dataesr/scientific-paragraphs-categorization huggingface dataset:
Morphometric and histological examination of fish tissues. Formalin-fixed distal intestine and pyloric caeca samples were dehydrated in ethanol, equilibrated in xylene and embedded in paraffin using standard histological techniques. Longitudinal sections of approximately 6 µm in thickness were prepared. The sections were stained with hematoxylin, eosin and Alcian blue 8 GX. Changes in villi length were captured using a DMLS light microscope (Leica Microsystems, Wetzlar, Germany) equipped with a Leica E3 digital imaging camera and LAS EZ v4. 9 software. Randomly selected villus of 18 distal intestine tissues from each dietary group (at least 80 measurements per group) was measured from the stratum compactum to the tip of the fold by ImageJ software. For histological evaluation, changes associated with intestinal tissues were blindly evaluated with a focus on the characteristic changes known for SBMIE in Atlantic salmon  9.
The histological scores were obtained through a semi-quantitative scoring system measuring changes in three morphological parameters: loss of supranuclear vacuoles in absorptive enterocytes; widening of lamina propria in mucosal folds; and increase of connective tissue between the base of folds and stratum compactum  9. Each parameter was given a score of 1-5, where 1-2 represents normal morphology; 3-4 mild and moderate enteritis; and 5 for severe enteritis (Supplementary Fig. S2a, b ). To measure changes associated with pyloric caeca, the longitudinal enterocytes area was selected (image 20×) and measured from the base to the apex. Measurement of enterocyte height was performed using the Easy Scan software. The total number and average mucous cell size in the caeca mucosal area were measured using the ImageJ software.
Figure6. Probing cell wall architecture of three autolyzed yeast species (A-C) with Atomic Force Microscopy, using naked tips (a-c) or tips functionalized with (mannan-binding) concanavalin A (d-l). The graphs show the distribution of the Young modulus (a-c), the relationship between adhesion force and rupture distance (d-f), the distribution of the length of mannoprotein unfolded (g-i), and the frequency of adhesion events with varying adhesion forces (j-l). In panels j-l, n. a. stands for non-adhesion. Rows A, B and C show results for Cyberlindnera jadinii, Blastobotrys adeninivorans and Wickerhamomyces anomalus, respectively. The data were obtained from 3 cells (3072 curves were analyzed with JPK data processing software before fitting Gaussian curves on the distribution). The blue circle highlights the rupture distance at the adhesion force for ConA.
Acknowledgements This work was supported by  Foods of Norway , a  Centre for Research-based Innovation  (the  Research Council of Norway ; Grant No. 237841/030 ). BM-L thanks to the Postdoctoral program from the  National Research and Development Agency of Chile  ( ANID-Chile 74200139 ). We acknowledge  Ricardo Tavares Benicio  for his technical support during the yeast processing and running of the fish trial. Also, we are grateful to  Lars Fredrik Moen ,  Oskar Bengtsson  and  Gergely Kosa  for their efforts during the fermentation and washing of the yeasts. Mathieu Castex  is acknowledged for his insightful review of the final draft of the manuscript.
Methods: We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions.
Following the method by Thomas and Harden  [76] , we initially developed descriptive themes by coding both direct participant quotes and researcher interpretations. We approached this by free-coding findings in an unstructured mind map, which we used to develop descriptive themes. Our research questions framed this coding process so that we coded anything related to information practices or the issues outlined in the Introduction section. We also considered the factors that shaped the way information was collected or shared in the presence of an EHR or the outcomes of changes in information practices. We intended for these descriptive themes to stay as close to the original findings as possible. Next, we used our review questions to develop the analytical themes. We integrated the quantitative data we had extracted during the textual analysis during this process.
Clinicians may have to go through a complex process of identifying what information they need and where they can access that information  [104]. Clinicians may also struggle with navigating the EHR because of the amount of information it contains, which is an issue when EHRs do not include search functions  [104, 106]. Finally, information may be collected for several purposes. The availability of information for one purpose, such as providing care, may not necessarily mean availability for another purpose, such as reporting  [100]. Larrison et al  [94]  found that for community mental health agencies, "capturing data to improve reporting capabilities" was a key motivation for implementing an EHR.
For example, natural language processing could be applied to free-text narratives to fill structured EHR fields  [24, 127]. The management of sensitive information was raised as a key concern in the adoption of EHRs. The definitions of and overlap between sensitive information and mental health information are unclear. The National Committee on Vital and Health Statistics  [131]  outlined the complexity of defining mental health information in that it can be collected in a variety of clinical settings and may be scattered throughout a person's health record. Data about physical health, collected in mental health settings, may also be considered mental health information. However, there appears to be a subsection of mental health information classified as sensitive for several reasons, such as the stigma related to certain diagnoses.
Samples We analyzed genomic data from 17 OCD case-control cohorts (11,312 OCD cases and 557,230 controls in total) not included in any previous OCD GWAS publications. In addition, three previously published GWAS datasets (two from the International OCD Foundation-Genetics Consortium (IOCDF-GC)  18  and one from The OCD Collaborative Geneticws Association Study (OCGAS)  19  ) were re-analyzed using newly matched control samples that were genotyped with the same microarrays as the cases (2,828 cases and 4,887 controls). In total, 20 cohorts comprising 14,140 OCD cases and 562,117 controls of European ancestry were included in the analyses. Among these, 323 cases were part of a parentproband trio; in these cases, parents were used as pseudo controls. A total of 12,607 cases met DSM-IV  26  or ICD10 criteria for OCD, while the remaining 1,533 cases were based on self-reported OCD diagnosis.
Genetic data formatting, cleaning, alignment, and individual GWASs First, the data of each participating cohort were analyzed individually (see Supplementary Methods for details). Genetic data were imputed using either the Haplotype Reference Consortium (HRC)  27  or 1000 Genomes Project Phase 3 reference panels  28. The resulting GWAS summary statistics were then harmonized before a conjoint meta-analysis was conducted. Each summary statistic data set was transformed to 'daner' file format following ricopili 29 specifications. Next, each dataset was cleaned of variants that were likely to have poor underlying genotype data. All variants had to meet the following criteria for inclusion: minor allele frequency (MAF) > 1% in cases and controls, imputationquality (INFO) score > 0. 8 and < 1. 2.
If the effect measure, p-value or standard error (SE) was missing or was out of bounds (infinite), the SNP was removed. Only biallelic SNPs were retained in the data. Once cleaned summary statistics were produced, all datasets were aligned to the HRC reference panel  27. If variants were reported on different strands, they were flipped to the orientation in the HRC-reference. Furthermore, strand-ambiguous A/T and C/G SNPs were removed if their MAF was > 0. 4. In case A/T and C/G SNPs showed a MAF < 0. 4 allele frequencies were compared to frequencies in the HRC-reference. If an allele frequency.
Polygenic risk scoring OCD polygenic risk scores (PRS) were calculated to assess the predictive value of the new meta-analysis. For this purpose, we conducted three 'leave-one-out' GWAS metaanalyses that each excluded one of three samples (IOCDF-GC, OCGAS, and Psych_Broad) and tested how well the PRS predicted case-control status in the excluded sample. We selected these three datasets based on their previous involvement in a recent meta-analysis (IOCDF-GC and OC-GAS; see above) and their availability at the site that conducted the analysis (Psych_Broad). PRS were generated at eight p-value thresholds (0. 001, 0. 05, 0. 1, 0. 2, 0. 3, 0. 4, 0. 5, and 1) as a weighted sum of the risk allele dosages. The contribution of PRS was measured by comparing Nagelkerke's preudo-R 2 of the full model (including principal components (PCs), sex, and PRS) and of the reduced model (adjusted for PCs and sex).
Tissue and cell-type enrichment analysis To determine if there are specific tissues or cell-types whose gene expression profiles are enriched for OCD risk variation, we used an analysis protocol recently described in Bryois et al. 38. Consistent with this we utilized their code-base for this analysis, and in particular the sets of genes that mark different highlighted tissue and cell-type datasets (see web-resources for data source). We selected 3 datasets that had been preprocessed by Bryois et al. 38  for inclusion in this analysis. The first features tissue-specific gene expression data derived from GTEx  39  , with a total of 37 tissues represented. The second and third datasets are derived from Zeisel et al. 40  , and represent 1) broad cell-type groups across the entirety of the mouse nervous system, and 2) a high-resolution single cell-type map of the same data.
In Zeisel et al. 40  , a total of 39 broad cell-type groups and 265 individual cell-types are represented. We followed the analysis protocol described in Bryois et al. 38  for the analyses of 37 tissues from GTEx and 39 broad celltype groups from Zeisel et al. 40  , and utilized a simplified approach for the analysis of each of the 265 individual celltypes from Zeisel et al. 40. For the tissue and broad cell-type group datasets, we conducted the full protocol from Bryois et al  38  which included analyzing tissue/cell-type enrichment using both LDSC  41  and MAGMA  31. For each dataset, pvalues were adjusted for a false discovery rate of 5%. We only considered a tissue or cell-type significantly enriched if the FDR-adjusted p-value was less than 0. 05 in both the LDSC and MAGMA-based tests.
Due to the high computational demands of analysing 265 individual cell-types across the mouse nervous system in LDSC, we limited the assessment protocol to using MAGMA only, and considered a celltype as significant if it had an FDR-adjusted p-value of less than 0. 05. All statistical analyses downstream of LDSC and MAGMA (namely, p-value adjustment) were done using R v3. 6. 1, and all plotting was done inside of R v3. 6. 1 using the package ggplot2 v3. 2. 1  42.
Overlap of the genome-wide significant locus with high-confidence chromatin interactions To derive chromatin interactions found in brain tissue that overlap with the genome-wide significant region(s) from the OCD GWAS, we submitted a corresponding analysis job to FUMA  36  on May 3rd 2020 using the OCD GWAS sumstats as input. We selected the following chromatin interaction datasets for inclusion: 1) Promoter anchored loops from the PsychENCODE project  43  , 2) loops from fetal cortex tissue, from Giusti-Rodriguez et al. 2019  44  , and 3) loops from adult cortex tissue, also from Giusti-Rodriguez et al. 2019  44. All other parameters were set to default settings (SNP2GENE: a) maximum p-value of lead SNPs < 5x10 -8 , maximum p-value cutoff <. 05, r 2 threshold to define independent significant SNPs ≥.
The source studies of the GWAS summary statistics used are summarized in Supplementary Table  S9. The genome-wide LD information used by these methods was based on European populations from the HapMap 3 reference panel  45;46  (see web resources). The GWAS summary statistics were filtered to only include SNPs that were part of the 1,290,028 million genome-wide HapMap 3 SNPs used in the original LD score regression studies  45;46. As a follow-up, we repeated the same analyses separately for three OCD subsets: 1) including only clinical samples, 2) including only (large-scale) biobank samples, and 3) including only data sets that were not primarily ascertained for OCD (AUS and iPSYCH).
Somewhat less prominent, but also significant was the positive correlation with childhood maltreatment. Genetic correlations with neurological, substance use, cardiovascular, and fertility traits were weak and non-significant. As follow-up sensitivity analyses, we repeated the genetic correlation analyses for clinically-ascertained samples and biobank samples separately, as well as for the full OCD GWAS sample excluding the two datasets that were not primarily ascertained for OCD (AUS and DOTS). The resulting correlations were similar, though not identical, across these three OCD sub-samples (see Supplementary Figure  S30 ). As an example for differences in the sub-samples, only the clinically-ascertained OCD samples had significant genetic correlations with ADHD and PTSD. Generally, the biobankderived OCD samples showed less pronounced correlations than the other two subsets, which might be expected due to their smaller case sample sizes.
However, further studies are needed to shed light on the genetic relationship and the overlap in genetic contributions to their etiology. One potential study limitation is the fact that, due to potentially higher than normal rates of psychiatric co-morbidity in many of our OCD datasets (e. g. through inclusion of individuals diagnosed with more severe OCD), our results may be biased towards identifying genes that contribute to multiple comorbid psychiatric disorders, or alternatively to an underlying trait related to general psychopathology, rather than to OCD specifically. A substantial proportion of new samples in the current study were derived from large-scale biobanks and national registers, two of which were drawn from studies that originally aimed to study other psychiatric disorders, such as depression (AUS), or that included a higher-than-average number of cases with depression, ADHD, and ASD (iPSYCH).
1, reference population: 1000G phase 3 EUR, maximum distance between LD blocks to merge into a locus < 250kb. b) Positional gene mapping: distance to genes or functional consequences of SNPs on genes to map: 50 kb, SNP filtering: minimum CADD score ≥ 12. 37, maximum RegulomeDB score = 7, annotation datasets eQTL catalogue: PsychENCODE, FANTOM5, Brain Open Chromatin Atlas. c) Gene eQTL mapping: Tissue types from Brain-Seq Brain, BRAINEAC, GTEx v8 Brain, eQTL; eQTL pvalue threshold: only significant snp-gene pairs with FDR <. 05. GENE2FUNC: all background genes used, gene expression datasets: GTEx v8:54 tissue types, 30 general tissue types, 29 different ages of brain samples, and 11 general developmental stages of brain samples, Benjamin-Hochberg.
A. A. and KJHV were supported by the Foundation Volksbond Rotterdam. MoBa is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. We thank the Norwegian Institute of Public Health for generating high-quality genomic data. This research is part of the HARVEST collaboration, supported by the Research Council of Norway (RCN) (229624). We also thank the NORMENT Centre for providing genotype data, funded by the RCN (223273), South East Norway Health Authority (SENHA) and KG Jebsen Stiftelsen. We further thank the Center for Diabetes Research, the University of Bergen for providing genotype data and performing quality control and imputation of the data funded by the ERC AdG project SELECTionPRE-DISPOSED, Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the NRC, the Novo Nordisk Foundation, the University of Bergen, and the Western Norway health Authorities.
The Anorexia Nervosa Genetics Initiative (ANGI), an initiative of the Klarman Family Foundation. JK has been supported by the Academy of Finland (grant 336823). This study is funded by "Gentransmed" Centre of Excellence, ERDF nr 2014-2020. 4. 01. 15-0012. MSA is a recipient of a Juan de la Cierva Incorporación contract (IJC2018-035346-I) from the Ministry of Science, Innovation and Universities, Spain. This investigation was supported by Instituto de Salud Carlos III (PI19/00721, P19/01224 and PI20/00041), and cofinanced by the European Regional Development Fund (ERDF), "la Marató de TV3" (092330/31), the Agència de Gestió d'Ajuts Universitaris i de Recerca-AGAUR, Generalitat de Catalunya (2014SGR1357 and 2017SGR1461) and the Pla estratègic de recerca i innovació en salut (PERIS), Generalitat de Catalunya (MENTAL-Cat; SLT006/17/287).
The EPOC study was funded by the German Re-It was furthermore funded by NIH R21 MH109938. Support was also provided by the German Research Foundation through grant KA815/6. The Research Council of Norway supported H. Ask, and T. Reichborn-Kjennerud (27611). A. Havdahl was supported by South East Norway Health Authority (2020022). Grant support was also provided from RCN (273291, 262656, 248778, 223273) and the KG Jebsen Stiftelsen. LifeGene was supported by grants from the Ragnar and Torsten Söderberg Foundation, AFA Insurance, the Swedish Research Council and Karolinska Institutet. It is currently a core facility at Karolinska Institutet. The Trøndelag Health Study (HUNT) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology NTNU), Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health.
1, reference population: 1000G phase 3 EUR, maximum distance between LD blocks to merge into a locus < 250kb. b) Positional gene mapping: distance to genes or functional consequences of SNPs on genes to map: 50 kb, SNP filtering: minimum CADD score ≥ 12. 37, maximum RegulomeDB score = 7, annotation datasets eQTL catalogue: PsychENCODE, FANTOM5, Brain Open Chromatin Atlas. c) Gene eQTL mapping: Tissue types from Brain-Seq Brain, BRAINEAC, GTEx v8 Brain, eQTL; eQTL pvalue threshold: only significant snp-gene pairs with FDR <. 05. GENE2FUNC: all background genes used, gene expression datasets: GTEx v8:54 tissue types, 30 general tissue types, 29 different ages of brain samples, and 11 general developmental stages of brain samples, Benjamin-Hochberg (FDR) correction was used to test for multiple testing in geneenrichment analyses, maximum adjusted p-value for gene set association <.
16-0125), the Estonian Research Council grant PUT (PRG184), and through the CoMorMent project. CoMorMent has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant agreement 847776. SEM is sup- ported by an Australian NHMRC Investigator Grant (APP1172917). The AGDS was primarily funded by National Health and Medical Research Council (NHMRC) of Australia grant 1086683. This work was further supported by NHMRC grants 1145645, 1078901 and 1087889. LCC is supported by a QIMR Berghofer Institute fellowship. NORDiC is funded by NIMH R01 MH110427 (PI Crowley), NIMH R01 MH105500 (PI Crowley) and the Swedish Research Council grant 2015-02271 (PI Mataix-Cols). Funding support for some of the Swedish controls was provided by the Klarman Family Foundation, the Swedish Research Council (Vetenskapsrådet, award: 538-2013-8864, and the NIMH Center for Collaborative Genomics Research on Mental Disorders (award U24 MH068457).
This study was also supported by NIMH R01MH085321, NIMH R01MH101493, NIMH R01MH58376, and NIMH K20MH01065. Technology (NTNU). EGOS was supported by a grant from the Beatrice and Samual A. Seaver Foundation to DEG. The OCD Collaborative Genetics Association Study (OCGAS) is a collaborative research study and was funded by the following NIMH Grant Numbers: MH071507 (G N), MH079489 (DAG), MH079487 (JM), MH079488 (AF), and MH079494 (JK). Yao Shugart and Wei Guo were also supported by the Intramural Research Program of the NIMH (MH002930-06). The International Obsessive Compulsive Foundation Genetics Collaborative (IOCDF-GC) was supported by a grant from the David Judah Foundation (a private, non-industry related foundation established by a family affected by OCD), MH079489 (DLP), MH073250 (DLP), S40024 (JMS), MH 085057 (JMS), and MH087748 (CAM).
A database corresponding to the accelerated aging of four roller bearings was used. These bearings were placed on a test bench by Rexnord Corp. The test bench was composed of four ZA-2115 roller bearings placed on a shaft, as shown in Figure  23. The characteristics of these bearings are detailed in Table  2. In order to verify the validity of this method for other types of bearings, we decided to use it to detect the appearance of defects in roller bearings. A database corresponding to the accelerated aging of four roller bearings was used. These bearings were placed on a test bench designed by Rexnord Corp. The test bench was composed of four ZA-2115 roller bearings placed on a shaft, as shown in Figure  23. The characteristics of these bearings are detailed in Table  2. In order to verify the validity of this method for other types of bearings, we decided to use it to detect the appearance of defects in roller bearings.
A database corresponding to the accelerated aging of four roller bearings was used. These bearings were placed on a test bench designed by Rexnord Corp. The test bench was composed of four ZA-2115 roller bearings placed on a shaft, as shown in Figure  23. The characteristics of these bearings are detailed in Table  2. 15. 17 The rotation speed was fixed at 2000 rpm and a radial load of 26,689 N was placed on the shaft and bearings by a spring mechanism. Vibration signals were recorded every 10 min with a sampling frequency of 20 kHz. The vibration signal of bearing number 3, recorded on the last day of testing, was used to assess the performance of the proposed approach. This signal corresponded to an inner race fault. The vibration signal of bearing 3 is shown in Figure  24 : From this signal, the EMD and the HT were performed in order to obtain the marginal spectral density of each IMF.
FUNDING This work was supported by  INSERM, European Research Council  (Endofood,  ERC-2010-StG-260515  and  CannaPreg ,  ERC-2014-PoC-640923 ,  MiCaBra , and  ERC-2017-AdG-786467 ),  Fondation pour la Recherche Medicale  ( DRM20101220445 ), and the  Human Frontiers Science Program ,  Region Nouvelle Aquitaine ,  Agence Nationale de la Recherche  ( ANR , NeuroNutriSens  ANR-13-BSV4-0006 ;  ORUPS ANR-16-CE37-0010-01 ;  CaCoVi ANR 18-CE16-0001-02 ; MitObesity  ANR 18-CE14-0029-01 ; and  BRAIN   ANR-10-LABX-0043 ).