dataesr/scientific-paragraphs-categorization huggingface dataset:
6%, 1. 1%, 1. 7%, and 2. 3% of the total number of birds sampled, respectively, and, due to their low occurrence, were not included in strain-specific analysis. Data from an additional 35 birds sampled in the UK as part of another body of work were analyzed  (Dunn et al. , 2018; Thomas et al. , 2022). When accounting for outliers in the dataset following threshold testing to remove uncommon taxa from analysis (Appendix 4), the threshold for the total proportion of samples within which a taxon had occurred was set at 7%, as this was the lowest threshold which did not introduce outliers, potentially skewing the results. All reported results are based on this data subset. DCA1 explains the geographical separation of samples, with the majority of samples from Senegal having positive values on dimension 1, and birds sampled from France largely having negative values (Figure  1 ).
Birds sampled in Senegal were distributed more widely across the poles, with greater dissimilarity between the points indicating more varied diet. DCA1 and DCA2 explained the majority of the variation observed in this dataset; therefore, only they were included as explanatory variables in GLM analysis (Appendix 5). The final dataset contained only one sample from Hungary, which was removed for GLM analysis following model assumption testing, as this point had leverage equal to zero, potentially disrupting the model. When assessing the relationship between infection status, season, and diet, season was the only variable to affect the presence of T. gallinae (GLM: LRT 4 = 33. 494, p < 0. 001). When considering individual strains, no variables were significantly associated with the presence of Tcl-1 or Type C, but season was significantly associated with the presence of GEO (GLM: LRT 4 = 48.
A PPE N D I X 3 BIOINFORMATIC ANALYSIS AND DATA CLEANING Bioinformatic analyses were carried out on both diet and parasite infection datasets using a custom pipeline designed for analysis of diet metabarcoding data from Illumina MiSeq  (Drake et al. , 2021). Data were first checked for truncation in the MID-tags, using the "grep" function to extract information of the number of reads with and without the full 10 base pair MID-tag before the primer. FastP  (Chen et al. , 2018)   mismatch was permitted; for DNA extracted from oral swabs, no primer mismatches were permitted, in order to prevent inflation in the number of OTUs generated and lead to the retention of higher quality reads, which are more likely to represent true variation  (Thomas, 2017). This was necessary for analyzing parasite samples as even one base mismatch leads to the assignment of a new strain of Trichomonas gallinae.
Reads were then clustered based on zOTUs, also in Unoise3. Clustering based on zOTUs uses a 100% clustering threshold, as opposed to the standard 97%, assuring high resolution and preventing variants being incorrectly collapsed into clusters  (Edgar, 2016). BLASTn v2. 7. 1  (Camacho et al. , 2009)  was then used to assign sequences to their closest matching taxa from GenBank, with the minimum percent identity set to 95% for DNA from fecal samples and 97% for DNA from oral samples in order to remove poor matches. BLAST results were filtered using the dplyr package in R to retain only the top bit score for each zOTU, calculated by combining the percent ID and e-value to indicate how good the alignment is. These BLAST outputs were analyzed using MEGAN v6. 15. 2  (Huson et al. , 2016)  to assign taxonomic names to each zOTU.
In instances where there was more than one top hit, zOTUs would be assigned to the highest common taxonomic rank. Data processing and cleaning following this initial bioinformatic processing differed for DNA extracted from fecal samples for diet analysis and DNA extracted from oral swabs for T. gallinae analysis. For DNA from fecal samples, following taxonomic assignment, results were manually checked to identify zOTUs not assigned to species level, generally resulting from variations in nomenclature or erroneous sequences on GenBank. Where possible, sequences were assigned to species level; however, where there were multiple top hits on GenBank, they were assigned to the highest taxonomic rank in common. The geographic range of all taxa identified was checked using Kew Science "plants of the world online" (POWO, 2019).
Cleaning the dataset There were two stages to the cleaning of data to remove artifacts from the final dataset, which cause false positives. The two stages of filtering complement each other to provide a more comprehensive method of data cleaning  (Drake et al. , 2021). The first stage of cleaning was to remove contamination. Contamination can occur at two stages: in the laboratory, exogenous contaminants may be introduced into the samples during extraction or PCR, or internal contamination, such as tag jumping, during library preparation or sequencing  (Taberlet et al. , 2018). In order to remove potential false positives occurring as a result of exogenous contamination, the highest read count in negative controls was identified for each taxon. The same was done for unused MIDtag combinations in order to account for tag-jumping.
In order to balance false positives and false negatives, a slightly more conservative threshold of 4% was selected, which removed most false positives from positive controls, without running the risk of removing too many genuine reads. This 4% threshold was applied across the dataset in a sample filtering basis, and read counts for any taxa failing to exceed 4% of total reads of a sample were replaced with zero. These two filtering methods were combined and only read counts exceeding both the maximum contamination threshold and the 4% per sample threshold were retained for further analysis. Following  To further clean the data, a cumulative depth curve was generated for each sample using the top 10 variants occurring in each sample  (Lighten et al. , 2014; Thomas, 2017). Based on the assumption that artifacts occur in much lower numbers than genuine reads, the presence of a clear inflection point (as seen in Appendix 3: Figure  A3a ) indicated the divide between a true sequence and an artifact, and any reads occurring beyond this inflection point would be considered artifacts and not included in further analysis  (Lighten et al.
DNeasy Blood and Tissue kit, 200-400 μL of culture medium was used. Samples from the UK dataset, and DNA extracted in 2018 used the modified ammonium acetate method, whereby isolated parasite samples were centrifuged at 3200 rpm for 5 min, the supernatant discarded and the pellet retained. 250 μL of Digsol buffer (20 mM EDTA, 50 mM Tris, 120 mM NaCl, 1% SDS, pH 8. 0) and 20 μL Proteinase K (Qiagen) were added and samples were incubated overnight at 37°C to allow digestion. Following digestion, 300 μL of 4M ammonium acetate was added, mixed by vortexing, and incubated at room temperature for 30 min, vortexing every 10 min. Samples were centrifuged at 13,000 rpm for 10 min and the supernatant transferred into a new tube, discarding the pellet. A volume of 1 mL 100% ethanol was added to the supernatant to allow precipitation of the DNA.
, 2010). Bearing in mind this polymorphism, slight variation in sequences produced by HTS may be produced by a different copy of an rRNA gene, as all reads generated during sequencing are returned (Porazinska et al. , 2010). Porazinska et al. (2010)  investigated the repeatability of using HTS to analyze nematode mock communities, also targeting SSU rRNA. By using mock communities, all expected sequences were known, and about half of the reads generated in the dataset differed from the presumed consensus sequence by at least 1 bp. Without extensive use of technical repeats, these polymorphisms cannot be distinguished from artifacts, but are likely to account in part to the high number of zOTUs differing from known sequences returned from sequencing in this study.
, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/edn3. 402 by Cardiff University, Wiley Online Library on [05/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License tion, analysis or interpretation of the data: REY (field work, lab work, analysis and interpretation); WOCS, JCD, IPV, JWM (analysis and interpretation); CJO; MK; MBD; MS (acquisition of data -field sampling in Senegal); HL; CE (acquisition of data -field sampling in France); OK (acquisition of data -field sampling in Hungary); RCT, JCD KCH, SJG (acquisition of data -provided UK dataset). Writing of the manuscript: REY. ACK N OWLED G M ENTS Natural Environment Research Council GW4+ Studentship NE/ L002434/1; Royal Society for the Protection of Birds project CASE partner, Genetics Society Heredity Fieldwork Grant; British Society for Parasitology International Training and Fieldwork Award.
This is possibly due to a relatively lower contribution of high-altitude rocks to the 10 Be MS signal when the massif is partly covered by ice. Based on these models, we show the following: -Simulations with constant precipitation or without flexure and without either ice cover or sea level changes do not produce the observed increase in 10 Be MS after 30-40 ka. -The large incision rate in the Vésubie River (compared to Bevera, Roya, Estéron and Paillon) can only be explained by the effect of isostatic rebound, the latter being smaller in the simulations with no glaciers. -Cosmic ray exposure (CRE) data on river-polished walls are not dense enough to discriminate between constant (0. 5 mm yr -1 ) or variable (0. 3 then 0. 7 mm yr -1 ) precipitation rates. -However, a constant precipitation induces more important denudation in the earliest stages of the predicted evolution (compared to the reference simulation where precipitation rate is low), hence producing on average a lower 10 Be MS at the end of the model.
e. over the area where post-glacial isostatic rebound is important. The sampling sites of these rivers being rather close to their headwaters, they are not sensitive to sea level variations. Quantification of each river sensitivity to local or regional processes like isostatic uplift, sea level changes, precipitation or ice cover, depending on the dating site location, could therefore be useful to better understand the respective importance of these external forcings on this southern Alpine margin. "Référentiel Géologique de France" (RGF-Alpes). This work has been supported by the French government, through the UCA-JEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01. Review statement. This paper was edited by Simon Mudd and reviewed by Yanyan Wang and Sebastien Carretier.
Acknowledgements. Fruitful discussions with Guillaume Duclaux (Geoazur) were greatly appreciated. The authors thank Sebastien Carretier and Yanyan Wang for their insightful comments on the first version of this paper and associate editor Simon Mudd for handling the review process and for the final review of this paper. Financial support. This study is part of a project that has been funded by the French Geological Survey (Bureau de Recherches Géologiques et Minières; BRGM) through the national programme.
Materials and methods A PubMed and Embase search was performed for entries between January 1996 and December 2022 using the following MESH terms: (glioma) AND (cingulate) OR (cingulate gyrus) OR (cingulate cortex) OR (cingulum). Inclusion criteria were as follows: peer-reviewed clinical studies of microsurgical series reporting resection of gliomas of the cingulate gyrus, written in English. Exclusion criteria were case reports  [6] , abstracts, book chapters, and conference papers. Two independent reviewers (SD, CT) assessed the data by applying the inclusion and exclusion criteria. There were no disagreements. This study was performed in agreement with the published Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines  [7]. Data extraction was done as per individual studies.
Statistical analysis For our meta-analysis, only studies reporting individual data were analyzed. Because of high variations in study characteristics, a statistical analysis using a binary randomeffects model (DerSimonian-Laird method) was performed using OpenMeta[analyst] software (Agency for Healthcare Research and Quality). Weighted summary rates were determined using meta-analytical models. Heterogeneity was tested for each meta-analysis; pooled estimates were obtained for all outcomes. Results of series concerning GTR, STR, and morbidity were compared using a meta-regression with a random effect. p values < 0. 05 were considered statistically significant. Using R package (version 2023. 06), we assessed the heterogeneity of studies under consideration, particularly for GTR, the primary outcome, and further for STR.
Two rounds of pilot testing occurred in July 2019 with 27 participants using a think-aloud exercise. The purpose of this pilot testing was to identify confusing language, explore participant understanding of the discrete choice experiment questions, and evaluate formatting preferences for the DCE questions. The first choice set of the randomly ordered set of eight was duplicated and presented as the ninth choice set for use as a consistency check. The responses to the first scenario was not analyzed; the last question was used for the main analyses. The experimental design was determined using the SAS 9. 4 (Optex) for a fractional factorial design and R (OptFederov) for blocking  (Brett-Major et al. , 2015; Inc, 2013; Team, 2019; Wheeler, 2004).
We ran a random intercept logit model accounting for individual-level heterogeneity using the full analytic dataset and including sex, race/ethnicity as covariates. We calculated the predicted probability of vaccinate willingness for the average individual and standardized the covariates at their means. We estimated standard errors using the delta method and compared the predicted probabilities and 95% CIs between the full sample of participants to the subset of participants that had passed a sensitivity test to compare them for differences in the predicted probabilities. In addition, we evaluated the partial utility of each parameter by using mixed multinomial logistic regression models with each subgroup identified in the sensitivity analysis. Stata 16 was used for all analyses (StataCorp, 2019).
Results Overall, 1,803 participants enrolled in the study. Nine participants who did not complete the demographics questions and 31 participants who did not complete the DCE questions were excluded from the analysis. The resulting analytic dataset consisted of 1,763 participants; 611 participants aged 18-40 years, 628 participants aged 41-64 years, and 524 participants aged 65 years and above (Table  2 ). Female respondents constituted between 58 and 65% of each age category. The proportion of respondents indicating All other race/ethnicity was 22% among those 18-40 years, 13% among those 41-64 years, and 6% among those 65 years and above. The majority of respondents reported living inside the seven-county Minneapolis-St Paul metro area (ranging from 76% to 86%) (Table  2 ). At least 88% of participants in each age group reported that they had ever received an influenza vaccination.
Sensitivity Analyses Three sensitivity analyses of Objective 1 restricted each analytic dataset to the subset of participants who passed one of the following sensitivity tests for duration, concordance, or non-straightliners. The predicted probability of influenza vaccine willingness in these sensitivity subsets did not result in substantial changes in the predicted probability of vaccine willingness, compared to the primary analysis (Table  S2 ). Substantial differences were not detected in the significance of the associations between parameters and vaccine willingness (Table  S4 ).
Sheep Sampling Serological diagnosis is considered adequate to define Leptospira-status among sheep herds and determine circulating leptospiral antibodies (with antibodies persisting several months) by the World Organization for Animal Health (WOAH)  [45]. At least 10% of the herd was sampled, in order to gather relevant data at the herd level as recommended by the WOAH  [45]. Site 3 hosted a herd of nearly 200 sheep. Consequently, samples were taken from 2 batches of 30 sheep at 2 time points: 6 months after the grazing period and 6 months after the initial sampling period, which was 2 weeks immediately after the next grazing period.
Data Analysis A vole was considered to be infected with Leptospira if at least one specimen was tested positive. The characteristics of infected and uninfected voles were compared using Rstudio (v. 1. 3. 1093, Apricot Nasturtium). An ANOVA (t. test function) was performed to evaluate the influence of length on infection status. A Chi-square test (chisq. test function) was used to assess the influences of sex, sexual maturity, category, and study site on infection status, including the estimate of p-value to consider small populations. The 95% confidence intervals for prevalence were calculated using the binom. test function.
Molecular Typing of Leptospira Strains in Voles We obtained the predicted 330-bp fragments when we conducted cPCR on Leptospira DNA obtained from the vole specimens (i. e. , kidney, lung, liver, blood, and urine) and successful cultures that had tested positive via RT-PCR (CT ≤ 40); the positive controls confirmed result reliability  [54]. In the BLAST search, all the sequences displayed high nucleotide affinity (97-100%) with L. kirschneri (GenBank accession number MK726123. 1).
Molecular Typing of Leptospira Strains in Voles We obtained the predicted 330-bp fragments when we conducted cPCR on Leptospira DNA obtained from the vole specimens (i. e. , kidney, lung, liver, blood, and urine) and successful cultures that had tested positive via RT-PCR (C T ≤ 40); the positive controls confirmed result reliability  [54]. In the BLAST search, all the sequences displayed high nucleotide affinity (97-100%) with L. kirschneri (GenBank accession number MK726123. 1). O-antigen typing was applied to DNA obtained from kidney samples (n = 19 samples) and successful cultures (n = 4) that had tested positive via RT-PCR (C T ≤ 40); the positive controls confirmed result reliability. Evidence was found for the presence of the Leptospira serogroup Grippotyphosa (GRIP) but not for the Leptospira serogroup Icterohaemorrhagiae (IH).
g. , over 50 to First_Order_Terms). We made heavy use of the Isar language  [19]  to write structured proofs, the Sledgehammer tool  [13]  for proof automation, and locales  [1] -Isabelle's parameterized module system-to structure our development and reuse existing components from the AFP entries. To ease associating the main results in this paper with their counterparts in the Isabelle development, names in monospace are taken verbatim from the formalization. The formalization follows the basic ideas of the existing formalizations of the first-order resolution calculus  [16]  and propositional CDCL calculi  [3, 4]. Compared to propositional logic, first-order logic adds a number of challenges: the extra term level requires to consider variables, substitutions, groundings, and the concept of factorization.
(  2 )  (Scanlon et al. 2018). Frappart and Ramillien (2018)  review in detail the current status, challenges, and limitations of GRACE-based GW monitoring. In particular, they provide a detailed overview on GRACE TWS processing and errors. Indeed, correlated noise contained in monthly GRACE solutions still represents a challenging issue and is addressed by sophisticated filters and/or spatial averaging  (Longuevergne et al. 2010 (Longuevergne et al. , 2013;; Ramillien et al. 2014; Chen et al. 2016 , see Appendix 1 for further details). For local applications regional GRACE solutions, like, e. g. , computed by  Ramillien et al. (2014)  for the African continent, provide better geographical localization of hydrological signals and are also available at higher temporal resolution. Few studies performed global analyses of GW variability and trends using different GRACE solutions and auxiliary data from hydrological models.
Other Related Climate Variables Remotely sensed precipitation (P) and evapotranspiration (ET) and their difference are further variables that are often involved in GW related studies to investigate the origin of water storage variations, e. g. , via time series analysis  (Khaki et al. 2018; Mohamed and Gonçalvès 2021; Frappart 2020). Furthermore, ET can serve for calibration purposes in hydrological models  (Poméon et al. 2018; Cheema et al. 2014)  with the aim of improving modeled GW storage evolution. Satellite-based rainfall measurements can be obtained from the Tropical Rainfall Measuring Mission (TRMM) operated from 1997 to 2015. Remotely sensed ET can be computed from data of the MODerate-resolution Imaging Spectroradiometer (MODIS) mission based on the Penman-Monteith equation  (Mu et al.
2003). While HMs model also GW and human abstraction, LSMs usually do not incorporate GW and have large temporally and spatially varying uncertainties  (Schewe et al. 2014; Scanlon et al. 2018; Vishwakarma et al. 2021a ), which limit their use for GW research. Uncertainties arise from insufficient realism of model equations and structure, imperfect model parameters, and imperfect forcing and surface data sets. Scanlon et al. (2018)  showed that both kind of hydrological models underestimate trends in TWS, and, thus also GW trends derived from these models have large uncertainties. Furthermore, in tropical and semi-arid catchments, most models underestimate seasonal TWS amplitudes  (Scanlon et al. 2019). Swenson and Lawrence (2015)  showed that deficits of LSMs in represented GW dynamics can be related to inappropriate storage capacity.
Ndehedehe et al. (2016)  applied PCA and MLR to GRACE data, GLDAS model outputs, and precipitation from TRMM and compared dominant spatio-temporal patterns also to satellite altimetry data in order to understand TWS variability over West Africa. They identified the contribution of strong rainfalls to interannual variability of TWSA, but also revealed inconsistencies between the individual data sets in specific parts of the study area. Using PCA, Awange (2022a) extracted simultaneous temporal patterns of TWSA, rainfall, and soil moisture over Ethiopia and corresponding dominant regions. water and fitted the temporal patterns to GRACE TWSA. Then, GW changes and trends were computed from GRACE TWSA and from the reconstructed storage compartments.
Observations and numerical models can also be combined using data assimilation concepts, e. g. , based on Ensemble Kalman Filter (EnKF) algorithms. Girotto et al. (2017)  showed that the assimilation of GRACE TWSA into a hydrological model can induce missing long-term trends to GW storage and improve interannual variability. Joint assimilation of GRACE TWSA and surface soil moisture has the potential to simultaneously improve GW and soil moisture estimates  (Tangdamrongsub et al. 2020). Khaki and Awange (2021)  jointly assimilated GRACE TWSA and lake levels of Lake Victoria derived from satellite altimetry into the World-Wide Water Resources Assessment Model (W3RA) and show detailed patterns of water storage evolution in different compartments with focus on an extreme water storage increase during the period 2019-2020.