dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset The analysis is performed for 28 EU countries (i. e. , Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and United Kingdom -UK), from 1998 to 2017 (data availability). Therefore, the dataset represents a panel with 28 cross-sections and 20 years (N > T). The dependent variable is the size of the underground economy (und_econ). The variable is denoted as a percentage of gross domestic product (GDP), revealing the extension of the underground economy compared with the official one. It is taken from  Medina and Schneider (2019). The variable of interest is the share of the refugee population, which is a constructed indicator.
" between the newcomers and the native population. Both components, in numbers of persons, are taken from the World Bank (2022a) online database. Three categories of control variables are used to isolate the effect of the variable of interest, being inspired by the "structural model" of the underground economy developed by  Medina and Schneider (2018). The first category includes macroeconomic variables, such as the tax burden (tax), GDP per capita (GDPc), and unemployment (u). The second category considers institutional determinants, such as institutional quality (inst_qual) and the size of government (gov), while a third is focused on trade openness variable (open). Migration and refugee flows can be correlated altering the quality of estimation. On the one hand, several pull factors are almost the same, and on the other hand, the common migration routes are intensively used by both illegal migrants and asylum seekers.
To tackle this issue, in addition to  Medina and Schneider's (2018)  model, the share of immigrant population variable (im_pop) is considered in order to control for migration flow. The variable denotes total immigrants as a percentage of the population in the destination country. In this way, the effect of refpop on und_econ is better isolated in respect to im_pop. The number of immigrants is reported by the Eurostat (2022) online database, while the population is taken from the World Bank (2022a) online database. According to the literature, we expect both negative and positive signs for this variable. Detailed information about the variables and their expected signs is presented in Table  A1  in the Appendix, according to  Medina and Schneider's (2018)  model. All variables are treated in percentages, so the marginal effect in a regression estimates their elasticities.
Results By using the threshold dynamic panel method proposed by  Kremer et al. (2013) , a set of models offers the results supporting the main interpretations and conclusions. As the first step of analysis, the VIF tests for detecting multicollinearity are performed by considering all determinants. The results are provided in Table  A2  in the Appendix. These 2 The estimations are performed based on STATA code freely offered by  Diallo (2020). clearly show that the variables used in the regressions are moderately correlated, as all test values are below the critical level of 5, as  Ringle et al. (2015)  suggest. The results are reinforced by the correlation matrix in Table  A3  in the Appendix. All coefficients of correlation are lower than the critical level of 0. 9 recommended by  Hall and Asteriou (2011, p.
The positive slope is in line with  Altındağ et al. (2020 ), Berdiev et al. (2020) , or Berdiev (2021), while the negative effect reinforces the results of  Akgündüz et al. (2018) , or  Cengiz and Tekguc (2018). The study is limited by the data availability; further research targeting the extension of the panel in terms of the period can be performed as soon as the new dataset is officially available.
Tree neighborhood Every tree of the public domain of Bordeaux is geolocalized, identified to the species level and has its age and height referenced. This information is available through the city database for urban trees (https://opendata. bordeaux-metropole. fr/explore/dataset/bor_arbres/information/). We described tree neighborhood around each focal horse chestnut by assessing tree density, tree diversity, number of conspecific horse-chestnuts and number of maple trees. Canopy cover was assessed in buffer of 100m. Tree diversity was assessed in buffer of 20m around each focal tree, both using tree species richness and Shannon diversity index. Number of conspecific horse chestnut and number of maple were assessed in buffer of 100m. Buffer sizes were chosen in regard to C. ohridella poor flight dispersal capacity  (Augustin et al.
Abundance of C. ohridella and associated damage In June 2020, we haphazardly collected 30 leaves on each sample trees. The leaves were collected with a pole pruner by rotating around the tree so as not to favor one aspect over another. We preserved the leaves in paper bags and brought them back to the lab for herbivory estimation. We estimated the damage caused by the horse-chestnut leafminer by evaluating the leaf area covered by mines. We took pictures of each leaves and processed them using the Inkscape software. Mines were manually cropped on the images and the percentage of leaf area damaged was calculated as the ratio between the area covered by mines and the total leaf area. Mean leaf area damaged was calculated across the 30 leaves collected on each tree. Additionally, we conducted two surveys to estimate the number of flying adults of the first and second generation around each sample horse chestnut.
Body mass was measured to the closest 20g (PW-200-FC, A&D co. , Tokyo, Japan) before and after each HWI session. Participants towel-dried themselves and donned dry underwear prior to measurement. Tympanic temperature (Thermoscan, Braun GmbH, Kronberg, Germany), HR (Fingertip Pulse Oximeter, ChoiceMMED, Beijing, China) and thermal sensation (using a 7-point scale from -3 "unbearably cold" to 3 "unbearably hot") [23] were recorded immediately pre and then at 5-min intervals during HWI sessions. The tympanic temperature recording device was stored at room temperature and was only exposed to the spa conditions for brief periods for recording. Test-retest reliability data is displayed for measures without a control group. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) collected from our laboratories are as follows: tympanic temperature (ICC = 0.
Statistical analyses Data are presented as mean (standard deviation) unless stated otherwise. All data were assessed for normaility using Shapiro-wilk normaility tests, with non normally-distributed data log-transformed before analysis. P-values are reported, however, no level of significance was set, as per recommendations from recent American Statisitician journal editorials and American Association of Statistics position statement  [27, 28]. Therefore, whilst p-values are reported in this study, effect size (Cohen's d) is the predominant statistic variable used for assessing change. Cohen's d (d) were calculated using the standard deviation of the mean difference for within-group changes, whilst between-group changes were calculated using the square root of average variance for between-group changes.
Discussion The aim of this current study was to investigate a) whether a longer-term (13 ± 2 session over a 6-week period) passive HWI protocol could elict physiological adaptations consistent with HA, and b) if such a protocol could improve intermittent running performance and athletes' belief in within-match running ability during the in-season (six matches were performed during the intervention period). This study is the first, to the authors' knowledge, to demonstrate that passive HWI may be of benefit in-season for team sport athletes. The findings of this study show the addition of HWI 2-3 x/week to a semi-professional ARF training program can induce HA and increase belief in match running performance. Furthermore, HWI may have the potential to improve intermittent running performance when compared to team-training alone, amongst semi-professional ARF athletes.
, 2019) , could help predict climate change impacts on their population dynamics and human health  (Nadeau et al. , 2017). A hard tick species Ixodes ricinus (Acari: Ixodidae) plays an important role in transmitting Lyme borreliosis, the most prevalent vector-borne disease in Europe. They spend the majority of their lives off-host, exposing themselves to surrounding environmental conditions. Like other tick species and invertebrate ectotherms, temperature and relative humidity have been shown to affect their survival  (Needham and Teel, 1991). However, experimental studies reporting the effects of both temperature and relative humidity on the survival time of I. ricinus are currently limited  (Herrmann and Gern, 2010; Lees, 1946; Milne, 1950). The most detailed reported dataset could be dated back to 1950, in which the sample size per experimental group was small (5 ticks/condition)  (Milne, 1950).
, 2017)  and assess the uncertainty of a judgment on the cause-of-death-specific mortality rate of wildlife  (Walsh et al. , 2018). To date, there has never been a report on expert opinion elicitation framework for the effects of multiple explanatory variables for survival analysis, particularly in ecological studies. Therefore, this study proposed a hierarchical Bayesian PSM (B-PSM) framework that incorporates expert opinion as supplementary information for estimating the effects of continuous explanatory variables on the mortality rate when the existing survival dataset is limited. We explored a historical survival dataset of I. ricinus ticks exposed to various combinations of controlled temperature and relative humidity with a limitation of small sample size. We conducted online interview sessions with multiple experts, eliciting the opinions of the experts, then combined them (by either pooling or averaging the opinions) as prior distributions of the environmental effects on survival time in the B-PSM.
Tick survival data The published survival dataset of I. ricinus ticks reported by  Milne (1950)  was used. Briefly, survival time T (in days) of female adult I. ricinus ticks was observed under a variety of laboratory conditions, controlling for temperature Q (5, 11, 19, and 25 • C) and relative humidity U (0, 50, 70, 85, and 95%). The age of adult ticks averaged 12 weeks post-moulting, at the beginning of the experiment. Five ticks were assigned to each exposure condition, pairing the temperature and relative humidity (Q, U). In total, the survival time of 100 ticks was observed in 20 different controlled conditions (Table  S1 ). Considering the expected difficulties in achieving and maintaining a perfect dehydrated condition (U = 0%) in the laboratory by the time of the study (~1950), we substituted the condition U = 0% with U = 10% in further analysis.
Weibull survival model We first assessed the most suitable distribution for the tick survival data from the exponential, Weibull, log-logistic, log-normal, logistic, and normal distributions, using the Frequentist Parametric Survival Model (F-PSM) in the survival package  (Therneau, 2020; Therneau and Grambsch, 2000). Here, temperature Q and relative humidity U were treated as categorical explanatory variables. The F-PSM models were compared using the Akaike information criterion (AIC). As a preliminary result, the Weibull distribution yielded the smallest AIC, suggesting that it is the most suitable distribution of the survival data. We subsequently validated the suitability of the Weibull distribution by exploring the Weibull property of linearity of the natural log of time ln(t)  (Kleinbaum and Klein, 2012)  where the relationship between ln(t) and the log negative log of the Kaplan-Meier survival estimates ln(-ln Ŝ(t)) should be linear.
Implementation of Markov chain Monte Carlo algorithm All the analyses in our study were performed using R programming language version 3. 6. 0 (R Core Team, 2019). The hierarchical Bayesian models were run by the Markov chain Monte Carlo (MCMC) algorithms in JAGS  (Plummer, 2003)  using rjags package  (Plummer, 2019). For each Bayesian model, we run three independent MCMC chains consisting of 10,500,000 iterations in total, with a burn-in period of 50,000. The autocorrelation was controlled by thinning out the MCMC samples, keeping every 700 values. The convergence of MCMC chains was inspected by trace plots and the Gelman-Rubin convergence test. Probability of direction (p-direction) was used as an index of effect existence for each parameter using bayestestR package  (Makowski et al. , 2019).
Model evaluation and validation The posterior distributions were evaluated by simulating survival time of 100 ticks in 20 conditions, then compared with the observed data. Subsequently, the validity of the Bayesian estimations was evaluated using posterior predictions. For each Bayesian model, a total of 15,000 values of each posterior estimates (B, p, and k) were used to simulate the replication of other previously published data: 1) Survival time of 30 female I. ricinus ticks in 6 conditions (5 ticks/condition), compared to survival time ranges by  Lees (1946) ; 2) Survival proportions of female I. ricinus ticks after 2-and 3-days post-exposure to 5 conditions (100 ticks/condition), compared against the survival proportion after 2 days reported by  Herrmann and Gern (2010). The 95% confidence intervals for the Herrmann and Gern data were calculated by assuming a binomial distribution.
, 2017; Cope et al. , 2019; Singpurwalla, 1988). To our knowledge, this study was the first to demonstrate an expert opinion elicitation framework on the survival regression coefficients for the effects of multiple continuous covariates and their interaction, explicitly in the ecological domain. Despite having a small sample size, tick survival data were initially assessed with frequentist parametric survival models (F-PSMs) to uncover the most suitable probability distribution and model formulation. This preliminary step was important for guiding the formulation of biological hypothesis, questions for expert opinion interviews, expert opinion elicitation, and hierarchical Bayesian framework. As suggested by the best-fitted F-PSM model, we designed the interview questions and elicitation scheme to capture the expert opinions on the effects of relative humidity, temperature, and their ambiguous interaction.
The study samples The patients included in this study were followed between 2013 and 2019 in a French private orthopedic practice and were diagnosed with a Class II malocclusion (mandible posterior to the maxillary arch) and OSAS (initial Apnea/Hypopnea Index (AHI) ≥1 event/hour of sleep). All these children were treated with the personalized, innovative oral appliance Ortho2D ® described in a previous paper combining rapid palatal expansion and nocturnal mandibular advancement  [17]. According to the French law n°2012-300 of March 5 th , 2012 on researches involving human beings, because this study was retrospective and non-interventional, and because data were anonymized, no signed consent was required. This study sample was divided into two groups: treated patients (TP), considered as cases, for whom data about the respiratory status were collected before (TPt0) and after (TPt1) the orthodontic treatment; and patients considered as control (CP) whose age matched that of the TPt1 patients, and who did not yet receive the orthopedic treatment at the time they undergone polysomnographic exams.
To really assert the therapeutic efficiency of simultaneous rapid maxillary expansion and mandibular advancement for the management of OSAS children, longitudinal study of untreated patients should be performed: to compare the same two sleep studies separated by the same amount of time between treated and control groups. As our study was a retrospective observational one, this would have implied to not treat the control children group, even though OSAS symptoms were identified. It is for these ethical reasons that only pre-treatment data were collected for the not-yet-treated control group. Anyway, these results also highlighted the benefit of acting at early ages: to reduce the length of intervention (9 ±3 months versus 12 months in the study of Villa et al. on rapid maxillary expansion therapy in 4-10 years old patients  [14]  or 13 to 15.
Plant Material and Marker-Assisted Backcross Breeding Introgression lines were generated through backcross breeding between FL478 (male donor, indica ssp. ) harboring the Saltol QTL  (Thomson et al. , 2010)  and OLESA, a salt sensitive, long grain japonica cultivar (female recipient) (Supplementary Figure  1 ). FL478 was obtained from the International Rice Research Institute (IRRI, accession number IRGC 117406). OLESA was provided by Càmara Arrossera del Montsià SCCL (Amposta, Spain, accession number NRVC20120346). It involved the initial cross and three backcrosses coupled to embryo rescue  (Ohnishi et al. , 2011) , and three selfing steps. Complementary approaches were used to monitor the introgression of indica segments into the japonica background of OLESA. The first step consisted in the screening of progenies for the presence of the Saltol QTL using the Saltol linked SSR marker SKC10  (Thomson et al.
RNA-Seq Library Preparation, Sequencing, and Data Analysis Plants (IL22, OLESA) were grown in hydroponic cultures as described above (phenotyping for salinity tolerance). Leaves from salt-treated (80 mM NaCl, 24 h of treatment) and untreated control plants were harvested (three replicates per genotype and condition, four plants per replicate). Samples were immediately frozen in liquid nitrogen and grinded by TissueLyser (Qiagen). Total RNA was extracted using the Maxwell RSC Plant RNA Kit (Promega). Details of library preparation and RNA-Seq analysis are presented in Supplementary Method. Statistical analysis of read counts was performed with R, with the HTSFilter package to remove low-expressed genes and the edge R package. Criteria used for calling differentially expressed genes (DEGs) were foldchange (log 2 fold change >0.
5 or <-0. 5, for up-regulated and down-regulated genes), significance of differences in expression (P ≤ 0. 05), and expression level (fragments per kilobase per million mapped reads-FPKMs ≥ 25). Gene Ontology (GO) enrichment of DEGs was carried out by Singular Enrichment Analysis (SEA) using AgriGO  (Tian et al. , 2017). Data sets for RNA-Seq analyses have been deposited at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) with accession number GSE167342).
Finally, genes that were down-regulated by salt stress classified into a broad spectrum of Biological Processes in both genotypes (IL22 and OLESA) (Supplementary Figures  11, 12 ). Gene expression correlation between RNA-Seq and RT-qPCR analyses in IL22 and OLESA plants could be observed for selected genes (Figure  7C ). For better understanding of salt-responsive mechanisms in IL22 plants, all salt-responsive genes of IL22 and OLESA plants were visualized using the MapMan (Version 3. 6. 0RC1) software. This analysis showed that the salt stress response mechanisms were complex and involved multiple mechanism and signal transduction pathways while confirming as a   (Supek et al. , 2011). The most enriched GO terms in the set of up-regulated and down-regulated genes are presented (upper and lower panels, respectively).
FUNDING This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 678168; NEURICE. MB, HM-C, JG-A, LC, and XS were funded by  H2020-SFS-2015-2 -NEURICE -678168. We acknowledge financial support from the  Spanish Ministry of Science and Innovation-State Research Agency (AEI) , through the " Severo Ochoa Programme for Centres of Excellence in R&D "  SEV-2015-0533  and  CEX2019-000902-S , and the " CERCA Programme/Generalitat de Catalunya ". We acknowledge support of the publication fee by the  CSIC Open Access Publication Support Initiative  through its  Unit of Information Resources for Research (URICI).
Metagenomic classifiers are widely used for the taxonomic profiling of metagenomics data and estimation of taxa relative abundance. Small subunit rRNA genes are a gold standard for phylogenetic resolution of microbiota, although the power of this marker comes down to its use as full-length. We aimed at identifying the tools that can efficiently lead to taxonomic resolution down to the species level. To reach this goal, we benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then compiled the best tools (BBTools, FastQC, SortMeRNA, MetaRib, EMIRGE, VSEARCH, BBMap and QIIME 2's Sklearn classifier) to build a pipeline called RiboTaxa. Using metagenomics datasets, RiboTaxa gave the best results compared to other tools (i.
e. Kraken2, Centrifuge, METAXA2, phyloFlash, SPINGO, BLCA, MEGAN) with precise taxonomic identification and relative abundance description without false positive detection (F-measure of 100% and 83. 7% at genus level and species level, respectively). Using real datasets from various environments (i. e. ocean, soil, human gut) and from different approaches (e. g. metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not discerned by current bioinformatics analysis opening new biological perspectives in human and environmental health.
Thus, using sequencing data, that allows access to all the 16S variable regions without primer and PCR biases, such as shotgun metagenomics or 16S-targeted gene capture by hybridization  (7) , appears to be the most suitable to describe precisely microbial communities based on a phylogenetic marker. Commonly, microbial profiling of shotgun metagenomics data is performed through annotation of reads, de novo assembled genes or metagenome assembled genomes  (8). However, deciphering taxonomic diversity is often limited by incomplete genome databases compared to large 16S rRNA gene repositories, despite relentless efforts to update them  (9). Direct read annotation is largely favoured because of computational arduousness during assembly. Consequently, several approaches have been proposed and compared to estimate microbial diversity and relative abundance of species using whole genome or marker reference databases  (10).
Most methods such as MEGAN  (11)  rely on sequence alignment based on matched database sequences and use the lower common ancestor (LCA) algorithm to assign taxa to the query sequence. However, the LCA algorithm fails to consider the differing degrees of similarity between the query and the database hit sequences. To overcome this problem, BLCA  (12)  has adopted a Bayesianbased LCA method whereby the taxonomic assignment of the query sequence is weighted by a Bayesian probability based upon the sequence similarity of the database hit to the query. Nevertheless, alignment-based approaches are generally memory and time consuming  (13)  while the advantage of k-mer based approaches is its fast computational speed. As an example, Kraken  (14) /Bracken  (15)  and SP-INGO  (16)  employ alignment-free method by using exact k-mer matches between reference databases and reads to report the microbial diversity based on LCA of every taxon.
Although k-mer approach is very efficient and fast on large metagenomics data, the choice of the length k highly influences the classification  (17, 18)  The hybrid pipeline METAXA2  (19)  allows rRNA extracting reads from large sequencing data sets using Hidden Markov Models (HMM)  (20)  and directly subjects putative rRNA reads to a BLAST  (21)  search against a specialized rRNA database. METAXA2 can report read origin (archaeal, bacterial, nuclear eukaryote, mitochondrial or chloroplast) and hierarchical classification down to genus or species level. Here, the step of rRNA read extraction accelerates the algorithm execution while enhancing specificity of the analysis. Although HMM was used in METAXA2, other methods can be employed such as general-purpose mappers such as BBMap (https://jgi.