dataesr/scientific-paragraphs-categorization huggingface dataset:
behavioral models of early dementia patients The approach did not address prodromal or and cognitive healthy function early functional impairments. NCT02290912 Wearable devices to detect effects of a large Target not further specified, aims at healthy number of life style interventions in middle- middle-aged individuals. aged individuals ICTRP JPRN-UMIN000023764 To develop machine learning algorithm to Data-driven approach targeting a broad range of provide objective measures for depression, different conditions, involving intrusive data bipolar disorder, and dementia using facial types such as video data and audiotapes of expression, body movement, voice, and daily spoken language activity data TCTR20160916001 Effect of cognition-specific computer training No use of ambient or wearable sensor systems for versus nonspecific computer training on the function detection.
OMIM database Fig. 1  Strategical abstract depicting the research strategy to identify new genetic elements in metabolism. The IMPC phenotyping data of 2016 knockout mouse strains was systematically evaluated for new links to human metabolic disorders. Nine hundred seventy-four knockout strains showed a strong metabolic phenotype. This set of genes was used as data mining resource. In a multiple line of evidence approach, we finally identified 23 genes that were linked to human disease-related SNPs EUMODIC  17, 18. Standardization, data quality control, an automated statistical analysis pipeline, and the phenotyping of reference strains to assess inter-center variation all help to ensure robust and reproducible data  [19] [20] [21] [22]. Adherence to each of these standards enables derivation of a high-quality, powerful hypothesis-generating resource that includes genes from a substantial proportion of the entire mouse genome.
047). New knockout mouse models for metabolic diseases. For 58 of our 666 candidate genes with known links to metabolic functions, the link was based only on GWAS data with no further evidence available. Based on a systematic search of the mouse genome informatics (MGI) curated database, knockout mouse models had not yet been described for 4 of these 58 candidate genes before our study: Lypla1 28 , Rfx6 26 , Slc6a14, and Slc6a3 29. Knockout mouse models had been generated previously for another 15 candidate genes but these had either no proven link to metabolism or were not reported to have been tested for metabolic parameters. Our phenotype data can therefore identify new genetic disease mouse models that allow in-depth investigation of disease mechanisms and fill the gap between genome-wide association studies and functional validation in a mammalian model organism.
Illuminating unexplored mouse metabolic genes. Many genes still lack any functional annotation. We conducted a systematic search of the MGI curated database to identify murine genes in our "strong metabolic phenotype" list with no previously known links to metabolism (see Supplementary Fig. 1  and Supplementary Data 4-Search results for unexplored metabolic genes). By careful stepwise evaluation, we found 429 of the 974 strong metabolic phenotype genes had no link to metabolic functions in mice. Fifty-one of these genes had no functional annotation at all (Fig. 1 , center part). Their knockout caused strong phenotypes regarding glucose and energy metabolism (Fig. 3 ). Our data analysis provides evidence for new links to metabolic functions for these hitherto uncharacterized genes. Function of new metabolism genes is linked to human disease.
We were interested in whether the strong metabolic phenotype   genes could be mapped to diseases in the OMIM database. Of all 515 genes that were linked to at least one OMIM disease, 264 (27. 1%) belonged to the strong metabolic phenotype genes, whereas 251 (24. 1%) were "non-strong metabolic phenotype" genes (Fig. 1 , lower left branch; also see Supplementary Data 5-OMIM mapping table). As a next step, we searched for non-annotated disease links to connect previously unknown strong metabolic phenotype genes to human disease. We found human orthologues for 402 of the 429 strong metabolic phenotype mouse genes. Note these 429 genes had no previously described link to metabolism and that includes the 51 completely unexplored mouse genes and 378 genes, which had no link to metabolism so far. Next, we extracted single-nucleotide polymorphisms (SNPs) in the SNiPA database  30  , from a ±2 kb region around the 402 human orthologues of strong phenotype genes (Fig.
1 , second lower branch from the left). The SNiPA database has both functional annotations and linkage disequilibrium information for bi-allelic genomic variants (SNPs and single-nucleotide variations), based on the 1000 Genome Project. We found 19,253 SNPs for 268 of the 402 orthologous genes. SNiPA SNPs have various annotation layers such as gene annotations, associated phenotypic traits, and expression based quantitative trait loci. For each SNP, we evaluated the extent of association across 16 type 2 diabetes-related traits using the cross phenotype meta-analysis method on data from the DIAGRAM, MAGIC, GIANT, GLGC, and ICBP GWAS consortia  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]  (Fig. 4 ). We applied different levels of evidence and p value thresholds to infer association with disease traits (Table  2 ).
First, we mapped our strong metabolic phenotype genes to metabolic pathways in the KEGG repository  42. At least one knockout gene for each metabolic phenotype analyzed mapped to a metabolic pathway (males and females, (Supplementary Data 7-Metabolic pathways in KEGG). We present three interesting findings: (i) genes from different pathways caused comparable metabolic phenotypes in males and females (Supplementary Data 8-14-Pathway maps). (ii) The 13 male and 13 female strong metabolic phenotype genes for triglyceride levels that were linked to the global metabolic pathway map of the KEGG database had one gene in common, Hpse. However, 13 male and 13 female strong metabolic phenotype genes mapped to the same pathway (out of 121 male and 121 female genes). (iii) Some metabolic pathways were only found in the set of female strong metabolic phenotype genes, and not in males affecting plasma triglyceride levels.
These pathways were "glycerophospholipid metabolism," "linoleic acid metabolism," and "ether lipid metabolism" pathways. We mapped the strong triglyceridephenotype genes to non-metabolic biological pathways in the KEGG database, this also revealed sex-specific differences (Supplementary Data 7-Metabolic pathways in KEGG). These results give insight into and confirm sexual dimorphism in genes and pathways associated with metabolic phenotypes in mice. Regulatory networks of strong metabolic phenotype genes. Metabolic pathways require simultaneous and coordinated presence of functionally connected proteins. Therefore, we analyzed next Fig. 2  Frequency distribution of mutant/wild-type ratios for metabolic parameters, separated for males and females. a T0 females, basal blood glucose after overnight food deprivation, b T0 males, basal blood glucose after overnight food deprivation, c AUC females, area under the curve of blood glucose excursions after glucose injection in a glucose tolerance test, d AUC females, area under the curve of blood glucose excursions after glucose injection in a glucose tolerance test, e TG females, plasma triglyceride concentrations, f TG males, plasma triglyceride concentrations, g body mass females, h body mass males, i MR females, metabolic rate obtained from a 21 h indirect calorimetry trial, j MR females, metabolic rate obtained from a 21 h indirect calorimetry trial, k VO 2 females, oxygen consumption obtained from a 21 h indirect calorimetry trial, l VO 2 males, oxygen consumption obtained from a 21 h indirect calorimetry trial, m RER females, respiratory exchange ratio, n RER females, respiratory exchange ratio.
6b ). Importantly, the two networks could be superposed, which thus validates combining our data-driven MORE set approach with experimental knowledge-based KEGG pathway mapping (Fig. 6c ). In summary, our analysis supports the concept that the various initially unconnected knockout genes form well-organized functional metabolic networks. These networks are supported at several system levels (phenotype, regulation, and metabolism). Prediction of metabolic genes. We hypothesized that the presence of MORE cassettes in currently unannotated genes could be used to functionally characterize them. Therefore, we proceeded determining which of the promoter sequences of the ~22,000 protein-coding genes in the mouse genome matched to the MORE cassettes defined in this study. We analyzed promoters of an independent set of genes that was not part of the original data set used to define the initial MORE cassettes (original data set: IMPC Release 4.
2 from Dec 2015; additional set of genes: IMPC Release 4. 3 from Apr 2016). The complementary data set contained 757 genes that had completed IMPC phenotyping after the earlier Release 4. 2. A batch query of phenotype terms, from the IMPC portal (www. mousephenotype. org), for these genes showed that 150 of 460 genes (32. 6%) with 1-11 MORE cassette matches had a metabolic phenotype, whereas only 68 of the total 297 genes (22. 9%) with no MORE cassette matches had such a metabolic phenotype (32. 6% vs. 22. 9%, p = 0. 004, Fisher's exact test; Fig. 1 , right lower branch). Thus, the presence of phenotype-associated MORE cassettes does indeed predict genes with a corresponding phenotype. The 9 genes with 6-11 MORE cassette matches associated with metabolic phenotypes were linked to the MORE set network defined in this study.
Discussion In this study, we have established and evaluated analysis and visualization tools that identify and select candidate genes with roles in glucose and energy metabolism starting from the pool of IMPC phenotype data of mouse knockout strains, which covers ~10% of mammalian protein-coding genes. This data set links new genes to disease-relevant metabolic phenotypes. As with all large-scale approaches, false discovery could be a major issue. We used 101 genes with a known or hypothesized link to obesity or type 2 diabetes to assess the false-negative discovery rate and our approach missed only one of the 101 genes. Therefore, metabolic phenotypes can be detected in almost all IMPC mouse models of published candidate genes for obesity and type 2 diabetes. The pioneering systematic approach of our IMPC database is an advance toward the final frontier of genome functional annotation as 44% of the strong metabolic phenotype genes have not been linked to metabolism in mice (429 of the 974).
Methods Mouse husbandry and phenotyping. We used the IMPC data resource to identify genes associated with strong metabolic phenotypes in mice. The IMPC phenotyping pipeline includes 14 mandatory and several optional tests that cover all major disease areas (www. mousephenotype. org/impress Bioinformatics. In general, only unique genes were included, i. e. , in the case that data for two different zygosities were available, we selected homozygotes instead of heterozygotes. We included lines in which data of males and females with the same genotype was available instead of having different genotypes for either males or females. Multiple entries for the same gene only occurred in reference strains. For each test procedure (indirect calorimetry, clinical chemistry, and glucose tolerance test), we received a csv file containing phenotyping data (one row per mouse, including characteristics such as sex, center, etc.
By adding a constant (e. g. , the predicted value for the mean body weight) to the residual, the sense of an actual VO 2 /metabolic rate value is conveyed. The new residual response variable is finally uncorrelated with the mean body weight  47. General statistics. The number of mutant strains assessed for each parameter varied depending upon (1) center-wide test implementation; and (2) whether both males and females were phenotyped; e. g. , indirect calorimetry providing VO 2 , MR, and RER was not conducted in every center and not always using both sexes. The statistical power of the phenotyping approach implemented by the IMPC alliance was evaluated by data obtained from the pilot program EUMODIC  21. Here a sample size of seven mutant mice per sex was found to be required to detect genotype effects.
In particular, we shuffled the phenotype ratios of the gene set analyzed by IMPC one million times and then we obtained sample distributions of the fraction of genes within the <5th >95th and the <20th and >80th range percentiles of the 101 genes in these random sets. These distributions were subsequently used to obtain the p values. Identification of unexplored genes. Starting with all genes with strong metabolic phenotype of the IMPC project (IMPC Release 4. 2 from Dec 2015), several filters were applied to discard genes with known molecular or functional annotation. First, we removed genes linked to mammalian phenotype (MP) terms related to metabolism (as described in the next section "Mapping to MP ontology terms"). Next, genes with annotated information in KEGG or gene ontology (GO) databases were removed following the procedure described in "Representation of genes in KEGG graphical pathways" and "Selection of GO terms describing metabolic processes" Method sections, respectively.
Selection of GO terms describing metabolic processes. Associations of genes to GO terms were extracted from Gene Ontology Consortium website  51. We analyzed terms of the "Molecular function" and "Biological processes" domains. To select genes involved in metabolism based on GO term annotation, we selected all GO terms under the "metabolic process" category and mapped them to the genes of interest. Mapping to MP ontology terms. MP terms for the genes of interest were extracted from the MGI database (http://www. informatics. jax. org/) using the file (MGI_PhenoGenoMP. rpt), which contains information on genes and their annotated phenotypes. In order to avoid circular discoveries, as the MGI database includes the IMPC phenotype data, IMPC entries were removed using internal filters. Human subjects, GWAS, and SNP analysis and statistics.
Orthologous/paralogous genes of mouse metabolism genes mapping to the human genome were used for analysis. We searched for SNPs in a ±2 kb region in SNiPA database. For each SNP occurring in or around genes, we evaluated the extent of this sharing for 19238 SNPs in 16 metabolic phenotype GWAs from various consortia, including DIA-GRAM, MAGIC, GIANT, GLGC, and ICBP  [30] [31] [32] [33] [34] [35] [36]. All participants of the studies contributing to the consortia have given their informed consent for genetic studies, which was confirmed by the appropriate ethics committees. For our present study, we used metadata without any link to individual IDs or data only. We used cross phenotype meta-analysis (CPMA), which detects association of a SNP to multiple, but not necessarily all, phenotypes  37.
Regarding promoter selection, sets of genes were collected from genes in the 5% outlier range (sex, high and low outliers were separately analyzed). The promoters of these gene sets were collected with the ElDorado database and the program Gene2Promoter (both Genomatix, Munich). Multiple organized regulatory elements (MORE) form a (partial) "fingerprint" that is present within a group of regulatory regions (promoters, enhancers, etc. ). Transcriptional MORE cassettes use transcription factor-binding sites (TFBSs) as elements, which are defined by a weight-matrix-based detection method (MatInspector, Genomatix, Munich). A MORE cassette is defined by several individual TFBSs and corresponding detection thresholds, their order and strand orientation in the DNA sequence, and the distance ranges and distance variations between pairs of TFBS elements (See Supplementary Notes 1 and 2 for more details).
Since there are too many potential MORE cassettes to collect in a database for standard enrichment analysis (see Supplementary Note 1 for details), we determined the number and structures of the MORE cassettes actually present in at least three promoters of each set. Each promoter set was analyzed separately for the presence of MORE cassettes shared by at least three promoters per set using the program FrameWorker (Genomatix, Munich). Parameters were set according to the supplier's defaults, except for Sequence quorum which was adapted top-down until MORE cassettes were found. The distance range variation was set to 20 bp, the minimum distance between elements was set to 10 and the minimum range of elements in MORE cassettes was set to 3-6. No further adjustments were made unless the minimum setting of three sequences did not reveal any MORE cassettes.
The resulting MORE cassette sets were treated as if they were individual MORE cassettes. However, detection in promoters by ModelInspector was always based on actual individual MORE cassettes. All MORE cassettes and the respective sets were located in promoter sequences by the program ModelInspector (Genomatix, Munich) in the following promoter sets: the set initially used for the detection of these MORE cassettes (to detect additional matches missed in the detection process, e. g. , located on the opposite strand), the promoter set derived from the opposite outlier group (plus or minus, respectively), the corresponding promoter sets from the other sex, and finally the set of all promoters from the mouse genome as provided in the ElDorado database. To analyze enrichment of MORE cassettes, we applied following procedures.
ebi. ac. uk/ pub/databases/impc/latest/). Information on strong metabolic phenotype gene, GWAS results and network analysis are provided in Supplementary Data files. The complete list of MORE cassette descriptions is available from the authors, consisting of the salient features: individual TFBS elements, their order, strand orientation, distance rage and distance range variations between the pairs. This allows location of the MORE cassettes, using any suitable TFBS program, without requiring access to the Genomatix Suite-for individual promoters. The list of MORE sets, consisting of the MORE cassettes belonging to each set is also available.
Acknowledgements This work was supported by the  German Federal Ministry of Education and Research: Infrafrontier  [no. 01KX1012 ] (M. HdA. ), the  German Center for Diabetes Research (DZD) ,  EU Horizon2020: IPAD-MD  [no 653961] (M. HdA. ), by  NIH  grants  U54 HG006370  (T. F. M. , P. F. , A. -M. M. , H. E. P. D. S. , and  S. D. M. B) ,  U42 OD011185  (S. A. M. ),  UM1 OD023222  (S. A. M. , K. L. S. , and R. E. B. ),  U54 HG006332  (R. E. B. and K. S. ),  U54 HG006348-S1  and  OD011174  (A. L. B. ),  1R24OD011883  (C. J. M. ),  U54 HG006364 , and  U42 OD011175 , and  UM1 OD023221 (K. C. K. L. ), and additional support provided by the The  Wellcome Trust, Medical Research Council   Strategic Award 53658  (S. W. and S. D. M. B. ),  Government of Canada through Genome Canada  and  Ontario Genomics  ( OGI-051 ) (C.
M. and S. D. M. B. ),  Wellcome Trust Strategic Award, National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (UDS) , the  "Centre Européen de Recherche en Biologie et en Médecine," the "Agence Nationale de la Recherche"  under the frame program " Investissements d'Avenir"  labeled  ANR-10-IDEX-0002-02 ,  ANR-10-INBS-07 PHENOMIN  to (Y. H. ), " EUCOMM: Tools for Functional Annotation of the Mouse Genome" (EUCOMMTOOLS)  project-grant agreement no. [FP7-HEALTH-F4-2010-261492 ] (W. G. W. ), the  Government of Australia  through the  National Collaborative Research Infrastructure Strategy  to the  Australian Phenomics Network Project (M. S. D. ) and by the Grants for  Korea Mouse Phenotype Consortium  ( 2013M3A9D5072550 ) funded by  National Research Foundation, Korean Government  (J.
STUDY DESIGN The study design, rationale, and procedures of CEN-TAUR (NCT02217475) have been reported previously. (15) This is a phase 2b, randomized, doubleblinded, placebo-controlled, and multinational study. The protocol was approved by the Institutional Review Board or Independent Ethics Committee for each center. The study is being conducted in accord with the Declaration of Helsinki and with all applicable laws/regulations of the study locations; all subjects gave written informed consent. Data were analyzed by Medpace, Inc. , and the sponsor. Authors had access to the data and participated in drafting the manuscript; editorial support was funded by the sponsor and provided by independent medical writers under author guidance. All authors approved the manuscript and assume full responsibility for data accuracy and completeness.
Subjects were randomized to receive CVC 150 mg or a matched placebo once-daily. After 1 year, half of the subjects receiving placebo crossed over to CVC, based on preplanned randomization, for a second year of treatment. At baseline, eligible subjects were assigned to the treatment arms using permuted block randomization stratified by NAFLD activity score (NAS) at screening (4 or 5) and fibrosis stage (2 or >2). Subjects were randomized 2:1:1 to arm A (CVC 150 mg once-daily for 2 years), arm B (placebo for 1 year then CVC 150 mg for 1 year), or arm C (placebo for 2 years). Randomization was accomplished by interactive voice response system. Subjects, the sponsor, investigators, and all site personnel involved with dispensing study medication, carrying out study procedures, evaluating subjects, entering study data, and/or evaluating study data remain blinded to individual treatment assignment until all subjects complete the 2-year study and the database is locked for all study data.
Discussion NASH is highly prevalent globally and represents an unmet medical need, based on related morbidity and mortality burdens, and the lack of approved therapies. (1) CENTAUR prospectively analyzed and reported on composite clinical efficacy endpoints currently being evaluated in phase 3 NASH studies (NCT02548351, NCT02704403, and NCT030287 40; https://clinicaltrials. gov), and demonstrated a benefit on fibrosis in subjects with NASH after only 1 year of treatment. Although the primary outcome was not met, twice as many subjects on CVC than placebo achieved the clinically important key secondary outcome of improvement in fibrosis by 1 stage and no worsening of SH. (5)  CENTAUR exclusively enrolled subjects with NASH and LF; additionally, subjects were required to have active metabolic dysfunction (T2DM or MetS), a well-known risk factor for disease progression.
4%), 2 (0. 4%) and 13 (2. 7%) isolates representing subtypes III, IV, V, and VI, respectively. Four (0. 8%) isolates gave inconsistent results (mixed subtype -I/II). The subtype I-VI isolates were obtained from both disease-associated and non-disease associated cases. Of two subtype IV isolates, one was obtained from a non-disease associated case, and the other one from a patient with unknown status. For two subtype V isolates, data concerning M. kansasii disease were unavailable. The highest frequency of M. kansasii subtype I isolations was observed for Poland (140/142; 98. 6%), and the lowest for Estonia (2/7; 28. 6%). This study demonstrated that subtype I represented the vast majority of M. kansasii clinical isolates worldwide. Since all M. kansasii subtypes detected (I-VI) were isolated from both disease-related and non-related cases, subtyping of the species does not permit differentiation between disease and non-disease states that did and did not cause definite disease.