dataesr/scientific-paragraphs-categorization huggingface dataset:
In efforts complementary to the technical advances made in multisite data collection initiatives such as the Alzheimer's Disease Neuroimaging Initiative, the Human Connectome Project, and the UK Biobank, ENIGMA's studies have required us to continually develop new scientific approaches to analyze data from diverse and independent populations. ENIGMA has made several technical advances that may be adapted to other domains, including creating, adapting, and extensively testing harmonized methods for distributed analysis, meta-analysis, and cross-site data integration (see Supplementary Appendix A: Technical Contributions of ENIGMA).
Driving discovery Neuroimaging and genetics are fields of both large and small effects. For common, complex chronic diseases, effects on brain metrics can be very subtle, but for rare monogenic disorders and across the field of neurologyincluding epilepsy, brain injury, stroke and neuro-oncology, disease effects can be relatively large (although not exclusively). In the 10 years since ENIGMA was founded, the primary lesson has been on the power of worldwide collaboration to discern subtle patterns in brain data, and advance neuroscience beyond the capacity of any one group of researchers collecting data on their own. New discoveries regarding the factors that influence brain organization and its association with health and disease are predicated on having adequate statistical power and on developing new neuroimaging approaches aiming to lead to more mechanistic explanations of the multi-scale organization of the brain.
Challenges and future directions Even given the advances made through ENIGMA during its first decade, as a growing consortium, ENIGMA faces important challenges. Thus far, ENIGMA has largely relied on existing data, which implies a degree of heterogeneity with respect to phenotyping-including clinical assessments, scanners and imaging protocols. Another limitation of this type of data is that the depth of phenotyping varies across centers, which can lead to a limited set of clinical and other scales shared by all centers. As we discuss below, ENIGMA is now beginning to address these limitations with a series of newly funded and planned studies  111, 112, 114, [118] [119] [120] [121]. The paucity of longitudinal data in the literature is also reflected within ENIGMA, which includes a limited number of longitudinal studies.
Consequently, the data-driven approach used in ENIGMA is complementary, but not always superior, to well-designed, hypothesis-driven, smallerscale prospective single-center or multi-center studies with in-depth phenotyping. Extending imaging modalities and computational approaches ENIGMA's future developments will include the coordinated analyses of new data modalities (such as resting state and task-related functional MRI  [160] [161] [162] [163]  , as well as geostatistical and mobile sensor data), and deeper or more refined analyses of current imaging modalities. Diffusion MRI, in particular, is moving towards multi-shell protocols that can better differentiate cellular and microstructural sources of variance that may explain patterns observed with DTI  17. Multimodal projects that pool data across imaging modalities are likely to boost the accuracy of machine learning methods for differential diagnosis, outcome prediction, and subtyping.
Unsupervised learning-applied to imaging and clinical data-may also help to identify homogeneous subgroups within and across disorders. Deep learning, for example, benefits from very large datasets, such as those analyzed in ENIGMA, and these and other artificial intelligence methods show promise in identifying unsuspected features and patterns in images beyond those derived using traditional methods. From its inception, ENIGMA has accommodated varying data sharing practices across institutions and countries, has used strategies (such as meta-analysis) to overcome some of these, and is working with field experts on novel strategies (like COINSTAC or other distributed analysis approaches)  164  to allow for more powerful analysis without sending data around the globe. On the 'omics' side, whole genome sequencing promises to refine our understanding of causal loci across all phenotypes, from plasma markers and brain metrics to environmental exposure and clinical measures of disease burden.
Cross-disorder analyses ENIGMA has recently created cross-disorder groups to answer transdiagnostic questions that draw on data from multiple WGs  165. An exemplar of this approach is the newly formed ENIGMA-Relatives WG which examines brain organization in the unaffected first-degree relatives of patients with psychiatric disorders. The first study from this group focused on identifying common and distinct anatomical patterns in patients with SCZ (N = 1016) or BD (N = 666) and their unaffected relatives (N for SCZ relatives = 1228 and for BD relatives = 852)  11. A remarkable finding from this study is that the first-degree relatives of BD patients had larger ICV compared to controls (d = 0. 16) while first-degree relatives of SCZ patients had smaller ICV and lower cortical thickness, and when controlling for ICV, had regionally smaller thalamic volumes.
Sex differences ENIGMA's sex differences Initiative is probing disease WG datasets to better understand sex disparities in risk factors, disease effects, or outcomes and their relationship with brain organization. In a new initiative, the ENIGMA-Transgender WG is contributing additional insights with respect to the biological underpinnings of sex assigned at birth versus gender identity  166. The first study from this group was based on more than 800 scans and pooled various MRI-based measures (cortical thickness, surface area, and volume) across eight international sites. While effects varied depending on the morphometric measure applied and the brain regions considered, a general pattern emerged: transgender men (assigned female at birth) mostly resemble cis-gender women, whereas transgender women (assigned male at birth) range between cis-gender men and cis-gender women  167.
Acknowledgements The work reported here was supported in part by many public and private agencies across the world. Individual authors' funding is listed in Supplementary Appendix B. Core funding for ENIGMA was provided by the  NIH Big Data  to Knowledge ( BD2K) program  under consortium grant  U54 EB020403 , by the  ENIGMA World Aging Center  ( R56 AG058854 ), and by the  ENIGMA Sex Differences Initiative  ( R01 MH116147 ). Additional support was provided by grants to the  ENIGMA-PGC PTSD Working Group  ( R01 MH111671 ; PI: RAM), the  ENIGMA-Addiction Working Group  ( R01 DA047119 ; to H. P. G. and P. J. C. ), the  ENIGMA Suicidal Thoughts and Behavior Working Group  ( R01 MH117601 ; to N. J. and L. S. ), the  ENIGMA Epilepsy Working Group  ( R01 NS107739 ; to C. R. M. ), a genotyping grant from the  Australian NHMRC  ( APP1103623  and  APP1158127 ; to SEM), a  German federal grant  to the  ENIGMA Task-Related fMRI Group  ( ER724/4-1  and  WA1539/11-1 ; to H.
Conclusion: The Next Decade This special issue, alongside the plethora of scientific reviews and mission concepts outlined in this article, demonstrate the enthusiasm and momentum for an ambitious, paradigm-shifting international mission to the Ice Giants in the coming decades. This will be the first dedicated mission to the last-remaining class of major planets to be explored, completing humankind's first reconnaissance of the eight planets. The potential for discovery is vast, and we urge our national and international space agencies to take up this challenge. We hope that the 2020 Royal Society Discussion Meeting will serve as a memorable stepping stone towards meeting this ambitious goal for the next generation of planetary explorers. Data Accessibility. No new data was used or generated in the preparation of this review article.
coli mastitis. Several criteria are considered: the systemic clinical score (Fig. 1b ), white blood cell (Fig. 1c ), and neutrophil (Fig. 1d ) counts; milk production (Fig. 1e ); the mammary clinical score (Fig. 1f ), and milk bacteriological load (Fig. 1g ). In addition, blood samples collected at day 0 (I0), and 7 days after the IM immunization (I7) and the booster injections (B7) were included in the analysis. In total, 107 samples from six dates and six animals in each of the three groups were prepared for transcriptome analysis; one sample (CONT, 12 hpi) was missing due to the low amount of RNA recovered after extraction. The whole-blood transcriptome was established using RNA sequencing with an average value of 10. 3 million (R1 + R2) reads for each sample (Supplementary Table  1 ). The dataset obtained with FeaturesCount after STAR alignment comprised 24,617 bovine genes.
At the acute phase of E. coli mastitis (12 hpi), the number of reads was significantly lower than at the other time points (Kruskal-Wallis, p < 0. 05), except in the IMM group (Supplementary Table  1 ). One sample (IM, B7) was removed from the dataset due to an outlier profile in the principal component analysis (PCA). In total, 106 samples were available for the statistical analysis. Whole-blood transcriptome through RNA-seq identifies the main biological functions altered by E. coli mastitis As a blood transcriptome related to bovine E. coli mastitis has never been reported before, we first investigated changes in gene expression in the CONT group. A PCA showed that the expression profile varied according to the time, with a clear separation between 0 and 12 hpi (PCA axis 1: 53. 8%), and between 0 and 40 hpi (PCA axis 2: 24.
2e ), with samples clustering according to the time. The top 500 most DEGs were divided into four modules that typified individual time points. The function modules were determined with ToppFun software and are presented with a dendrogram to evaluate correlations among functions. At 12 hpi, Cellular response to lipopolysaccharide and Regulation of cytokine mediated signaling pathway were the most activated functions, while Humoral response and Macrophage activation characterized the late phase at 40 hpi. Neutrophil migration and Cellular response to reactive nitrogen species were significantly detected at both times. Plasma concentrations of IL-6, tumor necrosis factor-α (TNFα), IL-10 cytokines, and CCL2, CCL4, CXCL10 chemokines were significantly increased at 12 hpi (Kruskal-Wallis, p < 0.
Blood transcriptome profile in IMM cured cows correlated with early cure We then paid attention to the comparison between the two IMM subgroups, whose clinical signs and blood transcriptome differed significantly at the late mastitis phase (40 hpi). To define the peculiarities of the cured cows, their blood profile was further scrutinized at all the available dates. A PCA was performed on the top 500 most variable genes from the I0 and B7 data (Fig. 5a ). At the B7 time point, PCA showed a significant difference between cured and non-cured cows indicating that the response to immunization of these particular cows was different and correlated with early cure. To further this hypothesis, a modular analysis was performed with the tmod R package. Two modules are common among all animals with Enrichment in monocytes and Immune activation-generic cluster as the main modules.
Profiles of DEGs were more related to the immunization than to the challenge status of the mammary quarters, suggesting that resident cells of the Th17 phenotype pre-existed the E. coli mammary challenge. Analysis of biological functions with ToppFun software is shown on a dendrogram presented in Fig. 6b. The top 500 genes were divided into four modules. Module 1 was related to the IMM group and correlated with Neutrophil degranulation and Regulation of BAD phosphorylation. Module 3 correlated with Th17 cell differentiation and CD40L signaling pathway. These modules were expressed in IMM infected Fig. 2  Whole-blood transcriptomic response of control cows during the course of an E. coli mastitis. Differentially expressed genes (DEGs) between times were assessed using Deseq2. a A principal component analysis on the top 500 most differentially expressed genes of control cows at 0, 12, and 40 h post infection (hpi) shows that the expression profile of blood cells changed according to the time during the E.
coli mammary infection. A Venn diagram presents the number of DEGs between time points, and the number of genes in common between the two comparisons (between 12 and 0 hpi and between 40 and 0 hpi) (b). Volcano plots were used for determining DEGs in the CONT group between 12 and 0 hpi (c), and 40 and 0 hpi (d). Thresholds were arbitrarily established for a q value < 0. 01 and a log2FoldChange > 2 and genes above these thresholds are depicted in red dots. e A Heatmap of the top 500 DEGs during infection in control group is presented using Euclidian distribution analysis. Results of the functional analysis with ToppFun is presented as a dendrogram showing the relationship among biological functions and blocks of commonly regulated genes. The list of DEG identified with DESeq2 analysis was used to evaluate a difference of pathway activation between the CONT and IMM groups upon infection with Ingenuity Pathway Analysis software.
Six previously challenged cows (3 CONT, 3 IMM) were inoculated in one healthy (SCC < 50, 000 cells/ mL), and not previously challenged quarter. Clinical data were not collected. SCCs were assessed and E. coli bacteria were re-isolated in order to check that inoculation was effective at inducing a mastitis. Cattle were euthanized 16 h after challenge and just before tissue harvest.
RNA-seq data processing and analysis Read quality was assessed with FastQC software. Reads were aligned in comparison to the bovine reference genome (UMD3. 1. 87) with STAR software  30. Count table was generated with Cufflinks/RSEM  31, 32. Identification of differentially expressed genes (DEGs) was performed with DESeq2 package within the R environment (R Core Team (2013)) following standard normalization procedures. False discovery rate was estimated with Benjamini and Hochberg correction. Expression changes were considered significant when both q value < 0. 01 and a AbsLog2Fold-Change > 1, except when otherwise indicated. DEGs were determined with group comparison at each time point. For control group analysis, the design matrix was defined by design = ~time. For group comparison analysis, the design matrix was calculated by design = ~time + group + time/group.
Determination of cytokine production Mammary tissue was weighed to normalization before grounding using ceramic beads (Bio-Rad) in a mixture containing Triton X-100 and a antiproteases cocktail (Sigma). Plasma, milk, and mammary tissue concentrations for 15 cytokines (IL-1α, IL-1β, IL-1RA, IL-2, IL-4, IL-6, IL-17a, IFNγ, CCL-2, CCL-3, CCL-4, CXCL8, CXCL10, and TNFα) were determined using a Custom bovine cytokines MilliPlex xMAP assay (Merck Millipore). Data were recorded on a MAGPIX flow cytometer using Xponent software (Luminex).
Weighted correlation network analysis (WGCNA) WGCNA was used to identify co-expressed genes associated with mammary and systemic clinical scores. The WGCNA R package was used for analysis  35. The minimum module size was set to 30, with a merge cut height of 0. 25. The soft-thresholding power as a function of the scale-free topology index was defined at six. In this construction, all pairwise comparisons were conducted between control, IM, and IMM groups. Modules with correlation (r ≥ 0. 5) and p value (≤0. 05) were extracted for further inquisition. WGCNA module annotation was carried out with ToppFun  36. A hypergeometric test with Bonferroni correction was applied to select statistically significant terms.
Data visualization PCA and volcanoplot were prepared with DESeq2 rlog-normalized RNAseq data only. For PCA, the top 500 most variable genes were used. The EnhancedVolcano R package was used for volcanoplot  37  and the pcaExplorer  38  was used for PCA. The pheatmap package was used for heatmap visualization  39. The clustering was based on Euclidean distance. Boxplox graphs were drawn using tmod R package  33, 34. To visualize gene expression and networks, Ingenuity Pathways Analysis (IPA, Qiagen) solfware was used to create network and to examine genes functions. Gene set enrichment analysis was represented in a dendrogram created with the IDEP R script  40. The network analysis was carried out with IPA Software (Qiagen). Barplot and dot plot representation were performed with GraphPad Prism version 6.
We were able to confirm earlier reports on the importance of the HPI in extra-intestinal virulence  [17] [18] [19] 21, 50, 51  , which showed the strongest signal in both the unitigs and accessory genome association analysis, and whose importance was validated in vitro and in an in vivo. The dist ibution of the HPI within the species resulting from multiple horizontal gene transfers via homologous recombination  52  has probably facilitated its identification using GWAS. We associated additional genetic factors to intrinsic virulence, such as the aerobactin and sitABCD operons, both related to iron scavenging together with the HPI. We also found mutations in the mtfA core gene to be associated with virulence, although its role in virulence remains elusive. Additional factors might have been overlooked by this analysis, due to the relatively small sample size; we however estimate that those putative additional factors might have a relatively low penetrance, based on our simulations in an independent dataset.
Mouse virulence assay Ten female mice OF1 of 14-16 g (4 week-old) from Charles River® (L'Arbresle, France) received a subcutaneous injection of 0. 2 ml of bacterial suspension in the neck (2•10 8 colony forming unit). Time to death was recorded during the following 7 days. Mice surviving more than 7 days were considered cured and sacrificed  14. In each experiment, the E. coli CFT073 strain was used as a positive control killing all the inoculated mice whereas the E. coli K-12 MG1655 strain was used as a negative control for which all the inoculated mice survive  26. For the mutant assays, 20 mice per strain were used to obtain statistical relevant data. The data was analysed using the lifeline package v0. 21. 0  65.
For both association analysis we used the number of unique presence/absence patterns to derive an appropriate p-value threshold for the association likelihood ratio test (2. 16E -08 and 5. 45E -06 for the unitigs and pangenome analysis, respectively). Unitigs significantly associated with the phenotype were mapped back to each input genome using bwa mem v0. 7. 17-r1188 71 and betools v. 2. 29. 2  72  , using the pangenome analysis to collapse gene hits to individual groups of orthologs. A sample protein sequence for each groups of orthologs where at least one unitig with size 20 or higher was mapped was extracted giving priority to strain IAI39 when available, given it was the only strain with a complete genome available  73  ; those sample sequences where used to search for homologs in the uniref50 database from uniprot 74 using blast v2.
Correlations with growth profiles The previously generated phenotypic data  27  for 186 over 370 strains were used to compute correlations with both the number of mice killed after infection and presence/absence of the associated virulence factors. The data was downloaded from the ecoref website ( https://evocellnet. github. io/ecoref/download/ ) and the pearson correlation with the s-scores was computed together with the correlation p-value. Prediction of tetracycline resistance was carried out using staramr v0. 7. 1 with the ResFinder database  76. Four predictors, one for virulence (number of killed mice post-infection) and one for presence of the HPI, aerobactin and the sitABCD operon were built using the random forest classifier algorithm implemented in scikit-learn v. 022. 0 77 , using the s-scores as predictors.
The input was column imputed, and 33% of the observations were kept as a test dataset, using a "stratified shuffle split" strategy. The remainder was used to train the classifier, using a grid search to select the number of trees and the maximum number of features used, through 10 rounds of stratified shuffle split with validation set size of 33% the training set and using the F1 measure as score. The performance of the classifiers on the test set were assessed by computing the area under the receiver operating characteristic curve (ROC-curve).
Code and data availability All input data and code used to run the analysis and generate the plots is available online at https://github. com/mgalardini/2018_ecoli_pathogenicity. Code is mostly based on the Python programming language and the following libraries: numpy v1. 17. 3  78  , scipy v1. 4. 0 79 , biopython v1. 75  80, 81  , pandas v0. 25. 3 82 , pybedtools v0. 8. 0 83 , dendropy 4. 4. 0 84 , ete3 v3. 1. 1  85  , statsmodels v0. 10. 2 86 , matplotlib v3. 1. 2 87 , seaborn v0. 9. 0 88 , jupyterlab v1. 2. 4  89  and snakemake v5. 8. 2  90.
Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (U e ) that includes the data uncertainties in both spatiotemporal scales.
The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the U e estimate is around 20 % higher than the classic uncertainty metrics. The deviation of U e from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for U e shows that U e has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly pro-posed U e and the two classic uncertainty metrics.
The new approach is implemented and analysed with multiple precipitation products of different types (e. g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. U e of the gaugebased precipitation products is the smallest, while U e of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new threedimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.