dataesr/scientific-paragraphs-categorization huggingface dataset:
In keeping with this, the delegation of testing to providers appears to have dramatically increased the detection rate among the rich. This finding highlights a potential trade-off between a more centralized system that was on average less efficient but more equitable, and a decentralized system that led to improvements but only in the wealthiest strata. By relating the recorded COVID-19 fatalities to the estimated cases from the CoVIDA data, we find an infection fatality ratio of 0. 34%, a value that is plausible in a relatively young population  4. Interestingly, recorded new daily cases are greater in the first wave than in the second one, but this is not the case if we use the CoVIDA data or registered deaths. Hence, part of the increase in detected cases is driven by an increase in detection rate over time (Supplementary Table  3 ).
Methods The RT-PCR test used and its reliability. The tests to be used in this study are based on the reverse transcriptase technique-PCR or one-step RT-PCR (the U-TOPTM COVID-19 detection kit). This kit was validated in the facilities of the Public Health Laboratory of the Secretaria Distrital de Salud, finding that the kit allows detection of the COVID-19 virus, with results comparable to the Charité technique, Berlin (Rev, January 13, 2020). Data description. The CoVIDA project was designed to help contain the spread of SARS-CoV-2 through active surveillance among mostly asymptotic individuals and to provide a range of information that differs from the self-selected symptomatic individuals tested in health facilities. The sample includes 59,770 RT-PCR tests of SARS-CoV-2 on 55,078 different individuals in Bogota from the beginning of June 2020 to March 3rd, 2021.
The ethics committee approved that the participants would receive the information via telephone and give their verbal consent, in order to comply with physical distancing and limit the restriction for a study is part of a public health surveillance strategy implemented jointly with the Health Secretary of Bogota. Our second database comes from administrative records, collected by the Health Secretary of Bogota (HSB, in Spanish the Secretaria de Salud de Bogota), that cover the universe of cases of Bogota residents that have been tested positive to SARS-CoV-2 by any laboratory using an RT-PCR test, starting from the beginning of the pandemic (January 23rd, 2020) until February 14th, 2021. All laboratories in Bogota must report any positive test to the HSB, which in turn reports it to the National Health Institute that provides national statistics used by the WHO.
Acknowledgements The authors acknowledge generous support from the  Interamerican Development Bank , the  Development Bank of Latin America (CAF) , the  University of Los Andes  and the  Universidad Nacional de Colombia. Duncan Webb  gratefully acknowledges support from the ED 465 at University Paris 1, and the  EUR  project  ANR-17-EURE-0001. We thank members of the  CoVIDA working group ,  Yessica Daniela Campaz Landazabal ,  Marylin Hidalgo ,  Juliana Quintero ,  Paola Betancourt ,  Pablo Rodríguez ,  Diana Sofia Rios Oliveros ,  Andrés Felipe Patiño ,  Jose David Pinzon Ortiz ,  Maribel Rincón ,  Alejandro Segura , and  Leonardo Salas Zapata.
In agreement with LD location and main role, the bulk of reported LD-sitting proteins is mostly cytoplasmic. Nevertheless, the published LD proteome datasets are numerous (a comprehensive list is compiled in  [121] ), and examples of nuclear or nucleus-related factors exist. For example, histones are LD-harboured in different species  [122] [123] [124]. In the Drosophila developing embryo, their timely release allows an accurate division pattern  [125, 126] , while in other species, the significance of this storage remains unassessed. Since, conceptually, "division pattern" is intimately related to "replication", and replication is extremely sensitive to histone levels  [127] , it is very tempting to speculate that LD may serve as a depot to fine-tune the number of histones supplied to the nucleus at a given time.
A limitation was still the 3D aspect, given that FA-binding pockets can represent complex cavities. We have therefore created PickPocket, which uses neural networks to train a ligand-binding prediction model. Using FA-like ligands, we can define pocket descriptors and secondary structures with an accuracy above 90% by using a dataset of 1740 manually curated ligand-binding pockets. Pickpocket successfully predicts ligand-binding pockets using unseen structural data  [188]. Using this tool, we have defined two central channel nucleoporins, Nup157 and Nic96, known to root the nuclear pore complex, as binders of FA, a feature that could regulate their dynamics  [77]. Amphipathic helices (AHs) are a feature of the secondary structure of proteins in which the aminoacids fold in a helical manner that segregates hydrophobic and polar residues between the two faces of the helix.
Upon proteolytic processing of the protein, the transmembrane segment stays anchored into the membrane while the released peptide diffuses within the nucleus to accomplish its task  [194, 195]. Initial predictions of potential transmembrane helices in nuclear proteins can be tackled using Phobius and TMpred software. More reliable predictions will be achieved if a given fragment is highlighted by both tools. Validation of bioinformatic predictions of putative lipid-related protein domains can be undertaken by cloning the motifs fused to a fluorophore of choice (see below). In the case of lipid-binding motifs, further confirmation can be obtained by cloning one-half of the Venus reporter protein fused to the candidate motif, while a nucleus-targeted lipid biosensor known to recognize the same lipid (see later) can be cloned fused to the other half of Venus.
Genotype data The 389 animals were genotyped using the Porcine 70k GGP Porcine HD Array (Illumina, San Diego, CA) containing 68,516 single nucleotide polymorphisms (SNPs). The quality control excluded SNPs with minor allele frequencies < 5%, rates of missing genotypes above 10%, and SNPs that did not map to the porcine reference genome (Sscrofa11. 1 assembly). Consequently, 42,641 SNPs were retained for subsequent analysis.
Network between microbial and immunity traits To better understand the relationship between microbial communities and immunity traits we implemented PCIT [35], a network-based approach that combines partial correlation coefficient with information theory to identify significant correlations between each possible combination of clr-transformed bacterial abundance and the immune-traits  [35]. The PCIT algorithm is a data-drive semi-parametric approach that does not require independence to make the results interpretable. Instead, it uses the definition of the partial correlation between "x" and "y" given "z" as being the strength of the linear relationship between "x" and "y" that is independent from "z". In doing so, PCIT tests all possible 3-way combinations in the dataset and only keeps correlations between traits if they are significant and independent of the association of another features.
Funding YRC is recipient of a  Ramon y Cajal post-doctoral fellowship  ( RYC2019-027244-I ) from the  Spanish Ministry of Science and Innovation. MB was recipient of a  Ramon y Cajal post-doctoral fellowship  ( RYC-2013-12573 ). LMZ is recipient of a Ph. D. grant from  Ministry of Economy and Science, Spain associated with 'Centro de Excelencia Severo Ochoa   2016-2019 ' award SEV-2015-0533 to CRAG. Part of the research presented in this publication was funded by Grants  AGL2016-75432-R ,  AGL2017-88849-R  awarded by the  Spanish Ministry of Economy and Competitiveness. The authors belong to Consolidated Research Group TERRA (AGAUR,  2017 SGR 1719 ).
D -0404511 (1095-9203 (Electronic)): 1915-1920. 28. E Bolyen JR Rideout MR Dillon NA Bokulich CC Abnet GA Al-Ghalith H Alexander EJ Alm M Arumugam F Asnicar 2019 Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 Nat Biotechnol 37 8 852 857 29. TZ DeSantis P Hugenholtz N Larsen M Rojas EL Brodie K Keller T Huber D Dalevi P Hu GL Andersen 2006 Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB Appl Environ Microbiol 72 7 5069 5072 30. PM VanRaden 2008 Efficient methods to compute genomic predictions J Dairy Sci 91 11 4414 4423 31. GF Difford DR Plichta P Løvendahl J Lassen SJ Noel O Højberg A-DG Wright Z Zhu L Kristensen HB Nielsen 2018 Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows PLoS Genet 14 10 e1007580 32.
Cohorts A detailed description of each dataset (Transdisciplinary Research for Cancer in Lung (TRICL, the traditional LC GWAS),  13  UK Biobank (GWAx and a subset left out of the GWAx, forming the germline PRS test set)  16  , The Cancer Genome Atlas (TCGA, somatic mutations and signatures analysis (https://www. cancer. gov/tcga)) and the GeniLuc cohort (somatic mutation and signatures replication cohort (unpublished)) can be found in the supplementary information.
Genome-wide association by using a family history and genetic correlations analysis The UK Biobank resource was accessed under project number 15825. The sample selection process and variant filtering from the UK Biobank is detailed in Supplementary Table  1  along with the UK biobank data ID fields. We performed a traditional GWAS on the family history status (GWAx) of LC and adjusted the betas and standard error as described previously. 14 Individuals diagnosed with lung cancer directly were not included in the GWAx. All samples were reported as having a European ancestry (confirmed with ancestry inferred by genetic profile) with non-European individuals excluded from the study due to different genetic architecture. Consent and ethics were approved for all cohorts used. Genetic correlation analysis was undertaken using the LDSC package.
17 Summary statistics were obtained from the LC traditional GWAS which has been previously published elsewhere. 13 Each summary statistic file from the Sequencing Consortium of Alcohol and Nicotine (GSCAN) consortium (with UK Biobank samples removed) along with the traditional LC GWAS and the GWAx were tested for genetic similarity using LDSC regression. 18 th the LC family history GWAx and the LC GWAS were meta-analysed using a fixed effect model. LD clumping was undertaken using PLINK (R 2 < 0. 1 and 10,000 kb). eQTL analysis was performed using GTEx version 8 data for both lung and brain tissues containing all variant and gene pairs. Coffee intake and forced vital capacity (FVC) summary statistics were obtained from the Benjamin Neale UK Biobank work (http://www. nealelab. is/ukbiobank/).
To estimate the colocalization between genetic associations of two traits at a given locus, we calculated the Bayesian posterior probability (PP4) for colocalisation of two datasets for the H 4 (one shared variant across both traits),  19  by firstly calculating the log bayes factor for each SNP in each dataset, then the PP4 was calculated by the COLOC package in python (https://github. com/anthony-aylward/coloc).
Raw sequencing data was processed by inhouse Nextflow pipelines (https://github. com/IARCbioinfo). Somatic mutations were defined using Mutect2 and germline calls using Strelka2. 21, 22 For germline genotypes from WGS tissue, PRS SNPs were extracted from VCF files and put in the PLINK BED format. 23 Individual PRS scores were generated using PRsice2 from the normal calls. 24.
Acknowledgements We would like to acknowledge the  TCGA Research Network  (https://www. cancer. gov/tcga)and the contribution of specimen donors and research groups involved in this resource. We also would like to acknowledge the  GTEx  project and the supporting bodies (https://commonfund. nih. gov/GTEx),specimen donors and research groups. Additionally, we would like to acknowledge the work carried about by the  Benjamin Neale lab  for their work on the  UK Biobank  (http://www. nealelab. is/uk-biobank/). The ILLCO consortium is listed in the supplementary text with affiliations.
Funding This work was supported by the  Institut National du Cancer (INCa)  ( GeniLuc 2017-1-TABAC-03-CIRC-1 -[TABAC 17 022 ],  NIH/NCI , Integral  NIH   5U19CA203654-03 ,  Cancer Research UK  [grant number  C18281/A29019 ], the  France Génomique National  infrastructure, funded as part of the  « Investissements d'Avenir » program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). Christopher Amos is a  Research Scholar  of the  Cancer Prevention Institute of Texas  and supported by RR170048.
Introduction Feature weighting is a crucial preprocessing step before creating a machine learning (ML) model. It is a technique employed to estimate the optimal degree of influence of features, individually, using a training set. When well-weighted, high weights are attributed to important features while lower weights are attributed to irrelevant and noisy features, i. e. , features that likely deteriorate the accuracy of the ML model  [1, 2]. In literature, there is a lack of investigation of the performance of metaheuristic optimization algorithms in feature weighting. Due to the good performance shown in a wide range of real-world optimization problems, including in ML  [3] [4] [5] , metaheuristic algorithms seem to be good candidates to be deployed for dataset preprocessing techniques, such as feature weighting.
is a function that takes a truth value inside and returns 1 or 0 accordingly, |. | denotes the cardinality of the dataset S, and ||. || indicates the Euclidean norm of a vector. s i ∈ R 1×nf represents the vector of nf features. φ is the set of possible modulation schemes. The expression inside the big parentheses returns the ith estimated modulation type that corresponds to the feature vector s i (i. e. , a vector of estimated HOS). v j is the theoretical HOS vector for the modulation φ j and ϕ i is the true modulation scheme for the sample i of the dataset (i. e. , the ith estimated HOS vector). denotes component-wise multiplication.
, 2019). They are an important basis of epigenetic regulation in the human placenta, in normal and pathological situations  (Hayder et al. , 2018; Apicella et al. , 2019). Structurally different ncRNAs engage diverse mechanisms that lead to different regulatory outcomes. The discovery of the diversity of functions played by the ncRNAs in the cell physiology has boosted the exploration of their role in placental development, physiology and pathology. The central role of the STB in the physiology of the placenta, suggests that deregulation of ncRNAs specifically required for its formation and/or maintenance could be potentially involved in placental diseases. Here we combined two microarray analyses, one carried out on the classical fusogenic trophoblast model BeWo (under the accession number GSE148088)  (Kudo et al.
, 2004; Ramos et al. , 2008; Orendi et al. , 2010; Shankar et al. , 2015; Zheng et al. , 2016) , and one carried out on total human placentas with normal controls, PE and IUGR placentas (under the accession number E-MTAB-9416). A crossanalysis was carried out with a drastic filtering in order to identify ncRNA that are associated to disease (in the placentas) and to fusion (induced by forskolin treatment in the BeWo cells), in parallel. This cross-analysis allowed the identification of a small subset of ncRNAs which are consistently modified both during fusion of trophoblast cells, and in the pathological placentas. We then carried on our analysis focusing upon the miRNA miR-193b (by a bioinformatic approach) and the lncRNA UCA1 (through knockdown (KD) experiments). In addition, we analyzed the effects of this KD in BeWo cells, overexpressing specifically one of the two major isoforms of the STOX1 transcription factor (STOX1A and STOX1B), previously identified as a key player in preeclampsia  (George and Bidwell, 2013) , and recently shown to modulate fusion through a specific equilibrium between its two isoforms  (Vaiman and Miralles, 2016; Ducat et al.
In the BeWo-FSK we detected substantially increased expression of key markers of syncytialization such as CGA (FC = 10. 1; P = 2. 1 × 10 -11 ), CGB1 (FC = 20. 5; P = 2. 81 × 10 -12 ) or ERVFRD-1 (aka Syncytin2, FC = 10. 6; P = 6. 23 × 10 -12 ). Up-regulated DEGs were associated with biological processes such as cell migration, vascular development, response to TGF-Beta and response to hypoxia. Down-regulated DEGs are mainly involved in cell cycle progression, amino-acid metabolism, and mitochondrial gene expression (Figure  2B ). In terms of hallmarks present in the GSEA Broad database, inflammation and hypoxia pathways were particularly enriched in up-regulated genes, while cell cycle, nutrient sensing via mTORC1 pathway, were strongly enriched in down-regulated genes, suggesting a whole silencing of basic pathways of cell physiology and energy expenditure slow down, accompanying the differentiation of the trophoblast cells into a syncytial structure.
Identification of Regulatory ncRNAs Targets Reveals Their Potential Roles in Syncytialization To investigate the role of these ncRNAs in the process of BeWo fusion we identified their putative targets (when known) using appropriate databases (miRBase, starBase v2. 0. and DianaTools LncBase v. 2). Since miRbase tends to provide a more limited list of putative target genes (∼10% of the others) for a given miRNA, and since these were generally largely covered in the other databases, this constituted the major basis for the establishment of our lists of targets. Next, we selected among these targets those which are indeed detected as DEGs in the BeWo-FSK relative to the BeWo-CO cells. These resulted in a list of 278 up-regulated and 572 down-regulated DEGs. Out of the deregulated DEG list (Supplementary Table  1 ), this represents 33.
A Small Subset of ncRNAs Involved in Syncytialization Is Also Associated With Preeclampsia and Intrauterine Growth Restriction To identify ncRNAs associated with syncytialization in BeWo which could be also involved in PE and/or IUGR we compared the list of ncRNAs identified in BeWo with those identified in a list from our study on total human placentas with normal controls, PE and IUGR placentas (E-MTAB-9416) and other published datasets (GSE114349, GSE114691, GSE75010, GSE93839, and GSE66273). Comparison of all differentially expressed ncRNAs, revealed that two miRNAs (miR-193b and miR-365a) and one lncRNA (UCA1) were found most consistently up-regulated in both PE and IUGR. Therefore, miR-193b is associated to trophoblast fusion, together with pathological placentation, suggesting its overall implication in normal placental function.
Alternatively, increased miR193b expression in PE could reflect a mechanism seeking to compensate an excessive apoptotic shedding of the syncytium by facilitating the entering of CTB into the fusion process. Since a dysfunctional syncytium could be at the origin of placental diseases, we systematically searched for lncRNAs involved in the CTBs fusion that were deregulated in placentas from PE or IUGR. An exhaustive literature search, as well and the reanalysis of datasets available in the GEO Database resulted in the identification of three lncRNAs consistently up-regulated in PE and IUGR and involved in syncytialization: miR193b, miR365a and UCA1. UCA1 (Urothelial Cancer Associated 1), is a lncRNA initially identified in a bladder cancer cell line  (Wang et al. , 2006). The involvement of lncRNAs in placental diseases has been previously described for the HELLP syndrome, a serious complication of preeclampsia  (van Dijk et al.
Functional Annotation of the Differentially Expressed Genes (DEGs) For the functional annotation of the DEGs we performed Over-representation analysis (ORA) using the WebGestalt 1 bioinformatics resource  (Liao et al. , 2019). Databases interrogated include: Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmarks. The significance of the detected enrichments was calculated using the Benjamini and Hochberg multiple test adjustment.
Prediction of ncRNAs Targets To identify targets for the differentially expressed ncRNAs in the BeWo-FSK relative to BeWo-Co cells we used ad hoc databases. 1 http://bioinfo. vanderbilt. edu/webgestalt 2 https://www. gsea-msigdb. org/gsea/index. jsp These include miRBase 3  , starBase v2. 0. 4  and the DianaTools LncBase v. 2 5.
ncRNAs Regulatory Network The ncRNAs and corresponding differentially expressed targets were used to generate a regulatory network. The network was constructed, visualized and analyzed using the Cytoscape 3. 2. 1 software 6  and its complementary applications  (Shannon et al. , 2003). The centrality parameters of the network were analyzed using the Cytoscape application NetworkAnalyzer  (Shannon et al. , 2003). Two topological parameters Betweenness Centrality (BC) and node degree were used to identify hub genes. The network is available as a Supplementary XML File.
Image analysis and segmentation To quantify ruffle lengths, maximum intensity projection images were generated and analysed using FIJI. The images, after thresholding, were skeletonized and parameters were extracted using the plugin 'analyse skeleton'  [61]. To measure the ruffle thickness, profiles across the cross section of ruffles from deconvolved single planes of the volumetric stack were measured. To measure the contour lengths, SyGlass VR was used  [26]. Images were surface rendered, and the contour lengths were drawn manually. The collated data was exported, and end-to-end distance was extracted from the first and last coordinates of each ruffle. The dependences of the squared end-to-end distance on the contour length were fit with a 2D worm-like chain model: ðR 2 ee Þ¼8l 2 p ½L=ð 2l p Þ-1 þ expf-L=ð2l p Þg (Eq.
Statistical analysis Statistical analyses were performed with Prism software (Graph Pad V. 8. 3). All datasets were tested for normal distribution using the Shapiro-Wilk normality test. Datasets were compared using either parametric two-tailed unpaired t-test, non-parametric two-tailed Mann-Whitney or Kruskal-Wallis tests with multiple comparisons Dunn's post-test, as appropriate. Significant differences in taxonomic abundances were assessed with nonparametric Kruskal-Wallis test with the false-discovery rate correction implemented in QIIME. Diabetes incidence was plotted according to the Kaplan-Meier method and statistical differences were analysed using the Mantel-Cox (log-rank test). Differences were considered significant at p<0. 05 (*/$p<0. 05, **p<0. 01, ***p<0. 001, ****p<0. 0001).
0 mg. mL -1 , Roche) solution. Digestion was stopped by adding a large volume of cold Roswell Park Medium Intermediate (RPMI) medium supplemented with 5% FCS, and cells were washed and filtered on cell strainers (40 µm, BD Falcon) before labeling. For islets cells isolations, pancreas were perfused with 3 mL of a collagenase P solution (1. 5 mg. mL -1 , Roche), and islets were isolated as previously described [2]. Flow cytometry. Labeling was performed in PBS containing 2% FCS and 0. 1% sodium azide. Antibodies, lectin, and tetramers are listed below. Data acquisition was performed using a BD Biosciences LSR Fortessa or FACS-ARIA III cytometer. Flow cytometric analyses were performed with the FlowJo analysis software V10. 6 (Tree Star). Intracellular labeling. For cytokine labeling, cells were stimulated in RPMI supplemented with 10% FCS, with PMA (10 ng/mL) and ionomycin (1 µg/mL) in the presence of Brefeldin A (10 µg/mL) for 4 h at 37°C (all reagents from Sigma-Aldrich) or cells were stimulated in vivo by i.