dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction, Illumina sequencing and data pre-processing Total RNA was isolated using the Nucleospin RNA plant kit (Macherey-Nagel, Duren, Germany) as per the manufacturer's instructions. The qualitative and quantitative assessments of these total RNA samples were conducted using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). RNA samples with RNA integrity (RIN) value ≥8 were pooled in equimolar amounts from three biological replicates prior to library preparation and subsequent sequencing. The cDNA libraries were prepared using an Illumina TruSeq RNA Sample Preparation Kit (Illumina Inc. , San Diego, CA, USA) following the manufacturer's instructions. Pair-end sequencing was performed in two sets: a set of 20 samples (nos 1-20) was sequenced using an Illumina HiSeq 2000 at Genotypic Technology Pvt.
Ltd, India and a second set of 10 samples (nos 21-30) was sequenced in-house using an Illumina HiSeq 2500 sequencing system. The raw sequencing data were subjected to quality check to ensure high quality reads for downstream analyses. Reads with Phred score <20, read length <50 bases, and consisting of any uncalled bases using NGSQC Box  (Katta et al. , 2015)  were filtered out.
Data analysis An open source software pipeline, Tuxedo suite  (Trapnell et al. , 2012a) , was used for analysing the RNA-seq data (deposited in NCBI Sequence Read Archive (SRA) database with BioProject ID PRJNA354681). The reads were mapped on the pigeonpea genome  (Varshney et al. , 2012;  http://www. icrisat. org/gt-bt/iipg/genomedata. zip) using a splice-aware alignment algorithm, TopHat (v 2. 1. 0) (Kim et al. , 2013). Thereafter, the mapped reads were assembled into transcripts and their abundances were estimated using Cufflinks (v 2. 1. 1) (Trapnell et al. , 2010).
Identification of differentially expressed genes The differentially expressed genes (DEGs) with log 2 fold change values ≥2 and ≤-2 (respectively up-regulated and down-regulated) and P-value ≤0. 05 were identified using Cuffdiff  (Trapnell et al. , 2012b)  displaying significance level as 'Yes'. CummeRbund  (Trapnell et al. , 2012b) , a part of the Tuxedo suite (http://compbio. mit. edu/cum-meRbund/index. htmland), was used to visualize the differential gene expressions between tissues as scatter plots, also called volcano plots. The identified DEGs were annotated using Blast2GO v 3. 3  (Conesa et al. , 2005)  against the NCBI non-redundant (nr) Viridiplantae protein database.
Co-expressed gene network analysis The co-expressed gene modules were identified and a topological overlap matrix (TOM) plot was generated using Weighted Gene Co-expressed Network Analysis (WGCNA;  Langfelder and Horvath, 2007, 2008). The identified gene modules were further visualized using Cytoscape v 3  (Lopes et al. , 2010). In addition, the PlantCARE  (Lescot et al. , 2002)  database was used to identify and study cis-acting regulatory elements in selected genes.
Identification of cis-regulatory elements and splice variants The cis-acting elements were identified by scanning 1500 bp upstream regions of the transcription start site of the selected genes using the PlantCARE database  (Lescot et al. , 2002). Further, the splice junctions were identified using Finesplice  (Gatto et al. , 2014)  in all 30 samples. Those alternative splice junctions that were supported by 10 or more reads were retained and considered for further study. The identified spliced junctions were then categorized into three alternative splicing events, namely alternative 5′ donor, alternative 3′ acceptor, and exon skipping. The splicing variants were visualized using the Integrative Genomics Viewer  (Robinson et al. , 2011)  and represented in the form of a Sashimi plot  (Katz et al.
e. sepal, petal, pistil, and stamen). Finally, at the senescence stage, petiole, leaf, root, and stem were collected. All samples were harvested in three biological replicates. For simplicity of usage, all the organs and organ system that were considered for the study were referred here as 'tissues' or 'samples'. Using Illumina sequencing platform, a total of 590. 84 million paired-end reads were generated from 30 samples (Fig. 1B  and Table  1 ). The low quality reads were filtered out (see Materials and methods, RNA extraction, Illumina sequencing and data pre-processing), retaining 559. 98 million paired-end reads (94. 5% of the total paired-end reads) for downstream analysis. An average of 94. 43% of the filtered reads mapped to the reference genome  (Varshney et al. , 2012)  using TopHat.
Subsequently, a total of 28 793 genes were identified from reference guided assembly and their expression was quantified as fragments per kilobase of transcript per million fragments sequenced (FPKM) using Cufflinks (Supplementary Table  S1 ). Gene expression profiling of 28 793 genes within different tissues and stages identified 27 132 genes with higher abundance (FPKM>1) and 1661 genes with lower abundance (FPKM<1). These results demonstrated sufficient sequencing coverage across the pigeonpea genome. In order to generate a comprehensive dataset for further analyses, genes were annotated based on previously annotated pigeonpea gene models  (Varshney et al. , 2012). Gene information such as the protein domain (UNIPROT), the putative cellular localization, the orthologous gene annotations in other species, the gene ontology (GO) IDs and names, and the putative corresponding pathways of each of these genes are indicated in Supplementary Table  S1.
Analysis of gene expression clusters To evaluate the quality of the generated gene expression atlas, a multi-dimensional scaling (MDS) analysis was first performed using the global expression dataset across the 30 samples. A clear repartition of the dataset in four major groups was observed, which represented the origin of the tissues, with aerial, underground, floral and embryo groups (Fig. 2A ). Simultaneously, Weighted Gene Co-expressed Network Analysis (WGCNA;  Langfelder and Horvath, 2007, 2008) , an R package, was utilized to identify sample outliers. Thirty samples clustered into two major clades, designated as clade I (Cl-I) and clade II (Cl-II) (Fig. 2B ). Clade Cl-I bifurcated into three subclades, Cl-Ia corresponding to floral tissues, Cl-Ib corresponding to mature seed tissues, and Cl-Ic corresponding to aerial tissues including shoot primordial tissues such as hypocotyl, SAM and seedling.
On the other hand, clade Cl-II comprised two subclades, Cl-IIa, which consisted of aerial tissue representing the reproductive and senescent stages, and Cl-IIb, which included all underground tissues (radicle, root, and nodule). As expected, there was no outlier detected from our set of samples, which validated the tissue sample preparation. From the transcriptomic dataset, a total of 1044 stably expressed genes have been identified within all tissues (i. e. displaying a coefficient of variation below 30%; Supplementary Table  S2 ). The catalogue of stably expressed genes represents a resource for identification of reference or housekeeping genes, which are necessary for comparative gene expression analysis to normalize transcript expressions due to developmental or environmental fluctuations.
In the dataset, 62 stably expressed genes within all the tissues with a coefficient of variation (CV) below 20% were identified (Supplementary Table  S2 ). These genes were mainly annotated as involved in basic cell functions such as RNA machinery  (C. cajan_18478, C. cajan_31580, C. cajan_19893, C. cajan_16707) , cell cycle machinery factors (C. cajan_10165, C. cajan_16931, C. cajan_25222) and actinrelated protein (C. cajan_31922). Based on the large diversity of analysed tissues in this study, it was difficult to identify genes displaying a CV lower than 10%. However, based on our comparative gene expression experiment, this catalogue of stably expressed genes could be refined to study specific tissues and/or stages. For instance, in the case of an experiment based on specific plant organs, 50 potential reference genes displaying a CV below 10% were identified for the study focused on underground tissues, 43 genes for the study of pod and seed tissues, and three genes for the study of aerial tissues (Supplementary Table  S2 ).
using WGCNA was used to analyse DEGs from different tissue combinations. A correlation matrix was generated among the 30 tissues using a set of 1076 DEGs resulting in three major modules, turquoise (containing 400 genes), blue (208 genes) and brown (197 genes). Here, modules are referred to the distinct groups formed by the clustering of genes, and each module has been designated by an arbitrary color to distinguish between them (Fig. 7A ). Module to tissue association was measured and resulted as a close relationship between the turquoise module and aerial tissues, whereas blue and brown modules were associated with underground and floral tissues (i. e. bud, flower, pistil, and stamen), respectively. As observed by Pearson's correlation coefficients and P-values, the strongest expression association was measured in the brown module, especially in the floral tissues (marked with a red square in Fig.
7B ). Therefore, a weighted correlation network for genes belonging to the brown module was visualized using Cytoscape. The circular network depicted 28 genes represented by nodes, interconnected with edges (connecting lines between genes) to identify three highly connected genes, referred to as 'hub' genes. WGCNA defines co-expression networks as weighted gene network, where the nodes correspond to gene expression profiles, and edges are determined by pairwise correlations between gene expressions. Genes within the co-expression module that display high connectivity form the 'highly connected genes' referred to as 'hub genes'  (Langfelder and Horvath, 2008). The hub genes identified in the co-expression network were annotated as encoding a sucrose-proton symporter 2 (C. cajan_35396), a pollen specific SF3 protein (C.
Discussion In this study, a comprehensive gene expression atlas of pigeonpea (CcGEA) has been developed, which catalogued more than 28 000 genes that were expressed in 30 diverse tissues of the plant and at five different developmental stages. This comprehensive dataset enhance the present understanding of the genes involved in various regulatory and metabolic processes, which could directly impact important agronomic traits. With the recent advances in genomics research, GAB has accelerated precision and efficiency of breeding in many crops  (Varshney et al. , 2013; Kole et al. , 2015). Development of the CcGEA together with the available genome sequence and other genomic resources offers an opportunity for pigeonpea researchers to not only solve biological questions, but also facilitate the GAB for pigeonpea improvement.
, 2008). It was revealed that eight genes potentially encoding orthologous proteins were transcriptionally active in the CcGEA dataset. Moreover, some tissues related to active developmental processes such as embryo and immature/mature seed displayed higher abundance of these genes, suggesting that post-transcriptional regulation plays a crucial role in seed and embryo development in pigeonpea. These genes could be studied further for their involvement in modulating important agronomical traits related to seed development and germination. A gene expression atlas has been developed in different legumes such as Medicago, Lotus, soybean, pea, black-eyed pea, and peanut with a focus on important traits. For instance, in the case of Medicago and pea, genes preferentially expressed in nodules have been emphasized, whereas in the case of the peanut, genes involved in geocarpy have also been described.
Acknowledgements This work has been undertaken as part of the  CGIAR Research Program on Genetic Gains. ICRISAT is a member of  CGIAR Consortium. Special thanks are due to  Dr Vinay Kumar ,  Ms Anu Chitikineni  of  ICRISAT and Genotypic Technology Pvt Ltd  for generating sequence data. We also acknowledge  Ms Akanksha Kulshreshtha  and  Mr M. Manikyam  for extending assistance with tissue collection and sample preparation.
Conclusions The gene expression atlas (CcGEA) developed in pigeonpea complements the genome sequence of pigeonpea and other genomic resources in understanding gene functions and their biological role. The CcGEA represents a comprehensive dataset of genes expressed in 30 diverse tissues across five developmental stages from embryo to senescence. The dataset has been analysed using pairwise comparison, clustering and correlation network analysis. The efficacy of the CcGEA has been demonstrated by identifying a gene network of 28 genes putatively regulated by a pollen-specific SF3 and a sucrose-proton symporter. Gene expression studies using two sets of male sterile and fertile genotypes revealed 20 genes crucial for pollen development. The role of these genes could also be ascertained in floral tissues with exclusive splicing variants identified in these tissues.
This study also provide genes that would be excellent candidates for a reverse genetics approach to determine their roles in pollen fertility and seed formation. Likewise, this dataset could be further analysed to identify candidate genes for various agronomic traits such as abiotic stress tolerance, especially for drought and heat stress. The CcGEA would also be useful in looking at the basal expression of genes when investigating mutant genotypes or any candidate gene expression for a specific agronomic trait. This resource will be valuable for studying the genes expressed in specialized tissues or organ systems such as nodules, flowers and pods in pigeonpea or related legumes. With further refinement of the existing draft genome assembly or the development of a pan genome, the CcGEA could be improved further and in that scenario, it will provide more and comprehensive insights into gene expression.
Study population As PBS almost exclusively affects boys, girls were excluded from analyses. Patient data were extracted from the ESPN/ ERA-EDTA Registry, a population-based cohort study. On an annual basis, the Registry collects individual patient data on all European children starting RRT, including date of birth, sex, primary renal disease, date and treatment modality at start of RRT, all subsequent changes in treatment modalities, and date and cause of death. Furthermore, data for several clinical parameters such as height and serum creatinine are collected for some countries  [11]. The ERA-EDTA coding system for renal diseases was used to identify male patients with PBS  [12]  from 35 countries who started RRT at <20 years of age between January 1990 and December 2013. Two different groups of patients with other congenital causes of renal disease who commenced RRT in the same period served as controls: C(1) male patients with COU with or without vesicoureteric reflux and (2) those with congenital RHD.
Statistical analysis To ensure complete coverage, incidence and prevalence of RRT for PBS were calculated for children <15 years of age and expressed per 100 million of the age-related population. General population data were derived from the Statistical Office of the European Communities (EUROSTAT)  [15]. Only countries contributing data over the entire follow-up period (1990-2013) were included when calculating mean yearly incidence, as was the case for analyses of comparisons over time. Differences in patient characteristics between groups were examined using chi-square tests for categorical and Kruskal-Wallis test for continuous parameters. Patient and peritoneal dialysis (PD) technique survival rates were determined using the Kaplan-Meier method (unadjusted survival) and Cox proportional hazards frailty analyses (adjusted survival), including country as a random effect to account for clustering of patients within the same country.
Incidence and prevalence Data were obtained on 2120 male patients. Of these, 88 (4. 2%) had PBS, while 904 (42. 6%) patients with COU and 1128 (53. 2%) with RHD served as controls. PBS patients were identified in 17 countries (Table  1 ). Within countries with complete follow-up, the average yearly incidence of RRT for PBS in boys <15 years was 15. 6 per 100 million age-related population in 1990-2013; point prevalence on 31 December 2013 of RRT for those boys was 68 per 100 million.
Acknowledgements We would like to thank the patients, their parents, and the staff of all the dialysis and transplant units who have contributed data via their national registries and contact persons. We also would like to thank  E. Levtchenko ,  D. Haffner ,  Z. Massy ,  A. Bjerre , and  C. Stefanidis  for being members of the ESPN/ERA-EDTA Registry Committee;  D. Shtiza ,  R. Kramar ,  R. Oberbauer ,  S. Baiko ,  A. Sukalo ,  K. van Hoeck ,  F. Collart ,  J. M. des Grottes ,  D. Pokrajac ,  D. Roussinov ,  D. Batinić ,  M. Lemac ,  J. Slavicek ,  T. Seeman ,  K. Vondrak ,  U. Toots ,  P. Finne ,  C. Grönhagen-Riska ,  C. Couchoud ,  M. Lasalle ,  E. Sahpazova ,  N. Abazi ,  N. Ristoka Bojkovska ,  K. Rascher ,  E. Nüsken ,  L. Weber ,  G. von Gersdorff ,  K. Krupka ,  B. Höcker ,  L. Pape ,  N.
Statistical Analysis Data are expressed as mean ± standard deviation. Geometric mean values of quantitative traits were compared using T-tests. Categorical variables were compared using chi-squared tests. Cox proportional hazards models were used to estimate adjusted hazard ratios and 95% confidence intervals for the incidence of NAFLD, comparing group B (HCT ≥ 49%) to group A (HCT <49%). The multivariate models were adjusted for variables that might confound the relationship between HCT and the incidence of NAFLD, including sex, age, BMI, ALT, AST, LDL, TG, TC, BP, FBG, smoking status, and regular exercise. Data were analyzed using an SAS software package (SAS, Version 9. 1. 3; SAS Institute Inc. , Cary, North Carolina, USA). A p value of less than 0. 05 was considered to be significant.
Acknowledgements This research was sponsored by the  Jiangsu Provincial Bureau of Health foundation  ( H201356 ), the  International Exchange Program , and  Jiangsu Six Talent Peaks Program  ( 2013-WSN-013 ). This study was also supported by the  Xuzhou Outstanding Medical Academic  Leader project and  Xuzhou Science and Technology grants  ( XM13B066  and  XZZD1242 ). The authors thank all subjects for participating in the study.
National to the Centre of Excellence in Medical Engineering funded by the  Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC ;  WT 088641/Z/09/Z ). The views expressed are those of the author(s) and not necessarily those of the National Health Service, the  National Institute for Health Research , or the Department of Health. The authors acknowledge the  British Heart Foundation  under project grant number  PG/14/66/30927. EV was supported by the  Agence National de Recherche  grant  ANR-13-MONU-0004-02.
PURPOSE. To correlate ellipsoid zone (EZ) defects on spectral-domain optical coherence tomography (SD-OCT) with retinal sensitivity loss on macular integrity assessment (MAIA) microperimetry in macular telangiectasia type 2 (MacTel). METHODS. Macular SD-OCT volumes and microperimetry maps were obtained during the international, multicenter, randomized phase 2 trial of ciliary neurotrophic factor for type 2 MacTel on two visits within 5 days of one another. Software was developed to register SD-OCT to MAIA scanning laser ophthalmoscopy images and to overlay EZ defect areas on the microperimetry maps generated from microperimetry sensitivity values at specific points and from interpolated sensitivity values. A total of 134 eyes of 67 patients were investigated. RESULTS. The semiautomated registration algorithm was found to be accurate, both qualitatively by visual inspection of the nearly perfect overlap of the retinal vessels and quantitatively as assessed by interobserver reliability metrics performed in 98 eyes of 49 patients (intraclass correlation of aggregate retinal sensitivity loss >0.
A prospective natural history study of macular telangiectasia type 2 (MacTel) initiated in 2005 has better defined the imaging characteristics of MacTel. One of the earliest signs is loss of luteal pigment centrally seen as fundus hyperautofluorescence. 1  This is followed by changes in optical coherence tomography (OCT), such as hyporeflective spaces in the inner and outer retina, and discontinuity or defects in the photoreceptor ellipsoid zone (EZ). 2  In addition, cone loss is observed by adaptive optics scanning laser ophthalmoscopy imaging. 3 OCT and confocal scanning laser ophthalmoscopy findings are associated with early mesopic function deficits that are first observed nasal to fixation. 4 These data suggest that photoreceptor loss is intrinsic to MacTel and precedes the well-known vascular changes.
g. , through summed voxel projection [SVP]), streamlines comparison of data acquired by functional 2-D imaging systems (Fig. 1 ). With this approach, it is possible to correlate loss of the EZ band as visualized on OCT and functionally relevant metrics to assess disease severity in MacTel. For example, in a study of 49 eyes of 28 patients, Sallo and colleagues 10 correlated visual field defects on Nidek microperimetry with en face OCT. These investigators showed that areas of retinal sensitivity defects were closely associated with cavitation of the outer retina and gaps in the EZ. The goal of our study was to create a novel robust semiautomatic software to aid in correlating function with structure in MacTel. We hypothesized that this software can more accurately demonstrate the association between EZ defects on SD-OCT scans with retinal sensitivity loss on macular integrity assessment (MAIA) microperimetry in a large cohort of patients with MacTel.
Study Participants Macular SD-OCT volumes and microperimetry sensitivity maps were obtained during screening and baseline visits of an international, multicenter, randomized phase 2 trial of ciliary neurotrophic factor for type 2 MacTel (NTMT02; Neurotech, Cumberland, RI, USA). The study protocol was in compliance with the Health Insurance Portability and Accountability Act (HIPAA) and Clinical Trials (NCT01949324, United States and Australia) guidelines, adhered to the tenets of the Declaration of Helsinki and HIPAA, and was prospectively approved by the institutional ethics committee of each participating center. Written informed consent was obtained from each participant after detailed explanation of the nature and possible consequences of the study.
Microperimetric Data Analysis Sensitivity thresholds measured within the EZ defect areas were compared with those measured external to the lesion. As previously described,  10  background sensitivity was defined as the average of retinal sensitivity values measured at all test points located outside the area of the EZ defect in each eye. To obtain the aggregate retinal sensitivity loss, the absolute difference relative to the background sensitivity was calculated at each test point within the area of the EZ defect and these differences were summed. Aggregate retinal sensitivity loss (expressed in decibels) thus reflects the EZ defect area and scotoma depth in a single variable. As an extension to the technique of Sallo et al. ,  10  a new metric termed ''interpolated aggregate sensitivity loss'' was calculated with interpolated microperimetry sensitivity values to provide measurement values in addition to the 85 loci tested by the microperimetry grid.