dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In the last decade, millions of individuals have been typed through genotyping chips for genetic association studies and the current accuracy of imputation software such as SNP2HLA may limit the statistical power for finding new associations on HLA genes. The use of HLA-check would certainly remove a small proportion of individuals, but could allow a higher accuracy in association detection justifying its use for research purposes. Moreover, these removed individuals could be individually retyped if needed (they are about 5% of typings). To this end, HLAcheck can be downloaded for its local use. HLA-check performs very quickly (on a personal computer): only a few seconds per tested individual are needed to obtain the final comparison value for a given HLA. This method should not have a direct impact on HLA typing for medical purposes since current sequencing methods already reach 100% of accuracy at G group  [27]  level (exons 2 and 3 for class I and exon 2 for class II).
Availability and requirements HLA-check is available under the MIT license at url https://github. com/mclegrand/HLA-check/. This license expressly allows for any use or modification for one's own needs. It is available as a platform-independent C++11 source code, and can be compiled with openMP to enable threading. Endnote 1 We also tried to compute the sum the log of (1-s) to obtain the combined probability of all markers, but this approach gave a considerable importance to imputation errors and ended up with more imprecise results than the simple sum.
Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys).
g. ,  (Seidlitz, Sponheim, et al. 2017; R. Matthew Hutchison 2012 ) ), and the potential for broad reaching cross-species studies remains to be explored. We introduce the Primate Data Exchange  (PRIME-DE)  to create an open science resource for the neuroimaging community that will facilitate the mapping of the non-human primate connectome. To accomplish this, we aggregate a combination of anatomical, functional, and diffusion MRI datasets from laboratories throughout the world, and make these data available to the scientific community.
METHODS Criteria for data contributions PRIME-DE welcomes contributions from any laboratory willing to openly share multimodal MRI datasets obtained from non-human primates, including but not limited to functional MRI, diffusion MRI and structural MRI. Contributors are responsible for ensuring that any data collected and shared were obtained in accordance with local ethical and regulatory requirements. There are no set exclusion criteria. We encourage the sharing of all data, independent of quality. This decision is based on the realizations that: 1) there is no consensus on acceptable criteria for movement in functional MRI or diffusion MRI data, 2) high motion datasets are essential to the determination of the impact of motion on reliability, and 3) new approaches continue to be developed to account for movement artifacts.
DATA RECORDS Overview At present, PRIME-DE contains 13 collections aggregated across 11 sites; data from a total of 98 monkeys is included to date (See Table  1  for information on each institution). To promote usage of a standardized data format, all data are organized using the Brain Imaging Data Structure (BIDS) format. All PRIME-DE datasets can be accessed through the PRIME-DE site (http://fcon_1000. projects. nitrc. org/indi/indiPRIME. html). Prior to downloading the data, users are required to establish a user account on NITRC and register with INDI (anticipated time: < 1 minute).
MRI data For each of the 11 PRIME-DE collections, for each unique ID #, at least one structural MRI (sMRI), and one corresponding R-fMRI dataset are available. In addition, one collection (NIMH) also provided cortical thickness data and R-fMRI data aligned to an anatomical template. Corresponding diffusion MRI (dMRI) datasets are available for five collections. Fieldmap images for fMRI correction are available for five collections. Consistent with its popularity in the imaging community and prior usage in INDI efforts, the NIFTI file format was selected for storage of the PRIME-DE MRI datasets. Table  2  lists the specific MRI scanners and head coils utilized for each collection. Specific MRI sequence parameters for the various data collections are summarized in Tables  2 , 3  , 4 and detailed on the PRIME website.
Across collections, R-fMRI acquisition durations varied from 8 to 345 minutes per subject; in two collections, subjects were in an awake state; in three collections, subjects were scanned both awake and under anesthesia; in the remaining eight collections, subjects were scanned under anesthesia. Along with R-fMRI, two collections provided Naturalistic Viewing fMRI. 3. 3. Data Licensing Contributors to PRIME will be able to set the sharing policy for their data in accord with their preferences and institutional requirements. For each sample, the contributor will set the sharing permissions for their data using one or more the following four policies: 1) Creative Commons -Attribution-NonCommercial Share Alike (CC-BY-NC-SA). https://creativecommons. org/licenses/by-nc-sa/4. 0/ Standard INDI data sharing policy.
TECHNICAL VALIDATION 4. 1 Automated Quality Assessment Consistent with the established policy of INDI, all data contributed to PRIME was made available to users regardless of data quality. The rationale of this decision has been the lack of consensus on optimal quality criteria in regards to specific measures or their combinations and cutoffs -a reality that is even more pronounced in nonhuman primate imaging given the variation in data quality and characteristics across scan protocols given the lack of harmonization. Following the tradition of recent INDI data-sharing consortia, a collection of automated, reference-free quality assurance measures, known as the Preprocessed Connectome Project Quality Assurance Protocol (PCP-QAP), is being made available with the PRIME datasets. These measures focus on structural and temporal (when appropriate) aspects of the datasets.
Additionally, commonly used pre-processing pipelines, used extensively with human neuroimaging datasets, often fail to work properly on non-human primate datasets. As a result, researchers commonly work to optimize individual steps for their datasets outside of traditional workflows, resulting in different pipelines and processing steps across groups. There are efforts underway to form best practices to guide this process and help researchers avoid the need to redefine pipelines themselves (e. g. ,  (Seidlitz, Sponheim, et al. 2017 ; "The Average Baboon Brain: MRI Templates and Tissue Probability Maps from 89 Individuals" 2016) ), however currently it is still necessary for researchers to do so.
2011)  ;  (Reveley et al. 2017)   (Seidlitz, Sponheim, et al. 2017 )  (Reveley et al. 2017; Saleem and Logothetis 2012; Seidlitz, Sponheim, et al. 2017 ) derived from analysis of histological tissue  (Saleem and Logothetis 2012; Paxinos, Huang, and Toga 1999; Paxinos 2009 ). These anatomical parcellations can be warped to individual subjects using standard linear and non-linear registration algorithms (e. g. , AFNI's 3dAllineate and 3dQwarp). Scripts to automate this alignment are available for the single-subject D99 template ( http://afni. nimh. nih. gov/pub/dist/atlases/macaque), and the recently published National Institute of Mental Health Macaque Template (NMT;  (Seidlitz, Sponheim, et al. 2017 ) ) ( https://afni. nimh. nih. gov/NMT ). The NMT is a high resolution (0. 25 mm isotropic) T1 template built from in vivo scans of 31 young adult macaques.
This volume (and accompanying surfaces) is representative of the adult population and provides anatomical detail akin to that of ex vivo templates, which require days of scanning to acquire. The NMT is available via the PRIME-DE website as well as on GitHub ( https://github. com/jms290/NMT ). The database also includes resting state data from 3 subjects that have been aligned to the NMT (see NIMH-Messinger in Table  1 ). A similar multi-subject template also exists for pre-pubertal rhesus monkeys  (Fox et. al, 2015). Other anatomical parcellations have been defined on the surface using the single-subject F99 template (available in Caret;  (David C. Van Essen et al. 2012 ) ), which can be used for analysis on the cortical sheet. For example, the cortical parcellation from  (Markov et al. 2014)  includes quantitative tract-tracing connectivity estimates for a subset of these regions.
Improving skull extraction, segmentation and registration. A high quality T1 image with isotropic voxels is important for skull extraction. There are a number of brain extraction algorithms and available tools, e. g. the Brain Extraction Tool (BET in FSL), 3dSkullStip in AFNI, the Hybrid Watershed Algorithm (HWA in FreeSurfer), BSE in BrainSuite, Robust Brain Extraction (BOBEX), and ANTs. Most of these tools do a good job for human data, however, the performance is suboptimal and variable in NHP due to the differences in brain structure (e. g. size, adipose tissue, olfactory bulb) and the quality of the T1 image (SNR, inhomogeneous intensity). Accordingly, the parameters and/or related atlas library need to be customized to optimize the brain extraction in NHP. For example, in AFNI the program "3dSkullStrip" with alternative options "-monkey" "-marmoset" and "-surface_coil" are available for brain extraction in NHP.
Population brain templates, such as the NIMH Macaque Template (NMT), can further improve and automate the registration and brain extraction process  (Seidlitz, Sponheim, et al. 2017). Standard segmentation algorithms can separate gray versus white matter but if the signal is not homogenous, which is typically the case at higher magnetic fields, segmentation in some parts of the brain will be better than others (especially subcortically). Registration of T2 datasets to T1 structural scans also remains a challenge. Affine or non-linear registration algorithms can work well provided that intermediate scans are available. For instance, a full brain T1 structural scan from the same monkey obtained along with T2 images (also with as much coverage of the brain as possible) could be crucial for registering T2 datasets to any of the freely available monkey template brains that are registered to macaque atlases.
One way to reduce or eliminate the manual intervention during brain extraction and tissue segmentation -using only the typically-acquired T1 scan -is to rely on priors defined on a high-resolution and high-contrast template. The multi-subject NMT includes manually refined masks of the brain, cortical gray matter, and various tissue types (including blood vasculature)  (Seidlitz, Sponheim, et al. 2017). Applying the inverse anatomical alignment transformations to the NMT brain mask produces an approximate single subject mask for brain extraction. A more precise individual brain mask and tissue segmentation can be obtained using the NMT's representative brain and tissue segmentation masks as priors. The NMT distribution includes scripts that use AFNI and ANTs to perform these mask refinements (as well as morphological analysis).
21 (SD=0. 03) but 0. 14 (SD=0. 07) during movie sessions (t=2. 82, p=0. 006). Regarding motion correction algorithms,those designed for human neuroimaging data similarly for NHP data. As such, most groups use SPM, AFNI, ANTs, or FSL software to estimate the motion parameters and remove motion artifacts. The estimates of the movement values can be used as regressors of no interest during the analysis of functional data, if desired. The grayplot proposed by  (Power 2017 ) can be used to illustrate the motion and the denoising effects. However, as with all neuroimaging data, image distortions or signal drop-out with caused movement correction to be suboptimal. Mean FD.
Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc. ) of the model.
, 2012). Since MaBoSS provides probabilistic results of V C The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals. permissions@oup. com all model entities (e. g. gene/protein/phenotype), theoretical results can be confronted to many types of experiments, including singlecell behaviors. MaBoSS has already proved its efficiency in biological interpretation of cancer-related models  (Calzone et al. , 2015; Cohen et al. , 2015; Remy et al. , 2015; Verlingue et al. , 2016)  and is constantly evolving. In addition to the initial core C þþ script (MaBoSS 1. 2. 2), the new version of MaBoSS (2. 0) includes an environment that facilitates the elaboration of a project in MaBoSS, in particular: model construction, visualization of solutions, simulations of mutations, drug treatments and sensitivity analyses (Fig.
Constructing a Boolean model using MaBoSS MaBoSS is a simulation software for continuous time Boolean modeling. The model is described by a network, where nodes have Boolean states. Transitions between the node states are governed by logical equations, with an associated rate (a real number 2 ½0; 1½). The model is written in a specific language and saved in a '. bnd' file (for Boolean Network Description). Modeling parameters, including initial conditions, maximum time, etc. are gathered in a '. cfg' file (for ConFiGuration file). The idea is to have a single. bnd file, and several. cfg files that account for different experimental situations (initial conditions, mutations, drug treatments, etc. ). That way, 'external variables', noted by the name of the variable with a preceding $ and representing real numbers, are defined in the.
bnd file and can be set and modified in the associated. cfg file. Writing a MaBoSS model can be facilitated by creating and exporting it using the user-friendly tool GINsim  (Naldi et al. , 2009)  (from http://ginsim. org/dev/). This export results in the creation of a. bnd file and a. cfg file with default parameters. Thus, any model in SBML qual standard format  (Chaouiya et al. , 2013)  (including multi-valued models), can be exported in MaBoSS format. Similarly, it is possible to export a MaBoSS model (. bnd and. cfg files) into a set of logical rules written in a text format. The output is a text format that is supported by the library bioLQM (available at https:// github. com/colomoto/bioLQM), which allows the export in SBML qual or other formats for other modelling tools such BoolNet (http://www.
colomoto. org/software/boolnet. html; Mu ¨ssel et al, 2010). Note that with a minor modification (a change of, into,), the output format can be imported directly in BoolNet. One important asset of MaBoSS 2. 0 is the possibility of using any probability distribution as an initial condition. For instance, for a set of 2 genes (A, B), the following probabilities can be assigned: p½ðA; BÞ ¼ ð0; 0Þ ¼ 0:7 and p½ðA; BÞ ¼ ð1; 0Þ ¼ 0:3, thus offering the possibility to characterize an heterogeneous cell population where each probability of network state corresponds to a subpopulation.
Running MaBoSS and processing the output files MaBoSS core script is accessible through command line. It estimates time dependent probabilities of 'network states', a network state is a vector of Boolean values associated to node states. The outputs of MaBoSS contain three files: a file for time dependent probabilities of network states (probtraj. csv file), a file for asymptotic probability decomposition in 'indecomposable stationary distribution' (statdist. csv file) and a file for the fixed points and their associated probability (fp. csv file). This latter file allows to quickly verify the existence of a limit cycle: when the sum of all fixed point probabilities is less than 1, it can be concluded that there exists at least one limit cycle that cannot be computed exactly but remains to be characterized.
Modifications of a master model One purpose of mathematical modeling is to predict the effect of specific perturbations (e. g. losses or gains of function, or combinations thereof). For instance, cancers can be considered as mutants of a model in normal conditions, which we refer to as 'master model'. To do so, some of the following scripts create new 'external variables' and vary them according to the task the modeler wishes to perform. Recall that external variables of MaBoSS correspond to parameters in the. bnd file that can be modified in the. cfg file (see reference card). Mutant simulations. Simulating gene alterations can be done by over-writing the rules of the corresponding components. The script MBSS_MutBnd allows the use of a single. bnd file for all different model variants (including wild type and mutant conditions).
The Fig. 1. Pipeline of the use of MaBoSS 2. 0 functionalities. A logical model can be simulated directly, modified by mutations, drug treatments or sensitivity analyses and all modifications can be gathered into a single command and simulated at once script adds two external variables to each gene that the user wishes to mutate: the first one representing a possible deletion or a loss of function, the other reproducing amplifications or gains of function. These external variables can then be modified and set as desired in specific. cfg files. Once several. cfg files have been constructed, the use of FormatTable is particularly suited, because it generates a folder with the name of the. cfg file in order to keep track of each mutant simulation. Parameter sensitivity analysis. The transition rates for each node can be varied to assess their importance in some outputs.
For that, it is important to control their value through external variables. The script MBSS_SensitivityAnalysis allows to vary the value of external variables separately by creating a. cfg file per modification. Then, a shell script is automatically created and can run the simulations for all the newly created. cfg files. Drug treatments. For the simulation of drug treatments, the script MBSS_DrugSim generates new nodes that will be used as drugs. They act as inhibitors of the specified targets. The strength of the inhibitor can be modulated to account for intermediate drug dosages. Also, in the course of a simulation, a drug treatment can be applied and removed after a certain time. For that, we have developed a script, MBSS_InitCondFromTrajectory, with which one can select the final probability distribution of a simulation and use it as the initial condition for another simulation of the model with the new drug condition.
Integrated functionalities In order to run a complete project including all needed perturbations into a single command, MBSS_PrepareProjectFile prepares a folder containing all. cfg files corresponding to all the modifications without launching the simulations. In addition, it creates a shell script for running MaBoSS for all possible. cfg files and processing the outputs files. For running multiple simulations on a server, the folder can be imported and the shell script can be adapted to the server environment. Note that the definition of multiple runs is more straightforward in MBSS_MultipleSim, but is more flexible in MBSS_PrepareProjectFile.
Conclusion MaBoSS 2. 0 and its associated environment have been developed to answer specific needs of modelers. They can be combined and used into the following pipeline (see Supplementary Material for an example): (i) Construct a logical model, by writing directly the. bnd and. cfg files or by exporting them from GINsim; (ii) Run the model, analyze the results within a spreadsheet, and visualize the results; (iii) Test parameter sensitivity; (iv) Confront the model to known experimental observations: mutations, drug effects and (v) Set up a protocol for comparing experiments to model results for new biological insights. In the future, we plan to construct a library version of MaBoSS in Python and in R to facilitate its implementation in these environments. We welcome the contribution of all future users to extend MaBoSS applications.
Funding GK is supported by the  Ligue Nationale contre le Cancer (Equipes labellisees) ,  Site de Recherche Integree sur le Cancer (IRIC) Socrates , the  ISREC Foundation ,  Agence Nationale pour la Recherche (ANR AUTOPH, ANR Emergence) , the  European Commission (ArtForce) , a  European Research Council   Advanced Investigator Grant , the  Fondation pour la Recherche Medicale (FRM) , the  Institut National du Cancer (INCa) , the  Fondation de France ,  Canceropole Ile-de-France , the  Fondation Bettencourt-Schueller , the  LabEx Immuno-Oncology  and the  Paris Alliance of Cancer Research Institutes. LC is partially funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  668858.
Background: Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. Methods: Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples.
Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. Results: A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93. 4% sensitivity and 94. 9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.