dataesr/scientific-paragraphs-categorization huggingface dataset:
0 mg kg -1 (EC Regulation n°1881/2006); these samples mainly consisted of dried seaweed or products derived from seaweed. Additionally, the cadmium levels in the food supplements containing dried seaweed were lower than those in the unprocessed seaweed in our dataset. This may be explained by the lower fraction of seaweed in the composition of food supplements compared to the unprocessed seaweed samples. Our estimation of cadmium dietary exposure in French adult seaweed consumers highlighted that seaweed consumed as such as a vegetable or condiment accounted for the majority of this intake (Table  4 ). Whereas contribution of food supplements containing seaweed was always minor among adult consumers. Indeed, among adult seaweed consumers, exposure to cadmium from seaweed consumed as a food represented on average about 16% of the TDI, and between 1.
35 mg kg -1 DM of unprocessed seaweed consumed as condiment or vegetable (confidence interval: [0. 18,1. 09]) was derived to ensure it. The application of this concentration reduced cadmium dietary exposure in French seaweed consumers. The contribution of seaweed to consumers in all its forms (food, ingredient, food supplement) relative to the TDI has been decreased by a factor of 1. 63 compared to the situation based on cadmium concentrations observed in our dataset and by a factor of 1. 34 compared to the situation based on the application of a maximum concentration in seaweed of 0. 5 mg Cd kg -1 DM recommended in France by the CSHPF (1997) (Table  5 ). However, as the confidence interval of our estimation included the maximum concentration of 0. 5 mg Cd kg -1 DM of seaweed, it is likely that this concentration would prevent 95% of the population from exceeding the TDI for cadmium.
Limitations of the study Our probabilistic approach could be improved with more detailed seaweed consumption data. The information about the consumption of seaweed as ingredients from industrial products was missing in our study as the access to their composition was difficult to obtain. In addition, only eating occasions were included in our estimation. Given the small dataset, our work failed to include children's consumption data of unprocessed seaweed and food supplements containing seaweed for exposure estimates. To investigate the frequency in adult and child specifically, the mode of consumption and quantity of seaweed consumed by species of seaweed according to the food product would provide a more accurate qualitative and quantitative data. These data would help to refine the estimate of exposure associated with the various types of seaweed consumption for the populations concerned and, where appropriate, of cadmium concentration limits in edible seaweed and recommendations for its consumption.
by the structural components of brown and red macroalgae(Almela et al. 2006; Superior Health Council 2015; NIFES 2016; Paz et al. 2018; Roleda et al. 2019; Banach et al. 2020). Lower range cadmium contamination was observed in microalgae compared to macroalgae in the dataset, probably due in part to production by phycoculture or seaweed farming through controlled conditions. It highlighted the recommendation to have set water quality standards for the harvesting of seaweed intended for feed and food production from fresh and coastal waters.
TableA1in the appendices describes the distribution of cadmium contamination in seaweed from the FAMCP database. Rodenas de laRocha et al. (2009). These data included cadmium contamination results from the analysis of 124 brown macroalgae (69%), 35 red macroalgae (19. 4%), 18 green macroalgae (10%) and 3 halophytes (glasswort, 1. 7%). A maximum concentration of 3. 18 mg Cd kg -1 DM was measured in a sample of red macroalgae (nori) from Japan. Red and brown macroalgae were the most contaminated with cadmium, with mean values above 0. 39 mg kg -1 DM compared to mean values below 0. 20 mg kg -1 DM for the green macroalgae and halophytes. Combining all data sources (FAMPC database, CEVA and Rodenas de la Rocha et al.
Acknowledgements We are grateful to the  Expert Panels of ANSES  on "Assessment of the physical and chemical risks in foods" for the proofreading and validation of this work. We are grateful to  Fernando AGUILAR  from  Risk assessment of Department of ANSES  for its help of the proofreading of the work. We are grateful to the  Directorate General for Competition ,  Consumer Affairs  and  Fraud Control  for their agreement for the use of results from  French Annual Monitoring and Control Plan  ( 2009-2017 ). We are grateful to the  Centre for Study and Promotion of Algae CEVA  for their help for the transmission of results of analysis of their studies in seaweed to ANSES.
TableA1in the appendices describes the distribution of cadmium contamination in seaweed from the FAMCP database. Rodenas de laRocha et al. (2009). These data included cadmium contamination results from the analysis of 124 brown macroalgae (69%), 35 red macroalgae (19. 4%), 18 green macroalgae (10%) and 3 halophytes (glasswort, 1. 7%). A maximum concentration of 3. 18 mg Cd kg -1 DM was measured in a sample of red macroalgae (nori) from Japan. Red and brown macroalgae were the most contaminated with cadmium, with mean values above 0. 39 mg kg -1 DM compared to mean values below 0. 20 mg kg -1 DM for the green macroalgae and halophytes. Combining all data sources (FAMPC database, CEVA and Rodenas de la Rocha et al. (2009)) and excluding 3 samples of seaweed powder, agar-agar and seaweed-based beverage, a total of 308 cadmium contamination data (255 samples of unprocessed seaweed, all species F combined, and 53 samples of food supplements containing dried seaweed) were considered.
In o this dataset, the mean cadmium contamination level over all seaweed species combined, r unprocessed, was 0. 60 mg kg -1 DM (min-max 0. 001-9. 4 mg Cd kg -1 DM). Mean cadmium P concentration in food supplements containing dried seaweed (0. 112 mg kg -1 DM) was lower e r e than unprocessed seaweed (Table R e v i e w O n l y Data from CEVA reported 35 out of 172 values as less than the quantification limit/LOQ of 0. 15 (n = 32)or LOQ of 0. 06 mg kg -1 DM (n = 3). Cadmium was quantified in the eight samples of seaweed analysed by Rodenas de laRocha et al. (2009). TableA2in the appendices describes the distribution of cadmium contamination in seaweed resulting of CEVA and.
O w i e w -Table 3. Input data for estimating dietary cadmium exposure for French edible seaweed -Table 4. Estimated dietary exposure to cadmium of French seaweed consumers by scenario and w consumers e i e v children according to the INCA 3 study (2014-2015) v R R e -Table 2. Number of consumption occasions, consumer rates and mean consumption of edible seaweed as a food, food ingredient and food supplement ingredient in French adults and e R e e r dried seaweed observed in the dataset, following the Upper Bound hypothesis r e P relatif aux nouveaux aliments et aux nouveaux ingrédients alimentaires. " P e -Table 1. Cadmium concentrations in unprocessed seaweed and food supplements containing e P List of Tables: o Martín Veronica, Revert Consuelo, Hardisson Arturo. 2019. Toxic metals (Al, Cd, Pb and Hg) in the most consumed edible seaweeds in Europe.
The species distribution data were collected from the China National Specimen Resources Platform (NSII, https://www. nsii. org. cn/), the China Digital Herbarium (CVH, https://www. cvh. org. cn), the Global Biodiversity Information Facility (GBIF, (https://www. gbif. org/) and the Botanical Information and Ecology Network (BIEN, https://bien. nceas. ucsb. edu/bien/biendata/). For the analysis, only data with precise geographical coordinates were selected. Duplicate records with similar geographical coordinates but extracted from different sources were removed to reduce geographical bias and spatial autocorrelation. Finally, we retrieved a total of 35,367 seed plant specimens with georeferenced occurrences collected between 1950 and 2018. We standardized the species names of all records according to the Taxonomic Name Resolution Service (TRNS, https://tnrs.
Climate change and human footprint on Mount Jinfo Using climatic data from the Nanchuan meteorological station located in Chongqing, within the Jinfo mountain range, we assessed changes in both mean annual temperature and annual precipitation throughout the past 58 years. Based on the meteorological station, we found a warming trend for mean annual temperature (Fig. 1b ) but no clear trend for changes in total annual rainfall (Fig. 1c ). In addition to extracting data from this specific meteorological station, we also analyzed data extracted from climatic grids provided on a yearly basis and at a spatial resolution of 1 × 1 km 2 by the climate dataset of the Resources and Environmental Data Cloud Platform of the Chinese Academy of Sciences (http://www. resdc. cn/). This spatial interpolation is based on daily observations from more than 2,400 meteorological stations across China  (Hutchinson, 1998).
Data analysis In order to evaluate the changes in species elevational distribution, we divided our dataset of species occurrences into two periods  : 1950-1988 and 1989-2018. We chose the cutoff year 1988 based on the assessment of previous studies relying on climate change time series in China  (Wang et al. , 2010). We cleaned the species occurrence data during both periods following two principles. First, we selected only those species occurring during both periods. Second, each selected species must occur at least ten times within each of the two studied periods to be able to compute reliable information on each species elevational distribution during a given time period. Following these principles, we focused on 231 species of seed plants, totaling 16,372 and 18,995 occurrence records  during 1950-1988 and 1989-2015 , respectively (Figure  S2 ).
Generalized linear model (GLMs) were used to analyze the relationship between elevational changes and changes in the upper/lower limits of the non-native and native plant species. In order to test whether there is sampling bias caused by specimen gatherers, we developed a null-model approach to estimate the null expectation of the elevational shifts of a species between the two studied periods  (1950-1988 and 1989-2018). The method works as follows  (Zu et al. , 2021) : first (step 1), we permuted species names of all occurrence records within the two time periods separately to generate randomized positions of each species occurrence for a given time period; then (step 2), we estimated the elevational shift for each species computing the exact same metrics as explained above for the empirical data but based on the randomized dataset generated by step 1; we repeated steps 1 and 2 for 1,000 times to generate 1,000 randomized iterations (step 3) of elevational range shifts for each range shift position (centroid and range margins); and finally (step 4), we tested the significance of the observed elevational range shifts of each species against the distribution of the null expectation generated by the 1,000 iterations.
To evaluate how climatic requirements of each native and non-native plant species influence the magnitude of elevational range shifts at the centroid of the range and at both margins, we fitted species distribution models (SDMs) for each of the 210 native and 21 non-native plant species. MaxEnt was used to predict species distributions, and the specific parameter setting was based on our past paper  (Zu et al. , 2021). The 231 species distributions data were obtained from global Biodiversity Information Facility (GBIF, https://www. gbif. org/), China National Specimen Resources Platform (NSII, https://www. nsii. org. cn/), China Digital Herbarium (CVH, https://www. cvh. org. cn), RAINBIO  (Dauby et al. , 2016) , and plant information with ecological network (BIEN, htt p://bien. nceas. ucsb.
edu/bien /biendata/). A total of 14,173 data distribution points were obtained after clearing the distribution records. We then thinned the occurrence records to a spatial resolution of 2. 5 arc minutes to reduce the potential influences of spatial biases  (Aiello-Lammens et al. , 2015). The climate data were obtained from the WorldClim database (https://www. worldclim. org) with a spatial resolution of 2. 5 arc minutes. Six major bioclimatic variables were finally selected after eliminating the collinearity between climatic variables. GLMs were used to test the relationships between elevation changes of the native and non-native plant species and the climatic factors constraining the distribution range. All analyses were conducted in R version 3. 4. 1  (Team, 2017).
Results Global effects of biocontrol. We depict in Fig. 2  the distributions of the profit gain ∆π and of the reduction in TFI, ∆TFI, compared to the absence of biocontrol. The mean profit gain is 5. 7 euros/ha (corresponding to 1. 3% of mean profit) and the 95% interquantile range  (-0. 1, 43. 6 ). The mean reduction in TFI is 1. 2 • 10 -2 units (corresponding to 0. 7% of mean TFI) and the 95% interquantile range for reduction in TFI (-0. 4, 0. 3). The average effect of biocontrol is thus relatively low when parameters are chosen at random within the ranges we explored. These distributions are computed over the whole simulated dataset. Of course, they can change if we consider a subset of parameters, or if we assume that some parameter values occur with a higher probability. Nevertheless, they give a first global picture of the potential effect of biocontrol.
1  to observe the difference between these two descriptors of the soil quality map) is known to be beneficial to pest population growth in the absence of predation  (Berestycki et al. , 2005) , which may explain this effect. Of course, the determination of the 'true' effect of biocontrol should rely on an array of approaches, not just one. In this respect, bringing our approach in the game appears very useful. We point out that, beyond the results discussed above, we have developed a generic tool which can be exploited under other conditions which are not necessarily those of this study. The Matlab codes used to generate the analyses conducted here are available and fully commented https://doi. org/10. 17605/OSF. IO/Z2QCX. These results, related to the effect of the agronomic context (land-use variability and fragmentation) on biocontrol, provide an indication on the changes that could be made in agrocosystem to increase biocontrol.
Data availability. All of the Matlab codes that led to the results in this paper are available in the Open Science Framework repository: https://doi. org/10. 17605/OSF. IO/Z2QCX. Funding. Funding for this research was provided by the project 'Facilitate public Action to exit from peSTicides (FAST)' as part of the French Priority Research Programme 'Growing and protecting crops differently' of the French National Research Agency (ANR-20-PCPA-0005). Author contributions statement. V. M. and L. R. contributed equally to this work. Competing interests. The authors declare no competing interests.
| Molecular blood meals data processing and analyses Reads were demultiplexed at the end of the sequencing using the embedded routine of the sequencer (Illumina Miseq) according to the dual index introduced during the library construction process. USEARCH version 11 pipeline  (Edgar, 2010)  was then employed to cluster all sequences into operational taxonomic units (OTUs) at cutoff value of 97% similarity. OTUs with <100 reads were discarded. Remaining OTU sequences were then compared against the NCBI Genbank database (www. ncbi. nlm. nih. gov/) using BLASTn. Only the top hits, with at least 95% similarity were conserved. Taxonomic assignment was then manually performed according to the following procedure. If top hits corresponded only to one species and the species is present in Ruaha NP, then the OTU was assigned to the species.
| Diversity of host species detected in blood meals Over the 182 pools of flies analysed, a total of 9,174,264 reads (average of 50,408 per sample [min: 1596; max: 83,667]) were obtained and trimmed. No difference in the amount of reads obtained from pools with or without fresh blood traces were observed. 148 OTUs were obtained from the 16S mtDNA sequences. 101 OTUs presented more than 100 reads and were then blasted on the global NCBI database (Table  S3 ). Despite precautions taken during manipulation of the samples and molecular treatments (extraction, PCR), three negative controls out of six contained several thousands of reads. Two contained reads from Homo sapiens and one from Phacochoerus africanus, thus indicating that intersample contamination has occurred during the different steps (Table  S4 ).
O R CO NTR I B UTI O N S S. M. , V. R. , C. P. , R. S. and F. P. designed the study. S. M. and F. P. collected the samples. S. M. , V. R. , C. A. , L. B. , E. M. , P. D. , D. J. , A. M. , S. M. , A. O. , Y. S. , B. R. , G. K. , F. C. , P. Y. B. -E. , F. B. , H. M. , C. P. , R. S. , F. P. performed research. S. M. , V. R. , C. P. and F. P. wrote the original manuscript, with input and revisions from all the coauthors. All authors contributed to and approved the final manuscript. ACK N OWLED G EM ENTS The authors thank the three reviewers as well as the editor for their very pertinent comments on a previous version of the manuscript. Data used in this study were (partly) produced through the GenSeq technical facilities of "the Institut des Sciences de l'Evolution de Montpellier" with the support of LabEx CeMEB, ANR "Investissements d'avenir" programme (ANR-10-LABX-04-01), Horizon 2020 Framework Progamme "ZIKAlliance (no.
734548) and the French LabEx ParaFrap (ANR-11-LABX-0024) and the National Reference Centre for Leishmaniasis. Authors also thank the CNRS, IRD, UM, CIRMF, IHI and Pandora Id Net consortium for their help and support. A special thanks to Christoline Motta, our park ranger, without which the tsetse collection could not have been done. We also thank Pascal Grébault for providing the traps.
, Inc. , Cary, NC, USA). Pair-wise t-tests were used to separate the main effect means and values are presented as least square means with the pooled standard error of the mean (SEM). Differences were considered statistically significant at p < 0. 05. Pearson correlations (r) were calculated using PROC CORR of the SAS software (SAS Inst. , Inc. , Cary, NC, USA) and p-values were calculated for r = 0.
Moreover, the systematic use of molecular tools/techniques to identify all sand flies collected from the field remains expensive. Therefore, morphological characteristics of sand fly eggs are of interest but there is currently no data on eggshell morphology for sand flies in Thailand. The objective of this study was to examine and characterize the ultrastructural morphology of eggs obtained from individual gravid female sand flies collected from Chiang Mai province, Thailand, using light microscopy (LM) and SEM.
Origin of the R. chalepensis L. Plant Several R. chalepensis L. plants are found in the Foret de Day National Park of the Republic of Djibouti (11 • 45 03. 6 N 42 • 41 13. 2 E). The aerial parts were air-dried and partially powdered. The species was identified by the first author in collaboration with the ESTK-USMS team (Superior School of Technology of Khenifra, University of Sultan Moulay Slimane) and was classified in the herbarium of the Medicinal Research Institute (CERD) of Djibouti with the accession number RC105-2019.
Cytotoxicity Activity All cancer cell lines (U87-MG, U2OS, RT4, PC3, NCI-N87, MRC5, MIA-Paca2, K562, JIMT-T1, HEK293, HCT116, A549, and A2780) were obtained from the standardized collections of the European Collection of Cell Cultures (ECACC, Porton Down, UK), the American Type Culture Collection (ATCC, Rockville, MD, USA), and the German Collection of Microorganisms and Cell Cultures of the Leibniz Institute (DSMZ, Braunschweig, Germany). Ethical approval for this study was granted by the ethics committee of ESTK-USMS (Morocco) and genetic information was archived under accession number: 2019-1215-0003. Cells were cultured in their appropriate media, supplemented with 10% fetal bovine serum, 1% glutamine, and 1% penicillin/streptomycin, and maintained at 5% CO2. Cultures were checked daily using an inverted microscope to assess the absence of microbial particles.
R. chalepensis L. essential oil was tested in different concentrations (0. 005, 0. 01, 0. 05, 0. 1, 0. 5, 1, 5, and 10 μg/mL); 10 µ L of each concentration was added to 90 µ L of culture medium containing the cancer cells. After incubation for 72 h, 100 μL of CellTiter Glo reagent was added to each well and incubated for 15 min. Luminescence was recorded using a spectrophotometer. The concentration-response curves were generated with EXCEL software and the 50% inhibition concentration values (IC50) were calculated from polynomial trend curves of the fourth degree  [34]. Vinblastine, doxorubicin, combrestatin A4, and monomethyl auristatin E (Sigma-Aldrich, St. Louis, MO, USA) were used as a positive control.
Cytotoxicity Activity All cancer cell lines (U87-MG, U2OS, RT4, PC3, NCI-N87, MRC5, MIA-Paca2, K562, JIMT-T1, HEK293, HCT116, A549, and A2780) were obtained from the standardized collections of the European Collection of Cell Cultures (ECACC, Porton Down, UK), the American Type Culture Collection (ATCC, Rockville, MD, USA), and the German Collection of Microorganisms and Cell Cultures of the Leibniz Institute (DSMZ, Braunschweig, Germany). Ethical approval for this study was granted by the ethics committee of ESTK-USMS (Morocco) and genetic information was archived under accession number: 2019-1215-0003. Cells were cultured in their appropriate media, supplemented with 10% fetal bovine serum, 1% glutamine, and 1% penicillin/streptomycin, and maintained at 5% CO 2. Cultures were checked daily using an inverted microscope to assess the absence of microbial particles.
R. chalepensis L. essential oil was tested in different concentrations (0. 005, 0. 01, 0. 05, 0. 1, 0. 5, 1, 5, and 10 µg/mL); 10 µL of each concentration was added to 90 µL of culture medium containing the cancer cells. After incubation for 72 h, 100 µL of CellTiter Glo reagent was added to each well and incubated for 15 min. Luminescence was recorded using a spectrophotometer. The concentration-response curves were generated with EXCEL software and the 50% inhibition concentration values (IC 50 ) were calculated from polynomial trend curves of the fourth degree  [34]. Vinblastine, doxorubicin, combrestatin A4, and monomethyl auristatin E (Sigma-Aldrich, St. Louis, MO, USA) were used as a positive control.
Statistical Analysis The calculations of all tests were statistically evaluated using the XLSTAT software 2016 (associated with EXCEL). Values are presented as mean ± uncertainty at a 5% significance level of three replicates for each experiment using the Student's t-test. Principal component analysis (PCA) is a mathematical tool to study the correlation between several parameters  [35]. The objective was to visualize and summarize the information concerning the effectiveness of the samples tested, including the essential oil, vinblastine, doxorubicin, combrestatin A4, and monomethyl auristatin E, showing the different values obtained in relation to cancer cell lines to have a summary representation allowing easier interpretation. More precisely, PCA allowed the grouping of the cell lines studied, which were linked by the active product.
Community Voice Method and the Miracle Question The Community Voice Method (CVM), based on filmed semistructured interviews, is an approach to public participation and participatory research that integrates a number of opportunities for participation and successive rounds of data analysis  (Ainsworth et al. , 2019). In the context of conflicts and transformative change, CVM has a number of strengths and opportunities. A CVM follows a step-wise process, consisting of in-depth interviews and analysis, which we followed in our research: Initially in-depth recorded and filmed interviews were conducted with stakeholders to understand the underlying discourses in each site. We designed a guide for semi-structured interviews (see Box S1 in Supplementary Material)  (Young et al. , 2018)  as a basis.