dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental recordings show an attack time (i. e. the interval from silence to mean intensity of the first vowel) of about 0. 03 s. This was reproduced by linearly increasing P s from 0 to 800 Pa in 0. 03 s. The same procedure was used to approximate the sound offset. The relationship between vocal folds' tension and fundamental frequency is almost lineal for Ps around 800 Pa  (Assaneo and Trevisan, 2013). In this way, the time trace of Q(t) was simply a scaling of the experimental pitch contour, extracted with Praat software  (Boersma and Weenink, 2013). For this experiment we used a fMRI block design. Stimuli were arranged in 9 blocks. Each block contained 8-10 stimuli of each category (NS, R, F and O). Blocks were 9 s long, separated by silences of 4. 5 s. Participants inside the scanner were instructed to listen to the stimuli and to grade them, at the end of each block, by pressing one of four fiber-optic triggers held in the right hand according to the following code: button 0 if "I am sure that the voice is not human"; button 1 if "The voice is likely to be non-human"; button 2 if "The voice is likely to be human"; button 3 if "The voice was definitely human".
fMRI data acquisition and processing Subjects were scanned on a Siemens 3T Verio MRI, 12-channel TIM system. First, high-resolution T1-weighted 3D volumes were acquired for anatomical localization (TR = 2. 3 s, TE = 2. 98 ms, matrix size 240 × 256 × 176, voxel size 1 × 1 × 1 mm). Second, whole brain functional images were collected using a T2*-weighted EPI sequence, sensitive to BOLD contrast (TR = 2. 02 s, TE = 25 ms, matrix size 66 × 66 × 40, voxel size 3 × 3 × 3 mm). The first four scans of all EPI series were not included in the analysis. Data processing and analyses were conducted using the SPM8 software (Wellcome Trust Centre for Neuroimaging, London, UK) and custom MATLAB code. Functional images were pre-processed as follows: slice timing, motion correction by realignment, co-registration of the T1-weighted image to the mean functional image, normalization of the T1-weighted image to the MNI template, normalization of functional images (resampled voxel size 3 × 3 × 3 mm) by applying the parameters of the anatomical normalization, and Gaussian smoothing (5 mm FWHM).
Vocal recognition and identity Speech carries information through two different channels: one of them communicates semantic content, and the other one information about the identity of the speaker. The identity information depends on extrinsic factors such as the speaker's accent and speaking habits and, more critically, on intrinsic factors such as the anatomy and physiology of the vocal system. The extraction of identity information can be as specific as recognizing a speaker from a database of known voices  (Formisano et al. , 2008; Lopez et al. , 2013) , but also includes more general tasks such as the recognition of the gender and approximate age of an unknown speaker  (Smith and Patterson, 2005). This broader sense of identity information has been recently explored  (Latinus et al. , 2013)  using morphing techniques, showing that voices are perceived as more atypical (and elicit more activation in the temporal vocal areas) as they become more distant from a male or a female prototypical voice in a 3-dimensional acoustical space.
Acknowledgements This work was supported by the  University of Buenos Aires (UBA) , the  Concejo Nacional de Investigaciones Científicas y Técnicas (CONICET)  and  ECOS A11S01 scientific collaboration program  ( France-Argentina ). JS and MFA thank  Mariam Chammat  and  Pablo Barttfeld  for their kind help with fMRI data analysis, and  Aaron Schurger  for revising the manuscript. MAT and MFA thank  Gabriel Mindlin  for inspiring conversations.
The origin of each mutant allele was confirmed by checking parental DNA members. All mutations showed co-segregations among available family samples. The 12 mutant alleles were counted as five different mutations, including three exonic mutations (c. 218G>C, c. 319T>C and c. 347C>T, S5 Fig;  NM_004928 ), and two intronic mutations (c. 545+1G>A and  c. 643-23A>T). Except c. 545+1G>A, all detected mutations were not reported in the Human Gene Mutation Database (HGMD). c. 545+1G>A has previously been reported as a causal mutation of cone-rod dystrophy  [7]. The skeletal phenotype of the patient with this mutation is not described in the publication.
Characterization of C21orf2 C21orf2 (OMIM: 603191) is an uncharacterized gene. C21orf2 was reported to have four alternative transcripts (NM_004928, NM_001271440, NM_001271441, and NM_001271442) in the Reference Sequence Database (RefSeq) and a previous report  [8]. NM_004928, NM_001271440 and NM_001271441 have some in-frame indels but share the same reading frame, while NM_001271442 uses a different ATG as a translation start codon. As the basis of clarifying the biological impact of the detected variations, we first validated the gene structure of C21orf2 by performing RT-PCR and sequencing of cDNAs from various tissues and celllines. Besides common tissues (brain, heart, lung, liver, kidney, etc. ), additional attention was paid to bone, cartilage and retina tissues as well as related cell lines, because they may have potential relationships with the axial SMD phenotype.
A pair of primers was designed to cover the whole coding DNA sequence (CDS) of transcripts NM_004928, NM_001271440 and NM_001271441. C21orf2 was expressed in all tissues and cell-lines tested, with a single band generated (S3 Fig). The ubiquitous expression of C21orf2 is consistent with records in gene expression databases (FANTOM5 and MGI Gene Expression Database). Sequencing of the PCR products from various tissues including chondrocyte, mesenchymal stem cell and ligament confirmed the existence of NM_004928 and NM_001271440, which differed by three nucleotides in the beginning of exon 6, resulting in one optional serine without changing the reading frame. NM_001271441 was not found in all samples examined. Primers based on NM_001271442 specific sequence could not yield targeted amplification (data not shown); probably it does not exist in tested tissues and cell-lines.
c. 643-23A>T was found in two Saudi Arabian families (F1 and F7), and was absent in all control groups, including ESP6500 and ExAC databases. c. 643-23A>T was predicted to be a branch-point splicing mutation by two prediction programs (SVM-BPfinder and Human Splicing Finder). c. 545+1G>A was found in F6, which was an obvious splice donor site mutation. c. 545+1G>A was reported in ExAC with a very low allele frequency (0. 001902%) and was absent in ESP6500. Several programs (ASSP, NetGene2, Human Splicing Finder, SplicePort and NNSPLICE) were utilized to predict its effect; however, each program generated a number of different results. A primer set spanning from exon 5 to exon 7 (  amplicon from the patient's cDNA, which appeared to have the same molecular size as the PCR product of normal control individuals, was demonstrated to have an abnormal sequence.
Exome sequencing and variation calling Exome sequencing was performed on 10 patients as previously described  [23, 24]. Briefly, DNA (3 μg) was sheared by an S2 system (Covaris) and processed by SureSelect Human All Exon kit or SureSelectXT Human All Exon V5 (Agilent Technologies). Captured DNAs were sequenced by using HiSeq 2000 (Illumina) with 101-bp pair-end reads with 7 indices. Image analysis and base calling were performed by using HCS, RTA and CASAVA softwares (Illumina). Reads were mapped to the reference human genome (hg19) by Novoalign-3. 00. 02 or 3. 02. 04. Aligned reads were processed by Picard to remove PCR duplicate. Variants were called by GATK (v1. 6-5 or v2. 7-4)  [25]  following the recommended workflow  [26]   [27] , and annotated by ANNOVAR  [28].
In silico analysis For a sequence conservation analysis, protein sequences of human (C21orf2, NP_004919. 1), chimpanzee (C21H21orf2, XP_514938. 2), cattle (C1H21orf2, NP_001069249. 1), mouse (1810043G02Rik, NP_080707. 2), rat (RGD1309594, NP_001008352. 1), chicken (C9H21ORF2, NP_001006544. 1), were downloaded from Genbank and aligned in ClustalX (ver. 2. 1)  [30]. Domain architecture was predicted by InterPro  [31]. Wild-type protein sequence of human C21orf2 (NP_004919. 1) and the mutant protein sequences with missense mutations found in this study were submitted to I-TASSER  [32, 33]  for 3D structure prediction. The effects of missense variations were annotated by SIFT  [34] , PolyPhen2  [35]  and Muta-tionTaster  [36] , through the pipeline of ANNOVAR. For the prediction on splicing mutations, genomic sequence of intron 6 of C21orf2 was submitted to SVM-BPfinder  [37]  and Human Splicing Finder  [38]  for prediction of the branch-point.
/start. php Reference Sequence (RefSeq) database, http://www. ncbi. nlm. nih. gov/refseq/ FANTOM 5, http://fantom. gsc. riken. jp/5/ MGI Gene Expression Database, http://www. informatics. jax. org/expression. shtml HomoloGene,http://www. ncbi. nlm. nih. gov/homologene Basic Local Alignment Search Tool (BLAST), http://blast. ncbi. nlm. nih. gov/Blast. cgi ClustalX, http://www. clustal. org/clustal2/ ESP6500, http://evs. gs. washington. edu/EVS/.
Acknowledgments We thank  N. Atsumi  for English revision. This study is supported and by  KAKENHI Grantsin-Aid for Scientific Research, Research Activity Start-up  (K. M. N. , No. 26893018 ), KAKENHI  Grant-in-Aid for Scientific Research (B)  ( N. Mi. , No. 25293235 ),  Takeda Science Foundation  (Z. W. ,  N. Mi. , N. Ma. ), and research grants from  Japan Agency For Medical Research and Development (AMED)  ( S. I. N. Ma , No. M15319 ).
Invasive aspergillosis is the primary opportunistic invasive fungal infection described in neutropenic hematologic patients, caused by the angioinvasive pathogen Aspergillus fumigatus. The molecular mechanisms associated with A. fumigatus infection in the vascular endothelium are poorly understood. In this context, we used a high-throughput proteomic approach to unveil the proteins modulated in HUVECs after interaction with a wild type strain and the UGM1 mutant (Δugm1) of A. fumigatus. The proteomic analysis was also performed in HUVECs challenged with a galactosaminogalactan (GAG) purified from A. fumigatus cell wall. The dataset presented here correspond to all proteins identified that fit a 2-fold change criteria (log 2 ratio Z 1 or r À1), disregarding the statistical validation cut off, in order to supplement the research article entitled "Modifications to the composition of the hyphal outer layer of Aspergillus fumigatus modulates the HUVEC proteins.
associated with inflammatory and stress responses" (G. W. P. Neves, N. A. Curty, P. H. Kubitschek-Barreira, T. Fontaine, G. H. M. F. Souza, M. Lyra Cunha, G. H. Goldman, A. Beauvais, J. P. Latgé, L. M. Lopes-Bezerra, 2016)  [1]. The mass spectrometry proteomic data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PRIDE: PXD002823. & 2016 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/).
Value of the data This dataset will be of value to the scientific community aiming to analyze the identified proteins modulated in HUVECs upon infection by A. fumigatus and the participation of the cell wall galactosaminogalactan (GAG) in endothelial activation. This data article contributes to the scientific research concerning the role of GAG in the physio- pathology of invasive aspergillosis. The data provided here identify key proteins involved in the response of HUVECs to different strains of A. fumigatus, contributing to the knowledge of angioinvasive molecular mechanism in invasive aspergillosis.
For this analysis, we used the quantitation criteria of a minimum 2. 0-fold change. Fig. 4. Ingenuity pathway analysis (IPA) showing the HUVEC routes commonly regulated through GAG and the Δugm1 mutant compared with the HUVEC uninfected control. The graphic shows the number of proteins with higher-abundance (black) and lower-abundance (gray) levels. For this analysis, we used the quantitation criteria of a minimum 2. 0-fold change. and critical point-dried with CO 2. The specimens were coated with gold in a Balzers sputtering apparatus and the micrographs were captured using a JEOL JSM-6510 LV SEM. The images were processed using ImageJ software. The obtained scanning electron microscopy (SEM) images of germlings of different A. fumigatus strains are shown in Fig. 1A. This experiment was performed twice with experimental duplicates.
Label-free protein analysis using mass spectrometry, database searching and quantification The proteins extracts used for proteomics assays were obtained as follows. The endothelial cell monolayers cultivated on 75-cm 2 culture flasks were infected with thimerosal-killed germlings of A. fumigatus  [4]  or were interacted with the purified GAG. After, HUVEC monolayers were washed twice with Hank´s balanced salt solution (Cultilab) and gently harvested using a cell scraper. Next, the cells were centrifuged at 200 g, for 10 min. The pellet was subsequently suspended in 250 mL of a lysis buffer (8 M urea, 1 M Tris, 4% (w/v) CHAPS, supplemented with 1 mM PMSF, 5 mM EDTA, 160 mM leupeptin, 1 mM pepstatin, and 0. 125 units/ml benzonase) and incubated for 1 h at 4 °C. Subsequently, the cell lysate was centrifuged at 13,000 g, for 15 min at 4 °C, and the supernatants (protein extracts) were collected and stored at À80 °C.
Protein identification and quantitative data packaging were performed as previously described  [1, [12] [13] [14] [15]. A cut-off Log2ratio values of þ1. 0 (2-fold) or higher was applied for determining the proteins with higher abundance levels and À 1. 0 (2-fold) or lower for proteins with lower abundance levels to compare pairs of experimental groups  [16, 17]. In silico prediction of protein-protein interaction and pathways of HUVEC proteins, with different abundance levels after interaction, was performed using QIAGEN's Ingenuity s Pathway Analysis (IPA s , QIAGEN Redwood City, www. qiagen. com/ ingenuity) and DAVID Bioinformatics Resources 6. 7. The proteomic data were deposited in the Pro-teomeXchange Consortium  [18]  via the PRIDE  [19]  partner repository with the dataset identifier PRIDE: PXD002823.
Acknowledgments The authors would like to thank  Glenda Sanches  for the skilled technical help in the preparation of HUVEC primary cultures. In addition, the authors would like to thank  Professor Marcia Cristina Veiga Amorim  for access to the scanning electronic microscope at the  Chemistry Institute and Carmela Dutra Hospital  and the volunteer placenta donors who contributed to this work. This work was financially supported through grants from the  Fundação Carlos Chagas de Amparo à Pesquisa do Estado do Rio de Janeiro, Brazil (FAPERJ) , grants  E-26/100. 329/2014and E-26/102819/2012. LMLB is a research fellow of  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  and  Faperj , and GWPN is a PhD Nota 10 fellow of FAPERJ.
Sources of studies We will conduct a thorough search for published systematic reviews investigating the impact of a genetic variant on an outcome. No restrictions will be placed on the type of outcome, the population studied or the type of genetic variant studied. However, only studies published in English and those conducted in human samples will be included. We will limit the search to only systematic reviews with meta-analyses conducted between 1 January 2014 and 31 December 2014 to obtain an appropriate sample size (see below). MeSH and free text terms will be used to identify relevant articles. The search strategy was created in consultation with a librarian and is presented in box 1. The following databases will be searched using the OVID interface: MEDLINE, Embase and Global Health.
Search using HuGE Literature finder: 'meta-analysis' OR 'systematic review' OR 'HuGE review'; filtered by Year '2014'*. *In searching HuGE, we did not restrict the search to 'humans' as this database is designed to capture only human studies. Furthermore, any non-English studies were manually removed during Title and Abstract Screening. Disagreements will be resolved by consensus. Articles that pass the screening phase will be carried forward to the data extraction phase. Agreement between reviewers will be assessed using Cohen's κ. A flow diagram displaying the screening process and a detailed table of the studies selected in the systematic review will be included in the publication, as per guidelines set by the Meta-analysis of Observational Studies in Epidemiology (MOOSE)  12  and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
Data extraction A total of 12 reviewers familiar with the conduct of genetic association studies will be recruited to appropriately represent users of the tool. 10 In pairs, reviewers will conduct a full-text assessment and data extraction from the assigned meta-analyses. Standardised forms with the scoring criteria will be given to each reviewer. Data extraction forms will be pilot tested to ensure feasibility and consistency in use among reviewers in a first round of review. A second round of review will be conducted with the same group of reviewers after addressing any concerns that may arise from the first round. The following will be extracted from all included systematic reviews: title of the journal, outcome studied, genetic variant studied, list of included studies with the associated risk estimate, sample size, SE, CIs and p values, as well as the statistical methods used for meta-analysis and information on design of included studies (ie, casecontrol, cohort, etc).
Application of the Q-Genie tool The Q-Genie tool is available from the Population Genomics Program website http://fhs. mcmaster. ca/ pgp/links. html. In the same pairs as data extraction, reviewers will independently rate studies from included meta-analyses using the Q-Genie tool. Their average score will be used. Reviewers will pilot test the Q-Genie on two or three studies in pairs and will meet to discuss disagreements in interpretation, if necessary. Agreement between pairs of reviewers will be reported with interrater G-coefficients, as this measure is able to produce a single estimate of reliability from all six rater-pairs. 18 As an adjunct, we will also report Cohen's κ for each pair as this is commonly used in systematic reviews to assess concordance. Lastly, an overall G-coefficient will be used to report the overall agreement, using data from all reviewers.
Qualitative assessment of the tool In addition to quantitative evaluation, we will conduct a qualitative assessment to explore (1) feasibility of the Q-Genie tool, (2) ease of use and comprehension and (3) relevance of the individual questions to the overall tool. The assessment will be used to compile changes to overcome proposed challenges in the presentation and administration of the tool. To accomplish this, a structured questionnaire will be administered to the 12 reviewers who participated in this study. This survey will consist of multiple-choice and open-ended questions on ease of use, comprehension and relevance of the content (table  1 ). This exercise will be valuable for gathering rich data on the numerous interpretations that may exist about the effectiveness of the Q-Genie tool.
These include SR-BI (Scavenger Receptor Class B, Member 1) and CD36 (CD36 Molecule, Thrombospondin Receptor) both involved in the uptake of carotenoids by intestinal cells, as well as BCO1 (Beta-Carotene Oxygenase 1) which catalyzes the oxidative cleavage of carotenoids into vitamin-A  [43, 44]. In addition, it is most likely that host genetic and epigenetic variations in metabolizing enzymes can be a major determinant of individual phenotypes. Pharmacogenomics studies have demonstrated that for particular drugs, including some phytochemicals of the alkaloid family like codeine, individuals can be categorized into poor, intermediate or extensive and ultra-rapid metabolizers and dosing has to be adapted to achieve equivalent plasma or tissue concentration of the active metabolite  [45]. Differences have been attributed to specific genes and enzymes of the xenobiotic metabolism pathways and the genetic variants associated have been compiled in online databases such as the Pharmacogenomics knowledgebase (PharmGKB)  [46]  and on the FDA Most plant food bioactives are absorbed and metabolized through the same polymorphic carriers and enzymatic systems as some of these drugs, and their pharmacokinetics is thus also likely to depend on genetic background.
Comprehensive profiles of metabolites present in urine, plasma or other biospecimens, including still unknown metabolites of plant food bioactives, can be obtained and compared in nutritional intervention studies for subjects consuming the same diet but stratified according to factors such as age, sex, gut microbiota composition, and genotype. The extent and the nature of the differences existing between the metabolomic profiles of the study groups, for example, between males and females after the same plant food intake is likely to provide valuable new insights for a better understanding of the inter-individual variation. Some technological and methodological improvements will have to be implemented to facilitate the use of metabolomics for plant food bioactive research. First initiatives have been launched in the framework of the COST Action POSITIVe with the development of a standardized combination of methods ensuring a wide coverage of plant food metabolites, the sharing of standards of rare metabolites, and the compilation of metabolism and analytical data in the online database PhytoHub dedicated to dietary phytochemicals (www.
phytohub. eu). The objective is to make possible the use of comprehensive annotated profiles of plant food bioactive metabolites in intervention trials and large-scale observational studies such as wide-association studies. Furthermore, despite the knowledge accumulated on the metabolism of plant bioactives in humans, and while most plant biosynthetic pathways of these compounds are accessible on-line at KEGG (http://www. genome. jp/kegg/pathway. html) or MetaCyc (http://metacyc. org/) databases, their metabolic pathways in humans or mammals have not yet been published in online databases. Up to now, caffeine is the only plant food bioactive for which the metabolism in humans is described in the KEGG database (http://www. genome. jp/ kegg/pathway/map/map00232. html). As modern biology increasingly relies on bioinformatics, there is an urgent need to fill this gap.
An in-depth characterisation of the subjects, including genotyping, gut microbiota analysis, food metabolome profiling has become achievable, although still quite expensive, making feasible the search for correlations between information-rich datasets and health outcomes in intervention and prospective studies. New bioactives as well as new molecular targets in population subgroups may be identified. However, for the majority of studies investigating the health effects of plant foods and for clinical applications, the subject stratification methods should be more cost-effective and easy to handle. Several approaches can be foreseen, including modeling and challenge tests. Models may be built from the acquired knowledge to predict the internal exposure of individuals based on their declared food intake and their status for the main factors controlling inter-individual variation in ADME (e.