dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019) , including the Global Omics Observatory Network (GLOMICONhttps://sites. google. com/ view/glomicon/home). (2) Genetic data archiving practices It is becoming standard practice to deposit genetic data into searchable and open access repositoriesthis is mandatory in several leading journals (Joint Data Archiving Policy (https://datadryad. org/docs/JointDataArchivingPolicy. pdf). Successful re-use and integration of these data sets, such as to calculate Genetic EBVs, depends on the archive, its interoperability, and the metadata requirements (Section III. 3). The International Nucleotide Sequence Database Collaboration repositories [INSDC;  Cochrane et al. , 2016 ; primarily a consortium of US, European and Japanese national databases, including the National Center for Biotechnology Information (NCBI)] are the largest publicly available nucleotide databases.
These databases host raw DNA sequence data in standard, interoperable formats and they can be queried and programmatically accessed (e. g. 'rentrez' R package, 'geomedb' R package). While standardized and easily accessible, the genetic data deposited into INSDC does not always include the minimum information required to recreate data sets appropriate for the calculation of Genetic EBVs. Archiving practices may also be inconsistentthe deposited DNA sequences are typically only the new, unique sequences derived from the study rather than all observed sequences, and do not provide frequency information that is needed to calculate EBVs reliably [see Section IV  (Pope et al. , 2015a; Paz-Vinas et al. , 2021) ]. For processed genetic data, such as microsatellite and SNP data sets, numerous other open access archives are used, including Dryad, FigShare, and GitHub.
Taxonomic and region-specific archives also exist (e. g. CartograTree, DIPNet, see Sections III. 3 and IV. 1), although it is not always easy to find them. Also, these repositories do not always require standard data file formats or have minimum metadata requirements and therefore these data are not easily searched or programmatically accessed. Most archived genetic data cannot be operationalized yet. Typically, less than 10% of sequences are georeferenced, and most cannot be geolocated based on text descriptions  (Gratton et al. , 2017; Miraldo et al. , 2016; Toczydlowski et al. , 2021). For example,  Theodoridis et al. (2020)  examined mammal mitochondrial genetic diversity globally, comprising >150,000 mitochondrial DNA sequences from BOLD and GenBank. They attempted to assign geographic coordinates to sequences without precise geolocation data using GeoNames.
org (http://api. geonames. org), but could do so for just 36% of sequences. In short, even for a very well-studied group (mammals), a majority of data could not be used. In addition, most genetic data sets are not directly linked to non-genetic global biodiversity databases (e. g. the Global Biodiversity Information Facility, GBIF), or environmental databases including widely used satellite and in situ data repositories, although links to these can be made through sufficient spatial data  (Kissling et al. , 2018). In some cases, the exact georeferences of the sampled organism or population may be purposely withheld, to protect the study organism (e. g. an endangered species, or highly desirable species) or at the request of Indigenous peoples for reasons of cultural sensitivity regarding this information.
Nonetheless, standardized practices of quality control and assurance when generating data and metadata, and during data archival, would help to avoid issues with compounding errors during data aggregation. (3) Metadata Metadata associated with archived samples, DNA, sequences, or other genetic data are essential for enabling re-use of these resources for calculating and interpreting EBVs. Ideally, genomic records should contain environmental information or a link to an environmental database with high-quality information about local conditions of the sampled organism. At the specimen level, metadata may include collection date, taxonomic designation, morphological measurements, latitude/ longitude, age, condition/health, spatial scale, and reproductive status. At the site or population level (by convention termed 'occurrence' or 'event' level) these may include habitat description or ecoregion classification, co-occurring species, sampling methodology, measured environmental variables (light, soil moisture, etc.
), and uncertainty in the geolocation. Metadata regarding the laboratory, sequencing, quality control, and bioinformatic steps may also be recorded, and are important for generating comparable genetic data but are difficult to document and reproduce in a standard way to derive the genetic data set from the raw data. As with laboratory protocols, open text fields for detailed description may be needed. Despite disparate study objectives, the research community has been active in developing standardized metadata Biological Reviews (2022) 000-000 © 2022 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behlaf of Cambridge Philosophical Society. This article has been contributed to by U. S. Government employees and their work is in the public domain in the USA.
EBVs to monitor global genetic biodiversity fields and vocabularies to capture ecological and environmental data for a sample and/or field sampling event, and laboratory and sequencing methodologies. These metadata standards are supported by the Biodiversity Information Standards community [administering the Darwin Core standard (https://dwc. tdwg. org/; Wieczorek et al. , 2012) ] and the Genomics Standards Consortium (https://press3. mcs. anl. gov/gensc/mixs/; Wooley, Field & Glöckner, 2009; Yilmaz et al. , 2011) , and operationalized through the Genomic Observatories Metadatabase (GEOME; https://geome-db. org/;  Deck et al. , 2017 , Riginos et al. , 2020). If widely adopted, standard metadata fields alongside deposited genetic data would ensure that they are uniformly queryable according to their machine readable metadata (using a web interface or programmatically using the 'geomedb' R package) and interoperable with other data aggregators (i.
2). The first category of macrogenetic studies involves integrating data sets from multiple species (tens to thousands) spanning large geographic scales (thousands of square kilometres to global coverage) with extensive environmental databases to identify spatial trends or patterns in EBVs (reviewed in  Leigh et al. , 2021). A study may test for correlations between genetic diversity and macro-ecological correlates such as latitude  (Miraldo et al. , 2016) , assorted environmental variables  (Manel et al. , 2020) , anthropogenic impact  (Millette et al. , 2020; Schmidt et al. , 2020) , species diversity  (Theodoridis et al. , 2020) , or life-history traits  (De Kort et al. , 2021). Such studies may generate multispecies genetic data from scratch (e. g. Taberlet et al. , 2012)  and/or extract large amounts of data from the literature or from public repositories like GenBank or BOLD  (Leigh et al.
, 2021). For example,  Manel et al. (2020)  repurposed 50,588 mitochondrial DNA barcode sequences [COI (cytochrome c oxidase subunit I) gene] from 5426 fish species to explore global determinants of freshwater and marine fish species' Genetic Diversity EBV (Table  2 ). The results from these studies can be effectively mapped in the spatial dimension of the GEO BON EBV 'data cube'. However, numerous statistical considerations apply to macrogenetic studies using data extracted from nucleotide databases, such as inconsistent data depositing practices (see  Section III and Paz-Vinas et al. , 2021). Further, these studies frequently do not assess true 'population' samples, but rather aggregated sequences according to spatial proximity or a grid (e. g. Theodoridis et al. , 2020; Millette et al. , 2020; Manel et al.
These results contrast with analyses by  Millette et al. (2020)  and  Lawrence et al. (2019) , emphasizing that some large-scale studies may have less power to detect change due to a limited time span, repurposing data from nucleotide databases, challenges in aggregation, and other reasons (reviewed in  Leigh et al. , 2021; Table 2). Unfortunately, true time series of observations of the same population or region with similar sampling are rare, making it difficult to construct a data object with a comprehensive time dimension that spans broad timescales. As with other EBV classes, statistical models will be essential to fill in spatial and temporal gaps (Section IV. 2). A third category seeks to test the effects of specific environmental or human impacts via 'space for time substitution' or 'case-control' (Table  2 ).
2. Such efforts at post-hoc data aggregation and mobilization are complemented by coordinated research networks where scientists are compiling their population genetic data sets into curated, standardized archives, supplementary to the general nucleotide databases like GenBank. This involves labourintensive retroactive application of archiving standards and requirements. For example, the Diversity of the Indo-Pacific Network project compiled more than 200 data sets for Indo-Pacific marine organisms (http://diversityindopacific. net/; Crandall et al. , 2019). Similar examples include: Ira Moana EBVs to monitor global genetic biodiversity (https://sites. massey. ac. nz/iramoana/), FishGen (www. fishgen. net) and CartograTree (https://treegenesdb. org/ ct). Other efforts are planning genetic assessment of many species as a part of a single project, and simultaneously cosampling them.
For example, the IntraBioDiv Consortium sampled 27 alpine plant species with the same strategy, in the same locations, with a common analysis method (https://www. wsl. ch/en/projects/intrabiodiv. html); this data set has been used for many studies (e. g. Taberlet et al. , 2012; Hanson et al. , 2017). The Oz Mammals Genomics Initiative (https://ozmammalsgenomics. com/) is using standardized methods to generate conservation genomic data sets for 14 threatened Australian mammals  (Eldridge et al. , 2020; von Takach et al. , 2021). All of these projects show that mining databases and the literature, and greater coordination of planning for data interoperability and FAIR practices, has great value for understanding and operationalizing Genetic EBVs at large scales. International groups such as GEO BON are working to network among these groups and share data standards, lessons learned, and best practices for operationalizing EBVs (https://geobon.
V FUTURE DIRECTIONS FOR GENETIC EBVs (1) What is needed for Genetic EBV operationalization? A number of challenges exist for Genetic EBV operationalization. These include standardizing bioinformatic pipelines, standardizing sampling for adequate characterization of each EBV, cost, scaling issues, and choosing target species. As  Navarro et al. (2017, p. 161)  explain, the EBV framework as a whole also 'faces challenges emerging from the lack of global monitoring schemes, the integration of datasets resulting from different collection methods, and technical issues related to data product structure, storage, workflow execution, and legal interoperability'. In Genetic EBVs, different spatial and temporal resolution and measurement units need to be harmonized and standardized to allow for integration of data sets from different sources.
Standardized temporal measures of genetic composition remain scarce due to limited longitudinal genetic monitoring, biases in location and taxonomy of the available data, and still unsolved statistical challenges in aggregating and harmonizing existing genetic data sets in large-scale analyses. (b) Standardization of genetic methods. Standard laboratory and informatic methodologies, and guidelines for consistent, routine archiving and reuse of genetic data, still require improvements to make all genetic data interoperable. (c) Better recording and use of genetic metadata. There are broad opportunities for increased use of genetic archival databases and biological and environmental metadatabases, and for better links between genetic and environmental repositories and observatories to improve data searching and inferences regarding the drivers of changes in Genetic EBVs.
Client pseudo-damage For the sake of illustration, we only keep the 6 efforts applied to the car's two front wheel axles. We calculate from them 12 scalar signal combinations (F j ) j∈{1,n d } using Eq. 7 over the 3 directions (X, Y, Z) with 4 angles of combination γ ∈ (0, 45, 90, 135). Pseudo-damage was calculated using Eq. 6 with a Basquin exponent β = 4. The rainflow counting on each load combination was performed on MATLAB using the toolbox WAFO  (Brodtkorb et al. (2000) ) following the rules of ASTM E1049-85 (2017). The Fig. 3  shows the values of pseudo-damage for a few outstanding clients and for each pseudo-damage component's mean. Each radius of this radar corresponds to one pseudo-damage component, individually centered and reduced. Each 120-degree sector corresponds to the 4 pseudo-damages calculated over one direction (X, Y, Z).
Thanks to innovations in sensor integration and Big Data methods  (Chojnacki (2021) ), small-cost integration of measurement sensors and post-processing routines becomes achievable in connected vehicles. A larger anonymous survey of client loads and uses will increase the precision of our knowledge of client uses. We also have isolated extreme client uses in our campaign I11 through client clustering. Pending the further analysis of larger databases using extreme value statistics, we can only draw partial conclusions on the distribution of all driver profiles over our client population. The existence and the specificity of prominent driver profiles raise however the question of the desired representativeness of the design load  (Rockafellar and Royset (2015) ). Its choice must account for the likelihood of all accepted uses in what we have classified as service uses.
| Chronic physical conditions and physical multimorbidity We included all 11 chronic physical conditions (angina, arthritis, asthma, chronic back pain, chronic lung disease, diabetes, edentulism, hearing problems, hypertension, stroke, and visual impairment) for which data were available in the SAGE. Chronic back pain was defined as having had back pain every day during the last 30 days. Respondents who answered affirmatively to the question "Have you lost all of your natural teeth?" were considered to have edentulism. The participant was considered to have hearing problems if the interviewer observed this condition during the survey. Hypertension was defined as having at least one of the following: systolic blood pressure ≥140 mmHg; diastolic blood pressure ≥90 mmHg; or selfreported diagnosis.
Visual impairment was defined as having severe/ extreme difficulty in seeing and recognizing a person that the participant knows across the road  (Freeman et al. , 2013). Diabetes and stroke were solely based on lifetime self-reported diagnosis. For other conditions, the participant was considered to have the condition in the presence of either one of the following: selfreported diagnosis; or symptom-based diagnosis based on algorithms. We used these algorithms, which have been used in previous studies using the same data set, to detect undiagnosed cases  (Arokiasamy et al. , 2017; Garin et al. , 2016). Specifically, the validated Rose questionnaire was used for angina  (Rose, 1962) , and other previously validated symptom-based algorithms were used for arthritis, asthma, and chronic lung disease  (Arokiasamy et al.
| Strengths and limitations The large sample size, the use of nationally representative datasets, and the identification of potential mediators in the association between physical multimorbidity and depression are the strengths of the present study. However, findings must be interpreted in light of the limitations of the study. First, the majority of the variables were based on self-report potentially introducing both social desirability and recall bias into the findings. Second, our list of chronic diseases included a variety of diseases which are highly prevalent in LMICs, but lacked some diseases (such as cancer), which are known to be related with higher risk of depression. Finally, temporal associations cannot be established in this study due to the cross-sectional design. For example, it is also possible for depression to precede physical multimorbidity, with this link being explained by unhealthy lifestyles.
ACKNOWLEDGMENTS This study uses data from  WHO's Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the  U. S. National Institute on Aging through Interagency Agreements   OGHA 04034785 ,  YA1323-08-CN-0020 ,  Y1-AG-1005-01 , and through research grants  R01-AG034479  and  R21-AG034263. Dr. Guillermo F. López Sánchez  is funded by the  European Union -Next Generation EU.
RNA Extraction Real Time PCR and RNA Sequencing TRIzol reagent (Thermo Fischer Scientific, Les Ulis, France) was used to extract total RNA from retroperitoneal adipose tissue as previously described  [27, 28]. Total RNA from three mice per group were used to prepare an RNA-seq library using the Illumina TruSeq Stranded mRNA kit. On the Illumina NextSeq 500 sequencer, libraries were sequenced paired-end. Sickle was used to eliminate reads having a phred score of less than 20 and a length of less than 25 bp (v1. 33). MultiQC was used to assess the quality of the trim reads (v1. 0). Trim readings were aligned with the STAR aligner (v2. 7. 0d) with the "outFilterMismatchNoverLmax" and "outFilter-MultimapNmax" options set to 0. 08 and 1, respectively. Cufflinks (v2. 2. 1) was used to find transcripts, with the "library-type" option set to fr-firstrand and a GTF file obtained from GENCODE ("Comprehensive gene annotation", vM1) serving as the genomic annotation.
Cuffmerge was used to integrate the GTF files created by Cufflinks for each sample. Unknown transcripts (class code "u") were identified using the "class code" supplied by Cuffmerge to each transcript. For further analysis, only de novo transcripts with counts greater than 0 in at least one RNA-seq sample were preserved. These newly created transcripts were joined with the GENCODE GTF file to create the final genomic annotation, which was then quantified using FeatureCounts (v1. 6. 1). DESEQ2 was used to compare gene expression patterns between conditions. Reads from Watson and Crick strands were picked using SAMtools (v1. 9) and sent into the RseQC program suite's bam2wig. py script to build bigwig files (v2. 6. 4). The IGV genome browser was used to visualise RNA-seq profiles. RNA-seq data are available at GEO (accession number: GSE206372).
Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) Differential expression data were filtered using the following parameters: padj < 0. 01 (p-value corrected for multiple tests using Benjamini and Hochberg method) and fold change (FC) FC > 1. 5 or <-0. 66. Those gene lists were implemented in Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software to highlight metabolic pathways differentially impacted by the maternal diet and subsequent LF/HF diet. Pathways were manually curated to retain only those related to inflammation in our study. Finally, quantitative analyses were performed to characterise the influence of the maternal diet and/or the adult diet in inflammation pathways in the offspring.
HF Diet and Maternal VDD Regulate Inflammation Pathways In order to highlight the inflammatory response in the WAT, RNA-seq analyses were set up in order to study the expression profile of the genes in the WAT. Using the significant regulated genes (Table  1 ), two sets of data were employed. The first set looked at the effect of maternal VDD in the same diet conditions (i. e. , CTRL-LF vs. VDD-LF and CTRL-HF vs. VDD-HF). The second data set evaluated the effects of a HF diet on the same type of maternal diet (CTRL-LF vs. CTRL-HF and VDD-LF vs. VDD-HF). Table  1. The total number of differentially expressed mRNA genes, including up-regulated and downregulated genes, in both males and females, was determined using data filtering with padj < 0. 01 and FC > 1. 5 or <-0. 66. In a first set of analysis, we investigated the impact of maternal VDD.
CTRL LF condition as revealed by the number of genes involved and the associated p-value (120 genes vs. 67 genes, and 6. 95 × 10 -8 p-value vs. 9. 48 × 10 -4 p-value, respectively). The same analyses were performed using IPA software and similar results were obtained. Namely, when comparing CTRL LF vs. CTRL HF, 6 differentially expressed canonical pathways were highlighted (Supplementary Table  S8 ) and 28 differentially expressed canonical pathways between VDD LF and VDD HF males (Supplementary Table  S9 ). Four pathways were found in common between the CTRL LF vs. CTRL HF and VDD LF vs. VDD HF (Figure  2B ). In females, GO analysis revealed 11 pathways related to inflammation in CTRL LF vs. CTRL HF (Supplementary Table  S10 ) and none in VDD LF vs. VDD HF females (Figure  2C ). Using IPA, 11 differentially expressed canonical pathways between CTRL LF and CTRL HF females (Supplementary Table  S11 ) and 2 canonical pathways between VDD LF and VDD HF females were observed (Supplementary Table  S12 ).
Discussion Maternal diet is now recognised as an important factor in fetal development and offspring metabolic programming  [31] , in line with the DOHaD concept  [32]. This concept states that early life environment may impact the risk of developing chronic diseases, formerly referred to as non-communicable diseases, from childhood to adulthood. In other words, the DOHaD corresponds to fetal or developmental programming. The fact that Phosphorylation levels of p65 and p38 were evaluated using ELISA in males (A,B) and females (E,F). The data are expressed as relative expression ratios (p65phosphorylated/p65total and p38 phosphorylated/p38total). Values are presented as mean ± SEM. Bars not sharing the same letter were significantly different in Fisher's LSD post hoc test. p < 0. 05. Scatter plot of the correlation between adiposity p65 in males (C) and females (G), and scatter plot of the correlation between adiposity and p38 in males (D) and females (H), using Pearson r correlation, p < 0.
After delivery, mice were all fed with control diet. At the age of 6 weeks, the offspring were randomised and received a low-fat (7% of total energy from fat) or high-fat diet (45% of total energy from fat) for 8 weeks to study the combined effect of the diet and maternal VDD on retroperitoneal adipose tissue inflammation. To evaluate the combined effect of VDD and HF, a morphological analysis was established. In agreement with already published data of our team  [11] , we observed in this model an increase in the body weight under HF diet in both CTRL and VDD males. Adiposity index, which is used as a good marker of fat pads accumulation, independently of total body weight, was not affected by VDD in the LF condition, but was dramatically increased in the HF condition compared with the other group (CTRL LF, CTRL HF, and VDD LF).
Methodology We use the Institut Pierre Simon Laplace coupled model (IPSL-CM6A-LR;  Boucher et al. , 2020)  which contributed to the 6th phase of the international Coupled Model Intercomparison Project (CMIP6;  Eyring et al. , 2016). The IPSL-CM6-LR uses the atmospheric component LMDZ6A  (Hourdin et al. , 2020)  which includes the land model ORCHIDEE version 2  (Cheruy et al. , 2020). It has a 79-layer vertical discretization ranging from about 10 m to 80 km above surface (top at 1 Pa) and a horizontal resolution of 144 × 143 points (2. 5° in longitude and 1. 25° in latitude). The ocean component is the Version 3. 6 stable of NEMO (Nucleus for European Models of the Ocean), which includes the ocean physics module OPA  (Madec et al. , 2017) , the sea ice dynamics and thermodynamics module LIM3  (Vancoppenolle et al.