dataesr/scientific-paragraphs-categorization huggingface dataset:
Normalized insurance data The insurance data used in this study are provided by the Caisse Centrale de Réassurance (CCR), the French public reinsurance company that manages a large database of its clients' contributions. It consists of the number of subsidence claims aggregated per year and per municipality, from 2000 to 2018. By "municipality", we mean a commune, the smallest administrative unit in France. The representativeness of the data varies over the years and the municipalities considered, depending on the proportion sent by insurers. Total and average annual costs were also provided by CCR, butt we focused on the number of claims, which gave more satisfactory results. The data is aggregated at the municipal level for confidentiality reasons. In addition, this is an official and frequently used zoning classification, so there is a lot of additional data available.
For normalization purposes, the large number of subsidence claims is divided by the number of single-family houses in the municipality located in areas prone to subsidence. This information comes from a dataset developed by the French Ministry of Ecology (MTES/CGDD/SDES) (Nouveau zonage d'exposition au retrait-gonflement des argiles : plus de 10,4 millions de maisons individuelles potentiellement très exposées, 2023). It provides the number of houses per municipality in different exposure classes (no exposure, low and medium to high). The zoning is based on a map of exposure to clay shrink-swell developed by the French Geological Survey (BRGM) (BRGM, 2023), coupling a national susceptibility map (based on geotechnical data and 1:50000 geological maps) and insurance statistics. We call the new variable obtained after normalization the "normalized number of claims".
Natural disaster (CatNat) decree database In France, financial losses to insured property following a natural catastrophe may be covered by a national compensation scheme, the "Régime d'indemnisation des Catastrophes Naturelles" (CatNat regime). Compensation under this scheme is, with a few exceptions, subject to the official recognition of the state of natural disaster through the publication of a decree, an "arrêté de reconnaissance de l'état de catastrophe naturelle" (CatNat decree), for a given year and municipality. The GASPAR After 2000, the publication of a CatNat decree is conditioned by a geotechnical criterion and by a meteorological criterion. The geotechnical condition requires the municipality to have at least 3 % of its surface mapped as shrinkable clay, based on the BRGM exposure to clay shrink-swell map.
Pair-wise ranking of years To assess the ability of the drought indices to represent subsidence damage for a given year, we compute the Kendall rank correlation coefficient  (Wilks, 2006)  between the two variables, over the entire study period. The Kendall rank correlation test compares the respective ranks of paired data by counting concordant and discordant pairs. It produces a coefficient ranging from 0 to 1, the Kendall tau, and a p-value (the null hypothesis being that the variables are not correlated). This test has the advantage of being nonparametric and robust to extreme values. To maximize the number of observations used to compute the Kendall tau, we merge data from multiple municipalities to form regional subsets. An association is performed to pair the municipal insurance data with the soil moisture available on the ISBA grid.
Validation The best drought index configuration is then applied to independent data to verify that we avoid over-fitting. For this purpose, the drought index is computed for all the municipalities not used in the calibration (forming a validation set) and confronted with the corresponding normalized number of claims. For further analysis, the optimal drought indices of both calibration and Finally, both the drought index and the normalized insurance data are compared to the set of historical CatNat decrees. For each subset, we assess the ability of the optimal drought index to predict claims by counting contingencies and deriving scores  (Wilks, 2006). First, we transform quantitative optimal drought magnitudes and normalized claim counts into Boolean occurrences of predictions and observations, respectively.
Differences of temporal and spatial resolution Uncertainty is also introduced by the different temporal and spatial resolution of the two main datasets compared in this study, i. e. soil moisture and number of claims. The annual temporal resolution of this study is forced by the insurance claims dataset, while the soil moisture simulations are more precise (daily). The coarse resolution of the first dataset is explained by the difficulty of insurers to precisely date the occurence of subsidence damage due to the slow nature of the phenomenon. For this reason, this work could not be carried out at a finer temporal scale. Nevertheless, there is an advantage to using daily soil moisture data: the SWI distribution is robust over a 19-year period. This is especially important in the context of a changing climate.
Zoning of clay shrink-swell exposure Clay shrinkage depends on triggering factors, such as drought, and predisposing factors, the most important of which are the presence of clay, its nature, and its ability to shrink and swell. In order to minimize the influence of these factors, we focused our analysis on the housing stock located in moderate to high hazard zones of municipalities with a significant history of subsidence damage. The blending of moderate and high exposure zones in the housing dataset is a first source of uncertainty, as the same drought is expected to trigger different responses in soils of different susceptibility. Despite the selection of the housing dataset, the uncertainty arising from the exposure to clay shrink-swell exposure map itself must be considered. The exposure map, made at a regional scale of 1:50000, does not integrate very local clayey soil occurrences.
Acknowledgments The PhD thesis work of  Sophie Barthélémy  was co-funded by  BRGM  ( Bureau de Recherches Géologiques et Minières) ,  CCR (Caisse Centrale de Réassurance)  and  Météo-France. The authors would like to thank the operational services of  Météo-France (DCSC) , especially  Jean-Marie Willemet , for providing the lai_clim soil moisture dataset. The authors declare no conflict of interest. https://doi. org/10. 5194/egusphere-2023-1366 Preprint. Discussion started: 27 July 2023 c Author(s) 2023. CC  BY 4. 0 License.
43 Predictors of response to biologic classes are currently being investigated as part of the ISAR initiative. Limitations include those common to observational studies such as missing data and recall bias. Reasons for choice of one class of biologic over another were also not collected, introducing the possibility of a phenotype selection bias, and criteria of eligibility were simplified to encompass eligibility for both biologic classes including a broad definition of allergic phenotype, defined as a positive skin prick or specific IgE test, but also, the presence of atopy and/or pre-therapy BEC cut-offs. More definitive evidence of allergy driven disease would have been preferable, but this is rarely collected in real life. Future work to assess the effectiveness of anti-IgE and anti-IL5/5R in patients eligible for both according to age of asthma onset is planned.
Study design and data sourceThis study was designed, implemented, and reported in compliance with the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) Code of Conduct (EMA 2014; EUPAS 38128), with all applicable local and international laws and regulation, and registered with ENCEPP (https://www. encepp. eu/encep p/viewR esour ce. htm?id=38129). Governance was provided by The Anonymous Data Ethics Protocols and Transparency (ADEPT) committee (registration number: ADEPT0920). All data collection sites in ISAR have obtained regulatory agreement in compliance with specific data transfer laws, country-specific legislation, and relevant ethical boards and organizations. The ISAR database has ethical approval from ADEPT (ADEPT0218) and is registered with the European Union Electronic Register of Post-Authorization studies (ENCEPP/DSPP/23720).
Joash Tan (BSc, Hons) of the Observational and Pragmatic Research Institute (OPRI), for editorial and formatting assistance that supported the development of this publication. FU N D I N G I N FO R M ATI O N This study was conducted by the Observational and Pragmatic Research Institute (OPRI) Pte Ltd and was partially funded by Optimum Patient Care Global and AstraZeneca Ltd. No funding was received by the Observational & Pragmatic Research Institute Pte Ltd (OPRI) for its contribution. CO N FLI C T O F I NTER E S T S TATEM ENT Paul E. Pfeffer has attended advisory boards for AstraZeneca, GlaxoSmithKline and Sanofi; has given lectures at meetings supported by AstraZeneca and GlaxoSmithKline; has taken part in clinical trials sponsored by AstraZeneca, GlaxoSmithKline, Novartis and Sanofi, for which his institution received remuneration; and has a current research grant funded by GlaxoSmithKline.
Nasloon Ali was an employee of OPRI at the time this research was conducted. OPRI conducted this study in collaboration with Optimum Patient Care and AstraZeneca. Ruth B. Murray declares no relevant conflicts of interest. Charlotte Ulrik has attended advisory boards for AstraZeneca, ALK-Abello, GSK, Boehringer Ingelheim, Novartis, Chiesi, TEVA,Covis Pharma, and Sanofi-Genzyme; has given lectures at meetings supported by AstraZeneca, Sandoz, Mundipharma, Chiesi, Boehringer Ingelheim, Orion Pharma, Novartis, TEVA, Sanofi-Genzyme, and GlaxoSmithKline; has taken part in clinical trials sponsored by AstraZeneca, Novartis, Merck, InsMed, ALK-Abello, Sanofi-Genzyme, GlaxoSmithKline, Boehringer Ingelheim, Regeneron, Chiesi, and Novartis; and has received educational and research grants from AstraZeneca, MundiPharma, Boehringer Ingelheim, Novartis, TEVA, GlaxoSmithKline, and Sanofi-Genzyme.
Regeneron, Novartis Canada, AstraZeneca, GlaxoSmithKline, and ValeoPharma. Mariana Muñoz-Esquerre has attended advisory boards for AstraZeneca, ALK-Abello, GSK, Chiesi, TEVA, and Sanofi-Genzyme; has given lectures at meetings supported by AstraZeneca, Chiesi, Novartis, TEVA, Sanofi-Genzyme, Ferrer, and GlaxoSmithKline; has taken part in clinical trials sponsored by AstraZeneca, GlaxoSmithKline, Regeneron, Palobiofarma, Chiesi, and Novartis; and has received educational and research grants from AstraZeneca, Novartis, ALK-Abello, TEVA, GlaxoSmithKline, Chiesi, and Sanofi-Genzyme. J. Mark FitzGerald reports grants from AstraZeneca, GSK, Sanofi Regeneron, Novartis paid directly to UBC. Personal fees for lectures and attending advisory boards: AstraZeneca, GSK, Sanofi Regeneron, TEVA. Peter G. Gibson has received speakers and grants to his institution from AstraZeneca, GlaxoSmithKline, and Novartis.
2 | ME THODS 2. 1 | 21 Study design and data sourceThis study was designed, implemented, and reported in compliance with the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) Code of Conduct (EMA 2014; EUPAS 38128), with all applicable local and international laws and regulation, and registered with ENCEPP (https://www. encepp. eu/encep p/viewR esour ce. htm?id=38129). Governance was provided by The Anonymous Data Ethics Protocols and Transparency (ADEPT) committee (registration number: ADEPT0920). All data collection sites in ISAR have obtained regulatory agreement in compliance with specific data transfer laws, country-specific legislation, and relevant ethical boards and organizations. The ISAR database has ethical approval from ADEPT (ADEPT0218) and is registered with the European Union Electronic Register of Post-Authorization studies (ENCEPP/DSPP/23720).
3) 0 (0. 0) p = 0. 763 ≥ 3, n (%) 1 (0. 4) 0 (0. 0) 3 (1. 0) 0 (0. 0) Abbreviations: Anti-IgE, anti-immunoglobulin E (omalizumab); anti-IL5/5R, anti-interleukin 5/5 receptor (benralizumab, mepolizumab or reslizumab); ER, emergency room; ISAR, International Severe Asthma Registry; SD, standard deviation. seen in our study (i. e. , Anti-IL-5/5R: 47. 1%; Anti-IgE: 38. 7%), most likely due to differences in patient cohorts, exacerbation definitions, exacerbation rates at baseline and the presence of other confound- ing factors such as country, LTOCS use, and presence of nasal pol- yps. However, a recently published US claims database reporting a 35  B L E 2 Comparative effectiveness of anti-IgE or anti-IL5/5R biologics in a matched cohort of patients with severe asthma from ISAR. Antisimilar anti-IL5/5R-induced exacerbation rate reduction to that seen in our study (55%) found that this level of reduction is associated with a reduction in exacerbation-related costs per patient of USD $6439.
Riyad Al-Lehebi has given lectures at meetings Farmaceutici, Circassia, Danone, Faes, Genentech, Guidotti Malesci, supported by AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, GlaxoSmithKline, Hal Allergy, Merck, MSD, Mundipharma, Novartis, and Sanofi, and participated in advisory board fees from Orion, Sanofi Aventis, Sanofi, Genzyme/Regeneron, Stallergenes, GlaxoSmithKline. Chin Kook Rhee declares consultancy and lecture UCB Pharma, Uriach Pharma, Teva, Thermo Fisher, and Valeas. Borja fees from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, G. Cosio declares grants from Chiesi and GSK; personal fees for advi- Mundipharma, MSD, Novartis, Sandoz, Sanofi, Takeda, and Teva- sory board activities from Chiesi, GSK, Novartis, Sanofi and Handok. Luis Perez-de-Llano declares non-financial support, personal AstraZeneca; and payment for lectures/speaking engagements from fees, and grants from Teva; non-financial support and personal fees Chiesi, Novartis, GSK, Menarini, and AstraZeneca, outside the sub- from Boehringer Ingelheim, Esteve, GlaxoSmithKline, Mundipharma, mitted work.
Del Dorscheid is supported by the following grants and and Novartis; personal fees and grants from AstraZeneca and Chiesi; clinical trials: Canadian Institutes of Health Research, British Columbia personal fees from Sanofi; and non-financial support from Menairi Lung Association, and Michael Smith Foundation for Health Research outside the submitted work. Diahn-Warng Perng (Steve) received and AstraZeneca, sponsorship to attend or speak at international meetings, honoraria for lecturing or attending advisory boards, and research grants from the following companies: AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Daiichi Sankyo, Shionogi, and Orient Pharma. Bassam Mahboub reports no conflict of interest. Eileen Wang has received honoraria from AstraZeneca, GlaxoSmithKline, Wefight, and Clinical Care Options.
Statistical analysis All statistical analyses were performed with the R software (version 4. 0. 2  70  ). We included only cases in which both males courted the female, and we observed copulation within the 30 min of the test. The departure from the random choice was tested with a two-tailed binomial test. To test for a treatment effect, mate-copying scores were analysed in a generalized linear mixed model (GLMM) with binary logistic regression (package lme4 71 ). All models included normalised air pressure as fixed effect as it was shown to influence mate copying in D. melanogaster, 45 however, accounting or not for air pressure did not change any conclusion. In experiments including pictures, we also included the photo ID as fixed effect. We also included a random block effect to account for non-independence of flies from the six trials that were run simultaneously.
ACKNOWLEDGMENTS We thank  Nathalie Parthuisot  and  Magdalena Monier  for their help with fly care,  Julien Favier  and  Audrey Dussutour  for access to their kitchen and materials. We thank  Alberto Ferrus  for providing the GAL4 lines C232, C739 and NP1131 and  Thomas Preat  for the lines Canton-S, w-, rut2080 and GAL4 G0050. Study supported by the '' Laboratoires d'Excellence (LABEX) ''  TULIP  ( ANR-10-LABX-41 ), the  Toulouse Initiative of.
Excellence '' IDEX UNITI '' ( ANR11-IDEX-0002-02 ) transversality grant and the  MoleCulture  ( ANR-18-CE37-0015 ) to GI and ED. SN acknowledges IAST funding from the  French National Research Agency (ANR)  under the  Investments for the Future (Investissements d'Avenir) program  ( ANR-17-EUR-0010 ). I. designed the study, S. N. performed the experiments under supervision of E. D. and G. I. S. N. wrote the first draft of the manuscript, and all authors contributed.
These parasites had all been cultured for 24 h to allow any atypically developing gametocytes to survive without being cleared by the spleen (Figures  S2A  and S2B ). To better understand gene expression in wild-type parasites, we first removed mutant cells from our dataset and combined all wild-type Smart-seq2 data with a high-resolution map of gametocyte development that we created from 6,191 dropletbased (10x) wild-type single-cell transcriptomes covering the asexual cycle, sexual commitment, and the bifurcation into either sex (Figure  S2C ). The two wild-type datasets were integrated to generate a combined uniform manifold approximation and projection (UMAP) plot (Figures  3A  and S3 ). Branching and pseudotime analysis on the combined wild-type data showed male and female gametocytes initially follow a common transcriptional trajectory after branching from the asexual cycle before they assume distinct sexual identities (Figures  3A, S3,  and S4 ).
Single-cell transcriptomes distinguish differentiation genes from putative sex ratio regulators To understand the nature of phenotypes from the single-cell data of the mutants, we first investigated their developmental progression in comparison to wild-type parasites (Figures  4A  and S5 ). We then used this integrated dataset to determine the maturation profile of cells for each mutant in each lineage compared with the wild-type reference (Figure  4B ). Through this analysis, we observe delays in the normal developmental program of some mutants, but we cannot define unique transcriptomic states associated with aberrant phenotypes. To explore the latter, we created a cellxgene visualization (http://obilab. molbiol. umu. se/gcsko/). Furthermore, we conducted a differen-tial expression analysis of mutants in relation to the wild-type maturation cluster they most correspond to for each sex (i.
Screen analysis Barcodes in Illumina sequencing data were counted and tabulated per library. To facilitate downstream analysis, these counts were expressed as log 2 proportions, with 0. 5 added to counts. To quantify the uncertainty induced by the fact that the barcodes that successfully entered libraries were a random sample of those in the actual population (with sampling occurring at the time of purification, sorting, pipetting of template material, and during amplification), standard deviations were calculated for each barcode in each sample, based on technical PCR duplicates. These standard deviations were made more precise by taking the moving average of values (k=11), with samples ordered by the abundance of the barcode in question, and enforcing monotonicity. This approach provided an estimate of the log 2 proportion made up by each barcode in each sample, with an associated uncertainty estimate.
FACS of single gene ko lines FACS was performed either on parasites revived from frozen stabilates (typically on day 5 post-injection at a parasitaemia of >2 %). or direct from transfection (typically on day 7-8 post-transfection at a parasitaemia of >2 %). Gametocytes were analysed directly from infected mouse blood or were purified from using gametocyte non-activation medium and Histodenz density gradient, and stained with Hoechst 33342 as above. Purified and stained gametocytes were resuspended in gametocyte non-activation medium, taking care to not expose parasite pellets to air and kept at room temperature at all times. Samples were immediately analysed for mCherry (female gametocytes) and GFP (males gametocytes) using a BD LSR Fortessa instrument and data were analysed using Flowjo (v.
Bulk-RNA-seq The generation and initial analysis of the additional time points for the bulk RNA-seq dataset was performed as described previously. 6 riefly, the PB GAMi line (engineered to overexpress the AP2-G transcription factor and undergo synchronous conversion into gametocytes upon the induction with rapamycin) was synchronised to the late schizont stage and either induced or treated with vehicle only. At different time points post induction, the blood containing the developing parasites was harvested. Plasmodipur filters (EuroProxima) were used to remove the leukocytes according to the manufacturer's instructions and red blood cells were lysed by resuspension in ice-cold 13 E-lysis buffer (1. 5M NH 4 Cl, 0. 1M KHCO 3 , 0. 01 EDTA). The resulting parasite pellet was washed with 1xPBS and stored in Trizol reagent for future RNA extraction.
Independently, in order to generate the reference transcriptome of male and female gametocytes, the 820 reporter line,  20  was used to sort 5x10 6 male and female gametocytes, as described in the previous section. The resulting male and female pellets were also stored in Trizol for further processing. Complete RNA was isolated from all the samples using Trizol/chloroform extraction followed by isopropanol precipitation and 1-2 mg of starting material was taken for mRNA isolation and stranded RNA-seq library construction. The libraries were prepared using NEBNext library preparation modules, with minor modifications of the protocol shown to improve the yield when sequencing AT-rich transcriptome of Plasmodium parasites. 61 The samples were pooled and sequenced using an Illumina HiSeq 2500 system according to the manufacturer's instructions.
A non-sorted negative control, and a positive 100-cell control were included on every plate. Library preparation and sequencing First and second strand cDNA synthesis and pre-amplification were performed as described in Reid et al. 62  with 96-well plates and 25 PCR cycles. Quality control of cDNA samples was monitored using a high-sensitivity DNA chip on the Agilent 2100 Bioanalyzer. Libraries were prepared using dual indexes as described in Reid et al. 62  and pools of 384 cells (4 plates) were sequenced. onto one lane on a HiSeq 4000 using v4 chemistry with 75 bases paired-end reads and run according to manufacturer's instructions. 10x Genomics chromium scRNA-seq mCherry hsp70 parasites were used for all experiments, so any data generated was comparable with Smart-seq2 data which used this background as a wild type control.
Bulk RNA-seq data analysis The raw data processing, generation of initial *. cram files and adapter removal was performed using the default analysis pipelines of the Sanger Institute. The raw data was transformed into paired *. fastq files using Samtools software (v. 1. 2). 63 The generated reads were re-aligned to the Plasmodium berghei ANKA genome (PlasmoDB-30 release) in a splice-aware manner with HISAT2 46 using the -known-splicesite-infile option within the splicing sites file generated based on the current genome annotation. Resulting *. bam files were sorted and indexed using Samtools (v. 1. 2) and HT-seq python library (v. 1. 3. 1). 47 was used to generate reads counts for all genes for further processing. The matrix of gene counts was combined with the time points published previously.