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opencc-by-4.0Jun 2021View details →

DMS multiphase chemistry mechanism and model results

Open Access DMS multiphase chemistry mechanism This repository contains the complete DMS multiphase chemistry mechanism developed and applied in Wollesen de Jonge et al. (2021). The DMS multiphase mechanism is located in the folder named DMS_chemistry. The executable mechanism consist of a number of Fortran f90 files, which were generated with the kinetic pre-processor (KPP) (Damian et al., 2002) using the KPP input file DMSchem.def and DMSchem.kpp file. Both the executable Fortran code and the KPP input files are stored in the subfolder DMS_multiphase_chem. The DMSchem.def file list all reactions and reaction rates in the DMS multiphase chemistry mechanism similar to the supplementary Tables S1 in Wollesen de Jonge et al. (2021).  The executable DMS multiphase chemistry mechanism consist of the following Fortran f90 files: DMSchem_Main.f90 DMSchem_Function.f90 DMSchem_Initialize.f90 DMSchem_Integrator.f90 DMSchem_Jacobian.f90 DMSchem_JacobianSP.f90 DMSchem_LinearAlgebra.f90 DMSchem_mex_Fun.f90 DMSchem_mex_Jac_SP.f90 DMSchem_Model.f90 DMSchem_Monitor.f90 DMSchem_Parameters.f90 DMSchem_Precision.f90 DMSchem_Rates.f90 DMSchem_Util.f90 DMSchem_Global.f90 These f90-files can be linked and compiled with gfortran using the provided Makefile.   The subfolder photolysis contain vectors with absorption cross sections (cs), quantum yields (qy) and the spectral actinic flux of the UV-lamps in the AURA chamber. A simplified model setup is provided to illustrate how the DMS-multiphase chemistry routines can be run. The DMS multiphase chemistry mechanism is called and run from a program named main.f90. In the main.f90 program the temperature, humidity, pressure and initial concentrations of all gas and aqueous phase species in the DMS multiphase chemistry are declared. After this the main program call the subroutines getKVALUES and getJVALUES from the Fortran module reaction_rates.f90. <ul> <li>getKVALUES calculates a number of complex reaction rates (mainly pressure dependent three-body reactions).</li> <li>getJVALUES calculates all gas phase photolysis rates in the DMS multiphase chemistry mechanism using the absorption cross sections, quantum yields and the spectral actinic flux files stored in the photolysis subfolder</li> </ul> The main.f90 program saves the concentration of all species in a file called conc.dat, the time step vector (time.dat) and all species names in SPC_NAMES.dat. A short Matlab script called plot_concentrations.m is provided to illustrate how the concentrations of all species listed in SPC_NAMES.dat can be plotted along the saved time vector. The simplified model (only used for demonstration purpose) can be compiled and executed with GFortran using the provided Makefile by typing the following commands in the command line (terminal): make ./main.exe   Stored model results presented in Wollesen de Jonge et al. (2021) We have saved all model data from each simulated smog chamber experiment and atmospheric relevant base case and sensitivity run presented in Wollesen de Jonge et al. (2021) in the form of 'OutputTable' files. The columns in the tables related to the DMS smog chamber experiments are classified as follows: <ul> <li>1 time [h] (simulation time starting from -1 h hour and ending at 15 h, time = 0 h is defined as the time when the UV-lights were turned on in the AURA smog chamber).</li> <li>2 PN_1.7nm [#/cm^3] (Total particle number concentration of particles 1.7 nm in diameter).</li> <li>3 PN_2.5nm [#/cm^3] (Total particle number concentration of particles 2.5 nm in diameter)</li> <li>4 PN_10nm [#/cm^3] (Total particle number concentration of particles 10 nm in diameter)</li> <li>5 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>6 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>7 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>8 DMS [ppbv] (Dimethyl sulfide gas phase concentration)</li> <li>9 O3 [ppbv] (Ozone gas phase concentration)</li> <li>10 PV [μm^3/cm^3] (Total particle volume concentration)</li> <li>11 PM [μg/m^3] (Total particle mass concentration)</li> <li>12 NH3 [ppbv] (Ammonia gas phase concentration)</li> <li>13 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>14 SO2 [ppbv] (Sulfur dioxide gas phase concentration)</li> <li>15 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>16 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>17 H2O2 [ppbv] (Hydrogen peroxide gas phase concentration)</li> <li>18 HO2 [#/cm^3] (Hydroperoxyl radical gas phase concentration)</li> <li>19 OH [#/cm^3] (Hydroxyl radical gas phase concentration)</li> <li>20-219 dN/dlogDp [#/m^3] (Particle number size distributions)</li> </ul>   The header line, rows 20-219, give the corresponding aerosol particle geometric mean diameters (Dp) in unit m, which were used to represent the modelled particle number size distributions (dN/dlogDp). The gas-phase concentrations given in unit ppb are given at the standard temperature and pressure of 273.15 K and 1E5 Pa.   The columns in the tables related to the atmospheric relevant runs are classified as follows: <ul> <li>1 time [h] ] (simulation time).</li> <li>2 DMS [#/cm^3]  (Dimethyl sulfide gas phase concentration)</li> <li>3 H2SO4 [#/cm^3] (Sulfuric acid gas phase concentration)</li> <li>4 MSA [#/cm^3] (Methane sulfonic acid gas phase concentration)</li> <li>5 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>6 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>7 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>8 UVflux [] (Relative UV light intensity, i.e. UVflux = 1 maximum sunlight, UVflux = 0 no sunlight)</li> <li>9 sinkCL [#/cm^3/s] (DMS loss rate by reactions with Cl radicals)</li> <li>10 sinkOHabs [#/cm^3/s] (DMS loss rate by reactions with OH via the abstraction pathway)</li> <li>11 sinkBrO [#/cm^3/s] (DMS loss rate by reactions with BrO radicals)</li> <li>12 sinkOHadd [#/cm^3/s] (DMS loss rate by reactions with OH via the addition pathway)</li> <li>13 sinkNO3 [#/cm^3/s] (DMS loss rate by reactions with NO3 radicals)</li> <li>14 sinkO3aq [#/cm^3/s] (DMS loss rate by reactions with O3 in the aqueous phase)</li> <li>15 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>16 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>17 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>18 PM_NO3 [μg/m^3] (Nitrate particle mass)</li> <li>19-218 dN/dlogDp [#/m^3]. (Particle number size distributions)</li> </ul>   In this case, the header line, rows 19-218, give the corresponding geometric mean diameters in unit m. The modelled smog chamber experiments result files were named according to the date when the DMS experiments were performed: Exp. DMS1 - 20180405 Exp. DMS2 - 20180519 Exp. DMS3 - 20180521 Exp. DMS4 - 20180523 Exp. DMS5 - 20180526 Exp. DMS6 - 20190226 Exp. DMS7 - 20190301   Details for each output table are given here: OutputTable_AtmMain: Base case atmospheric model run OutputTable_lowWindAtm: Atmospheric model run with 2 m/s wind speed OutputTable_PolAtm: Atmospheric model run with higher O3 and NOx concentrations OutputTable_woAqAtm: Atmospheric model simulation without aqueous phase chemistry reactions OutputTable_woCloudAtm: Atmospheric model simulation without clouds   OutputTable20180405_1: Base case smog chamber simulation experiment DMS1 OutputTable20180519_1: Base case smog chamber simulation experiment DMS2 OutputTable20180521_1: Base case smog chamber simulation experiment DMS3 OutputTable20180523_1: Base case smog chamber simulation experiment DMS4 OutputTable20180526_1: Base case smog chamber simulation experiment DMS5 OutputTable20190226_1: Base case smog chamber simulation experiment DMS6 OutputTable20190301_1: Base case smog chamber simulation experiment DMS7    OutputTable20180519_2: MSIA+OH rate analogues to Yin et al, exp. DMS2  OutputTable20180519_3: MSIA+OH rate analogues to Lucas & Prinn et al. , exp. DMS2  OutputTable20180519_4: MSIA+OH rate set to 0, exp. DMS2  OutputTable20180519_5: No gas partitioning to the liquid water film on the chamber walls, exp. DMS2  OutputTable20180519_6: MCM gas-phase chem. setup, exp. DMS2  OutputTable20180519_9: CH3SOO isomerization set to 0, exp. DMS2  OutputTable20180519_10: HPMTF pathway set to 0, exp. DMS2    OutputTable20190226_2: HPMTF pathway analogues to Veres et al. , exp. DMS6  OutputTable20190226_3: HPMTF pathway analogues to Yin et al. , exp. DMS6  OutputTable20190226_4: HPMTF patway set to 0 , exp. DMS6  OutputTable20190226_5: No gas partitioning to the liquid water film on the chamber walls , exp. DMS6  OutputTable20190226_6: CH3SOO isomerization set to 0 , exp. DMS6  OutputTable20190226_7: MSIA+OH rate set to 0 , exp. DMS6    OutputTable20180405_12: O3 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_12: O3 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_12: O3 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_12: O3 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_12: O3 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_12: O3 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_12: O3 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_13: O3 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_13: O3 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_13: O3 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_13: O3 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_13: O3 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_13: O3 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_13: O3 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_16: DMS wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_16: DMS wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_16: DMS wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_16: DMS wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_16: DMS wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_16: DMS wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_16: DMS wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_17: DMS wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_17: DMS wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_17: DMS wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_17: DMS wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_17: DMS wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_17: DMS wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_17: DMS wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS1  OutputTable20180519_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS2  OutputTable20180521_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS3  OutputTable20180523_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS4  OutputTable20180526_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS5  OutputTable20190226_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS6  OutputTable20190301_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS7    OutputTable20180519_20: Liquid water content on walls (LWC wall) = 0.3 mg/m^3, exp. DMS2  OutputTable20190226_20: Liquid water content on walls (LWC wall) = 1.5 g/m^3, exp. DMS6  OutputTable20190301_20: Liquid water content on walls (LWC wall) = 15 g/m^3, exp. DMS7    OutputTable20180519_21: Liquid water content on walls (LWC wall) = 30 mg/m^3, exp. DMS2  OutputTable20190226_21: Liquid water content on walls (LWC wall) = 150 g/m^3, exp. DMS6  OutputTable20190301_21: Liquid water content on walls (LWC wall) = 1000 g/m^3, exp. DMS7   References Wollesen de Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., Lüdemann, D., Bilde, M., and Roldin, P.: Secondary aerosol formation from dimethyl sulfide – improved mechanistic understanding based on smog chamber experiments and modelling, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-2020-1324">https://doi.org/10.5194/acp-2020-1324</a> (2021)    Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567–1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002.

opencc-by-4.0Jul 2024View details →

WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data

The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.

opencc-by-4.0Oct 2024View details →

Dataset of a multiphase flow and reactive transport benchmark for radioactive waste disposal

The files include the full dataset (tables and figures) of the comparion the results of a multiphase flow and reactive transport benchmark for radioactive waste disposal. The codes INVERSE-FADES-CORE V2, DuMuX , TOUGHREACT and iCP were benchmarked with 6 test cases of increasing complexity, starting with conservative tracer transport under variably unsaturated conditions and ending with water flow, gas diffusion, minerals and cation exchange.

opencc-by-4.0Jul 2023View details →

Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"

This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.

zenodo40/100

The multilayer volume-of-fluid method for multiphase flows across scales: breaking waves, microfluidics, and membrane-less electrolyzers

Supplementary movies to PhD thesis <a href="https://doi.org/10.3929/ethz-b-000547518">10.3929/ethz-b-000547518</a>

opencc-by-4.0May 2022View details →

zenodo40/100

LiverHccSeg: A Publicly Available Multiphasic MRI Dataset with Liver and HCC Tumor Segmentations and Inter-Rater Agreement Analysis

Please cite our data paper published in "Data in Brief": https://www.sciencedirect.com/science/article/pii/S2352340923007473 Background Liver cancer ranks as the third leading cause of cancer-related mortality worldwide [1] and alarmingly, both the incidence and mortality rates of liver cancer are increasing [2; 3]. Among the various types of primary liver cancer, hepatocellular carcinoma (HCC) stands out as the most prevalent, accounting for approximately 70-85% of liver cancer cases [4]. Leveraging the advantages of magnetic resonance (MR) imaging, HCC can be reliably detected and diagnosed without the requirement of an invasive biopsy [5]. MR imaging offers high tissue contrast, which can be further enhanced through contrast-enhanced multiphasic magnetic resonance imaging (mpMRI) techniques. This enables accurate identification and non-invasive diagnosis of HCC [6]. Objective Precise segmentation of the liver plays a crucial role in volumetry assessment and serves as a vital pre-processing step for subsequent tumor detection algorithms [7]. However, accurate liver segmentation can be particularly challenging in patients with cancer-related tissue alterations and deformations in shape [8]. Accurate HCC tumor segmentation is essential for the extraction of quantitative imaging biomarkers such as radiomics and can be used for studies on treatment response assessment and prognosis evaluation and provides critical information about the tumor biology. In order to enhance the reproducibility of liver and tumor segmentation, automated methods utilizing image analysis techniques and machine learning have been developed. These methods have demonstrated promising results [7; 8]; however, most algorithms were tested only on small internal test sets and therefore do not guarantee generalizable and consistent performance on external data.Publicly available datasets allow for fair and objective comparisons between different algorithms, techniques, or approaches. Researchers can evaluate the strengths and weaknesses of their methods in relation to existing solutions and establish benchmarks for performance evaluation. In addition to providing a benchmark with this dataset, we also assess the inter-rater variability between two different sets of tumor segmentations. This analysis serves as a measure of reproducibility for human segmentations, highlighting the consistency or variability that may exist among different human raters. Understanding the reproducibility of human segmentations is essential in assessing the reliability of manual annotations and establishing a baseline for algorithm performance comparison. By introducing LiverHccSeg, we aim to fill the gap of lacking publicly available mpMRI HCC datasets and offer researchers and developers a valuable resource for algorithmic evaluation on external data and imaging biomarker analyzes. Materials and MethodsInclusion of Patients All available scans from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436">https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436</a>) were downloaded [9]. One multiphasic MRI scan (pre and triphasic post contrast) per patient was included. Patients who did not exhibit a tumor or residual tumor were excluded from the tumor segmentation dataset; however, they were included in the liver segmentation dataset. MR Imaging Data Subsequently, all imaging data was converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format with the dcm2nii (v2.1.53) package [10] and available header information was extracted using the pydicom (v.2.1.2) package [11]. Multiparametric MR sequences were labeled with a consistent syntax ('pre', 'art', 'pv', 'del', for the pre-contrast, arterial, portal-venous and delayed contrast phases, respectively). All images were already de-identified by the TCIA website. Images were acquired between the years 1993 and 2007 on Philips and Siemens scanners with field strengths of 1.5 and 3 Tesla. Full details of the imaging parameters can be found in Table 5. Briefly, the median repetition time (TR) and median echo time (TE) were 365.8 ms and 26.4 ms, respectively. The median slice thickness was 9.5 mm, the median bandwidth 536.9 Hz. Scientific Reading After conversion, all images were read in a scientific reading by two board-certified abdominal radiologists (S.A. and S.H with 9 and 10 years of experience, respectively). Any disagreement between the two raters was discussed in a consensus meeting. All HCC lesions were classified according to LI-RADS criteria [6]. Image Registration The co-registration of pre-contrast, portal-venous, and delayed-phase images with arterial phase images was performed using the software BioImage Suite (v3.5) [12]. A non-rigid intensity-based registration approach was applied, employing a parameterized free-form deformation (FFD) with 3D B-splines [13]. The optimal FFD transformation was estimated by maximizing the normalized mutual information similarity metric [14] through gradient descent optimization. To enhance the optimization process, a multi-resolution image pyramid with three levels was utilized. The final B-spline control point spacing was set to 80 mm. The estimated transformation was then employed to warp the moving images (pre-contrast, portal-venous, and delayed-phase) into the reference image space, specifically the arterial phase image. Liver and Tumor Segmentation and Statistical Analysis All livers and tumors were manually segmented under the supervision of two board-certified abdominal radiologists using the software 3D Slicer (v4.10.2) [15]. To compare the segmentation agreement between the two sets of liver and tumor segmentations, we calculated segmentation metrics using the Python package seg-metrics (v1.0.0) [16]. All segmentation metrics and statistics were calculated in Python (v3.7). Data description The data that appears in this article include:<ol><li>dicoms.zip: This zip file contains all the raw MR images from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) [1] in the Digital Imaging and Communications in Medicine (DICOM) format used for the curation of this dataset. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique unidentified patient ID, DATE is the date of the image acquisition, and SEQUENCE is the name of the MR sequence.  </li><li>LiverHccSeg_MetaData.xlsx: This spreadsheet contains all the metadata from the DICOM headers along with the data from the scientific image readings.  </li><li>nifti_and_segms.zip: This zip file contains all MR images along with the liver and tumor segmentations in the Neuroimaging Informatics Technology Initiative (NIfTI) format. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique anonymized patient identifier, DATE is the date of the image acquisition, and SEQUENCE is the name of the MRI sequence or segmentation image. The NIfTI files are named as follows: pre.nii.gz : Pre-contrast T1-weighted MRI art.nii.gz: Arterial-phase T1-weighted MRI pv.nii.gz: Portal-venous-phase T1-weighted MRI del.nii.gz: Delayed-phase T1-weighted MRI art_pre.nii.gz: Pre-contrast T1-weighted MRI registered to the corresponding arterial-phase T1-weighted image art_pv.nii.gz: Portal-venous-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI art_del.nii.gz: Delayed-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI The corresponding manual segmentations are named after the rater and the type of segmentation and follow the format 'RATER_ROI.nii.gz' where RATER denotes the human rater and ROI denotes the region of interest that was segmented, for example, 'rater1_liver.nii.gz', 'rater2_liver.nii.gz', 'rater1_tumor1.nii.gz', and 'rater2_tumor1.nii.gz'. For tumor segmentations, an integer indicates the tumor identification number for different tumor ROIs, for example, 'rater1_tumor1.nii.gz' and 'rater2_tumor1.nii.gz'. The segmentations can be used for the arterial phase NIfTI file as well as the corresponding co-registered pre-contrast (art_pre.nii.gz), portal-venous (art_pv.nii.gz), and delayed-phase (art_del.nii.gz) images.  </li><li>segm_metrics.xlsx: This spreadsheet summarizes the segmentation agreement between the two sets of liver and tumor segmentations by the two board-certified abdominal radiologists.</li></ol> References1          Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-2492          Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7-343          White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820.e8154          Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP (2006) The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol 45:529-5385          Hamer OW, Schlottmann K, Sirlin CB, Feuerbach S (2007) Technology insight: advances in liver imaging. Nat Clin Pract Gastroenterol Hepatol 4:215-2286          Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 289:816-8307          Bousabarah K, Letzen B, Tefera J et al (2020) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol. 10.1007/s00261-020-02604-58          Gross M, Spektor M, Jaffe A et al (2021) Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 16:e02606309          Erickson BJ, Kirk S, Lee Y et al (2016) Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection. The Cancer Imaging Archive. 10.7937/K9/TCIA.2016.IMMQW8UQ10        dcm2nii DICOM to NIfTI converter. <a href="https://github.com/rordenlab/dcm2niix">https://github.com/rordenlab/dcm2niix</a> Accessed: 2021-12-07.11        Mason D, scaramallion;, rhaxton; et al (2020) pydicom/pydicom: pydicom 2.1.2, v2.1.2. Zenodo12        X. Papademetris MJ, N. Rajeevan, H. Okuda, R.T. Constable, L.H Staib BioImage Suite: An integrated medical image analysis suite, Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. <a href="http://www.bioimagesuite.org">http://www.bioimagesuite.org</a>.13        Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans Med Imaging 18:712–72114        Studholme C, Hill DL, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32:71-8615        Fedorov A., Beichel R., Kalpathy-Cramer J. et al (2012) 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323-134116        Ordgod (2020) Ordgod/segmentation_metrics: seg-metrics, v1.0.0. Zenodo - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -ChangeLogVersion 1.1: Fixed incorrect liver segmentation mask.

opencc-by-4.0May 2023View details →

opencc-by-4.0Sep 2020View details →

Data for: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences

Microscale processes in three-phase suspensions (mixtures of gas, liquids, and solids) can affect the macroscale behavior of the whole suspension. To visualize these small-scale processes at high speed and in 3D, we use a recently developed imaging system: Swept Confocally-Aligned Planar Excitation (SCAPE) microscopy. This dataset contains 3D videos  taken with SCAPE microscopy of experiments where different phases interact with each other. Each zipped folder contains raw data and processed data for a single experiment. "Case 1" experiments show CO2 bubbles growing on PMMA (acrylic) particles in sparkling water. The "Case 2" experiment shows water droplets suspended in canola oil and flowing through a porous medium made of packed PMMA particles. "Case 3" experiments show growth of injected air bubbles in particle suspensions (either glass beads in immersion oil, or PMMA particles in a refractive index matched liquid). All scaling parameters are provided in Table 1. "info.txt" files contain metadata for the processed hyperstacks. The experiments provided here are discussed in the following publication: Oppenheimer, J.*, Patel, K.*, Lindoo, A., Hillman, E. M. C., and Lev, E.: High-Speed 3D Imaging of Multiphase Systems: Applying SCAPE Microscopy to Analogue Experiments in Volcanology and Earth Sciences. Geochemistry, Geophysics, Geosystems. (In press, 12/2020)

dryad36/100

Data from: Response to persistent er stress in plants: a multiphasic process that transitions cells from prosurvival activities to cell death

The unfolded protein response (UPR) is a highly conserved response that protects plants from adverse environmental conditions. The UPR is elicited by endoplasmic reticulum (ER) stress, in which unfolded and misfolded proteins accumulate within the ER. Here, we induced the UPR in maize (Zea mays) seedlings to characterize the molecular events that occur over time during persistent ER stress. We found that a multiphasic program of gene expression was interwoven among other cellular events, including the induction of autophagy. One of the earliest phases involved the degradation by regulated IRE1-dependent RNA degradation (RIDD) of RNA transcripts derived from a family of peroxidase genes. RIDD resulted from the activation of ZmIRE1 for promiscuous ribonuclease activity that attacks the mRNAs of secreted proteins. This was followed by an upsurge in expression of the canonical UPR genes indirectly driven by ZmIRE1 due to its splicing of Zmbzip60 to make an active transcription factor that directly upregulates many of the UPR genes. At the peak of UPR gene expression, a global wave of alternative RNA processing led to the production of many aberrant UPR gene transcripts, likely tempering the ER stress response. During later stages of ER stress, ZmIRE1's activity declined as did the expression of survival modulating genes, Bax inhibitor1 and Bcl-2-associated athanogene7, amidst a rising tide of cell death. Thus, in response to persistent ER stress, maize seedlings embark on a course of gene expression and cellular events progressing from adaptive responses to cell death.

opencc-zeroDec 2017View details →

opencc-by-4.0May 2022View details →

Dataset supporting the publication, "Constraints on the role of Laplace pressure in multiphase reactions and viscosity of organic aerosols"

This dataset supports the publication, "Constraints on the role of Laplace pressure in multiphase reactions and viscosity of organic aerosols". This project was funded by the U.S. National Science Foundation Postdoctoral Fellowship Award #AGS-1624696.

opencc-by-4.0Apr 2023View details →

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

Model outputs associated with “Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

opencc-by-4.0Sep 2023View details →

Reactive Multiphase Flow in Porous Media at the Darcy Scale: a Benchmark proposal

Description and data files for a "Reactive Two-phase flow Benchmark" Modeling  reactive multiphase multicomponent flow in porous media leads to a highly nonlinear coupled system of degenerate partial differential equations and algebraic and/or ordinary differential equations, requiring special numerical treatment. The Benchmark consists of five test problems in total (both in 1D and in 2D), with varying degrees of difficulty, designed to verify the algorithms and the codes dedicated to simulate coupled isothermal Hydro-Chemical processes during injection and storage of CO2 in the subsurface. It is intended to be used as a basis for comparing codes in order to better understand different couplings such as chemical reactions with two-phase flow, phase behavior with equilibrium reactions, dissolution and precipitation

opencc-by-4.0Oct 2023View details →

Dataset for Multiphase turbulent flow explains lightning rings in volcanic plumes

Datasets for all figures in "Multiphase turbulent flow explains lightning rings in volcanic plumes." Data comes from observations of the Hunga Tonga-Hunga Ha’apai (HTHH) eruption on January 15, 2022, and from numerical simulations of the Boussinesq equations with inertial particles using the GHOST code. Observational data is provided in CSV and Matlab FIG format. Data from numerical simulations is provided in NetCDF files with Python scripts giving examples on how to read the files.

ClinicalTrials.gov36/100

Rapid Evaluation of Innovative Intervention Components to Maximize the Health Benefits of Behavioral Obesity Treatment Delivered Online: An Application of Multiphase Optimization Strategy

ClinicalTrials.gov study NCT04520256. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

dryad36/100

Data from: Response to persistent er stress in plants: a multiphasic process that transitions cells from prosurvival activities to cell death

Open the record for dataset details and reuse information.

publicMay 2019View details →

opencc-by-4.0Aug 2019View details →

Dataset (.esg files) of a multiphase aggregate representative of a lower mantle composition compressed at High P/T in an R rDAC

A collection of .esg files for use in the software package Materials Analysis Using Diffraction (MAUD; Lutterotti et al., 1997). Seven files are included, spanning the range used in data analysis. All are post-conversion from San Carlos olivine. Files are named in the following format: MPBrgFp: Stands for Multiphase, primary phases bridgmanite and ferropericlase ###: Number of the diffraction pattern taken during the experiment ##GPa: Approximate temperature, rounded to the nearest whole number. This is to cross-correlate .esg files with experimental Pt pressures cited within the paper.

opencc-by-4.0Apr 2024View details →

An improved multiphase chemistry mechanism for methylamines: Significant dimethylamine cloud production

Processing data for Figures in publication.

opencc-by-4.0Sep 2024View details →

Supplemental materials for "Boosting Barlow Twins reduced order modeling for machine learning-based surrogate models in multiphase flow problems"

Supplemental materials for "Boosting Barlow Twins reduced order modeling for machine learning-based surrogate models in multiphase flow problems" in Water Resources Research. Detailed information is available in readme.md.

opencc-by-4.0Apr 2023View details →

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity”.