2026-04-05T05:05:36Z https://rodare.hzdr.de/oai2d

oai:rodare.hzdr.de:1257 2021-11-12T13:04:56Z openaire_data user-health user-rodare

Moldovan, Rares-Petru Gündel, Daniel Teodoro, Rodrigo Ludwig, Friedrich-Alexander Fischer, Steffen Toussaint, Magali Schepmann, Dirk Wünsch, Bernhard Brust, Peter Deuther-Conrad, Winnie 2021-11-09 The σ2 receptor (transmembrane protein 97), which is involved in cholesterol homeostasis, is of high relevance for neoplastic processes. The upregulated expression of σ2 receptors in cancer cells and tissue in combination with the antiproliferative potency of σ2 receptor ligands motivates the research in the field of 2 receptors for the diagnosis and therapy of different types of cancer. Starting from the well described 2-(4-(1H-indol-1-yl)butyl)-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline class of compounds, we synthesized a novel series of fluorinated derivatives, bearing the F-atom at the aromatic indole/azaindole subunit. RM273 (2-[4-(6-fluoro-1H-pyrrolo[2,3-b]pyridin-1-yl)butyl]-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline) was selected for labelling with 18F and evaluation regarding detection of σ2 receptors in the brain by positron emission tomography. Initial metabolism and biodistribution studies of [18F]RM273 in healthy mice revealed promising penetration of the radioligand into the brain. Preliminary in vitro autoradiography on brain cryosections of an orthotopic rat glioblastoma model proved the potential of the radioligand to detect the upregulation of σ2 receptor in glioblastoma cells compared to healthy brain. The results indicate that the herein developed σ2 receptor ligand [18F]RM273 has potential to assess by non-invasive molecular imaging the correlation between the availability of σ2 receptors with properties of brain tumors such as tumor proliferation or resistance towards particular therapies https://rodare.hzdr.de/record/1257 10.14278/rodare.1257 oai:rodare.hzdr.de:1257 url:https://www.hzdr.de/publications/Publ-32485 url:https://www.hzdr.de/publications/Publ-33346 doi:10.14278/rodare.1256 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode σ2 receptor transmembrane protein 97 azaindoles binding affinity radiochemistry fluorine-18 labeling positron emission tomography (PET) brain-penetration glioblastoma orthotopic Data publication: Design, radiosynthesis and preliminary biological evaluation in mice of a brain-penetrant 18F-labelled σ2 receptor ligand info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1381 2022-08-10T12:38:36Z openaire_data user-health user-rodare

Moldovan, Rares-Petru Gündel, Daniel Deuther-Conrad, Winnie Ueberham, Lea Kaur, Sarandeep Otikova, Elina Teodoro, Rodrigo Lai, Thu Hang Clauß, Oliver Scheunemann, Matthias Bormans, Guy Kopka, Klaus Bachmann, Michael Brust, Peter 2022-08-09 The cannabinoid receptor type 2 (CB2R) is an attractive target for diagnosis and therapy of neurodegenerative diseases and cancer. Recently, we reported a novel naphthyrid-2-one based positron-emission tomography (PET) radioligand for imaging of the CB2R in the brain ([18F]5). In this study we aimed at the development of a novel 18F-labeled CB2R radioligand with improved binding properties and metabolic stability. Starting from the structure of 5, we developed a novel series of fluorinated derivatives by modifying the substituents at the naphthyrid-2-one subunit. Compound 28 (LU13) was identified with the highest binding affinity and selectivity versus CB1R (CB2RKi = 0.6 nM; CB1RKi/CB2RKi > 1000) and was selected for radiolabeling with 18F and biological characterization. The radiofluorination was performed starting from the corresponding bromo-precursor (31) bearing a fully deuterated N-alkyl chain to protect against defluorination. The in vitro evaluation of [18F]LU13 proved the high binding affinity of the radioligand towards rat (rCB2RKD = 0.2 nM) and human (hCB2RKD = 1.1 nM) CB2R. Metabolism studies in mice revealed a metabolic stability at 30 min p.i. with fractions of parent compound of >80% in the brain and 90% in the spleen with only trace of defluorination products detected in plasma. PET imaging in a rat model of vector-based/related overexpression in the striatum revealed a high signal to background ratio, demonstrating the ability of [18F]LU13 to reach and selectively label the hCB2R in the brain. Thus, [18F]LU13 is a novel and highly promising PET radioligand for the imaging of up regulated CB2R expression under pathological conditions in the brain. https://rodare.hzdr.de/record/1381 10.14278/rodare.1381 oai:rodare.hzdr.de:1381 url:https://www.hzdr.de/publications/Publ-34030 url:https://www.hzdr.de/publications/Publ-33987 doi:10.14278/rodare.1380 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode cannabinoid receptor type 2 naphthyrid-2-one binding affinity Data Publication: Structure-Based Design, Optimization and Development of [18F]LU13, a novel radioligand for CB2R Imaging in the Brain with PET info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2381 2024-08-12T07:55:04Z openaire_data user-health user-rodare user-hzdr user-elbe user-direct-electron-beam-at-elbe user-draco-elbe user-oncoray

Metzkes-Ng, Josefine Brack, Florian-Emanuel Kroll, Florian Bernert, Constantin Bock, Stefan Bodenstein, Elisabeth Brand, Michael Cowan, Thomas Gebhardt, René Hans, Stefan Helbig, Uwe Horst, Felix Ernst Jansen, Jeannette Kraft, Stephan Krause, Mechthild Leßmann, Elisabeth Löck, Steffen Pawelke, Jörg Püschel, Thomas Reimold, Marvin Rehwald, Martin Richter, Christian Schlenvoigt, Hans-Peter Schramm, Ulrich Schürer, Michael Seco, Joao Szabó, Emília Rita Umlandt, Marvin Elias Paul Zeil, Karl Ziegler, Tim Beyreuther, Elke 2023-07-24 Source data, scripts and parts of figures to generate the figures in publication https://rodare.hzdr.de/record/2381 10.14278/rodare.2381 oai:rodare.hzdr.de:2381 url:https://www.hzdr.de/publications/Publ-37304 url:https://www.hzdr.de/publications/Publ-37303 doi:10.14278/rodare.2380 url:https://rodare.hzdr.de/communities/direct-electron-beam-at-elbe url:https://rodare.hzdr.de/communities/draco-elbe url:https://rodare.hzdr.de/communities/elbe url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Laser-Plasma Acceleration FLASH Radiobiology Laser-driven proton acceleration TNSA UHDR Ultra-high dose rate Cancer Radiotherapy Data publication: The DRESDEN PLATFORM – A Research Hub for Ultra-high Dose Rate Radiobiology info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3672 2025-06-03T09:24:15Z openaire_data openaire_data openaire_data openaire_data openaire_data user-health user-rodare user-fwi user-ibc

Alsadig Ahmed Mohammed, Ahmed Peng, Xuan Boutier, Hugo Rodrigues Loureiro, Liliana Raquel Feldmann, Anja Hübner, René Cabrera, Humberto Kubeil, Manja Bachmann, Michael Baraban, Larysa 2025-04-07 The precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities. https://rodare.hzdr.de/record/3672 10.14278/rodare.3672 oai:rodare.hzdr.de:3672 doi:10.17815/jlsrf-3-159 url:https://www.hzdr.de/publications/Publ-41021 doi:10.14278/rodare.3671 url:https://rodare.hzdr.de/communities/fwi url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/ibc url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/1.0/legalcode Fibroblast activation protein immunotherapeutic target modules gold nanoparticles thermal lens spectroscopy Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:801 2025-07-18T10:19:41Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/801 10.14278/rodare.801 oai:rodare.hzdr.de:801 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4130 2026-02-13T12:09:51Z openaire_data user-health user-hzdr user-oncoray user-rodare

Schaart, Dennis Huizenga, Jan Jagt, Thyrza Wecker, Franziska Römer, Katja Wolf, Andreas Müller, Sara Urban, Konstantin Kieslich, Aaron van Zanten, Julian Kreuger, Rob Kögler, Toni 2026-01-01 Contact person(s): Jagt, Thyrza; Kögler, Toni Project leader(s): Kögler, Toni This dataset contains data gathered in the experimental run of July and August 2025, designed to characterize newly developed Multi-Feature Treatment Verification (MFTV) detectors. MFTV is the next generation of Prompt Gamma-Ray Treatment Verification. Detectors, experimental setup, data acquisition, and data processing are described in the Documentation.pdf. For questions regarding the database, please refer to the beforementioned contact persons. https://rodare.hzdr.de/record/4130 10.14278/rodare.4130 oai:rodare.hzdr.de:4130 eng url:https://www.hzdr.de/publications/Publ-43006 doi:10.14278/rodare.4129 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess Multi-Feature Treatment Verification Prompt-Gamma Timing Proton Range Verification Experimental data of first characterization experiment of novel Multi-Feature Treatment Verification detectors info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2644 2024-01-09T12:44:33Z user-health user-rodare

Peng, Xuan Janićijević, Željko Lemm, Sandy Laube, Markus Pietzsch, Jens Bachmann, Michael Baraban, Larysa 2024-01-09 summary over: (a) raw data for the metabolic assays (b) raw data for the analysis of the permeability of the capsules (c) unpublished images: typical cross section analysis of the capsules and organoids https://rodare.hzdr.de/record/2644 10.14278/rodare.2644 oai:rodare.hzdr.de:2644 doi:10.22541/au.165830418.86011497/v1 url:https://www.hzdr.de/publications/Publ-35501 url:https://www.hzdr.de/publications/Publ-35494 doi:10.14278/rodare.2643 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Data publication: Shell engineering in soft alginate-based capsules for culturing liver spheroids info:eu-repo/semantics/other image-other

oai:rodare.hzdr.de:4143 2026-01-12T06:47:21Z openaire_data user-rodare user-zrt user-health

Kogler, Jürgen Donat, Cornelius Trommer, Johanna Kopka, Klaus Stadlbauer, Sven 2025-11-19 Analytical data for chemical synthesis (HPLC, NMR, HRMS), biological data for in vitro and in vivo evaluation (binding assays, small animal imaging) https://rodare.hzdr.de/record/4143 10.14278/rodare.4143 oai:rodare.hzdr.de:4143 doi:10.1186/s41181-025-00398-9 url:https://www.hzdr.de/publications/Publ-42251 url:https://www.hzdr.de/publications/Publ-42101 doi:10.14278/rodare.4142 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/zrt info:eu-repo/semantics/restrictedAccess FAP FAPI PET fluorescence-guided surgery noninvasive molecular imaging Data publication: Synthesis and preclinical evaluation of FAP-targeting radiotracers for PET and optical imaging info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4199 2026-01-07T15:21:39Z software user-health user-hzdr user-pet-center user-rodare user-zrt

Maus, Jens Nitschke, Janina Nikulin, Pavel Hofheinz, Frank Barth, Mareike Lemm, Sandy Richter, Lena Pietzsch, Jens Braune, Anja Ullrich, Martin 2026-01-07 Collection of neural network models for automatic image segmentation of microscopic tumor spheroids. Intended to be used with nnU-Net deep-learning framework. Trained and tested on a total of microscopic images of mouse pheochromocytoma (MPC) tumor cells. In addition to the trained network model, a PyQt5-based graphical user interface tool is provided. This tool provides a complete pipeline for handling microscopic spheroid image data, running deep-learning–based delineation, and curating results for continuous model improvement. For installation and usage instructions, please visit https://github.com/hzdr-MedImaging/pyMarAI Please cite nnU-Net and the respective paper when using pyMarAI. List of available model types: pyMarAI-1.0.0-ecat.zip: nnUNetv2 ready network (for ECAT7) pyMarAI-1.0.0-nifti.zip: nnUNetv2 ready network (for NIFTI) https://rodare.hzdr.de/record/4199 10.14278/rodare.4199 oai:rodare.hzdr.de:4199 url:https://www.hzdr.de/publications/Publ-42498 url:https://www.hzdr.de/publications/Publ-42497 url:https://github.com/hzdr-MedImaging/pyMarAI doi:10.14278/rodare.4198 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/pet-center url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/zrt info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/4.0/legalcode Tumor Spheroid Imaging Radiopharmacological Treatment Response Assays Delineation Cancer Deep-Learning Artifical Intelligence Convolutional Neural Networks Network model pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation info:eu-repo/semantics/other software

oai:rodare.hzdr.de:3176 2024-10-01T06:14:09Z openaire_data user-energy user-health user-rodare

Fahmy, Karim Günther, Alix Bertheau, Rahel Pape, David 2024-09-30 The Excel file contains heat flow data from Schizophyllum commune cultures grown at 30 °C at different glucose concentrations. Measurements were carried out with a TAMIII instrument (TA-Waters) using 4 mL ampoules filled with 2 mL of growth medium.The heat flow curves show an oxidative phase followed by a fermentative phase at high glucose concentration. The two corresponding peaks can be evaluated indepndently by chosing the appropriate heat range. (The publication DOI:10.14278/rodare.3152 contains these data with the according analysis results). The Excel file serves also as a template for users to paste in their raw data. The format must not be changed for successful upload in METABOLATOR (DOI: 10.14278/rodare.3150). METABOLATOR is still being developed. Comments, reports on errors, suggestions can be sent to metabolator@hzdr.de https://rodare.hzdr.de/record/3176 10.14278/rodare.3176 oai:rodare.hzdr.de:3176 url:https://www.hzdr.de/publications/Publ-39694 doi:10.14278/rodare.3175 url:https://rodare.hzdr.de/communities/energy url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode metabolator microcalorimetry microbes bacteria growth kinetics Heat flow data from the fungus Schizophyllum commune: example file for the software tool METABOLATOR info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2872 2024-08-12T08:05:48Z openaire_data user-rodare user-health user-hzdr user-oncoray

Werner, Rahel-Debora Franke, Anna Makarevich, Krystsina Kögler, Toni Kögler, Toni Stach, Daniel Weinberger, David Wolf, Andreas Dreyer, Anne Makarevich, Krystsina Schellhammer, Sonja Pausch, Guntram Römer, Katja Tiebel, Jessica Turko, Joseph Alexander Bunker Wagner, Andreas Kögler, Toni 2024-05-16 The dataset contains the data reported on https://www.hzdr.de/publications/Publ-39073 where 2 proton bunch monitors (PBMs), namely the diamond detector and the cyclotron monitoring signal Uphi, are established, characterized, and applied for correcting the prompt gamma-ray timing (PGT) data. Experimental setup, irradiation modalities, data acquisition, and data pre- and postprocessing are described there. The process is summarized in the following: Experimental setup: A homogeneous cylindrical PMMA phantom was irradiated with a proton beam. Two sets of measurements were considered: S1) measurements at the horizontal fixed beamline with the control of the beam time structure and current. These data establish the relation between the investigated PBMs and calibrate them to the scattering setup that provides the proton bunch arrival time in the experimental room. The phantom was irradiated with 7 different proton energies Ep = {70, 90, 110, 130, 160, 190, 224} MeV. For each Ep, 3 irradiation modalities were applied: CW-mode represented the continuous beam lasting for 30 s, the beam current Ibeam = 2 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA); Plan I represented a clinically realistic plan with a spot duration of 4 ms and a spot repetition time of 7 ms. The beam current Ibeam = 1 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA); Plan II aimed to reproduce the measurements of Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). For that, the spot duration was set to 69 ms, and the repetition time was 72 ms. The beam current Ibeam = 1 nA for all Ep excluding 70 MeV (for 70 MeV, Ibeam = 0.5 nA). S2) measurements at the pencil beam scanning (PBS) beamline were similar to those at the clinical beam delivery nozzle. The PBS beamline delivers the beam as spots of given intensity (expressed in MU), (x,y)-coordinates, and energy (corresponds to the penetration depth or z-coordinate). These data comprise data from the PGT detector and PBMs and are used to correct the PGT data employing the investigated PBMs. The phantom was irradiated with 8 different proton energies Ep = {70, 90, 110, 130, 162, 180, 200, 220} MeV. For every energy, 2 spot intensities were considered: 0.1 MU per 1 spot (~1e7 protons) and 1 MU per 1 spot (~1e8 protons). For Ep = 162 MeV, an additional spot intensity of 10 MU per 1 spot (~1e9 protons) was applied to reproduce the measurements of Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). Data preprocessing: The raw data of each measurement were converted from the binary list-mode format to ROOT TTrees. The data were corrected for the photomultiplier gain drift, and digitalization time non-linearities, and the integral signal was converted into deposited energy. For the measurements at the fixed beamline, the coincidence analysis was applied additionally for non-PBM detectors. The data were assigned to individual corresponding spots for the PBS beamline measurements. Data structure: The ROOT files are named u100-p00XX-yyyy-mm-dd_HH.MM.SS+TZ.root where p00XX is the detector’s number, yyyy-mm-dd_HH.MM.SS is the time of the measurement, and TZ is the time zone. Here, p0012 and p0019 mean scintillating detectors that were used both at the fixed beamline, and only detector p0012 was used for PGT measurements at the PBS beamline. P0015 is the diamond detector, and p0017 contains data of the Uphi signal. In general, the data structure inside the ROOT files is different depending on the purpose of the detector. However, there are some general includes: data (TTree) contains list-mode data which comprises uncorrected data: before corrections and calibrations steps; corrected data: after correcations and calibrations steps; meta (TTree) is a measurement metadata (applied detector voltage, the start time of the measurements, etc.); histograms is a directory with selected example histograms (uncorrected); analysis is a directory with histograms with corrected data used for the analysis. For further questions, please refer to the contact persons stated above. https://rodare.hzdr.de/record/2872 10.14278/rodare.2872 oai:rodare.hzdr.de:2872 eng doi:10.14278/rodare.2872 url:https://www.hzdr.de/publications/Publ-39104 url:https://www.hzdr.de/publications/Publ-39104 url:https://www.hzdr.de/publications/Publ-39104 doi:10.14278/rodare.2872 url:https://www.hzdr.de/publications/Publ-39104 doi:10.14278/rodare.2872 url:https://www.hzdr.de/publications/Publ-39073 url:https://www.hzdr.de/publications/Publ-39104 doi:10.14278/rodare.2871 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode prompt gamma timing PGT proton bunch monitor PBM proton range verification Experimental data for investigating proton bunch monitors for clinical translation of prompt gamma-ray timing info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1501 2025-02-06T08:24:12Z software user-hzdr user-health user-rodare

Abdussalam, Wildan 2022-03-24 Software to synchonise the data between various data sources and casus database server. For Unix users please use MigrateWhere2test_0.7Unix.zip and for WIndows users please use MigrateWhere2test_0.7Win.zip. In order to use the scripts, please use the following instructions: Windows 1. Create the postgreq sql database and set the port 5432 2. Create folder C:\Workspaces and unzip the unix file. 3. Create folder in workspaces, com.com.casus.env.where2test.migration\COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION. 4. Set run Develop and run the .bat file on the folder MigrateWhere2test_0.7Unix to run in localhost. Unix 1. Create PostgreSQL with port 32771. 2. Create folder /home/wildan/Workspaces and unzip the unix file. 3. Open the file MigrateWhere2test/MigrateWhere2test_run.sh and change the mode "Default" by "Production" 4. Create folder in workspaces, com.com.casus.env.where2test.migration.unix/COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION. 5. run the MigrateWhere2test_run.sh in the "Production" mode. https://rodare.hzdr.de/record/1501 10.14278/rodare.1501 oai:rodare.hzdr.de:1501 eng url:https://www.hzdr.de/publications/Publ-34430 doi:10.14278/rodare.1500 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode data pipeline Data synchronizator of Where2test pipeline info:eu-repo/semantics/other software

oai:rodare.hzdr.de:2473 2025-01-20T13:09:04Z openaire_data user-health user-rodare

Liou, Natasha De, Trina Urbanski, Adrian Khasriya, Rajvinder Yakimovich, Artur Horsley, Harry 2023-09-12 Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files Objects Count Pixel Values img 300 Raw data 0-255 bin_mask 300 Background/Foreground 0/1 mult_mask 300 Background/Class 0 Rod 1697 1 RBC/WBC 1056 2 Yeast 41 3 Miscellaneous 550 4 Single EPC 182 5 Small EPC sheet 26 6 Large EPC sheet 10 7 Total 3562 https://rodare.hzdr.de/record/2473 10.14278/rodare.2473 oai:rodare.hzdr.de:2473 eng url:https://www.hzdr.de/publications/Publ-37531 doi:10.14278/rodare.2472 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode clinical microscopy urine microscopy widefield transmission light image segmentation binary segmentation multiclass segmentation Clinical urine microscopy for urinary tract infections info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3772 2025-06-03T09:24:15Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data user-fwi user-health user-ibc user-rodare

Alsadig Ahmed Mohammed, Ahmed Peng, Xuan Boutier, Hugo Rodrigues Loureiro, Liliana Raquel Feldmann, Anja Hübner, René Cabrera, Humberto Kubeil, Manja Bachmann, Michael Baraban, Larysa 2025-04-07 The precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities. https://rodare.hzdr.de/record/3772 10.14278/rodare.3772 oai:rodare.hzdr.de:3772 doi:10.17815/jlsrf-3-159 url:https://www.hzdr.de/publications/Publ-41021 doi:10.14278/rodare.3671 url:https://rodare.hzdr.de/communities/fwi url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/ibc url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/1.0/legalcode Fibroblast activation protein immunotherapeutic target modules gold nanoparticles thermal lens spectroscopy Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3773 2025-06-27T09:18:30Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data user-fwi user-health user-ibc user-rodare

Alsadig Ahmed Mohammed, Ahmed Peng, Xuan Boutier, Hugo Rodrigues Loureiro, Liliana Raquel Feldmann, Anja Hübner, René Cabrera, Humberto Kubeil, Manja Bachmann, Michael Baraban, Larysa 2025-04-07 The precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities. https://rodare.hzdr.de/record/3773 10.14278/rodare.3773 oai:rodare.hzdr.de:3773 doi:10.17815/jlsrf-3-159 url:https://www.hzdr.de/publications/Publ-41021 doi:10.14278/rodare.3671 url:https://rodare.hzdr.de/communities/fwi url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/ibc url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/1.0/legalcode Fibroblast activation protein immunotherapeutic target modules gold nanoparticles thermal lens spectroscopy Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:794 2025-07-18T10:19:40Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/794 10.14278/rodare.794 oai:rodare.hzdr.de:794 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1849 2025-07-18T10:19:41Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild Neubeck, Cläre Von Haase, Robert Lühr, Armin Dietrich, Antje Nexhipi, Sindi 2022-09-21 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. Note: There are ongoing corrections taking place with the B6 mouse strain data (P2A_B6_M1, P2A_B6_M2, P2A_B6_M6, P2A_B6_M10). If you are interested in working with these data, please wait for the new version to be uploaded or contact the authors of https://doi.org/10.1016/j.radonc.2023.109591 Chunked zip: The histological data are stored as chunked .zip files (*.zip.001 - *.zip.0XX). In order to unpack the data, download all chunks into the same directory, then unpack. https://rodare.hzdr.de/record/1849 10.14278/rodare.1849 oai:rodare.hzdr.de:1849 eng info:eu-repo/grantAgreement/EC/H2020/730983/ doi:10.3389/fonc.2020.598360 url:https://www.hzdr.de/publications/Publ-31469 url:https://www.hzdr.de/publications/Publ-32124 url:https://www.hzdr.de/publications/Publ-32394 url:https://www.hzdr.de/publications/Publ-32394 doi:10.1016/j.radonc.2023.109591 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3900 2025-08-04T13:19:03Z openaire_data user-health user-rodare

Wyrzykowska, Maria della Maggiora, Gabriel Deshpande, Nikita Mokarian, Ashkan Yakimovich, Artur 2024-08-30 How to cite us Wyrzykowska, Maria, Gabriel Della Maggiora, Nikita Deshpande, Ashkan Mokarian, and Artur Yakimovich. "A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy." Scientific Data 12, no. 1 (2025): 1-11. @article{wyrzykowska2025benchmark, title={A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy}, author={Wyrzykowska, Maria and Della Maggiora, Gabriel and Deshpande, Nikita and Mokarian, Ashkan and Yakimovich, Artur}, journal={Scientific Data}, volume={12}, number={1}, pages={1--11}, year={2025}, publisher={Nature Publishing Group} } Data sources Raw data used during the study can be found in corresponding references. VACV: Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF. Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion. PLoS One. 2015 Sep 28;10(9):e0138760. doi: 10.1371/journal.pone.0138760. PMID: 26413745; PMCID: PMC4587671. HADV: Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Witte R, Puntener D, Greber UF. Microscopy deep learning predicts virus infections and reveals the mechanics of lytic-infected cells. iScience. 2021 May 15;24(6):102543. doi: 10.1016/j.isci.2021.102543. PMID: 34151222; PMCID: PMC8192562. HSV, IAV, RV: Olszewski, D., Georgi, F., Murer, L. et al. High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections. Sci Data 9, 610 (2022). https://doi.org/10.1038/s41597-022-01733-4 Data organisation For each virus (HADV, VACV, IAV, RV and HSV) we provide the processed data in a separate directory, divided into three subdirectories: `train`, `val` and `test`, containing the proposed data split. Each of the subfolders contains two npy files: `x.npy` and `y.npy`, where `x.npy` contains the fluorescence or brightfield signal (both for HADV, as separate channels) of the cells or nuclei and `y.npy` contains the viral signal. The data is already processed as described in the Data preparation section. Additionally, Cellpose masks are made available for the test data in separate masks directory. For each virus except for VACV, there is a subdirectory `test` containing nuclei masks (`nuc.npy`). For HADV cell masks are also available (`cell.npy`). Data preparation Each of VACV plaques was imaged to produce 9 files per channel, that need to be stitched to recreate the whole plaque. To achieve this, multiview-stitcher toolbox has been used. The stitching was first performed on the third channel, representing the brightfield microscopy image of the samples. Then, the parameters found for this channel were used to stitch the rest of the channels. VACV dataset represents a timelapse, from which timesteps 100, 108 and 115 have been selected to produce the data then used in the experiments. Images have been center-cropped to 5948x6048 to match the size of the smallest image in the dataset (rounded down to the closest multiple of 2). The data was additionally manually filtered to remove the samples that constituted only uninfected cells (C02, C07, D02, D07, E02, E07, F02, F07). The HAdV dataset is also a timelapse, from which only the last timestep (49th) has been selected. For the rest of the datasets (HSV, IAV, RV) only the negative control data was used, which was selected in the following way: from the data collected at the University of Zürich, from the Screen samples only the first 2 columns were selected and from the ZPlates and prePlates samples only the first 12 columns. All of the datasets were divided into training, validation and test holdouts in 0.7:0.2:0.1 ratios, using random seed 42 to ensure reproducibility. For the time-lapse data, it was ensured that the same sample from different timesteps only exists in one of the holdouts, to prevent information leakage and ensure fair evaluation. All of the samples were normalised to [-1, 1] range, by subtracting the 3rd percentile and dividing by the difference between percentile 99.8 and 3, clipping to [0, 1] and scaling to [-1, 1] range. For the brightfield channel of HAdV, percentiles 0.1 and 99.9 were used. These cutoff points were selected based on the analysis of the histograms of the values attained by the data, to make the best use of the available data range. Specific values used for the normalization are summarized in Figure 3 of the manuscript in Related/alternate identifiers. To prepare the cell nuclei masks, Cellpose model with pre-trained weights cyto3 has been used on the fluorescence channel. The diameter was set to 7 for all the datasets except for HAdV, for which the automatic estimation of the diameter was employed. Cell masks were prepared using Cellpose with pre-trained weights cyto3 with a diameter set to 70 on brightfield images stacked with fluorescence nuclei signal. The data preparation can be reproduced by first downloading the datasets and then running scripts that are located in `scripts/data_processing` directory of the [VIRVS repository](https://github.com/casus/virvs), first modifying the paths in them: for HAdV data: `preprocess_hadv.py` for VACV data: `stitch_vacv.py` + `preprocess_vacv.py` for the rest of the viruses: `preprocess_other.py` to prepare Cellpose predictions: `prepare_cellpose_preds.py` (for cells) and `prepare_cellpose_preds_nuc.py` (for nuclei) Additional Dataset in v1.2: GFP-transgenic human coronavirus OC43 (CoV-GFP) This dataset comprises raw fluorescence microscopy images acquired from a 384-well control plate, half of which was infected with GFP-transgenic human coronavirus OC43 (CoV-GFP). The plate was imaged using two fluorescence channels: CoV-GFP to visualize viral infection, and Hoechst 33342 to stain cell nuclei. The raw images of two plates are provided in the cov_raw.zip. Each plate has half a plate infected with CoV-GFP and another is a mock-infected (no virus). Images were captured using a 4× objective on an ImageXpress Micro imaging system (Molecular Devices). The dataset was derived from a published high-throughput screening study by Murer et al. [1], aimed at identifying broad-spectrum antiviral compounds. Murer, L. et al. Identification of broad anti-coronavirus chemical agents for repurposing against SARS-CoV-2 and variants of concern. Current Research in Virological Science, 3, 100019 (2022). https://rodare.hzdr.de/record/3900 10.14278/rodare.3900 oai:rodare.hzdr.de:3900 eng url:https://www.hzdr.de/publications/Publ-39523 url:https://www.hzdr.de/publications/Publ-40031 doi:10.14278/rodare.3129 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode virus infected cell microscopy deep learning virtual staining A Dataset for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2285 2023-05-22T07:24:33Z user-rodare user-health

Podlipec, Rok 2023-05-03 Time-lapse videos of cells and microtubule dynamics (in green) after exposure to different nanoparticles (in red) taken with confocal fluorescence microscopy. S1 - control experiment; S2-S3 - the exposure to titanium dioxide (TiO2) nanotubes measured at different image planes; S4-S5 - the exposure to TiO2 nanocubes measured at different image planes; S6-S7 - the exposure to multiwall carbon nanotubes (MWCNTs) measured at different image planes. https://rodare.hzdr.de/record/2285 10.14278/rodare.2285 oai:rodare.hzdr.de:2285 doi:10.14278/rodare.2287 url:https://www.hzdr.de/publications/Publ-36911 url:https://www.hzdr.de/publications/Publ-36910 doi:10.14278/rodare.2284 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Different effect of anatase TiO2 nanotubes and nanocubes on microtubule fragmentation, mitotic arrest and aneuploidy indicating plausible carcinogenicity info:eu-repo/semantics/other video

oai:rodare.hzdr.de:4437 2026-01-27T07:45:57Z openaire_data user-health user-hzdr user-oncoray user-rodare

Makarevich, Krystsina Kieslich, Aaron Markus Römer, Katja Schellhammer, Sonja Wagner, Andreas Kögler, Toni 2026-01-20 The dataset contains the data used for evaluating the performance of the Prompt Gamma-ray Timing (PGT) system under clinical-like conditions. Experimental setup: Clinically realistic dose plans were applied to an anthropomorphic head phantom at the pencil beam scanning (PBS) beamline. Two phantom positioning schemes were employed: noseφ setup: the geometric center of the head phantom was aligned with the beamline isocenter, and the phantom’s nose pointed in a given direction defined by an angle φ (in the bird’s-eye view) gantry-like Gθ setup: the phantom was placed according to a positioning template so that a hypothetical tumor, contoured on the phantom’s CT images, was aligned with a beamline isocenter, and the PBS nozzle position relative to the phantom corresponded then to a gantry rotational angle θ. The photographs of the experimental setup and the schematic of the target positioning are provided in Figure 1 of the 0_Materials_and_Methods.zip file. The positioning template for the Gθ setups is given in Figure 2 in 0_Materials_and_Methods.zip. Three types of irradiation fields were used for the study: EqualMU fields: square fields of about 8.4 cm × 8.4 cm, comprising 15×15 spots arranged on a regular grid with a lateral spacing of 6 mm. Spots within the same energy layer share an identical weight. DistalLayer fields: fields comprising 5+15×15+5 spots arranged on a regular grid with a lateral spacing of 6 mm. The main sequence of spots (15×15) forms a square field of about 8.4 cm × 8.4 cm and has varying spot weights. The additional 10 outermost lateral spots (5 before and after the main sequence) are used to determine the field orientation. Gθ fields: these are treatment fields developed to target a hypothetical tumor delineated in the phantom’s CT images. They define complex field shapes consisting of multiple energy layers and spots with widely varying weights. The employed irradiation fields are provided as *.pld files in 0_Materials_and_Methods.zip. For several measurements, a beam range shifter with a water-equivalent thickness of 7.38 cm was inserted into the beamline. It was rigidly attached to the snout holding the detection units, ensuring a fixed position throughout the measurements. Produced gamma rays were measured with eight scintillation detectors placed at: 0° (detector p0012); 180° (detector p0008); 45° (detector p0017); 225° (detector p0006); 90° (detector p0015); 270° (detector p0013); 135° (detector p0009); 315° (detector p0019). Measurements: the four experimental studies were conducted, and the data from these studies are given in the corresponding zip archives: Evaluate the count-rate capacity of the PGT system: the phantom was in the G270 orientation; an EqualMU plan comprising 9 energy layers (combinations of {150, 120, 90}MeV and {0.01, 0.1, 1}MU was used. Due to the limitations on the minimal spot weight imposed by the beam delivery system, the 0.01 MU spots actually weighed 0.0101 MU. Data only for the 7 detectors employed in this experiment are provided in 1_Count_rate_capacity.zip. Experimental and clinical machine log files are not given (due to internal regulations). Investigate the range shifter contribution to the PGT data: The data are provided only for the detector p0006 (at 225°) placed inside a hollow cylindrical lead collimator (r1=2'', r2=2''+1 cm). The range shifter was inserted in the beamline; the phantom was in nose45 orientation; two DistalLayer plans with 104 MeV and 187 MeV energy layers were applied. After passing the range shifter, these correspond to proton energies of 30 MeV and 150 MeV, respectively. Each plan comprised 24 identical energy layers and delivered a total of 1009 MU. Data from these measurements are provided in 2_Range_shifter_contribution.zip. Study spot-position dependence in scanned fields: the phantom was positioned as nose0; the range shifter was removed from the beamline to ensure only a single (target-related) peak in time distributions; EqualMU fields of {90, 120, 150} MeV and with spots of 1 MU weight were applied, each field comprised 8 identical layers and was delivered 2 times. Note that during the second repetition of the 120 MeV field, the file for p0012 was corrupted; therefore, the field was applied for the third time, and for this repetition, the file for p0015 was corrupted. Therefore, there are 3 data files for all detectors except for p0012 and p0015. Data files are in 3_Spot_position_dependence_in_scanned_fields.zip. Investigate the stability of the PGT mean with irradiation time: phantom was in the nose0 orientation; the range shifter was removed from the beamline; EqualMU fields with energy layers of {90, 120, 150}MeV and spot weights of either 0.2 MU or 1 MU were delivered. Fields with 0.2 MU spots included 40 identical energy layers, while those with 1 MU spots included 8 layers. Each field was delivered twice, in a random order. Since studies 3 and 4 overlap (they comprise the same measurements with {90, 120, 150} MeV and 1 MU fields), only the data from {90, 120, 150} MeV and 0.2 MU fields are included in 4_Stability_of_PGT_mean.zip. The remaining files for {90, 120, 150} MeV and 1 MU fields have already been given in 3_Spot_position_dependence_in_scanned_fields.zip. Data preprocessing: The raw data of each measurement were converted from the binary list-mode format to ROOT TTrees. The data were corrected for the photomultiplier gain drift and digitalization time non-linearities. The integral signal was converted into deposited energy. The data were assigned to individual corresponding spots. Data structure: The ROOT files are named u100-p00XX-yyyy-mm-dd_HH.MM.SS+TZ.root, where p00XX is the detector’s number, yyyy-mm-dd_HH.MM.SS is the time of the measurement, and TZ is the time zone. In general, the data structure inside the ROOT files includes: data (TTree) contains list-mode data, which comprises uncorrected (original measured) data. It contains branches: Triggertime (in time stamps, when the event triggered the data acquisition) Livetime (in time stamps, when the detector was idle) Energy (in a.u., normalized integral over the pulse) HeadEnergy (in a.u.) corrected and calibrated data. It comprises branches: EnergyGainCorrected (in a.u., pulse integral after applying correction for a photomultiplier gain drift). EnergyCalibrated (in MeV, calibrated pulse integral). FineTimeCorrected (in ns, detection time within the cyclotron acceleration period after correcting for time non-linearities). GlobalSpotID (in a.u., assigns a global ID to a spot, which incrementally increases for each new spot. If there is no beam, the counter is 0). LayerID (in a.u., an ID of the current energy layer. Outside the layer (no beam), the counter is 0). LocalSpotID (in a.u., a spot ID within the current layer. Outside the spot (no beam), the counter is 0). SpotMU (in MU, a spot weight of the current spot extracted from machine log files. If there was no spot irradiated, this value is 0). SpotEnergy (in MeV, the energy of the current energy layer taken from machine log files. Outside energy layers, this value is 0). SpotXCoordinate, SpotYCoordinate (in mm, the measured X- and Y-coordinates of the current spot. Outside the spot (no beam), these values are 10000). meta (TTree) is measurement metadata (applied detector voltage, the start time of the measurements, etc.); histograms is a directory with selected example histograms (uncorrected); analysis is a directory with histograms to correct and calibrate data, which are later saved into the data TTree. The main subdirectories here are: 00_General_Information contains data from machine log files: how many energy layers were irradiated, of which energies, how many spots each layer comprised, etc. 01_Layers_and_Spots_Detection contains histograms with the start and stop time of every energy layer and spot. 02_Gain_Correction includes histograms used to correct for photomultiplier gain drift. The procedure is described in Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). 03_Energy_Calibration contains data of the performed energy calibration of the detector. 04_Fine_Time_Linearization comprises histograms used to correct for differential and integral time non-linearities. The procedure is described in Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). Further, the authors typically employed an energy selection window of 0.7-7.40 MeV and subtracted time-uncorrelated background using the closest neighbor algorithm, as described in the dedicated publication. For further questions, please contact the persons stated above. https://rodare.hzdr.de/record/4437 10.14278/rodare.4437 oai:rodare.hzdr.de:4437 url:https://www.hzdr.de/publications/Publ-42868 url:https://www.hzdr.de/publications/Publ-42869 doi:10.14278/rodare.4436 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess prompt gamma timing PGT prompt gamma-ray timing proton range verification proton range monitoring Data publication: Performance of the Prompt Gamma-ray Timing system prototype under clinical-like conditions info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3828 2025-07-04T06:53:15Z openaire_data user-fwk user-health user-hzdr user-oncoray user-rodare

Turko, Joseph Lutz, Benjamin Meric, Ilker Müller, Sara Tabea Ratliff, Hunter Römer, Katja Ellen Urban, Konstantin Kögler, Toni 2025-06-24 This data set contains the experimental raw data from the measurement campaign at PTB in March 2024 funded by the European Innovation Council (EIC). Setup: The miniNOVO prototype (version 4) consists of 14 organic scintillator elements (7 × M600 and 7 × organic glas scintillator) of the dimensions \(12 × 12 × 140~\text{mm}³\). The scintillator bars have dual readout composed of 2 × Hamamatsu R7378A (1’’) PMTs1, 4 × Hamamatsu S14161-3050HS-04 SiPM1 + U3012 (+ custom front-end electronics) and 8 × Hamamatsu R2059-01 (2’’) PMTs1. The data was recorded with 2 CAEN V1730S3 14-bit, 16-channel digitizers (named dta and dtb) with a sampling frequency of 500 MS/s. A 1’’ CeBr3-detector was employed as a reference detector and positioned centrally behind the array. This detector was used for time calibration and time-of-flight measurements as start detector with a Pu-238 source. The detector array was irradiated head-on with mono-energetic neutron fields at the PIAF accelerator facility (Tandetron accelerator) of the energies \(E_n = \{ 1.2, 2.5, 6.5, 14.8, 17.0, 19.0\}~\text{MeV}\). The array position was shifted in two dimensions in 1 cm increments for the \(14.8~\text{MeV}\) measurements, in 5cm increments for \(17.0~\text{MeV}\) and at 1, 2 and 5 cm in both directions for the remaining energies. Data structure: The directory calibration contains six subdirectories dedicated to the time calibration with the reference detector, the position calibration with a Sr-90 source, the energy calibration with a Bi-207 and a Na-22 source, the gate optimisation and the gain matching. In the neutron_beam folder the measurements with the different neutron fields can be found, sorted into the corresponding subdirectory by energy. Waveform data recorded with a Pu-238 source is saved in the waveform_data folder and measurements with the reference detector can be found in the reference_detector directory. All other measurements and test runs are stored in the tests folder. influxDB holds the slow control data entries in a csv file and the main configuration files for the digitizers are saved in the DDAQconfig folder. In documentation a pdf-file of the elog providing more detailed information about the individual data files and a pdf-file with the detector setup are stored. Data Format: All data is saved in root files which each contain two root trees, one for each digitizer, named “dta” and “dtb”. The trees hold the following information in the form of listmode data for each event: digitizer channel ("channel"), charge integrated over long gate ("Elong"), charge integrated over short gate ("Eshort"), digitizer flags ("flags") and the timestamp (separated in three parts: "timestamp", "timestampExtended", "time"). Additionally, the root files also contain an TArrayD which denotes the start time of the measurement in UNIX time at its first index and the stop time at its second. There are two configuration files for each data file (named “filename_dtx.config”), one for each digitizer card. These text files contain the information about the digitizer settings for each run. [1] Hamamatsu Photonics Deutschland GmbH, Arzbergerstr. 10, 82211 Herrsching am Ammersee, Germany. [2] Target Systemelektronik, Heinz-Fangman-Straße 4, 42287 Wuppertal, Germany. [3] CAEN S.p.A., Via Vetraia 11, 55049 Viareggio (LU), Italy. The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester has received funding from UK Research and Innovation under grant agreement No. 10102118 https://rodare.hzdr.de/record/3828 10.14278/rodare.3828 oai:rodare.hzdr.de:3828 eng url:https://www.hzdr.de/publications/Publ-41530 doi:10.14278/rodare.3827 url:https://rodare.hzdr.de/communities/fwk url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess NOVO Neutron imaging Dual particle imaging Monoenergetic neutron fields Range verification in proton therapy PTB Neutron imaging and light output calibration with the miniNOVO prototype at the Physikalisch-Technische Bundesanstalt (PTB) Braunschweig info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4161 2026-03-24T01:07:10Z software user-health user-pet-center user-rodare user-zrt

Nikulin, Pavel Hoberück, Sebastian Apostolova, Ivayla Maus, Jens Hüttmann, Andreas Dührsen, Ulrich Kroschinsky, Frank Kotzerke, Jörg von Bonin, Malte Bundschuh, Ralph Braune, Anja Hofheinz, Frank 2025-11-26 Collection of neural network models for metabolic tumor volume segmentation in (non-Hodgkin) lymphoma patients in FDG-PET/CT images. Intended to use within nnU-Net deep learning framework. For installation and usage instructions, please visit https://github.com/hzdr-MedImaging/LyROI https://rodare.hzdr.de/record/4161 10.14278/rodare.4161 oai:rodare.hzdr.de:4161 url:https://www.hzdr.de/publications/Publ-42318 url:https://www.hzdr.de/publications/Publ-42236 url:https://github.com/hzdr-MedImaging/LyROI doi:10.14278/rodare.4160 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/pet-center url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/zrt info:eu-repo/semantics/closedAccess FDG PET Total Metabolic Tumor Volume TMTV Non-Hodgkin Lymphoma Convolutional Neural Network Delineation LyROI – nnU-Net-based Lymphoma Total Metabolic Tumor Volume Segmentation info:eu-repo/semantics/other software

oai:rodare.hzdr.de:4177 2026-03-24T01:07:10Z software software user-health user-pet-center user-rodare user-zrt

Nikulin, Pavel Hoberück, Sebastian Apostolova, Ivayla Maus, Jens Hüttmann, Andreas Dührsen, Ulrich Kroschinsky, Frank Kotzerke, Jörg von Bonin, Malte Bundschuh, Ralph Braune, Anja Hofheinz, Frank 2025-11-26 Collection of neural network models for metabolic tumor volume delineation in (Non-Hodgkin) lymphoma patients in FDG-PET/CT images. Intended to use within nnU-Net deep learning framework. Trained with a total of 1192 [18F]FDG-PET/CT scans from 716 patients with Non-Hodgkin lymphoma participating in the PETAL trial. For installation and usage instructions, please visit https://github.com/hzdr-MedImaging/LyROI Please cite nnU-Net and the respective paper when using LyROI. List of available models: LyROI_Orig.zip: regular U-Net LyROI_ResM.zip: residual encoder U-Net (medium) LyROI_ResL.zip: residual encoder U-Net (large) https://rodare.hzdr.de/record/4177 10.14278/rodare.4177 oai:rodare.hzdr.de:4177 url:https://www.hzdr.de/publications/Publ-42318 url:https://www.hzdr.de/publications/Publ-42236 url:https://github.com/hzdr-MedImaging/LyROI doi:10.14278/rodare.4160 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/pet-center url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/zrt info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/4.0/legalcode FDG PET Total Metabolic Tumor Volume TMTV Non-Hodgkin Lymphoma Convolutional Neural Network Delineation LyROI – nnU-Net-based Lymphoma Total Metabolic Tumor Volume Delineation info:eu-repo/semantics/other software

oai:rodare.hzdr.de:2562 2025-01-20T13:09:04Z openaire_data user-health user-rodare

Liou, Natasha De, Trina Urbanski, Adrian Khasriya, Rajvinder Yakimovich, Artur Horsley, Harry 2023-09-12 Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files Objects Count Pixel Values img 300 Raw data 0-255 bin_mask 300 Background/Foreground 0/1 mult_mask 300 Background/Class 0 Rod 1697 1 RBC/WBC 1056 2 Yeast 41 3 Miscellaneous 550 4 Single EPC 182 5 Small EPC sheet 26 6 Large EPC sheet 10 7 Total 3562 https://rodare.hzdr.de/record/2562 10.14278/rodare.2562 oai:rodare.hzdr.de:2562 eng url:https://www.hzdr.de/publications/Publ-37531 doi:10.14278/rodare.2472 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode clinical microscopy urine microscopy widefield transmission light image segmentation binary segmentation multiclass segmentation Clinical urine microscopy for urinary tract infections info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2563 2025-01-20T13:09:04Z openaire_data user-health user-rodare

Liou, Natasha De, Trina Urbanski, Adrian Khasriya, Rajvinder Yakimovich, Artur Horsley, Harry 2023-09-12 Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. How to cite us Liou, Natasha, Trina De, Adrian Urbanski, Catherine Chieng, Qingyang Kong, Anna L. David, Rajvinder Khasriya, Artur Yakimovich, and Harry Horsley. "A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection." Scientific Data 11, no. 1 (2024): 155. @article{liou2024clinical, title={A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection}, author={Liou, Natasha and De, Trina and Urbanski, Adrian and Chieng, Catherine and Kong, Qingyang and David, Anna L and Khasriya, Rajvinder and Yakimovich, Artur and Horsley, Harry}, journal={Scientific Data}, volume={11}, number={1}, pages={155}, year={2024}, publisher={Nature Publishing Group UK London} } Download Timeout Troubleshooting Use "-C" flag of curl in case you experience timeout of the download: curl -C - https://rodare...tar.gz_part1\?download\=1 --output ...tar.gz_part1 Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files Objects Count Pixel Values img 300 Raw data 0-255 bin_mask 300 Background/Foreground 0/1 mult_mask 300 Background/Class 0 Rod 1697 1 RBC/WBC 1056 2 Yeast 41 3 Miscellaneous 550 4 Single EPC 182 5 Small EPC sheet 26 6 Large EPC sheet 10 7 Total 3562 https://rodare.hzdr.de/record/2563 10.14278/rodare.2563 oai:rodare.hzdr.de:2563 eng url:https://www.hzdr.de/publications/Publ-37531 url:https://www.hzdr.de/publications/Publ-40019 doi:10.14278/rodare.2472 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode clinical microscopy urine microscopy widefield transmission light image segmentation binary segmentation multiclass segmentation Clinical urine microscopy for urinary tract infections info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1261 2021-12-15T14:31:24Z openaire_data user-health user-rodare

Moldovan, Rares-Petru 2021-11-11 Cannabinoid receptors type 2 (CB2R) represent an attractive therapeutic target for neurodegenerative diseases and cancer. Aiming at a positron emission tomography (PET) radiotracer to monitor receptor density and/or occupancy during a CB2R-tailored therapy, we developed here cis-[18F]1-(4-fluorobutyl-N-((1s,4s)-4-methylcyclohexyl)-2-oxo-1,2-dihydro-1,8-naphthyridine-3-carboxamide ([18F]LU14) starting from the corresponding mesylate precursor. First biological evaluation revealed that [18F]LU14 is a highly affine CB2R radioligand with >80% intact tracer in brain at 30 min p.i. Its further evaluation in a well-established rat model of CB2R overexpression by PET demonstrated its ability to selectively image the CB2R in the brain and its potential as tracer to further investigate diseased related CB2R changes in expression. https://rodare.hzdr.de/record/1261 10.14278/rodare.1261 oai:rodare.hzdr.de:1261 url:https://www.hzdr.de/publications/Publ-33389 url:https://www.hzdr.de/publications/Publ-32557 doi:10.14278/rodare.1260 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Cannabinoid receptor type 2 naphtyrid-2-one binding affinity radiochemistry fluorine-18 labeling brain positron emission tomography Data Publication: Development of [18F]LU14 for PET Imaging of Cannabinoid Receptor Type 2 in the Brain info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4128 2026-02-19T14:55:49Z openaire_data user-health user-novo user-rodare

Blangiardi, Francesco Ratliff, Hunter Kögler, Toni 2025-11-14 Introduction This dataset corresponds to the simulation data used within AI methods in _"Fast proton transport and neutron production in proton therapy using Fourier neural operators"_ [CITE]. It has been extracted from the corresponding PHITS dataset [1] related to the same work, and is used by the codebase provided in [2] implementing all important AI methods within the paper. The purpose of this entry is to provide a more easily accessible version of the data in [2] ready to be used for AI applications. The size of the dataset has been greatly reduced, and put into a format allowing the access of the phase space density at each individual depth in the phantom for both protons and neutrons and in the form of discretized histograms. A concise description of the simulation setup is provided in [2] please refer to the paper for detailed discussion, description, analysis, and further results derived from this dataset. General information The phase space density data is divided into discretized histograms as defined in the related paper. This follows the approximation within said paper where only 4 dimensions are kept, related to the depth, radial distance (R), energy (E) and azimuthal divergence (θ) of the particles. The depth dimension is considered as a pseudo-time dimension, meaning that time is not provided within the data. In order to simulate examples of different beams propagatng through different materials, a total of 47 phantoms have been simulated, each with a unique starting energy. Phantoms have been divided into slabs along the depth dimension which are assumed to be of homogeneous material along the dimensions perpendicular to the beam axis, but are composed of different materials among them. The proton density is provided as the Monte Carlo simulated protons appropriately binned into the defined discretizations whenever one of the surfaces of each slab is crossed. When it comes to the neutron phase space density, this is instead provided as the angle, energy and radius distributions of secondary neutrons produced within each slab. Both densities are to be considered as integrated with respect to time. For each slab, also the energy deposited by the proton is provided, coming as an energy deposition probability distribution along E and R. Moreover, each of the 47 phantoms has been irradiated according to three different sets of treatment head paramenter, leading to the creation of three dataset: ES8, ES9 and NES8. For the sake of reproducibility, weights for each of the models discussed in [2] are also provided. Parametrization The densities are observed through discretizations as identified in the paper. Within this work, the resolution along the beam depth is fixed to 0.5mm, the energy resolution is set to 1 and 2 MeV for the proton and neutron fluences respectively, while the radial distance and angle is handled differently among the two particles. For protons these are discretized in logarithmically spaced bins, with the first bin also comprising 0, and ranging up to 95.9 mm and 58.76 ° respectively. Instead, for neutrons both dimensions are uniformly discretized, ranging from 0 up to 60 mm and 180 ° respectively. The R, E and θ dimensions are divided into 30x250x30 bins within the proton data, and into 30x125x30 in the case of the neutrons, which are provided at each discretized depth. Data about energy deposition follows the same radial binning as in the case of the proton density, but the energy binning is instead logarithmic ranging from 1.0e-3 up to 97.7 MeV. As already mentioned, the ES8, ES9 and NES8 datasets differ in terms of the treatment head parameters. More details about the specifics of each dataset can be found in [1]. As ES8 and ES9 share the same treatment head parameters with the exception of the intensity, the proton density is not provided for the ES9 dataset to limit storage size. Model weights for each surrogate trained on each of the provided datasets (called MES8, MES9 and MNES8) are also provided, abiding to the surrogate structure defined in [2]. In particular, each surrogate is composed of a proton and neutron model for both density and intensity prediction. Models can be used as detailed in the GitHub repository [3] related to [2]. File description Both the aforementioned density discretizations are named internally as "phits_logfull" and "hn_phits" for the proton and neutrons respectively, with the energy deposition one following the same convention as the protons. All files contained within this datasets are therefore named according to the discretizations as either "phits_logfull_cube_protons_\<depth in millimeters\>_data.nc", "phits_logfull_cube_dose_\<depth in millimeters\>_data.nc" or "hn_phits_cube_neutrons_\<depth in millimeters\>_data.nc". Each nc file contains an `xarray` variables, containing the MC-approximated histogram, details of the discretization, as well as important parameters such as the CT number of the considered slab, its density and the material's ID within the PHITS environment. Surrogates are provided in separate .zip files. Each surrogate contains 4 subfolders related to each surrogate component. The PDF components come in the form of pytorch checkpoints encapsulating Fourier Neural Operator models defined through package `neuraloperator` [4] [5] with version 0.3.0. Intensity components are instead .pickle files containing XGBoostRegressor objects defined through package `XGBoost` [6]. Each component also comes with a pickled dictionary containing important metadata related to model hyperparameters. Folder Structure The provided data consists of three different .zip files, each related to the ES8, ES9 and the NES8 datasets. Each .zip file comes already divided within the train, validation and test split on the basis of the starting energy. Within each split folder, simulations are represented through folders named in the format "\<Starting Energy\>MeV_05mm_800layers, and each contain the related proton and neutron fluences in files with the previously specified naming convention. It should be noted that, although the total size of the proposed dataset is of around 7GB, uncompressing the files requires a total size of 180.2 GB. References [1] H. N. Ratliff, F. Blangiardi, PHITS simulations of neutron and gamma-ray production from and transport of 70–250 MeV protons in hetero-geneous 1D tissue phantoms, Rodare, (in preparation for submission)(2025). [2] "Fast proton transport and neutron production in proton therapy using Fourier neural operators" (to be filled) [3] Blangiardi, F. (2025). AI_phase_space_PT [Computer software]. GitHub. [https://github.com/f-blan/AI_phase_space_PT](https://github.com/f-blan/AI_phase_space_PT) [4] J. Kossaifi, N. Kovachki, Z. Li, D. Pitt, M. Liu-Schiaffini, R. J. George, B. Bonev, K. Azizzadenesheli, J. Berner, A. Anandkumar, A library for learning neural operators (2024). arXiv:2412.10354. [5] N. B. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. M. Stuart, A. Anandkumar, Neural operator: Learning maps between function spaces, CoRR abs/2108.08481 (2021). [6] T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, ACM, 2016, p. 785–794. doi:10.1145/2939672.2939785. URL http://dx.doi.org/10.1145/2939672.2939785 Acknowledgements The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester has received funding from UK Research and Innovation under grant agreement No. 10102118 https://rodare.hzdr.de/record/4128 10.14278/rodare.4128 oai:rodare.hzdr.de:4128 eng url:https://www.hzdr.de/publications/Publ-42226 doi:10.14278/rodare.4127 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/novo url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Proton Therapy Surrogate Modelling Proton Transport Neutron Production Deep Learning Neural Operators Monte Carlo Proton and Neutron reduced phase space for surrogate modeling of Proton Therapy from PHITS simulations info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2262 2024-08-12T08:02:00Z openaire_data user-ecfunded user-rodare user-health user-matter user-elbe user-draco-elbe user-oncoray

Reimold, Marvin Assenbaum, Stefan Beyreuther, Elke Bodenstein, Elisabeth Brack, Florian-Emanuel Eisenmann, Christoph Englbrecht, F. Kroll, Florian Lindner, F. Masood, U. Pawelke, Jörg Schramm, Ulrich Schneider, Moritz Sobiella, Manfred Umlandt, Marvin Elias Paul Vescovi Pinochet, Milenko Andrés Zeil, Karl Ziegler, Tim Metzkes-Ng, Josefine 2023-04-18 The data set comprises raw data, processed data and detector data for the OCTOPOD detector applied for characterization of proton bunches at a proton cyclotron and a laser-driven proton accelerator. https://rodare.hzdr.de/record/2262 10.14278/rodare.2262 oai:rodare.hzdr.de:2262 eng info:eu-repo/grantAgreement/EC/H2020/871124/ doi:10.17815/jlsrf-2-58 doi:10.1017/hpl.2023.55 url:https://www.hzdr.de/publications/Publ-36823 url:https://www.hzdr.de/publications/Publ-36751 doi:10.14278/rodare.2261 url:https://rodare.hzdr.de/communities/draco-elbe url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/elbe url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/matter url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess laser-plasma acceleration of protons proton detector tomographic reconstruction Data publication for: OCTOPOD - single bunch tomography for angular-spectral characterization of laser-driven protons info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3770 2025-06-03T09:24:15Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data user-fwi user-health user-ibc user-rodare

Alsadig Ahmed Mohammed, Ahmed Peng, Xuan Boutier, Hugo Rodrigues Loureiro, Liliana Raquel Feldmann, Anja Hübner, René Cabrera, Humberto Kubeil, Manja Bachmann, Michael Baraban, Larysa 2025-04-07 The precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities. https://rodare.hzdr.de/record/3770 10.14278/rodare.3770 oai:rodare.hzdr.de:3770 doi:10.17815/jlsrf-3-159 url:https://www.hzdr.de/publications/Publ-41021 doi:10.14278/rodare.3671 url:https://rodare.hzdr.de/communities/fwi url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/ibc url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/1.0/legalcode Fibroblast activation protein immunotherapeutic target modules gold nanoparticles thermal lens spectroscopy Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3001 2024-06-06T08:03:15Z openaire_data user-health user-rodare

De, Trina Urbanski, Adrian Thangamani, Subasini Wyrzykowska, Maria Yakimovich, Artur 2024-06-05 Data Description: This dataset comprises fluorescence micrographs of HeLa cells, specifically labelled to identify nuclei and cell cytoplasm. These images were acquired as a technical calibration for a high-content screening study detailed and published in [1]. The HeLa cell line (ATCC-CCL-2), a widely used immortalised cell line in laboratory research, was cultured under standard conditions. Post-cultivation, the cells were fixed and stained with fluorescent dyes to visualise the nuclei and cytoplasm. The nuclei were stained with DAPI (4',6-diamidino-2-phenylindole), a blue-fluorescent DNA stain, while fluorescent-labeled phalloidin was used to detect actin filaments and delineate the cytoplasm. The entire process of cell culture, fixation, staining, and imaging adhered strictly to the protocols described in [1]. The preprocessed dataset includes 2,676 8-bit RGB images, each with a pixel resolution of 520 x 696 pixels. In these images, only two of the RGB channels are utilized: the red channel represents the cytoplasm, and the blue channel represents the nuclei. The dataset is divided into training, validation, and test subsets in a 70:20:10 ratio. The entire dataset is accompanied by instance segmentation masks for nuclei and cytoplasm objects obtained through a specialised CellProfiler [2] software. Notably, the test subset was annotated manually by a specialist, ensuring high-quality annotations. The original raw images are of a higher resolution, 1040 x 1392 pixels, and have a bit depth of 16 bits, providing more detailed information for advanced analyses. File Description: The file structure of the zip files is as follows: HeLaCytoNuc_{train/validation/test}.zip -> - images -> {filename}.tif - nuclei_masks -> {filename}.tif - cytoplasm_masks -> {filename}.tif HeLaCytoNuc_raw_images.zip -> {filename}.tif HeLaCytoNuc_test_cellprofiler_masks.zip -> - nuclei_masks -> {filename}.tif - cytoplasm_masks -> {filename}.tif References: 1. Rämö, Pauli, Anna Drewek, Cécile Arrieumerlou, Niko Beerenwinkel, Houchaima Ben-Tekaya, Bettina Cardel, Alain Casanova et al. "Simultaneous analysis of large-scale RNAi screens for pathogen entry." BMC genomics 15 (2014): 1-18. 2. Carpenter, Anne E., Thouis R. Jones, Michael R. Lamprecht, Colin Clarke, In Han Kang, Ola Friman, David A. Guertin et al. "CellProfiler: image analysis software for identifying and quantifying cell phenotypes." Genome biology 7 (2006): 1-11. https://rodare.hzdr.de/record/3001 10.14278/rodare.3001 oai:rodare.hzdr.de:3001 eng url:https://www.hzdr.de/publications/Publ-39181 doi:10.14278/rodare.3000 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Fluorescence microscopy high content microscopy cytoskeleton cell nuclei HeLaCytoNuc: fluorescence microscopy dataset with segmentation masks for cell nuclei and cytoplasm info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:915 2025-07-18T10:19:41Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. Chunked zip: The histological data are stored as chunked .zip files (*.zip.001 - *.zip.0XX). In order to unpack the data, download all chunks into the same directory, then unpack. https://rodare.hzdr.de/record/915 10.14278/rodare.915 oai:rodare.hzdr.de:915 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 url:https://www.hzdr.de/publications/Publ-31469 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1436 2025-01-20T13:10:52Z openaire_data user-health user-rodare

Sharma, Vaibhav Yakimovich, Artur 2023-01-16 Sample preparation artefacts represent a significant source of errors in high-content screening datasets leading to misinterpretation of results in drug discovery. To address this we have created a multispectral high-content imaging dataset with typical sample preparation artefacts added to the samples. This dataset consists of high-content images of cultured HeLa ATCC cells in the presence of typical sample preparation artefacts. The aim of this dataset. HeLa cells imaged in this dataset were cultured in a black 96-well (rows A to H and columns 1 to 12) polystyrene imaging plate (Corning, Sigma). To obtain a dataset similar to the experimental setup of a high-content image-based screening we have used a 96-well (rows A to H and columns 1 to 12) black polystyrene imaging plate (Corning, Sigma). HeLa cells were seeded a day prior to the experiment in 200 µL volume (per well) containing 250000 cells per mL in Dulbecco’s Modified Eagle’s Medium (Sigma) containing 10% fetal calf serum (Sigma) 4500 mg/L glucose (Sigma), sodium bicarbonate (Sigma), L-glutamine (Sigma), sodium pyruvate (Sigma), and non-essential amino acids (Sigma). To obtain a gradient of cell density, the cell suspension was stepwise diluted at 1:2 ratio during seeding (columns 2 to 12). The first column was reserved as no-cells control. Upon seeding, the HeLa cells were incubated overnight at 37° C in a 5% CO2 atmosphere with humidity control. The next day after seeding, cells were fixed with 4% paraformaldehyde (Sigma) solution prepared in phosphate buffer saline (PBS, Sigma). Upon fixation, HeLa cell nuclei were stained with Hoechst 33342 dye (Sigma) at 40 µg/mL concentration prepared in PBS. Row A was kept unstained as the control without Hoechst dye. Upon preparation of the bona fide artefact-free experimental plate, we have collected samples of dust across the approximately 100 m2 laboratory and prepared a suspension of these dust samples in PBS. Next, we added this suspension to rows A to G of the 96-well plate, leaving row H as an artefact-free control. The dataset consists of images obtained with 4x and 10x objectives using fluorescence cube assemblies for DAPI, CFP, GFP, TRITC and Cy5 channels. For hardware reasons, images with the CFP filter cube were obtained separately from images with DAPI, CFP, GFP, TRITC and Cy5 filter cubes. Furthermore, CFP images (and in some cases DAPI images) were obtained with varying exposure times corresponding to “_w1”, “_w2” and so on filename suffixes. Images were obtained using ImageXpress Micro XL high-content microscope (Molecular Devices). Images are organised into the following folders: 4x-cfp 4x-dapi-gfp-tritc-cy5 10x-6cfp 10x-6dapi 10x-cfp dapi-gfp-tritc-cy5 filters_spectra Here, folders A and B correspond to 4x magnification and contain images obtained with the CFP (folder A) and the other filter cubes respectively (folder B). Each folder contains “TimePoint_1” subfolder containing the raw images. In the case of 4x images, each field of view (“site” designed with “_s1”, “_s2” etc. suffixes) corresponds to a nearly perfect quarter of a 96-well plate well. In addition to the raw images in the “TimePoint_1”, a subfolder “Stitched” contains images of the entire wells. In the case of folder B containing all other fluorescence channels, “_w1”, “_w2”, “_w3”, and “_w4” correspond to a single optimal exposure time of DAPI, GFP, TRITC and Cy5 filters respectively. Similarly, folders C - F correspond to 10x magnification and contain images of multiple exposures of CFP and DAPI (folders C and D) and single exposures of CFP and other channels (folders E and F). In the case of CFP and DAPI multiple exposures folders, varying exposure times correspond to “_w1”, “_w2” etc. Finally, folder G contains metadata on filter cubes used in the dataset, including the emission and excitation filters spectra for each filter cube. https://rodare.hzdr.de/record/1436 10.14278/rodare.1436 oai:rodare.hzdr.de:1436 url:https://www.hzdr.de/publications/Publ-36282 doi:10.14278/rodare.1435 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode fluorescence microscopy high-content microscopy sample preparation artefacts High-content multi-spectral fluorescence microscopy sample preparation artefacts info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4185 2025-12-19T13:30:26Z openaire_data user-health user-rodare user-oncoray

Kieslich, Aaron Markus Singh, Yerik Palkowitsch, Martina Starke, Sebastian Hennings, Fabian Troost, Esther Gera Cornelia Krause, Mechthild Bensberg, Jona Lühr, Armin Heinzelmann, Feline Bäumer, Christian Timmermann, Beate Depauw, Nicolas Shih, Helen A. Löck, Steffen 2025-12-16 This repository contains the outputs, model checkpoints and result data of our deep-learning-based experiments for the approximation of Monte-Carlo-simulated linear energy transfer distributions and uncertainty estimation, which build the foundation for the corresponding article. https://rodare.hzdr.de/record/4185 10.14278/rodare.4185 oai:rodare.hzdr.de:4185 url:https://www.hzdr.de/publications/Publ-42451 url:https://www.hzdr.de/publications/Publ-42419 doi:10.14278/rodare.4184 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode proton radiotherapy linear energy transfer deep learning pencil beam scanning double scattering uncertainty quantification Data publication: Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton plans with uncertainty-aware external validation info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4360 2026-01-12T06:47:21Z openaire_data user-rodare user-zrt user-health

Kogler, Jürgen Donat, Cornelius Trommer, Johanna Kopka, Klaus Stadlbauer, Sven 2025-11-19 Analytical data for chemical synthesis (HPLC, NMR, HRMS), biological data for in vitro and in vivo evaluation (binding assays, small animal imaging) https://rodare.hzdr.de/record/4360 10.14278/rodare.4360 oai:rodare.hzdr.de:4360 eng doi:10.1186/s41181-025-00398-9 url:https://www.hzdr.de/publications/Publ-42251 url:https://www.hzdr.de/publications/Publ-42101 doi:10.14278/rodare.4142 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/zrt info:eu-repo/semantics/restrictedAccess FAP FAPI PET fluorescence-guided surgery noninvasive molecular imaging Data publication: Synthesis and preclinical evaluation of FAP-targeting radiotracers for PET and optical imaging info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4444 2026-02-23T08:29:00Z openaire_data user-novo user-rodare user-health user-oncoray user-hzdr

Müller, Sara Tabea Akgun, Bora Bekkevoll, Anna Blorstad Thu, Sander Engebertsen, Anders Jagt, Thyrza Pausch, Guntram Phan, Than Binh Ratliff, Hunter Römer, Katja Smeland Ytre-Hauge, Kristian Stokkevag, Camilla Tarakoglu, Engin Turko, Joseph Wolf, Andreas Yazici, Berkay Meric, Ilker Kögler, Toni 2026-01-22 This data set contains the experimental raw data of the NOVO compact detector array (NOVCoDA) from the measurement campaign at OncoRay Dresden, Germany in December 2025. This experiment is the first test of the NOVCoDA prototype at a clinical proton beam. The aim of the measurement campaign was to characterize the response behavior of the scintillators used under high-energy neutron irradiation (especially the pulse-shape discrimination behavior), as well as to test the imaging, range-shift, and rate-processing capabilities of the system. Setup: Measurements 01.12.-09.12.: miniNOVO (version 5): The prototype consists of 12 organic scintillator elements (6 × M600 and 6 × organic glas scintillator) of the dimensions 12×12×140 mm3 Measurements 10.12.-12.12.: miniNOVO (version 5.1): The prototype consists of 14 organic scintillator elements (7 × M600 and 7 × organic glas scintillator) of the dimensions 12×12×140 mm3 The scintillator bars have dual readout composed of 2 × Hamamatsu R7378A (1’’) PMTs1, 4 × Hamamatsu S14161-3050HS-04 SiPM1 + U3012 (+ custom front-end electronics) (only 2 × for miniNOVO version 5) and 8 × Hamamatsu R2059-01 (2’’) PMTs1. The data was recorded with 2 CAEN V1730S3 14-bit, 16-channel digitizers (named dta and dtb) with a sampling frequency of 425.216 MS/s. The detector array was placed at 90° w.r.t. to the fixed-beam research beam line of the Dresden proton therapy facility at OncoRay, Dresden. A cylindrical PMMA (solid/with air gap/with bone insert) was placed centrally in front of the detector head and irradiated with proton energies from 75-225 MeV and varying currents between 10-2000 pA at various positions (± 180 mm w.r.t. central position). In addition measurements with the online-adaptive RAPTOR phantom in different configurations (air insert/bone insert/swelling/no swelling) were executed. Data structure: The directory DOI_calibration contains the position calibration measurements with a Sr-90 source. Energy_calibration holds the energy calibration measurements with a Na-22 and Cs-137 source. In efficiency_measurement the measurements with a Na-22 source at phantom position (with and without PMMA phantom) can be found. PMMA_phantom is dedicated to all the beam measurements with the cylindrical phantom (with and without various inserts) while the directory online_adaptive_phantom provides the same for the measurements with the RAPTOR phantom. All measurements for which waveforms were recorded are stored in waveforms and backend_comparison is comprised of repeat measurements with the cylindrical PMMA phantom where one detector (dtb, ch2 and ch3) was connected to an alternative back-end system for comparison. All other measurements and test runs are in the tests folder. The PDF-files 2025-12_ NOVO-first-proton-facility-tests-PGTV-Wiki.pdf and 2025-12_ NOVO-first-proton-facility-tests-Week-2-PGTV-Wiki.pdf hold information about the setup of the experiment and and more details about the individual measurements (elog). The file 2025-12_ NOVO-first-proton-facility-tests-Run-List-PGTV-Wiki.pdf contains the run list with all parameters for each measurement. In 2025-12_OncoRay_HEBC_Monitor_Data.zip csv-files with the beam control meta data can be found (one file for each measurement day). The main configuration file for the digitizers is called template_main.cfg. Data Format: All data is saved in root files which each contain two root trees, one for each digitizer, named “dta” and “dtb”. The trees hold the following information in the form of listmode data for each event: digitizer channel ("channel"), charge integrated over long gate ("Elong"), charge integrated over short gate ("Eshort"), digitizer flags ("flags") and the timestamp (separated in three parts: "timestamp", "timestampExtended", "time"). Additionally, the root files also contain an TArrayD which denotes the start time of the measurement in UNIX time at its first index and the stop time at its second. There are two configuration files for each data file (named “filename_dtx.config”), one for each digitizer card. These text files contain the information about the digitizer settings for each run. [1] Hamamatsu Photonics Deutschland GmbH, Arzbergerstr. 10, 82211 Herrsching am Ammersee, Germany. [2] Target Systemelektronik, Heinz-Fangman-Straße 4, 42287 Wuppertal, Germany. [3] CAEN S.p.A., Via Vetraia 11, 55049 Viareggio (LU), Italy. The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester have received funding from UK Research and Innovation under grant agreement No. 10102118 https://rodare.hzdr.de/record/4444 10.14278/rodare.4444 oai:rodare.hzdr.de:4444 eng url:https://www.hzdr.de/publications/Publ-43021 doi:10.14278/rodare.4443 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/novo url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess NOVO Neutron imaging Prompt gamma ray imaging Dual particle imaging Range verification in proton therapy OncoRay First tests of the NOVO Compact Detector Array at a Proton Facility (OncoRay) info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:66 2018-10-30T12:42:21Z openaire_data user-fwk user-health user-hzdr user-matter user-rodare

Obst-Huebl, Lieselotte Ziegler, Tim Brack, Florian-Emanuel Branco, João Bussmann, Michael Cowan, Thomas E. Curry, Chandra B. Fiuza, Frederico Garten, Marco Gauthier, Maxence Göde, Sebastian Glenzer, Siegfried H. Huebl, Axel Irman, Arie Kim, Jongjin B. Kluge, Thomas Kraft, Stephan Kroll, Florian Metzkes-Ng, Josefine Pausch, Richard Prencipe, Irene Rehwald, Martin Rödel, Christian Schlenvoigt, Hans-Peter Schramm, Ulrich Zeil, Karl 2018-10-30 This data repository contains analyzed data files of the shown figures and simulation input files. Please see the according README.txt files in the individual directories and the original manuscript for guidance. Manuscript title: All-optical structuring of laser-driven proton beam profiles Authors: Lieselotte Obst, Tim Ziegler, Florian-Emanuel Brack, Joao Branco, Michael Bussmann, Thomas E. Cowan, Chandra B. Curry, Frederico Fiuza, Marco Garten, Maxence Gauthier, Sebastian Göde, Siegfried H. Glenzer, Axel Huebl, Arie Irman, Siegfried H. Glenzer, Axel Huebl, Arie Irman, Jongjin B. Kim, Thomas Kluge, Stephan Kraft, Florian Kroll, Josefine Metzkes-Ng, Richard Pausch, Irene Prencipe, Martin Rehwald, Christian Rödel, Hans-Peter Schlenvoigt, Ulrich Schramm, Karl Zeil Submitted to: Nature Communications (2018) Responsible for the data repository: Lieselotte Obst-Huebl, TU Dresden and HZDR Axel Huebl, TU Dresden and HZDR Tim Ziegler, TU Dresden and HZDR Thomas Kluge, HZDR https://rodare.hzdr.de/record/66 10.14278/rodare.66 oai:rodare.hzdr.de:66 url:https://www.hzdr.de/publications/Publ-28136 doi:10.14278/rodare.65 url:https://rodare.hzdr.de/communities/fwk url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/matter url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode All-optical structuring of laser-driven proton beam profiles data sets info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2668 2025-10-09T14:31:40Z openaire_data user-rodare user-health

Li, Rui Della Maggiora Valdes, Gabriel Eugenio Andriasyan, Vardan Petkidis, Anthony Yushkevich, Artsemi Kudryashev, Mikhail Yakimovich, Artur 2024-01-12 How to cite us Li, R., Della Maggiora, G., Andriasyan, V., Petkidis, A., Yushkevich, A., Deshpande, N., ... & Yakimovich, A. (2024). Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model. Communications Engineering, 3(1), 186. @article{li2024microscopy, title={Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model}, author={Li, Rui and Della Maggiora, Gabriel and Andriasyan, Vardan and Petkidis, Anthony and Yushkevich, Artsemi and Deshpande, Nikita and Kudryashev, Mikhail and Yakimovich, Artur}, journal={Communications Engineering}, volume={3}, number={1}, pages={186}, year={2024}, publisher={Nature Publishing Group UK London} } Download Timeout Troubleshooting Use "-C" flag of curl in case you experience timeout of the download: curl -C - https://rodare...tar.gz_part1\?download\=1 --output spa.tar.gz_part1 Dataset This dataset contains a sample of 600 fluorescently labelled nuclei of cultured cells imaged using widefield fluorescence microscopy and confocal fluorescence microscopy at different focal planes. Image preprocessing Notably, the hardware precision of the sectioning process led to variations in the step size when shifting the focal plane between the two devices. This resulted in distinct z-dimensions between the datasets obtained from the two microscopy techniques. The confocal stacks in raw data comprised 92 focal planes, whereas the widefield stacks consisted of only 40 slices. Each focal plane image had a shape [2048, 2048, 1]. Assuming the central slice of each stack to be the in-focus, we performed z-direction registration by downsampling the confocal stacks from the central slice (46th) to match the 40 slices of the widefield stacks. Due to the instrumental limitations, a slight drift was noticeable between images. To address this, we used the phase cross-correlation algorithm [2] to compensate for the offsets on the x-y plane for the z-dimension registered image stacks. Having completed the registration and alignment along three dimensions, we then partitioned the original images into non-overlapping patches with dimensions of [128, 128, 1] in the xy plane. This partitioned dataset serves as the test dataset for validating our blind-deconvolution model, conducted without the specific Point Spread Function (PSF) parameters [3]. Files description The Widefield-confocal Microscopy Dataset is stored in the '*.npz' format, encompassing the variables 'c_img' and 'w_img.' These handles respectively denote the confocal images and their corresponding widefield microscopy images. Both types of data undergo registration, alignment, and normalization, with values scaled to range between [0.0, 1.0]. For each category, the data has a shape of [600, 128, 128, 40], where the first dimension denotes the individual field of view and the last dimension signifies the z-dimension representing changes in the focal plane for virtual sectioning. The first dimension corresponds to the patch number, each with a patch size of [128, 128]. Sample preparation and microscopy A549 lung carcinoma cell line cells were seeded in 96-well imaging plates a night prior to imaging, then fixed with 4% paraformaldehyde (Sigma) and stained for DNA with Hoechst 33342 fluorescent dye (Sigma). Cell culture was maintained similarly to the procedures described in [1]. Next, stained cell nuclei were imaged using ImageXpress Confocal system (Molecular Devices) in either confocal or widefield mode employing Nikon 20X Plan Apo Lambda objective. To obtain 3D information images in both modes were acquired as Z-stacks with 0.3 µm and 0.7 µm for confocal and widefield modes respectively. Confocal z-stack was Nyquist sampled. The excitation wavelength was 405 nm and the emission was 452 nm. Using these settings, we obtained individual stacks for both modalities, with each stack covering 2048 by 2048 pixels or 699 by 699 µm. References Yakimovich, Artur, et al. "Plaque2. 0—a high-throughput analysis framework to score virus-cell transmission and clonal cell expansion." PloS one 10.9 (2015): e0138760. Alink, Mark S. Oude, et al. "Lowering the SNR wall for energy detection using cross-correlation." IEEE transactions on vehicular technology 60.8 (2011): 3748-3757. Li, Rui, et al. "Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model." arXiv preprint arXiv:2306.02929 (2023). https://rodare.hzdr.de/record/2668 10.14278/rodare.2668 oai:rodare.hzdr.de:2668 eng arxiv:arXiv:2306.02929 url:https://www.hzdr.de/publications/Publ-37066 doi:10.48550/arXiv.2306.02929 url:https://www.hzdr.de/publications/Publ-38497 url:https://www.hzdr.de/publications/Publ-37066 doi:10.14278/rodare.2667 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode fluorescence microscopy widefield confocal corelative microscopy Correlated Widefield-confocal Microscopy Dataset info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1967 2024-07-18T06:57:30Z user-hzdr user-health user-fwm user-fwc user-rodare

Starke, Sebastian Zwanenburg, Alex Leger, Karoline Zöphel, Klaus Kotzerke, Jörg Krause, Mechthild Baumann, Michael Troost, Esther Gera Cornelia Löck, Steffen 2022-11-24 We include the input data, analysis scripts, analysis results and scripts to create the visualizations and plots used in the manuscript and supplement to our article "Longitudinal and multimodal radiomics models for head-and-neck cancer outcome prediction". https://rodare.hzdr.de/record/1967 10.14278/rodare.1967 oai:rodare.hzdr.de:1967 doi:10.3390/cancers15030673 url:https://www.hzdr.de/publications/Publ-35309 url:https://www.hzdr.de/publications/Publ-35560 url:https://www.hzdr.de/publications/Publ-35309 url:https://www.hzdr.de/publications/Publ-39339 doi:10.14278/rodare.1966 url:https://rodare.hzdr.de/communities/fwc url:https://rodare.hzdr.de/communities/fwm url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/closedAccess radiomics head-and-neck cancer loco-regional control survival analysis computed tomography positron emission tomography cox proportional hazards longitudinal imaging Data publication: Longitudinal and multimodal radiomics models for head-and-neck cancer outcome prediction info:eu-repo/semantics/other other

oai:rodare.hzdr.de:558 2025-07-18T10:19:40Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/558 10.14278/rodare.558 oai:rodare.hzdr.de:558 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:791 2025-07-18T10:19:40Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/791 10.14278/rodare.791 oai:rodare.hzdr.de:791 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:810 2025-07-18T10:19:41Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild von Neubeck, Cläre Haase, Robert Lühr, Armin Dietrich, Antje 2021-01-20 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/810 10.14278/rodare.810 oai:rodare.hzdr.de:810 eng info:eu-repo/grantAgreement/EC/H2020/730983/ url:https://www.hzdr.de/publications/Publ-32124 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2442 2025-01-20T13:10:52Z openaire_data user-health user-rodare

Sharma, Vaibhav Yakimovich, Artur 2023-01-16 How to cite us Sharma, Vaibhav, and Artur Yakimovich. "A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy." Scientific Data 11, no. 1 (2024): 232. @article{sharma2024deep, title={A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy}, author={Sharma, Vaibhav and Yakimovich, Artur}, journal={Scientific Data}, volume={11}, number={1}, pages={232}, year={2024}, publisher={Nature Publishing Group UK London} } Download Timeout Troubleshooting Use "-C" flag of curl in case you experience timeout of the download: curl -C - https://rodare...tar.gz_part1\?download\=1 --output ...tar.gz_part1 Dataset Description Sample preparation artefacts represent a significant source of errors in high-content screening datasets leading to misinterpretation of results in drug discovery. To address this we have created a multispectral high-content imaging dataset with typical sample preparation artefacts added to the samples. This dataset consists of high-content images of cultured HeLa ATCC cells in the presence of typical sample preparation artefacts. The aim of this dataset. HeLa cells imaged in this dataset were cultured in a black 96-well (rows A to H and columns 1 to 12) polystyrene imaging plate (Corning, Sigma). To obtain a dataset similar to the experimental setup of a high-content image-based screening we have used a 96-well (rows A to H and columns 1 to 12) black polystyrene imaging plate (Corning, Sigma). HeLa cells were seeded a day prior to the experiment in 200 µL volume (per well) containing 250000 cells per mL in Dulbecco’s Modified Eagle’s Medium (Sigma) containing 10% fetal calf serum (Sigma) 4500 mg/L glucose (Sigma), sodium bicarbonate (Sigma), L-glutamine (Sigma), sodium pyruvate (Sigma), and non-essential amino acids (Sigma). To obtain a gradient of cell density, the cell suspension was stepwise diluted at 1:2 ratio during seeding (columns 2 to 12). The first column was reserved as no-cells control. Upon seeding, the HeLa cells were incubated overnight at 37° C in a 5% CO2 atmosphere with humidity control. The next day after seeding, cells were fixed with 4% paraformaldehyde (Sigma) solution prepared in phosphate buffer saline (PBS, Sigma). Upon fixation, HeLa cell nuclei were stained with Hoechst 33342 dye (Sigma) at 40 µg/mL concentration prepared in PBS. Row A was kept unstained as the control without Hoechst dye. Upon preparation of the bona fide artefact-free experimental plate, we have collected samples of dust across the approximately 100 m2 laboratory and prepared a suspension of these dust samples in PBS. Next, we added this suspension to rows A to G of the 96-well plate, leaving row H as an artefact-free control. The dataset consists of images obtained with 4x and 10x objectives using fluorescence cube assemblies for DAPI, CFP, GFP, TRITC and Cy5 channels. For hardware reasons, images with the CFP filter cube were obtained separately from images with DAPI, CFP, GFP, TRITC and Cy5 filter cubes. Furthermore, CFP images (and in some cases DAPI images) were obtained with varying exposure times corresponding to “_w1”, “_w2” and so on filename suffixes. Images were obtained using ImageXpress Micro XL high-content microscope (Molecular Devices). Images are organised into the following folders: 4x-cfp 4x-dapi-gfp-tritc-cy5 10x-6cfp 10x-6dapi 10x-cfp dapi-gfp-tritc-cy5 filters_spectra Here, folders A and B correspond to 4x magnification and contain images obtained with the CFP (folder A) and the other filter cubes respectively (folder B). Each folder contains “TimePoint_1” subfolder containing the raw images. In the case of 4x images, each field of view (“site” designed with “_s1”, “_s2” etc. suffixes) corresponds to a nearly perfect quarter of a 96-well plate well. In addition to the raw images in the “TimePoint_1”, a subfolder “Stitched” contains images of the entire wells. In the case of folder B containing all other fluorescence channels, “_w1”, “_w2”, “_w3”, and “_w4” correspond to a single optimal exposure time of DAPI, GFP, TRITC and Cy5 filters respectively. Similarly, folders C - F correspond to 10x magnification and contain images of multiple exposures of CFP and DAPI (folders C and D) and single exposures of CFP and other channels (folders E and F). In the case of CFP and DAPI multiple exposures folders, varying exposure times correspond to “_w1”, “_w2” etc. Finally, folder G contains metadata on filter cubes used in the dataset, including the emission and excitation filters spectra for each filter cube. https://rodare.hzdr.de/record/2442 10.14278/rodare.2442 oai:rodare.hzdr.de:2442 url:https://www.hzdr.de/publications/Publ-36282 url:https://www.hzdr.de/publications/Publ-39995 doi:10.14278/rodare.1435 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode fluorescence microscopy high-content microscopy sample preparation artefacts High-content multi-spectral fluorescence microscopy sample preparation artefacts info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:255 2024-07-18T06:56:26Z openaire_data user-health user-rodare

Starke, Sebastian Leger, Stefan Zwanenburg, Alex Leger, Karoline Lohaus, Fabian Linge, Annett Schreiber, Andreas Kalinauskaite, Goda Tinhofer, Inge Guberina, Nika Guberina, Maja Balermpas, Panagiotis Grün, Jens von der Ganswindt, Ute Belka, Claus Peeken, Jan C. Combs, Stephanie E. Böke, Simon Zips, Daniel Richter, Christian Troost, Esther Gera Cornelia Krause, Mechthild Baumann, Michael Löck, Steffen 2020-02-27 These are the results from the analyses presented in a paper submitted to Scientific Reports. The zip file contains the trained model files and the plots that were used in the manuscript. Code for reproduction of our analyses can be obtained from https://github.com/oncoray/cnn-hnscc. There, you also find instructions on how to load our models. https://rodare.hzdr.de/record/255 10.14278/rodare.255 oai:rodare.hzdr.de:255 doi:10.1038/s41598-020-70542-9 url:https://www.hzdr.de/publications/Publ-30759 url:https://www.hzdr.de/publications/Publ-30750 url:https://www.hzdr.de/publications/Publ-39339 doi:10.14278/rodare.254 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc/4.0/legalcode convolutional neural networks Keras Deep learning head and neck cancer loco-regional-recurrence Cox proportional hazards 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3742 2025-08-04T12:15:02Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data user-health user-rodare

Wyrzykowska, Maria della Maggiora, Gabriel Deshpande, Nikita Mokarian, Ashkan Yakimovich, Artur 2024-08-30 How to cite us Wyrzykowska, Maria, Gabriel Della Maggiora, Nikita Deshpande, Ashkan Mokarian, and Artur Yakimovich. "A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy." Scientific Data 12, no. 1 (2025): 1-11. @article{wyrzykowska2025benchmark, title={A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy}, author={Wyrzykowska, Maria and Della Maggiora, Gabriel and Deshpande, Nikita and Mokarian, Ashkan and Yakimovich, Artur}, journal={Scientific Data}, volume={12}, number={1}, pages={1--11}, year={2025}, publisher={Nature Publishing Group} } Data sources Raw data used during the study can be found in corresponding references. VACV: Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF. Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion. PLoS One. 2015 Sep 28;10(9):e0138760. doi: 10.1371/journal.pone.0138760. PMID: 26413745; PMCID: PMC4587671. HADV: Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Witte R, Puntener D, Greber UF. Microscopy deep learning predicts virus infections and reveals the mechanics of lytic-infected cells. iScience. 2021 May 15;24(6):102543. doi: 10.1016/j.isci.2021.102543. PMID: 34151222; PMCID: PMC8192562. HSV, IAV, RV: Olszewski, D., Georgi, F., Murer, L. et al. High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections. Sci Data 9, 610 (2022). https://doi.org/10.1038/s41597-022-01733-4 Data organisation For each virus (HADV, VACV, IAV, RV and HSV) we provide the processed data in a separate directory, divided into three subdirectories: `train`, `val` and `test`, containing the proposed data split. Each of the subfolders contains two npy files: `x.npy` and `y.npy`, where `x.npy` contains the fluorescence or brightfield signal (both for HADV, as separate channels) of the cells or nuclei and `y.npy` contains the viral signal. The data is already processed as described in the Data preparation section. Additionally, Cellpose masks are made available for the test data in separate masks directory. For each virus except for VACV, there is a subdirectory `test` containing nuclei masks (`nuc.npy`). For HADV cell masks are also available (`cell.npy`). Data preparation Each of VACV plaques was imaged to produce 9 files per channel, that need to be stitched to recreate the whole plaque. To achieve this, multiview-stitcher toolbox has been used. The stitching was first performed on the third channel, representing the brightfield microscopy image of the samples. Then, the parameters found for this channel were used to stitch the rest of the channels. VACV dataset represents a timelapse, from which timesteps 100, 108 and 115 have been selected to produce the data then used in the experiments. Images have been center-cropped to 5948x6048 to match the size of the smallest image in the dataset (rounded down to the closest multiple of 2). The data was additionally manually filtered to remove the samples that constituted only uninfected cells (C02, C07, D02, D07, E02, E07, F02, F07). The HAdV dataset is also a timelapse, from which only the last timestep (49th) has been selected. For the rest of the datasets (HSV, IAV, RV) only the negative control data was used, which was selected in the following way: from the data collected at the University of Zürich, from the Screen samples only the first 2 columns were selected and from the ZPlates and prePlates samples only the first 12 columns. All of the datasets were divided into training, validation and test holdouts in 0.7:0.2:0.1 ratios, using random seed 42 to ensure reproducibility. For the time-lapse data, it was ensured that the same sample from different timesteps only exists in one of the holdouts, to prevent information leakage and ensure fair evaluation. All of the samples were normalised to [-1, 1] range, by subtracting the 3rd percentile and dividing by the difference between percentile 99.8 and 3, clipping to [0, 1] and scaling to [-1, 1] range. For the brightfield channel of HAdV, percentiles 0.1 and 99.9 were used. These cutoff points were selected based on the analysis of the histograms of the values attained by the data, to make the best use of the available data range. Specific values used for the normalization are summarized in Figure 3 of the manuscript in Related/alternate identifiers. To prepare the cell nuclei masks, Cellpose model with pre-trained weights cyto3 has been used on the fluorescence channel. The diameter was set to 7 for all the datasets except for HAdV, for which the automatic estimation of the diameter was employed. Cell masks were prepared using Cellpose with pre-trained weights cyto3 with a diameter set to 70 on brightfield images stacked with fluorescence nuclei signal. The data preparation can be reproduced by first downloading the datasets and then running scripts that are located in `scripts/data_processing` directory of the [VIRVS repository](https://github.com/casus/virvs), first modifying the paths in them: for HAdV data: `preprocess_hadv.py` for VACV data: `stitch_vacv.py` + `preprocess_vacv.py` for the rest of the viruses: `preprocess_other.py` to prepare Cellpose predictions: `prepare_cellpose_preds.py` (for cells) and `prepare_cellpose_preds_nuc.py` (for nuclei) https://rodare.hzdr.de/record/3742 10.14278/rodare.3742 oai:rodare.hzdr.de:3742 eng url:https://www.hzdr.de/publications/Publ-39523 url:https://www.hzdr.de/publications/Publ-40031 doi:10.14278/rodare.3129 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode virus infected cell microscopy deep learning virtual staining A Dataset for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4516 2026-02-23T08:29:23Z openaire_data user-rodare user-health

Li, Rui Yushkevich, Artsemi Kudryashev, Misha Yakimovich, Artur 2026-02-18 ProteinNet3D is a curated large-scale dataset of 3D macromolecular density volumes designed to support representation learning and benchmarking in structural biology. The dataset is derived from the publicly available Electron Microscopy Data Bank (EMDB), a comprehensive repository of experimentally determined cryo-electron microscopy (cryo-EM) maps spanning diverse macromolecules, molecular assemblies, and subcellular structures. ProteinNet3D focuses specifically on individual macromolecules resolved by single-particle analysis (SPA) or subtomogram averaging (STA), ensuring methodological consistency across samples. To emphasize biologically meaningful structures while avoiding extreme cases, entries were restricted to a molecular weight range of 100–1500 kDa. This criterion excludes small domains and excessively large complexes, resulting in a dataset well-suited for learning size-robust structural representations. All volumes are standardized through isotropic resampling, spatial normalization to a fixed grid (64³ voxels), and intensity normalization to zero mean and unit variance. Background regions are masked using annotated contour levels to reduce noise contributions. To enhance diversity and rotational invariance, each structure is augmented with multiple random 3D rotations. Overall, ProteinNet3D comprises 26,110 processed samples and captures substantial structural heterogeneity, experimental variability, and realistic noise characteristics, making it a rigorous benchmark for 3D deep learning in cryo-EM. https://rodare.hzdr.de/record/4516 10.14278/rodare.4516 oai:rodare.hzdr.de:4516 url:https://www.hzdr.de/publications/Publ-43018 doi:10.14278/rodare.4515 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode cryo-EM deep learning proteins EMDB proteinNet3D info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1811 2024-08-12T08:08:43Z openaire_data user-rodare user-health user-hzdr user-oncoray

Pausch, Guntram Schellhammer, Sonja Kögler, Toni Berthold, Jonathan Römer, Katja Rinscheid, Andreas Werner, Theresa Hueso-González, Fernando Kögler, Toni Petzoldt, Johannes Schellhammer, Sonja Pausch, Guntram 2023-05-10 This dataset comprises the data reported on by Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). Please refer to this publication for details on the experimental setup, data acquisition and preprocessing. The process is summarised in the following. A static, pulsed pencil beam was delivered to a target without and with cylindrical air cavities of 5 to 20 mm thickness and prompt gamma-ray timing distributions were acquired. Experimental setup: A homogeneous cylindrical phantom comprised of poly(methylmethacrylate) was used. Air cavities of varying thickness ∆R ∈ {0 mm, 5 mm, 10 mm, 20 mm} were successively introduced into the phantom to mimic anatomical variations leading to range deviations. For each air cavity thickness, the phantom was irradiated with proton pencil beams of two different kinetic energies (E_1 = 162 MeV and E_2 = 227 MeV) and a micropulse repetition rate of 106.3 MHz. Prompt-gamma ray timing distributions were measured with a detection unit consisting of a single ∅2 ” × 2 ” CeBr_3 crystal by Scionix, a Hamamatsu R13089-100 photomultiplier and a U100 digital spectrometer by Target Systemelektronik, which was placed at a backward angle of 130° . A static pencil beam was directed centrally at the phantom. The beam was pulsed in spots with a spot duration of 69 ms, a period of 72 ms and 1e9 (!) protons per spot (corresponding approximately to the combined signal of 8 prompt-gamma ray detection units for one strongly weighted clinical pencil beam scanning spot). One measurement consisted of 100 spots. Overall, the experiment comprised eight measurements covering the set of four cavity thicknesses ∆R and two beam energies E_1 and E_2. Experiments were carried out in the patient treatment room of OncoRay, Dresden. Data preprocessing: The raw data of each measurement was preprocessed as follows: The binary data was converted to ROOT. The photomultiplier gain drift was corrected for and the integral signal charge was converted into deposited energy. Time digitalisation nonlinearities were corrected for. The calibrated data was then saved in list-mode format. The data was assigned to the spot number and the detection time relative to the accelerator radiofrequency (fine time) was used to populate a prompt gamma-ray timing histogram for each spot. No background or phase shift correction were applied. Data structure: The dataset contains one root file for each measurement, named by the detector number in the format u100-p00XX and the measurement time. The spreadsheet MeasurementIndex_20160716_SingleSpot.xlsx contains the details of each measurement. The corrected and calibrated PGT spectra can be found in the root file at analysis/05_PGT_for_Layers_and_Spots. Each root file contains the following directories: analysis 01_Layers_and_Spots_Detection: association between spot number and measurement time 02_Gain_Correction: energy gain drift correction curve 03_Energy_Calibration: energy calibration curve 04_Fine_Time_Linearization: timing non-linearity calibration curve 05_PGT_for_Layers_and_Spots: final PGT spectra - for each spot of each layer: PGT_*_all: timing spectrum of the whole energy range PGT_*_2,5to7MeV: timing spectrum for events between 2.5 and 7 MeV only PGT_*_3to5MeV: timing spectrum for events between 3 and 5 MeV only ESpec: energy spectrum EoT: two-dimensional energy-timing spectrum data: list-mode data (not histogrammed) uncorrected: before the correction and calibration steps corrected: after the correction and calibration steps meta: measurement meta data (log file containing applied detector HV etc.) histograms: selected example histograms For further questions, please refer to the contact persons stated in the Contributors section. https://rodare.hzdr.de/record/1811 10.14278/rodare.1811 oai:rodare.hzdr.de:1811 eng url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-769231 url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-840468 doi:10.1088/1361-6560/ab176d doi:10.3389/fphy.2022.932950 url:https://www.hzdr.de/publications/Publ-36784 doi:10.14278/rodare.1810 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess proton therapy treatment verification prompt gamma-ray timing experimental data Experimental prompt gamma-ray timing data for proton treatment verification in a clinical facility using a fixed beam info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:1946 2025-02-06T08:24:13Z software user-health user-hzdr user-rodare

Abdussalam, Wildan 2022-11-10 Software to synchonise the data between various data sources and casus database server. For Unix users please use MigrateWhere2test_2.0Unix.zip and for WIndows users please use MigrateWhere2test_2.0Win.zip. In order to use the scripts, please use the following instructions: Windows 1. Create the postgreq sql database and set the port 5432. 2. Apply w2testRegion.sql to the Postgresql database. It creates the schema of the database. 3. Create folder C:\Workspaces and unzip the unix file. 4. Create folder in workspaces, com.com.casus.env.where2test.migration\COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION. 5. Install requirement.txt. We use some python scripts for downloading and cleaning the data 6. Set the memory on the file MigrateWhere2test_run.bat minimal to 13 GB 7. Set run Develop and run the .bat file on the folder MigrateWhere2test_2.0Unix to run in localhost. Unix 1. Create PostgreSQL with port 32771. 2. Apply w2testRegion.sql to the Postgresql database. It creates the schema of the database. 3. Create folder /home/wildan/Workspaces and unzip the unix file. 4. Open the file MigrateWhere2test/MigrateWhere2test_run.sh and change the mode "Default" by "Production" 5. Create folder in workspaces, com.com.casus.env.where2test.migration.unix/COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION. 6. Install requirement.txt. We use some python scripts for downloading and cleaning the data 7. Set the memory on the file MigrateWhere2test_run.sh minimal to 13 GB 8. run the MigrateWhere2test_run.sh in the "Production" mode. https://rodare.hzdr.de/record/1946 10.14278/rodare.1946 oai:rodare.hzdr.de:1946 eng url:https://www.hzdr.de/publications/Publ-34430 doi:10.14278/rodare.1500 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode data pipeline Data synchronizator of Where2test pipeline info:eu-repo/semantics/other software

oai:rodare.hzdr.de:4030 2025-10-09T15:20:00Z openaire_data user-health user-rodare

Li, Rui Della Maggiora Valdes, Gabriel Eugenio Andriasyan, Vardan Petkidis, Anthony Yushkevich, Artsemi Kudryashev, Mikhail Yakimovich, Artur 2024-01-12 How to cite us Li, R., Della Maggiora, G., Andriasyan, V., Petkidis, A., Yushkevich, A., Deshpande, N., ... & Yakimovich, A. (2024). Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model. Communications Engineering, 3(1), 186. @article{li2024microscopy, title={Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model}, author={Li, Rui and Della Maggiora, Gabriel and Andriasyan, Vardan and Petkidis, Anthony and Yushkevich, Artsemi and Deshpande, Nikita and Kudryashev, Mikhail and Yakimovich, Artur}, journal={Communications Engineering}, volume={3}, number={1}, pages={186}, year={2024}, publisher={Nature Publishing Group UK London} } Download Timeout Troubleshooting Use "-C" flag of curl in case you experience timeout of the download: curl -C - https://rodare...tar.gz_part1\?download\=1 --output spa.tar.gz_part1 Dataset This dataset contains a sample of 600 fluorescently labelled nuclei of cultured cells imaged using widefield fluorescence microscopy and confocal fluorescence microscopy at different focal planes. Image preprocessing Notably, the hardware precision of the sectioning process led to variations in the step size when shifting the focal plane between the two devices. This resulted in distinct z-dimensions between the datasets obtained from the two microscopy techniques. The confocal stacks in raw data comprised 92 focal planes, whereas the widefield stacks consisted of only 40 slices. Each focal plane image had a shape [2048, 2048, 1]. Assuming the central slice of each stack to be the in-focus, we performed z-direction registration by downsampling the confocal stacks from the central slice (46th) to match the 40 slices of the widefield stacks. Due to the instrumental limitations, a slight drift was noticeable between images. To address this, we used the phase cross-correlation algorithm [2] to compensate for the offsets on the x-y plane for the z-dimension registered image stacks. Having completed the registration and alignment along three dimensions, we then partitioned the original images into non-overlapping patches with dimensions of [128, 128, 1] in the xy plane. This partitioned dataset serves as the test dataset for validating our blind-deconvolution model, conducted without the specific Point Spread Function (PSF) parameters [3]. Files description The Widefield-confocal Microscopy Dataset is stored in the '*.npz' format, encompassing the variables 'c_img' and 'w_img.' These handles respectively denote the confocal images and their corresponding widefield microscopy images. Both types of data undergo registration, alignment, and normalization, with values scaled to range between [0.0, 1.0]. For each category, the data has a shape of [600, 128, 128, 40], where the first dimension denotes the individual field of view and the last dimension signifies the z-dimension representing changes in the focal plane for virtual sectioning. The first dimension corresponds to the patch number, each with a patch size of [128, 128]. Sample preparation and microscopy A549 lung carcinoma cell line cells were seeded in 96-well imaging plates a night prior to imaging, then fixed with 4% paraformaldehyde (Sigma) and stained for DNA with Hoechst 33342 fluorescent dye (Sigma). Cell culture was maintained similarly to the procedures described in [1]. Next, stained cell nuclei were imaged using ImageXpress Confocal system (Molecular Devices) in either confocal or widefield mode employing Nikon 20X Plan Apo Lambda objective. To obtain 3D information images in both modes were acquired as Z-stacks with 0.3 µm and 0.7 µm for confocal and widefield modes respectively. Confocal z-stack was Nyquist sampled. The excitation wavelength was 405 nm and the emission was 452 nm. Using these settings, we obtained individual stacks for both modalities, with each stack covering 2048 by 2048 pixels or 699 by 699 µm. References Yakimovich, Artur, et al. "Plaque2. 0—a high-throughput analysis framework to score virus-cell transmission and clonal cell expansion." PloS one 10.9 (2015): e0138760. Alink, Mark S. Oude, et al. "Lowering the SNR wall for energy detection using cross-correlation." IEEE transactions on vehicular technology 60.8 (2011): 3748-3757. Li, Rui, et al. "Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model." arXiv preprint arXiv:2306.02929 (2023). https://rodare.hzdr.de/record/4030 10.14278/rodare.4030 oai:rodare.hzdr.de:4030 eng arxiv:arXiv:2306.02929 url:https://www.hzdr.de/publications/Publ-37066 doi:10.48550/arXiv.2306.02929 url:https://www.hzdr.de/publications/Publ-38497 url:https://www.hzdr.de/publications/Publ-37066 doi:10.14278/rodare.2667 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode fluorescence microscopy widefield confocal corelative microscopy Correlated Widefield-confocal Microscopy Dataset info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4525 2026-02-19T16:09:54Z openaire_data user-health user-novo user-rodare

Blangiardi, Francesco Ratliff, Hunter Kögler, Toni 2025-11-14 Introduction This dataset corresponds to the simulation data used within AI methods in _"Fast proton transport and neutron production in proton therapy using Fourier neural operators"_ [CITE]. It has been extracted from the corresponding PHITS dataset [1] related to the same work, and is used by the codebase provided in [2] implementing all important AI methods within the paper. The purpose of this entry is to provide a more easily accessible version of the data in [2] ready to be used for AI applications. The size of the dataset has been greatly reduced, and put into a format allowing the access of the phase space density at each individual depth in the phantom for both protons and neutrons and in the form of discretized histograms. A concise description of the simulation setup is provided in [2] please refer to the paper for detailed discussion, description, analysis, and further results derived from this dataset. General information The phase space density data is divided into discretized histograms as defined in the related paper. This follows the approximation within said paper where only 4 dimensions are kept, related to the depth, radial distance (R), energy (E) and azimuthal divergence (θ) of the particles. The depth dimension is considered as a pseudo-time dimension, meaning that time is not provided within the data. In order to simulate examples of different beams propagatng through different materials, a total of 47 phantoms have been simulated, each with a unique starting energy. Phantoms have been divided into slabs along the depth dimension which are assumed to be of homogeneous material along the dimensions perpendicular to the beam axis, but are composed of different materials among them. The proton density is provided as the Monte Carlo simulated protons appropriately binned into the defined discretizations whenever one of the surfaces of each slab is crossed. When it comes to the neutron phase space density, this is instead provided as the angle, energy and radius distributions of secondary neutrons produced within each slab. Both densities are to be considered as integrated with respect to time. For each slab, also the energy deposited by the proton is provided, coming as an energy deposition probability distribution along E and R. Moreover, each of the 47 phantoms has been irradiated according to three different sets of treatment head paramenter, leading to the creation of three dataset: ES8, ES9 and NES8. For the sake of reproducibility, weights for each of the models discussed in [2] are also provided. Parametrization The densities are observed through discretizations as identified in the paper. Within this work, the resolution along the beam depth is fixed to 0.5mm, the energy resolution is set to 1 and 2 MeV for the proton and neutron fluences respectively, while the radial distance and angle is handled differently among the two particles. For protons these are discretized in logarithmically spaced bins, with the first bin also comprising 0, and ranging up to 95.9 mm and 58.76 ° respectively. Instead, for neutrons both dimensions are uniformly discretized, ranging from 0 up to 60 mm and 180 ° respectively. The R, E and θ dimensions are divided into 30x250x30 bins within the proton data, and into 30x125x30 in the case of the neutrons, which are provided at each discretized depth. Data about energy deposition follows the same radial binning as in the case of the proton density, but the energy binning is instead logarithmic ranging from 1.0e-3 up to 97.7 MeV. As already mentioned, the ES8, ES9 and NES8 datasets differ in terms of the treatment head parameters. More details about the specifics of each dataset can be found in [1]. As ES8 and ES9 share the same treatment head parameters with the exception of the intensity, the proton density is not provided for the ES9 dataset to limit storage size. Model weights for each surrogate trained on each of the provided datasets (called MES8, MES9 and MNES8) are also provided, abiding to the surrogate structure defined in [2]. In particular, each surrogate is composed of a proton and neutron model for both density and intensity prediction. Models can be used as detailed in the GitHub repository [3] related to [2]. File description Both the aforementioned density discretizations are named internally as "phits_logfull" and "hn_phits" for the proton and neutrons respectively, with the energy deposition one following the same convention as the protons. All files contained within this datasets are therefore named according to the discretizations as either "phits_logfull_cube_protons_\<depth in millimeters\>_data.nc", "phits_logfull_cube_dose_\<depth in millimeters\>_data.nc" or "hn_phits_cube_neutrons_\<depth in millimeters\>_data.nc". Each nc file contains an `xarray` variables, containing the MC-approximated histogram, details of the discretization, as well as important parameters such as the CT number of the considered slab, its density and the material's ID within the PHITS environment. Surrogates are provided in separate .zip files. Each surrogate contains 4 subfolders related to each surrogate component. The PDF components come in the form of pytorch checkpoints encapsulating Fourier Neural Operator models defined through package `neuraloperator` [4] [5] with version 0.3.0. Intensity components are instead .pickle files containing XGBoostRegressor objects defined through package `XGBoost` [6]. Each component also comes with a pickled dictionary containing important metadata related to model hyperparameters. Folder Structure The provided data consists of three different .zip files, each related to the ES8, ES9 and the NES8 datasets. Each .zip file comes already divided within the train, validation and test split on the basis of the starting energy. Within each split folder, simulations are represented through folders named in the format "\<Starting Energy\>MeV_05mm_800layers, and each contain the related proton and neutron fluences in files with the previously specified naming convention. It should be noted that, although the total size of the proposed dataset is of around 7GB, uncompressing the files requires a total size of 180.2 GB. References [1] H. N. Ratliff, F. Blangiardi, PHITS simulations of neutron and gamma-ray production from and transport of 70–250 MeV protons in hetero-geneous 1D tissue phantoms, Rodare, (in preparation for submission)(2025). [2] "Fast proton transport and neutron production in proton therapy using Fourier neural operators" (to be filled) [3] Blangiardi, F. (2025). AI_phase_space_PT [Computer software]. GitHub. [https://github.com/f-blan/AI_phase_space_PT](https://github.com/f-blan/AI_phase_space_PT) [4] J. Kossaifi, N. Kovachki, Z. Li, D. Pitt, M. Liu-Schiaffini, R. J. George, B. Bonev, K. Azizzadenesheli, J. Berner, A. Anandkumar, A library for learning neural operators (2024). arXiv:2412.10354. [5] N. B. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. M. Stuart, A. Anandkumar, Neural operator: Learning maps between function spaces, CoRR abs/2108.08481 (2021). [6] T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, ACM, 2016, p. 785–794. doi:10.1145/2939672.2939785. URL http://dx.doi.org/10.1145/2939672.2939785 Acknowledgements The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester has received funding from UK Research and Innovation under grant agreement No. 10102118 The NOVO project has received funding from the European Innovation Council (EIC) under grant agreement No. 101130979. The EIC receives support from the European Union's Horizon Europe research and innovation programme. Partners from The University of Manchester has received funding from UK Research and Innovation under grant agreement No. 10102118 https://rodare.hzdr.de/record/4525 10.14278/rodare.4525 oai:rodare.hzdr.de:4525 eng url:https://www.hzdr.de/publications/Publ-42226 doi:10.14278/rodare.4127 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/novo url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/restrictedAccess Proton Therapy Surrogate Modelling Proton Transport Neutron Production Deep Learning Neural Operators Monte Carlo Proton and Neutron reduced phase space for surrogate modeling of Proton Therapy from PHITS simulations info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:2807 2025-10-02T06:04:31Z openaire_data user-hzdr user-fwk user-health user-rodare user-elbe user-γelbe user-oncoray

García Rivas, Iris Fernández Prieto, Antonio Kögler, Toni Römer, Katja Hueso González, Fernando 2024-04-16 This repository contains raw experimental data acquired during the gELBE beam time performed in October 2023 under proposal number 23203137-ST, at Helmholtz-Zentrum Dresden - Rossendorf. In this setup, a bremsstrahlung beam of up to 12.5 MeV energy in 13 MHz pulses irradiates a CeBr3 scintillation detector (by Hilger®) of Ø 1'' x 1'', coupled to a Hamamatsu® R13408-100 PMT, custom voltage divider and shaping electronics, and a commercial digitizer (SFMC01+SIS1160) by Struck®, containing an AD9689 chip that supports a data sampling rate of 2.5 Gsps and 14-bits. This detector is developed in the context of the coaxial prompt gamma-ray monitoring method [1], where very high count rates are expected [2]. The dead-time-free data acquisition is programmed in-house using ROOT [3]. In addition, a plastic scintillation detector (paddle) was placed inbetween the beam and the CeBr3 crystal to serve as reference beam monitor. An Arduino is used to monitor the high-voltage supply for the PMT and active divider electronics in terms of current, voltage and temperature. A Comet Systems® T7310 is used to monitor ambient temperature, humidity and pressure. The published data consist of the raw signal waveforms acquired during ~450 measurements. Each measurement is stored in a separate folder, its name being the acquisition time start, and lasts between 3 and 20 seconds (16 GiB up to 100 GiB). The data format is little-endian binary. Each sample uses two bytes, being the 14 first bits the digitized signal in a 1.7 Vpp range, and the 15th bit the (negated) logic status of the reference beam monitor (paddle). Samples are stored consecutively, without headers. Sample time separation is 0.4 ns (2.5 Gsps). The digitizer is phase-locked to the accelerator radiofrequency (RF), so that each 2500 stored samples correspond to 13 consecutive periods of 13 MHz. The data can be directly opened using the open-source pulse visualization software (PulseSurfer) available in this link: https://igit.ific.uv.es/ferhue/pulse-surfer/, with ROOT as a dependency. One just needs to run: root -l test_gui.cpp+(\"/path-to-folder/chA.bin\") and then set 192.307692307692307696 in the "Cycle" box. Use the slider in the bottom to navigate across different consecutive frames. To visualize the paddle counter (negated) logic status, change the "Mask" box from 3FFF to 4000. There is also a checkbox to activate the baseline subtraction. In addition to the raw waveform data (chA.bin), each folder contains following metadata: log.root a ROOT file storing all the measurement and hardware settings as TObjString. It also contains the T7310 monitoring as a TTree ("pth") chA.root a ROOT file storing a TTree that benchmarks the readout speed of the DAQ for this channel zdt.log a text file storing the output printed by the DAQ software to terminal gui.png Screenshot of the DAQ window hv.txt a test file storing the monitoring of the high-voltage supply and electronics This activity has received funding from the European Union's 2020 research and innovation programme under grant agreement No 101008126, corresponding to the RADNEXT project. Also we received funding from the Conselleria de Educación, Investigación, Cultura y Deporte (Generalitat Valenciana) under grant number CDEIGENT/2019/011 In addition we received funding from the: Industrial Doctorates Program of the Xunta de Galicia (Consellería de Cultura, Educación, Formación Profesional e Universidades), the CONSOLIDACIÓN 2022 GRC GI-1490 - Grupo de Física de Altas Enerxías - GAES -- ED431C 2022/30 and the Studying Leptopic Flavour Universality and nuclear structure with the enhanced LHCb experiment -- PID2019-110378GB-I00 and from the PTCOG Project Funding 2024 - Physics https://rodare.hzdr.de/record/2807 10.14278/rodare.2807 oai:rodare.hzdr.de:2807 eng doi:10.58065/24020 doi:10.17815/jlsrf-2-58 doi:10.1016/j.nima.2022.166701 doi:10.1016/j.nima.2018.09.062 doi:10.1016/j.sna.2023.114859 doi:10.1109/TRPMS.2019.2930362 doi:10.1088/1361-6560/ab176d doi:10.1109/JSEN.2021.3062428 url:https://www.hzdr.de/publications/Publ-39098 url:https://www.hzdr.de/publications/Publ-41810 doi:10.14278/rodare.2806 url:https://rodare.hzdr.de/communities/elbe url:https://rodare.hzdr.de/communities/fwk url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare url:https://rodare.hzdr.de/communities/γelbe info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Proton Therapy Range Verification High-count rate photon detection high speed digitizers pile-up deconvolution High-count rate photon detection with scintillators coupled to photomultiplier tubes and fast digitizers info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3771 2025-06-27T09:18:30Z openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data openaire_data user-fwi user-health user-ibc user-rodare

Alsadig Ahmed Mohammed, Ahmed Peng, Xuan Boutier, Hugo Rodrigues Loureiro, Liliana Raquel Feldmann, Anja Hübner, René Cabrera, Humberto Kubeil, Manja Bachmann, Michael Baraban, Larysa 2025-04-07 The precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities. https://rodare.hzdr.de/record/3771 10.14278/rodare.3771 oai:rodare.hzdr.de:3771 doi:10.17815/jlsrf-3-159 url:https://www.hzdr.de/publications/Publ-41021 doi:10.14278/rodare.3671 url:https://rodare.hzdr.de/communities/fwi url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/ibc url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/1.0/legalcode Fibroblast activation protein immunotherapeutic target modules gold nanoparticles thermal lens spectroscopy Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3131 2025-07-18T11:18:48Z openaire_data user-ecfunded user-health user-rodare

Müller, Johannes Suckert, Theresa Beyreuther, Elke Schneider, Moritz Boucsein, Marc Bodenstein, Elisabeth Stolz-Kieslich, Liane Krause, Mechthild Neubeck, Cläre Von Haase, Robert Lühr, Armin Dietrich, Antje Nexhipi, Sindi 2022-09-21 The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons. In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP). The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix. We provide the data in raw format and as aligned data sets, as well as their spatial transformations. https://rodare.hzdr.de/record/3131 10.14278/rodare.3131 oai:rodare.hzdr.de:3131 eng info:eu-repo/grantAgreement/EC/H2020/730983/ doi:10.3389/fonc.2020.598360 url:https://www.hzdr.de/publications/Publ-31469 url:https://www.hzdr.de/publications/Publ-32124 url:https://www.hzdr.de/publications/Publ-32394 url:https://www.hzdr.de/publications/Publ-32394 doi:10.1016/j.radonc.2023.109591 doi:10.14278/rodare.557 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Preclinical Image fusion Proton radiation Medical imaging Histology Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D. info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:192 2021-12-15T14:06:56Z software user-health user-rodare

Kornek, Dominik Berthold, Jonathan Kögler, Toni 2020-01-17 Single plane Compton imaging (SPCI) is a novel approach to medical imaging of gamma radiation [1]. The possible range of applications includes nuclear imaging and range verification in proton therapy. For the purpose of image reconstruction, a software tool written in ROOT [2] and named SPCI-Reconstruction [3] has been developed. The implementation features the well-established MLEM algorithm for binned data [4] as well as a Monte-Carlo based algorithm called Origin Ensemble [5]. Given a precalculated system matrix and a file containing the measurements, the emission densities of the gamma radiation source can be backprojected into a voxel-based image space. [1] Pausch G et al. A novel scheme of compton imaging for nuclear medicine. 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD). [2] CERN. ROOT – Data Analysis Framework. Release 6.12/04 - 2017-12-13. https://root.cern.ch/content/release-61204. [3] Kornek D. Anwendung von Maximum-Likelihood Expectation-Maximization und Origin Ensemble zur Rekonstruktion von Aktivitätsverteilungen beim Single Plane Compton Imaging (SPCI). Master's thesis. TU Dresden. 2019. [4] Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982; 1(2):113-22. [5] Sitek A. Representation of photon limited data in emission tomography using origin ensembles. Phys Med Biol. 2008 June; 53(12):3201-3216. https://rodare.hzdr.de/record/192 10.14278/rodare.192 oai:rodare.hzdr.de:192 eng url:https://github.com/dkornek/SPCI-Reconstruction url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-376408 url:https://gitlab.hzdr.de/fwmp/invivodos/SPCI_ReCo url:https://www.hzdr.de/publications/Publ-30631 doi:10.14278/rodare.191 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode single plane compton imaging compton camera image reconstruction maximum-likelihood expectation-maximization origin ensemble nuclear medicine range verification in particle therapy SPCI-Reconstruction info:eu-repo/semantics/other software

oai:rodare.hzdr.de:2044 2022-12-22T10:41:13Z openaire_data user-health user-rodare

Abdussalam, Wildan 2022-12-22 We provide post-processing data of daily infected COVID-19 cases for a municipality (Obec) level. The current data for municipality level is prepared on Czech_Obec_COVID19_Infections.csv. The file consists of five columns such as region, name, date, infected and population. The region denotes the ID of a county/state followed by its name in the next column. The inserted date of data is prepared in the third column followed by the number of dead and infected cases. Last but not least, the population of the county is provided in the last column. This data hub was partially funded by the Where2Test project, which is financed by SMWK with tax funds on the basis of the budget approved by the Saxon State Parliament. This data hub was also partially funded by the Center of Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. https://rodare.hzdr.de/record/2044 10.14278/rodare.2044 oai:rodare.hzdr.de:2044 eng url:https://www.hzdr.de/publications/Publ-35983 doi:10.14278/rodare.2043 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Covid-19 Czechia Covid-19 Infections in Czechia info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:116 2019-03-06T14:02:35Z openaire_data user-ecfunded user-health user-hzdr user-matter user-rodare

Huebl, Axel Rehwald, Martin Obst-Huebl, Lieselotte Ziegler, Tim Garten, Marco Widera, René Zeil, Karl Cowan, Thomas E. Bussmann, Michael Schramm, Ulrich Kluge, Thomas 2019-03-06 Supplementary materials for our paper "Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration". Additional high-resolution, raw HDF5 files using the openPMD standard (DOI:10.5281/zenodo.1167843) increase simulation output data to 4.7 TByte and are available from the corresponding author upon reasonable request. This project received funding within the MEPHISTO project (BMBF-Förderkennzeichen 01IH16006C). https://rodare.hzdr.de/record/116 10.14278/rodare.116 oai:rodare.hzdr.de:116 eng info:eu-repo/grantAgreement/EC/H2020/654148/ url:https://www.hzdr.de/publications/Publ-28969 doi:10.14278/rodare.115 url:https://rodare.hzdr.de/communities/ecfunded url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/hzdr url:https://rodare.hzdr.de/communities/matter url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/4.0/legalcode LPA laser-ion acceleration TNSA multi-species cryogenic target particle-in-cell Supplementary Data: Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:3130 2025-08-04T12:15:02Z openaire_data user-health user-rodare

Wyrzykowska, Maria della Maggiora, Gabriel Deshpande, Nikita Mokarian, Ashkan Yakimovich, Artur 2024-08-30 How to cite us Wyrzykowska, Maria, Gabriel della Maggiora, Nikita Deshpande, Ashkan Mokarian, and Artur Yakimovich. "A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy." bioRxiv (2024): 2024-08. @article{wyrzykowska2024benchmark, title={A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy}, author={Wyrzykowska, Maria and della Maggiora, Gabriel and Deshpande, Nikita and Mokarian, Ashkan and Yakimovich, Artur}, journal={bioRxiv}, pages={2024--08}, year={2024}, publisher={Cold Spring Harbor Laboratory} } Data sources Raw data used during the study can be found in corresponding references. VACV: Yakimovich A, Andriasyan V, Witte R, Wang IH, Prasad V, Suomalainen M, Greber UF. Plaque2.0-A High-Throughput Analysis Framework to Score Virus-Cell Transmission and Clonal Cell Expansion. PLoS One. 2015 Sep 28;10(9):e0138760. doi: 10.1371/journal.pone.0138760. PMID: 26413745; PMCID: PMC4587671. HADV: Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Witte R, Puntener D, Greber UF. Microscopy deep learning predicts virus infections and reveals the mechanics of lytic-infected cells. iScience. 2021 May 15;24(6):102543. doi: 10.1016/j.isci.2021.102543. PMID: 34151222; PMCID: PMC8192562. HSV, IAV, RV: Olszewski, D., Georgi, F., Murer, L. et al. High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections. Sci Data 9, 610 (2022). https://doi.org/10.1038/s41597-022-01733-4 Data organisation For each virus (HADV, VACV, IAV, RV and HSV) we provide the processed data in a separate directory, divided into three subdirectories: `train`, `val` and `test`, containing the proposed data split. Each of the subfolders contains two npy files: `x.npy` and `y.npy`, where `x.npy` contains the fluorescence or brightfield signal (both for HADV, as separate channels) of the cells or nuclei and `y.npy` contains the viral signal. The data is already processed as described in the Data preparation section. Additionally, Cellpose masks are made available for the test data in separate masks directory. For each virus except for VACV, there is a subdirectory `test` containing nuclei masks (`nuc.npy`). For HADV cell masks are also available (`cell.npy`). Data preparation Each of VACV plaques was imaged to produce 9 files per channel, that need to be stitched to recreate the whole plaque. To achieve this, multiview-stitcher toolbox has been used. The stitching was first performed on the third channel, representing the brightfield microscopy image of the samples. Then, the parameters found for this channel were used to stitch the rest of the channels. VACV dataset represents a timelapse, from which timesteps 100, 108 and 115 have been selected to produce the data then used in the experiments. Images have been center-cropped to 5948x6048 to match the size of the smallest image in the dataset (rounded down to the closest multiple of 2). The data was additionally manually filtered to remove the samples that constituted only uninfected cells (C02, C07, D02, D07, E02, E07, F02, F07). The HAdV dataset is also a timelapse, from which only the last timestep (49th) has been selected. For the rest of the datasets (HSV, IAV, RV) only the negative control data was used, which was selected in the following way: from the data collected at the University of Zürich, from the Screen samples only the first 2 columns were selected and from the ZPlates and prePlates samples only the first 12 columns. All of the datasets were divided into training, validation and test holdouts in 0.7:0.2:0.1 ratios, using random seed 42 to ensure reproducibility. For the time-lapse data, it was ensured that the same sample from different timesteps only exists in one of the holdouts, to prevent information leakage and ensure fair evaluation. All of the samples were normalised to [-1, 1] range, by subtracting the 3rd percentile and dividing by the difference between percentile 99.8 and 3, clipping to [0, 1] and scaling to [-1, 1] range. For the brightfield channel of HAdV, percentiles 0.1 and 99.9 were used. These cutoff points were selected based on the analysis of the histograms of the values attained by the data, to make the best use of the available data range. Specific values used for the normalization are summarized in Figure 3 of the manuscript in Related/alternate identifiers. To prepare the cell nuclei masks, Cellpose model with pre-trained weights cyto3 has been used on the fluorescence channel. The diameter was set to 7 for all the datasets except for HAdV, for which the automatic estimation of the diameter was employed. Cell masks were prepared using Cellpose with pre-trained weights cyto3 with a diameter set to 70 on brightfield images stacked with fluorescence nuclei signal. The data preparation can be reproduced by first downloading the datasets and then running scripts that are located in `scripts/data_processing` directory of the [VIRVS repository](https://github.com/casus/virvs), first modifying the paths in them: for HAdV data: `preprocess_hadv.py` for VACV data: `stitch_vacv.py` + `preprocess_vacv.py` for the rest of the viruses: `preprocess_other.py` to prepare Cellpose predictions: `prepare_cellpose_preds.py` (for cells) and `prepare_cellpose_preds_nuc.py` (for nuclei) https://rodare.hzdr.de/record/3130 10.14278/rodare.3130 oai:rodare.hzdr.de:3130 eng url:https://www.hzdr.de/publications/Publ-39523 url:https://www.hzdr.de/publications/Publ-40031 doi:10.14278/rodare.3129 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode virus infected cell microscopy deep learning virtual staining A Dataset for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy info:eu-repo/semantics/other dataset

oai:rodare.hzdr.de:4575 2026-03-25T10:10:20Z software user-health user-oncoray user-rodare

Kieslich, Aaron Markus Makarevich, Krystsina Zwanenburg, Alex Kögler, Toni Löck, Steffen 2026-03-25 Satz von Skripten zur Verarbeitung von PGT Daten, sowie Ausführung einer Machine-learning Pipeline und Modellauswertung. https://rodare.hzdr.de/record/4575 10.14278/rodare.4575 oai:rodare.hzdr.de:4575 url:https://www.hzdr.de/publications/Publ-43176 doi:10.14278/rodare.4574 url:https://rodare.hzdr.de/communities/health url:https://rodare.hzdr.de/communities/oncoray url:https://rodare.hzdr.de/communities/rodare info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode proton radiotherapy treatment verification prompt gamma-ray timing machine learning multivariate modelling Software publication: Range-shift prediction with prompt gamma timing in clinically realistic proton therapy fields info:eu-repo/semantics/other software