dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , IF_cond_alpha and EIF_cond_exp_isfa_ista in Figure  4 ), and there is an experimental implementation which allows PyNN scripts to export a full model description in NeuroML v2 format. For example, whereas from pynn import nest at the start of a PyNN script indicates that the model should be executed in NEST, from pynn import neuroml2 indicates that the script should save a declarative description of the model in NeuroML. In addition, code generation to allow all cell/synapse types in NeuroML to be used in PyNN scripts is under active investigation. NineML  (Raikov et al. , 2011 ) is a language for describing new models of spiking neurons in a machine-readable format, which has been developed in parallel to LEMS. NineML shares a number of features with LEMS, allowing new models of spiking neurons to be specified (in the Abstraction Layer).
libNeuroML AS AN OBJECT-MODEL FOR THIRD-PARTY APPLICATIONS One potential use of libNeuroML is to provide an object model for third-party applications such as visualization, modeling, and simulation libraries. This is the route taken by the in-development Pyramidal project (http://pyramidal. readthedocs. org), which seeks to create an API for running multicompartmental simulations across different simulators. In contrast to libNeuroML, which loads the model elements into an internal object tree for manipulation, Pyramidal interacts with the underlying simulator to store the model in that application's native format. The advantages are that the modeler only deals with model elements defined in NeuroML and interacts with them through libNeuroML, while the simulator uses its own efficient, internal representation, and scripts developed in this way are portable across simulators.
JAVA LIBRARIES FOR NeuroML AND LEMS In addition to the Python APIs described here for working with NeuroML and LEMS, there are corresponding libraries in the Java language for reading, editing, writing, and validating NeuroML (https://github. com/NeuroML/org. neuroml. model) and LEMS (https://github. com/LEMS/jLEMS) documents. jLEMS is a more mature package than PyLEMS and is the reference simulator implementation of the LEMS language. These libraries are complemented by other Java packages for importing and exporting multiple formats such as SBML, NEURON, and Brian into LEMS using code generation. A tool jNeuroML (https:// github. com/NeuroML/jNeuroML) exists which bundles the functionality of these Java packages, allowing easy access to these features from command line. Options for allowing some of these code generation features to be accessible from Python are under investigation.
ACKNOWLEDGMENTS Michael Vella  is funded by a  Medical Research Council (MRC)   Capacity Building Studentship. Michael Vella 's initial contributions to libNeuroML were supported by the  Google Summer of Code Program 2012 , with the INCF as mentoring organization. The contributions of  Sharon Crook ,  Gautham Ganapathy  and  Robert C. Cannon  were supported in part by the  US National Institute of Mental Health  under grant  R01MH061905. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  US National Institutes of Health. Contributions of  Sharon Crook  were also supported in part by grant  R01EB014640  from the  US National Institute of Biomedical Imaging and Bioengineering. The contributions of  Robert C. Cannon ,  R. Angus Silver , and  Padraig Gleeson  were supported by the  Wellcome Trust  ( 086699/101445 ).
, large models can be stored in a MongoDB database at a fraction of the disk requirements for equivalent NeuroML documents. One MongoDB limitation is that JSON files exceeding 16 MB can not be stored; large documents (more than 100,000 segments) should therefore be stored as individual files. Serialization file sizes. For reconstructed morphologies, the file sizes of the different serialization formats vary linearly with the number of segments in the morphology with NeuroML requir- ing 316 B/segment, JSON requiring 62 B/segment, and HDF5 requiring 41 B/segment. For a typical reconstructed morphology of 2000 segments, a NeuroML (XML) file would require 617 KB, and the corresponding JSON and HDF5 files would require 80 and 41 KB, respectively. Data Format 5, http://www. hdfgroup. org/HDF5) is a set of file formats and tools for stor- ing and organizing large amounts of numerical and hierar- chical data.
HDF5 serialization is provided by libNeuroML, although presently the level of serialization support which is provided only extends to morphological reconstructions of neu- rons. The main advantage of HDF5 support is the relatively low memory footprint and support for rapid read/write opera- tions, which is demonstrated in the benchmarks section of this article. MongoDB. MongoDB (http://www. mongodb. org/) is a document-oriented "NoSQL" database which departs from the traditional table-based relational database paradigm toward JSON-like documents with dynamic schemas. Its documentoriented approach is particularly suitable for storing NeuroML documents. The MongoDB support provided by libNeuroML is particularly useful for users wishing to store large amounts of data on a server, such as for use with a website.
Methods Mineral chemistry. Chemical analyses of major elements in minerals (Supplementary Data 1) were performed on polished thin sections with a CAMECA SX100 electron microprobe at the Research School of Earth Sciences (RSES), Australian National University (ANU), operating in the wavelength-dispersive mode. Acceleration voltage and beam current were 15 kV and 20 nA with a beam diameter of 1mm for pyroxene and garnet and 5 mm for micas. Counting times per element were 20 s for Na, Mg, Si, Al, K, Ca and Fe and 60 s for Ti, Cr and Mn. K and Na were always analysed first in the routine. Several analyses on each phase were performed to obtain representative compositions, and core-rim analyses were done to assess zoning patterns. Standards were natural and synthetic minerals, which were all determined on the Ka emission peak.
In-house mineral standards were used as secondary standards. Imaging of internal zoning. Zircons were separated from the crushed rocks using conventional heavy liquid and magnetic techniques. Grains were mounted in epoxy resin and polished down to expose the near-equatorial section. Sensitive high resolution ion micro probe. Zircon was analysed for U, Th and Pb in the epoxy mount using the SHRIMP-II at the RSES at ANU in Canberra and the SHRIMP-IIe at USP in Sa ˜o Paulo. For zircon, instrumental conditions and data acquisition were generally as described previously  63. The data were collected in sets of six scans throughout the masses, and a reference zircon (TEMORA  64  ) was analysed each fourth analysis. The analyses (Supplementary Data 4) were corrected for common Pb on the basis of the measured 207 Pb/ 206 Pb ratios as described previously  63.
The common Pb composition was assumed to be that predicted by the model of ref. 65. U-Pb data were collected over five analytical sessions using the same standard, with the different sessions having calibration errors between 1. 23% and 2. 16% (2 sigma), which was propagated to single analyses. Data evaluation and age calculation were done using the software Squid and Isoplot/Ex, respectively  66, 67. Average 206 Pb/ 238 U ages are quoted at the 2-sigma confidence level and forced to at least 1% to account for external errors. Figure  5  | Pressure-temperature paths for the high-pressure and ultrahigh-pressure terranes from West Gondwana Orogen. Peak metamorphic conditions for Mali and Togo eclogites are discussed in the text and data are available in the electronic appendix (dashed curves are inferred).
Acknowledgements C. E. G. de. A. thanks  Y. Agbossoumonde  ´for field guidance in Togo and  J. Peucat  and  P. R. Menot  for providing extra zircons for sample DKE-350. We thank  R. Weinberg  and  I. Campbell  for careful reading of the manuscript. C. E. G. A. , U. G. C. and M. A. S. B. thank  Sa ˜o Paulo Research Foundation  ( FAPESP) 2012/00071-2  for the financial support and the  Brazilian National Research Council (CNPq)  for the grant  246206/2012-8  to  C. E. G. de. A. D. R. acknowledges the financial support of the  Australian Research Council   DP110101599. This is a contribution to the IGCP-628, Gondwana Map Project.
1 Cycle Sequencing Kit (Applied Biosystems) and M13 primers on ABI PRISM 3130 automated sequencer (Applied Biosystems). Chimeras were eliminated by analyzing sequence with ChromasPro Software. Sequences were compared with sequences available in the GenBank database using BLAST. A 97% sequence similarity and 95% coverage with a described species were used for molecular identification  [7]. All the sequences obtained in this work have been deposited in GenBank database with accession number KF768259-KF768340.
Discussion Here, further exploration of gut microeukaryotes in stool specimens collected in seven individuals living in four tropical countries yielded new data about this poorly explored component of the gut microbiota. Fifteen species were observed for the first time in the human gut. S. roeseli detected in stool from India and V. campanula in stool from Amazonia, are two ciliates previously described from environment in particular in freshwater  [22, 23]. Moreover, environmental fungi Puccinia poarum, Rhosporidium babjevae, Phytophthora pinifolia, Alternaria alternata, Aspergillius restrictus, Bispora christiansenii, D. tassiana, Davidiella sp. and W. cylindrical were previously described as plant pathogen or from fresh water  [24] [25] [26] [27] [28] [29] [30]. F. capsuligenum was previously found in fruit, brewery and in soil  [31].
Conclusions A diversity of 41 microeukayotes species including 38 fungal species and three protist was detected in stool samples collected from four different tropical locations. A total of 13 fungal species and two protists Stentor roeseli and Vorticella campanula were observed in the human gut for the first time. Indeed, these microeucaryotes have not been detected among the 249 fungi and the 36 protists cultured and detected from the human gut so far. These data plea for more extensive studies being performed in specimens collected from various geographical regions to further establish the human gut microeukaryote repertoire.
Data collection Standard 12-lead ECGs were recorded using a novel ECG machine (CS200 excellence, Schiller AG) with a sampling rate of 1000/s and an amplitude resolution of 1. 0 mV. In addition to standard electrophysiological catheters deployed in the right atrium and ventricle, a reference catheter (REF-STAR, Biosense Webster, a Johnson & Johnson company) was placed on the back of the patient. The electromechanical mapping system (NOGA XP, Biosense Webster) and the navigation and mapping method were described previously. 11 In brief, a conventional 7F deflectable-tip mapping catheter (NAVI-STAR, Biosense Webster) was deployed in the LV via a retrograde aortic approach from the femoral artery. Intracardiac unipolar and bipolar electrograms and spatial position of the catheter tip were recorded at 200 equally distributed LV endocardial locations.
The sequentially recorded intracardiac electrogram and position data were temporally aligned using the simultaneously recorded 12-lead body surface ECG. For the three-dimensional (3D) LV reconstruction during the acquisition process, the geometric filling threshold was set at 10 mm to reduce artificial interpolation. Cardiac magnetic resonance data were acquired with a 3T Siemens Magnetom Skyra scanner with 36 channel coils. Following localizer acquisitions, cine ECG-triggered segmented steady-state free precession images were acquired in the two-, three-, and four-chamber orientations, as well as in a stack of contiguous short-axis slices covering the ventricles (slice thickness 8 mm, 20% inter-slice gap, 25 phases). After intravenous bolus injection of gadolinium (Gadobutrol, 0. 2 mmol/kg body weight) ultra-fast gradient-echo 'VIBE' images were obtained to describe thoracic anatomy and to detect the location of the precordial ECG electrodes on the chest.
Construction of anatomical models Patient-specific anatomical models were reconstructed using CMR data. The outline of the ventricular musculature was traced semi-automatically in the end-diastolic frame of the short-axis cine dataset. The base of the ventricles, outflow tracts, outline of the atrial cavities, aortic arch, and caval veins were traced manually in the FLASH dataset. The lungs and the torso surface were traced from the VIBE data. From the contours thus obtained surfaces were created using Blender (The Blender Foundation) as 3D editing software. A computational mesh was constructed and mesh nodes were labelled as myocardium, blood, connective tissue, lung, or skeletal muscle, depending on the surfaces in which they were enclosed. Fibre orientations were assigned to the ventricular nodes using a rule-based method.
20 t 1 ms intervals, computed transmembrane currents were injected in the torso model and the bidomain equation was solved for the electric potential throughout the torso, from which the 12-lead ECG and local electrograms were extracted. 18 Wilson's central terminal was used as reference for electrograms and precordial ECG leads. All simulations were performed using propag-5, a cardiac simulation package suitable for large-scale parallel computers. 12, 21 The computations were performed on 2048 cores of a Cray XE 6 supercomputer and took about 20 min for a single heartbeat.
Patient 1 Patient 1 was a 72-year-old woman with an idiopathic dilated cardiomyopathy diagnosed by CMR and coronary angiography. Her ECGs fulfilled conventional criteria for LBBB, which include a QRS duration. 120 ms, QS or rS morphology in lead V1, and a monophasic R-wave with no Q-waves in leads V6 and I. Moreover, a broad notched or slurred R-wave in leads I, aVL, V5, and V6 was noted, which is consistent with the ECG criteria of the American Heart Association, American College of Cardiology Foundation, and the Heart Rhythm Society. The comparison between the measured and simulated data for this patient is shown in Figure  1. The correlation coefficient between measured and simulated activation times was r ¼ 0. 91. Measured surface QRS duration (130 ms) was very close to the simulated QRS duration.
Patient 2 Patient 2 was a 69-year-old man with a QRS duration of 190 ms with previous anterior myocardial infarction diagnosed by cardiac CMR and coronary angiography. With a QS complex in lead V1 and broad, notched, monophasic R-waves in leads I and V6, his ECG fulfilled conventional criteria for LBBB. The comparison between the measured and simulated data for this patient is shown in Figure  2. The correlation between measured and simulated activation times was r ¼ 0. 87. The measured LV breakthrough was in the anterior wall, far from the septum. Attempts were made to reproduce this in the model by increasing the tissue anisotropy ratio, but the simulated breakthrough was still closer to the septum. The simulated leads I and V6 were more notched than the measured signals, while in lead V5 the opposite was true.
Funding This work was supported by a grant from the  Swiss National Supercomputing Centre (CSCS)  under project  ID   397. The authors gratefully acknowledge financial support by the  Theo Rossi di Montelera Foundation , the  Mantegazza Foundation , and FIDINAM. This work was previously supported by the  'Iniziativa Ticino in Rete'  and the  'Swiss High Performance and Productivity Computing' (HP2C) Initiative. Funding to pay the Open Access publication charges for this article was provided by.
Statistical Analysis The evolution of diabetes has been chosen as the primary end point of the study. It was evaluated on the basis of a score of effectiveness. Remission of diabetes (score 1) was defined as HbA1c level lower than 6. 5% and interruption of antidiabetic treatment. Improvement (score 2) was defined as either a decrease in HbA1c level or a reduction in the antidiabetic treatment. Score 3 corresponded to a stabilisation of diabetes and score 4 to a degradation of the glycaemic pattern. As the latter is a qualitative parameter, statistical analysis of the data used a  2 test. Similar scores and statistical procedures were used to evaluate the changes in the following comorbidities: arterial hypertension, sleep apnoea syndrome, and serum lipid pattern. The serum HbA1c levels, measured, respectively, before and 6 to 12 months after surgery, were compared with the Wilcoxon test.
Acknowledgments We are thankful to  Olivier Lemaire  ( INRA Colmar ) for providing us the antibodies directed against the P90 protein, to  Catherine Reinbold  ( INRA Colmar ) for the transmission electron microscopy pictures, to  Daniele Scheidecker  ( CNRS-IBMP Strasbourg ) for protoplasts isolation. We also would like to thank  Lionel Ley  and the members of the experimental unit of  INRA-Colmar  for their valuable technical assistance for plant production. We are grateful to  Allen Miller  and  Etienne Herrbach  for critical reading of the manuscript and to the two reviewers for helping us to improve the manuscript.
g. [44] [45] [46] ), but the available information concerning the species' genetic content is limited. To date, this is the case for most sparids, except for the two protandrous species of Sparus aurata and Acanthopagrus schlegelii, which account for more than 90% of the Sparidae sequences available in GenBank. With modern sequencing technologies, this lack of knowledge can be overcome and ultimately allow the comparison of the genetic networks involved in sex determination and differentiation among closely related species that exhibit different reproductive modes. Here, we sought to identify and understand the molecular toolkit underlying the sex differences of the expressed transcriptome in the rudimentary hermaphrodite sharpsnout seabream. To that end, we employed an RNA-Sequencing (RNA-Seq) approach  [47]  aiming at capturing the gene content of sharpsnout seabream that exhibits sex-biased expression pattern.
We chose to study both gonad and brain tissues. Gonads were selected to get a comprehensive overview of the genes responsible for the divergence of the primary sex-related structure; brain was included to increase the species genic information and to understand how sex affects brain functions, as shown in gonochoristic fishes (see  [48]  and references therein). Our results revealed major expression differences between male and female gonads, but only minor differences between male and female brains. Most genes involved in primary pathways of sex determination and differentiation in other vertebrates are present also in the sharpsnout seabream; this implies a conservation of pathways between gonochorists and hermaphrodites. Finally, we constructed a dataset of genes and a resource of genetic markers that will assist future genetic research at the species and family level.
Transcriptome assembly and annotation For the assembly, we pooled the filtered reads of all samples and implemented three different trials using SOAP-DENOVO  [53]  (kmer = 35, min length 200 nucleotides), Velvet/Oases  [54, 55] ) (kmer = 35, min length 200 nucleotides) and Trinity  [56]  (trinityrnaseq_r2013-02-25; default kmer 25, min length 200 nucleotides). The three candidate assemblies were evaluated by BLASTn  [57]  against Oreochromis niloticus, Oryzias latipes and Gasterosteus aculeatus cDNA dataset downloaded from Ensembl database  [58]  with an e-value threshold of 10 -9. The assembly produced by Trinity had the highest number of significant similarity with unique genes from all three teleost species and was selected. To assess the assembled transcripts and exclude the spurious ones, we pooled all the reads and mapped them to the selected assembly with Bowtie  [59]  within RSEM  [60]  using the script available in trinity utilities run_RSEM_align_n_estimate.
pl. Putative transcripts with less than 1% of a locus reads mapped to that particular isoform (IsoPct < 1) were eliminated (as proposed in  [61] ). The same was done for those with Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values less than 0. 3. The choice of FPKM threshold was To annotate the assembled transcripts, we conducted a BLASTx similarity search against the NCBI protein database nr (e-value threshold 10 -9 ; keeping the top ten hits). BLASTx was done in parallel using NOBlast  [62]. The output was used in Blast2GO  [63]  where gene ontology terms were retrieved and assigned to the transcripts (only the longest transcript was used per locus). The open reading frames (ORF) were extracted per sequence with the EMBOSS program getorf. InterProScan  [64]  was run on the longest ORF per transcript.
Differential expression The paired reads of each sample were mapped to the assembly with Bowtie and abundance was estimated with RSEM v. 1. 2. 4, as implemented in the trinity script run_RSEM_align_n_estimate. pl. The estimated expected counts for each sample, at the gene level, were extracted and used for the analysis of differential expression conducted in DESeq  [65] , a software considered accurate and conservative for differential expression analyses  [66, 67]. Samples were grouped according to sex and expression was compared for each tissue separately, following the developers' manual (FDR threshold of 0. 05). Due to a reported difficulty in assessing differential expression when a gene is expressed only in one group  [66] , we considered as significant those genes only when the expression in the other group was higher than 30 normalized counts in total.
Detection of genetic markers The assembled sequences were scanned for microsatellites with Phobos  [68]. We detected non-exact Short Tandem Repeats (STRs) with 2-10 repeat unit length and a minimum length of 20 nucleotides. A custom Perl script was used to parse the output. Single Nucleotide Polymorphisms (SNPs) were detected with SAMTools  [69]  and VCFtools  [70]. Alignment (. bam) files produced through mapping for gonads and brain were merged for each individual. Alignment files were further filtered for mapping quality (threshold 75), number of mismatches to the reference ('ΝΜ' threshold 10) and for proper pair mapping with BamTools  [71]. Then, they were sorted and piled (with SAMTools function 'mpileup') to detect candidate SNPs. Custom Perl scripts were used to eliminate SNPs that formed clusters (>1 variant every 4 bases) to exclude hypervariable regions where accurate alignment is difficult.
Those with SNP QUAL < 25, total high quality bases coverage below 15 and minor allele read ratio < 0. 2 were excluded  [72]. In each locus we kept only the SNPs belonging to the transcript with the highest SNP number. Finally, SNPs were categorized to synonymous, non-synonymous and those that belong to the transcripts UTRs. For that, we first retrieved the predicted ORF per transcript with the highest similarity to tilapia proteins (e-value threshold 10 -10 ). Then, we evaluated whether each SNP is located within the coding region defined by the ORF, and for those located within the coding region if it causes a synonymous or a nonsynonymous mutation using a Perl script. To identify SNPs for which genotyping can be conducted robustly we applied extra filtering steps. Those included the genotype quality ('GQ') score > =20 as provided by SAMtools and VCFtools pipeline and required minimum coverage of the SNP site per sample > 5 for all samples.