dataesr/scientific-paragraphs-categorization huggingface dataset:
e. observations of seed bank persistence or otherwise), the number of 'trials' needed to be included as a weight in the binomial models. For this, we used the number of seed bank assessments (i. e. data rows) for each species in the LEDA traitbase. To maximise the power of this analysis, we included all 840 species for which both species temperature indices and seed bank longevity index were available, even considering species that were not present in the seed bank database. Statistical significance of predictor variables in generalised linear models was determined using a p-value threshold of 0. 05. To assess the potential correlates of other plant traits on seed longevity index, we reran the above models with seed bank longevity index as the response variable and either species mean temperature index or species temperature range index as the predictor variable, this time including plant life span, seed mass and seed number as additional predictor variables  (Models 1c and d).
Thirtythree plots had seed bank or herb layer communities consisting only of species for which temperature indices could not be calculated, and were therefore excluded from the community-level analyses, leaving 2763 plots (Grassland: 1298, Intermediate: 864, Forest: 601). Next, the community mean temperature index was regressed against the gridded (CHELSA) macroclimate mean annual temperature data for each plot to verify if a climatic control on seed bank and herb layer communities was present in the dataset. This was performed using a linear mixed effects model (function: lmer in r package lme4 version 1. 1. 29;  Bates et al. , 2014). Data were arranged so that each community occupied a separate row, that is, there were two data points per plot, one for seed bank and one for herb layer. The response variable in the model (Model 2a) was community mean temperature index.
Fixed predictor variables were annual mean temperature (at the plot) and source (seed bank or herb layer), as well as their interaction. This allowed us to first test for climatic control of the plant communities, but also whether there was a difference in community mean temperature index between the seed bank and herb layer, and whether climatic control of the community differed according to source. Plot identity, nested into the identity of the original dataset from which the plot was taken (of the 54 component datasets) was added as a random effect. Another model  (Model 2b)  with community temperature range index as the response, source as the predictor and the same random effect structure as the previous model tested whether seed bank and herb layer communities differed in terms of community temperature range index.
| Environmental drivers of differences in community temperature indices (Question 3) To explore the potential drivers of any differences in community temperature index between the seed bank and herb layer, we built a further linear mixed model (Model 3a). The difference between seed bank and herb layer community mean temperature index was the response variable (seed bank index minus herb layer index, with positive values indicating that the seed bank is characterised by warm-adapted species with higher species mean temperature indices relative to the herb layer). Predictor variables were landuse category (categorical variable with intermediate successional habitats as base factor for comparison) and mean annual temperature from the seed bank database, the seed bank community temperature range index and the first two eigenvectors of a principal coordinate analysis derived from a neighbour matrix (PCNM) of the spatial coordinates of each plot (function: pcnm in r package vegan version 2.
6. 2;  Borcard & Legendre, 2002; Oksanen et al. , 2016). These variables were included to account for spatial autocorrelation because the nested random effect structure above was not possible because there was only one row per plot in the data frame. The original dataset of each plot was included as a single random effect. Two-way interactions were included for predictor variables, excluding PCNM eigenvectors. In this model, numerical predictor variables were standardised (mean = 0 and SD = 1; R function: scale) to allow interpretation of both main effects and interactions  (Schielzeth, 2010). S3 ). Including plant life span, seed mass and seed number in the models together with the temperature indices resulted in no significant effects on seed bank longevity index, indicating that when holding other traits constant, there are no clear associations between each trait and seed bank persistence (Models 1c and d; Table  S3 ).
DATA AVA I L A B I L I T Y S TAT E M E N T This study made use of three existing, published databases. Seed bank longevity was calculated from the LEDA traitbase  (Kleyer et al. , 2008 ; https://uol. de/en/lande co/resea rch/leda). Species climatic indices were taken from ClimPlant (Vangansbeke et al. , 2021a (Vangansbeke et al. , , 2021b ; now updated to include species calculated for this study: https://doi. org/10. 6084/m9. figsh are. 12199 628. v2). And seed bank and herb layer community data were taken from https://doi. org/10. 5061/DRYAD. KSN02 V72G  (Plue et al. , 2020 (Plue et al. , , 2021)).
13652745, 2023, 5, Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1111/1365-2745. 14074 by EVIDENCE AID -BELGIUM, Wiley Online Library on [12/05/2023]. 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 Licenseperennials as 1). The order of predictors in the model formulae did not affect the model outputs. 2. 2. 2 | Comparison of community temperature indices between the seed bank and herb layer (Question 2) In the next step, we calculated two community temperature indices for each seed bank and herb layer community for each plot in the seed bank database. First, we calculated the community mean temperature index (CMTI; often referred to in the literature as the community temperature index) as the average of the species mean temperature index for all species present within the seed bank or herb layer community.
Hence, studies on mesozooplankton shifts require a more comprehensive assessment of the changes in the whole compartment to understand ecosystem-level implications. In this study, we investigated potential structural changes in the mesozooplankton compartment over the last five decades in the southern North Sea by considering a high diversity of taxa and life strategies. Our two main aims were to (1) identify potential tipping years in the structure of the mesozooplankton community and (2) compare the results obtained from the complete mesozooplankton community to those obtained from several subsets of taxa in order to investigate how taxonomic selection affects tipping year identification and trends over time. To do so, we analysed a broad diversity of zooplankton groups, including both holo-and meroplankton, using the long-term dataset of Helgoland Roads  (Greve et al.
Data sources In this study, we analysed the long-term time series of Helgoland Roads (54 • 11 18 N 7 • 54 E), in which zooplankton organisms have been sampled three times per week since 1974  (Greve et al. , 2004). The data can be found on PANGAEA: doi: 10. 1594/PANGAEA. 873032  (Boersma et al. , 2017). The sampling was done through oblique hauls with the HYDRO-BIOS quantitative collection device net (150 μm mesh size, aperture 17 cm, net length 100 cm). For a more detailed description of the sampling protocol, we refer the reader to  Greve et al. (2004). We used data from 1975 to 2018. During this period, all species encountered were identified at 38 constant taxonomic levels. We selected 22 taxa that individually accounted for >0. 1% of the total abundance. This selection permitted to remove very rare (zero-inflated) taxa while retaining a substantial share of the complete community, representing 99.
Data preparation We first calculated monthly mean abundances for each taxon. This was done in order to smooth the data and obtain a constant time step, necessary for time series analyses. Thus, the fauna datasets had 528 rows (12 months for 44 years). Taxon abundances were then log transformed [log (nb. m -3 + 1); nb = number of individuals] and decomposed into three constituent components, namely, seasonality, trend, and random (package stats). The function isolates the fixed seasonal component using a moving average method, while the trend component is obtained by applying a locally weighted regression to the data after removing the seasonal component  (Shumway and Stoffer, 2017). The trend component was used for further analysis.
The first two axes explained 64% of the variations observed in the dataset (47% and 17%, respectively). Most taxa (12 taxa) were affiliated with PCoA 1 (Figure  2b ), all of them in the positive part of the axis. Decapoda zoea, Appendicularia, Oithona spp. , Cirripedia, and Chaetognatha were affiliated with the positive part of the second axis, while Echinodermata juvenile, Temora longicornis, and Acartia spp. to its negative part. The taxa Mollusca and Phoronida were affiliated with neither the first nor second axis (contribution to axes lower than average). Subsequently, PCoA coordinates (months of each year) along both axes were plotted over time (Figure  2c ). The two axes showed different trends over time. The community represented by the first PCoA axis increased in abundance until the beginning of the 1990s, followed by a decline, especially sharp after the mid-2000s.
Tipping year identification and trend comparison The previous analyses carried out on the complete dataset were repeated on different subsets in order to compare the trend and timing of significant tipping years in community change depending on the taxa considered (Table  3 ). For the three functional group subsets, the MRT analysis showed that three periods were sufficient to reach 50% of explained variation in the structure composition over time. The first significant tipping year was identified in 1981 for the copepod subset, 1983 for non-copepods, and 1989 for the ten most abundant taxa, showing a detection of changes varying up to 8 years. In the copepod and non-copepod subsets, only cyclopoids and echinoderm juveniles were linked to the first period, respectively, as in the analyses of the whole data set.
K E Y W O R D S biodiversity, constancy, ecosystem functioning, eutrophication, functional traits, remote sensing, resistance uncorrelated or can display either positive or negative covariation  (Donohue et al. , 2013; Polazzo & Rico, 2021). Analysis of the relationships between productivity and different components of its stability is therefore crucial to understand whether productivity is consistently linked to stability or whether some stability components increase with productivity whereas others decrease. Here, we used 19,884 vegetation plots across France to quantify the relationships between grassland productivity and two components of its stability (constancy and resistance) and to disentangle the joint roles played by abiotic and biotic factors on these relationships. The dataset covers four major types of grassland habitats, namely calcareous, mesic, mountain and ruderal grasslands, which are characterized by contrasting growing season length (GSL; computed from both temperature and soil water balance) and nitrogen inputs  (Borgy, Violle, Choler, Denelle, et al.
| Ecosystem productivity and stability in French permanent grasslands We first delineated all grasslands in France at a resolution of 500 m × 500 m by combining data from Corine Land Cover (2010), a European-scale land cover database, and from the Registre Parcellaire Graphique, an administrative database of land management based on on-farm survey data (Supporting Information Figure  S1 ). To select only the most representative pixels, we kept 93,524 pixels with a grassland cover >70%. For all selected pixels and for the years 2000-2020, we computed the kernel-normalized difference vegetation index (kNDVI;  Camps-Valls et al. , 2021). The kNDVI is a nonlinear generalization of the normalized difference vegetation index (NDVI), which quantifies photosynthetically active vegetation from the ratio of red and near-infrared reflected light.
| Vegetation, habitat types and plant traits The dataset of permanent grasslands in France includes 96,132 botanical plots and 2930 species, coming from multiple data sources and gathered in the DivGrass project  (Borgy, Violle, Choler, Denelle, et al. , 2017; Denelle et al. , 2020; Violle et al. , 2015). The area of each plot varied from 50 to 100 m 2 , and they were visited a single time. Most of the plots (75,872) consist of plant species relative cover estimated among a six-level scale: 0-1, 1-5, 5-25, 25-50, 50-75 and 75-100%. The median of each class provides a quantitative scale. Only botanical plots surveyed after 1980 and data considering all the species and estimating their cover were retained (80% of DivGrass database). Each plot was assigned to one of four types of grassland habitats (i.
e. , dry calcareous, mountain, mesic and ruderal), defined from an analysis of modularity of the global species-site network  (Denelle et al. , 2020). Dry calcareous grasslands were characterized by higher soil pH and low water availability caused either by low precipitation or by fine, sloping soil that does not retain water. Mountain grasslands included sites with lower annual temperature and higher precipitation. Mesic grasslands represented sites at low elevation, with mean annual temperature and precipitation that lay in between those of the other habitats. Sites associated with high temperatures, low precipitation and more ruderal species represented disturbed ruderal communities with many Mediterranean taxa. For each species, we extracted five functional traits from several databases and local datasets (including the TRY database;  Kattge et al.
, 2020) ; see  Borgy, Violle, Choler, Denelle, et al. (2017)  and  Violle et al. (2015)  for details about trait data compilation. We considered three traits related to resource use strategy, namely specific leaf area (SLA; in metres squared per gram), mass-based leaf nitrogen content (LNC; in milligrams of N per gram) and leaf dry matter content (LDMC), which discriminate resource-acquisitive versus conservative plant strategies well  (Garnier et al. , 2016; Wright et al. , 2004). Plant height (in metres) characterized plant size. Finally, flowering time (Julian day) represented reproductive phenology, a dimension of the functional space of plants that is rather independent from resource use strategy and size  (Segrestin et al. , 2020). We considered mean trait values for each species, assuming that interspecific variation in trait values has more influence than intraspecific variation on the functional turnover of community composition at large biogeographical scales  (Siefert et al.
| Abiotic and biotic drivers We used an index of growing season length (GSL) that accounts for both daily temperature and soil water content that has been calculated by  Borgy, Violle, Choler, Denelle, et al. (2017)  for the DivGrass dataset. Briefly, this index corresponds to the mean number of days in the year for which: (1) mean daily temperature was >5°C, and (2) the ratio between soil available water content and soil water-holding capacity was >. 2 for the 1961-1990 period. We chose to use this integrative index rather than mean annual precipitation and temperature because  Borgy, Violle, Choler, Denelle, et al. (2017)  demonstrated that it best predicted the distribution of plant functional traits in the DivGrass dataset. Nitrogen input was computed as the sum of organic fertilization, mineral fertilization and atmospheric deposition.
Data were obtained from the census Nopolu-Agri information system of the French Ministry of Agriculture (http://www. stati stiqu es. devel oppem ent-durab le. gouv. fr) and the European Monitoring and Evaluation Program (http://www. emep. int/). These data were available for each French municipality, whose mean area is c. 8 km 2 (see details in the paper by  Borgy, Violle, Choler, Denelle, et al. , 2017). Biotic drivers corresponded to taxonomic diversity, trait diversity and community-weighted mean (CWM) values of SLA, LNC, LDMC, plant height and flowering time. The exponential of the Shannon diversity index [exp(H′)] quantified taxonomic diversity as an effective species number  (Jost, 2006). We measured the multivariate functional dispersion index (FDis) to quantify the functional diversity of each community  (Laliberté & Legendre, 2010).
| Data analyses Initially, we compared ecosystem productivity, constancy and resistance among the different grassland habitats using ANOVA and Tukey's post-hoc tests. Next, we analysed ecosystem productivity-stability relationships for all grassland habitats together and then separately for each of the four types of grassland habitats. We computed the Pearson correlation index between ecosystem constancy and ecosystem productivity, between ecosystem resistance (log 10 -transformed) and ecosystem productivity, and between both components of ecosystem stability. To sort out the roles of climate (GSL), nitrogen input and biotic factors [exp(H′), FDis and CWMs of each trait] on grassland productivity, constancy and resistance, SEMs were used. Given that we expected that abiotic conditions would affect the roles of plant diversity and functional traits on ecosystem productivity and stability, we built one SEM per habitat, with the four grassland habitats displaying contrasting environmental conditions (Supporting Information Figure  S1 ).
14668238, 2023, 4, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/geb. 13645 by Cochrane France, Wiley Online Library on [13/03/2023]. 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 as many times as necessary to ensure that each habitat was represented in the final dataset. The final dataset comprised 19,884 plots and 2002 plant species spread across 1914 5 km pixels. Of these, 477, 889, 353 and 195 5 km pixels corresponded to dry calcareous, mesic, mountain and ruderal grasslands, respectively.
| Statistical analysis Statistical analysis was performed using Prism v. 6. 0 (GraphPad Software, Inc. , San Diego, CA). The normality of the distribution of data was checked with the Kolmogorov-Smirnov test. For parametric data, comparisons between the two groups were assessed using the paired or unpaired Student's t-test. Comparisons between more than two groups were performed using one-way or two-way analysis of variance (ANOVA) followed by Bonferroni's post hoc test. For non-parametric data, comparisons between the two groups were assessed using the Mann-Whitney U test. Data are presented as the means ± SEM. Differences were considered significant at P less than. 05.
35  On the other hand, ADT21, an IDE inhibitor derived from the protein sequence of varicella-zoster, showed delayed onset of diabetes in NOD mice (a preclinical model of T1D) and improved oral glucose tolerance in diet-induced obese mice. 43  F I G U R E 5 Continuous administration of sPIF recovers β-cell function in islets isolated from diet-induced obese mice. A, Mice were fed a SD or a HFD, and saline or sPIF was continuously administered as described in the Materials and Methods section. Afterwards, islets were isolated and GSIS was assessed. Data are reported as means ± SEM. SD (n = 2 mice), 9 islet groups; 5 islets per group. HFD-SALINE (n = 3 mice), 12 islet groups; 5 islets per group. HFD-sPIF (n = 3 mice), 12 islet groups; 5 islets per group. **P <. 01; ***P <. 001 by two-way ANOVA.
B, Fold change from insulin secretion of panel A. Data are reported as means ± SEM. SD (n = 2 mice), 9 islet groups; 5 islets per group. HFD-SALINE (n = 3 mice), 12 islet groups; 5 islets per group. HFD-sPIF (n = 3 mice), 12 islet groups; 5 islets per group. *P <. 05 by one-way ANOVA. C, Intracellular insulin content of mice islets measured after GSIS. Data are reported as means ± SEM. SD (n = 2 mice), 9 islet groups; 5 islets per group. HFD-SALINE (n = 3 mice), 12 islet groups; 5 islets per group. HFD-sPIF (n = 3 mice), 12 islet groups; 5 islets per group. ANOVA, analysis of variance; GSIS, glucose-stimulated insulin secretion; HFD, high-fat diet; SD, standard diet; sPIF, synthetic preimplantation factor. Less attention has been paid to the development of IDE activators. We have shown the principle-of-proof that hepatic gainof-function of IDE is beneficial for improving glucose tolerance and insulin sensitivity in diet-induced obese mice.
non-high-density lipoprotein cholesterol and triglyceride levels were investigated in 4138 patients with obstructive sleep apnea in the European Sleep Apnea Database (ESADA) cohort, divided into those with periodic limb movements during sleep index ≥ 15 per hr (n = 628) and controls (n = 3510). ANCOVA adjusted for age, sex, body mass index, apnea-hypopnea index, alcohol intake, smoking status, diabetes, insomnia and study site was used to assess differences in lipids between periodic limb movements during sleep and controls. Patients with periodic limb movements during sleep (24% female, 54. 4 ± 12. 1 years, body mass index 31. 9 ± 5. 8 kg m À2 , apnea-hypopnea index 36. 7 ± 25. 4 per hr) had higher triglyceride (1. 81 ± 1. 04 versus 1. 69 ± 0. 90 mmol L À1 , p = 0. 002) and lower high-density lipoprotein cholesterol (1.
, 2014). The European Sleep Apnea Database (ESADA) is a large collaborative network that at the time of our analysis included > 30,000 patients  (Hedner et al. , 2011). As part of their assessment, many subjects had fasting blood samples, including lipid profile and polysomnography (PSG) as a diagnostic test. These features make the database suitable to investigate the relationship between PLMS and dyslipidaemia in patients with OSA. Therefore, the aim of this study was to compare blood lipid values in patients with OSA with and without PLMS. We also performed predefined sensitivity analyses to validate our findings.
| Statistical analyses The JASP 0. 14 (JASP Team, University of Amsterdam, Amsterdam, The Netherlands) software was used for statistical analysis. Demographic and clinical characteristics were compared using t-test, Mann-Whitney test and Chi-square test. ANCOVA adjusted for age, sex, body mass index (BMI), AHI, alcohol intake, smoking status, the presence of type II diabetes, insomnia and the study site was used to assess the difference in lipid levels between patients with and without PLMS. The results are expressed as mean ± standard deviation and beta coefficient with standard error for the IPTW analysis. A p-value < 0. 05 was considered significant.
ACKNOWLEDGEMENTS Andras Bikov  is supported by the  NIHR Manchester BRC. The ESADA network was founded during the COST action B26 supported by the  European Union. The ESADA network is one of the  Clinical Research Collaborations  (CRC) funded by the  European Respiratory Society (ERS). Unrestricted seeding grants from the  ResMed Foundation  and the  Philips Respironics Foundation  for establishment of the database in 2007 and 2011 are gratefully acknowledged. The ESADA has received an unrestricted grant from  Bayer AG  for collaborative data analysis. LG and JH are supported by the  Swedish Heart and Lung Foundation  ( 20180567  and  20180585 ) and the agreement concerning research and education of doctors ( ALFGBG 725601  and  ALFGBG 721521 ). Support was also provided by the  European Sleep Research Society (ESRS)  and the ERS in terms of logistics for communication, meetings and data presentations for the ESADA collaborators.