dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of CD44 variants containing a single exon insert was carried out using qPCR. The expression of each CD44 variant in fibroblast (black circles) was compared with myofibroblasts (black squares) at 0 h and 72 h timepoints. (C-F) represent; CD44 (standard) (C), CD44v3 (D), CD44v6 (E), CD44v10 (F). The expression of each CD44 variant in fibroblasts (black circles) was compared with IL-1β (1. 0 ng/ml) stimulated fibroblasts (black squares) at 0 h and 6 h timepoints. G-J represent; CD44 (standard) (G), CD44v3 (H), CD44v6 (I) and CD44v10 (J). For the mRNA expression of other CD44 variants in basal fibroblasts TGF-β 1 and IL1-β stimulation including CD44v2, CD44v4, CD44v7, CD44v8, and CD44v9, see Figures S1 and S2. CD44v5 was not expressed in fibroblasts or myofibroblasts. Data are shown as mean ± SD from three experimental repeats with statistical significance represented by; *p ≤ 0.
CD44s protein expression was knocked down over a time course of 0-72 h. Cells were treated with siRNA CD44s (+) or with an siRNA negative control (-). Western blot was used to identify CD44s in cell lysates and GAPDH as a loading control (E). Western blots were quantified using Image J software to perform densitometry (using CD44/GAPDH ratios). Proteins from fibroblasts transfected with negative siRNA controls (black circles) were compared with protein from fibroblasts transfected with an siRNA to CD44 (black squares) over a 0-72 h time course (F). Data are shown as mean ± SD from three experimental repeats with statistical significance represented by; *p ≤ 0. 05, **p ≤ 0. 01, ***p ≤ 0. 001, and ****p ≤ 0. 0001. ICC shows Fibroblasts (G and J) and myofibroblasts (H, I, K, and L) following CD44s siRNA transfection (J-L) compared with cells transfected with a negative siRNA (G-I).
Data Analysis and Statistical Methods All the collected data were quantitative variables, and descriptive analysis of the sociodemographic and clinic characteristics of the participants were conducted. Chi-square test was used for bivariable analysis of possible association between BC and explicative variables. Multivariable analysis with conditional logistic regression was used to select risk factors associated with BC occurrence. All statistical tests were conducted using Epi Info (V 3. 5. 3) and SPSS (V 17. 0) software. The Odds ratio (OR) of each risk factor was then calculated. Data are presented as average ± standard deviation (SD). All tests were considered significant at P< 0. 05.
These models are well suited for testing pre-established hypotheses about theoretical constructs (e. g. to answer questions like "how much higher is mosquito abundance for each additional millimeter of rainfall?"); however, to explore hypotheses and extract knowledge in complex systems, machine-learning (ML) "algorithmic" models might be more suitable  [20, 24]. In fact, these models are inherently able to capture complex patterns (such as nonlinear relationships and complex interactions between variables) contained in data. After a good predictive algorithm is fitted to a dataset, post hoc interpretation methods may uncover the complex relationships contained in the data and learned by the model, which in turn can be carefully linked to prior knowledge to identify meaningful-possibly unforeseen-causeeffect relationships, valuable thresholds or interactions  [25] [26] [27].
This predict-then-explain modeling workflow is being increasingly used to generate knowledge from complex datasets  [24, 26]  and is commonly referred to as "interpretable machine learning" (IML)  [25, 26]. The main objective of this study was to improve our overall understanding of the ecological niche and determinants of the biting rates of the main malaria vectors in a rural area of southwestern Burkina Faso. To do so, we used entomological collections and environmental variables extracted from high-resolution EO data, in a datadriven and IML modeling framework. In this research article, after a presentation of the methods and results, we discuss the environmental (landscape and meteorological) drivers of the human-biting activity of the main malaria vectors in the Diébougou area. We also briefly present some potential practical uses of our results to support the conceptualization and deployment of locally tailored VC interventions.
Entomological data Anopheles human-biting activity was monitored as part of a study carried out in the Diébougou rural health district located in southwest Burkina Faso  [28]. Twenty-seven villages in this 2500 km 2 wide area were selected according to the following criteria: accessibility during the rainy season, 200-500 inhabitants per village, and distance between two villages greater than 2 km. Seven rounds of mosquito collection were conducted in each village between January 2017 and March 2018. The periods of the surveys span some of the typical climatic conditions of this tropical area (three surveys in the "dry-cold" season, two in the "dry-hot" season, one at each extremum of the rainy season) (see Additional file 1: Summary of the meteorological conditions around the sampling points).
Landscape data A land cover map of the study area was produced by carrying out a geographic object-based image analysis (GEOBIA)  [34]  using multisource very-high-and high-resolution satellite-derived products. The GEOBIA involved the following main steps: acquisition/collation of the satellite products (Satellite Pour l'Observation de la Terre (SPOT)-6 image acquired on 2017-10-11, Sentinel-2 image acquired on 2018-11-16, and a digital elevation model (DEM) from the Shuttle Radar Topography Mission  [35] ), acquisition of a ground-truth dataset composed of 420 known land cover samples by both fieldwork (held in November 2018) and photo-interpretation of satellite images, and classification of the land cover over the whole study area using a random forest (RF) algorithm  [36]. The definitions of land cover classes were those proposed by the Permanent Interstate Committee for Drought Control in the Sahel  [37].
The resulting dataset was a georeferenced raster image, where each 1. 5 × 1. 5 m pixel was assigned a land cover class. The confusion matrix was generated using the internal RF validation procedure based on the out-of-bag observations, and the quality of the final classification was assessed by calculating the overall accuracy from the confusion matrix  [38]. Spatial buffers were then defined to characterize the environmental conditions at the neighborhood of each HLC collection point. Four buffer radii were considered: 250 m, 500 m, 1 km, 2 km. The distance of 2 km was chosen as the largest radius to minimize overlaps among buffers coming from different villages and because local dispersal of Anopheles beyond this distance can be considered negligible  [29, [39] [40] [41]. We calculated the percentage of landscape occupied by each land cover class in each buffer zone around each collection site.
Additional indices related to the presence of water were calculated. The theoretical stream network was produced for the study area by first generating a flow accumulation raster dataset from the DEM and then applying a threshold value to select cells with accumulated flow greater than 1000  [42]. The quality of the product was assessed visually by overlaying it on the SPOT-6 satellite image. We then derived two indices for each collection site: the length of streams in each buffer zone, and the shortest distance to the streams. In order to describe attractiveness and penetrability of households for malaria vectors, the geographical location of the households in the villages were recorded and two indices were computed. First, the Clark and Evans aggregation index  [43]  was calculated to describe the degree of clustering of the households in each village, as it has been suggested that scattered habitations in a village might increase the attractiveness for some vector species  [44].
Bivariate analysis using correlation coefficients The bivariate relationship between the presence/abundance of each vector species and the environmental variables was assessed using multilevel Spearman correlation coefficients  [55]  with the village entered as a random effect. Multilevel correlations, contrary to simple correlation, account for non-independency between observations in a dataset, by introducing a factor as a random effect in the correlation (on the same principle as random effects in mixed linear regressions). Landscape variables: The correlation coefficient was calculated for each landscape variable (i. e. percentage of landscape occupied by each land cover class in each buffer zone). Meteorological variables: Past weather is likely to influence the size of the sampled mosquito generation with varied delays.
Doing so enables us to limit overfitting and over-optimistic performance metrics due to spatial autocorrelation  [59]. For the presence models, precision-recall plots were then generated from the observed and predicted values, and the PR-AUC was calculated and compared to the baseline of PR curve (i. e. the PR-AUC of a random-guess classifier for the dataset). The PR-AUC is a measure of predictive accuracy of a binary classification model particularly suitable for imbalanced classification problems  [60]. It makes sense when compared to the baseline PR-AUC, which is the rate of "presence" observations in the dataset. For example, a model with a baseline of 0. 01 and a PR-AUC of 0. 2 performs 0. 2/0. 01 = 20 times better than a random-guess, or no-skill, classifier. We also calculated sensitivity and specificity at the optimal probability threshold (i.
Software used The software packages used in this work were all free and open-source. The R programming language  [62]  and the RStudio environment  [63]  were used as the main programming tools. An R package was developed  [64]  to extract the NASA meteorological data (MODIS and GPM). The land cover layer was generated using the following R packages: "RSAGA"  [65] , "rgrass7"  [66] , "raster"  [67] , "sf "  [68] , "rgdal"  [69]  and "randomForest"  [70]. The "spatstat"  [71]  package was used to compute the Clark and Evans aggregation index. The QGIS software  [72]  was used to create the map of the study area. The "landscapemetrics" package  [73]  was used to calculate the percentage of landscape occupied by each land cover class in the buffer areas. The "correlation"  [55]  package was used for the correlation analysis.
The "caret"  [74]  and "ranger"  [75]  packages were used to fit the random forest models in the statistical analysis. The "CAST"  [76]  package was used to create the temporal folds for cross-validation. The "MLmetrics"  [77]  package was used to calculate the model evaluation metrics. The "iml"  [78]  and "pdp"  [79]  packages were used to generate the partial dependence plots. The "patchwork"  [80]  package was used to create various plot compositions. The "ggmap"  [81]  package was used to generate the map of the vector biting rates. The "precrec"  [82]  package was used to generate the precision-recall plots for the presence models. The "tidyverse" meta-package  [83]  was used throughout the entire analysis.
Entomological data A total of 1512 nights of HLC were conducted among the 27 villages during the seven entomological surveys. Altogether 3056 vectors belonging to the Anopheles genus were collected: 1322 An. coluzzii, 708 An. funestus, 616 An. gambiae s. s. and 410 from other species. An. funestus was present in 12% of the nights of HLC (182 times), while both An. coluzzii and An. gambiae s. s. appeared on 20% of the nights of HLC (respectively 297 and 302 times). The distribution of the biting rates in the positive sessions (i. e. sessions with at least one bite) was highly left-skewed (for An. funestus: median = 2, SD = 4. 7, max. = 36; for An. gambiae s. s. : median = 2, SD = 1. 5, max. = 10; for An. coluzzii. : median = 2, SD = 6. 8, max. = 50). Figure  1  shows the distribution of the biting rates of the three main vector species by village and survey.
manuscript. PT was supported by the French Institute of Research for Sustainable Development (IRD) through an international volunteer fellowship and the French National Research Agency (ANR) through the ANORHYTHM project (ANR-16-CE35-008). This work was supported by public funds received in the framework of GEOSUD, a project (ANR-10-EQPX-20) of the program "Investissements d' Avenir" managed by the French National Research Agency.
Acknowledgements We thank populations of the villages for their kind support and collaboration. We also thank all the  field and laboratory  staff for their strong commitment to the  REACT  project. We thank all the anonymous persons that make datascience web forums alive by asking and answering technical questions; these forums were extensively used to elaborate this modeling work. Map data copyrighted by OpenStreetMap contributors and available from https:// www. opens treet map. org. This work contains modified Copernicus Sentinel data (2018).
A total of 689 samples from 397 participants were therefore eligible for the analysis. Among them, 60 HTLV-positive patients were followed over time in a longitudinal study and tested to analyze the evolution of their serological profile. However, in order to have independent data for each HOST participant, we included in the study only the first sample tested of each participant, more specifically, 199 HTLV-positive samples from 199 participants (100 HTLV-1 from 100 patients and 99 HTLV-2 from 99 patients) and 198 negative samples from 198 participants (Fig  1 ; S2 Appendix).
Trans-Hit Bio (Canada) We obtained 65 HTLV-2 samples from a commercial source. All patients came from USA and had available data on ethnicity and ages. Samples were collected between 2013 and 2016, screened with an HTLV-1/2 ELISA (brand not specified by Trans-Hit Bio) and subsequently confirmed as HTLV-2 positive by WB (brand not specified by Trans-Hit Bio).
In contrast to other confirmatory immunoassays, we excluded the detection of anti-P24 antibodies in the Multi-HTLV test because it has been shown in WB assays that indeterminate samples frequently show an isolated P24 band attributable to false reactivity  [36]. Relevant antigens were designed and obtained synthetically according to the reviewed and published non-redundant sequences available at the repository www. uniprot. org (Table  2 ). Each antigen composition was individually optimized to obtain the best performance in terms of sensitivity and specificity, and by calculating the areas under the Receiver Operating Characteristic curve (AUC), using SPSS Statistics software (IBM, USA, version 19. 0). This optimization step was performed by using a panel of HTLV-infected and seronegative samples from EFS.
Statistical analysis Dataset. Data from three cohorts (HOST, EFS, Trans-Hit Bio) were pooled. Gold standard HTLV type assignments were available for the HOST cohort samples by either PCR or type specific ELISA and INNO-LIA results. For the two other studies, sample type assignment was only available based on INNO-LIA test results. The pooled dataset was split into a development and validation set. All data from EFS and Trans-Hit Bio were allocated to the development set and the HOST samples were allocated randomly (p = 0. 5) to either the development or the validation dataset. Only HOST samples were included in the validation set because of PCR-based HTLV classifications. PCR has high confirmation power and strain discrimination, which in turn leads to more accurate validation analysis. The validation set consisted of 96 positive samples (53 HTLV-1 and 43 HTLV-2) and 103 negative samples.
The addition of EFS and Trans-Hit Bio data to HOST data for the development set allowed a larger sample size, which is beneficial for statistical methods and machine learning algorithms. It comprised 308 positive samples (180 HTLV-1 and 128 HTLV-2 samples) and 123 negative samples. For both HTLV detection and typing, a logistic regression (LR) model was trained. Outliers were expected in the development set due to possible imperfect INNO-LIA labeling of EFS and Trans-Hit Bio samples. Sample deviances were used to detect outliers, which were omitted if the human interpretation of the Multi-HTLV assay did not match the INNO-LIA label. Due to the gold standard labeling of HOST samples, only samples with deviances higher than the maximum deviance of HOST samples were checked. Deviances below this threshold were expected to arise from regular variability but not necessarily from mislabeling.
Interpretation algorithm. Logistic regression was used as a baseline positive/negative classification method using GP21-I/II (Confirmation), P19-I/II (Confirmation), GP46-I/II (Confirmation), P19-I, GP46-I and GP46-II as independent variables (features). To prevent (quasi-) complete separation problems, Bayesian analysis with non-informative prior assumptions was applied, by use of the R arm package (version 1. 11-2)  [37]. A backward selection procedure was used to remove non-significant features, based on Akaike's information criterion, to achieve the final minimal adequate model  [38]. A Random forest model was also examined but no performance improvements were found. This analysis is therefore not included in this article. We have combined an additional algorithm to logistic regression formulas, to categorize questionable samples into an indeterminate category.
First, a minimal intensity value of 10 was determined for labeling confirmation antigens as positive. A 2-cluster Gaussian mixture model was fitted using the R mclust package (version 5. 4. 7), and the clusters' intersection point was used as threshold. Then, we developed an algorithm based first on counting the number of positive antigens per sample and then applying logistic regression formulas. The algorithm is as follows: if the number of confirmatory antigens is zero, then the sample is directly considered to be negative; if the number of antigens is equal or higher than one, then a first logistic regression formula for confirmation is applied. It permits classification of samples with one reactive antigen among negative (LR probability (p)<0,5) or indeterminate categories (p> = 0,5), and samples with number of antigens higher than one in indeterminate (p<0,5) or positive (p> = 0,5) categories.
Because HOST samples were tested and typed by both PCR and INNO-LIA HTLV, for sake of clarity, we describe here the results of this fully documented cohort. The performance of the algorithm is presented for both the development and validation datasets to evaluate the reactivity of all samples of the HOST cohort. However, the strength of the algorithm can only be evaluated on the validation dataset, since it was not used to develop the algorithm. Sensitivity and specificity were calculated based on perfect matching of the results as compared to PCR gold standard. We obtained a sensitivity of 100% (96,4-100 development set; 96,15-100 validation set) in both sets and a specificity of 97,90% (92,65-99,42) and 98,06% (93,  47)  for the development and validation sets, respectively. The proposed new method achieves slightly improved performances compared to INNO-LIA in terms of reduced indeterminates (4 vs.
Acknowledgements We are grateful to  Salomé Clémente  and  Leonor Rodrigues  for their help in collecting data for the first experiment, to  Inês Santos  for the maintenance of the spider mite populations and the plants, and to  Inês Fragata  and  Fabrice Vavre  for useful discussions and suggestions. This work was funded by an  FCT-ANR  project  ( FCT-ANR//BIA-EVF/0013/2012 ) to SM and  Isabelle Olivieri  and by an  ERC   Consolidator Grant  ( COMPCON, GA 725419 ) to SM. MC was funded through an  FCT PhD fellowship  ( SFRH/BD/136454/2018 ), and FZ through an  FCT Post-Doc fellowship  ( SFRH/BPD/125020/2016 ). Funding agencies did not participate in the design or analysis of experiments. Version 5 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi.
Quality of Control Program Data of freedom from infection. Differences in disease status between countries and nonstandardized approaches to assess freedom from infection pose a risk for countries with CPs for non-regulated diseases as infected animals may influence the progress of the disease control or eradication program. The implementation of output-based standards allows estimation and comparison of the probability of freedom for non-regulated cattle diseases in European countries. The aim of the current study was to assess the existence and quality of data that could be used for estimating freedom from infection in European countries. The online data collection tool was sent to 32 countries participating in the SOUND control COST Action and was completed by 24 countries. Data on cattle demographics and data from CPs of IBR and BVD exist in more than 50% of the response countries.
SOUND control covers 32 countries and provides a great opportunity to assess at a large scale the data currently available for estimating the probability of freedom from infection for EU non-regulated cattle diseases and provide recommendations to support the future development of output-based standards. Our study was conducted within the scope of the SOUND control Working Group 2 (WG2) activities  (22)  and aimed to (i) provide an overview of the existence of potential data required as inputs for estimating freedom from infection in the 32 Action member countries, (ii) evaluate and compare the quality of these data using a standardized approach, and (iii) review data sources of available data. JD, IBR, and BVD are among the diseases most frequently targeted by CPs implemented in the Action member countries  (23).
Online Data Collection Tool A thorough description of the online data collection tool, including its development, key lessons learned during the process, and definitions of the variables, can be found in van Roon et al. (24). The online data collection tool was designed using LimeSurvey software  (25). Data from the previous year (or the most recent available) were requested for data collection. Briefly, the online data collection tool was divided into two main parts with four sections and (Figure  1 ): • I. General information. This section included five basic questions about the time period of the data assessed in the questionnaire, country, contact information of the respondent, and definitions of dairy and beef cattle. Part 1: • II. Demographics. This section included 10 questions about the cattle population in the country or region considered in the questionnaire: number of cattle and herds, average herd size, number of births, number of herds with calves, cattle density, and number or percentage of farms with small ruminants and mixed farms (mixed farms are defined as all dairy herds that also have a type of beef cattle, such as veal calves, suckler cattle, etc.
• Completeness. Refers to whether there are missing and/or unknown data fields in the database (e. g. , for the variable "number of cattle/herds in territory, " completeness would represent the percentage of farmers entering this information in the data base: 85% of all farmers having filled in the data means completeness of this variable is 85%). • Accuracy. Aims at assessing to what extent the stored values for an object are the correct values (e. g. , when data validation procedures are implemented on a regular basis, it is more likely that data is accurate). Data quality was evaluated using a standardized scoring method (Figure  1B )  (24). First, for each quality criteria, a score of three ("good"), two ("fair"), or one ("poor") was given. Second, the overall quality of the available data was then calculated using the sum of the scores obtained for each quality criterion.