dataesr/scientific-paragraphs-categorization huggingface dataset:
II. NUMERICAL MODEL The GYSELA code is a first-principles, nonlinear, global and flux-driven 5D gyrokinetic code that evolves the gyro-averaged distribution function, Fs , for each species s, in time and phase space z = (x G , v G , µ G ), x G being the gyro-center position, v G the parallel velocity and µ = mv 2 ⊥ /2B the magnetic moment, based on a semi-Lagrangian method for numerical computations  [27, 31]. The nonlinear gyrokinetic Vlasov equation is expressed as follows: ∂ Fs ∂t + 1 B * ∇ z • ( żB * Fs ) = C( Fs ) + S heat + S mom , (1) where we use B * = b•B * with the generalized magnetic field B * = B+(m s v G /eZ s )∇×b, the charge number Z s , the mass m s , the collision operator C( Fs ) and S heat , S mom represent the heat and toroidal momentum sources respectively. The evolution of Fs is self-consistently coupled to the quasi-neutrality equation to ensure the balance between the total density N eq and the electric potential φ.
V. CONCLUSION In the present paper, the effects of toroidal rotation on both turbulent and neoclassical W transport are addressed using the nonlinear, global, flux-driven 5D gyrokinetic code GYSELA. The injection of toroidal momentum via the external source term drives W to the supersonic regime, while the toroidal velocity of the main ions remains low. By leveraging the multi-species collisional operator and external source terms implemented in GYSELA, both turbulent and neoclassical W transport in a toroidally rotating plasma are treated self-consistently. Unlike the conventional approach, which treats neoclassical and turbulent transport separately, the self-consistent treatment used in this study captures the interplay between these two channels of W transport. Through a series of nonlinear simulations involving different levels of momentum injection, it has been observed that centrifugal forces cause W to accumulate in the outboard region, creating an in-out poloidal asymmetry.
Acknowledgments This work has been carried out within the framework of the  EUROfusion  Consortium, partially funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). The Swiss contribution to this work has been funded by the  Swiss State Secretariat for Education, Research and Innovation (SERI). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the  European Union , the  European Commission or SERI. Neither the  European Union  nor the European Commission nor SERI can be held responsible for them. The simulations presented herein were carried out on the CINECA Marconi supercomputer under FUA35  GSNTITE  project.
Metabolic changes in the gut after DOR administration To explore the metabolic and functional effects of microbial community changes across the different treatment groups, we used evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG), Carbohydrate-Active enZymes (CAZy) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation programs to get the full breadth of functions in each group. We identified several hundred differentially expressed genes between the 200 N and V N groups as well as between the 200 A and V A groups (Table  S1 -2). These data showed that DOR administration led to a substantial decrease in the representation of the KEGG categories of 6 main processes (Figure  6A ), 24 eggNOG orthologues main processes (Figure  S4A ) and 6 CAZy main processes (Figure  S5A ) in the 200 N group.
For example, the relative abundance of genes encoded in KEGG level 2 pathways shifted so that carbohydrate metabolism, amino acid metabolism, translation, and membrane transport among others were the most significantly diminished among the microbiota of the 200 N group (data not shown). In addition, PCA analysis revealed that the 200 N group tends to cluster separately from other groups, implying that this sample has high similarity in community structure (Figure  6B , Figure  S4B , and Figure  S5B ). The abundance clustering heatmap based on significantly different functions indicates that the 200 N group is extremely different from the other groups (Figure  6C , Figures  S4C,  and Figures S5C ). Furthermore, we speculate that the distinctive antitumor cellular immune response in this group is related to DOR and, particularly, with the metabolic processes involved in DOR uptake by the cells.
To verify this hypothesis, we used the Metastats method, and a significant difference function box plot was drawn based on the q-value. We found that 9 KOs, including K03088, K01990, K02003, K07484, K02004, K06147, K03205, K03496, and K00558, were significantly decreased in the fecal microbiota of the 200 N compared with the V N groups (q-value < 0. 05). Next, we aimed to study whether the decreased orthologs matched with certain physiological processes. For this, we examined the overrepresentation of these orthologs in KEGG pathways. Our analysis showed several intriguing associations. Specifically, among 9 KOs that were significantly decreased in the fecal microbiota of 200 N compared with V N mice, we found that the microbiota of the V A group versus that of V N mice was significantly decreased in orthologs correlated with ABC type transport system ATP-binding proteins, such as K01990 (ABC-2 type transport system ATP-binding protein) (Figure  6D ), K02003 (putative ABC transport system ATP-binding protein) (Figure  6E ), and K02004 (putative ABC transport system permease protein) (Figure  6F ), emphasizing the significance of these pathways for metabolizing DOR.
However, the effects of DOR on KEGG pathways were all abolished by antibiotic administration (Figure  S7B ), suggesting that DOR activity was largely mediated by the above-mentioned pathways. Through the comprehensive analysis of the pathways in which differential metabolites are positioned (including enrichment analysis and topology analysis), we selected the crucial pathways that are most closely related to these metabolites. We mapped authoritative metabolite databases such as KEGG and PubChem across differential metabolites. The outcome of the metabolic pathway analysis was represented in a bubble plot; each bubble corresponded to a metabolic pathway, and the size of the bubble signified the level of enrichment. Figure  7A  shows a strong enrichment of cyanoamino acid metabolism, alanine, aspartate, and glutamate metabolism, aminoacyl-tRNA biosynthesis, glycine, serine, and threonine metabolism.
Common functional database annotations We adopted DIAMOND software (V0. 9. 9) to blast unigenes to functional databases including KEGG database (version 2018-01-01, http://www. kegg. jp/kegg/), eggNOG database (version 4. 5, http://eggnogdb. embl. de/#/app/home), and CAZy database (version 201801, http://www. cazy. org/) and the alignment result with the highest score (> 60) and E-value < 1e-5 were selected for subsequent analysis. The relative abundance of different functional hierarchy was clustered, and, based on the function annotation result and gene abundance table, the gene number table of each sample in each taxonomy hierarchy was obtained. In addition, the relative abundance profile, the abundance cluster heat map, the decrease-dimension analysis of PCA and NMDS, the Anosim analysis of the difference between/within groups based on functional abundance, comparative analysis of metabolic pathways, and the Metastat and LEfSe analysis of functional difference between groups were performed.
LC-MS/MS Analysis LC-MS/MS analyses were performed using an UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2. 1 mm × 100 mm, 1. 7 µm) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo Fisher Scientific). The mobile phase consisted of 25 mmol L -1 ammonium acetate and 25 ammonia hydroxide in water (pH = 9. 75) (A) and acetonitrile (B). The auto-sampler temperature was 4ºC, and the injection volume was 2 μL. The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur, Thermo Fisher Scientific). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as following: sheath gas flow rate as 30 Arb, aux gas flow rate as 25 Arb, capillary temperature 350°C, full MS resolution as 60000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray voltage as 3.
Data preprocessing and annotation The raw data were converted to the mzXML format using ProteoWizard and processed with an in-house program, which was developed using R and based on XCMS, for peak detection, extraction, alignment, and integration. Then an in-house MS2 database was generated by Shanghai Biotree Biotech Co. , Ltd. (Shanghai, China) and applied in metabolite annotation. The cutoff for annotation was set at 0. 3.
Analysis workflow of metabolomics data, metabolic pathways, and statistical analysis In this study, a total of 12 experimental samples related to 4 groups (V N, 200 N, V A, 200 A) were compared and metabolites were left after relative standard deviation de-noising. Then, the missing values were filled up by half of the minimum value. Also, the total ion current (TIC) normalization method was employed for each sample in this analysis. The final dataset containing the information of peak number, sample name and normalized peak area was imported to SIMCA16. 0. 2 software package (Sartorius Stedim Data Analytics AB, Umea, Sweden) for multivariate analysis. Data were scaled and logarithmic transformed to minimize the impact of both noise and high variance of the variables. After these transformations, PCA, an unsupervised analysis that reduces the dimension of the data, was carried out to visualize the distribution and the grouping of the samples.
95% confidence interval in the PCA score plot was used as the threshold to identify potential outliers in the dataset. In order to visualize group separation and find significantly changed metabolites, supervised orthogonal projections to latent structures-discriminate analysis (OPLS-DA) was applied. Then, a 7-fold cross validation was performed to calculate the value of R2 and Q2. R2 indicates how well the variation of a variable is explained and Q2 means how well a variable could be predicted. To check the robustness and predictive ability of the OPLS-DA model, 200 times permutations were further conducted. Afterward, the R2 and Q2 intercept values were obtained. The intercept value of Q2 represents the robustness of the model, the risk of overfitting and the reliability of the model, which will be the smaller the better.
Furthermore, the value of variable importance in the projection (VIP) of the first principal component in OPLS-DA analysis was obtained. It summarizes the contribution of each variable to the model. The metabolites with VIP > 1 and p < 0. 1 (Student's t-test) were considered as significantly changed metabolites. Volcano plots were made using GraphPad Prism (version 9. 3. 1). Volcano plots can visually display the overall distribution of metabolite differences between groups. HCA was generated using an integrative toolkit-Tbtools  (53). Meanwhile, we calculated the Euclidean distance matrix (EDM) for the quantitative value of differential metabolites and clustered the differential metabolites by complete linkage method. Then, we mapped authoritative metabolite databases such as KEGG (http://www.
Statistics analyses The experiments in this study were set up using 3-5 samples or animals per independent group (mouse experiments), condition or repeat. Each experiment was independently repeated. Immunoblot detection, quantitative PCR with reverse transcription, and immunofluorescence staining experiments were performed at least three times. The statistical analyses were performed using GraphPad Prism (version 9. 3. 1). Unless otherwise noted, all data are shown as mean ± standard error of the mean (SEM). Comparisons were analyzed using a two-way ANOVA or unpaired two-tailed Student's t-test. Differences in Kaplan-Meier survival curves were assessed by the log-rank test. anti-CD3ε and 1 μg mL -1 anti-CD28 antibodies) for 15 min in the presence of increasing quantities of DOR or DMSO (vehicle control) were analyzed for expression of the indicated proteins by Western blotting.
E: Mouse naive CD8 + T-cells were activated in medium containing DOR (2. 5 μM) or DMSO (vehicle control) prior to stimulation with 5 μg mL -1 plate-coated anti-CD3ε and 1 μg mL -1 soluble anti-CD28 (1. 5 h at 37ºC and 5% CO 2 ). All media were added with 10% dialysed FBS. The abundance of cellular metabolites was analyzed by LC-MS/MS. The heat map shows the relative abundance of metabolites in CD8 + T-cells stimulated in DOR-supplemented relative to control medium. Data are representative of three independent experiments. The horizontal axis is the sample grouping, and the vertical axis is the relative abundance of the corresponding function. The horizontal line represents two groups with significant differences (*q-value < 0. 05; **q-value < 0. 01). Each bubble in the plot represents a metabolic pathway.
Acknowledgments We thank  Xiaqing Yu  and  Qiong Luo  from the  Department of Nuclear Medicine, Shanghai Tenth People's Hospital , and  Yingying Li  from  School of Pharmacy, East China University of Science and Technology, Shanghai, China  for their technical assistance, the  Flow Cytometry core  ( Meier Bao ), and the  Animal facility at the Central Laboratory, Clinical Medicine Scientific and Technical Innovation Park (Shanghai Tenth People's Hospital, Tongji University, Shanghai, China ). The authors are grateful to  Dr. Olivera J. Finn  ( Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA ) for her critical review of the manuscript and providing excellent support during the writing process. This work was supported by grants from the  Natural Science Foundation of Shanghai  [ 21ZR1449600 ].
Solution methods This section implements an exact method for small-scale problems to solve the proposed model presented in the previous section. Furthermore, a case study as a large-scale problem is solved by a new meta-heuristic algorithm because location-allocation problems are NPhard to solve optimally, so using the exact method is time-consuming. Moreover, the proposed model is mixed-integer non-linear (MINLP), making it more challenging to be solved in a reasonable time. Consequently, the generalized algebraic modeling system (GAMS) software to obtain optimal values for small-scale problems and Grasshopper Optimization Algorithm (GOA) as a meta-heuristic approach for the large-scale problem are used.
Computational experiments In this part, two small-scale problems are defined and solved using both exact and meta-heuristic methods. The proposed model is an MINLP, so we used BARON solver in GAMS software for small-size problems. However, due to their complexity, large-scale problems are more realistic, closer to real-world conditions, and solved by GOA in MATLAB 2016B software. Small-scale problems are only solved to investigate the performance of GOA, which is then employed to solve a real case study as a large-scale problem. A laptop computer solves test problems with Intel(R) Core (TM) i7-6700HQ CPU 2. 60 GHz and 12 GB RAM. For the first small-scale problem, permanent and temporary blood collection centers are assumed to be located in three potential sites, along with three donation groups.
Parameter tuning A meta-heuristic algorithm's effectiveness is strongly related to its parameters, and GOA is not an exception. It has three parameters that should be properly adjusted to provide more accurate solutions. Taguchi method is used to determine the best values for Grasshoppers Population Size (GPS), Maximum Iteration (MI), and Decrement Factor (DF) by MINITAB software. Different levels and factors used to design an experiment are provided in Table  3. First, we normalized data with Related Percentage Deviation (RPD) Equation (  46 ) to make them independent from their range, and then, based on factors and levels, we applied L9(3**2) design in order to adjust parameters properly. Corresponding Mean and SNR graphs are depicted in Figs. 8  and 9  after the Taguchi method execution.
Change in Sahelian savannah communities following drought episodes Recent studies combine both satellite data and historical botanical survey data in northern Senegal. Woody vegetation changes before, during and after the drought period are studied  (Dendoncker et al. , 2020). This work was partly based on the use of historical vegetation databases, in particular the Flotrop database  (Taugourdeau et al. , 2019)  which contains more than 340,000 observations of plants between the 5th and the 25th parallel north for the African continent between 1920 and 2012 (figure 2. 1). Data freely available on GBIF 2. This study illustrates that tree density decreased between 1965 and 2008 but remained stable between 2008 and 2018 (around 10 trees per hectare). However, significant changes in species composition were noted, indicating an interspecific change in ligneous communities.
Study of intraspecific adaptation using herbariums Historical data are also preserved in the form of herbarium samples. Herbariums can be used to study changes in the flora (interspecific adaptation) but can also be used to identify variations within species, in particular morphological characteristics such as leaf area from images of these samples. We measured leaf areas on typical Sahelian species from images available at the Recolnat 3. For example, there is a relationship between the leaf area of Zornia glochidiata and the rainfall index in the Sahel (figure 2. 2). For this annual species, the surface area was lower in dry years, showing a morphological adaptation to rainfall. The intensity of the dots represents the data density of the online botanical surveys. The greater the intensity, the greater the density.
❚ Genetic characterization of local breeds adapted to tropical and Mediterranean conditions Over the last twenty years, the genomic revolution accompanied by the development of new high-throughput 5 genotyping and sequencing tools has greatly facilitated the fine genetic characterization of ruminant breeds. These tools have, for example, provided reference genotyping data for many cattle and sheep breeds stored in the Widde database 6  (Sempéré et al. , 2015). Exploring the structure of genetic diversity in local breeds (through multivariate approaches or supervised and unsupervised hierarchical clustering) applied to individual genotyping data is an essential step in describing these breeds and a prerequisite for further study of their demographic and adaptive histories. In cattle, for example, this exploratory approach has made it possible to better characterise the genetic diversity structure of some local breeds by comparing them to a panel of breeds representative of the genetic diversity of the species and to suggest historical hypotheses based on their origin, as exemplified by the Zebu of Mayotte and the Mediterranean cattle breeds  (Ouvrard et al.
The Bacillus anthracis spores can persist in the soil for years and climatic factors such as temperature and precipitation are decisive in the occurrence of anthrax epizootics. The network was obtained using the Ingenuity Pathway Analysis software. Genes under selection are highlighted in yellow, those associated with at least one climate variable are in red. Shaded genes are not associated with any climate variable. The shape of the molecules is representative of their different families. Figure 2. 11. Gene network comprising genes under selection and those associated with climate variables in 21 Mediterranean breeds  (Flori et al. , 2019). Taken together, these results indicate that local breeds are valuable genetic resources that should be preserved and integrated into appropriate management and genetic improvement schemes.
They correspond to the transfer of fodder and crop residues from the cropping system to livestock systems and to the contribution of manure for crop fertilisation. The farms studied were represented as networks, where the links between compartments represent biomass flows within the farm. Most of the biomass and nutrient flows were quantified thanks to on-farm measurements (biomass production, feed consumption, etc. ), laboratory measurements for nutrient contents, while some data were estimated (nutrient and carbon contents of meat, milk, eggs). Four scenarios were designed to explore intensification practices in production systems: • (S1) nitrogen supply for dairy cows is increased by increasing the intake of concentrate feed, • (S2) nitrogen supply for rice production is increased by increasing the supply of mineral fertiliser, • (S3) improving nitrogen conservation during manure storage (covering the manure pile) and during fertiliser application (rapid incorporation into the soil) • and (S4) the combination of the first and third scenarios.
g. , through the use of municipal land use plans). Daniel Pinillos' thesis generated a first dataset to quantify ecosystem services in the municipality of Paragominas and to carry out simulations according to local regulations  (Pinillos, 2021a). Comprehensive landscape efficiency measures are underway, with the aim of producing a territorial certification label that guarantees the transparency and attractivity of the territory with regards to responsible investors or industries. These principles of landscape efficiency have already inspired the municipality's new "territorial intelligence and development plan", enacted in 2019.
Elements of agroecology as suggested by the With regard to the implementation of the principles of a circular economy in livestock grazing systems, modelling to support territorial stakeholders plays an essential role in the development of scenarios for the implementation of innovative collective practices based on circular economy schemes. This notably applies to the management of biomass produced by livestock and agriculture in a territory such as on the island of Reunion using the Ocelet software  (Vigne et al. , 2021a). The agroecological transition will also depend to a large extent on the downstream part of the supply chains and consequently on consumption patterns. With regard to animal products, consumers, notably urban dwellers, often have high expectations of regaining access to traditional animal products of satisfactory health quality at an affordable price.
The portal (http://portal. wiktrop. org) consists of: (i) a computer-assisted plant identification tool (Idao) accessible to non-botanists; (ii) an interface for posting observations and identification requests/confirmations via photographs; (iii) a documentary database of around 800 species; (iv) a system for mapping weed observations; (v) an exchange interface by type of agricultural production. The functionalities are available on PC and smartphone (Wiktrop on Google Play). The know-how, data, information, media and documents are distributed on the portal under a Creative Commons license (CC 2017).
To accurately quantify spatially-resolved regional deposition features such as bronchial versus alveolar NP dose from terabyte LSFM lung imaging datasets, an automatic, highly efficient, and quantitative approach is essential. Current imaging and quantification tools cannot reliably determine the particle bronchial fractions  14, 16, 21, 29, 30  , highlighting the need for AI-driven methods, such as convolutional neural networks (CNNs)  31. The state-of-the-art approach, nnU-Net, has shown great promise in semantic segmentation in the biomedical domain 32. We optimized nnU-Net beyond its default configuration to achieve excellent labeling of complete bronchial trees, enabling accurate determination of the spatially-resolved deposition features for four application routes and longitudinal analysis.