dataesr/scientific-paragraphs-categorization huggingface dataset:
Species time series and population abundance. Annual Species Abundance Indices (SAI) for birds came from national breeding bird surveys coordinated by the PanEuropean Common Bird Monitoring Scheme (PECBMS)  (39)  (see examples in SI Appendix,  Appendix 4). Skilled volunteers in each country use comparable standardized protocols (point counts, line transects, and territory mapping) and conduct annual counts at given sites. Overall, the data that are collated in this paper represent the monitoring of more than 20,000 sites, which is one of the largest databases of its kind ever analyzed (see also refs. 6 and 58). TRends and Indices for Monitoring (TRIM) software was used to produce national population indices from site counts, taking into account error estimates and missing observations  (59, 60).
The dataset initially contained national indices for 170 bird species in 28 countries: 25 from the European Union (excluding Croatia and Malta), the United Kingdom, Norway, and Switzerland (see SI Appendix, Appendix 5 for a list of species and the number of species by country). In addition to the SAI, which corresponds to a relative value of annual population size, national estimates of the number of breeding pairs were manually extracted from Birdlife Data Zone which collates data from the EU Bird Directive Reporting, to evaluate annual absolute abundance (national population estimate)  (13)  (see methods for species abundance in SI Appendix, Appendix 1). Supranational analyses (i. e. , trend analyses at the European scale) of change in abundance were based on species-monitoring data that were available from 1980.
We focused on the period 1980 to 2016 to maximize spatial and temporal coverage, with limited uncertainty. The longest time series covered the period 1966 to 2016, but most schemes began only in the 1990s and before 1980, data were only available for six countries in northern Europe. These criteria resulted in a dataset of 1,983 time series, for 115 bird species (among the 170), in the 28 countries over 37 years. Supranational indices (i. e. , species indices at the European level) were obtained using the rtrim R package  (59, 61)  with national population size used for weighting. Abundance indices for the whole species pool, or specific (e. g. , main habitat) subgroups, were obtained from supranational indices using a multispecies index (MSI) reconstruction  (62) , adapted for abundance data (see SI Appendix, Appendix 1 for relative abundance MSI and biomass MSI).
National analyses were based on the best trade-off between spatial and temporal cover data. We therefore selected time series beginning in 1996 (±1 y) and ending in 2016. This resulted in a dataset of 1,041 time series, for 141 species (83% of the 170 species), in 14 countries, covering 60% of the area of the 28 countries (see SI Appendix, Appendix 6 for the influence of the choice of period on data distribution). Both supranational and national trajectories were assessed using a method incorporating data uncertainty and testing for nonlinear trends  (63). Bird habitat and ecological traits. We followed the latest PECBMS classification (https://pecbms. info/) to classify farmland and forest species according to their predominant habitat. Species affinity for urban areas was obtained from the European Nature Information System (EUNIS) database (https://eunis.
eea. europa. eu) (64). The Bird EUNIS database provides information on the associations between bird species and habitat types grouped by Mapping and Assessment of Ecosystems and their Services ecosystems (12 types: coastal, cropland, grassland, heathland/ shrub, marine/inlets, ocean, rivers/lakes, shelf, sparsely vegetated, urban, wetland, and woodland/forests). All species that have been reported to breed, even partially, in urban areas are considered as urban species. This study covers a continental scale from the Mediterranean to the arctic parts of Europe. Within such large areas, bird species may vary in habitat affiliation. So, national statistics related to birds and biodiversity may include different sets of species than those used here, meaning that the statistics here presented for individual countries may differ from the countries' own national statistics.
The PLS coefficient of each explanatory variable corresponds to the effect average across each of the selected components. In this study, the response variable was the national species trend between 1996 and 2016, and the explanatory variables encompassed four anthropogenic pressures, both in terms of baseline conditions and temporal changes: high-input farm cover and its temporal trend, forest cover and its temporal trend, urbanization and its temporal trend, and finally temperature and its temporal trend. The PLS analysis was performed using the plsRglm R package  (67) , which can handle incomplete data. It also provides significance levels for explanatory variables through bootstrapping and can compute bias-corrected and accelerated CIs (BC a ) (68) that correct for skewness and bias of the bootstrap distribution.
ACKNOWLEDGMENTS. We thank the volunteers who took part in national breeding bird surveys in  Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Ireland, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland , and the  United Kingdom. We acknowledge the use of the E-OBS dataset from the  EU-FP6  project  Uncertainties in Ensembles of Regional ReAnalyses (UERRA)  (https://www. uerra. eu)and the  Copernicus Climate Change Service , and data providers working with the  ECA&D  project (https://eca. knmi. nl). We also thank all those funding and otherwise supporting the national breeding surveys.
Methods Hospitalised dengue case data. Linelist data of hospitalised dengue cases was from the Queen Sirikit National Institute of Child Health (QSNICH) in Bangkok, Thailand. QSNICH is the only public children's hospital at the tertiary level in Bangkok that serves dengue cases in children requiring hospitalisation  19. All suspected dengue cases that underwent hospitalisation at QSNICH were tested using reverse transcriptase polymerase chain reaction or IgM/IgG serology at the Armed Forces Research Institute of Medical Sciences  19. Primary or secondary infection was determined using dengue hemagglutination inhibition assay and/or dengue IgM/IgG capture enzyme-linked immunosorbent assay (ELISA)  19. The infecting serotype was determined using serotype-specific PCR and/or antigen-capture ELISA  19.
10. 4 34 , under a HKY codon substitution model 35 , a relaxed lognormal clock model 36 , and a skygrid population size model 37. Three independent chains were run for each serotype, with parameters sampled every 10,000 iterations. Runs were optimised using the GPU BEAGLE 4 library 38. For each serotype, combined chains were manually checked for convergence using the Tracer software 39 , with effective sampling size (ESS) values > 200. For DENV-2 to DENV-4, the chains were run for 1 to 1. 5 billion iterations, where we manually removed the initial 10% iterations as burn-in depending on the serotype. For DENV-1, considering the large size of the sequence dataset, we allowed some ESS to be between 150 and 200 to avoid prohibitive computational time. Two chains were run for 900 millions iterations with the initial 18% iterations as burn-in, while the third chain was run for 1.
Antigenic characterisation of the representative dengue viruses isolated from Bangkok. Among the 1,848 dengue viruses isolated from Bangkok, 348 antigenically representative strains, including 87 DENV-1, 80 DENV-2, 90 DENV-3, and 91 DENV-4 strains, were selected for the antigenic measurements using PRNT assay. The titration experiments used a panel of 20 antisera from African green monkeys  13. The selection criteria of the representative viruses and antisera for antigenic measurements and the adjustment of titres for experimental conditions were described previously  13. We constructed the original antigenic map of dengue viruses using the approach of antigenic cartography  40, 41  , as described previously  13. DENV -1 in 2003, 2008 and 2009, DENV-2 in 2009, and DENV-3 in 1999 and 2007 only had sequencing data but no antigenic measurements.
In our case, let ( )   , ji D v v  be the whole dataset of the antigenic distance between the two viruses that are responsible for an individual's primary and secondary infections. We partition the range of antigenic distance data  -partition the range of antigenic distance data into 1 Lbins with equal quantiles. Our disease model described by Eq. (  4 ) is built with cubic polynomials with degree d = 3. Model comparison results suggest the use of L = 10 knots in the full model to achieve sound fitting performance (Table  S2 ).
Acknowledgements This research was supported by the  European Research Council  Grant  804744  (L. W. , H. S. , A. H. and N. L), the  National Institute of Allergy and Infectious Diseases and National Institutes of Health  Grant  R01AI114703-01  (D. A. T. C. , L. C. K. , A. C. E. , A. H. , N. C. , I. M. B. , R. J. , L. W. and H. S. ),  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases  (S. S. W. and L. C. K. ), the  Military Infectious Disease Research Program  (I. M. B. and R. J. ). This study was performed using resources provided by the  Cambridge Service for Data Driven Discovery (CSD3)  operated by the  University of Cambridge Research Computing Service  (www. csd3. cam. ac. uk), provided by  Dell EMC  and  Intel  using Tier-2 funding from the  Engineering and Physical Sciences Research Council  (capital grant  EP/T022159/1 ), and  DiRAC  funding from the  Science and Technology Facilities Council  (www.
Study population ERAS is a large, French, national, repeated, anonymous, self-administered, voluntary, cross-sectional online survey of MSM conducted in mainland France from 26 February to 11 April 2021. Its main aim was to assess trends in HIV prevention strategies among MSM living in France since the introduction of PrEP in the country in 2016  [33]. Secondary aims included an assessment of bSTI screening and diagnosis. Participants were recruited through various digital media. More speci cally, banners were posted directly on gay dating websites, gay geolocation dating apps (e. g. , Grindr, Scruff, Hornet), gay a nity news sites, and social media networks (e. g. , Facebook). Clicking on these banners redirected the user to the survey website, where information about the survey's objectives, the conditions for participation, and data con dentiality were presented.
Data collection Outcomes: The present study's outcomes were factors associated with bSTIs screening uptake and diagnosis. They were measured using the data collected for the included participants from the following questions regarding STIs in the ERAS questionnaire: 'In the past 12 months, have you had been tested at least once for the following? a. Hepatitis C, b. Hepatitis B, c. Syphilis, d. Chlamydia infections, e. Gonococcal disease, f. Condyloma, venereal vegetations, genital warts (HPV)'. Participants who answered 'yes' were then asked the following question: "What was the result of your most recent test for each STI you were screened for in the past 12 months?". Participants who answered they tested positive for at least one bSTI (i. e. , c. Syphilis, d. Chlamydia infections, e. Gonococcal disease) were categorized as having a bSTI in the previous 12 months.
Data analysis Outcomes were compared rst between MSM who had been screened for at least one bSTI in the 12 previous months and MSM who had not. They were then compared between MSM in the screened group who had tested positive for at least one bSTI at their most recent screening and those who did not. Pearson's χ2 test or Fisher's exact test were used for dichotomous and categorical variables as appropriate, while the Wilcoxon-Mann-Whitney test was used for continuous variables. The number and proportion of positive syphilis, Ct and NG infections at the most recent screenings were calculated according to HIV status and to PrEP use status. Binary logistic regression models were used to identify factors associated with bSTI diagnosis in the previous 12 months. Variables with a p-value <0. 20 in the univariate analyses were considered eligible to enter the multivariate model.
Acknowledgements The authors would to thank  Nathalie Lydié ,  Nicolas Etien  ( Santé Publique France ),  Bérangère Gall  and  Julien Vivant  ( BVA Institute ) for their involvement in the implementation of the ERAS survey, and our partners from various associations for their support and for raising awareness of our survey throughout their networks. Finally, we would like to thank all the ERAS participants for taking the time to complete the ERAS survey questionnaire.
Our code and practical implementation details All our neural network based numerical tests were performed in Python 3. 6 and using the TensorFlow 1. 13. 1 library  [20]. The code provided in the original paper on PINNs  [21]  was used as the starting point for our own code developments, and we have followed similar guidelines to generalize and enlarge it where needed. In the same way, for each numerical method, derivatives of functions v ∈ K are computed using automatic differentiation. The numerical optimization procedure used in order to compute an approximation of θ * a minimizer of problem (26) is given by the quasi-Newton L-BFGS algorithm  [22]. The code used to generate the examples shown here is available at https://github. com/agussomacal/ConDiPINN The interested reader can reproduce our results and test the impact of the variations of certain parameters such as ε, K, the sampling method, and the machine precision.
| Analysis Focus group discussions and interviews were audio-recorded, transcribed verbatim, and translated from Malagasy to English. Transcripts were uploaded to NVivo 12 (QSR International Pty Ltd, 2020). Responses were coded following the pre-selected pattern of the preliminary framework but with the flexibility to add codes as necessary. To check for consistency, a second member of the research team (JY) double-coded two of the transcripts. The coded responses were reviewed to confirm the relevance of existing themes within the preliminary framework, identify areas of ambiguity or potential for misinterpretation, and identify any additional relevant themes not yet included. We then refined the framework based on these findings. Finally, based on interviewee recommendations, we identified key stakeholders (i.
| METHODS We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. 14 fore starting the project, we agreed on a systematic review protocol, including the choice of databases to be searched, search terms, eligibility criteria, and data to be extracted. Methods to aggregate data and to solve any dispute were also decided. The protocol was registered in PROSPERO database (registration number: CRD42021247888). 15 PRISMA-P abstract and study checklists are provided as Supporting Information: materials. IRB approval is not required for this study type. Studies were selected according to the following criteria.
Preterm neonatal lung ultrasounds of infants who received surfactant treatment were compared with those who did not receive surfactant. The following relevant clinical variables were also compared between the same group: gestational age (GA); oxygen saturation over inspired oxygen fraction (S/F); small for gestational age (SGA); gender and prenatal steroid administration. We excluded "grey" literature, unpublished, or nonpeerreviewed reports. Information Sources and search strategy. The databases PubMed, SCOPUS, Biomed Central, and Cochrane library were searched between January 2011 and December 2021. The 10-year interval provides a comprehensive search of the topic as Brat and coworkers reported the numerical score for the first time in 2015. 9 e above databases were searched using keywords: Lung ultrasound and surfactant and neonate.
| Data collection process and items Data were extracted using a standardized form derived from the Cochrane data collection template and reported in a Microsoft Excel (Microsoft, 2013) spreadsheet. The following data were extracted from the studies: author, year of publication, number of neonates included, number of areas scored, LUS or lung ultrasound profile predictive for surfactant treatment, and area under the curve (AUC); reported (or derived) raw true negative, false negative, true positive, and false positive were extracted to compute sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive and negative likelihood ratios, diagnostic odds ratio (DOR). The following clinical characteristics of surfactant treated, and nontreated infants were recorded: GA in weeks (median); oxygen saturation over inspired oxygen ratio (S/F ratio); sex (percentage of male infants), SGA, and use of prenatal steroids.
The BK polyomavirus (BKPyV) is an opportunistic pathogen, which is only pathogenic in immunosuppressed individuals, such as kidney transplant recipients, in whom BKPyV can cause significant morbidity. To identify broadly neutralizing antibodies against this virus, we used fluorescence-labeled BKPyV virus-like particles to sort BKPyV-specific B cells from the PBMC of KTx recipients, then single-cell RNAseq to obtain paired heavy-and light-chain antibody sequences from 2,106 sorted B cells. The BKPyV-specific repertoire was highly diverse in terms of both V-gene usage and clonotype diversity and included most of the IgM B cells, including many with extensive somatic hypermutation. In two patients where sufficient data were available, IgM B cells in the BKPyV-specific dataset had significant differences in V-gene usage compared with IgG B cells from the same patient.
Furthermore, some clinical studies and case reports have indicated that infusion of intravenous immunoglobulin-containing BKPyV-specific neutralizing antibodies  (21)  can prevent active BKPyV replication in KTx recipients  (22)  and successfully treat PyVAN  (23, 24, 25). These encouraging observations have stimulated the search for monoclonal antibodies against BKPyV, specifically those with broad specificity, capable of neutralizing all four BKPyV genotypes  (26). Indeed, two such antibodies are currently in clinical trials (NCT04294472 and NCT05358106). However, the robust humoral response after BKPyV reactivation does not always coincide with control of virus replication  (15, 19, 27) , and persistent BKPyV DNAemia in the face of a strong humoral response appears to be related to the emergence of neutralization escape mutations in the virus capsid  (28, 29).
These characteristics are expected for antigen-specific B-cell repertoires: diversity (as measured by the D50 and the Shannon indices) is lower, because the number of antigen-specific clonotypes is smaller than the total B-cell repertoire, whereas the expansion of dominant clones generates inequality in antigen-specific repertoires, leading to a higher Gini coefficient. Having access to the BCR repertoire in total B cells and the sorted BKPyV-specific B cells allowed us to test directly the possibility that antibody sequences in the SpecB dataset may have been contaminated by sequences from the total B-cell population. In patient 3. 1, the TotB dataset contained one dominant clone (clone ID 4968), represented by 46 cells, and 21 expanded clones with at least three cells in the dataset per clonotype.
In the SpecB dataset from the same patient, clone 4,968 was not found, and of the 21 expanded  clones, only three (clone IDs 152, 81, and 304. 1) were represented in the SpecB dataset, where they were among the top 5 expanded clones. The specificity of these three clones was validated experimentally (Fig  6 ). The expanded clonotypes in the TotB dataset that were not detected in the SpecB dataset (and therefore non-specific for BKPyV) made up 6. 2% of the TotB BCR repertoire (105 cells of 1,691), and this allowed us to estimate the potential contamination of the 1,542 SpecB BCR sequences with non-specific sequences. For example, if the SpecB repertoire contained 10% sequences from total B cells, then we would expect that 0. 6% of the SpecB repertoire, or 10 cells, would carry BCR sequences from the nonspecific expanded clonotypes in the TotB repertoire.
Because this was significantly different (P < 0. 001, Fisher's exact test) from the observed number (i. e. , zero cells), we can therefore conclude that the contamination of the SpecB dataset with non-specific antibody sequences was less than 10%, and possibly less than 5% (P < 0. 05), for patient 3. 1. As the cells for the other patients were sorted at the same time, this estimate is likely to be valid for the other patients, and indeed, the same analysis carried out on clonotypes found in SpecB and TotB datasets for patient 3. 2 gave similar results (Table  S3 ). Sufficient data were available for patients 3. 1 and 3. 2 to analyze the BKPyV-specific repertoire overlap in independent PBMC samples from the same individual (Fig  2G ). The BKPyV-specific repertoire overlap found in two samples from patient 3.
2, calculated as the number of shared clonotypes divided by the number of clonotypes in the smaller dataset, was 51%, whereas for the three PBMC samples that provided sufficient data from patient 3. 1, three independent two-by-two measures of repertoire overlap were obtained (3. 1 T2 versus 3. 1 T3: 132/177 = 74. 6%; 3. 1 T2 versus 3. 1 T4: 64/ 97 = 66. 0%; 3. 1 T3 versus 3. 1 T4: 62/97 = 63. 9%), giving a mean repertoire overlap of 68%. The α-diversity of the BKPyV-specific BCR repertoire, representing the total number of BKPyV-specific clonotypes in each patient, was calculated for patients 3. 1 and 3. 2 using the Chao1 (32) and first-order Jackknife (33) estimators. Similar results were obtained in both patients, with a more precise estimate of 400-500 distinct clonotypes in patient 3. 1 (437 ± 25 by Chao1, 449 ± 99 by Jackknife1) compared with 200-700 clonotypes in patient 3.
In both patients 3. 1 and 3. 2, the IGHV4-39 gene, which showed the greatest enrichment in the overall BKPyV-specific BCR repertoire, was used equally in both IgG and IgM antibodies, whereas several other VH-genes showed preferential use by either IgG or IgM antibodies (Fig  3D  and E ). In patient 3. 1, IGHV1-3, IGHV3-53, IGHV3-66, IGHV4-31, and IGHV4-61 were significantly more frequent in BKPyVspecific IgG compared with IgM, whereas for IGHV1-18, IGHV3-11, IGHV3-48, IGHV3-7, and IGHV5-51, the reverse was true. Although the dataset was smaller, a similar pattern was seen in patient 3. 2, with IGHV2-5 and IGHV4-61 more frequently used by BKPyV-specific IgG, whereas IGHV5-51 was more frequent in BKPyV-specific IgM. Significant disparities were also observed in light-chain V-gene usage between BKPyV-specific IgG and IgM (Fig  S9 ).
Identification of a "41F17-like" family of broadly BKPyV neutralizing antibodies Broadly neutralizing BKPyV-specific monoclonal antibodies, including the high-affinity clones 41F17 and 27O24, have previously been isolated from the PBMC of a healthy adult donor  (26). Because four of the KTx recipients we studied had successfully suppressed BKPyV replication, we investigated whether their BCR repertoire included antibodies with similar broadly neutralizing properties. In order to identify antibodies in our dataset with similar heavy-and light-chain sequences to the 41F17 and 27O24 monoclonals, concatenated CDR3 amino acid sequences were clustered as a phylogenetic tree (Fig 5A ). We were able to identify a cluster of Κ light-chain IgG antibodies (green clade; Figs  5A  and S11 ) that shared a CDR3 sequence motif with the 41F17 antibody (Fig  5B ).