dataesr/scientific-paragraphs-categorization huggingface dataset:
CART was performed in R using the package rPART  42  for three target variables shown to be most relevant for modelling the loss reduction by the vegetative filter strip 23 : • Total water inflow (volume of runoff water + precipitation): relative reduction (%) of total inflow (ΔQ) • Eroded sediment mass: relative reduction (%) of sediment load (ΔE) • Pesticide load: relative reduction (%) of pesticide load (pesticides leaving the field) by the vegetative filter strip (ΔP) Following Reichenberger et al. 41  the following criteria were used as measures of prediction accuracy: Pearson (r 2 ), coefficient of determination (R 2 , corresponds to the Nash-Sutcliffe coefficient [NSE]), percentage bias (PBIAS) and root mean square error of prediction (RMSEP).
Soil conservation measures For soil conservation measures, the main data sources were (i) a plot experiment database of annual surface runoff and soil loss compiled by Maetens et al. ,  18  (ii) a field study with event-based data on surface runoff, erosion and pesticide loss,  43  and (iii) two reviews with data on pesticide loss over the whole season. 19, 44  These two reviews analyzed in total 14 studies executed in the United States. Due to the heterogeneity of the available data in terms of temporal resolution and the availability of experimental variables, the evaluation had to be done separately for (i), (ii) and (iii). Furthermore, there were differing definitions of various soil conservation measures. For a substantial share of the data only the information that the plot was not tilled with a moldboard plough was available and thus crucial information for differentiating the type of measure and judging the quality of the study was missing.
The efficiency of the measures was assessed by comparing runoff, soil loss (erosion) and pesticide loss with the control treatment (conventional tillage). The resulting quantities were runoff ratio (RR, runoff with soil conservation measure/runoff with conventional tillage), soil loss ratio (SLR, soil loss with soil conservation measure/soil loss with conventional tillage) and pesticide loss ratio (PLR, pesticide loss with soil conservation measure/ pesticide loss with conventional tillage). The annual and seasonal data were analyzed by means of box-and scatterplots using runoff ratio, soil loss ratio and pesticide loss ratio, while for the single study with event-based results,  43  runoff CNs  25  were calculated (a detailed description of the method is given in Sittig et al. 15  ).
Micro-dams For the analysis of micro-dams we mainly relied on the eventbased data compiled by Sittig et al. ,  15  which comprised five studies in potatoes and two in maize (in Belgium, Germany and France). CNs for each event had already been fitted by Sittig et al. In addition, the study of Sui et al. 45  on micro-dams in maize in China and the study of Keshavarz et al. 46  on micro-dams in furrow irrigation (bare soil) in Iran were evaluated. Again, for each event, CNs were calculated and compared between micro-dams and conventional tillage.
Qualitative analysis by expert consultation In expert interviews, 16 mitigation measures were qualitatively evaluated (Table  S4 ). Regarding economic viability two risk mitigation measures were consistently considered to be particularly applicable: no-till and reduced tillage. Six further measures primarily relating to cultivation methods (mulch tillage, strip tillage, cover crops in general, cover crops with deep roots and microdams) were assessed to be economically neutral. Eight measures were classified as slightly uneconomical among them various types of vegetative buffer strips (vegetative filter strip, riparian and thalweg buffers) with noticeable differences in the assessment between the different experts (Table  S4 ). The controllability was the criterion with the most positive ratings but varied between measures: while off-field measures can be controlled by remote sensing if data with a high spatial and temporal resolution are available, in-field measures usually require field inspections.
g. , in Schulz  53  ). The fact that VFS effectiveness has a stronger dependence on hydraulic load than on VFS width is closely linked to the issue of VFS performance under concentrated flow conditions (where the runoff enters the VFS in concentrated form and flows over only a small area fraction of the VFS) because it is essentially equivalent whether the volume of water entering the VFS is doubled or the area of the VFS over which runoff flows is halved: the hydraulic load on the VFS is the same in both cases. The impact of flow concentration/convergence on VFS performance has been investigated in several publications (e. g. , Arora et al. 54  Boyd et al. 55  Poletika et al. 56  Helmers et al. 57  ), several of which have been included in the dataset compiled by Reichenberger et al. 41  Even though flow concentration was clearly found to impact the effectiveness of VFS (e.
Soil conservation measures A number of soil conservation measures were assessed for their potential to reduce the frequency of occurrence and the magnitude of runoff events in the field. Only no-till showed a clear reduction in all variables assessed, i. e. , runoff, sediment load and pesticide losses. As for the other soil conservation measures, no effect on annual runoff was found in the data of Maetens et al. 18  while median pesticide losses decreased by about 25-50%. 19, 44  These limited effects do not mean that soil conservation measures have no potential to reduce runoff, but this would require further event-based studies that additionally include relevant parameters such as soil cover. In the event-based study  43  evaluated, soil cover was monitored throughout the whole season, which confirms that this particular study was performed in an exemplary manner (see details in Tables  S7-S11 ).
ACKNOWLEDGEMENTS We would like to thank all contacted experts for taking the necessary time for interviews and questionnaires. Furthermore, we would like to thank  Volker Prasuhn  from  Agroscope Switzerland  and  Hans Ramseier  from the  School of Agriculture, Forestry and Food Sciences  for helpful comments, and  Willem Maetens  for providing his data compilation on runoff and erosion. The  German Environment Agency  funded this project ( Umweltbundesamt , project  ID FKZ3719654091 ). Open Access funding enabled and organized by  Projekt DEAL.
Pathogen surveillance using whole-genome sequencing (WGS) provides an unprecedented resolution for identifying clusters of cases and sources of contamination, as well as predicting the virulence and antimicrobial resistance profiles of strains, useful for risk assessment  (5, 10). Previous studies have confirmed the presence of Lm in various foods in Costa Rica, with contamination levels ranging from 5% to 20% in processed meat products and fresh cheese, respectively  (11, 12). However, because listeriosis is not yet a notifiable disease in Costa Rica, data are scarce and the impact of the disease is unknown. Here we used WGS to characterize 92 Lm recovered over an 11-year period from clinical (n=16), food (n=64), and production environment (n=9) samples in Costa Rica (Appendix), to understand the diversity and potential risk of circulating strains.
Acknowledgments This work includes MLST profiles publicly available on BIGSdb-Listeria. We thank the submitters for depositing their data in public databases and  Institut Pasteur  teams for the curation and maintenance of BIGSdb-Pasteur databases at https://bigsdb. pasteur. fr/. We also thank the  CONAGEBIO  from the  Costa Rican Ministry of Environment and Energy (MINAE)  for providing the permits to the access to biological material presented here (file CM-ITCR-003-2021).
Funding This work was developed within the framework of agreements between  Institut Pasteur  and the  University of Costa Rica , and between  Institut Pasteur  and  Instituto Tecnológico de Costa Rica  (Amendment No. 1 to the memorandum of understanding dated February 21st 2018), and supported by  Institut Pasteur ,  Inserm ,  Santé Publique France , the  French government 's  Investissement d'Avenir program   Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ), the  Vice Rectory of Research of the Instituto Tecnológico de Costa Rica  (Research Project # 1510160 ), and the  Vice Rectory of Research at the University of Costa Rica  (Projects  B2104  and  B9026 ). MGZ travel funds were provided by the  Instituto Tecnológico de Costa Rica  and the  Consejo Nacional para Investigaciones Científicas y Tecnológicas (CONICIT).
Measurement of amphioxus body size and shape changes Photographs were taken from the top of each tank at the beginning and at the end of the 16 days experiment in the four conditions (0, 50, 500, 5000 particles/L). Amphioxus body lengths were measured with the ImageJ software (Panasonic DMC-FZ300) and by considering the straight-line distance between the anterior and posterior tip of each individual. The amphioxus body shape was expressed by the tangent intersection angle of the anterior and posterior tip of the amphioxus.
Gene expression Triplicate RNA samples were extracted from gut and hepatic caecum tissues using the RNeasy Mini Kit, following manufacturer's instruction (Qiagen). RNA quality and concentration were verified with DeNovix, RiboGreen dye (Invitrogen) using Victor3™ (Perkin Elmer) and also Agilent 2100 (Agilent) bioanalyzers. The Nanostring technology was applied using molecular barcodes and microscopic imaging to detect and quantify up to several hundred transcripts in hybridization reaction  (Kulkarni, 2011). A total of 59 genes were selected to target the immune system, stress response, apoptosis and housekeeping, based on the literature  (Huang et al. , 2011; Zhang et al. , 2016)  and online databases (UCSC database  (Karolchik et al. , 2003) , Amphiencode database  (Marlétaz et al. , 2018) , JGI database  (Putnam et al.
128 database  (Quast et al. , 2013). Eukaryote, chloroplast, mitochondria and archaea sequences were removed and all the samples were re-sampled to the same depth (1012 tags per sample) by using phyloseq R package  (McMurdie and Phyloseq, 2013). The α-diversity indexes (Chao1 richness, Pielou evenness and Shannon diversity) were calculated with Microbiome R package  (Lahti et al. , 2017). Taxonomy composition was analyzed with web-based platform of Micro-biomeAnalyst  (Chong et al. , 2020). Relative abundance of ASVs (>5% for each library) within each condition was averaged, and visualized by heatmap with online tool of ClustVis  (Metsalu and Vilo, 2015). In addition, the eukaryotic sequences (without paired merge) from the MP samples were analyzed with the procedure mentioned above. All the SSU rRNA data are available in the NCBI SRA repository (accession number PRJNA732566).
Statistical analysis For the gene expression assays, student t-tests were conducted on pairwise comparisons on the data for each gene, which was normalized with internal standard followed by the geometric mean of the housekeeping genes, and p-value was normalized with the method from Benjamini & Yekutieli (nSolver software)  (Benjamini and Yekutieli, 2001). Linear regression analysis was used to evaluate the relation between the differentially expressed genes and MP concentrations. Unweighted-pair group method with arithmetic (UPGMA) dendrogram based on Bray-Curtis similarities was used for visualization of 16S rRNA-based beta-diversity (PRIMER 6). A similarity profile test (SIMPROF, PRIMER 6) was performed on the null hypothesis that a specific sub-cluster can be recreated by permuting the entry species and samples.
Acknowledgement We are grateful to  Kevin Magré  and  Michaël Fuentes  and  EMBRC-FR  staff for the amphioxus sampling, to  David Pecqueur  ( BIOPIC platform ) for the support on microscopy, to  Guigui PA , VF, JPJS, and the three anonymous reviewers for insightful comments on the manuscript. This work benefited from access to the  Observatoire Océanologique de Banyuls-sur-Mer , an  EMBRC-France  and  EMBRC-ERIC  site.
| METHODS The European Surveillance of Congenital Anomalies (EUROCAT) is a collaborative network of populationbased congenital anomaly registries (EUROCAT, 2021a). Forty-two registries in 22 countries use multiple sources to collect data on anomalies occurring in live births, spontaneous fetal deaths from 20 weeks gestation and terminations of pregnancy for a congenital anomaly at any gestation following prenatal diagnosis. All registries use the WHO International Statistical Classification of Diseases and Related Health Problems version 9 or 10 (ICD-9 or ICD-10). For this study, 21 of 42 registries agreed to participate, all of which provided maternal age data. We included cases born between January 1, 1995 and December 31, 2015 from 21 regional registries in 12 European countries. Denominator data were provided by EUROCAT (EUROCAT, 2021b) and aggregated according to the year of birth and maternal age at delivery.
, 2012). All other CHD cases were eligible and coded as nonsyndromic where these CHD occurred without the presence of any other noncardiac major congenital anomaly (Figure  1 ). We investigated the association between maternal age and the prevalence of all nonsyndromic CHD in Europe between the years 1995 to 2015. We also examined the association according to three CHD severity groupings based on the EUROCAT classification of the severity of CHD  (EUROCAT, 2000)    variables as denominator data for these variables were not available. We applied a Bayesian approach as the credible intervals presented may be interpreted as probability intervals, meaning that there is the specified probability of the true value lying within this interval. This is not technically the case for a frequentist confidence interval.
Noninformative priors were used to reflect a position of prior ignorance  (McNeish, 2016). Bayesian multilevel Poisson regression modeling (with levels defined by different registers) was used to estimate the IRR of CHD according to maternal age at delivery, adjusted for year of delivery (as a continuous variable) and imposed a linear trend over the 20 years. We also performed a subgroup analysis of each CHD subtype separately for further investigation among the different age group categories using Bayesian Poisson regression models of individual CHD subtypes without any adjustment of multiple testing. All analyses were performed using Stata software (version 15; StataCorp, College Station, Texas) for both descriptive and multilevel analysis (Table  1 ).
Model intercomparison studies (e. g. ,  Bohn et al. , 2015; Chang et al. , 2023)  imply that the wetland models can simulate different magnitudes of fluxes though using standardized input data and protocols. The uncertainty, likely coming from differences in model structure and parameterization of CH 4 processes such as methanotrophic production and transports, would be evident for cold-season fluxes. Using flux measurement and model-derived datasets (WETCHIMP and WetCHARTs),  Treat et al. (2018)  concluded that wetland models have underestimated cold-season CH 4 emissions. The models have estimated the cold-season flux to be negligible because microbial CH 4 production and consumption are strongly inhibited in cold and frozen soils  (Bartlett & Harriss, 1993; Christensen, 1993; Ito, 2019).
, 2021). The protocol requires the use of two climate datasets (surface air temperature, humidity, pressure, precipitation, wind speed, and incoming shortwave and longwave radiation): the Climate Research Unit 4. 06  (Harris et al. , 2020)  and the Global Soil Wetness Project Phase three datasets. This study chose the former because it covers the longest period of the two, and we did not find a substantial difference between the results using the two climate datasets. In this study, we analyzed simulated monthly mean 0. 5° × 0. 5° gridded CH 4 flux data (kg CH 4 m -2 s -1 ). For composite model mapping, data of several models (Table  1 ) were regridded using the "remapbil" function from Climate Data Operators (https://code. mpimet. mpg. de/projects/cdo/).
Observational CH 4 Datasets Observed CH 4 flux data were used only for evaluation of the simulation results. The field CH 4 flux observations were made by eddy covariance method, which generally have a local-scale footprint (on the order of m 2 to km 2 ). Therefore, we adopted the data set of  Peltola et al. (2019) , which comprised CH 4 flux data observed by the eddy covariance method at 25 northern wetlands sites scaled up with a machine learning algorithm to generate a continuous field for the land area north of 45°N at a spatial resolution comparable to that of the wetland models. This data set provides the monthly CH 4 fluxes during 2013-2014 for three different base wetland maps; we used the results for the Global Lakes and Wetlands Data set base map  (Lehner & Döll, 2004). Additionally, to examine in situ CH 4 fluxes closely, we used the FLUXNET-CH 4 version 1.
19448007, 2023, 14, Downloaded from https://agupubs. onlinelibrary. wiley. com/doi/10. 1029/2023GL103037 by Cochrane France, Wiley Online Library on [25/07/2023]. 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 cold-season emission patterns appear comparable to those of the upscaled flux data but differ in the magnitude of their contributions to annual flux (FigureS5in Supporting Information S1). In the upscaled flux data, the contributions of emissions at subzero air temperatures were substantial in far East Siberia and Alaska, and non-summer emissions were substantial across a wide area of Central and East Siberia and northern North America. We used dendrograms to identify clusters in the spatial patterns simulated by the different models and the upscaled observations (FigureS6in Supporting Information S1).
Acknowledgments This study is a contribution to the global CH 4 synthesis organized by the  Global Carbon Project , and all model simulations were conducted using the protocol and input data prepared by the project. AI was supported by the  Environmental Research and Technology Development Fund  ( JPMEERF21S20830  and  JPMEERF21S12007 ) of the  Ministry of the Environment  and the  Environmental Restoration and Conservation Agency of Japan , and the  Arctic Challenge for Sustainability II  ( JPMXD1420318865 ) of the  Ministry of Education, Culture, Sports, Science and Technology, Japan. PH is grateful for support from a  University of Birmingham  Fellowship and BEAR computing facilities. FJ acknowledges support by the  Swiss National Science Foundation  (# 200020_200511 ). TK acknowledges support from the  German Federal Ministry of Education and Research (BMBF)  through the project  PalMod , Grant  01LP1507B.
Data Availability Statement Datasets contributing to the Global Methane Budget by the GCP including wetland model estimates are archived in the International Carbon Observation System: https://doi. org/10. 18160/gcp-ch4-2019. Tier one (CC-By-4. 0) FLUXNET-CH 4 data are available from https://fluxnet. org/data/fluxnet-ch4-community-product/. Upscaled flux data by  Peltola et al. (2019)  are available from: https://doi. org/10. 5281/zenodo. 3247295. WetCHARTs data are available from: https://doi. org/10. 3334/ORNLDAAC/1915.
Episodic memory tasks Organizable words-based episodic memory task and semantic organization Participants were shown once a categorized 20-word list (five categories of four words, see  Taconnat et al. , 2020). The words were arranged and presented in pseudo-random order so that two words from the same category were never presented sequentially. Participants were not informed about the possible structuring of lists. The words for the five categories were selected from a table  (Marchal & Nicolas, 2003)  which was constructed from category classes generated by young and older adults. The categories were matched with respect to word length, word frequency (Brulex database,  (Content et al. , 1990)  and typicality of semantic category, similar for both younger and older adults. The words were 5-8 letters long, with 2-3 syllables, and were all concrete nouns.
Data analyses Data were analyzed using R version 4. 1. 0 (Team R Core, 2021). We first analyzed age-related differences on recall (number of words) and ARC and PF using Linear Mixed Model (LMM; Hypothesis 1). First of all, on recall, we fit a LMM with age group (younger adults, older adults) as a between-subjects factor, memory task (organizable, nonorganizable) and trial number (1, 2, 3) as within-subject factors and Participants as a random factor. This model was fit with the lme4 and afex packages  (Bates et al. , 2015; Singmann et al. , 2020). We then examined the effects of age group and memory task on semantic organization using the variable ARC as dependent variable. As this variable comprises scores from 0 and 1 and included 0 and/or 1, this was not suitable to be fit using a GLMM with a Binomial distribution.
𝑛 where n is the sample size  (Smithson & Verkuilen, 2006 ) This Beta Regression was fit using the betareg package  (Cribari-Neto & Zeileis, 2010). Following this analysis, we examined the effects of age group and inter-trial (first trial to second trial or 1-2 inter-trialsecond trial to third trial or 2-3 inter-trial)but not trial number as for ARC, given that PF needs at least two trials to be computedas betweensubjects and within-subject factors and Participant as random factor on subjective organization (PF) using another LMM. For all the analyses described above, the package emmeans  (Lenth, 2020)  was used to perform the pairwise comparisons with Tukey adjustments when there were multiplicity issues. Age-related differences on the different cognitive tests (cognitive control and working memory) were analyzed using t-tests (Hypothesis 2).
We then conducted a Structural Equation Modelling (SEM) analysis using the package lavaan  (Rosseel, 2012)  to examine whether the cognitive tests were representative of cognitive control and working memory (Hypothesis 3). To do so, we fit and compared two SEM models where all cognitive tests (observed variables) accounted for one latent variable (model 1) and where the WCST, SCWT and TMT loaded onto one latent variable (cognitive control) and the N-Back test, Digit Span and Alpha Span loaded onto another latent variable (working memory; model 2). From this analysis, we then took the best model to test how the latent variable(s) was/were predicted of recall in both memory tasks (organizable, nonorganizable), semantic organization (ARC) and subjective organization (PF) using a third SEM model (Hypothesis 4).
Previous permeability-depth relationships Permeability typically decreases with depth due to tectonic stresses, compaction, diagenesis and weathering  14, 18, 19  and permeability-depth relationships have been derived for a variety of environments using various approaches (Table  1 ). A prime example of such a relationship was generated using data from geothermal and metamorphic environments  15, 20  (Fig. 2b ), which extended down to depths of 40 km, including the 10 km thickness of brittle crust nearest to the ground surface. Permeabilities of geothermal systems were estimated by determining the amount of fluid flow required to produce enough advection to replicate observed temperature distributions. In contrast, in metamorphic systems, permeabilities were estimated using the time-integrated fluid flux required to produce the observed amount of mineralization.