dataesr/scientific-paragraphs-categorization huggingface dataset:
Geographical and botanical data Two methods were used to collect GPS points related to different ecological stages of land-unit classes. First, after determining the main classes of land units corresponding to local perceptions of the terrestrial landscape, different places were visited with a local assistant. During these visits, these land-unit classes were subdivided into subclasses based on structure and botanical composition corresponding to different ecological states of land unit development that could be used in analyses of remotely sensed data. For these ecological subclasses, multiple criteria were considered: (a) soil water regime (terra firma, floodable or permanent swamp), (b) maximum canopy height, (c) canopy openness, (d) botanical composition (dominant species and species of interest to the population), and (e) potential uses by the human population.
Data on floristic composition were first collected in Myéné language according to the knowledge of the assistant, and a photographic database of the flora of interest for each ecological subclass was created. In a second step, species were identified with their scientific names, when possible, based on the first author's personal knowledge from previous experience in Central Africa and consultation of three books on the flora of Gabon  (White and Abernethy, 1997; Meunier et al. , 2015; Vande Weghe et al. , 2016).
Recent studies agree on the advantages and potential of Sentinel-2 satellite images for remote sensing analyses, especially in tropical areas  (Frampton et al. , 2013; Verhegghen et al. , 2016; Van Passel et al. , 2020). While satellite data for this region of Africa are mostly of poor quality due to high atmospheric humidity, cloud cover was almost non-existent in the image we used, which was the most recent image of such high quality. All bands at 10-m and 20-m spatial resolution were selected for the various pixel-oriented classifications, corresponding to visible and infrared light wavelengths. This selection thus accounts for variations in plant biomass and water level with high spatial resolution  (Frampton et al. , 2013). Note that the 20-m bands were first recalibrated to 10-m using the linear function of the Orfeo ToolBox extension of QGIS before conducting the analyses on ENVI, a specialized remote-sensing software.
This SES approach integrates the land-cover map with local practices and provides an essential tool for Ramsar site managers to identify the portions of the landscape that are most used by the population, in a way to implement integrated and locally acceptable conservation policies in the future. This SES approach also confirms similar studies conducted elsewhere in Africa, demonstrating the contributions of local knowledge about human practices in landscape analyses using remote sensing  (Yiran et al. , 2012; Sulieman & Ahmed, 2013). Finally, it is also important to note that in this study, local knowledge, not remote sensing, was used as the primary source of data in the field. Deploying an ethnographic approach to the initial analysis of the landscape allows looking at land use through the prism of perceptions and practices of local people.
This work is part of the  VULCAR - FATE  project (Global change impact on vulnerable carbon reservoirs: carbon sequestration and emissions in soils and waters From the Arctic To the Equator,  2021-2024 ), supported by the  Belmont Forum  and funded by the  French National Research Agency  [grant numbers  ANR-21-SOIL-0005 ]. In addition, the  Belmont Soils 2020 International Scholarship  awarded by the U. S. National Science Foundation also allowed extension of the initial duration of the field mission for this study.
DATA AVAILABILITY STATEMENT The remote sensing image (2017 Sentinel-2 satellite image) is available online (https://earthexplorer. usgs. gov). Because of restrictions imposed by ethics committee, other data have not been publicly deposited, but our main results can be found on the Vulcar-Fate project website (https://vulcar-fate. obs-mip. fr/). Upon reasonable request, the corresponding author may make available certain data following agreement by the principal investigator of the research project of which this study is a part.
Incremental vs. constant load As explained above, constant load protocols are probably the most used fatiguing designs in the literature. They consist in sustained contractions, either submaximal [e. g. ,  (28)  or maximal [typically 2-min MVC, e. g. ,  (33) ], or intermittent contractions that can again be maximal or submaximal (Figure  1A ). When fatigability is only measured at task failure, it must be assumed that the motivation of the participant is maximal. Data showing that the variability in times to task failure is greater in clinical populations  (34)  suggest that it may less be the case than in healthy subjects. Furthermore, both clinical and healthy populations rarely reach exhaustion in activities of daily living, limiting the transfer of the results to ecological situations.
Previous CH 4 inversion studies divide emissions into anthropogenic, biospheric, and other categories (e. g. ,  [30, 31] ). The category of anthropogenic emissions include all CH 4 emissions from human activity, while biospheric emissions include emissions from wetlands and soil sinks. The other emissions include geological, ocean, and termite emissions. In this study, we challenge the traditional division of emissions and instead group the emissions based on their isotopic signatures and origin. In this study, we categorize the emissions based on their isotopic signatures: fossil-based and more enriched in 13 CH 4 and biogenic with depleted 13 CH 4. We performed two inversion runs using a CarbonTracker Europe -CH 4 data assimilation system using CH 4 mole fraction data. The TM5 atmospheric chemistry model was run using the posterior fluxes and process-specific isotopic signatures to examine atmospheric δ 13 CH 4 and CH 4 trends.
CH 4 and 13 CH 4 Fluxes In this study, we use inventories and process-based model data for global CH 4 flux fields from anthropogenic and natural sources. All the CH 4 fields are pre-processed to a 1 • × 1 • grid to match the TM5 model resolution. The 13 CH 4 fluxes are obtained using Equation  (2). CH 4 anthropogenic flux data are taken from the EDGAR inventory v6. 0  [47, 48]. The data used in this study cover the years 2000-2020. The anthropogenic emissions include emissions from coal, oil and gas, residential, rice cultivation, landfills and wastewater treatment (LWW), and enteric fermentation and manure management (EFMM). EDGAR v6. 0 includes the seasonal cycle of emissions. Anthropogenic emissions can be of fossil or biogenic origin, and the grouping between them is based on the processes by which CH 4 is formed, using isotopic signatures.
In general, fossil sources are more enriched in 13 CH 4 than emissions of biogenic origin. Emissions from wetlands, soil sinks, and termites are taken from Saunois et al. [1]. Wetland emissions are the climatological monthly means from the bottom-up models for 2000-2017. Biomass burning emissions are taken from the Global Fire Emissions Database (GFED) v4. 1  [49] , ocean emissions from Weber et al. [50] , and geological emissions from Etiope et al. [10]. Based on the Intergovernmental Panel on Climate Change (IPCC) AR6 WG1 report  [51] , we scaled the geological emissions to obtain the total global emissions of 23 Tg.
CH 4 Observations for Constraining CH 4 Fluxes Atmospheric observations for CH 4 mole fractions were obtained from NOAA/GML and other national and international networks and assimilated in the CTE-CH 4. The CH 4 observations include surface observations from continuous and flask samples, i. e. , weekly discrete air samples. The hourly data from the continuous in situ CH 4 observations were processed similarly to Tsuruta et al. [30]  before the inversions were performed. Observation uncertainties were defined for each site and observation based on the site's characteristics and the measurement accuracy. The observation uncertainty is defined by taking into account both the observation error and the TM5 model error. The model error arises from the ability of the TM5 to simulate the observations.
δ 13 CH 4 Observations for Evaluation We used observations of δ 13 CH 4 to evaluate the emission trends and model estimates. Anomalies in the trends were used for comparison. We used curve fitting methods from  Thoning et al. [57]  to calculate the trend. The trend curve represents the long-term trend with seasonal cycles removed. Anomalies were obtained by subtracting the mean of the time series. In this study, we compared the model with observations at 13 stations, which has a long enough data time series, as shown in Table  1  and Supplementary Table  S1.
Acknowledgments: We acknowledge obspack_ch4_1_GLOBALVIEWplus_v5. 0_2022-10-17providers fo their valuable work:  Kenneth N. Schuldt ,  Tuula Aalto ,  Arlyn Andrews ,  Shuji Aoki ,  Francesco Apadula ,  Jgor Arduini ,  Bianca Baier ,  Jakub Bartyzel ,  Peter Bergamaschi ,  Tobias Biermann ,  Sebastien C. Biraud ,.
No permits were required for fieldwork. We used plant cover data from control, +NPK, Fence and +NPK & Fence plots from any site that met three criteria: (1) recorded pretreatment cover data, (2) had at least 1 year of posttreatment cover data, (3) had NPK treatments, fencing treatments or both. This resulted in 90 sites ranging in duration from 1 to 15 years of treatment data (Table  S1 ). We converted yearly species cover values within a plot into percentage rank abundances. Rank abundance curves array species within a plot ordinally from most (1) to least (n) abundant and provide a numerically convenient approach for understanding the role of abundant species across sites  (Avolio, Carroll, et al. , 2019). Recasting the ordinal rank abundance as a percentage rank (where 1 is the most abundant species, 0.
In contrast, higher HDL lipoprotein levels and larger HDL particles were associated with Asymptomatic cases. Neither amino acids nor glycoprotein acetyls were associated with COVID symptom duration. In both the clinically validated variables, and the entire metabolomics dataset, only 7/249 (2. 8%) variables were significantly different in Long COVID (OSC28 or PCS84 combined) compared to Non-COVID illness (NC28 and NC84 combined) as the reference group (Supplementary Table  6 ). Of note, HDL-Cholesterol was also raised in Acute COVID-19 Illness (OR 1. 24 [FDR P-value 0. 005] for ACI vs Non-COVID illness) and further raised in Asymptomatic illness (OR 1. 44 [FDR P-value 0. 007] for Asymptomatic vs. Non-COVID illness). The Infectious diseases score. The multi-biomarker infectious diseases score (ID Score) was calculated, whereby higher values were previously associated with hospitalization for COVID-19  19.
Correlation between metabolomic and microbiome analysis. Correlations between the metabolites and microbial taxa were assessed using Spearman's rank coefficient. We found no evidence that microbiome taxa were associated with differences in those metabolites associated with symptom duration in this dataset (Fig. 2 ). Negatively correlated metabolites and microbial species clustered in the top right-hand corner, with mild positively correlated associations throughout the remainder of the heatmap. A distinctive pattern was unable to be elucidated. The primary species driving the negative correlation were Alistipes finegoldii, an anaerobic, mesophilic, rod-shaped bacterium and Bacteroides cellulosilyticus, a cellulolytic bacterium.
Strengths + Limitations. Our study benefits from large size, with a long duration of prospective symptom reporting, and availability of both metabolite and microbiome data on the same community-based participants. These platforms are well validated, including for clinical use of the metabolomics data. Our participants also reflect the spectrum of COVID cases in the community. As participants were recruited prior to vaccination in the wild-type era, this reduces complexity by variance attributable to virus variant, and type and timing of vaccination  55. Infection status was reconfirmed at enrolment, ensuring accuracy of classification by gold standard methodology. During this period, there were also relatively few acute COVID-19 specific treatments available, and none routinely used in UK community-managed individuals allowing our study to reflect the natural history of COVID-19.
The first 301 samples that would maintain a balance for BMI, age, and sex, were selected to undertake a pilot investigation of microbiome differences. Metagenome quality control and pre-processing. All metagenomes were quality controlled using the pre-processing pipeline (available at https:// github. com/ Segat aLab/ prepr ocess ing). Briefly, pre-processing consisted of three main steps: (i) read-level quality control, (ii) removal of host sequence contaminants, and (iii) splitting and sorting of cleaned reads. Read-level quality control removes low-quality reads (quality score < Q20), fragmented short reads (< 75 bp), and reads with ambiguous nucleotides (> 2 Ns), using trim-galore (https:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ trim_ galore/). Host sequences contaminant DNA were identified using Bowtie 2  61  with the "-sensitive-local" parameter to remove both the phiX 174 Illumina spike-in and humanassociated reads.
Functional potential profiling of metagenomes was performed with HUMAnN 3 (v. 3. 0. 0. alpha. 3 and UniRef database release 2019_01)  62, 67  that produced pathway profiles and gene family abundances. We assessed beta diversity by computing a Principal Coordinates Analysis (PCoA)/Metric Multidimensional Scaling (MDS) based on the pairwise Bray-Curtis dissimilarity metric. Additional covariates. BMI was derived from self-reported weight and height. Other self-reported information (obtained from ZCS app-reported data) included smoking; and co-morbid illness ('heart disease' , 'diabetes' (and type of diabetes), 'lung disease' (including asthma), hay fever, eczema, 'kidney disease' and current cancer (type, and cancer treatment). Address data was linked to the UK Index of Multiple Deprivation (IMD), with the IMD rank decile used as a categorical variable measuring local area deprivation  [68] [69] [70] [71].
Frailty was assessed using the Prisma-7 scale, with a score > 2 indicating frailty  72. A subset of participants had participated in a dietary assessment during the COVID-19 pandemic, also recruited through the ZCS (published previously  73, 74  ). This included detailed information on vitamin supplementation (including omega-3 oils), physical activity, alcohol consumption, dietary habits and a food frequency questionnaire. These data were used to derive a diet quality score  73  , and a plant-based diet index  73  , analysed as continuous variables. Both have previously been associated with cardiovascular disease  75  , Type 2 Diabetes 76 , a lower risk of COVID-19 illness, and a lower risk of hospitalisation for COVID-19 during the early waves of the pandemic  73.
Statistical analysis. The statistical analyses were performed using R software (v. 4. 0. 5) and Stata (v. 17, StataCorp). Baseline characteristics were described by frequency and percentages. Descriptive data on those invited and those enrolled, are presented in Supplementary Table 2 + 3. Metabolites were all log-transformed and standardised (mean 0, standard deviation 1) as per protocol  19. To account for 0 values, prior to log transformation, a pseudo-count of 1 was added to all values. Initial analysis examined association between duration of illness and each metabolite individually, using multinomial logistic regression (all adjusted for age, sex, and BMI). The Asymptomatic group was used as the reference category of the outcome variable. We also performed a secondary analysis, using the non-COVID-19 participants as a reference category.
Correlation analyses were conducted using R version 3. 6. 0. The package 'corrplot_0. 90' was used to compute the variance and the covariance or correlation, 'pheatmap_1. 0. 12' and 'cor. mtest' were used to visualise the heatmap, calculate associated P values. Hierarchical clustering of both top 39 most abundant microbial species and 39 metabolic profiles was conducted using hclust, implementing the Ward. D2 agglomeration method. The package 'p. adjust' was used to perform Benjamini-Hochberg multiple testing correction  77.
Acknowledgements We would like to acknowledge support from the  Exetera  development team ( Benjamin Murray ,  Liyuan Chen ,  Jie Deng ,  Eric Kerfoot ), the  TwinsUK laboratory  and administrative teams ( Gulsah Akdag ,  Andy Anastasiou ,  Andrei Beleanu ,  Julia Brown ,  Maria Paz Garcia ,  Rachel Horsfall ,  Genevieve Lachance ,  Ayrun Nessa ,  Alyce Sheedy ,  Dovile Vaitkute ,  Sam Wadge ,  Sivasubramaniam Rajan Wignarajah ,  Darioush Yarand ). We are also grateful to the  CSS Biobank Volunteer Advisory Group  for their input on the development of the biobank, and feedback on the significance of this work for affected individuals and the wider public.
Data availability Data collected in the ZCS smartphone application are shared with other health researchers through the UK National Health Service-funded Health Data Research UK (HDRUK) and Secure Anonymised Information Linkage consortium, housed in the UK Secure Research Platform (Swansea, UK). Anonymised data are available to be shared with researchers according to their protocols in the public interest (https:// web. www. healt hdata gatew ay. org/ datas et/ fddcb 382-3051-4394-8436-b9229 5f142 59). The code is available in: https:// gitlab. com/ KCL-BMEIS/ covid-zoe/ vacci nation. Access to data in the CSS Biobank is available to bona fide health researchers on application to the CSS Biobank Management Group. Further details are available online at: https:// cssbi obank. com/ infor mation-for-resea rchers including application forms and contact information.
Materials and Methods The list of the AS reported in Morocco (and their geographical coordinates) was compiled from (i) freely available web databases, including the Global Biodiversity Information Facility GBIF (http://www. gbif. org/ (accessed on 1 April 2022)) and the Global Invasive Species Database GISD (http://www. iucngisd. org/gisd/ (accessed on 1 December 2022)); (ii) an extensive literature search through published articles ; (iii) our own research, whether published or in progress  [11] [12] [13] [14] [15] [48] [49] [50] [51] [52] [53] [54] [55] ; and other personal observations. The introduction pathways of each species were designated by an extensive analysis of literature dealing with biological invasions and summarized in seven categories, following the same method as previously published articles on the same topic of alien animals and aquatic species (e.
Results Forty-one AS are present in Moroccan inland waters, plus 15 considered doubtful or unconfirmed. Only half of them (21 species) have well-established populations across the country (Table  1 ). About 560 records of alien animal species occupying the inland waters of Morocco have been gathered with the GPS coordinates of the localities. 374 records come from specialized published literature, of which 104 are our publications (among which some are also present in GBIF), followed by 142 new records (presented in the Supplementary Materials of this work), and completed by 24 records coming from GBIF. The geographical positions of alien animal species present in the inland waters of Morocco, collected from the bibliography, in addition to our data (published and unpublished), are offered in Supplementary Materials (Table  S1 ).
Discussion Given the lack of a robust national database recording alien species on a regular basis and the huge gaps in research on invasive species in Morocco in particular or in Africa in More than half of the AS present in Moroccan inland waters are of American origin (52. 54%), followed by Asian (27. 12%) and European (16. 95%) taxa (Figure  4b ). To date, only a few studies have addressed the impact of alien species on native ones or their autoecology in the inland aquatic ecosystems of Morocco. Most of them were published recently or are in progress (Table  2 ).
Discussion Given the lack of a robust national database recording alien species on a regular basis and the huge gaps in research on invasive species in Morocco in particular or in Africa in general, studies of the distribution and detection of invasive species are extremely important and useful for researchers working on the spread, impacts, and management of biological invasions  [56]. The present work represents a first step toward the management of AS in Morocco by providing the first annotated list of AS present in the inland waters of the country. This list includes 41 confirmed AS, some of which are ranked in the top hundred worst invasive species worldwide (Stegomyia albopicta, Micropterus salmoides, Cyprinus carpio, Gambusia holbrooki, Oncorhynchus mykiss, Trachemys scripta elegans, and others)  [63].
(2013). However, NGC 4214 alone is not a good test sample for the m M max ecl relation due to its relatively low SFR (0. 2 M e yr -1 ), which implies low numbers of observed clusters, and thus, low-number statistics. Progress on this controversy can be aided by the data products from the Legacy ExtraGalactic UV Survey (LEGUS), a Hubble treasury program that has obtained imaging data for ∼50 nearby star-forming galaxies (distances ∼ 3. 5-18 Mpc) in five bands from near-ultraviolet (NUV) to I  (Calzetti et al. 2015b). As part of the effort, star cluster catalogs for 31 LEGUS galaxies have been produced, which include visual identification, location, and photometry for each star cluster; for clusters detected in more than four bands, physical parameters such as age, mass, and color excess, E(B -V ), derived from spectral energy distribution (SED) fitting  (Adamo et al.
Data LEGUS is an HST Treasury Program (Cycle 21) which collected five band images (UV, U, B, V, and I) of 50 starforming galaxies within 12 Mpc with the Wide-Field Camera 3 (WFC3) using the filters F275W, F336W, F438W, F555W, and F814W  (Calzetti et al. 2015b). Archival images obtained with the Advanced Camera for Surveys (ACS) wide field in the filters F435W, F606W, and F814W are also used when they are available to complement the LEGUS observations. The pixel scale of ACS/WFC is slightly larger, 0 05 pixel -1 , than that of WFC3 (0 0396 pixel -1 ), but the LEGUS collaboration aligned and resampled the images to the WFC3 ones. YSC catalogs were produced using a combination of SExtractor  (Bertin & Arnouts 1996)  for the automatic search of extended sources and human identification. SED fits for all sources detected with a signal-to-noise ratio of 3 or better in at least four bands were performed, and the sources were assigned ages, masses, and extinctions with uncertainties.
Photometry In order to measure the Hα luminosity of each star cluster, we retrieve from the Hubble Legacy Archive images of the five galaxies in the F547M/F550M (V-short) and in the F657N/ F658N (Hα+[N II]) filters obtained with WFC3/ACS. We give preference for the medium-band F550M or F547M over the F555W filter to avoid the strong [O III] emission line which affects the broader filter. We align the images to the F814W (I band) images of the LEGUS data for aperture photometry and additional corrections. Aperture photometry is performed on all 375 clusters in all three bands using the PHOTUTILS package in ASTROPY with a 5 pixel aperture radius, corresponding to 0. 35-0. 5 RStromgren  (Calzetti et al. 2010) , and with an annulus between a 7 and 9 pixel radius for the local background. The positions of the selected clusters are taken from the LEGUS catalogs.