dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data were obtained from the INRAE Experimental Unit of La Fage (UE 321 agreement A312031, Roquefort, France) between 2015 and 2019. The dataset consisted of 795 adult Lacaune dairy ewes in mid-lactation (109 ± 16. 0 days in milk) under the same housing conditions. The ewes (weighing 76. 9 ± 8. 68 kg) had two or more lactations and lambed one or more lambs. animal management. The protocol used for animal experimentation received approval from the Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation -Animal ethics committee with the following approval number APAFIS#6292-2016080214271984 v8. The genetic structure of the INRAE La Fage flock includes four independent divergent genetic lines with two selected for milk somatic cell score (SCS) and two selected for milk persistency (PERS).
Each year, two extreme groups of ewes were created, one with high persistence (PERS+) and one with low persistence (PERS-), for milk production. Furthermore, ewes selected for the gene suppressor of cytokine signaling 2 (Socs2)  7  were also included in the dataset. That population was derived from the oldest SCS lines and is currently selected for Socs2 to increase the mutant allele frequency in the experimental population to investigate possible associations with traits other than SCS. The dataset contains information from 795 ewes, including 94 from SCS+, 204 from SCS-, 200 from PERS+ and 202 from PERS-, and 95 under the Socs2 selection. All ewes were raised indoors, and to meet their requirements, they were fed the same mixed ration of on average 90. 0% meadow hay and silage plus 10. 0% barley (on a gross matter basis) supplemented with approximately 150 g of a commercial protein concentrate (38.
0% of crude protein on dry matter basis) distributed in the milking parlor. Ration distribution was collective and takes into account a 15. 0% refusal level to ensure that each individual was fed ad libitum. Because of variations in forage, the adjustment of the percentage of concentrates and forages was done each year according to the feeding value of the forages to cover the needs of the ewes, which therefore received the same amounts of nutrients over the five years. On average, over the five years, the ewes ingested 3. 27 kg of dry matter in total, comprised of 16. 0% crude protein and 30. 0% crude fiber. The authors advise using their data corrected for the year effect as in Martinez Boggio et al. 4. Rumen sampling. The rumen sampling was performed from each ewe using a gastric tube consists of a flexible silicon PVC tube (5 mm of wall thickness and 8 mm of internal diameter; Stomaflex, Genia, France) with a rounded open tip with a lateral eye to prevent solid material from accumulating in the tube, and was rinsed with clean water and drained between samplings to avoid cross-contamination.
005% of total sequences 12. As a result of the bioinformatics process, we retained 9,536,442 DNA sequences, representing 46. 0% of the initial total DNA sequences (Fig. 1 ), and 2,059 OTUs, representing 0. 03% of initial OTUs. The final step in the process was to give each OTU a taxonomic affiliation using the SILVA database (version 138)  3. The abundance table, delivered by FROGS, had 795 samples (as rows) and 2,059 affiliated OTUs (as columns), represented by 750 to 168,617 sequences (median of 1,755 DNA sequences per OTU). Rumen samples contained between 214 and 1,874 OTUs (mean of 1,322 ± 239) and between 2,034 and 33,043 sequences per sample (mean of 11,970 ± 5,196) (Fig. 2 ). In general, the 2,059 OTUs were assigned to six taxonomic levels with different frequencies of unknown taxonomic affiliation according to OTUs (Table  1 ).
The ewes were sampled only once during morning and afternoon milking. From these milk samples we quantified daily milk yield (MY). In addition, samples were analyzed at the Interprofessional Milk Analysis Laboratory (Agrolabs, Aurillac, France) to quantify the somatic cell count using a Fossomatic cell counter (Foss, Nanterre, France), and to produce the mid-infrared (MIR) spectra of milk using a Milko-ScanTM FT6000 instrument (Foss, Nanterre, France). This method allows to obtain simultaneously several components of interest for milk production. The somatic cell count was transformed to somatic cell score with the following formula: [SCS = 3 + log2(SCC/100,000)], and fat content (FC) and protein content (PC) were obtained from MIR spectra and expressed in grams per 100 ml of milk. Morning and afternoon milking data were weighted on average for daily composition.
Moreover, we predicted by pre-established equations the fine profile of milk proteins (Ferrand et al. 13  ) and fatty acids (Ferrand-Calmels et al. 14  ) from MIR spectra. These fine milk components were averaged by milk quantity to recover the weighted average of the daily composition. The proteins included in the dataset were four caseins, namely, alpha-S1-casein, alpha-S2-casein, beta-casein and kappa-casein, and two soluble proteins, namely, alpha-lactalbumin and beta-lactoglobulin. The fatty acids included in the dataset were butyric acid (C4:0), caproic acid (C6:0), caprylic acid (C8:0), capric acid (C10:0), lauric acid (C12:0), palmitic acid (C16:0), oleic acid (cis-9 C18:1), rumenic acid (cis-9 trans-11 C18:2) and alpha-linoleic acid (C18:3ω-3).
Data Records The sequence read archive (SRA) is publicly available on the National Library of Medicine repository under the BioProject (https://www. ncbi. nlm. nih. gov/bioproject/) called Rumen microbiota of Lacaune dairy sheep from INRAE La Fage (accession number: PRJNA723543)  15. In addition, the raw data is available in Data INRAE repository (https://doi. org/10. 15454/IOET0P)  16 under the name "DataINRAE_795Lacauneewes. tab". This dataset includes environmental effects and milk yield and fine milk composition traits for each of the 795 ewes. In the dataset the first two columns correspond to the accession number and sample ID to link with the corresponding SRA in the BioProject  15. In addition, from column 3 to 11 there are environmental effects such as: genetic line (five levels: SCS+ and SCS-, and PERS+ and PERS-, and SOCS2), year of sampling (five levels: 2015, 2016, 2017, 2018, 2019), run of sequencing or batch effect (seven levels) which refers to the laboratory process of sequences, number of lactations (three levels: 2, 3, or 4 lactations), days in milk (DIM: 28 to 133), litter size (two levels: 1 lamb and 2 lambs or more), total number of sequences per sample (seven levels: ≤5,000; >5,000 and ≤10,000; >10,000 and ≤15,000; >15,000 and ≤20,000; >20,000 and ≤25,000; >25,000 and ≤30,000 and >30,000), order of sampling (eight levels) and hour of sampling (two levels: morning or afternoon).
In the present study, we complete the previous work by investigating the effect of transgenerational exposure to OA on the hepatic transcriptome of European sea bass. Differential gene expression analysis was performed by RNAseq technology on RNA extracted from the liver of two groups of 18 months F2 juveniles that had been exposed since spawning to the same AO conditions as their parents (F1) to either actual pH or end-of-century predicted pH levels (IPCC RCP8. 5), respectively. Here we show that transgenerational exposure to OA significantly impacts the expression of 236 hepatic transcripts including genes mainly involved in inflammatory/immune responses but also in carbohydrate metabolism and cellular homeostasis. Even if this transcriptomic impact is relatively limited compared to what was shown in the olfactory system, this work confirmed that fish transgenerationally exposed to OA exhibit molecular regulation of processes related to metabolism and inflammation.
Ligated cDNAs were PCR amplified for 15 cycles and the PCR products were purified using AMPure XP Beads (Beckman Coulter Genomics, Brea, CA, USA). The size distribution of the resulting libraries was monitored using a Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA) and the libraries were quantified using the KAPA Library quantification kit (Roche, Basel, Switzerland). The libraries were denatured with NaOH, neutralized with Tris-HCl, and diluted to 150 pM. Clustering and sequencing were performed on a NovaSeq 6000 (Illumina, San Diego, CA, USA) using the single read 100 nt protocol on 2 lanes of a flow cell SP. Image analyses and base calling were performed using the NovaSeq Control Software and the Real-Time Analysis component (Illumina). Demultiplexing was performed using Illumina's conversion software (bcl2fastq 2.
Transcriptomic and Gene Ontology (GO) analysis Raw reads were processed using Trim Galore v0. 6. 7  [27]  to perform quality trimming (Q = 15) and to remove the T overhang at 5' end of reads, according to Illumina recommendations for stranded mRNA Prep protocols  [28]. Trimmed reads were mapped to the European sea bass genome v1  [29]  using STAR v2. 7. 9a  [30]  with standard parameters. STAR provided matrices of raw counts which were used to perform differential analysis. To minimize the false-positive rate, the count matrices were filtered for low expressed transcripts (minimum counts > 10 in at least half of the samples). Differentially expressed genes (DEGs) between acidified and control samples were identified using DESeq2 v2. 1. 34 [31]  with R v4. 1. 2 (https://www. R-project. org/) and a collection of graphics and statistics packages : rafalib  [32]  ; pheatmap  [33] ; Sartools  [34]  ; gplots  [35]  and ggplot2  [36].
Statistical analysis Liver mass and HSI Shapiro-Wilk normality test were performed to ensure the normality of the data related to liver mass and hepatosomatic index. Data were log transformed to allow analysis of variance (ANOVA). Fisher's test was used to test if the variances were equal. Two-way ANOVA was performed to determine the potential effect of pH condition and sampling day on the liver mass and the hepatosomatic index, with significant p-value fixed at 0. 05. RNAseq: In this study the statistical design considered two factors: pH condition and sampling day. The statistical model was built using 'counts ~ sampling_day + group' design formula, where the qualitative variable 'group' indicates sample group (acidified/control). Fold changes were adjusted using Bayesian shrinkage estimators for effect sizes with apeglm package  [37].
All features with adjusted p-value smaller than 0. 01 (Benjamini-Hochberg method) were reported as differentially expressed (DEGs). Over-and under-representation of Gene Ontology (GO) terms associated with DEGs were tested using goatools v0. 8. 12  [38]. Goatools process is based on Fisher's exact test and GO database from geneontology (go-basic. obo release 2017-07-07, https://geneontology. github. io/docs/download-ontology/).
Participants and procedure The study was conducted among two convenience samples with 591 participants (Sample 1) and 474 participants (Sample 2) after data cleaning. For the first sample, data were collected during France's second lockdown from November 23 to December 14, 2020, and for the second sample during the third lockdown from April 19 to May 10, 2021. The samples have an average age of  Ms1 = 37. 0 (SDs1 = 13. 4)  and Ms2 = 38. 3 years (SDs2 = 13. 1 ). Sociodemographic characteristics of the samples are presented in Table  1. We invited participants to create a code from their personal information in order for us to be able to identify participants who answered twice. These participants were removed so that the samples of this cross-sectional analysis were composed only of different participants.
The data were collected online. Participants were recruited via the social networks of the researchers, using online advertisements or e-mails sent via a variety of media: e-mails and distribution to friends and family, communication from the University of Nantes, solicitation of the French local press, specialized blogs, distribution on social and professional networks (Facebook, Linkedin, Twitter). Participants did not receive any remuneration. All people under the age of 18 and/or not residing in France were excluded. The questionnaires were completed anonymously.
| Article identification strategy In 2019 there was a large Cochrane review of healthier food and drink availability studies, so we first identified potentially eligible articles by searching the reference list and list of excluded studies from this review. 12 We next ran an updated search for papers published since the searches were carried out by Hollands and colleagues (i. e. 23 July 2018-16 June 2021). Three databases were searched for relevant keywords: PsycINFO, SCOPUS, and MEDLINE (see Supplementary Information for full set of terms used). We also used a snowballing approach to identify further articles for screening, including forward citation tracking for all eligible articles and the 2019 review, as well as searching the reference lists of all eligible articles. We also contacted authors of eligible articles to enquire as to whether they had published any additional relevant papers, and searched the OSF preprint archive for unpublished and gray literature.
One author screened potential articles from the 2019 review and completed title/abstract screening. A second author checked 20% of title/abstracts conducted by the first author to assess consistency (no eligible articles were missed by the first author) and conducted the grey literature searches (e. g. unpublished work, preprints, forward citation tracking). Both authors conducted full-text screening for all potentially eligible articles independently, and any inconsistencies were resolved through discussion. An updated search was conducted on 11 May 2022 (databases and OSF preprint archive), which identified no additional papers.
| Data extraction Two authors independently extracted data, and any discrepancies were resolved through discussion. We extracted information about participants sampled (e. g. demographics and type of sampling used), study characteristics (e. g. study type and setting, details about study design, SEP measure, and categorization). When studies reported categorizing participants as "higher" and "lower" in SEP, the categorization used in the paper was retained. For example, lower SEP was most often defined as up to A level (US equivalent: Advanced Placement [AP] examinations) and higher SEP as higher education (US equivalent: above high school) (e. g. 11, 14, 17 ). Other studies categorized lower SEP as GCSE/equivalent or below (US equivalent: US High school diploma or below) (e. g. 10, 20 ), and higher SEP as degree level and above  10  or A level and above (US equivalent: AP examinations).
20 en SEP was collected but not categorized as "higher" and "lower" (e. g. data was collected as part of baseline demographic information but not analyzed), for the primary indicator (education), to be consistent with the majority of included studies, lower SEP was defined as up to A level and higher SEP was defined as higher education. If studies reported more than two SEP groups, we planned to extract and analyze data for the two most extreme groups (i. e. , highest vs. lowest SEP grouping). We extracted outcome data on food choice (% choosing the healthier food), kcal content of items selected, and energy intake (kcal), obtained separately for lower and higher SEP to enable comparisons. In instances in which studies manipulated availability across different contexts (e. g. participants chose from multiple menus),  13  we extracted outcome data averaged across the different context (e.
Results Characteristics of the participants in discovery and replication analyses. A flowchart of the primary analyses is presented in Fig. 1. In the discovery analysis, the total sample size was 7277 from three cohorts. The mean age was 46 in the Framingham Heart Study Third Generation cohort (FHS Gen3) and 75 in the Atherosclerosis Risk in Communities (ARIC) study and Cardiovascular Heart Study (CHS). FHS included White participants only. ARIC and CHS included Black and White participants (17% and 14% Black, respectively, Supplementary Data 1). The protein measures were quantified using an aptamer-based platform (SomaScan, Supplementary Data 2). For the primary replication using the SomaScan platform, the total sample sizes were 8891 for general cognitive function aged ≥25, 5268 for aged ≥65, and 5478 for performance on the Digit Symbol Substitution Test (DSST)  12  aged ≥65 from 4 cohorts (Supplementary Data 3).
We also attempted exploratory replication using data from the Olink proteomic platform from 5 cohorts with a total sample size of 2925 for general cognitive function aged ≥25 and 2225 for aged ≥65 and 1744 for DSST aged ≥65, Supplementary Data 4). Discovery and replication results. We use protein measures to refer to the quantified protein levels given that some proteins were quantified by more than reagent. A total of 1049 protein measures annotated to 1043 unique proteins were tested for general cognitive function among participants aged ≥25. For general cognitive function, the first unrotated principal component generated from cognitive scores from 3 or more different domains (Supplementary Data 5-7), we identified 79 significant protein measures (p-value < 4. 77E-5 = 0. 05/1049, Table  1 , Fig.
We did not identify any enriched pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) library using any of the three meta-analysis results. Proteins significantly affecting or affected by general cognitive function based on MR analysis. In the MR analysis of protein effect on general cognitive function, 202 of the 246 proteins that were significant in the discovery meta-analysis had one or more cis proxy single nucleotide polymorphisms (SNP) available. MR analysis implicated two proteins, inactive tyrosine-protein kinase 7 (PTK7) and DnaJ homolog subfamily B member 12 (DNAJB12), had effects on general cognitive function (inverse variance weighted fixed effect [IVW FE] p < 3. 96E-5, Supplementary Data 25). Each had only one genetic proxy (proxy SNP I2 in protein quantitative trait locus in cis [cis-pQTL] metaanalysis, DNAJB12: 8.
2, PTK7: 85. 7, Supplementary Data 26). In the results of the Genotype-Tissue Expression (GTEx) project  17  , these proxy SNPs were significantly associated with the expression levels of multiple cis-genes (Supplementary Data 27). In a database of expression quantitative trait locus (eQTL) of the brain cortex  18  , these proxy SNPs did not associated with the expression levels of the protein encoding gene at FDR < 0. 05. Colocalization analyses for these two proteins provided support for a single shared causal variant for DNAJB12 (posterior probability [PP] for H4 = 0. 86, Supplementary Data 28, Supplementary Fig. 12 ) and weak support for a shared causal variant for PTK7 (PP H4 = 0. 68, Supplementary Fig. 13 ). The sum of single effect (SuSiE) method, which supports multiple signals in colocalization analysis, did not identify any credible set in the region of the encoding gene of these two proteins for the genetic association of general cognitive function.
In the MR analysis of general cognitive function on protein levels, genome-wide proxy SNPs of general cognitive function were analyzed against 243 proteins as outcome after excluding 3 protein complexes (Methods). Of these, MR analysis indicated that general cognitive function had effects on one protein (SLIT and NTRK-like protein 3 [SLITRK3], IVW multiplicative random effect [MRE] p = 2. 45E-6), with support from one or more MR methods robust to pleiotropy (Supplementary Data 29, Supplementary Fig. 14 ). The proxy SNPs of general cognitive function had modest heterogeneity in the pQTL meta-analysis of SLITRK3 (I2, median (1st, 3rd quartile): 0 (0, 35. 4), Supplementary Data 30). Proteins significantly affecting or affected by AD susceptibility from MR analysis. In the MR analysis of protein effect on AD susceptibility, 202 of the 246 proteins that were significant in the discovery meta-analysis had one or more cis proxy SNPs available.
There was evidence that one protein (nectin cell adhesion molecule 2, NECTIN2) significantly increased the risk of AD (OR 18. 0 per SD of protein levels, Wald ratio p = 2. 54E-26, Table  2 ). This significant effect of NECTIN2 was also observed using two other summary statistics datasets with AD or AD-by-proxy as the outcome (Supplementary Data 31). NECTIN2 had one proxy SNP (rs440277, I2 in cis-pQTL meta-analysis: 39. 1, Supplementary Data 26). This variant is in intron 1 of NECTIN2. Its G allele was significantly associated with higher circulating protein levels and higher gene expression levels of NECTIN2 in the liver, pancreas, and whole blood in the results from GTEx (p < 1e-30, Supplementary Data 32). This variant (rs440277) was also associated with the expression of AC084219. 4, a long non-coding RNA, in the pancreas with much weaker effect (p = 2.
08E-05). These lookup results support rs440277 as a specific proxy for circulating NECTIN2. In an eQTL dataset of the brain cortex, rs440277 was not associated with the expression levels of NEC-TIN2 at FDR < 0. 05  18. Although the encoding genes of NECTIN2 and APOE are in close proximity (26 kb), the correlations between rs440277 and the two APOE ε4 variants were low (1000 G EUR: r2 = 0. 018 with rs429358 and 0. 001 with rs7412) with moderate D', a measure of linkage disequilibrium (1000 G EUR: D' = 0. 42 with rs429358 and 0. 11 with rs7412). We also replicated a previous finding of higher levels of SVEP1 (SomaScan ID: 11109_56 and 11178_21) as potentially causal for AD susceptibility (IVW FE p = 3. 28E-3 and 3. 76E-3, respectively, Supplementary Data 33)  9. Colocalization analyses within the 500 kb region on both sides of the NECTIN2 promoter, which included the APOE gene, did not support a single shared causal variant underlying NECTIN2 protein levels and AD susceptibility (PP of H 4 : 2.
0E-11, Supplementary Data 28), but did support the H 3 hypothesis that both traits are associated, but with different causal variants (H 3 > 0. 9) (Supplementary Fig. 15 ). An analysis using SuSiE also did not support shared causal variants between NECTIN2 and AD susceptibility (all pairwise H 4 PP < 3. 5E-04, Supplementary Data 34). **P-value of NECTIN2 on AD susceptibility was obtained using Wald ratio since NECTIN2 only had one genetic proxy. P-value of AD susceptibility on protein was obtained using the inverse variance weighted multiplicative random effect method. a The beta of protein on AD susceptibility was in the unit of log (odds ratio for AD) per SD of inverse normal transformed protein levels. The beta of AD susceptibility on protein was in the unit of SD of inverse normal transformed protein levels per log(odds ratio for AD).
For the analysis of AD susceptibility on protein levels, genomewide proxy SNPs of AD susceptibility were analyzed against 243 proteins as outcome after excluding 3 protein complexes (Methods). Using clinically diagnosed AD as the exposure (Kunkle et al. stage 1 dataset)  19  , we identified 8 proteins as potentially affected by AD susceptibility (IVW MRE p < 1. 99E-4, Table  2 , Supplementary Data 35, Supplementary Figs. 16-23). Of these, 7 were supported by one or more methods robust to pleiotropy at p < 0. 05 (Supplementary Data 36). In individuallevel data, these protein measures were modestly correlated (abs(Pearson correlation of log2 transformed values, median [1st and 3rd quartile]: 0. 13 [0. 08, 0. 18], Supplementary Data 37). The strongest effect was on C-reactive protein (CRP) with higher AD susceptibility leading to lower CRP levels (beta: -0.