dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Goddard affiliates are thankful for support from  GSFC Sellers Exoplanet Environments Collaboration (SEEC) , which is funded by the  NASA Planetary Science Divisions Internal Scientist Funding Model. M. T. acknowledges the use of the computing resources on OCCIGEN (CINES,  French National HPC ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 832738/ESCAPE. M. W. and A. D. acknowledge funding from the  NASA   Astrobiology Program  through participation in the Nexus for  Exoplanet System Science (NExSS). The  THAI GCM  intercomparison team is grateful to the  Anong 's  THAI Cuisine restaurant in Laramie  for hosting its first meeting on November 15, 2017. We would like to thank the reviewer  Daniel D.
Code availability. ExoCAM  (Wolf and Toon, 2015)  is available on Github, https://github. com/storyofthewolf/ExoCAM. The Met Office Unified Model is available for use under licence, see http://www. metoffice. gov. uk/research/modelling-systems/unified-model. ROCKE-3D is public domain software and available for download for free from https://simplex. giss. nasa. gov/gcm/ROCKE-3D/. Annual tutorials for new users take place annually, whose recordings are freely available on line at https://www. youtube. com/user/NASAGISStv/playlists?view= 50&sort=dd&shelf_id=15. LMD-G is obtainable upon request from Martin Turbet (martin. turbet@lmd. jussieu. fr) and François Forget (francois. forget@lmd. jussieu. fr).
PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patientspecific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors.
RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
INTRODUCTION We present an open-source software suite, SlicerDMRI, that enables research using diffusion magnetic resonance imaging (dMRI) for noninvasive, in vivo investigation of the organization of the white matter in individual neurosurgical patients. dMRI measures the random molecular motion or diffusion of water molecules, which enables estimation of the underlying cellular microstructure in tissues. 1  Brain mapping using dMRI can provide critical preoperative information,  2, 3  including preoperative maps of critical brain connections (white matter fiber tracts) and insight into the tissue microstructure that surrounds brain tumors. White matter fiber tracts are composed of myelinated axons and are categorized as association, projection, or commissural, depending on the brain regions they connect.
17, 18 The most popular measures used in neurosurgical patients are derived from modeling water diffusion using tensor mathematics (diffusion tensor imaging [DTI]  19  ). These measures include fractional anisotropy (FA) and mean diffusivity (MD). In neurosurgical patients, alterations in FA and MD can reflect combinations of increased water content from edema and/or tumor infiltration. 20 ain mapping using dMRI requires sophisticated computational processing to enable visualization and quantification, and multiple software packages exist for this purpose. In fact, several packages are US Food and Drug Administration (FDA) approved and purpose-built for neurosurgical brain mapping, neuronavigation, and radiation therapy planning. However, these dedicated packages do not make cutting-edge developments in dMRI analysis available to clinicians or are specifically designed for research needs.
SELECTED EXAMPLE NEUROSURGICAL PATIENT DATA SETS Retrospective imaging data sets from two neurosurgical patients were selected to illustrate two different clinical scenarios: a high-grade glioma displacing the corticospinal tracts (CSTs), with significant surrounding edema, and a high-grade glioma abutting the arcuate fasciculus (AF). Preservation of these tracts is critical for voluntary motor movements (CSTs)  26  and language function (AF). 27 The data were acquired at Brigham and Women's Hospital. The use of the data was approved by the Partners Healthcare institutional review board, and informed consent was obtained from the patients before scanning. All data analyses in the current work were performed retrospectively for research purposes only.
MRI Acquisition All magnetic resonance data were acquired on a 3T MAGNETOM Prisma MRI scanner (Siemens Healthineers, Erlangen, Germany) with a 20-channel head coil. Anatomic imaging included volumetric T2 weighted and gadoliniumenhanced T1 weighted. dMRI was acquired with a repetition time of 3,500 ms and echo time of 58 ms with a flip angle of 90°, a voxel size of 2 × 2 × 2 mm, a b value of 1,000 seconds/mm 2 , six baselines, and a matrix of 112 × 112 with 66 axial slices. Functional MRI (fMRI) was acquired as the patient performed motor and language tasks as clinically indicated, and data were processed using FDA-approved software (BrainEx; NordicNeuroLab AS, Bergen, Norway). Ultrasound data were acquired with the BK5000 system (BK Medical North America, Peabody, MA) with a slice thickness of 0.
Patient Data Set 1 A 69-year-old, right-hand-dominant man with a recurrent left-sided frontoparietal glioma initially presented with a diffuse large fronto-insular infiltrative pattern lesion. A biopsy sample was found to have a WHO grade 2 astrocytoma IDH1 wild type, which was treated with chemoradiation. Follow-up imaging at 3 years revealed progression in the left-side perirolandic area, which did not respond to salvage systemic therapy. Radiographic progression and clinical deterioration (motor weakness) led to the decision to pursue surgical resection for debulking and tissue diagnosis. Image-guided left frontal craniotomy was performed with intraoperative motor-and somatosensoryevoked potentials and cortical and subcortical mapping. Gross total resection of enhancing tumor (now grade 4) was accomplished with stable motor function immediately postoperatively and improved motor function relative to baseline at 1-week follow-up.
DTI Analysis for Visualization and Quantification of Tissue Microstructure The analysis of scalar measurements from DTI can give insight into tissue microstructure differences between the tumor and surrounding tissues. Figure  1  illustrates this analysis in patient data set 1, and the following paragraphs describe the workflow using SlicerDMRI. Diffusion tensor estimation and visualization. SlicerDMRI provides the DiffusionTensorEstimation module to calculate DTIs from diffusion-weighted image sequences. The 3D Slicer Volumes module allows visualization of diffusion tensors with multiple options, including both two-dimensional (2D) and three-dimensional (3D) displays. Diffusion-derived scalar measure computation and visualization. SlicerDMRI supports computation of quantitative diffusion measures, such as FA and MD, on the basis of the estimated diffusion tensors using the Dif-fusionTensorScalarMaps module.
Quantification of region-specific diffusion measures. SlicerDMRI, together with other modules in the 3D Slicer core, enables quantification of region-specific diffusion measures. This can be done using 3D fiducials to define regions of interest (ROIs) and Data Probe to view diffusion measures from an ROI. This process can also be performed with the SegmentEditor module that enables manual drawing of an ROI and the LabelStatistics module that calculates statistics for different ROIs.
These two seeding strategies are provided in the TractographySeeding and Tractogra-phyROISeeding modules. Tractography visualization is performed using the TractographyDisplay module, where multiple tract visualization modes are provided (eg, fiber color by diffusion scalar measure, visualization as lines or tubes). Tract quantification. SlicerDMRI provides multiple tools to enable quantification of tract diffusion and geometric measures. For example, the TractographyMeasurement module allows for computation of multiple statistics (mean, maximum, etc) of diffusion-derived values of the fiber points along a tract. The TractographyToMask and LabelStatistics modules together enable the measurement of tract volume, and the WhiteMatterAnalysis package enables computation of the fiber length distribution of a tract.
7] [38]  Thus, UKF can better match the established understanding of surgical neuroanatomy than traditional DTI tractography. However, it should be noted that such comparisons are difficult because of the lack of ground truth, and tractography validation and standardization remain an open problem. 39, 40 gure  3  illustrates the advanced tractography analysis in patient data set 1, which features a comparison between single-fiber DTI tractography and the multifiber plus free 'water UKF method. The following paragraph describes the UKF tractography fiber tracking workflow using SlicerDMRI. Multifiber UKF tractography with advanced tissue modeling options. SlicerDMRI provides the InteractiveUKF and UKFTractography modules for fiber seeding from an interactively modifiable 3D object and an ROI mask, respectively.
Automated Identification of Critical Fiber Tracts Conventional identification of critical fiber tracts requires expert manual interpretation, which is time consuming and has variable results across experts. 43  of machine learning and expert anatomic annotation, we created an anatomically curated white matter atlas. 44 The atlas contains 58 deep white matter tracts, including major long-range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections. This atlas is released as an open source as part of SlicerDMRI. 45 Using this atlas and our accompanying open-source software, we have demonstrated automatic identification of fiber tracts in patients with brain tumors, despite the challenges of edema and tract displacement as a result of mass effect.
44, 46 Figure  4  illustrates the application of this technology to automatically identify critical fiber tracts in patient data set 2, and the following paragraphs describe the workflow using SlicerDMRI. Fiber clustering tract identification. SlicerDMRI provides the WhiteMatterAnalysis Python package to perform automated tract identification using a data-driven fiber clustering pipeline. A one-step batch script is provided to run the pipeline for identification of fiber tracts according to the anatomically curated white matter atlas. 44, 45, 47, 48   he 3D Slicer Ruler function can be used to measure the distance of the tracts to other image data (eg, a tumor 3D model) displayed in the 3D Viewer. Recent advances using deep learning tract parcellation. In addition to WhiteMatterAnalysis, in the near future, SlicerDMRI will also support fast tractography segmentation using deep learning techniques.
HOW TO GET SlicerDMRI SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager, an app store for distribution of software extensions to 3D Slicer, with A B installation instructions and tutorials provided. 62 SlicerDMRI is open source, so all code is available. 63 User and software development support for SlicerDMRI is available through the 3D Slicer forums. 64.
6righam and Women's Hospital and Harvard Medical School, Boston, MA2Massachusetts Institute of Technology, Boston, MA3Isomics, Cambridge, MA4The University of Chicago, Chicago, IL5Parietal, Inria Saclay-lle de France, Neurospin CEA, Universit é Paris-Saclay, Palaiseau, France6University of Bremen and Fraunhofer MEVIS, Bremen, Germany 7 University of Iowa, Iowa City, IA CORRESPONDING AUTHOR Fan Zhang, PhD, Harvard Medical School, 1249 Boylston St, Boston, MA 02115; e-mail: fzhang@bwh. harvard. edu. SUPPORT Supported by National Cancer Institute Informatics Technology for Cancer Research Grants No. U01CA199459 (Open Source Diffusion MRI Technology for Brain Cancer Research), P41EB015898 (National Center for Image-Guided Therapy), P41EB015902 (Neuroimaging Analysis Center), R01MH119222, R01CA235589, R01EB027134, and HHSN261200800001E.
Data From NGO shipbreaking platform, Number of LDT dismantled by ship type  (General Cargo, Bulk Carrier and Oil Tanker)  and by country (Bangladesh, India, Pakistan, China, Turkey and others) were estimated in excel worksheet (NGO Shipbreaking Platform 2017). Secondly, type and quantity of material recovered are identified in percentage by ship type from  Adak (2013)  and  Anderson et al. (2001). The material recovery types include ferrous scrap, remelting scrap, cast iron, nonferrous scrap, machinery, electrical and electronic compounds, minerals, plastics, liquids, chemicals and gases, joinery and miscellaneous. The percentage is then multiplied with the quantity each country represents by the ship type. This gives us the amount of metal/material recovered in each material category in tons (Rk), which is then multiplied by the environmental impacts per kg (Ik), taken from ecoinvent database version 3.
1 using TRACI method (e. g. , data of 1 kg steel metal, steel, low-alloyed, at plant metals/extraction in the ecoinvent database). This gives the total avoided impact of the primary metal/material production (I). Impacts of Ferrous and remelting scrap category is calculated based on steel production impact item in ecoinvent; impacts of Cast iron are calculated by the cast iron production impact item (Equ. 1). Environmental impacts of non-ferrous scrap are estimated by the impact of copper (14%) , zinc (43%) and bronze (43%) which were calculated by the non-ferrous recovery data. For example, Table  S1  shows the conversion of 1 kg cast iron to its corresponding impacts. We exclude the other material category (Machinery, Electrical and electronic compounds, Minerals, Plastics, liquids, Chemicals and Gases, Joinery and Miscellaneous) from impact calculation due to the lack of appropriate conversion factor.
(2015). The proportion of waste landfilled, incinerated and bilge oil processed was estimated as 74%, 7% and 19% respectively. To estimate impacts of waste disposal, hazardous waste landfill, hazardous waste incineration and bilge oil incineration were chosen from ecoinvent database at global scale. TRACI impact estimation method was used. It is noteworthy that IMPACT 2002+ were used to estimate domestic processing impacts in  Rahman et al. (2016) , of which four impact categories were converted to compare with avoided environmental impacts and waste disposal impacts in equ. (2).
Bangladesh generates about 23 thousand tons while Pakistan generates 33 thousand tons. India generates about 46 thousand tons (Table  2 ). Transforming waste to energy may be an option to manage this waste for these substandard recycling countries, which economically and environmentally feasible. In Indian context, waste is treated by landfilling, incinerating and disposal of bilge oil, with about 74% waste landfilled, 7 % incinerated and 14% bilge oil treated  (Deshpande et al. 2013 , Hiremath et al. 2015). Using TRACI, environmental impacts are estimated in nine categories from eco-invent database. Hazardous waste landfill, hazardous waste incineration and bilge oil incineration were chosen as the dominant disposal methods for global scale. According to equation. (2), disposal impact and domestic processing impact were estimated in addition to total avoided impact.
Geant4-DNA19-22 is an extension of Geant4 for the modeling of biological damage induced by ionizing radiation at the nanometer scale. To complement comparisons of our results, we have used published data obtained with TOPAS-nBio. 23 TOPAS is a Monte Carlo24 system that wraps and extends Geant4 to facilitate the use of Monte Carlo simulation of radiotherapy by medical physicists. TOPAS-nBio is the extension of TOPAS to model radiobiological at the cellular and subcellular scale. Since the release of Geant4 version 10. 1, Geant4-DNA provides a computational framework for the physicochemical and chemical stage in which the SBS method, that includes Brownian motion and chemical reactions between molecules resulting from water radiolysis, is available for users. 11 Applications are mainly related to the validation of radiolytic yields and the evaluation of radiation-induced DNA damage.
In that example, as well as in this work, the DNA model was generated with the DNAFabric tool. 6 DNAFabric is a stand-alone software which enables complex 3D geometries to be generated and visualized, in particular DNA structures from the nucleotide constituents (2-deoxyribose, phosphate, and DNA bases) to the whole genome representation. The geometrical model generated consisted of a piece of a 40 nm heterochromatin fiber including 3640 nucleotide pairs in a cubic voxel of 40 nm centered in a box of 2 |im side made of liquid water (1 g/cm3). These DNA elements representing the 2-deoxyribose, phosphate, and DNA bases (adenine, guanine, cytosine, and thymine) were considered as static spherical traps. Using Protein Data Bank information,32 atom's vol umes and positions of each of these molecules were assembled in a unique sphere.
3. A. Resultson radiolytic yields First, the reliability and performance of our IRT method implementation in Geant4-DNA are examined by using this method in the simulation to calculate time-dependent G-values of the chemical species created by water radiolysis, and comparing the results with other simulated data and experimental data from the literature. These quantities were calculated with our IRT method implementation (with both reaction schemes described in Section 2. B), the SBS-dynamic time step of the current public release of Geant4-DNA (Geant4 10. 6), and the IRT method of TOPAS-nBio code (Topas-IRT). 23 Note that the physical stage and physicochemical stage31 are the same for these three approaches. Figure  3  shows G-values as a function of time for different species ('OH, H3O+, H', e-q, H2, and H2O2) produced by 80 keV electrons depositing a total energy of 1 keV.
Projects in the life sciences continue to increase in complexity as they scale to answer deeper and more diverse questions. They employ technologies that generate increasingly large 'omic' datasets and research teams regularly include experts ranging from animal care technicians, veterinarians, human health clinicians, geneticists, immunologists, and biochemists to computer scientists, mathematical modelers, and data scientists, often located at different institutions. Providing the cyberinfrastructure support framework (IT, data management, communication, documentation, and aspects of project management related to these areas) for these projects requires a diverse set of technical tools and soft skills. These skills must be able to meet both the broad needs of data generators and consumers within the project and the needs of the larger scientific community.
Introduction Creating and supporting the cyberinfrastructure (IT, data management, communication, documentation, and related project management needs) for a systems biology project is as much about facilitating communication and education as technology, data management, and data integration. Systems biology is, by nature, 'transdisciplinary', bringing together diverse teams of researchers with equally rich expertise. This can create a 'Tower of Babel' scenario where researchers don't speak exactly the same  (Zook, et al. , 2017)  technical language. What is a 'gene'? It may translate as a sequence, a function, a protein, or be taken for jargon. What is a ' database'? Is it an Excel spreadsheet, a file archive, or a relational architecture? Agreement to be the 'informatics' support team for such a project is challenging technologically and socially.
Tip 5 is longer and organized into sub-sections that describe the roles of team members, and the main services Art. 24, page 3 of 12 and resources the team will provide on a day-to-day basis. The last four Tips focus on activities that become more relevant as the project matures, though they should be planned for as early as possible. This manuscript results from experience. In this case, a five-year National Institute of Allergy and Infectious Diseases (NIAID/NIH) contract awarded in 2012 to a large, multi-institutional, transdisciplinary group of researchers to establish a systems biology program to study host-pathogen interactions and the development of pathogenesis in malaria; the Malaria Host-Pathogen Interaction Center -MaHPIC  (MaHPIC, 2019). The project involved multiple Plasmodium parasite species, their mosquito vectors, and nonhuman primate (NHP) hosts.
At its peak, 26 terabytes of data residing in nearly one million files generated in a dozen project performance sites needed to be managed, including institutional project approvals and partnering agreements. The DMCIT ensured secure data transfer within and across institutions, internal data validation and analyses and ultimately data publication and data deposition via public repositories and websites. Figure  2  shows a generalized view of key cyberinfrastructure components based on MaHPIC's experience, and the possible roles and responsibilities of the DMCIT. Figure  2  is intended as a guide to readers as they consider how they will relate to and serve their own systems biology project. It is difficult to overstate the consistent support the MaHPIC DMCIT received from project leadership and team members.
Data producers, often wet lab scientists, may have different needs than data consumers, often mathematical modelers, data scientists, or researchers outside the project's experimental team. It is important to determine what metadata and standard operating procedures (SOPs) will need to be collected and shared within the project and with the community to make them FAIR. Consider aligning preparations for both internal and external data consumption so that duplication of effort is minimized. Early familiarity with the submission requirements for the archival repositories that will eventually be utilized can avoid last-minute scrambles for details. It is critical that you know who you are serving, how they view the project and its goals, and what they hope to gain from the data. In the case of the MaHPIC, experts from different disciplines might seek knowledge helpful for vaccine development, drug development, treatment of severe illness, or blocking the transmission of infectious parasites to mosquitoes.