dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq data analysis 24 samples of C57BL6 strains of mice were sequenced using RNA-Seq technology 2,3 , averagely generating 45,188,386 raw sequencing reads and then 45,120,825 clean reads after filtering low quality (see Data Filtering in method page). Table  1  briefly summarizes information of sequencing data for each sample. After filtering, clean reads are mapped to reference using HISAT  10   identified genes is also increased. However, when the number of sequenced reads reaches a certain amount, the growth curve of identified genes flattens, indicating that the number of identified genes tends to reach the saturation. The distribution of the reads on reference gene reflects whether each part of the gene body is evenly sequenced. If the randomness is good, the reads in every position (from 5' terminal to 3' terminal) would be evenly distributed.
If the randomness is poor, reads preference to specific gene region will directly affect subsequent bioinformatics analysis. We use the distribution of reads on the reference genes to evaluate the fragmentation randomness, as shown in Fig. 1. Gene's expression level is quantified by a software package called RSEM  12. The number of identified expressed genes were counted and its proportion to total gene number in database for each sample was calculated. Meanwhile, the distribution of gene number on different expression level for each sample are shown as Fig. 2 , to get a general idea about the expression levels of each gene. The file all. gene. FPKM. xls available on Figshare repository is an expression table for all samples with brief gene description and annotation. Figure  1. Reads distribution on reference gene.
Because of variable lengths of reference genes, the average length of genes is divided into N equal parts. Each equal part is called a window. X-axis shows the relative position of genes, and Y-axis shows the number of reads in each window. DEGs (Differentially Expressed Genes) screening is aimed to find differential expressed genes  14  between samples and perform further function analysis on them. Poisson distribution method was used to perform this analysis. All expressed genes of each pairwise are stored in *. GeneDiffExp. xls and screened DEGs are stored in *. GeneDiffExpFilter. xls. They have the same file format and are listed in Figshare repository (Data Citation 1). For result list of each control-treatment pair above (*. GeneDiffExp. xls), scatter plots were draw for all expressed genes (Fig.
3 ) to present the distribution of DEGs in screening threshold dimensions. Also, a histogram is shown to demonstrate the number of genes that are significantly up-down in each pairwise comparison (Fig. 4 ). Genes with similar expression patterns usually have same functional correlation. We also provide clustering analysis of differentially expressed genes with cluster  15, 16  and javaTreeview software  17  according to the provided cluster plans for DEGs. Complete intersection and union DEGs heatmap for each cluster plan were provided (Fig. 5 ). Annotation analysis of Gene Ontology (GO) are performed for screened DEGs and then generate a report named GOView. html under the data folder of Figshare (Data Citation 1). After getting GO annotation for DEGs, WEGO software  18  was used to do GO functional classification to help understand the distribution of gene functions of the specie from the macro level.
Genes usually interact with each other to play roles in certain biological functions. We perform pathway enrichment analysis of DEGs based on KEGG database  19  and generate a report for DEGs in each pairwise respectively, stored in Figshare folder (Data Citation 1). In addition, we generate a scatter plot for the top 20 of KEGG enrichment results (Fig. 6 ) and a bar plot for the statistics of KEGG terms types (Fig. 7 ). Different proteins often form complex proteins through complicated interactions to perform their biological functions. Protein-protein interaction network analysis integrates several famous interaction network databases such as BIND, BioGrid and HPRD and constructs interaction networks for DEGs' coding proteins. The analysis result documents are showed by Medusa software  20  and webpage version is presented by the report network_en.
Quality validation and analyses We applied FastQC v0. 11. 5 software to determine data quality and analysed several measures, including the sequence quality per base, GC content per sequence, sequence duplication levels, and quality score distribution over all sequences of the fastq files  13. A representative summary plots are depicted in Figshare repository (Data Citation 3). Here, the quality scores per base were high, with a median quality score above 30 suggesting high quality sequences across all bases. The GC distribution per base over all sequences was examined. The GC composition pattern was similar to theoretical distribution indicating that the samples were contaminant free. In addition, sequence complexity was examined. Approximately 15% of sequences were shown over 10 to 100 times, either from highly expressed transcripts duplications.
Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date. Objective: We aimed to identify existing evidence for genemacronutrient interactions and T2D and to examine the reported interactions in a large-scale study. Design: We systematically reviewed studies reporting genemacronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e. g. , total carbohydrate) or indicators of quality (e. g. , dietary fiber) by use of self-report or objective biomarkers of intake.
To minimize publication bias, we searched the WHO International Clinical Trials Registry Platform, gray literature (e. g. , GreyNet), and names of key authors and diabetes trials and we also hand-searched relevant reviews. Medical Subject Headings and specific terms (i. e. , title, abstract, and key words) were also used wherever possible to ensure sensitivity within respective databases. No restrictions were placed on language, age, publication date, or study design. Authors of 3 published studies  (13, 25, 26)  and 1 unpublished study (clinicaltrials. gov NCT01168297) were contacted to either assess eligibility or collect further data to conduct the review. Two studies, 1 published (26) and 1 unpublished, were subsequently determined to be ineligible. Studies were screened by title and abstract for eligibility for full-text review.
We extracted information from each publication meeting the eligibility criteria through the use of an agreed-upon data extraction form on cohort characteristics (e. g. , study design, sample size, ethnicity, etc), covariates, statistical analyses, and estimates of associations between 1) macronutrient intake and T2D, 2) genetic variant and T2D, and 3) genemacronutrient interactions and T2D. Narrative synthesis was undertaken. An assessment for confounding, bias (selection, measurement, attrition, outcome, and reporting), and genetic-specific issues (genotyping quality, population stratification, multiple testing) was undertaken through the use of a modified version of the Cochrane guidelines for nonrandomized studies of interventions to incorporate genetic issues highlighted by HuGENet  (23, 27).
EPIC-InterAct study To investigate the reproducibility of the statistically significant interactions identified by our systematic review, we examined them in a large-scale study (EPIC-InterAct). EPIC-InterAct participants provided informed consent and an ethics committee approved the study  (21). EPIC-InterAct is a case-cohort study nested within the EPIC study (28) composed of 12,403 individuals with T2D and a randomly assigned subcohort of 16,154 individuals, as previously described  (21). Data on lifestyle variables were collected from questionnaires that participants completed at baseline (from 1991). Follow-up was censored at the date of T2D diagnosis, 31 December 2007, or the date of death, whichever occurred earlier. Our current analyses were based on a smaller subset of EPIC-InterAct with available macronutrient and genomewide genotyping data, representing 8 European countries (n = 9403 cases and 11,745 noncases for analyses on macronutrient intake; n = 9937 cases and 12,336 noncases for analyses on circulating FAs).
Self-reported and objective biomarkers of macronutrient intake Habitual self-reported macronutrient intake data were derived from the validated self-or interviewer-administered countryspecific food-frequency questionnaire (FFQ) or dietary histories taken at baseline  (29, 30) , with nutrient composition derived from the EPIC Nutrient DataBase  (31). Baseline circulating plasma phospholipid FAs were profiled using a high-throughput automated gas chromatography method  (32).
For 2 replication analyses, we excluded EPIC-InterAct centers (Potsdam and Malmo ¨) that contributed to previous analyses  (10, 35). For example, the interaction between caveolin 2 (CAV2) and total fat and SFAs identified by Fisher et al. was examined in the EPIC-Potsdam study, so Potsdam was excluded from our EPIC-InterAct analysis. We undertook complete case analyses so that those with missing macronutrient intake, genetic data, or covariates were excluded. Stata software (version 14; StataCorp LP) was used for all analyses, with a P-interaction , 0. 05 judged as statistically significant on the basis that each interaction was considered an independent replication attempt.
Supported by the  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) , and  Statistics Netherlands  (to AMWS, IS, and  NCO-M). In EPIC Utrecht , verification of the Dutch diabetes cases was additionally funded by  NL Agency  (grant  IGE05012 ) and an  Incentive Grant  from the  Board of the UMC Utrecht. EPIC Ragusa  acknowledges for funding from the  Sicilian Regional Government and AIRE-ONLUS RAGUSA  (to RT). This is an open access article distributed under the  CC-BY license  (http://creativecommons. org/licenses/by/3. 0/). Supplemental Tables 123456, Supplemental Figures  1234 , and Supplemental at http://ajcn. nutrition. org.
5 MgCl 2 ; 4 Na 2 ATP; 0. 4 NaGTP; 10 Na-Phosphocreatine; and 0. 6 EGTA. Traces were recorded using a Multiclamp 700B amplifier (Molecular Devices, Sunnyvale, CA, USA), sampled at 10 kHz and filtered at 2 kHz. Cells were excluded from analysis if the series resistance exceeded 25 MΩ. Miniature excitatory postsynaptic currents (mEPSCs) were recorded at -70 mV in the prescence of Tetrodotoxin (1 μM) and Picrotoxin (100 μM). Paired pulse ratios (PPR) were recorded in the presence of Picrotoxin (100 μM) in ACSF containing 4 mM CaCl 2 and 4 mM MgCl 2 , at a holding potential of -70 mV, following stimulation of the Schaffer collaterals and calculated as peak2/peak1 after correcting for any residual current at the second pulse. Miniature recordings were analysed using the Mini Analysis Program (Synaptosoft, Fort Lee, NJ, USA).
A constitutively active renilla luciferase plasmid was also co-transfected to normalize for transfection efficiency. Luciferase values were measured using the Dual Luciferase Reporter Assay System (Promega) and normalized to an empty GFP control vector. Data were evaluated using a one-way analysis of variance test followed by a pairwise comparison of means using a Student's t-test (with Bonferroni correction for multiple comparisons). Data are displayed as a box and whisker plot with a central line for the median, a box comprised of the first-third quartiles and whiskers showing the minimum and maximum values. n = 3 independent experiments, with the displayed value for each being the average of duplicate plates of cells. *P o0. 05. labelling with a clear synaptic localization that was confirmed by PSD95 co-labelling experiments (Figures  2a  and b ).
These results thus suggest that postsynaptic targeting of PTCHD1 is not specifically mediated by the scaffolding action of PSD95 or SAP102. Similar findings were previously described for the excitatory postsynaptic NLGN1-GFP and NR1-GFP proteins, also known to interact with PSD95. 29, 30 ltogether these data indicate that PTCHD1 is partly localized at synapses. The C-terminal intracellular tail is essential for the dendritic sorting of PTCHD1, whereas its binding to the key molecules of synapse assembly PSD95 and SAP102 would not be strictly required for its synaptic targeting. PTCHD1 does not activate canonical SHH pathway in Ptch1 -/- mouse embryonic fibroblasts A major question remains regarding the direct contribution of PTCHD1 to the SHH signalling pathway. We performed Gli-luciferase reporter assays that are standardly used to measure canonical SHH signalling in mouse embryonic fibroblasts isolated from Ptch1 -/ -embryos.
The numbers indicate the amount of genes annotated with each term in the upregulated gene set and genomewide, respectively. Almost 25% of the PSD genes are upregulated in Ptchd1 -/y hippocampi. (e) Enrichments for experimental binding sites from Cistrome chromatin immunoprecipitation-seq database. Upregulated genes are enriched in the binding sites of Suz12, Acsl1 and Rest, whereas downregulated genes are not. Complete data in Supplementary Table  S9. test conditions. In addition, we found that Ptchd1 -/y males had an increased number of rears in the actimetric cages and in the open field (Figure  3a  and Supplementary Figure  S2b ), a parameter reflecting stereotypic behaviour,  32  in line with clinical observations and common features of attention deficit and hyperactivity disorder (ADHD) or ASD syndrome.
The effects of Ptchd1 gene inactivation were also evaluated on different aspects of learning and memory processes. When animals were allowed to discriminate between a novel object and a familiar one 3 h after an acquisition trial, only WT, but not Ptchd1 -/y mice, displayed object recognition performance above the chance (Figure  3g ), suggesting altered recognition memory following Ptchd1 inactivation in males. Ptchd1 -/y males also showed altered working memory in the Y-maze test, as revealed by significantly reduced percentage of spontaneous alternations (P40. 05; Figure  3f ). These data are in line with attentional and cognitive features in developmental diseases, including ADHD and ASD syndromes. 33, 34 In the fear conditioning test, the baseline levels of immobility during habituation to the conditioning cages was lower in Ptchd1 mutants as compared with WT (P o 0.
We next assessed the release probability of Schaffer collateral axons, the main excitatory input onto CA1 pyramidal cells, by PPR. We found that the PPR is significantly decreased in Ptchd1 -/y compared with WT brain, thus revealing an increased release probability at these synapses (Figures  5h  and i ). Together, these data indicate that pyramidal cells receive a reduced number of excitatory synapses in Ptchd1 -/y mice but that these synapses present a higher release probability.
10, 13 Circadian activity measurements also revealed increased activity during the light phase suggesting altered sleep, similarly to the clinical observations. 10 When looking at cognitive abilities, our data reveal specific alteration in working memory, in line with attentional and cognitive features in neurodevelopmental disorders, including ADHD and ASD. Indeed, executive dysfunction (including working memory) is considered a promising endophenotype in neurodevelopmental disorders, such as ASD and ADHD (for a review, see Craig et al. 47  ). However, contrasting results were reported: working memory deficits were more pronounced either in ASD or in ADHD depending on the studies; even deficits were not observed in some studies reporting ASD or ADHD clinical investigations. 47 Our results are consistent with potential deficits described either in ASD or ADHD.
A total of three independent experiments were performed. (d) PTCHD1 does not repress Gli reporter activity in Ptch1 -/-mouse embryonic fibroblasts (MEFs). Luciferase activity was assessed in Ptch1 -/-MEFs co-transfected with PTCHD1-GFP or PTCH1, and an 8 × Gli Luciferase Reporter Plasmid. A constitutively active renilla luciferase plasmid was also co-transfected to normalize for transfection efficiency. Luciferase values were measured using the Dual Luciferase Reporter Assay System (Promega) and normalized to an empty GFP control vector. Data were evaluated using a one-way analysis of variance test followed by a pairwise comparison of means using a Student's t-test (with Bonferroni correction for multiple comparisons). Data are displayed as a box and whisker plot with a central line for the median, a box comprised of the first-third quartiles and whiskers showing the minimum and maximum values.
ACKNOWLEDGMENTS This work was funded by grants from  European Union  ( FP7 Gencodys , grant  241995 ) to FL, YH and PB; grants from the  'Association pour le Développement de la Neurogénétique ' to FL; the  Jérôme Lejeune foundation , the  'Agence Nationale de la Recherche ' ( ANR-15-CE16-0015-01 ) and  French Government  for the ' Investments for the Future' LabEx 'SIGNALIFE'  ( ANR-11-LABX-0028-01 ) to SM; the  IDEX02 and labex INRT  ( ANR-10-IDEX-0002-02 ;  ANR-10-LABX-0030-INRT ) and the  National Infrastructure for Biology  and health  PHENOMIN  ( ANR-10-INBS-07 ) to YH; and a  doctoral fellowship  from the  'Région Centre Val de Loire ' to DCU. Electron and Confocal Microscopy data were obtained with the technical assistance of  J Rousseau  and  J Burlaud-Gaillard  at the  IBiSA Electron Microscopy Facility of University François-Rabelais of Tours.
A more frequent detection of perfusion defects in the inferior wall in half time study in male patients, not supported by coronary angiography results, was identified as one of causes of discordances between interpretations of full time and half time studies reconstructed without AC. Those defects were small but were observed consistently in a shortened study. Attenuation correction reduced differences between full time and half time studies to a large extent. After dichotomous categorization of study results into normal and abnormal ones attenuation correction improved an agreement from 81% to 87%. Those values are not as high as the ones presented by Ali et al. [6] , Valenta et al. [7] , and Armstrong et al. [8] , who also made use of "Evolution for Cardiac" software package. They obtained agreement between full time and half time studies equal to 95% without AC and 96% with AC  [6] , 96% with 100% agreement in a clinical interpretation  [7] , and 83% to 96% (in case of acceptance of minor inconsistencies)  [8].
Although Gutstein et al. [10]  studied a diagnostic efficacy of low count studies reconstructed with Myovation Evolution protocol in relation to coronary angiography and obtained sensitivity and specificity equal to 89% and 75%, respectively, their studied material was small, especially a group of patients without critical stenoses of arteries, which consisted of 16 people. Besides, they did not apply attenuation correction while reconstructing studies. To our knowledge this is the first publication presenting not only agreement between results of full and reduced count studies processed with "Evolution for Cardiac" software package, but also comparing their diagnostic efficacy with and without attenuation correction. Other results of works comparing diagnostic efficacy of full and reduced count studies can be found in the literature  [11, 12] , but they do not concern "Evolution for Cardiac" software.
Conclusions Myovation Evolution protocol dedicated to reconstruction of myocardial perfusion studies with reduced number of counts should include attenuation correction, otherwise a perfusion pattern in inferior wall in male patients may be misinterpreted as abnormal. Discordances observed during visual assessment of normal and reduced count studies reconstructed with AC are statistically not significantly larger than disagreements between dual assessment of a full count study. Shortened studies reconstructed with "Myovation Evolution" package without AC reveal a tendency toward reduction of accuracy of the study in detection of CAD. Attenuation correction makes up for this reduction.
Population Patients were eligible if i) they were consulting for back pain condition lasting more than 3 months, ii) previous treatments (medications and/or physiotherapy) had been ineffective and iii) they had no previous history of surgery or multidisciplinary rehabilitation program or dedicated educational intervention. All patients signed an informed consent. The study was approved by the regional ethics committee (Comitede Protection des Personnes Sud Mediterranee III, 2011. 06. 05), recorded by French authorities (RCB ID n˚2011-A00270-41 delivered by AFSSAPS), and declared on Clinical trials (NCT01389999).
On the other hand, in a divergent hypothesis, the BBQ would likely not be correlated with HAD (anxiety or depression) and VAS (pain). This validity was measured using the nonparametric Spearman rank correlation coefficient (CI 95%). Supplementary analyses: A parametric item response theory (IRT) model was used to characterise the BBQ scale properties such as difficulty and discrimination of each item  [21]. All the analyses were performed using SAS software version 9. 4 (SAS Institute Inc. , Cary, NC).
In this study, patients, were enrolled because of chronic LBP and were evaluated the same day of the inclusion. It is possible therefore that the educational questioning provided the first day would have led to changes in back pain thoughts because of the discussions with the team and/or exchanges between participants. This hypothesis however, needs to be confirmed by studies with no educational assessment. Finally, data on the professional status of the patients included was not specified so we are not able to extrapolate our results to the general LBP population.
HRQoL was assessed using Functional Assessment of Cancer Therapy-Prostate (FACT-P) version 4  [15] [16] [17]  (both trials) and EQ-5D  18  (PREVAIL only) instruments (see Methods in the Supplementary Information). Higher scores represent better health states. In PREVAIL, nine FACT-P domains were assessed: FACT-P total score, FACT-General (FACT-G) score, FACT-Trial Outcome Index (TOI), FACT Advanced Prostate Symptom Index, physical wellbeing, functional wellbeing, emotional wellbeing, social/family wellbeing and prostate cancer subscale. Patients completed FACT-P at baseline, weeks 5 and 13, and then every 12 weeks until study drug discontinuation. EQ-5D data were collected at baseline and week 13, and then every 12 weeks until treatment discontinuation. In AFFIRM, three FACT-P domains were assessed: FACT-P total score, FACT-G and prostate cancer subscale.
Data were prospectively collected at baseline, weeks 13, 17, 21 and 25, and every 12 weeks thereafter until study drug discontinuation. In both studies, HRQoL instruments were administered before patients were interviewed by their physicians or received study medication. 'Post-SRE assessment' was the first assessment made o100 days after an SRE occurred. Patients without an assessment during that interval were considered to have missing post-SRE assessments.