dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology term associations Gene Ontology (GO) terms for DE genes were retrieved using g:Profiler  [40]  with the "significant only" option un-checked so as to retrieve all GO terms. While GO terms fall into three different groups (biological process, molecular function, and cellular component), only biological process and molecular function were used for this analysis. Upon classifying GO terms into more general categories, the number of unique transcripts linked to each broad category was plotted for each contrast and direction of differential expression. Because a single gene may return multiple GO terms, transcripts were allowed to fall into multiple categories.
l. , suggesting conserved functions, most likely related to basic sterol homeostasis and 20-hydroecdysone biosynthesis functions that have been ascribed to these proteins in D. melanogaster  [89, 93, 94]. In our dataset, only An. gambiae NPC1a (AGAP008137) is modestly down-regulated and only in the 6IVM:6BF contrast (Table  2 ). The NPC2 proteins from An. gambiae s. s  [92] , D. melanogaster  [90]  and Ae. aegypti  [91]  have been previously subjected to phylogenetic analysis. These latter two analyses placed these proteins in 4 broad groups. From this basis, BLASTp against Ae. aegypti representatives of the 4 previously-described groups (AAEL009557: group 1, AAEL001650: group 2, AAEL004120: group 3, AAEL006854: group 4) identified 108 NPC2-like proteins in An. gambiae s. l. and in the outgroup species An.
BLASTp and phylogenetic analysis of AAEL009557 returned forty proteins in six subgroups (group 1, blue font), AAEL001650 returned thirty proteins in five subgroups (group 2, gold font), AAEL004120 returned nine proteins in one subgroup (group 3, black font), and AAEL006854 returned twenty-nine proteins in four subgroups (group 4, pink font) The genome location of An. gambiae s. s orthologues are listed after each gene. Groups with >50 % bootstrap support are red. An. gambiae orthologues appear in a larger font with the IVM and blood meal-responsive transcripts in the largest font. Note the duplication appearing in the same physical location in group 4a of AGAP002847 in the 6DPE contrast compared to the 2DPE contrast may influence the differential age susceptibility we observed. These data highlight the need for future studies to investigate this potential connection and further characterize these proteins.
Comparison of our dataset with work done by Dinglasan et al. [97]  revealed DE of a high number of putative peritrophic matrixassociated genes upon IVM ingestion in both age contrasts (Table  3 ), suggesting that PM gene transcription is responding directly to IVM treatment. In general the 2DPE contrast showed both an increased number of significantly DE transcripts as well as higher fold-changes than the 6DPE contrast, but interestingly, both age contrasts had DE genes unique to their own age group. Additionally, gene ontology analysis revealed downregulation of chitin processing transcripts upon blood-  feeding (Additional file 9: Figure  S3 ) yet up-regulation of this category after an IVM blood meal (Fig. 6 ). IVM is known to significantly decrease the rate of blood meal digestion  [21, 101] , even though the glutamate-gated chloride channel target is not expressed in the An.
Transcripts related to immunity significantly differentially expressed transcripts following IVM ingestion. Inclusion and family classifications based on data provided by the insect immunoDB database [108]  and other publications. Descriptions in blue text were annotated based on BLASTp results (e-value ≤ 10 -3 ) (Additional file 10) AMP anti-microbial peptide, CLIP clip-domain serine protease, FibR fibrinogen-related, LYSP lysozyme, PGRP peptidoglycan recognition proteins, SC signaling-cascade related, SR scavenger receptor, SRP serine protease, SRPN serine protease inhibitor.
Acknowledgments We would like to thank  Benjamin Krajacich  for collection and feeding of field-caught mosquitoes in Burkina Faso. We thank those at the IRSS for their assistive collaboration in allowing us to use mosquitoes from their colony. We thank  Meg Gray  for assistance with mosquito rearing and bioassay experiments. We appreciate the work of those at  Beckman-Coulter Genomics  for performing sequencing reactions and ensuring RNA-seq library integrity. We thank those in the  Ebel Laboratory at Colorado State University  for training and use of their Bioanalyzer and qPCR instruments.
CX3CR1 mRNA in schizophrenia Many transcriptomic microarray studies in SCZ have been made available in public domains such as the Gene Expression Omnibus (GEO) from NCBI  (Barrett et al. , 2013) , ArrayExpress from EBI  (Rustici et al. , 2013) , and the Stanley Medical Research Institute (SMRI) online genomics database  (Higgs et al. , 2006). Raw data from both postmortem and blood SCZ studies can be easily retrieved from these databases for use in a meta-analysis with enough sample size and sensitivity for the identification of differentially expressed genes and biological processes. With increasing numbers of analyzed samples, it is important to apply normalization procedures that will balance effects that may arise from the heterogeneity in tissue regions, microarray platform, and sample quality that could collectively deteriorate the meta-analysis.
Microarray Datasets Microarray datasets were selected on the basis of whether they used either postmortem brain or blood tissue (i. e. cell lines were excluded), the availability of raw data, and information on covariates (i. e. age and sex for blood samples, as well as pH and postmortem interval (PMI) for brain tissues). The treatment status and the acute or remitted status of patients were not taken into account for the selection of datasets. Each dataset was comprised of neuropathologically normal and SCZ subjects, as diagnosed and reported in their respective studies (Table  1  retrievable (including some microarrays with custom designs that did not cover the whole genome), many represented repeated runs of the samples from the same subjects, so datasets were selected that do not allow overlap between samples and/or individuals.
Two additional studies of whole blood were obtained from the investigators  (Gardiner et al. , 2013; Kumarasinghe et al. , 2013). In total, ten datasets represented various postmortem brain regions (prefrontal, frontal and temporal cortex, cerebellum, hippocampus, striatum and thalamus) and five utilized blood (whole blood and PBMCs) (see Table  1  for details of each data set). CX3CR1 mRNA in schizophrenia.
Analysis of Microarray Data After downloading the raw data, microarray analysis was conducted using an in-house pipeline inspired by Turnbull et al. and developed under the R statistical language  (Turnbull et al. , 2012). Using R/Bioconductor software  (Gentleman et al. , 2004)  Affimetrix datasets  (Gautier et al. , 2004). Probes were assigned to gene symbols based on the annotation files provided by GEO. For genes with multiple expressed probe sets, mean expression intensity was calculated within each microarray dataset. Next, according to their Entrez ID annotation, genes in common between Affymetrix (postmortem samples) and Illumina (blood samples) platforms were selected to merge the datasets into one single postmortem dataset (8655 genes) and one single blood dataset (16 661 genes).
Batch effects across the postmortem and blood datasets were removed using the methods implemented in the ComBat algorithm, that exploits variance moderation during data adjustment, using the sva R package (http://www. bioconductor. org/packages/release/bioc/html/sva. html) (Johnson et al. , 2007). Differences between SCZ and control groups were assessed by analysis of variance (ANOVA) using the maanova R package (http://www. bioconductor. org/packages/release/bioc/html/maanova. html). The resulting pvalues were further adjusted for multiple testing using the q-value method to control the false discovery rate (FDR)  (Storey and Tibshirani, 2003).
Gene ontology analysis Lists of genes with significant changes were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID)  (Huang da et al. , 2009)  to identify statistically relevant biological pathways altered in SCZ using medium classification stringency, p-value < 0. 05 and FDR ≤ 10%. A Venn diagram of meta-signatures was built using the jvenn tool  (Bardou et al. , 2014).
Real-time RT-PCR RNA (1 μg) was reverse transcribed with the High Capacity cDNA archive kit (Applied Biosystems, Foster City, CA, USA). cDNA was combined with a TaqMan® universal PCR Master Mix (Applied Biosystems) and PCR reactions were performed on an ABI PRISM 7900HT thermocycler according to the manufacturer's recommendations (Applied Biosystems). All PCR reactions were performed in triplicate. For CX3CR1 mRNA expression, primer sets and probes were selected using the web portal of the manufacturer (Applied Biosystems: Hs00365842_m1). The relative fold change expression (FC) was calculated using the 2 -ΔΔCt formula with the DataAssist software (Applied Biosystems, v3. 0). The differential expression was also compared to a calibrator sample; specifically to the mean of all control samples.
Statistical Analysis in validation study Skewness and Kurtosis were calculated for each continuous demographic and clinical variable to test for normality. Variables were compared between patients and matched-controls with a CX3CR1 mRNA in schizophrenia chi-squared (χ 2 ) test for qualitative variables and ANOVA or t-test for quantitative variables. Gene expression differences between groups were analyzed using General Linear Models (GLMs) including groups as fixed factors and potential confounding factors as covariables. Confounding factors were defined if demonstrating a difference between groups with p-value < 0. 2 in univariate analyses. Correlation between clinical dimension and gene expression were calculated using Pearson's correlation test. All statistical tests were conducted using the IBM SPSS Statistics v20 software.
Meta-analysis in postmortem brain We first compiled a brain cohort from 204 SCZ patients and 212 normal controls by pooling data from 10 studies (Tables  1  and 2 ). Brain regions investigated included the prefrontal, frontal and temporal cortices, cerebellum, hippcampus, striatum and thalamus. Analysis of the demographic variables identified a significant difference in PMI and pH, but not age and gender between the SCZ subjects and controls (Table  2 ). After normalization and the removal of underexpressed probes and batch effect (see Methods for details), 8655 genes remained in common from the 10 datasets that were pooled from the postmortem brain dataset. ANOVA was carried out to identify differentially expressed genes in SCZ versus control after taking into account age, gender, pH and PMI as covariables.
Meta-analysis in blood A second blood cohort consisted of 240 SCZ patients and 238 healthy controls with similar age distribution but significant overrepresentation of men in the SCZ group (Tables  1  and 2 ). After raw signal processing and quality control measures, 16 661 expressed genes remained in common to the five datasets that were pooled for the blood dataset. ANOVA was performed CX3CR1 mRNA in schizophrenia with age and gender added as covariables to address their influence on gene expression. To obtain a similar number of genes compared to the brain meta-analysis, we adjusted the threshold q-value to 0. 010 and identified 132 and 123 genes that were down-and up-regulated in SCZ patients compared to controls, respectively. Ontological annotations with DAVID revealed that two groups of biological processes were overrepresented (FDR < 10%) in the blood gene list: wounding/defense response/inflammatory response/lymphocyte activation/cytokine binding and protein kinase cascade (Table  4 ).
Convergence of gene expression profiles in brain and blood To explore whether a common meta-signature was shared between brain and blood samples, we drew a Venn diagram based on down-and up-regulated genes in brain (q ≤ 0. 001) and blood (q ≤ 0. 05) cohorts (the statistical thresholds have been adjusted to compare approximately the same number of genes between tissues) (Figure  1 ). Out of the 1247 dysregulated genes in total, 13 genes were differentially expressed in the same direction in both brain and blood datasets. Of these, 10 genes (ABCF1 and CX3CR1 down-regulated; ADM, BCL6, LCN2, NCF4, S100A8, S100A12, TSPO, and TLR2 upregulated) have roles in inflammation, immunity and wound responses. Indeed, a comparison of Tables  3  and 4  demonstrates that the brain and blood datasets share in common dysregulation of the defense/wounding/inflammatory response pathways from which we highlighted 3 genes present in our meta-signature: ADM, S100A8 and CX3CR1.
018). CX3CR1 mRNA expression could be influenced by antipsychotic medication since our patients were either on risperidone or aripiprazole monotherapy, but this association was not significant (p = 0. 363). Since clinical data was collected for the validation cohort, it was assessed whether any clinical dimension was correlated with CX3CR1 expression within SCZ patients. Interestingly, we identified a significant correlation of CX3CR1 expression with the depression-anxiety dimension of the 5dimension PANSS (r = -0. 36; p = 0. 048) and with the general psychopathology score of the classical 3-dimension PANSS (r = -0. 46; p = 0. 012). Furthermore, we observed a strong correlation between CX3CR1 expression and the score of the CDSS (r = -0. 632; p = 0. 00023) as well as with both STAI subscales: S-Anxiety (r = -0.
Discussion This study has presented for the first time a large-scale evaluation of the consistency of overlap between peripheral blood and postmortem brain gene expression in SCZ with the aim to discover transcriptional biomarkers of the disorder. A decade ago,  Glatt et al. (2005)  were the first to compare the gene expression profiles of blood and brain. Although they used a small cohort of SCZ cases and controls, their pioneering work has paved the way for using gene expression to identify biomarkers in psychiatric diseases  (Glatt et al. , 2005). In the present study, we conducted two meta-analyses in blood and brain using the raw data of 15 microarray studies to investigate gene expression from nearly 1000 subjects, equally divided between SCZ patients and non-psychiatric controls. To our knowledge, no meta-analysis has been previously conducted on blood microarray data from SCZ, although several studies had combined data from two to seven human postmortem brain tissue microarray studies  (Hagihara et al.
). Sources for data include GEO (http://www. ncbi. nlm. nih. gov/geo), ArrayExpress(http://www. ebi. ac. uk/arrayexpress/), and the Stanley database (https://www. stanleygenomics. org/). GEO and ArrayExpress studies were identified by extensive manual and combinatorial keyword searches (schizophrenia, psychosis, control, human, brain, blood, microarray) until November 1st 2014. Although additional datasets were.
0 28 SCZ (BA10) PMI, pH et al. , 2009) (54681) GEO, GSE21138 Narayan et al. , Affymetrix, 30 CTL Prefrontal Cortex ArrayExpress 2008 (Narayan U133Plus2. 0 29 SCZ (BA46) et al. , 2008) (54681) GEO, GSE21935 Barnes et al. , Affymetrix, 19 CTL Temporal Cortex ArrayExpress 2011 (Barnes U133Plus2. 0 23 SCZ (BA22) et al. , 2011) (54681) GEO, GSE53987 Affymetrix, U133 19 CTL Hippocampus, ArrayExpress Plus2. 0 (54681) 19 SCZ Prefrontal Cortex (BA46) and Striatum GEO GSE27383 van Beveren et Affymetrix, U133 29 CTL PBMCs al. , 2012 (van Beveren et al. , Plus2. 0 (54681) 43 SCZ 2012) GEO GSE38481 de Jong et al. , Illumina 22 CTL Whole Blood 2012 (de Jong HumanRef-8 v3. 0 15 SCZ et al. , 2012) expression beadchip (24526) GEO GSE38484 de Jong et al. , Illumina 96 CTL Whole Blood 2012 (de Jong et al. , 2012) Gardiner et al.
Observations along the southwestern Atlantic WOCE A17 line made during the Dutch GEOTRACES-NL programme (2010-2011) were compared with historical data from 1994 to quantify the changes in the anthropogenic component of the total pool of dissolved inorganic carbon ( C ant ). Application of the extended multilinear regression (eMLR) method shows that the C ant from 1994 to 2011 has largely remained confined to the upper 1000 dbar. The greatest changes occur in the upper 200 dbar in the Subantarctic Zone (SAZ), where a maximum increase of 37 µmol kg -1 is found. South Atlantic Central Water (SACW) experienced the highest rate of increase in C ant , at 0. 99 ± 0. 14 µmol kg -1 yr -1 , resulting in a maximum rate of decrease in pH of 0. 0016 yr -1. The highest rates of acidification relative to C ant , however, were found in Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW).
Data The two data sets used in this study are the results from the CO 2 survey data from the WOCE '94 A17 section (public data at http://cdiac. ornl. gov/oceans/woce_a17c. html) and the Dutch West Atlantic GEOTRACES programme, completed in 2011 (GEOTRACES-NL, 2010/2011; public data available at http://www. bodc. ac. uk/geotraces/data/idp2014/). The respective stations from the two campaigns are shown in Fig. 1. The GEOTRACES-NL (2010/2011) section was carried out in two parts. The shown stations north of the Equator were occupied in July 2010 by the Dutch RV Pelagia (expedition 64PE321: from Hamilton, Bermuda, to Fortaleza, Brazil), and the Southern Hemisphere was sampled during March 2011 by the British RRS James Cook (JC057: from Punta Arenas, Chile, to Las Palmas, Gran Canaria).
pH calculations From DIC, A T and supplementary data (salinity, temperature, pressure, Si(OH) 4 , PO 4 ), pH, and pCO 2 were also calculated in situ for both data sets using CO2_SYS  (Lewis and Wallace, 1998)  adapted for MATLAB (van Heuven, 2011a), applying the acid dissociation constants of  Mehrbach et al. (1973) , refitted by  Dickson and Millero (1987) , and the KSO 4 constant of  Dickson (1990).
Consistency between data sets In a later section, we employ the extended multi-linear regression (eMLR) method  (Wallace, 1995; Friis et al. , 2005)  to infer C ant between the two cruises. The eMLR method considers various biogeochemical properties (in this case salinity, DIC, NO 3 , Si(OH) 4 and apparent oxygen utilization (AOU = [O 2 ] sat -[O 2 ] obs )) and is particularly sensitive to large-scale ("secular") changes in the distributions of these properties, as well as to analytical biases in their measurement. In order to assess the magnitude and distributions of these changes, we gridded the values of salinity, DIC, NO 3 , Si(OH) 4 and AOU of each data set, and the gridded WOCE data set was subtracted from the GEOTRACES grid. Grid spacing was every 2 • of latitude, with 80 layers in the vertical direction, with increased density towards the surface.
In the lower Circumpolar Deep Water (lCDW; conceivably the most stable water mass in the section), the differences average -0. 01 ± 0. 015 (salinity), -4. 2 ± 12. 1 (DIC), -1. 92 ± 0. 78 (NO 3 ), -5. 05 ± 3. 3 (Si(OH) 4 ) and -3. 13 ± 3. 9 µmol kg -1 (AOU). By limiting the comparison to just lCDW, the number of data points available is limited; as such, we further performed a more robust crossover analysis of the GEOTRACES 2010/2011 data set with data from the CARINA database  (CARINA Group, 2009; Tanhua, 2010). This was done for all the tracers used at depths deeper than 3000m. We find an offset of +1 ± 0. 8 % for NO 3 , -1 ± 0. 5 % for dissolved oxygen, -0. 004 ± 0. 001 for salinity, -1 ± 0. 3 % for Si(OH) 4 , +1. 17 ± 2. 8 µmol kg -1 for DIC, and +5. 3 ± 4. 4 µmol kg -1 for A T. These values are all within the threshold values of the CARINA synthesis  (Key et al.
Acknowledgements. We thank the captains and crews of  RV Pelagia  and  RRS James Cook. The Earth and  Life Sciences division (ALW) of the Netherlands Organisation for Scientific Research (NWO)  supported this research under project number  817. 01. 004. The research leading to these results was supported through the  EU   FP7  project  CARBOCHANGE "Changes in carbon uptake and emissions by oceans in a changing climate ", which received funding from the  European Commission 's  Seventh Framework Programme  under agreement no. 264879. We also thank  M. Álvarez  and an additional, anonymous referee for their helpful comments and suggestions. Edited by:  G. Herndl.
Background: To evaluate novel grading system used to quantify optical coherence tomography (OCT) scans for cystic macular lesions (CML) in Usher syndrome (USH) patients, focusing on CML associated alterations in MOY7A and USH2A mutations. Methods: Two readers evaluated 76 patients' (mean age 42 ± 14 years) data prospectively uploaded on Eurush database. OCT was used to obtain high quality cross-sectional images through the fovea. The CML was graded as none, mild, moderate or severe, depending on the following features set: subretinal fluid without clearly detectable CML boundaries; central macular thickness; largest diameter of CML; calculated mean of all detectable CML; total number of detectable CML; retinal layers affected by CML. Intra-and inter-grader reproducibility was evaluated. Results: CML were observed in 37 % of USH eyes, while 45 % were observed in MYO7A and 29 % in USH2A cases.
Subjects and database This prospective observational cohort study was conducted according to the tenets of the Declaration of Helsinki and received authorization from Ile-de-France V ethical board committee for human research (June 4th 2013) and from the French regulation agency for medication (September 3rd 2013) (http://clinicaltrials. gov/ct2/ show/NCT01954953). The Eurush database (www. eurushdatabase. org) was used where subjects' data were uploaded on a prospective manner. The participants were recruited at the Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, Paris, France. The study procedure was fully explained to all patients, and their informed written consent was obtained. Seventy six patients with a clinical diagnosis of USH were enrolled (age 42 ± 14 years). The inclusion criteria were: clinical characteristics for USH1, USH2 and USH3 as defined by the Usher syndrome consortium  [23] ; informed consent and agreement to participate in the study.
Statistical analysis Statistical analysis was carried out using SPSS software (version 19. 0, SPSS Inc. , Chicago, USA). We analyzed CML in the whole cohort and in two groups associated with mutations in USH2A and MYO7A. Pearson correlation (r) was used to assess the correlation between central macular thickness and age in a whole cohort and associated genes, namely USH2A and MYO7A. One-way analysis of variance (ANOVA) was performed to detect differences between CML severity in associated gene USH2A and MYO7A. The percent agreement and the kappa statistics were calculated as measures for categorical variables (quality of scan and presence of CML). Percent agreement was computed as the number of concordant grading pairs divided by the total number of grading pairs multiplied by 100. All numerical data are presented here as arithmetic means ± standard deviation (SD).
Quality assurance data for a novel grading system The evaluation of the proposed system is done using Eurush database estimating intra-grader and inter-grader Fig. 2  The prevalence of cystic macular lesions severity in Usher syndrome cases. The prevalence of cystic macular lesions in USH 1 and USH 2 a, the prevalence of cystic macular lesions in USH2A and MYO7A b CML required in averaged 5 min ± 1 min for the first and 6 min ± 1 min for the second grader (not statistically significant p > 0. 05). The difference in mean CML diameter was 2 μm ± 1. 5 μm between the two graders performed grading of OCT scans. Reproducibility rates were high for the categorical variables scan quality at the fovea and CML. For the inter-grader repeatability analysis, agreements rates were 97 % (95 % CI 0. 915-1.
Acknowledgment The project is supported by the by national funding organizations of  France (ANR) ,  Germany (BMBF) ,  Netherlands (ZonMW) ,  Portugal (FCT)  under the frame E-RARE 2, the  ERA-Nett for Reasearch on Rare Diseases ERAREl   N°58 :  Eur-USH , http://eur-ush. eu,http://www. e-rare. eu. TP is funded by  NIHR BMRC at MEH and IoO, London, UK.