dataesr/scientific-paragraphs-categorization huggingface dataset:
GGERVRAAMEKLAETGTGFARKVAVNYLETHKSLIS The crystal structure of a bBE3/bait complex was obtained with a variant of the bait protein possessing different side chains on its concave surface. The sequence differences between the bait protein used for the selection (see sequence above) and the bait protein that was crystallized (PDB code 8AW4) are all located on the concave surface of the bait protein which does not interact with the "back binder" protein. In Figure  S1a , the variable surface of the "back binder" protein (bBE3 in orange) interacts with the outside surfaces of the first helices of repeat I1 (green), I2 (cyan) and I3 (purple) of the bait protein. In the crystal structure, the N cap module and the second helix of the C cap of the bait protein were not visible in the electron density map.
ACKNOWLEDGEMENTS The authors thank  B. Gigand  and  V. Campanacci  for technical assistance in kinetic absorbance measurements. This work was funded by the  Agence Nationale de la Recherche  ( ANR , Contracts No. ANR-14-CE08-0004-ARTEMIS ,  ANR-16-CE09-0027-HYBNAP ,  ANR-18-CE44-0013 - Scaffold-Art ,  ANR-21-CE09-0045-ProteOrigami ). J. Miller  acknowledges the support of the  Office for Science & Technology of the French Embassy  in the United States through a  Chateaubriand Fellowship. This work has benefited from the  Electron Microscopy, Photonic Microscopy and Protein Interactions Platforms of I2BC , supported by  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05. F. A. acknowledges the  Région Bretagne  and  Rennes Métropole  for the X-ray setup support.
Data analysis The correlations between physicochemical parameters were studied using a principal component analysis (PCA) on the basis of the environmental variables measured at each station throughout the study period. PERMANOVAs were used to test the responses of the physicochemical parameters (with Euclidian distance as a similarity measure) or of the faunal assemblages (with Bray-Curtis similarity index) to the factors 'site' and 'season' using Primer 6 software (PRIMER-E). Between-season differences were compared statistically for each environmental parameter using Wilcoxon's matched pairs test. The analysis was carried out using the program Statistica 7 (StatSoft). Taxonomic richness, richness in Ephemeroptera, Plecoptera, Trichoptera and Coleopteran (EPTC) and Shannon & Weaver's diversity index were calculated using the faunal list of each site and for each sampling date in order to study the effects of site and season.
Correlations between each physicochemical variables and faunal taxonomic richness were determined by Spearman correlation tests. A two-way ANOVA was performed to test the variability of the indices across sites and seasons, and Tukey's HSD test was used for pairwise comparisons. The analysis was carried out using the program Statistica 7 (StatSoft). A distance-based redundancy analysis (db-RDA) was used to detect linear relationships between physicochemical parameters and faunal assemblages  (Legendre and Anderson, 1999). Prior to similarity matrix calculations, macroinvertebrate abundance matrix was ln (x þ 1) transformed to reduce the weight of superabundant taxa upon the analysis. The similarity matrix, calculated using Bray Curtis distances, was used as the input of a principal coordinates analysis (PCoA).
Data The present study is conducted on a sample of 37 Organization for Economic Co-operation and Development (OECD) countries over 2000-2019. Data on various forms of nonrenewable and renewable energy consumption (in Exajoules) have been captured from the British Petroleum (2021). The data on Environmental Patent (proxy for environmental technology) and Environmentally related Tax Revenue (proxy for environmental tax) are extracted from the OECD statistics (OECD, 2022). The globalization index has been taken from  Dreher (2006). The data on Palma Ratio (proxy for income inequality) have been extracted from the World Inequality Database. The data on Gender Inequality Index have been collected from the United Nations Development Programme (UNDP). The data on labor force (total), the number of labors in three sectors (agricultural, industrial, and service), and Government expenditure on education as percentage of government expenditure (proxy for education expenditure) have been collected from the World Development Indicators  (World Bank, 2021a).
The data on governance quality have been collected from the Worldwide Governance Indicators  (World Bank, 2021b). For the robustness check, the data on gross capital formation (constant 2017 USD) have been collected from the World Development Indicators (World Bank, 2021a), the data of gross value added by kind of economic activity have been collected from United Nations Statistics Division (UNSD, 2021), the data on final energy consumption by sector have been collected from International Energy Agency (IEA, 2021b).
The amplicon paired-end libraries (PE250) targeting an insert size of 350 bp were sequenced on Illumina NovaSeq 6000 platform aiming for 30K raw tags per sample. DNA extraction was performed at "Chromas" Core Facility, Saint Petersburg State University (Peterhoff, Russia), and library preparation, quality control, and sequencing were performed at Novogene (Novogene, Cambridge, UK). The data presented in the study are deposited in the NCBI Sequence Read Archive (SRA) database, accession number PRJNA958125.
Bioinformatics methods Raw reads were trimmed from adapter reads using bbduk. CutAdapt 4. 3 was used to trim primer reads and to discard reads without primers  (Martin 2011). Quality filtering was applied with the Dada2  (Callahan et al. 2016 ) command «filterAndTrim» with total expected errors = 1 for forward reads and 2 for reverse reads. For reads visualization and quality checking FastQC tool was used on each of previous steps. Standard Dada2 pipeline was used for further ASV producing. Taxonomy assignment was carried out using the BLASTN (McGinnis and Madden 2004) algorithm using the NCBI nucleotide database as a reference. ASVs with fewer than 10 copies were removed to minimize impact of likely spurious reads. The number of reads of each sample was rarefied by the value of the sample with the smallest number using the phyloseq (McMurdie and Holmes 2013) function «rarefy_even_depth».
Not surprisingly, they are the most abundant in the H. giganteus sample, where the beetle was collected from a cattle dung pad. But a reasonable amount of reads were also found in the H. fissicollis sample from Amber Mountain National Park, collected by pitfall traps a few kilometers inside the park. A high number of reads of humans (more than 2/3 in our analyses) and cattle, which was similar for different samples, regardless of the taxonomy of the beetles and collecting localities, shows that humans and cattle can be considered as the main food producers at least for some native Madagascar dung beetle taxa. In all our samples (except for Epilissus splendidus) we revealed the sequences of Norway rat (Rattus norvegicus (Berkenhout)). However, with the data available we cannot be sure that in all these cases rats were the real food producers for the beetles.
Acknowledgements We are thankful to the  Malagasy Institute of Conservation of Tropical Ecosystems (MICET)  for the support in the acquisition of permits and logistic support during the expedition to  Madagascar. Michele Rossini  and  Thomas Merrien  ( Finnish Museum of Natural History, Helsinki, Finland ) helped with fieldwork. Aleš Bezděk  ( Institute of Entomology ASCR, České Budějovice, Czech Republic ) commented the draft manuscript. This research was funded by  Russian Science Foundation , grant number  22-24-00715  to A. F. The scarab beetle collection of the  Zoological Institute  is maintained under the  Russian State research program   122031100272-3.
g. ,  Anderson et al. , 2013)  due to their long-life cycles in contrast the life cycles of enchytraeids are much shorter and thus calibration data can be rapidly generated. For example, the generation time is 4 weeks for the model species Enchytraeus crypticus. The development of a DEB-TKTD modelling approach with this species could, therefore, facilitate efficient ecological risk assessment for terrestrial annelids. In this investigation we developed an experimental design capable of providing the data required to calibrate a DEB-TKTD model for E. crypticus. A toxicity test following this design was then performed with the pyrethroid insecticide cypermethrin. The data from this study were subsequently used to calibrate the most-simplified DEB-TKTD model  (DEBtox, Jager, 2020). Finally, we show how the calibrated model can be used to calculate standard ecotoxicological metrics such as the EC50, and also to derive mechanistically important information, such as the physiological mode of action (pMOA) for the chemical.
Eleven vessels were prepared for each treatment and sacrificed over time for data acquisition. In addition, for each treatment, three vessels were prepared in the exact same condition than the experimental samples, and were frozen (-20 • C) at day 0, 7, and 28. These samples were then defrosted, 0. 5 g of soil sample were spiked with recovery standard tau-Fluvalinate for the quantification of cypermethrin using liquid chromatography coupled to a triple quadrupole mass spectrometer (Waters ACQUITY UPLC system coupled with a Waters XEVO TQ-XS Tandem Mass Spectrometer; UK) interfaced with a Unispray source in positive MRM mode and operated using Masslynx software (version 4. 2). The measured cypermethrin concentration was used to model the true exposure of enchytraeids and used as input in the DEB-TKTD model.
All individuals and cocoons extracted were placed in a petri dished filled with ethanol, and pictures were taken on a graph paper (e. g. , Fig. S1, S2 ). Image J software was used to measure the length of adult and juvenile enchytraeids and the diameter of cocoons  (Schneider et al. , 2012). For practical reasons, we directly used this physical length for the DEB modelling, rather than the estimated volumetric length. In theory, as long as it is used consistently, and the animal does not change shape during growth, any length measure can be used for DEB-TKTD model parametrisation  (Jager, 2020). An E. crypticus cocoon may contain a variable number of eggs, ranging from few (2-5) to more than twenty (personal observation). Therefore, to accurately estimate the reproduction over time, we chose to estimate the number of eggs to characterise reproduction, rather than using the absolute number of cocoons.
Model fitting All model calculations were performed in Matlab 2021 with the BYOM v. 6. 0 modelling platform (http://www. debtox. info/byom. html), with the package DEBtox2019. The optimisation of the parameter values was performed with the parameter-space explorer  (Jager, 2021). This algorithm combines grid search, a genetic algorithm, and likelihood profiling, giving the Confidence intervals (CIs) of the parameter values. Regarding exposure, cypermethrin dissipates over time, therefore individuals are exposed to a time-varying concentration that is modelled following a first-order kinetic as described in the supplementary material (Fig. S1 ). For the survival data, each time point is a different population, therefore survival may increase in time in independent observations which does not make ecological sense; to address this, we used an independent binomial likelihood function, recently implemented in the BYOM platform.
Strengths, weaknesses, and recommendations for future improvement of the method for ecological risk assessment This case study demonstrates that the simplest DEB-TKTD model can be applied not only to aquatic organisms, but also to soil organisms, and the use of E. crypticus is promising due to its high ecological importance in contributing to soil functions  (Pelosi et al. , 2021a (Pelosi et al. , , 2021b;; Pelosi and Römbke, 2016). The novel experimental design successfully provided the data required to calibrate a DEB-TKTD model, and allowed a complete TKTD analysis of the effects on survival, growth and reproduction, simultaneously over time. The destructive sampling technique provides independent observations (i. e. , independent populations of test organisms are sampled) at each time point, making the analysis and model calibration more robust compared to other experimental designs, because the likelihood functions assume independence, which is normally violated in regular DEB-TKTD model calibration with aquatic organisms (in which the same individuals are monitored throughout their life cycle).
The experimental approach presented can be improved in the future. For instance, the length measurement with the ImageJ software could be readily automated  (Duckworth et al. , 2019). For reproduction, we monitored the overall cocoon production but did not take into account a potential hazard to embryonic development due to transfer of cypermethrin from the mother to the eggs, nor the production of unviable cocoons. Furthermore, the potential for accumulation of cypermethrin into cocoons through soil exposure was also not accounted for, which could lead to a decrease in hatching success. These effects could have an ecologically relevant impact, and could be addressed by investigating the exposure of healthy cocoons in contaminated soils and monitoring their hatching success, as presented in  Bicho et al.
(2015). To also measure the hazard to embryonic development, monitoring of juvenile production could be added to the experimental design (as is presented in the OECD guideline OECD 220, 2016). However, retrieving of hundreds of juveniles at several time points is more time consuming than retrieving cocoons (personal observation), and monitoring of both requires additional time, therefore there are resource implications for extending studies to monitor this parameter. Further research would be needed to find a balance between experimental effort and the minimum data required for the calibration of a DEB-TKTD, which provides a much better insight than ad hoc analyses with traditional summary statistics. Where needed (for example for comparative purposes) the model can be used to derive traditional ecotoxicological metrics (LC x and EC x for reproduction and growth) at any time point (Table  2 ).
Acknowledgements We would like to thank all of the inhabitants of the communities of Huancarani, Rinconada, and Pilhuani in the Puno region for their hospitality and participation in this study. We are grateful to  Angel Mujica ,  Mario Tapia ,  Marco Chevarria Lazo ,  Jose Manuel Prieto ,  Gladys Nelida Sosa Maquera , and  Edgar Pinto Mayta  for their help in developing and testing the methodology applied in this study and in establishing connections with the farmers' communities. We would like to thank  Francesca Fagandini Ruiz  for her support to mapping the study site location. We would like to express our thanks to  Arnold Bregt  for his support and helpful advice in the final drafting of the article. We would like to thank  Carl Timler  for teaching the  WUR-SESAM program  team how to use the Q methodology.
1996; Oda et al. , 2019)  but also for CO and NOx  (Elguindi et al, 2020). Commonly used bottom-up emission inventories such as the Emissions Dataset for Global Atmospheric Research (EDGAR;  Janssens-Maenhout et al, 2015)  including various gases such as NOx, CO2 and VOC for the entire globe  (Crippa et al, 2020) , the Copernicus Atmosphere Monitoring Service regional scale (CAMS-REG) including various chemical species  (Kuenen et al, 2021)  or the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) providing globally CO2 emissions from fossil fuel combustions  (Oda and Maksyutov, 2011)  have provided gridded emissions estimates that can be combined with ground-based or satellite atmospheric measurements of greenhouse gases to perform atmospheric inversions  (Lauvaux et al. 2016; Staufer et al.
The air transportation contributes to 1. 9 % of the total emissions, and the residential emissions to 0. 3 %. Fig. 3  shows the results for January 2010, overall similar to June 2010. Considering the CO2 emissions, the EDGAR Greenhouse Gas Emissions (ghg) version 6. 0 dataset of 2018 (Joint Research Centre Data Catalogue, 2021) was preferred  (Monforti Ferrario et al. , 2021). Because CO2 is not reacting with other atmospheric species, we selected a later version to compare and to diagnose any changes in the point source locations compared to the EDGAR htap v2. 2 dataset. In addition, the CO2 EDGAR dataset consists of emissions from 18 sectors: energy, agriculture, industry, iron production, non-energy fuels, non-ferrous metals, non-metalic minerals, chemicals production, fossil fuel fires, fuel production, other products, residential, oil refineries, soil waste and transportation (vehicles, airplanes and ships).
Fig. 4  displays the contribution of the different sectors to the total CO2 emissions similarly to Fig. 3. However, the results show a strong seasonal variability in the energy sector contributing to 38. 7 % of the total emissions, followed by the industry sector with 16 %, the oil refineries with 13. 9 %, the ground transportation with 12. 6 %, the chemicals production with 8. 1 % and the rest of the sectors amounting to 10. 4 % in January 2018. These results are quite different compared to June 2018, where the energy sector is contributing to 61. 9 % of the total emissions, followed by the ground transportation sector with 10. 3 %, the industry with 9. 9 %, the oil refineries with 6. 7 %, the chemicals production with 4. 9% and the remaining sectors accounting for 6. 3 %. The EDGAR datasets provide emissions for the general molecular form of the various species (e.
Comparison of satellite TROPOMI observations and WRF-Chem simulation We compared the NO2 tropospheric column concentrations from TROPOMI and from our WRF-Chem simulations using the full chemistry scheme for the months of January and June 2021 (cf. Fig. 10 ). More particularly, we compared the average value for the months of January and June 2021 at approximately 10:00 UTC for the nested domain of the simulation after regridding the WRF outputs to the TROPOMI observations and applying the same averaging kernel to the vertical levels. A 270 Gaussian filter has also been applied in order to avoid some of the background noise  (Goldberg et al, 2019; Goudar et al, 2023). During the month of January, the quality of the observations was frequently subpar, as seen in Fig. 7  but the overall coverage remains sufficient to evaluate the model-data mismatches over the entire month.
The last noticeable plume is located over the western part of the domain, where the city of Riyadh is located (zone 1), with a wider and more intense plume in TROPOMI observations. We also note a secondary plume in TROPOMI data, south of the main one, which is not observed in the model. In Fig. 11 , we present a direct comparison of NO2 concentrations between the satellite observations and the model, separated into four distinct zones (as described above). We selected these boxes as they include isolated plumes separable from other sources and estimated the sum of the 80th percentile concentrations for the tropospheric column (raw values -no Gaussian filtering) for each day of June and January 2021. The model-data correlations are larger for zone 1 despite the presence of a  In order to examine whether the model performances correspond to an overall underestimation or overestimation of the emissions, we applied the emissions scaling factors (in this case the slope of the linear fit) to the EDGAR emissions for the 4 zones separately and we summed the values for each, thus estimating the scaling factors for the sum.
For the quantification of the enhancement ratios, we applied the method described in  Macdonald et al. (2022)  summarized here. The plume corresponds to the 80 th percentile of the NO2 concentration in each subregion (zones), consequently masked while the remaining field is considered as background. To avoid missing retrievals, the masked area was filled in using the nearest-neighbor interpolation technique and the resulting background field was subtracted from the plume values in order to obtain the enhancement fields δNO2 and δXCO2. Fig. B1  and B2 show each step of the method for the TROPOMI and OCO-3 data respectively over the urban area of Riyadh. The TROPOMI satellite data were regridded to the model resolution and the units were converted from moles cm -2 to ppb. The same method was applied to the WRF outputs for the same days.
Due to the scarcity of OCO-3 observations and the asynchronous sampling times of TROPOMI and OCO-3, we identified only 6 coincidental images over the time period, with 4 images for the urban area of Riyadh and 2 images for the urban area of Dammam. Fig. 13  shows two examples of observed and modeled δNO2 and δXCO2 plumes. Despite the plumes being correctly oriented in the WRF-Chem simulations, their widths appear lower than observed, for both TROPOMI and OCO-3. Considering that both gases are impacted by the same error, we suspect that transport model errors are responsible for the mismatch (wind speed, vertical mixing, or stability conditions) or the activity data used in EDGAR to map emissions are not diffused enough. We decreased the potential impact of transport errors in our analysis by focusing on the integral over each plume, limiting the mislocation errors (due to incorrect wind speed and direction) that would affect a pixel-based analysis of the plumes.
We defined four zones with large and distinct plumes in order to perform a spatial comparison with the TROPOMI satellite observations. The comparison revealed an underestimation of the NO2 concentrations by the model of approximately 1. 5-2 times for two zones and an overestimation of 3-4 times for the other two. By applying the specific scaling factors to the raw emissions for these four zones (linearity assumption) and summing them, we derived an overall overestimation by the EDGAR inventory equivalent to a factor of 2. 5. Finally, we simulated the CO2 enhancements with WRF-Chem for June and January 2021. The estimation of the enhancement ratios showed values of approximately 0. 7, which agrees with other relevant studies (e. g. Macdonald et al. , 2022; Lei et al. , 2022). Similar to the analysis of the NO2 concentrations showing positive and negative corrections of the NO2 emissions, the CO2 model-data mismatches (OCO-3 retrievals) also showed similar mismatches.
Acknowledgements This work is a contribution to the  EU-Funded EMME-CARE research project  (under grant no. 856612 ). The authors thank the  French Alternative Energies and Atomic Energy Commission  and the  Cyprus Institute  for funding this project. We acknowledge the use of Satellite data produced by the OCO-3 project at the  Jet Propulsion Laboratory, California Institute of Technology , and obtained from the OCO-3 data archive maintained at the  NASA Goddard Earth Science Data and Information Services Center. The wind date were retrieved from the METAR archive of the Iowa Environmental Mesonet, Iowa State University, https://mesonet. agron. iastate. edu/request/download. phtml?network=SA__ASOS, accessed on 23 October 2023. The atmospheric emissions data were retrieved from the Joint Research Centre Data Catalogue, 2017 & 2021 updates, http://jeodpp.
3. 9. 1. 1. emissions guide (2018). A recent study from Rey Pommier et al (2023) has produced a detailed infrastructure map with the known locations of the power plants and factories over the Middle East region, with 62 emitting sites in total. We show in Fig. 5 the locations of power plants and factories over the EDGAR-htap v2. 2 NOx and EDGAR-ghg v6. 0 emissions maps from the energy sector to unveil possible deficiencies of the energy point sources in the EDGAR datasets for our nested domain. The EDGAR NOx emissions lack some of the point sources along the Saudi Arabian and the Qatarian coastlines as well as near the city of Riyadh (cf. Fig. 5(a)).