dataesr/scientific-paragraphs-categorization huggingface dataset:
Field Data Acquisition Ground surveys were conducted during the image acquisition to estimate the canopy structure of hedgerows under different amounts of canopy branch cover and clumping. At the date of image acquisition, the trees were leafless as shown by the hemispherical photographs taken the same day (Figure  3 ). A series of 55 hemispherical photographs distributed over 15 hedgerows were taken in April 2012 using a Canon EOS 7D camera with a Circular Fisheye for APS-C sensor (with a focal length of 10 mm, a diagonal angle of view approximately 180° and a minimum opening of F22). Each hemispherical photograph was geolocated using differential GPS (GeoXH Trimble, accuracy 0. 10 m). Figure  3  presents different canopy fragmentation patterns encountered on the study site with more or less dense cover, showing different branch layouts, since at this period of the year the trees had no leaves.
Satellite Data One dual-polarization TerraSAR-X image was acquired 19 April 2012 in High Resolution Spotlight mode with HH and VV polarizations, a ground spatial resolution of 1. 5 m and an azimuth resolution of 2. 2 m. An incidence angle of 37 degrees was chosen to maximize the vegetation penetration. Generally we consider that small angles are more sensitive to ground surfaces  [32, 33]  and higher/medium angles are more sensitive to vegetation roughness  [34]. The TerraSAR-X image was acquired during leaf-off period. Indeed, it should be not possible to characterize the hedgerow structure during summer as X band cannot penetrate the vegetation foliage to access the inner structure of hedgerows. A Lee refined filter  [36]  was then applied using a window of 3 × 3 pixels to reduce speckle noise.
The image was then geocoded using the Shuttle Radar Topography Mission (SRTM) data to correct the topographic deformations. The images were also geometrically corrected to the Lambert-RGF93/IGN-69 system using 55 ground control points selected from orthophotoplan images with a 0. 5 m resolution. The geometric correction accuracy is less than one pixel (i. e. , 1 m). The backscattering coefficients σ° HH and σ° VV were then calculated in decibels (dB): (3).
Polarimetric Parameters A 2 × 2 covariance (C 2 ) matrix was firstly extracted from the scattering matrix images (Figure  4 ) using PolSARpro v4. 0 software (Polarimetric SAR Data Processing and Educational Toolbox)  [37]. A Lee refined filter  [36]  was applied using a window of 3 × 3 pixels to reduce speckle noise. The geocoding process was in this case directly applied on the elements of the 2 × 2 C 2 matrix which are independent of the polarimetric absolute phase  [38]. The Shannon entropy (SE), which corresponds to the sum of two contributions related to the intensity and the degree of polarization  [38] , was then calculated from the 2 × 2 covariance matrix C 2. SE measures the disorder encountered in polarimetric SAR images. Each pixel of the TerraSAR-X images is defined as a complex 2D target vector k that follows a 2D circular Gaussian process with a zero mean and a covariance C 2 matrix  [38] :  (4)  where T* stands for transpose-conjugate.
Extraction of Hedgerows Figure  5  shows the general classification scheme applied to extract hedgerows. An object-oriented approach was used to classify the Shannon entropy and the single bounce (HH + VV) layers using eCognition Developer V 8 software  [29]. The first step aimed to segment and extract objects with high SE values in order to extract the hedgerows using the multi-threshold segmentation technique implemented in eCognition Developer V 8 software  [29]. This algorithm segments the image into objects based on a threshold value that splits the image object domain and classifies the resulting image objects based on a defined pixel value threshold. This threshold can be user-defined, dividing the selected set of pixels into two subsets so that heterogeneity is increased to a maximum.
Discussion This study is the first to describe the inner structure of woody linear features such as hedgerows using SAR images. This study provides a methodology to detect hedgerow networks using polarimetric parameters. Moreover, the results show that one polarimetric parameter, i. e. , the Shannon entropy, is highly sensitive to the canopy structure. The extraction of hedgerow networks was achieved with an object-oriented method using two polarimetric parameters: the single bounce parameter and the Shannon entropy index derived from one TerraSAR-X image. As proved in this study, SAR imagery can be used to identify hedgerows in agricultural landscapes, the accuracy of hedgerow classification being over 90% (KIA = 0. 92). These results are comparable to those from similar studies that aimed to develop methods to automatically extract linear landscape features such as hedgerows from VHRS optical data using object-oriented  [16, 21, 22] ; hybrid pixel-based and object-oriented  [47] , or waveform recognition-based  [21]  classification algorithms.
e. , the single and double bounces, the correlations are low (respectively NS and 0. 4 for the double bounce parameter). This result is in accordance with radar theory because even if the double bounce mechanism is higher than the single bounce in vegetation cover  [38] , it is not the dominant backscattering mechanism. Indeed, volume scattering (HV polarization) is generally more sensitive to the tree structure  [38]. This was not studied here because the TerraSAR-X image we used presented only HH and VV polarizations. In spotlight mode, only HH-VV dual pol TerraSAR-X data are available with a very high spatial resolution (1 m to 2 m). HV images in dual pol mode are only delivered in stripmap mode  [48]  with a lower spatial resolution which is not compatible with the extraction of hedgerow networks.
Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene-and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases.
Gene coexpression studies offer complementary perspectives on gene changes in the context of transcriptome studies. Here, two genes are defined as coexpressed in a dataset if their patterns of expression are correlated across samples. Coexpression reflects possible shared function between genes, and may arise through multiple biological pathways including cellular coexpression and common regulatory pathways (e. g. , hormone signaling, transcription factors)  [23, 24]. Hence, coexpression links have been used to build gene networks, and to identify communities, or modules, of genes with shared functions  [25, 26]. Notably, by incorporating multiple interactions among a large number of genes, the study of gene coexpression networks provides an approach to tackle the complexity of biological changes occurring in complex polygenic disorders  [24].
Materials and Methods Figure  1  illustrates the meta-clustering and validation methods of the approach. In step I, we identified 50 robust co-regulation modules in human brains by combining 11 transcriptome datasets collected from several brain regions in different cohorts of subjects with MDD and non-affected comparison subjects. Steps II and III were performed to identify MDD-related gene modules, and exclude other gene modules linked to biological functions not related to MDD. In step II, we collected different sets of genes located nearby SNPs identified by GWAS for MDD, neuropsychiatric disorders, related traits, and for systemic diseases often associated with psychiatric disorders, and performed gene set analysis to identify MDD-related gene modules. In step III, we performed functional annotations of gene module members by using 2,334 gene sets collected from MSigDB (http://www.
Transcriptome Data Sets Eleven MDD microarray datasets generated in our lab were used here. Cohorts and brain areas investigated are listed in Table  1  and details were provided in  [30, 31]. Among these studies, six used Affymetrix Human Genome U133 Plus 2. 0 platforms (Affymetrix Inc. , Santa Clara, CA), two used Affymetrix Human Genome U133A platforms, and the remaining three used Human HT-12 arrays from Illumina (Illumina Inc, San Diego, CA). Figure  S1  provides a diagram and results of the transcriptome dataset preprocessing procedures. Data has been deposited to the NCBI Geo database with accession numbers: GSE54562, GSE54563, GSE54564, GSE54565, GSE54566, GSE54567, GSE54568, GSE54570, GSE54571, GSE54572 and GSE54575. For gene matching across studies, when multiple probes or probe sets match to one gene symbol, we choose the probe set with the largest variation (largest interquartile range; IQR) to represent the gene  [32].
Genotyped data were generated from Affymetrix GeneChip Human Mapping 500 K using BRLMM algorithm. 449 SNPs were selected by p-value less than 0. 001, and 155 genes were identified to have contained one or more selected SNPs in the 10 kilobases (kb) up-and down-stream extension of the coding regions. -The second gene list was obtained from the MDD 2000+ project that included a meta-analysis of MDD studies with 2,431 MDD cases and 3,673 controls  [38]. Similarly, 532 SNPs with p-value less than 0. 001 were mapped to gene coding regions (including 10 kb upstream and downstream regions) and 159 genes were identified. -The third gene list was obtained from a mega-analysis of GWAS for MDD  [11]. The associated 202 SNPs' p-values were less than 10 (3) all neurological disorder and brain phenotypes studies, (4) all medical illnesses sharing increased risk with MDD.
05) in more than three GWAS results (Figure  3 ). Only four out of the 550 modules had more than 14 genes (,15% of the 88 genes; indicated by blue arrows in Figure  3 ) that overlapped with module #35. Hence, the metaclustering approach efficiently combined weak signals in single studies to identify a stable and biologically more meaningful gene module. In other words, module #35 would not have been discovered without combining 11 studies. We next tested the meta-clustering approach using transcriptomic data from control subjects only (i. e. , removing all MDD subjects) from the same 11 studies. Out of the 50 modules generated, no module was enriched in more than two GWAS studies (p-value threshold 0. 05) among the eight GWAS results (see Table  S5  and heatmap in Figure  2 (b) ), indicating that the inclusion of the MDD cases was necessary for the detection of significant module/GWAS overlap (i.
The result from the full ''cases+ controls'' is also shown for reference (blue cross). We also tested the meta-clustering approach using transcriptomic data from MDD subjects only (i. e. , removing all control subjects) from the same 11 studies. Among the 50 modules generated, one module (module #15 with 169 genes) was enriched in six out of the 8 GWAS categories (p,0. 05) but notably not in the gene set corresponding to the Mega GWAS MDD (p = 0. 29) and to MDDrelated studies (p = 0. 43) in the catalog of GWAS (Table  S7 ). This module only has 3 genes overlapped with the 88 genes (ST8SIA3, GRM7 and MYCBP2) of module #35 extracted from the case and control combined analysis. Pathway analysis of this module indicated an over-representation of signal transduction pathways (Table  S8 ). Overall, the statistical significance of results using MDD data only was lower and potentially inconclusive (i.
Discussion Using methods we developed to identify meta-analyzed coexpression modules across transcriptome datasets, we report the identification of a module consisting of 88 genes that is significantly enriched in genetic variants located nearby genes otherwise associated with major depression and related phenotypes. The finding of a significant intersection of two unbiased large-scale approaches (transcriptome and GWAS) provide robust evidence for the putative recruitment and contribution to molecular and cellular mechanisms of MDD of a biological module that is formed by the identified gene set. This module includes numerous genes encoding proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA-related proteins (GABRA2, GA-BRA4, CALB1), and neurotrophic and development-related molecules [e.
In genomic applications, it was shown that allowing scattered genes can improve clustering performance with better biological knowledge discovery  [34]. (3) Integration and validation with external databases: Integration with rich GWAS and pathway knowledge databases for biological and disease interpretation identified a robust module with 88 genes that is consistent with current knowledge about depression, hence providing some level of ''internal control'' for the methods. (4) Case and control combined coexpression analysis: We showed that the combination of case and control coexpression analysis was necessary to reveal the coexpression perturbation originating from the disease. This is an important observation as coexpression studies rely on subtle differences in expression patterns compared to differential expression between two groups.
Hence disease-related coexpression modules could have been predicted to be unique to the disease groups and ''diluted'' when combined with control data. However, we show that the opposite is true, resulting in increased power in the combined dataset. For technical validation, we have performed the following: First, we fine-tuned the parameters to be used in the final meta-clustering analysis (i. e. number of modules, percentage of allowed scattered genes in penalized K-medoid method) and tested those parameters in three studies using ''surrogate'' information, i. e. gene families and biological pathways broadly associated with psychiatric disorders (See Methods). Second, subsampling and bootstrap simulation were applied to investigate the stability of the identified gene modules. Third, three non-psychiatric related GWAS gene sets (cancer, human body indexes and disease traits unrelated to mental functions) served as negative controls.
e. , from normal to disease brain functions), consistent with a dimensional understanding of biological mechanisms of brain disorders. The fact that borderline significance in gene overlap was also observed for categories of disorders sharing clinical risk with MDD (i. e. cardiovascular diseases, inflammation and metabolic syndrome) suggest that the same gene sets may also contribute to dysfunctions in peripheral organs through pleiotropic functions of common genes, hence providing putative biological links for the clinical and symptom co-morbidity. Follow-up studies of coexpression patterns obtained in datasets across these disorders may be necessary to further investigate these interesting hints. So while these studies provide insight into the biology of complex disorders, one may reasonably ask how they may contribute to the generation of novel hypotheses and predictions.
Two directions are worth mentioning. First, for the purpose of therapeutic development and target identification, the application of graph theory and other network analysis may help identify critical genes within the identified module or upstream factors, as potential mediators of the function of this module in disease state. Preliminary analyses of the network properties of module #35 did not provide clear insight into hub genes or other parameters of interest (data not shown); however these studies may be confounded by circular analyses within the same datasets. Thus, testing these hypotheses in other large-scale disease related datasets are needed to, firstly, refine gene membership into the identified module, in view of the reasonable and significant conservation of module structure across datasets, although not to absolute levels; and, secondly, to identify key network nodes with conserved crossstudies functions, as potential targets to modulate the functional outcome of the identified gene module.
e. , p-value and genomic distance). Moreover, we used only a small fraction of the datasets and predefined pathways related to psychiatric disease to decide on the number of clusters and sets of scattered genes during the method development phase, so the result of the clustering approaches may still show some instability and may vary based on different numbers of clusters and applied thresholds. Indeed, although we performed extensive validation analyses to select the clustering parameters and increase stability of modules, the 88 genes in module #35 will inevitably vary slightly under additional data perturbation (e. g. , when adding additional MDD or related studies). An additional limitation is that generating gene coexpression modules using cluster analyses is known to be sensitive to small data perturbation.
To mitigate these effects, we combined multiple studies and concentrated on tight modules by leaving out scattered genes. While this approach increased the power of the meta-clustering method, it also meant combining datasets from different brain regions, hence potentially diluting the effects of local coregulation patterns that may be important for disease mechanisms. The integration of multiple datasets comes at the expense of variable technical platforms, including inclusion of different probesets across array types. We investigated this potential issue and showed considerable overlap in genomic region targeted by the various probes for a same gene (Figure  S3 ), hence lowering the potential impact of this array differences. So these results should be considered proof-of-concept, rather than experimentally and biologically optimized.
© 2014 Woerner et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Secondly, does the NIH global score predict mortality risk when it is applied at time points after the onset of treatment for chronic GVHD? Thirdly, does the NIH global score correlate with risk of recurrent malignancy? To address these important clinical questions, Inamoto et al. performed an impressive set of analyses on data collected in a prospective, multicenter, longitudinal, observational study of patients with chronic GVHD. The authors analyzed 574 adult patients with chronic GVHD, using multivariate time-varying analysis accounting for serial changes in severity of disease in eight individual organ sites over time. They randomly divided the cohort into a training set (482 patients who underwent 1602 visits) and a validation set (including 150 patients with 600 visit ratings). In the training set, severity of skin, mouth, gastrointestinal tract, liver and lung involvement were independently associated with the risk of non-relapse mortality.
We performed the annotation of the non-coding RNA (ncRNA) and repeated elements (Methods; Supplementary Tables  9 10 11 12 ) and predicted protein sequences were functionally annotated using Interpro domains, gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Methods). Pfam annotation yielded 17,505 predicted domains (3,460 distinct families found in 52% of the termite proteins). Proteins of Z. nevadensis, eight reference insect species, the crustacean Daphnia pulex and a non-arthropod species Caenorhabditis elegans were clustered in orthologue groups, which were used along with functional annotation for genome quality assessment (Methods; Supplementary Note 3; Supplementary Tables  13  and 14 ). High orthologue and annotation coverage, despite the deep rooting of the termite lineage in the insect phylogeny (see following section), highlight the quality of the final assembly and gene models (Fig.
Acknowledgements We thank the administrators of the  Pebble Beach Company  for permission to collect termites and  Navdeep Mutti  for initial help in RNA and DNA sampling. This work was supported by the  Agriculture and Food Research Initiative  Competitive Grant number  2007-35302-18172  from the  USDA National Institute of Food and Agriculture  to J. L. and C. S. B. ; and a research grant from the  Deutschen Forschungsgemeinschaft (DFG)  to J. K. ( KO1895/6 ) and  LOEWE Research Focus 'Insect Biotechnology ' to A. V. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  US Department of Agriculture. USDA  is an equal opportunity provider and employer.
More details about contig/ scaffold number and length are provided in Supplementary Table  3. Genome sequence and annotation data are available at http://www. termitegenome. org/ ?q=consortium_datasets. A genome browser is available at http://www. termitegenome. org/?q=browser. Protein-coding prediction. As a first step, three different methods were used to predict gene models: homology-based, RNA-seq-based and de novo. For homology-based gene models, protein sequences from A. mellifera, D. melanogaster, Homo sapiens and two ants (Camponotus floridanus and H. saltator) were used. For each of these five species, the prediction pipeline included the following steps: (1) use of TBLASTN (E-valueo1e À 5) for homology search; (2) selection of the most similar gene loci when there were multiple candidates; (3) exclusion of regions with identity o50%; (4) use of GeneWise v2.
termitegenome. org/?q=consortium_datasets. We obtained an integrated gene set (named OGS v2. 1) of 17,737 gene models (Supplementary Table  8 ). Most of them have expression support (73%) while just 2. 7% are predicted by de novo approaches only (Supplementary Fig. 2 ). Subsequently, B1,186 genes were identified as transposon-related genes through Interpro domain annotation and orthoMCL clustering (see below). These genes were not considered in some of the following analyses (for example, the transcriptomics analysis Supplementary Note 2), and they were removed from the next gene set release (OGS v2. 2-consisting of 15,876 proteins). Finally, the OGSv2. 2 contains additional gene models built through manual curation. ncRNA annotation. Four types of ncRNAs were annotated in our analysis: transfer RNA (tRNA), ribosomal RNA (rRNA), microRNA and small nuclear RNA (Supplementary Table  4 ).
tRNA genes were predicted by tRNAscan-SE with eukaryote parameters. rRNA fragments were identified by aligning the rRNA template sequences from invertebrate animals to the Z. nevadensis genome using BLASTN with an E-value cutoff of 1e À 5. MicroRNA and small nuclear RNA genes were inferred by the INFERNAL software, using release 9. 1 of the Rfam database. Repeat annotation. The presence of repeats in the genome was examined using two different approaches: First, known transposable elements (TEs) were identified using RepeatMasker (version 3. 2. 6) against the Repbase. This step identified 13 Mb of known TEs, comprising 2. 6% of the genome (Supplementary Table  5 ). Second, a de novo repeat library was constructed using RepeatScout with default parameters. The generated consensus sequence for each repeat family was then used as reference in RepeatMasker to identify additional high and medium copy repeats (410 copies) in the genome assembly.