dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-nucleotide variants and indels were subsequently called by the SAMtools suite (mpileup, bcftools, vcfutil). All calls with a read coverage ≤5× and a Phred-scaled SNP quality of ≤20 were filtered out. Substitution and variation calls were made with the SAMtools pipeline (mpileup). Variants were annotated with an in-house Paris Descartes bioinformatics platform pipeline based on the Ensembl database (release 67). Exome sequencing quality data were homogeneous with an average mean depth higher than 100X. Coverage depth greater than 15X and 5X were obtained for ~97% and ~99% of the target. We analysed variants affecting coding regions and essential splice sites and excluded all variants with frequencies higher than 1% in multiple genome databases including dbSNP, 1000 Genomes, the NHLBI Exome Variant Server (EVS), the Exome Aggregation Consortium (ExAC), and a local Paris Descartes Bioinformatics platform database.
Multiplex targeted sequencing using smMIPs. 7 -We designed a pool of 35 smMIP oligonucleotides targeting the coding sequences of PIK3R2. smMIPs were tiled across a total of 3340 base pair (bp) of genomic sequence, including all 2202 coding nucleotides of the targeted genes. 100 ng capture reactions were performed in parallel. Massively parallel sequencing was performed using the Illumina HiSeq. Variants were filtered against the public databases (dbSNP, 1000 Genomes, EVS, ExAC) mentioned above. smMIP sequencing data was processed with MIPgen and PEAR 0. 8. 1, 8 both with default options, with the exception of introducing a penalty of 80 for soft clipping during the BWA mem mapping, to produce high quality smc-reads (single molecule consensus reads). smc-reads were analyzed with GATK v3. 1-1 as recommended using the IndelRealigner and HaplotypeCaller tools on the targeted regions.
smc-reads were processed with Freebayes using the -F 0 option to capture low frequency variants. All variants with at least two reads were retained for downstream analysis. Variants were merged across all samples and allele balances calculated. Amplicon sequencing-To screen for the recurrent PIK3R2 mutation, p. Gly373Arg, we performed locus-specific amplification of genomic DNA followed by GS Junior sequencing. We designed fusion primers containing genome-specific sequences along with distinct MIDs (multiplex identifier sequences) used to differentiate samples being run together on the same plate and sequencing adapters to generate amplicons ranging in size from 290 to 310 bp using primer3plus software. Primer sequences are available upon request (Dr. Renzo Guerrini). Small DNA fragments were removed using Agencourt AMPure XP (Beckman Coulter, Beverly, MA) according to the manufacturer's protocol.
All amplicons were quantified using the Quant-iT PicoGreen dsDNA reagent (Invitrogen Corporation, Life Technologies, Carlsbad, CA), pooled at equimolar ratios, amplified by emulsion PCR using the GS Junior Titanium emPCR kit (Lib-A kit, Roche Applied Science, Mannheim, Germany) and pyrosequenced in the sense and antisense strands on a GS Junior sequencer (Roche) following the manufacturer's instructions. We performed data analysis using the GS Amplicon Variant Analyzer version 3. 0 (AVAv3. 0) software (Roche). Sanger sequencing-We performed confirmation of constitutional mutations by direct Sanger sequencing. PCR amplification was performed with 50 ng of genomic DNA using Taq DNA polymerase (Applied Biosystems). Primers used to amplify the coding and flanking noncoding regions of PIK3R2 were designed using Primer 3.
Double-stranded DNA sequence analysis was performed using the Big Dye Terminator chemistry (Applied Biosystems), and reactions were run on the ABI 3730_l Genetic Analyzer (Applied Biosystems). Sequence chromatograms were analyzed using Mutation Surveyor software version 3. 30. Sequences were compared with normal control samples and the reference sequences for PIK3R2. Statistical analysis-P-values were calculated by using Fisher's exact test. 95% confidence intervals were calculated by using the method introduced by Newcombe. 9.
Role of the funding source This study was funded by the US National Institutes of Health under NINDS grants K08NS092898 (to G. M. M. ), NS058721 (to W. B. D. ), and by EU Seventh Framework Programme (FP7) under the project DESIRE grant agreement N602531 (to R. G. and J. C. ), E-RareJTC2011 (grant to R. G. and J. C. ) and FRM (Equipe FRM; J. C. -DEQ20130326477). All authors had full access to all data in the study and had final responsibility for the decision to submit for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding sources. The funding sources had no role in study design, data collection, data analysis, data interpretation, manuscript writing or decision to submit the manuscript for publication. The MIPgen Design Software is open-source and freely available for academic use but copyright/patent protected (by J.
Filtering of exome data and search for variations in the same gene in unrelated subjects revealed the same recurrent mutation (c. 1117G>A, p. Gly373Arg), in PIK3R2 in two patients (Patients  18  and 19 ). This mutation was not present in any of the public databases. However, close look at the reads generated by the high throughput sequencing using the Integrated Genome Viewer (IGV) interface revealed that this variant is present in 10 reads out of 86 (12%) for patient 18 and 20 reads out of 132 (15%) for patient 19. This deviation from 50% of reads bearing the variant or alternate allele expected for heterozygous constitutional mutations was suggestive of somatic mosaicism of this mutation in PIK3R2. As with standard WES, variation in read depth between DNA samples is due to quantity and quality of the initial DNA, efficiency of DNA binding to target, amplification of the final library and clusters, and sequencing efficiency.
Gly373Arg). Another patient with MPPH (patient 5) was independently studied by clinical WES and found to have the same PIK3R2 mutation as well. One patient was identified to have a de novo missense mutation within the same functional domain of the PIK3R2 gene, p. Lys376Glu. Eighty additional patients with polymicrogyria broadly were tested only for the recurrent PIK3R2 mutation (c. 1117G>A, p. Gly373Arg) by amplicon sequencing and were found to be negative. The clinical characteristics of all patients included in this study are summarized in Table  1. As the same PIK3R2 mutation was detected in a subset of patients with polymicrogyria among a cohort of 126, we calculated the probability for the recurrent PIK3R2 mutation occurring by chance in our cohort. Comparing the allele frequency of the PIK3R2 nonsynonymous variant in our cohort (19/(126 • 2)); with the one reported in the largest public database (ExAC; 0/33,113) showed an overwhelming significant enrichment of PIK3R2 variant in our cohort using Fisher's exact test (p-value < 2.
2 × 10 -16 ) (Supplementary Tables  3 4 5 6 ). Across the cohort, mutations were constitutional in 12 and mosaic in eight patients. Among the mosaic patients, we observed substantial variation in alternate (mutant) allele levels within individual samples, ranging from 2•6 (10/377) to 36•7% (39/106) of reads, equivalent to 5•2-73•4% of cells analyzed. Levels of mosaicism varied from undetectable to 17•1% (37/216) of reads in blood-derived compared to 29•4 (2030/6889) to 43•3% (275/634) in saliva-derived DNA. To exclude artifactual low frequency variant detection due to sample cross-contamination or index cross talk, we confirmed mutations using independent captures or Sanger sequencing. Patient 12 had a different de novo missense mutation of PIK3R2 (c. 1126A>G, p. Lys376Glu), that was not present in any of the public databases and is predicted to be pathogenic using in silico analysis.
WAIS-IV. Abbreviations: AED = anti-epileptic drugs; CBTE = cerebellar tonsillar ectopia; CC = corpus callosum; CSPV = cavum septum pellucidum et vergae; DB = database number; F = female; FSIQ = full scale intellectual quotient; GERD = gastro-esophageal reflux; GI = gastrointestinal; ID = intellectual disability; IQ = intelligence quotient; LD = learning disability; LGA = large for gestational age; M = male; mo = months; MC = myoclonic; OFC = occipito-frontal circumference; PIQ = performance intellectual quotient; PMG = polymicrogyria; POI = perceptual organization index; PSI = processing speed index; PV = perivascular; SD = standard deviation; US = ultrasound; VCI = verbal comprehension index; VIQ = verbal intellectual quotient; VSD = ventricular septal defect; WM = white matter; WMI = working memory index; Wt = weight; yrs = years, BSID = Bayley scale of infant development.
The genomic coordinates for these mutations are: chr19:g. 18273784G>A (p. Gly373Arg), and chr19:g. 18273793A>G (p. Lys376Glu) a Alternate allele fractions (AAF) are based on the number of alternate or non-reference/total alleles (%). b This patient underwent trio-based clinical whole exome sequencing. 99•5% of PIK3R2 was covered at a minimum of 10X. Overall mean depth of coverage was 759X, with a quality threshold of 99•8%. c Poor DNA quality. Therefore, next generation sequencing was not performed. No other tissue sources were available to analyze on this family. d This mutation is not present in any of the public databases (dbSNP138, 1000 genomes, EVS, ExAC Server). It affects an evolutionarily conserved amino acid residue and is predicted to be damaging using multiple in-silico prediction programs (SIFT, Polyphen-2, MutationTaster).
Acknowledgments Funding. This study was funded by the  US National Institutes of Health  under  NINDS  grants  K08NS092898  (to G. M. M. ),  NS058721  (to W. B. D. ), and by  EU   7th Framework Programme  ( FP7)  under the project  DESIRE  grant  N602531  (to R. G. and J. C. ),  E-RareJTC2011  (to R. G. and J. C. ) and  FRM (Equipe FRM ;  J. C. DEQ20130326477 ). We thank the patients, their families and referring physicians for their contribution to our ongoing work on these disorders.
cDNA change Amino acid change Germline or mosaic Tissue tested Alternate allele fractions (AAF) a The genomic coordinates for these mutations are: chr19:g. 18273784G>A (p. Gly373Arg), and chr19:g. 18273793A>G (p. Lys376Glu) a Alternate allele fractions (AAF) are based on the number of alternate or non-reference/total alleles (%). b This patient underwent trio-based clinical whole exome sequencing. 99•5% of PIK3R2 was covered at a minimum of 10X. Overall mean depth of coverage was 759X, with a quality threshold of 99•8%. c Poor DNA quality. Therefore, next generation sequencing was not performed. No other tissue sources were available to analyze on this family. d This mutation is not present in any of the public databases (dbSNP138, 1000 genomes, EVS, ExAC Server). It affects an evolutionarily conserved amino acid residue and is predicted to be damaging using multiple in-silico prediction programs (SIFT, Polyphen-2, MutationTaster).
The cells were washed with PBS and the nuclei were counterstained with Hoechst 33258. Coverslips were mounted with glycergel mounting medium (Dako, Carpenteria, CA, USA). Slides were observed in oil immersion with an AxioImager Z1 (Carl Zeiss), numerical aperture, ECL-PLAN NEOFLUAR × 40 NA 1. 3, with a monochrome Zeiss AxioCam MRm camera and Zen Blue acquisition software. Cell death determination. Cells were trypsinized and analyzed with the Tali Apoptosis Kit (Life Technologies). Briefly, cells were harvested by centrifugation and incubated with Alexa Fluor 488-conjugated Annexin V and PI. Then staining was measured with a Tali image-based cytometer (Life Technologies) to determine the amounts of unstained, singly stained, and doubly stained cells. In vitro calpain cleavage and mass spectrometry.
The resulting peptide mixture was eluted from the gel, desalted, and spotted onto a MALDI plate with freshly dissolved α-cyano-4hydroxycinnaminic acid (10 mg/ml in 50% CH 3 CN, TFA 1/1000). Mass spectrometry was performed by MALDI-TOF-TOF Autoflex Speed (Bruker Daltonics, Fremon, CA, USA). MS and MS/MS data were analyzed with BioTools software (Fremon, CA, USA). Peptides were identified with Mascot, http://www. matrixscience. com/. Tumor sample preparation. After surgery, tumor samples were divided into two parts. One was frozen in a Snapfrost fast freezing system (Excilone, Aperio, CA, USA) and stored at -80 °C, and the other was formaldehyde fixed and paraffin embedded (FFPE). For western blot analysis, frozen tumor samples were sliced into pieces ~1. 2 mm in diameter and transferred into Lysing Matrix type D tubes containing ceramic beads (MP Biomedicals, Santa Ana, CA, USA) in the presence of RIPA buffer (50 mM Tris-HCl, pH = 7.
The data set used for this study (www. doi. pangaea. de/ 10. 1594/PANGAEA. 828348; Fig. 1 ) constitutes a compilation of Alfred Wegener Institute's Arctic macrozoobenthos data (PANABIO, Pan-Arctic Database of Benthic Biota, in progress) selected using the following criteria: (1) abun-dance and biomass data available on species level; (2) comparable sampling and sample treatment (comparable sampling device and sample area and sieving of samples with 250 mm or 500 mm sieve sizes) to keep comparison errors to a minimum; and (3) data distributed along a transect from the Fram Strait (788N) to the central Arctic (908N), with a focus on the Eurasian basins. The samples were taken during several RV Polarstern cruises between 1991 and 2012 (Table  1 ). Data from the cruise ARK-VIII/3 in 1991 (Fu ¨tterer 1992; 47 stations from northern Svalbard, Yermak Plateau, Morris Jesup Rise and Arctic ridges and basins) were published by Kro ¨ncke  (1994, 1998)  and samples from cruise ARK-XXVII/2 (11 stations from the long-time deep-sea observatory Hausgarten, herein referred to as a group as NW Spitsbergen) by Soltwedel 2013.
Data harmonization All geographical coordinates were converted to decimal degree. The station data were plotted on a modified polar stereographic International Bathymetric Chart of the Arctic Ocean base map (www. ibcao. org; Jakobsson et al. 2012)  in the WGS84 coordinate system using ESRI ArcGIS 10. 1. The taxonomic name of each species was matched with the World Register of Marine Species (WoRMS) as the first authority and also with the Integrated Taxonomic Information Service for reasons of comparability with other data sets. When abundance and biomass data were not already provided per m 2 from the start they were recalculated to individuals and g wet mass per m 2. A complete list of species taxonomy, abundance, biomass and P can be found in the PANGAEA open access library (www. doi. pangaea.
Environmental data Water depth refers to the recorded depth at the time the sampling device was deployed at the seafloor; bottom water temperature (8C) data were compiled using the PANGAEA open access library (www. pangaea. de). If temperature was not measured during sampling, we used data from nearby conductivityÁtemperatureÁdepth stations from the same cruise. If no such data were available, we searched for the spatially and temporally closest measurement available from other cruises. This approach is reasonable as the seasonal variations in bottom water temperature from stations below 800 m depth are negligible  (Langehaug & Falck 2012). Information about sea-ice concentration (%) per station was extracted from GeoTiff pictures of sea-ice concentration for the respective year and month (25 km raster cells).
The P/B model Estimation of benthic P was performed using the empirical artificial neural network model developed by  Brey (2012). The difference and advantage of this model compared to other empirical models based on multiple linear regression is that it can model complex, non-linear and non-continuous relationships between independent and dependent variables by learning and generalizing from example data  (Brey 2012). The P/B model used here is based on an initial database of 1258 data sets, each providing information on annual P, biomass, M, annual P/B ratio, taxonomy and ecology per species as well as the applied methods. The final model (which is implemented in an Excel spread sheet and can be accessed at www. thomas-brey. de/science/virtualhandbook/ [Brey 2001] ) consists of three continuous and 17 categorical input parameters: M (log(M), [J]), temperature (1/T, [K]), water depth (log(D), [m]), five taxonomic categories (Mollusca, Annelida, Crustacea, Insecta, Echinodermata), seven lifestyle categories (infauna, sessile, crawler, facultative swimmer, herbivore, omnivore, carnivore), four environmental categories (lake, river, marine, subtidal) and a marker for exploitation.
All categorical variables were binary (0 or 1). The necessary ecological information for each species was extracted from the literature and online resources (see below and Supplementary file for details). M was calculated for each species by dividing biomass by abundance. Biomass data were previously converted to Joule, using the conversion factor database of  Brey (2012, database version 4, www. thomas-brey. de/science/virtualhandbook). When no conversion factor was found for a certain species the conversion factor of the next higher taxonomical level was used. Species that did not belong to any of the five taxonomic categories of the model were grouped by the category their body form most resembled. Accordingly we grouped Porifera, Tunicata, Cnidaria and Bryozoa by category Mollusca, and Sipuncula, Nemertea, Entoprocta and Cephalorhyncha were grouped by category Annelida.
GIS For mapping benthic abundance, biomass and P, ArcGIS Desktop (Release 10, 2011, Environmental Systems Research Institute, CA, Redlands, USA), was used. Shapefiles containing the geo-referenced sea-ice extent from 2013 and a 30-year mean were provided by the National Snow and Ice Data Center (www. nsidc. org/data/; Fetterer et al. 2002).
As P/B and M are known to be largely influenced by temperature, we tested for a correlation of temperature with P/B and M and also for regional differences in bottom temperature. Statistical approaches included regression, ANOVA, multi-way ANOVA, ANCOVA and post hoc tests (Student's t) using the JMP † software package, version 10. 0 (1989Á2007, SAS Institute Inc. , Cary, NC, USA). Because of the limited number of samples, we performed one-way ANOVAs and ANCOVAs with water depth used as co-variable to test for significant differences between stations (grouped by regions, latitudes and sea-ice concentration) after eliminating the generally acknowledged impact of depth on benthic communities. As depth and temperature co-vary in the Arctic Ocean, we performed an ANOVA on the residuals of a temperature versus depth regression to test for temperature differences among regions.
The Makarov Basin region was excluded from the statistical comparison of regions on account of the small sample size of only two stations; all other regions contained 4Á20 stations (Table  2 ). To exclude potentially distorting effects of sampling procedure (i. e. , sieve size, sample area, year of sampling) on our regional comparison we performed a three-way ANOVA of these factors on the residuals of an ANOVA of abundance versus depth and biomass versus depth. Data from regions that were sampled in 1991 and 2012 (Nansen and Amundsen basins) were additionally tested with ANCOVA for an effect of time. Data were transformed using power (BoxÁ Cox) and log transformation. ANOSIM was used to test for differences in the relative contribution of different feeding types to overall biomass and P. SIMPROF and ANOSIM were performed with PRIMER, Version 6  (Clarke & Gorley 2006).
2010). The high GPP is supported by Atlantic water supply and the fertile conditions generally found along the MIZ  (Sakshaug 2004) , which covers a large fraction of northern Fram Strait  (Sakshaug 2004; Wassmann et al. 2010). Along ice edges POC fluxes of !300 mg C m (2 d (1 are Table  4  Mean community production (P) and productivity (P/B) values found in literature, ordered after increasing water depth. When originally given in other units, data were converted to carbon using conversion factors from the database of  Brey (2012, database version 4, www. thomas  ). While the stations north-west of Spitsbergen benefit from the conditions mentioned previously, the stations in the High Arctic are far from any input from the MIZ and the productive shelf areas. We assume that benthic P at the Lomonosov Ridge could be fuelled by organic matter that gets transported with sea ice along the Transpolar Drift, enhancing export via seasonal melting processes.
Acknowledgements The authors thank the captains, crews and shipboard parties of the  RV Polarstern ARK-VIII ,  ARK-XIII/2 ,  ARK-XXVII/2  and  ARK-XXVII/3  cruises for help with work at sea. The authors thank  Jo ¨lund Asseng  ( Alfred Wegener Institute ) for information and help in importing sea-ice concentration data into Arc GIS and  Professor Dr Ursula Schauer  ( Alfred Wegener Institute ) for helpful information about Arctic seawater temperatures. The authors further thank  Sergey Y. Gagaev ,  Alexey V. Golikov  and  Victor Petrjashev  from the  Zoological Institute of the Russian Academy of Sciences  for the taxonomical work on ARK-XIII/2 samples. The authors also thank the anonymous reviewers for their constructive comments. This work was supported by the  European Research Council   ABYSS advanced grant  ( 294757 ) to AB.
R. Fröhlich et al. : Intercomparison of ME-2 organic source apportionment The following software packages were used. Q-ACSM: version 1. 4. 4. 5. of the ACSM DAQ software (Aerodyne Research Inc. , Billerica, Massachusetts) during data acquisition and version 1. 5. 3. 2 of the ACSM local tool (Aerodyne Research Inc. , Billerica, Massachusetts) for Igor Pro (Wavemetrics Inc. , Lake Oswego, Oregon) for Q-ACSM data treatment and export of PMF matrices (see Supplement for discussion of changes in most recent software version 1. 5. 5. 0). ToF-ACSM: TOFDAQ version 1. 94 (TOFWERK AG, Thun, Switzerland) during acquisition and Tofware version 2. 4. 2 (TOFWERK AG, Thun, Switzerland) for Igor Pro for data treatment. ToF-ACSM PMF matrices were calculated manually in accordance with the procedures employed in the AMS software SQUIRREL v1.
(2013a)  was used (reference spectra from  Crippa et al. (2013a)  are labelled with the subscript Paris in the following). The HOA factor, if possible, remained unconstrained or was extracted from a previous PMF solution with a higher number of factors similar to the retrieval of the COA factor in the HR-PMF in Sect. 3. 3. This procedure was favoured because for most ACSM an increase of the factor number produced an HOA factor with similar or better covariance with the time series of NO x and BC ff as opposed to the application of external reference HOA spectra. For this purpose unconstrained PMF runs with three, four, five and six factors were performed for each ACSM and the HOA profiles corresponding to the highest combined R 2 between factor time series and external data were saved and subsequently used as anchor profiles in the four-factor constrained ME-2 runs.
HOA reference profiles retrieved this way are individual for each instrument and denoted HOA indv in the following. A COA factor could not be extracted for the ACSM with this method. The HOA factors in the four-factor constrained ME-2 runs were left unconstrained if their time series correlations with NO x and BC ff were better or similar to the constrained case. The two additional factors in the 4 factor constrained ME-2 were left completely free and the results resembled OOA and BBOA for each instrument. Extraction of individual reference profiles directly from the data is not always possible and a more common approach is the adaptation of reference spectra from a database of previous experiments. Therefore the ME-2 results acquired with the use of the database profiles HOA Paris and COA Paris are shown as well for comparison.