dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007). The autocovariate variable allows for estimating how much the food web structure of a given lake reflects the structure of the neighboring ones. However, as the composite food web descriptors could show a spatial autocorrelation because the environment itself is autocorrelated, we then modeled each autocovariate variable (one for each composite food web descriptor) to the set of environmental variables using a bootstrap aggregating model with the randomForest function from the R package "randomForest"  (Liaw & Wiener, 2002). Bootstrap aggregating prediction models is a general method for fitting multiple versions of a prediction model and then combining (or ensembling) them into an aggregated prediction  (Breiman, 1996). This technique reduces the variance and bias in the sample data and results in tackling the overfitting of the models.
This approach is directly integrated into the gam function of the R package "mgcv" using the argument "select = TRUE". Using single food web metrics or the composite food web descriptors from the PCA axes yielded similar qualitative results and relationships with the explanatory variables (see Supporting Information S8). Therefore, here we present results on PCA axes as they summarize the main features of food web structures among the 67 lakes. The data and codes are available online  (Leclerc, 2023).
DATA AVA I L A B I L I T Y S TAT E M E N T Temperature and fish data are available from the dashboard of Pole ECLA (http://dashb oard. ecla. inrae. fr/). Productivity data are available from the Naïades database (http://www. naiad es. eaufr ance. fr/). The data and the code that support the findings of this study are openly available in Zenodo at https://doi. org/10. 5281/.
Acknowledgements This research was funded by grants of the  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP ;  2015/16235-2 ,  2018/02645-2 ,  2018/14617-3 ,  2018/05892-0 ,  2019/17732-0 ,  2019/16066-7 ,  2019/ 16271-0 ,  2019/12132-5 ,  2021/10949-4 ), the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq ;  308045/2013-0  and  307353/2019-2 ), and the  Fundação de Amparo à Pesquisa do Minas Gerais (FAPEMIG ; project  APQ-03793-16 ). We acknowledge  Adriana de Cássia Zapparoli , formerly at  Vale company , for her help to access the Vale's drill core library in Carajás. J. Pereira ,  D. Vasconcelos ,  A. Mazoz  and  A. R. Alkmim  are acknowledged for assistance during sample preparation, CL imaging (Microscopy and Microanalysis  Network of Minas Gerais ), and data acquisition.
Although these concepts were introduced by EFSA more than ten years ago, risk categorisation of abattoirs and the role of all relevant components necessary for it have not been researched widely and developed further. A targeted database search from 2012 onwards identified only four relevant published studies investigating abattoir risk categorisation  (Alvseike et al. , 2019; Cegar et al. , 2022; Hauge et al. , 2023; Nastasijevic et al. , 2016). Abattoir process hygiene assessment in cattle and pig  (Nastasijevic et al. , 2016)  and cattle and sheep abattoirs  (Alvseike et al. , 2019)  was carried out with the aim to risk-categorise the investigated abattoirs using a two-fold approach: (i) auditing based on the scoring systems used in the UK (hygiene assessment system, HAS), in Australia (meat hygiene assessment, MHA) and Norway (hygiene performance rating, HPR) and (ii) results of PHC testing.
Data collection and analysis The questionnaire was developed as an online survey using Google Forms software (https://docs. google. com/forms). The survey was emailed to the network of National Contact Points from the RIBMINS COST Action, 1  representing 35 European countries. Data were collected between 28th April and June 5, 2022. Respondents' answers were quantitatively analysed depending on the type of animal species and whether the country had implemented risk categorisation of abattoirs or not. When they were provided, the systems used for risk categorisation are described below.
Data accessibility Data accessibility to CAs at central level varied depending on the criteria that were or could be included in the risk categorisation systems (Fig. 3 ). The data most commonly available to central CAs are the outcomes of official controls, indicated by 72% of respondents as available on a central computerised database and by 22% of respondents as available upon request to local authorities. Similarly, respondents reported the following data are easily available, either centrally or locally by food safety authorities: the history of food safety alerts and product withdrawals (56% on a central computerised database and 17% upon request to local authorities), export agreements (56% and 28%, respectively), the category of animal slaughtered (72% and 17%, respectively), the production figures of the abattoirs (67% and 22%, respectively) and the results of microbiological testing performed by the CA (61% and 33%, respectively).
Data Data come from the Horizon 2020-funded LifeCycle Project-EU Child Cohort Network,  24  which comprises 19 pregnancy and childhood cohorts in Europe and Australia investigating early life health and its determinants, starting from pregnancy. LifeCycle cohorts collected data on demographic and socioeconomic characteristics, including maternal health, marital status, living arrangements, parental education, family income and parental health behaviours, among other variables. 24  total of nine cohorts collected data on migration status and were able to harmonise variables on socioeconomic and migrant status associated with health at birth. The results presented in the main text are based on five cohorts with a sufficiently large sample size (>1700 children of immigrants) to conduct multivariate analyses.
Measures The dependent variable is the child's birth weight, a continuous variable measured in grams. Different specifications of our dependent variable are tested (see the Data analysis section). Children's migration status is measured in four categories: second-generation children, with both parents born abroad; 2. 5 generation children with a mother born abroad, and native-born father; 2. 5 generation children with a father born abroad and a nativeborn mother; and natives (reference), which included children with both parents born in the host country. Mothers' region of birth was measured in 10 categories: host country (reference for each cohort study), Western Europe, Eastern Europe, Other Europe and Central Asia, East Asia and Pacific, South Asia, Middle East and North Africa, sub-Saharan Africa, Latin America and the Caribbean, and North America.
Data analysis After descriptive statistics analyses, we selected our analytical sample following a complete-case analysis approach. We used ordinary least square and logistic regressions to model different specifications of birth weight. First, we modelled birth weight as a continuous variable, including all births (DV1). Second, we excluded babies born with a birth weight higher than 4500 g (DV2) given their higher risk of negative health outcomes. Finally, we modelled low birth weight as a 0-1 binary variable where 1 indicates birth weight below 2500 g (DV3). As the results for these three models were very similar, only DV1 is presented in the Results section. Models using the other specifications are available as online supplemental appendices 3 and 4. Analyses were conducted separately for each cohort.
Acknowledgements The ELFE survey is a joint project between the  French Institute for Demographic Studies (INED)  and the  National Institute of Health and Medical Research (INSERM) , in partnership with the French blood transfusion service (Etablissement français du sang, EFS),  Santé publique France , the  National Institute for Statistics and Economic Studies (INSEE) , the  Direction générale de la santé (DGS , part of the  Ministry of Health and Social Affairs) , the  Direction générale de la prévention des risques (DGPR, Ministry for the Environment) , the  Direction de la recherche, des études, de l'évaluation et des statistiques (DREES, Ministry of Health and Social Affairs) , the  Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture) , and the  Caisse nationale des allocations familiales (CNAF) , with the support of the  Ministry of Higher Education and Research and the Institut national de la jeunesse et de l'éducation populaire (INJEP).
Funding The LifeCycle Project received funding from the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 733206 LifeCycle). The ELFE study received a government grant managed by the  National Research Agency  under the ' Investissements d'avenir' programme  ( ANR-11-EQPX-0038 ). Core funding for  Born in Bradford (BiB)  has been funded by the  Wellcome Trust  ( WT101597MA ), a joint grant from the  UK Medical Research Council (MRC)  and  UK Economic and Social Science Research Council (ESRC)  ( MR/N024397/1 ); the  British Heart Foundation  ( CS/16/4/32482 ) and the  National Institute for Health Research ARC Yorkshire and Humber  ( NIHR200166 ). The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the  Department of Health and Social Care or Wellcome Trust.
The GECKO Drenthe birth cohort was funded by an unrestricted grant of  Hutchison Whampoa Ld ,  Hong Kong  and supported by the  University of Groningen ,  Well Baby Clinic Foundation Icare, Noordlease, Paediatric Association Of The Netherlands, Youth Health Care Drenthe and European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 733206 LifeCycle). The Raine study has been funded by programme and project grants from the  Australian National Health and Medical Research Council , the  Commonwealth Scientific and Industrial Research Organisation, Healthway  and the  Lions Eye Institute in Western Australia. The Raine study Gen2-17 years of follow-up was funded by the  NHMRC Programme  Grant (Stanley et al, ID 353514). The Raine study participation in LifeCycle was funded by a grant from the  National Health and Medical Research Council, Australia  ( GNT1142858 ).
733206 , 2016; EUCAN-Connect grant agreement no. 824989 ;  ATHLETE , grant agreement no. 874583 ). VJ received funding from a Consolidator Grant from the  European Research Council  ( ERC-2014-CoG-648916 ). INMA-Sabadell data collections were supported by grants from the  Instituto de Salud Carlos III  ( Red INMA G03/176 ),  CIBERESP , and the  Generalitat de Catalunya-CIRIT  ( 1999SGR 00241 ). The NINFEA cohort was partially funded by the  Compagnia San Paolo  and by the  Piedmont Region. The  Piccolipiù  project was financially supported by the  Italian National Center for Disease Prevention and Control  (CCM grants years 2010 and 2014) and by the  Italian Ministry of Health  (art 12 and 12 bis D. lgs  502/92 ). Childhood Obesity Project (CHOP): the study reported has been carried out with partial financial support from the  Commission of the European Community ; specific research,  technological development and demonstration programme 'Quality of Life and Management of Living Resources ', within the European Union's  Seventh Framework Programme  (FP7/2007-2013); project  EarlyNutrition  under grant agreement no.
In the corresponding heatmap and unsupervised hierarchical clustering, LS separates from NL and HC skin, whereas NL and HC cluster together (Supplementary Figure  S1b ). The top differentially expressed genes between conditions (LS/NL/ HC) are shown in Figure  1b  and represent genes that are associated with AD. Furthermore, we compared our differentially expressed gene list with those from two other studies, namely GSE121212 in which skin was sampled by 5 mm punch biopsies  (Tsoi et al. , 2019)  and a metaanalysis-derived AD signature  (Ewald et al. , 2015). As shown in Figure  1c , there is a substantial overlap between the three AD signatures. On the basis of an across-study pathway enrichment analysis, we found that at the pathway level, the AD skin transcriptome derived from 1. 5 mm biopsies showed enrichment patterns comparable with those in AD-relevant pathways with previous studies that used larger biopsies as well as laser capture microdissection (Figure  2 ).
Finally, we performed an in silico cell sorting analysis to estimate the cell type composition of skin biopsies. This analysis (found in Supplementary Materials and Methods) showed that the 1. 5 mm biopsies used in our study had a cell composition comparable with that of the 5 mm biopsies in the GSE121212 study. In summary, the AD skin transcriptome derived from 1. 5 mm punch biopsies is comparable with that obtained by larger biopsies, including the gene, pathway, and cell composition level.
In our study, the spatial variation in gene expression could mainly be attributed to differences in skin anatomy as reflected by genes specific to hair and hand (palmoplantar epidermis). Most notably, the largest variation was observed for hair-specific genes, including K27/71 gene   S2 ). (b) Heatmap showing selected DEGs between conditions (LS/NL/HC). Log 2 -transformed counts were z-scaled; higher/lower than average is depicted in red/blue. The anatomic region from which each sample was collected is indicated above the heatmap; samples were obtained from the arm (270), leg (24), hand (15), wrist (10), elbow (9), back of knee (  7 ), and feet (4). (c) Veen diagram showing the overlap between DEGs (upregulated/downregulated) identified in our study (GENAD [Gentofte AD]), 5 mm biopsies (GSE121212), and a MADAD signature.
194, P ¼ 0. 07), and lichenification (r ¼ -0. 075, P ¼ 0. 62) in LS skin. The dominant signal in AD LS skin stems from keratinocytes, macrophages, and dendritic cells, which is reflected by a decreased Shannon index (0. 753) compared with that in NL Figure  2. Bubble plot illustrating the comparable functional enrichment pattern of AD across five studies using different sampling methods. The five AD studies include acute versus chronic  (Tsoi et al. , 2020) , GSE107361  (Brunner et al. , 2018) , GSE120721  (Esaki et al. , 2015) , GSE121212  (Tsoi et al. , 2019) , and our GENAD (Gentofte AD) cohort. The sampling method and biopsy size are indicated above the plot. We selected AD-relevant pathways from gene ontology biological process. The extent of enrichment, estimated by NES, determines bubble color, whereas the significance of enrichment (-log 10 transformed P-value) determines bubble size.
Transcriptomic profiling Total RNA was extracted using the QIAsymphony RNA kit (catalog number 931636, Qiagen, Hilden, Germany), eluted in water, and profiled by INVIEW Transcriptome service at Eurofins Scientific (Aarhus, Denmark). The library was prepared using NEBNext Ultra II DNA Lib Prep Kit (New England BioLabs, Hitchin, United Kingdom). Paired-end (2 Â 150 base pair) sequencing on NovaSeq 6000 (Illumina, San Diego, CA) resulted in an average sequencing depth of 35 M reads per sample. Raw reads were cleaned by fastp, version 0. 19. 4  (Chen et al. , 2018) ; mapped to the human reference genome GRCh38 by hisat2, version 2. 2. 0  (Kim et al. , 2019) ; and annotated by GENCODE  (Frankish et al. , 2021). Counts were normalized to the library size and variance-stabilizing transformed. We averaged the expression of biological replicates from each subject in the differentially expressed gene analysis by DESeq2  (Love et al.
Bioinformatics and statistics Principal component analysis. Read counts were scaled, centered, and log 2 transformed. Principal component analysis was performed by the reduce_dimensions function of R/Bioconductor package tidybulk  (Mangiola et al. , 2021). In silico cell sorting analysis. Multisubject single-cell deconvolution  (Wang et al. , 2019)  was used for in silico cell sorting with single-cell RNA-sequencing data GSE147424  (He et al. , 2020)  as the reference. The cell composition data, generated by in silico cell sorting, has the unit-sum constraint  (Greenacre, 2021) , that is, for each sample, the sum of all cell types (x i ) equals 1 x 1 ; x 2 ;. ; x j P j X j ¼ 1.
Functional enrichment analysis. We performed functional enrichment analysis by R/Bioconductor package fast gene set enrichment analysis (Sergushichev, 2016 1 ) using Molecular Signature database, version 7. 4, as the reference, including Reactome, gene ontology biological process, and gene ontology molecular function. Genes were ranked by the fold change for each contrast. To counteract the bias introduced by get set size variation, normalized enrichment score was calculated by random sampling of the gene sets of the same size using the parameter minSize ¼ 8, maxSize ¼ 500, scoreType ¼ SD, iteration of permutation ¼ 10 4 , and eps ¼ 0. In Figure  2 , the AD-relevant pathways were selected from gene ontology biological process. Similar patterns were observed in Reactome and gene ontology molecular function.
However, owing to the redundancy in the gene sets, only gene ontology biological process results were included in the visualization. Detailed functional enrichment results are available online (https://bit. ly/ad-time-space). Overrepresentation test was performed by Enrichr  (Xie et al. , 2021)  using ARCHS4_Tissue  (Lachmann et al. , 2018)  and Human Gene Atlas  (Wu et al. , 2009)  databases.
ACKNOWLEDGMENTS We acknowledge  Li Jiang ,  Steven Dennis Pilger ,  Maja Kirkegaard Jensen , and  Thomas Farkas  for data analysis suggestions;  Peter Scheipers  for insightful comments on genetic associations;  Georg Duenstl  for neuron and Schwann cell discussions;  Paola Lovato  and  Maria Helena Hoeyer-Hansen  for pointing to IL-34 and IL-37 as indicators of atopic dermatitis pathology; and  Anne Saaby Schmidt  and  Hasan Go ¨kcer Tekin  for helping with the sample collection. We also acknowledge  Computerome -Danish National Life Science Supercomputing Center  for providing computation infrastructure. We sincerely acknowledge the anonymous reviewers and the editors for their many insightful and constructive comments and suggestions. This study was conducted in Ballerup, Denmark.
CONFLICT OF INTEREST TH, DAE, IH, and TL are LEO Pharma employees. JCdR was a former employee of  LEO Pharma. LS has been an adviser, investigator, and speaker for  AbbVie ,  Eli Lilly ,  Novartis ,  Sanofi ,  Celgene ,  LEO Pharma ,  BMS ,  UCB , and BI, outside the submitted work. LS reports nonfinancial support from  AbbVie ,  Sanofi , and  Janssen  and grants from  Novartis ,  Janssen ,  BMS , and  Sanofi. TT has been an investigator for  Novartis, AbbVie ,  Dr Wolff ,  Galderma , and  Almirall. This study was funded by  LEO Pharma A/S.
The mean radiant temperature was computed from the globe temperature using the function psychrometrics. t_mrt from the pythermalcomfort python package 60. Fanger's PMV index was also computed using this package. Physiological. Skin temperature was measured in 16 areas of the body: neck ( T skin,neck ), chest ( T skin,chest ), arm ( T skin,arm ), forearm ( T skin,forearm ), anterior thigh ( T skin,ant thigh ), calf ( T skin,calf ), and shin ( T skin,shin ) at both the left and right sides of the participant plus hand ( T skin,hand ) and finger ( T skin,finger ) at the left side. See Fig. 2  for the exact placement of the thermocouples. The finger thermocouple was on the back of the 4th finger, close to the fingernail. All body-part skin measurements, except hand and finger, were calculated as the mean of the left and right sides of the body.
The distal skin temperature ( T skin,distal ) was calculated as the mean of the finger and hand skin temperatures. Skin temperatures were measured every 30 s using contact thermometry which consists of temperature sensors positioned in direct contact with the skin surface and, therefore, relies on conductive heat exchange between skin and sensor. We used thermocouples of type T (± 0. 2 °C accuracy, TCSA manufacturer), also known as copper-constantan thermocouples, with a diameter of 0. 2 mm. The thermocouples were connected to a multichannel data acquisition system (Campbell CR1000 data logger + AM16/32B multiplexer). Before taking the measurements, the thermocouple system was calibrated for the temperature range of 20 to 50 °C. These thin thermocouples were fixed onto the skin with a breathable medical tape ensuring good contact and a rapid response time (< 10 s), which is necessary for the dynamic conditions being studied.
Limitations In this study, the thermal adaptation time was equal to 70 min which is more than double the 30 min considered the norm in thermal comfort experiments. Despite this long adaptation time, skin temperatures up to 70 min from the start of the experiment were influenced by the previous thermal exposure in the office where the participants were required to stay for two hours to ensure that they were all having the same initial metabolic rate. As the main limitation of this study, we did not control the thermal conditions in the offices because we did not anticipate they could influence the results of the experiment. Because of this limitation and to eliminate any effect of the initial thermal state we only analysed time-of-day and sex effects on the physiological and subjective data collected 120 min from the start of the tests.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  884556. A. L. Pisello  and  I. Pigliautile  would like to thank the  Italian Ministry of Research  for supporting the NEXT. COM (Towards the NEXT generation of multiphysics and multidomain environmental COMfort) project (Prin 2017-20172FSCH4). I. Pigliautile would also like to thank the Italian funding programme  Fondo Sociale Europeo REACT EU-Programma Operativo Nazionale Ricerca e Innovazione 2014-2020  (D. M. n. 1062 del  10 Agosto 2021 ) for supporting her research in the RED-TO- GREEN  project framework.
Given that, in healthy samples, most of the cfDNA comes from lymphomyeloid lineages, we could identify the model parametrisation with the best accuracy in those lineages using publicly available datasets of healthy patients' cfDNA. Our results show that (1) the way tissue-specific gene classes are defined matters more than what fragmentation features are included, and (2) in particular, lncRNAs are more tissue specific than coding genes and stand out in terms of both sensitivity and specificity in our results.
Annotations of lncRNAs are provided in several databases  [19]. FANTOM CAT compiles an atlas of more than 25,000 lncRNAs with high-confidence 5 ends and expression profiles across samples from many primary cell types and tissues  [23]. As a follow-up to previous work aimed at recognising the tissue of origin of cfDNA based on fragmentation patterns around TSSs  [14, 15] , we addressed here the question of how tissue specificity could be used and defined to maximise the prediction accuracy. To this end, we comprehensively studied the characteristics of circulating DNA fragments around TSSs that increase the ability of statistical models to recognise the tissue of origin of cfDNA in healthy individuals. We evaluated how the sensitivity of the method depended on what sets of genes were to be classified.
Plasma-Seq data In this study, we re-analysed the dataset BH01 obtained and described in  [15] : it corresponds to cfDNA extracted from a pool of plasma from several healthy donors, deeply sequenced (100x) using paired-end reads of length 101 nt. Unprocessed read data are available from the NCBI Sequence Read Archive under the accession number SRX1120757 from the PRJNA291063 bioproject. In this study, we started off with the BAM files available from Martin Kircher's website (accessed 2018). Information January 16, 2023 3/25 regarding data processing in the original article can be found on Jay Shendure's laboratory's GitHub repository cfDNA.