dataesr/scientific-paragraphs-categorization huggingface dataset:
Human studies reflect the population at hand, yet often rely on indirect measures, obscuring the full picture. Although both share a common goal of curing stroke via the repair and reorganization of the injured brain, what is missing is a translational bridge to effectively span the divide between basic mechanisms and dynamic human brain systems. At the same time, the neuroscience community is immersed in collecting large datasets to provide greater understanding of brain function and dysfunction. Such initiatives span normal function (Human Connectome Project), development (NIH Pediatric Database), and brain disorders, such as Alzheimer's disease (ADNI) and mental illness  (Research Domain Criteria Project). Although these initiatives provide the necessary empirical foundation, quantitative tools are missing to integrate these multiple datasets to "reconstruct" the brain, and provide the link between these data and those from a single person.
TVB has been established and applied to normative datasets  (Deco et al. , 2012)  and for learning and plasticity  (Roy et al. , 2014) , yet a proof of concept needs to be established based on pathological states. The objective of the present study using the TVB platform was to determine changes in local and global biophysical parameters to better understand individualized brain dynamics after stroke. In this approach, the model parameters act as a means to assess brain health, analogous to blood samples assessing physical health, and hence, parameter changes could ideally be used as potential biomarkers of stroke and/or stroke recovery. So far, such biomarkers have mostly focused on stable architectures, from behavior to fine anatomical and functional levels  (Burke and Cramer, 2013). In contrast, our aim is to create a synergistic amalgamation of mathematical models with neuroimaging, where the biomarker derives from the dynamical model itself.
Brain imaging Imaging data were acquired on a 3 Tesla Philips Achieva scanner using the following sequences: (1) High-resolution anatomical images were acquired with a 3D magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence: FOVϭ 250 ϫ 250, res-olutionϭ1 ϫ 1ϫ1 mm, SENSE reduction factor ϭ1. 5, TR/ TEϭ7. 4/3. 4 ms, flip angleϭ8, sagittal orientation, number of slicesϭ301 covering the whole brain. (2) Diffusion tensor imaging (DTI) was acquired with the following sequence: FOVϭ224 ϫ 224, TR/TEϭ13030/55, 72 slices, slice thicknessϭ 2 mm, resolutionϭ0. 875 ϫ 0. 875 ϫ 2, 2 mm post-processing iso-voxel with bϭ1000 s/mm 2 (and bϭ0), 32 diffusion directions. (3) Functional imaging acquisition at rest covering the whole brain (37 slices) was acquired using single-shot echo-planar MR (EPI) with slice thickness ϭ 4.
Virtual brain transplantation Because of mechanical deformation consequent to large cortical strokes, the anatomical parcellation on T1w images using semiautomated methods is very difficult to achieve. Hence, a "virtual brain transplant" process was performed in accordance with a previous approach  (Solodkin et al. , 2010). This method replaces the cortical lesion with the homologous image from the contralesional hemisphere from the same subject. With this, brain parcellation is possible using semiautomatized software. The process consisted of the following steps: (1) Lesion segmentation by hand. (2) Using the AFNI 3dcalc function  (Cox, 1996) , the homologous region in the nonlesioned hemisphere was dissected and transplanted into the stroke region, effectively filling in the missing portions of the brain.
Resting state fMRI preprocessing Preprocessing was done in AFNI  (Cox, 1996)  and included the following steps: motion correction of functional and anatomical datasets  (Cox and Jesmanowicz, 1999) , 3D spatial registration to a reference acquisition from the rsfMRI run, registration of functional images to the T1-w volume, de-spiking and mean normalization of the time series, motion correction (Ͼ1 mm;  Johnstone et al. , 2006)  and regression of CSF and white matter signals to remove slow-wave components (eg, physiological noise;  Lund et al. , 2006).
0122 ms. (5) The BOLD signals were derived from the field potentials using a hemodynamic response function implemented with a gamma kernel  (Boynton et al. , 1996; Sanz-Leon et al. , 2015). (6) Assessing reliability of the simulated time series: comparison between the empirical and simulated BOLD time series was done in terms of amplitude, frequency, and phase. Amplitude: we calculated the range of amplitude by identifying the highest and lowest peaks present in the time series across all regions. Frequency: fast Fourier transforms of the raw and simulated time series were obtained using MATLAB's "fft" function with a sampling frequency of 0. 5 Hz, to determine the range, profile, and peak frequencies  (Ritter et al. , 2013). Phase: this was assessed by comparing the functional connectivity matrices of the simulated and empirical time series.
Finally, neither lesion characteristics nor patient demographics correlated with recovery, highlighting the unique predictive potential of these parameters. The question then becomes to what extent these parameter estimates can be used as predictors of recovery at the individual patient level. Although a cross-validation approach using the current dataset could serve to answer this question, a new and larger stroke cohort is ideal in obtaining estimates of the sensitivity and the specificity of our markers, due to high variance in stroke. However, there is clear value of our observations even with this limitation. At present, biomarkers for stroke recovery have been limited by the use of "substitute or surrogate" measures derived from brain imaging or electrophysiology, mainly due to the inability to measure in vivo more ideal basic elements, ie, at molecular or cellular levels  (Burke and Cramer, 2013).
Gaseous and condensed phase chemical schemes An explicit description of long-chain hydrocarbon oxidation processes up to the final production of CO 2 involves millions of reactions (e. g. ,  Aumont et al. , 2005). Such explicit chemical schemes are too large to be written manually. The GECKO-A computer program has been developed to overcome this difficulty. This tool is used here to generate explicit chemical schemes for the long-chain hydrocarbons. The GECKO-A tool generates chemical schemes for a given precursor on the basis of a prescribed protocol. Re- Exp. [HC] 0 action pathways and kinetic data are assigned from experimental data if available or structure activity relationships (SARs) if not. The protocol implemented in GECKO-A is described in  Aumont et al. (2005) , with chemistry updates performed by  Aumont et al.
Acknowledgements. This research work has been supported by the  French National Research Agency (ANR)  within the  ON-CEM  ( ANR-12-BS06-0017 ) and  MAGNIFY  ( ANR-14-CE01-0010 ) projects and the  French National institute for Geophysical Research (CNRS-INSU)  within the  LEFE-CHAT program  through the  Dodec-AOS  project. PJZ acknowledges support from the  National Science Foundation (NSF)  under grants  AGS-1219508  and  AGS-1420007. The  National Center for Atmospheric Research  is sponsored by the  National Science Foundation. JLT and SM were partly supported by a  US Department of Energy  grant  BER/DE-SC0006780. Edited by:  N. L. Ng.
2) The algorithm layer contains the retrieval and reuse steps of the CBR cycle and the bolus calculator formula. Whenever the user requests a new bolus advice, the retrieval algorithm compares the current scenario with existing cases in the case-base and returns the solution (i. e. bolus calculator parameters) and, if necessary, adapts the retrieved solution to the current scenario (reuse step). The bolus calculator formula uses this solution to calculate the recommended insulin dose, which is sent to the presentation layer to display the advice, through the graphical user interface, to the patient. 3) The safety layer implements risk mitigation and risk control measures to ensure maximum safety of the system. Risk mitigation is implemented to ensure that only safe (i. e. physiological) values are entered via the user interface as well as to verify each parameter retrieved from the database.
Risk control limits the maximum amount of insulin to be advised which can be pre-defined for each user by the clinical expert. 4) The data layer is responsible for storage, maintenance, security of data stored in the local databases as well as providing secure transmission to the clinical platform. The data layer contains three databases: 1) An event-database which contains log book entries and information about all glucose related user entries and insulin requests; 2) a case database (i. e. case base) containing all generated cases and information about their usage and 3) a settings-database to store security information, patient details and personal settings. The data layer also manages access to automatic input parameters (e. g. exercise information through external accelerometer. ) ABC4D Clinical Revision Platform: 1) The presentation layer is responsible for the graphical user interface of the revision platform.
It allows the clinician to import the log book and cases retrieved from the patient platform as well as additional data (e. g. CGM data) required by the revision algorithm. During the revision process, the user interface displays glucose graphs, meal information, selected parameters (e. g. exercise) and retrieved cases used for each scenario where an insulin advice has been requested. A suggested adaptation to the solution of the retrieved case is presented to the clinical expert who needs to approve or decline each case adaptation. 2) The algorithm layer holds the revision and reuse steps of the CBR algorithm. The revision algorithm calculates adaptations to the solution of each case that has been used. After all case adaptations have been revised, the approved cases are updated into the case base of the patient platform (retain step).
The time is automatically retrieved from the system time of the phone when requesting an advice. Glucose data from a CGM is used for case revision to assess the outcome of a suggested insulin bolus by evaluating the minimum postprandial glucose value within a pre-defined time window. The length of the time window (e. g. 4 hours) is depended on the insulin-actiontime of the individual user and can be defined a priori by the clinical expert. For more details about the adaptation metric, the reader is referred to  [9]. Patient Smartphone Platform: The ABC4D patient platform is built on the presented architecture and has been implemented in an off-the-shelf smartphone (Hardware: iPhone 4S, Apple Inc. California; Programming Environment: X-Code/Objective-C; Database: SQLite3). Special attention was put on designing the graphical interface to make it as userfriendly as possible.
Users could give reasons for declining bolus advice through selecting one of following check-boxes: • Too much insulin • Too little insulin • Other/Manual user comment Declined recommendations by the user were used for revision. However, instead of the solution of the retrieved case, the solution proposed by the user is revised and, if non-optimal outcome, adapted. All user input and recommendations are locally stored in a relational database management system (i. e. SQLite3) on the phone. This enables the user to have access to past glucose information and recommendations at all times. Data essential for case revision can be exported as an Excel (Microsoft) file and sent encrypted via email to the clinical expert. Clinical Revision Platform: The clinical revision platform has been implemented in MATLAB (The MathWorks, Inc) and is designed to run on a desktop computer.
Each participant used the patient platform continuously for six weeks to receive insulin bolus advice and log additional diabetes events such as snacks, additional capillary blood glucose measurements and correction boluses of insulin. A blinded CGM was worn by the participants for the whole duration of the pilot study. Its glucose data was uploaded once a week by the research team for case revision. At the end of the study, all participants were asked to complete a usability questionnaire. Furthermore, we recorded the number of times the software had been used and the time needed to request and review a bolus advice. A clinician, a clinical research nurse and an engineer used the clinical platform at the end of each study week to approve case adaptations for each accepted bolus advice. Safety measures (e.
No reasons were provided by the user for the remaining 4% of declined advice. While participants used the log book function of the application less in the last week of the study (p<0. 05), no statistically significant change was observed when analysing the average number of requested bolus recommendations when comparing the initial study week with the last week. The mean time spent for requesting a bolus advice using the application (i. e. time from opening the software until closing it) was 100±63 seconds in the first week. This value was significantly (p<0. 01) reduced to 62±36 seconds in the last week. Table  III  shows the outcome of the questionnaire assessing system usability and acceptability after completion of the six week study. The majority of people considered the ABC4D platform as user-friendly, to trust the generated advice and to be happy to use the platform.
g. snacks, exercise or stress), the number of insulin advice requests did not significantly change over the study period. Further findings show, that the time needed to request an insulin advice was significantly reduced in the last week compared to the start of the study. As the software did not provide a function to re-use previously entered manual inputs (e. g. library of profiles), this reduction results from the learning curve of the user to enter data more efficiently when becoming more familiar with the software. However, some participants still found the use of ABC4D more time consuming compared to their conventional way of calculating the insulin dose. To address this, future work could see the system being integrated into a blood glucose meter or insulin pump to reduce the number of manual user inputs.
It is important to note that the presented platform can hold various algorithms for each of the CBR steps. While the ABC4D can potentially hold other revision algorithms that do not rely on CGM data (e. g. post-prandial capillary measurements  [15] ), the algorithm implemented in the presented system utilises retrospective CGM data to learn from previous case outcomes and further adapt the bolus calculator parameters. However, even without CGM, the patient platform is able to provide real-time bolus advice. In this scenario, the revision and retain steps will not be performed. For long-term usage, and once the bolus calculator parameters have been optimised, CGM could be used periodically (e. g. one month every four months) to adapt to changes in the users environment. This is important as CGM sensors are expensive and some users may not want to continuously use CGM for longer periods.
Bolus Calculator 2. Algorithm Layer Retrieval Reuse 1. Presentation Layer Data Export 4. Data Layer 3. Safety Layer Risk Mitigation Risk Control Data Security Database Presentation Logic Graphical User Interface Maintainance 2. Algorithm Layer 1. Presentation Layer Data Export 4. Data Layer 3. Safety Layer Risk Mitigation Risk Control Data Security Database Presentation Logic Graphical User Interface Maintainance Clinical Platform Revision Retain Patient Platform MANUAL INPUT PARAMETERS CASE REVISION INSULIN BOLUS MANUAL CASE ADVICE APPROVAL CASE BASE UPDATE AUTOMATIC INPUT PARAMETERS.
In this article, we present epiPALEOMIX, an open-source and user-friendly software package that automates the characterization of ancient epigenomes. The epiPALEOMIX pipeline implements all the methods used to characterize the first ancient epigenome, that from the hair of a 4-kyr-old Palaeo-Eskimo  (Pedersen et al. 2014) , including methylation mapping, nucleosome calling and phasogram analyses. It can be readily applied to any BAM alignment file obtained from ancient specimens. We apply epiPALEOMIX to the shotgun sequence data underlying a large subset of ancient genomes characterized with an average depth-of-coverage >2-fold (2. 19-to 42. 0-fold). Our study thus represents the largest ancient epigenomic study, including 35 samples from AH, ancient anatomically modern humans (AMH), horses and aurochs.
New Approaches In this study, we present epiPALEOMIX, a new open-source package for the analysis of ancient epigenomes, which encloses one optional and three main modules. The MethylMap module (fig. 1A ) implements the methodology from  Pedersen et al. (2014)  and  Gokhman et al. (2014)  to calculate methylation scores (Ms) and reconstruct methylation profiles within regions provided by users as BED coordinate files. Such files can correspond to genomic regions of interest, such as promoters, gene bodies, CpG Islands (CGIs) (fig. 2 ), but also to sliding genomic windows (fig. 10 ). The methodology exploits the relative number of CpG!TpG read mis-incorporations found within a given region, which is assumed to mostly reflect post-mortem deamination events at methylated epialleles (Seguin-  Orlando, Hoover, et al.
2015). The MethylMap module can accommodate single-stranded and double-stranded DNA library types and can also restrict the analyses to specific read positions, which proves useful in cases where particular read positions show inflated error rates. These features, and the direct applicability of the methodology to nonarchaic hominin sequence data, represent important increments over RoAM. As specifically developed for the analysis of epigenomic signatures from aDNA data, epiPALEOMIX also fills a niche that is presently not covered through the multiple Bioconductor tools tailored to the detection of methylation and nucleosome regions  (Gentleman et al. 2004; Huber et al. 2015). In addition to MethylMap, epiPALEOMIX provides two modules to reconstruct patterns of nucleosome positioning along the genome.
The first module, called NucleoMap, implements the methodology from  Pedersen et al. (2014)  to map peaks of nucleosome occupancy (fig. 1B ). It builds on the natural DNA decay taking place post-mortem, resulting in an over-representation of nucleosome-protected DNA regions post-sequencing  (Dong et al. 1997; Pedersen et al. 2014). More specifically, NucleoMap exploits patterns of coverage variation along the genome, where nucleosomes are inferred at positions maximizing depth variation within 147 nucleotide blocks, reflecting the size of a nucleosome dyad, and their flanking regions. The analysis can be performed genome-wide or within pre-defined genomic regions (as shown on fig. 7 ). The second module implements the phasogram analyses from  Valouev et al. (2011)  and aims at detecting periodicity patterns between DNA fragments and from adjacent nucleosomes (fig.
1C ). We also provide an optional module, GCcorrect, that builds on previous methodology  (Benjamini and Speed 2012)  to Fast, Accurate and Automatic Ancient Nucleosome and Methylation Maps. doi:10. 1093/molbev/msw184 MBE estimate depth-of-coverage observed at the single nucleotide level, corrected for local %GC content. This can prove critical to account for sequence bias introduced during DNA library amplification  (Dabney and Meyer 2012). The epiPALEOMIX package is written in Python 2. 7 and builds on the implementation of the PALEOMIX pipeline  (Schubert et al. 2014). It supports multi-threading and requires a simple makefile as input indicating the location of sequence alignment files (BAM format) and the parameters of the analyses to be run. The epiPALEOMIX package is compatible with the analysis of HTS data generated from shotgun and captureenrichment experiments.
Analyses restricted to a single or a minimum number of loci are typically performed within minutes while up to half an hour is necessary to investigate approximately half a million loci scattered along the genome (supplementary table  S1 , Supplementary Material online). 1. Three main methods implemented in epiPALEOMIX. (A) The MethylMap module exploits the deamination of cytosines naturally taking place post-mortem at CpG sites and resulting in the conversion of unmethylated CpGs epialleles into UpGs and methylated CpGs epialleles (mCpGs) into TpGs. CpG (green) to TpG mis-incorporations observed when aligning sequencing reads against a reference genome (top sequence) will mostly indicate the presence of mCpG epialleles, which can be summed over regions of interest to estimate regional methylation scores (Ms).
For single-strand library construction methods  (Meyer et al. 2012 ), TpGs at both read starts (blue) and ends (red), as well as their reverse complementary CpAs, are used. For other library construction methods based on the ligation of adapters to double-stranded templates, TpGs (and their reverse complementary CpAs) are only considered towards starts (ends). Nucleotide mis-incorporations found in other dinucleotide contexts are ignored. (B) The NucleoMap module calls nucleosomes through patterns of depth-of-coverage variation along the genome. The size of the sliding window was selected to reflect the average size of nucleosomal DNA (147 bp;  Luger et al. 1997). Whenever the center of a given window displayed maximal local read depth, the nucleosome score is calculated as the coverage at the center (green) minus the mean read depth of the two 25-bp flanking regions (red).
Regional Methylation Patterns We stratified the human genome into four main genomic classes expected to show strong differential methylation levels but low cross-tissue variation  (Ziller et al. 2013). We first investigated CGIs, including their up-and downstream flanking shores (0-2 kb) and shelves (2-4 kb). CGIs are wellknown hypomethylated genomic regions, showing gradually increasing methylation levels from shores to shelves  (Deaton and Bird 2011). We next focused on three categories of   (Ball et al. 2009 ). We then contrasted Ms in exons, introns and their splice sites, as 5 0 (3 0 ) exonic DNA shows higher methylation levels than 5 0 (3 0 ) intronic DNA  (Laurent et al. 2010). Finally, we examined mtDNA as this locus shows minimal, if any, methylation  (Rebelo et al. 2009). Almost all ancient HTS data sets investigated displayed the expected methylation patterns, regardless of the molecular tools used for generating data (fig.
Conclusions In this article, we have developed epiPALEOMIX, the first computational package dedicated to the reconstruction of genome-wide methylation and nucleosome maps in ancient individuals. We applied epiPALEOMIX to the largest panel of genome-scale sequence data from ancient mammals and have shown that genome-wide epigenetic landscapes can be reconstructed over a considerable geographical and temporal span. For all ancient samples showing significant levels of DNA damage and prepared using USER treatment or Phusion amplification, inferred methylation profiles are reminiscent of modern methylomes, and show some level of tissue specificity. Importantly, this was also true (although less pronounced) for some samples that had not been USER treated or Phusion amplified due to the faster post-mortem deamination rate at methylated cytosines.
Additionally, we retrieved hg18 genomic coordinates of CTCF binding sites from  Fu et al. (2008)  and converted these to hg19/build37 using liftOver (http:// hgdownload. soe. ucsc. edu/admin/exe/linux. x86_64/; last accessed September 1, 2016). Our final set of genomic coordinates included 2 kilobases centered at each of 5,735 CTCF occupied and 5,635 unoccupied CTCF binding sites. We excluded sex chromosomes, which display depth-of-coverage variation between males and females, from all our analyses, except when noted otherwise. CGI coordinates for equids were converted from hg19/build37 using liftOver to horses (EquCab2. 0) and represented 4. 9 megabases spread across 7,685 regions. The gene annotation and intron/exon splice sites coordinates for horses (EquCab2. 0) and Aurochs (btau_ umd3.