dataesr/scientific-paragraphs-categorization huggingface dataset:
• MMRA step3: network analysis ARACNE information-theoretic algorithm (http://wiki. c2b2. columbia. edu/califanolab/index. php/ Software/ARACNE) was used to infer interactions between each miRNA selected in the previous step and any gene from the paired dataset. The software was available through the Cyni Toolbox panel under the Infer Network tab, implemented in Cytoscape (https://cytoscape. org/). Quantile normalization was performed as standard ARACNE pre-processing. Only edges connecting miRNA to genes were considered, thus having the miRNA as the only hub of the network. A mutual information (MI) p-value significance threshold fixed at 10 -7 was used as recommended. Each of the consensus networks was tested for significant enrichment in "up" and "down" gene signature of each biological condition with Fisher's exact test.
• MMRA step4: Step-wise Linear Regression (SLR) analysis SLR is here used to filter out weak miRNA-gene interactions identified in the previous steps. In the SLR analysis, the gene log2-expression levels were considered as response variables, and the log2-expression levels of miRNAs linked by ARACNE or by databases to the gene were considered as the explanatory variables. Akaike information criterion was used as the stop criterion. At the end of this step, for each biological condition, we obtained a list of miRNAs with their associated signature genes.
Molecular Pathway Analysis with GeneTrail2 Molecular signatures from HFepa vs. reference, HFoleic vs. reference and HFepa vs. HFoleic comparisons were explored using gene set enrichment analysis (GSEA) implemented in GeneTrail2  [22, 23]. In order to limit the effect of outliers, the unweighted version of GSEA implemented in GeneTrail2 evaluated whether the genes of different biological categories from KEGG-, Reactome-and Wiki-pathway datasets were randomly distributed or accumulated on top or bottom of the list. To this aim, a Kolmogorov-Smirnov test was applied on FC of genes with a differential expression between 2 groups at p < 0. 05 with no FDR adjustment. As recommended by the GeneTrail2 algorithm, only sets of genes identified at p < 0. 05 after adjustment for multiple testing using the Benjamini-Yekutieli method were considered.
Logical Modeling of Gene Regulatory and Metabolic Networks A Boolean modelling approach implemented in GINsim software (v3. 0. 0b, http://ginsim. org) was used to build a dynamic model of the associations between the highlighted signaling pathways and the metabolic phenotypes related to obesity and insulin resistance  [26]. This approach consisted in a simplified reconstruction of biochemical networks to connect metabolic components based on prior knowledge. The model inherently provided links of causality and an input-output relationship at the molecular level, allowing the in silico analysis of the dynamical behavior of the network (presence/activation, absence/deactivation of the different components) under different combinations of inputs or perturbations in the system  [27]. Each node in the graph was represented by a single Boolean variable which exhibited two possible levels, 0 (OFF) if the element was absent/inactive or 1 (ON) if the element was present/active.
For some exceptions, this two-state representation may be biologically too restrictive and 3 levels should be necessary, 0 (OFF), 1 (LOW), and 2 (HIGH), representing high or low activation of this node regulator in the network  [28]. The model was represented on a regulatory graph where each node represented a component of the network. Edges represented the regulatory positive or negative influences between nodes. Next, we defined logical rules to determine the level of activity of each target node depending on the levels of regulators. Computational modeling through in silico simulations and perturbations revealed dynamical properties of the network. The model was then used to test and predict biological hypotheses. The model was built using input information from the literature, the KEGG database  [29] , and the present experiment.
Effect of High Fat Diet and Transgenerational Supplementation with EPA in Gene and miRNAs Expression The impact of the obesogenic diet on fat mass accumulation, increase in liver weight, plasma glucose, and insulin levels was reduced in mice that received a transgenerational supplementation with EPA as compared to the oleic/control lineage (Table  1 ). The global workflow of our study was based on an integration of bioinformatic and statistical blocks, including the use of R packages, and software applications such as Cytoscape and GINsim (Figure  1 ). In order to go into the molecular mechanisms that could mediate the positive effect of supplementation with omega 3 FA in mice challenged with an obesogenic HFD; we characterized the adaptations at the level of miRNA and gene in the liver. We then analyzed differential expression between 3 biological conditions (HFepa, HFoleic and reference) as described in Sections 2.
Identification of Differentially Regulated Cellular and Signaling Pathways Global pathway enrichment analysis on the three biological comparisons (HFoleic vs. reference, HFepa vs. reference and HFepa vs. HFoleic) using significant gene expression regulations (p < 0. 05) with no FDR correction was performed with GSEA to relate genes and their biological functions. Enriched pathways were identified using the Wiki, Reactome, and KEGG databases. Pathways were mostly enriched in the HFoleic condition compared to the reference condition for which 8, 11 and 7 enriched pathways were found respectively (Figure  3a  and Supplementary Table  S3 ). Figure  3a  exhibited a summary of this analysis, illustrating an enrichment of pathways related to oxidative phosphorylation, cholesterol metabolism, peroxisome proliferator-activated receptor (PPAR) signaling pathway, eicosanoid metabolism.
Computational Modeling Predicts that Dynamism in Genes and miRNAs Expression Leads to Specific Cell Metabolic Phenotypes To achieve an integrated comprehension of our results and the dynamic of gene expression regulations involved in the mechanisms of insulin resistance and obesity, a logical bioinformatics model was built using the software GINsim (Figure  6 ). Our model included three inputs which could not be affected by other components of the network: glucagon, glucose, and FA; five outputs: gluconeogenesis, glycolysis, lipogenesis, lipolysis, and adipogenesis; and several internal elements like Irs2, Pck1, G6pc, Pgc-1α, Pparγ, and Pparα. The model was based on the observation that a high level of Pgc-1α expression during fasting shifts the ratio of Irs1:Irs2 toward a higher Irs2 expression to prime the insulin receptor response toward a more efficient inhibition of gluconeogenesis after feeding  [30].
Acknowledgments: We are grateful to the staff of the  Installation Expérimentale de Nutrition (INRAE of Saint Genès Champanelle-Theix)  for providing everyday care to the animals;  Elodie Pitois  for her expert technical assistance to the study. We thank  Claire Naylies  and  Yannick Lippi  for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT-TRiX Genopole Toulouse Midi-Pyrénées facility (Toxalim (Research Centre in Food Toxicology ),  Université de Toulouse ,  INRA, ENVT, INP-Purpan, UPS, Toulouse, France ). We also thank  Dragan Milenkovic  for its critical reading of the manuscript.
Statistical Analysis All presented results correspond to mean ± standard deviation (SD). Statistical analyses were carried out by the mean of GraphPad Prism 5 (GraphPad Software Inc. , San Diego, CA, USA). A two-way analysis of variance (ANOVA) test was employed to determine the p-values: * p < 0. 05, ** p < 0. 01, and *** p < 0. 001. Duncan's test means comparison test was used to compare the different treatments performed at the p < 0. 05 probability level.
a dona-tion by Elior; European Research Area Network on Car-diovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Pro-ject Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China (GDW20171100085), Institut National du Cancer (INCa); Inserm (HTE); Institut Univer-sitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology (ANR-18-IDEX-0001); the RHU Torino Lumière; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and the SIRIC Cancer Research and Personalized Medicine (CARPEM). CONFLICT OF INTEREST D. C-G. , G. K. and F. M. are the scientific co-founders of Samsara Therapeutics, a company that develops novel pharmacological autophagy inducers. A. Z. has interests in Samsara Therapeutics.
ACKNOWLEDGMENTS The authors are grateful to the  Austrian Science Fund FWF  ( SFBLIPOTOX F3007&F3012 ,  W1226 ,  P29203 ,  P29262 ,  P27893 ) and the  Austrian Federal Ministry of Education, Science and Research and the University of Graz  for grants " Unkonventionelle Forschung" and "flysleep"  ( BMWFW-80. 109/0001-WF/V/3b/2015 ). The authors also acknowledge the funding of  DK Metabolic and Cardiovascular Disease (FWF)  and the  Doctoral College "Metabolic and Cardiovascular Disease"  ( FWFW1226 ) as well as support from  NAWI Graz  and the  BioTechMed-Graz  flagship.
2019). Instead, we have previously developed a truly two-dimensional lineshape analysis strategy, based on fitting observed two-dimensional spectra to complete quantum mechanical simulations of the underlying pulse sequence  (Waudby et al. 2016). The analysis has been implemented in a software tool, TITAN, which has since been applied to study a wide range of biomolecular interactions  (Danhart 2017; McShan 2018; Srb 2019). Two-dimensional lineshape analysis has two particular differences from the well-known one-dimensional theory. Firstly, there are now two chemical shift differences, Δ I and Δ S , to consider and therefore it is not always possible to classify resonances as being simply in fast or slow exchange regimes. Secondly, in experiments such as the HMQC, chemical shift evolution occurs as a combination of zero and double quantum coherences, with frequencies S ± I , and it is the differences in these frequencies between states that should be compared to the exchange rate rather than the difference in single quantum frequencies.
Acquisition and processing All NMR data were acquired using Bruker spectrometers operating with Topspin 3. 5. Pulse sequences for the SOFAST-H(Z/D)QC and BEST-ZQ-TROSY experiments are provided in supporting information. NMR data were processed using nmrPipe  (Delaglio 1995). SOFAST-H(Z+D)QC spectra were sheared and recombined in nmr-Pipe using the shear. M macro. Scripts and additional processing macros for the analysis of the SOFAST-H(Z/D)QC experiment are provided in supporting information.
Lineshape analysis Two-dimensional lineshape fitting, and simulations of HSQC, HMQC, HZQC and HDQC spectra, were carried out using TITAN (v1. 6)  (Waudby et al. 2016). HZQC and HDQC experiments have been implemented by a simple modification of the original HMQC implementation  (Waudby et al. 2016)  to isolate the subspace of ZQ or DQ coherences. Fitting of the Hsp90 titration data was carried out in two stages. The chemical shifts and linewidths of the unbound state were initially fitted using only the first spectrum, acquired in the absence of ligand. These parameters were then fixed, and the chemical shifts and linewidths of the bound state were fitted together with the dissociation constant and the dissociation rate. Bootstrap error estimates were calculated using 100 replicas. A second estimate of uncertainty in the dissociation constant and dissociation rate was also determined using a jackknife algorithm, in which fitting was repeated multiple times, with each spin system being omitted in turn.
Acknowledgements We thank  Anais Cassaignau  for providing the sample of  FLN5 Y719E , and  Frank Delaglio  for help preparing nmr-Pipe macros. We acknowledge the use of the  UCL Biomolecular NMR Centre  and the staff for their support. This work was supported by the  Francis Crick Institute  through provision of access to the  MRC Biomedical NMR Centre. The  Francis Crick Institute  receives its core funding from  Cancer Research UK  ( FC001029 ), the  UK Medical Research Council  ( FC001029 ), and the  Wellcome Trust  ( FC001029 ). This work was supported by a  Wellcome Trust   Investigator Award  (to J. C. ,  206409/Z/17/Z ).
Statistical analyses All analyses were undertaken in patients that were treated with at least one dose of study drug and had baseline information for assessment of MetS available. Descriptive data are given as mean ± standard deviation, or proportions (%). We split the population into two subgroups (with vs. without MetS at baseline) based on whether the criteria for MetS were fulfilled or not. Incidence rates per 1000 patient years of follow-up were calculated in the placebo group. The association of subgroup to outcomes and the treatment effect of pooled empagliflozin vs. placebo was explored by Cox proportional hazards models with terms for age, sex, geographic region, baseline HbA1c, baseline eGFR, treatment, subgroup and subgroup*treatment interaction. In addition, we assessed if there was any treatment interaction with subgroups based on how many of the diagnostic criteria for MetS that were fulfilled among those with MetS at baseline using the same Cox model.
(38, 39). Adipocyte images were obtained at 340 magnification, and morphological analyses were performed with use of Adiposoft software (Center for Applied Medical Research, University of Navarra). Adipocyte size and number were measured in two fields of 1,000 3 1,000 mm using a validated, optimized sequence of image analysis steps in Adiposoft. On average 267 adipocytes were measured per participant.
Area under the curve (AUC) analysis was performed with use of the trapezoidal method for interstitial glycerol, lactate, and pyruvate release during the clamp. The Shapiro-Wilk test was used to determine the normality of the distribution in all cases where a parametric test was used. In the event that the normality assumption failed, the Wilcoxon signed rank test was used instead. The significance level for this study was set at P , 0. 05. Statistical analyses were performed using GraphPad Prism 5 software (GraphPad Software, Inc. ) and SPSS statistical software, version 24.
Acknowledgments. The authors thank all participants for their involvement in the study. The authors are grateful for the constant support of the U. K. and  French ALMS  patients' organizations. The authors thank  Shaun Bolton  ( Queen Elizabeth Hospital NHS Foundation Trust ),  Vishy Veeranna  ( Queen Elizabeth Hospital NHS Foundation Trust ), and  Wouter Lamers  ( University of Maastricht ) for input. Funding and Duality of Interest. In the U. K. , this work was supported by  Queen Elizabeth Hospital NHS Foundation Trust and Prometic Pharmaceuticals. In Australia , this work was supported by  Flinders University and Vaxine. In France , this work was supported by the  French National Institute for Health and Medical Research (INSERM) , the  University of Strasbourg , and the  SATT Conectus Alsace maturation program.
Statistical Analysis Passing-Bablok regression analysis was used to compare the results of the PETIA assays with the BRAHMS PCT Kryptor© assay. The scatter of differences was visualized by means of Bland-Altman plots  [19]. The CUSUM test and the recommendations of Emerson et al. [20]  were used to detect deviation from expected values in linearity studies. Statistical analyses were performed using XLSTAT ® software, version 2016. 06. 35661 (Addinsoft, Paris, France). The concordance between the methods for classification of patients according to clinical algorithms was assessed using Cohen's κ-test on the population divided into four categories: PCT values < 0. 25, 0. 25-0. 5, 0. 5-1. 99, ≥ 2. 0 µg/L  [21]. For all comparisons, p-value < 0. 05 was considered statistically significant.
35 (recovery 148. 7%) down to the last dilution. This positive bias that may derive from some interference could affect the specificity of the test. The DiaSys assay displayed a more acceptable linearity than Diazyme's over the most clinically relevant range for PCT (0. 5 µg/L). The CUSUM test for both assays did not show significant deviation from linearity. However, the difference of recovery (%) was higher than 20% for both assays. There were no significant effects on PCT values measured by DiaSys assay with the addition of RF, triglycerides, bilirubin (unconjugated and conjugated) and Hb. The variation in each spiked serum sample compared to the baseline sample remained much lower than 10%. No interference was observed with unconjugated and conjugated bilirubin with the Diazyme assay, as described in the technical data as well.
Between 1998 and 2012 , several scientific expeditions in Dzanga-Sangha Special Reserve and Dzanga-Ndoki National Park led to the collection of many Mantodea specimens from Central African Republic (CAR). Among these specimens, several males of an undescribed species were discovered. Morphologically, this species most closely resembles to Chlidonoptera vexillum Karsch, 1892 and Chlidonoptera lestoni Roy, 1975. A new lineage was revealed by DNA barcoding. Therefore, a new species is described, Chlidonoptera roxanae sp. nov. Habitus images, genitalia illustrations and descriptions, measurement data, a key to species, natural history information, and locality data are provided. These results add to the evidence that cryptic species can be found in tropical regions, a critical issue in efforts to document global species richness.
DNA barcoding DNA barcoding, the analysis of a standardised segment of the mitochondrial cytochrome c oxidase subunit I (COI) gene, was performed on a representative selection of specimens (n = 25). Tissues were sent to CCDB at the University of Guelph for DNA extraction, polymerase chain reaction (PCR), and sequencing. DNA was extracted from dry legs using a routine silica-based 96-well extraction automation protocol  (Ivanova et al. 2006). The 658bp region of COI proposed for use as a 'DNA barcode'  (Hebert et al. 2003)  was amplified with the PCR primers C_LepFolF/C_LepFolR  (Hebert et al. 2004 ). Data are currently managed under the following projects: "Mantodea of Gabon -Project 1 [ECO-TROP 2014]," "Mantodea of Gabon -Project 2 [ECOTROP 2011]," "DNA Barcoding Mantodea -Collection N. Moulin" Barcode of Life Data Systems (BOLD, Biodiversity Institute of Ontario, Canada; http://www.
Chlidonoptera lestoni: Roy 1975: 297; Ehrmann 2002: 95; Otte and Spearman 2005: 87. Type material examined. (1♂). Chlidonoptera lestoni: 1 ♂ paratype, Tafo, Ghana, 09. XI. 1967, UV Trap, Coll. D. Leston, genitalia preparation Roy 2067, Insects -Small orders & Odonates MNHN Database (EP) #2488, MNHN. Chlidonoptera roxanae sp. nov. http://zoobank. org/E7FBAAE9-E506-4154-A762-3008A1D6AF44 Figures 2A, 3, 4F, 5, 6 Repository. Holotype male. Muséum national d'Histoire naturelle, Paris, France. Holotype label: Pinned. Central African Republic, Dzanga-Ndoki National Park, base camp, Lake #1, 2. 4881, 16. 2330, light, 4. II. 2012, BOLD NMMAN11-0404, Genitalia NM0181, Coll: Sangha 2012 Team. Paratypes males. Philippe Annoyer Personal Collection (PAPC), Sainte-Croix-Volvestre, France; Research Collection of Nicolas Moulin (RCNM), Montérolier, France; Muséum national d'Histoire naturelle, Paris, France.
Acknowledgments Many thanks to  Philippe Grandcolas ,  Frédéric Legendre ,  Roger Roy  and the  Muséum national d'Histoire naturelle  for giving me access to the Muséum's specimens. I would also like to thank the  Government of the Central African Republic  for permitting access for scientific research. I also thank all of the team of the Epiphyte 2008 and  SANGHA2012  ' Biodiversité en Terre Pygmée ' expeditions. DNA barcoding was supported by the  International Barcode of Life  project led by  Paul Hebert  at the  Biodiversity Institute of Ontario (Guelph, Canada ) and by the funds dedicated to the research of the author's company. Furthermore, I wish to thank the managers and contributors of the  Mantodea Species File (MSF , http://mantodea. species. file. org)for the valuable work they have been undertaking for dictyopterists worldwide.
CellMiner datasets and comparisons The National Cancer Institute 60 (NCI-60) cell line datasets used for this study were accessed in CellMiner and downloaded from the Download Data Sets tab\Download Processed Data set (https://discover. nci. nih. gov/ cellminer). The drug activities (growth inhibition 50%) were accessed from Download Processed Data Set\Compound activity: DTP NCI-60. All drug data were generated by the Developmental Therapeutics Program (https://dtp. nci. nih. gov/). The activities of 129 drugs were evaluated. The numbers of molecular markers measured by each molecular marker platform were 23,232 for aCGH, 17,553 for DNA methylation, 12,706 amino acid changing variants, 9143 protein function-affecting variants, 25,040 transcripts as measured by microarray, 23,826 transcripts as measured by RNAseq, 3162 proteins as measured by mass spectrometry, 93 proteins as measured by antibodies by NCI, and 347 proteins as measured by http://dx.
Relationships between specific pharmacological response and molecular alterations We assessed molecular data for DNA copy number, methylation, amino acid-affecting mutations, protein-function-affecting mutations, transcript levels as assessed by microarray and RNAseq, total protein levels as assessed by mass spectrometry and antibody, and phosphoprotein levels as assessed by antibody for 167 genes compared to 128 FDA-approved or clinical trial drug activities (Supplemental Table  1 ). This yielded 3978 drug-gene comparisons with direct linkage, i. e. the same gene that is a biomarker for, targeted by, or causally linked to a given drug is assessed for change at the molecular level. Table  1  presents a subset of that data that exhibit significant correlations between molecular and activity data.
There are, of course, other cell line sets that might have been used. These include, but are not limited to, the Cancer Cell Line Encyclopedia (CCLE, https://portals. broadinstitute. org/ccle), Cancer Therapeutics Response Portal (CTRP, https://portals. broadinstitute. org/ctrp/), and Genomics of Drug Sensitivity in Cancer (GDSC, https://www. cancerrxgene. org/). Each of these have the advantage of having more cell lines (~1000), and thus represent more tissue of origin types and subsets, as well as combinations of molecular features. The NCI's cell line set has the more complete set of molecular and phenotypic data, including some 22,000 compound and drug activities, as well as 5355 two-drug combinations. Going forward, users will be well advised to choose those subsets of data that are of greatest relevance to them and to mine drugs responses and genomic parameters across all database using the CellMiner tools (http://discover.
Molecular dynamics simulations, co-immunoprecipitation and pressure tuning fluorescence spectroscopy were used to assess the complex formation capacity of the mutant proteins. Zebrafish dkc1 mutants were generated using CRISPR/Cas9. Pseudouridylation was assessed In patients and zebrafish by immuno-Northern and HPLC-MS. Whole transcriptome analysis was performed in zebrafish mutants. The localisation of the mutant protein was determined by immunocytochemistry. Results: Linkage analysis in Family A idenified a 5 Mb region in Xq28 and a homozygous region of 2. 6Mb in 15q14 in FamB. We found a DKC1 mutation (c. 616 G>A, p. Glu206Lys) in FamA which appeared de novo in the second generation in parallel with the disorder and a homozygous NOP10 mutation (c. 47C>T, p. Thr16Met) in FamB. Both mutations affected evolutionary conserved amino acids, were predicted to be pathogenic and were absent from population databases.