dataesr/scientific-paragraphs-categorization huggingface dataset:
e!DAL: Plant Genomics and Phenomics Research Data Repository The IPK is hosting a plant genomics and phenomics research data repository, which is based on the e!DAL data sharing and publication infrastructure  (Arend et al. , 2014). It features the publication of plant research data that is out of scope of existing domain databases, too huge, or less structured. In compliance to international standards, such as DOI, DataCite, and OpenAIRE, plant genomic and phenotpyic datasets are published. In a particular focus are studies of plant genetic resources from the system plant from the root to bloom and seed, as well from sequence analysis to systems biology. Examples are genomic datasets of Triticeae (DOIs: 10. 5447/IPK/2015/0, 10. 5447/IPK/2015/1, and 10. 5447/IPK/2015/2).
Triticeae Genome Data and Tools at The Genome Analysis Centre The TGAC Browser is an open-source genomic browser developed to visualize genome annotations such as genes, variations, and markers for species whose reference sequence may be contiguous or highly fragmented; the IBSC's barley genome and the wheat CSS represent two such fragmented references (Fig. 7A, 7B ). Traditional datatypes, such as the reference assembly and gene annotations, reside in an Ensembl schema database. Larger datasets are stored in standard file formats such as SAM, BAM, bigwig, and VCF. The TGAC Browser has an integrated BLAST functionality, essential for identifying and accessing regions of interest in fragmented genomes, and an interface to enable manual annotation of genomic features. Homeologous genes can also be explored through the Aequatus browser (Fig.
Discussion Outlook Great progress has been made recently in sequencing, annotating, and analyzing the complex genomes of Triticeae species including bread wheat and barley. The data generated has great potential for applications in plant breeding, experimental plant biology, and comparative genomics. However, to make the best possible use of the large and heterogeneous data sets produced, data archiving, integration, visualization, and access are essential. A variety of platforms provide different types of analysis and presentations, but it can be difficult for users to use these resources in combination. Many of the partners within the transPLANT project are actively involved in past or ongoing Triticeae genome initiatives and the development of resources. A major focus of the project is ensuring interoperability to maximize their collective value.
Critical to this is the development of common standards and formats. transPLANT partners use accepted standards to share and disseminate data wherever possible and are involved in ongoing efforts to standardize plant phenotypic data and metadata (that is, the description of material, experimental conditions, and results  [Krajewski et al. , 2015] ). We have also been working with common data mining interfaces (BioMart  [Smedley et al. , 2015] , InterMine  [Smith et al. , 2012] ), and developing RESTful web services to support integrative programmatic access to data. We have also been developing cloud computing environments to support downstream analyses. This has translated into a number of concrete benefits for end users working with complex Triticeae genome data, including the following: • Extensive and standardized data exchange and synchronization between partners; all data is served on common reference sequence, and portable annotation tracks can be visualized at different sites    The result of these efforts is a rich collection of interfaces using common reference data, searchable through a single entry point at the central transPLANT web hub.
They also simplify the identification of suitable datasets and databases for research using Triticeae genome data, assist in data acquisition, and provide powerful tools to analyze data in the context of other plant species. The training videos provide example use cases illustrating how users can take advantage of different resources in combination to interrogate the data and perform complex analyses. With the expected emergence of additional genome sequence data from the Triticeae, the framework for data integration, exchange, and aggregation established within the transPLANT project will help to address the challenges involved with even more distributed data repositories and heterogeneous data types. In this context, a project has started recently with the objective to set up an International Wheat Information System (WheatIS) to support the wheat research community.
The main objective is to provide a single-entry web-based system to access the available data resources and bioinformatics tools. The WheatIS project is an international project lead by an Export Working Group of the Wheat Initiative (Table  1. 20). The Wheat Initiative is supported by the G20 Agricultural Ministers to coordinate worldwide research efforts in the fields of wheat genetics, genomics, physiology, breeding and agronomy. The WheatIS project is driven by a network of 21 experts from Australia, Canada, France, Germany, Mexico, United States, and United Kingdom that congregates a group of volunteers willing to participate in the WheatIS project. The WheatIS (Table  1. 21) will operate as a hub integrating wheat data produced and submitted to the public repositories by the community, extending the model and technologies established in transPLANT for the coordination of dispersed resources.
Acknowledgments All authors and institutions would like to acknowledge funding of the transPLANT project by the  European Commission  within its 7th  Framework Programme , under the thematic area Infrastructures, contract number  283496. EBI acknowledges funding from the  United Kingdom Biotechnology and Biological Sciences Research Council  grants  BB/ I008071/1  and  BB/J00328X/1 , and grant  52930112  from the  United States National Science Foundation. The  IPK resources Barley Blast Server, BARLEX , and Kmasker e!DAL were supported by the  Leibniz Association (WGL)  in the context of the  Pakt für Forschung und Innovation/ WGL  and the  German Federal Ministry of Education and Research (BMBF)  in the frame of the projects BARLEX ( FKZ 0314000A ), and  TRITEX  ( FKZ 0315954A ) and DPPN ( FKZ 031A053B ).
URGI likes to acknowledge funding from  INRA , french  Research National Agency  ( ANR-09-GENM-025-003 )  3BSEQ  project , Investment for the Future  ( ANR-10-BTBR-03 ,  France AgriMer, FSOV)   BreedWheat  project, European commission within 7th  Framework Program TriticeaeGenome  ( KBBE-212019 ) and  WHEALBI  ( FP7-613556 ) projects. TGAC likes to acknowledge funding from the  Biotechnology and Biological Sciences Research Council  grants  BB/L002124/1  and  BB/L024144/1. RRG is supported by a  Norwich Research Park PhD Studentship and The Genome Analysis Centre Funding and Maintenance Grant. PGSB acknowledges funding from the  German Federal Ministry of Education and Research (BMBF)  in the frame of the projects BARLEX ( FKZ 0314000A ), and  TRI-TEX  ( FKZ 0315954A ) as well as  Deutsche Forschungsgemeinschaft (DFG)  funding to project  SFB924  Molecular mechanisms regulating yield and yield stability in plants.
inra. fr/ INRA URGI wheat database 8 INRA http://wheat-urgi. versailles. inra. fr/Seq-Repository IWGSC wheat sequence repository 9 INRA https://urgi. versailles. inra. fr/gb2/gbrowse/wheat_phys_pub Wheat physical maps browser 10 INRA https://urgi. versailles. inra. fr/blast/ URGI wheat BLAST search tool 11 INRA http://urgi. versailles. inra. fr/Wheat3BMine/ Wheat 3B data warehouse 12 IPK http://lailaps. ipk-gatersleben. de LAILAPS search engine 13 IPK http://webblast. ipk-gatersleben. de/barley IPK barley BLAST server 14 IPK http://barlex. barleysequence. org Barlex home page 15 TGAC www. tgac. ac. uk/tools-resources TGAC tools and resources homepage 16 TGAC http://polymarker. tgac. ac. uk TGAC Polymarker 17 TGAC http://www. tgac. ac. uk/grassroots-genomics TGAC grassroots genomics website 18 transPLANT http://www.
Image analysis. We used Matlab Image Analysis Toolbox to detect cell contours in the DiD channel using a Canny edge detection algorithm. We created a mask by dilating the detected edges by 5 pixels and calculated pixel statistics corresponding to the mask in both the DiI and DiD channels. We calculated the DiI to DiD ratio image through pixel-by-pixel division of pixel values (converted to double precision) and extracted the mean ratio for each image cycle. We visually checked that edges were correctly detected by the algorithm for every DiD image.
Therefore, we used the sodium-potassium ATPase as a plasma membrane protein loading control (detected using an anti-sodium potassium ATPase antibody, Abcam, MA, USA). To analyse Western Blots, we set the band intensities for VAMP2 or VAMP2-4X to 100% after normalizing each band with respect to the intensity of the loading control band. We repeated the experiments three times. Electrophysiology. Flipped t-SNARE HeLa cells (tCells) grown in a 3 cm culture dish were placed onto the temperature-controlled stage (TC-202A by Harvard Apparatus, or Thermo Plate by Tokai Hit) of an inverted microscope (Olympus IX71, Olympus Corp. ) equipped with an EMCCD camera (DU-885K, Andor) controlled by Solis software (Andor). Recording pipettes (borosilicate glass, BF 150-86-10, Sutter Instruments) were pulled using a model P-1000 pipette puller (Sutter Instruments) and polished using a micro-forge (MF-830, Narishige).
Pipette resistances were 5-10 MΩ in NaCl-based solution. The bathing medium was composed of (in mM): 125 NaCl, 4 KCl, 2 CaCl 2 1 MgCl 2 , 10 HEPES, (pH adjusted to 7. 2 using NaOH) for the cell-attached or whole-cell recordings. The medium was supplemented with 10 mM glucose before use. All voltage-clamp recordings were made using a HEKA EPC10 Double USB amplifier (HEKA Electronics, Inc. ), controlled by the Patchmaster software (HEKA). Currents were recorded using 5-20 mV/pA gain, and a sampling rate of 10 or 20 kHz and filtered at 3 kHz. All experiments were done at 35-37 °C. Cells were kept < 2 hours on the microscope stage during recordings. To measure SNARE-mediated single fusion pore currents in the cell-attached mode  60  , electrodes were filled with a solution containing (in mM) 125 NaCl, 4 KCl, 1 MgCl 2 , 10 HEPES, 13 or 26 tetraethylammonium-Cl (TEA-Cl, K + -channel antagonist), adjusted to pH 7.
The device can generate reproducible and rapid pressures (both negative and positive) within milliseconds. HSPC-1 was controlled via analogue signals sent from the HEKA EPC10 Double USB amplifier. Pressure values were also monitored using the analogue-to-digital input of the EPC10, according to the calibration values provided by the manufacturer of the HSPC-1. A cell was patched in the cell-attached configuration. While the pipette was held at -40 mV, current was monitored at a given pressure for a period of 10 or 40 s. Pressure was increased in increments of 10 mm Hg from 0 mm Hg to 100 mm Hg and the current measurements repeated. All experiments were performed at 37 °C. The open probability (P o ) of single channels (currents larger than or equal to1 pA and opening longer than 10 ms) was calculated by dividing the total time spent in the open state by the total time of continuous recording (10 s or 40 s) in stretches of data containing due to a single active channel.
Data analysis. Patchmaster (HEKA Electronik) current traces were exported to Matlab (Mathworks). An interactive graphical user interface was designed in Matlab to identify, crop, and process single fusion pore currents. Traces were low-pass filtered using a pass-band of 280 Hz (2. 8 kHz for high time resolution recordings), and sharp frequency peaks due to digital noise and/or line voltage identified from the baseline were removed using notch filtering, using zero-phase digital filtering to avoid signal distortion. Filtered traces were averaged in blocks of 40 or 80 points to achieve 4 ms between points (125 Hz bandwidth), with rms baseline noise ~0. 2 pA (span 0. 06-0. 7 pA). Traces with larger than 0. 7 pA noise were rejected. For high time resolution recordings, blocks of 8 successive points were averaged, such that in the final traces points were spaced by 0.
3 ), we first computed the probability density functions (PDFs) for individual fusion pores, then averaged the PDFs, to give equal weight to all fusion pores in the averaged PDF. All distribution fits used maximum likelihood estimation. For aligning conductance traces, we shifted the time axis such that t = 0 corresponded to the first data point in a burst. Matlab Curve Fitting Tool was used to fit double exponentials to the averaged conductance traces in Supplementary Fig. 13. To estimate fusion pore size from conductance, we approximated the pore as a cylinder and used the relation  39  ρλ π = r G ( / ) po po 1/2 , where ρ is the resistivity of the solution, λ = 15 nm is the length of the cylinder, and G po is the open-pore conductance. For assessing statistically significant differences in pairwise comparisons of sample means we used either the two-sample t-test, or the nonparametric two-sample Kolmogorov-Smirnov test (ttest2 or kstest2, Matlab Statistics Toolbox), as indicated in figure legends.
Acknowledgements We thank  F. Sigworth  and  D. Zenisek  ( Yale University ) for expert advice and discussions,  J. E. Rothman ,  M. Caplan  ( Yale University ),  B. O'Shaughnessy  ( Columbia U. ) and members of the  Karatekin and Rothman laboratories  for helpful discussions. Jing Wang  ( Rothman lab ) kindly provided C45 lipids. We are grateful to  James E. Rothman ,  Pietro De Camilli ,  Frederick Sigworth ,  David Zenisek ,  Yongli Zhang ,  Ben O'Shaughnessy , and  Thomas Melia  for critically reading the manuscript. We thank  Dr. Vladimir Polejaev  and the  West Campus Imaging Core  for single-cell lipid mixing and calcium influx recordings. This work was supported by  NIH  grant  R01GM108954  and a  Kavli Neuroscience Scholar Award  to EK. SMA, OB, and SSK are members of the  Rothman laboratory , supported by  NIH   R01DK027044.
We implemented a proportional-integral control algorithm using MATLAB (MathWorks, Natick, MA, USA) and the applied voltage was modulated to maintain the delivering power within 3% of the target. Therefore, a voltage boundary condition is hence applied at the active electrode surface. On all the outer surfaces of the model, except the bottom surface, a null electrical flux is imposed. The voltage on the bottom surface is set to 0 V to model the dispersive electrode. For the thermal boundary conditions, a null thermal flux is used on the symmetry plane and a constant temperature of 37ºC is fixed on the outer model surfaces. For the velocity boundary conditions, an inlet velocity boundary condition is applied on the left surface of the fluid volume to impose a blood flow velocity of 0. 1 m s -1  [5].
An outlet boundary condition of zero pressure is fixed on the right surface of the fluid volume. The saline irrigation flow is taken into account by an inlet velocity condition into the blood region. A no slip condition is applied on the upper surfaces of the fluid volume, at the symmetry plane and at the tissue-blood and electrode-blood interfaces. The model is based on a coupled electric-thermal-flow problem, which is solved numerically using the Finite Element Method (FEM) with COMSOL Multiphysics software (COMSOL, Burlington, MA, USA). The governing equation for the thermal problem is the Bioheat Equation, modified by the enthalpy method  [4]  that includes the phase change to model tissue vaporization: ( ) ( ) h k T q c T t ρ ρ ∂ = ∇ ⋅ ∇ + - ⋅ ∇ ∂ u ( 1 ) where ρ is density (kg m -3 ), h enthalpy, t time (s), k thermal conductivity (W m -1 K -1 ), T temperature (ºC), and q the heat source caused by RF power (W m -3 ).
Acknowledgements This work was supported in part by the  Basque Government  through the  BERC 2014-2017 program  and by  Spanish Ministry of Economy and Competitiveness MINECO  through the  BCAM Severo Ochoa excellence accreditation   SEV-2013-0323 , and also by the  Spanish "Plan Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad " under Grant  TEC2014-52383-C3-R (TEC2014-52383-C3-1-R ) and Grant  MTM2015-69992-R. A. González-Suárez  has a  Postdoctoral Grant  ( APOSTD/2016/045 ) from the  Generalitat Valenciana.
Data processing and meta-analytic methods Raw intensity data were obtained from 57,577 subjects from 43 separate datasets (Supplementary Table  2 ). After CNV calling and quality control (QC), 41,321 subjects were retained for analysis. We developed a centralized pipeline for systematic calling of CNVs for Affymetrix and Illumina platforms. (Methods and Supplementary Figure  1 ). The pipeline included multiple CNV callers run in parallel. Data from Illumina platforms were processed using PennCNV  15  and iPattern  16. Data from Affymetrix platforms were analyzed using PennCNV and Birdsuite  17. Two additional methods, iPattern and C-score  18  , were applied to data from the Affymetrix 6. 0 platform. In order to ensure proper normalization of the X chromosome, male and female subjects were normalized separately.
The CNV calls from each program were converted to a standardized format and a consensus call set was constructed by merging CNV outputs at the sample level. Only CNV segments that were detected by all algorithms were retained. We performed QC at the platform level to exclude samples with poor probe intensity and/or an excessive CNV load (number and length). A final set of rare, high quality CNVs was defined as those >20kb in length, at least 10 probes, and <1% MAF. Genetic associations were investigated by case-control tests of CNV burden at four levels: (1) genome-wide (2) pathways, (3) genes, and (4) CNV breakpoints. Analyses controlled for SNP-derived principal components, sex, genotyping platform and data quality metrics. Multiple-testing thresholds for genome-wide significance were estimated from family-wise error rates drawn from permutation.
Discussion The present study of the PGC SCZ CNV dataset includes the majority of all microarray data that has been generated in genetic studies of SCZ to date. In this, we find definitive evidence for eight loci, surpassing strict genome-wide multiple testing correction. We also find evidence for a contribution of novel CNVs conferring either risk or protection to SCZ, with an FDR < 0. 05. The complete results, including CNV calls and statistical evidence at the gene or breakpoint level, can be viewed using the PGC CNV browser (URLs). Our data suggest that the undiscovered novel risk loci that can be detected with current genotyping platforms lie at the ultra-rare end of the frequency spectrum and still larger samples will be needed to identify them at convincing levels of statistical evidence.
Collectively, the eight SCZ risk loci that surpass genome-wide significance are carried by a small fraction (1. 4%) of SCZ cases in the PGC sample. We estimate 0. 85% of the variance in SCZ liability is explained by carrying a CNV risk allele within these loci (Supplementary Note). As a comparison, 3. 4% of the variance in SCZ liability is explained by the 108 genome-wide significant loci identified in the companion PGC GWAS analysis. Combined, the CNV and SNP loci that have been identified to date explain a small proportion (<5%) of heritability. The large dataset here provides an opportunity to evaluate the strength of evidence for a variety of loci where an association with SCZ has been reported previously. Of 44 published findings from the recent literature, we find evidence for 8 loci at a false discovery rate of 5% and nominal support for an additional 17 loci (uncorrected p < 0.
05, Supplementary Table  6 ). Thus, nearly half of the existing candidate loci retain some support in our combined analysis. However we also find a lack of evidence for many of the previously identified loci, underscoring the value of meta-analytic efforts to assess the validity of such reports. A lack of strong evidence in this dataset (which includes samples that overlap with many of the previous studies) may in some cases simply reflect that statistical power is limited for very rare variants, even in large samples. However, it is likely that some of the earlier findings represent chance associations; indeed, the loci that are not supported by our data consist largely of loci for which the original statistical evidence was weak (Supplementary Table  6 ). Thus, our results help to refine the list of promising candidate CNVs.
Our results provide strong evidence that deletions in SCZ are enriched within a highly connected network of synaptic proteins, consistent with previous studies  2, 6, 10, 28. The large CNV dataset here allows a more detailed view of the synaptic network and highlights subsets of genes account for the excess deletion burden in SCZ, including synaptic cell adhesion and scaffolding proteins, glutamatergic ionotropic receptors and protein complexes such as the ARC complex and DGC. Modest CNV evidence implicating Dystrophin (DMD) and its binding partners is intriguing given that the involvement of certain components of the DGC have been postulated  29  ,  30  and disputed  31  previously. Larger studies of CNV are needed to define a role for this and other synaptic sub-networks in SCZ. Our current study is well-powered to detect CNVs of large effect that occur in >0.
Overview We assembled a CNV analysis group with the goal of leveraging the extensive expertise within the Psychiatric Genomics Consortium (PGC) to develop a fully automated centralized pipeline for consistent and systematic calling of CNVs for both Affymetrix and Illumina platforms. An overview of the analysis pipeline is shown in Supplementary Figure  1. After an initial data formatting step we constructed batches of samples for processing using four different methods, PennCNV, iPattern, C-score (GADA and HMMSeg) and Birdsuite for Affymetrix 6. 0. For Affymetrix 5. 0 data we used Birdsuite and PennCNV, for Affymetrix 500 we used PennCNV and C-score, and for all Illumina arrays we used PennCNV and iPattern. We then constructed a consensus CNV call dataset by merging data at the sample level and further filtered calls to make a final dataset Supplementary Table  2.
4. For Illumina data, we first determined the genome build and converted to hg18 if necessary and created analysis batches based on the plate ID or genotyping date. Composite Pipeline-The composite pipeline comprises CNV callers PennCNV 2 , iPattern  3  , Birdsuite 4 and C-Score 5 organized into component pipelines. We used all four callers for Affymetrix 6. 0 data and we used PennCNV and C-Score for Affymetrix 500. Probe annotation files were preprocessed for each platform. Once the array design files and probe annotation files were pre-processed, each individual pipeline component pipeline was run in two steps: 1) processing the intensity data by the core pipeline process to produce CNV calls, 2) parsing the specific output format of the core pipeline and converting the calls to a standard form designed to capture confidence scores, copy number states and other information computed by each pipeline Merging of CNV data and Quality control (QC) filtering-Merging of CNV data and Quality control (QC) filtering is described in detail in the supplementary material.
Briefly, for each subject CNV calls were made using multiple algorithms. Only CNV calls that were made using multiple algorithms were included in the call set. Sample level QC filtering was performed by removing arrays with excessive probe variance or GC bias and removal of samples with mismatches in gender or ethnicity or chromosomal aneuploidies. The final filtered CNV dataset was annotated with Refseq genes (transcriptions and exons). After this stage of quality control (QC), we had a total of 52,511 individuals, with 27,034 SCZ cases and 25,448 controls. To make our final dataset of rare CNVs for all subsequent analysis we filtered out variants that were present at >= 1% (50% reciprocal overlap) frequency in cases and controls combined. We included in the call set CNVs that were ≥20 kb and ≥10 probes in length and overlapped < 50% with regions tagged as copy number polymorphic on any other platform.