dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were collected in a structured questionnaire embedded in an Excel (Microsoft Co. , 2013) database, termed "the CIF Action day"  [3, 9]. Demographic, clinical, CIF, underlying disease, IVS and HPN program characteristics were collected. A total of 3362 patients were enrolled: 320 with malignant CIF and 2919 with benign CIF. The mechanisms of benign CIF were SBS-IF in 64. 3%, enterocutaneous fistulas in 7. 0%, intestinal dysmotility in 17. 5%, mechanical obstruction in 4. 4% and extensive mucosal disease in 6. 8%  [3]. For the purpose of the present study, only patients with benign SBS-IF were analyzed.
Discussion The data represent the largest study to-date demonstrating that clinically-relevant differences exist in SBS-IF types and underlying diseases between different countries, collected from multiple international centres at the same time point. Notably, the dataset also confirms differences between genders and SBS types, while also providing a global picture of the characteristics of SBS-IF cohorts within different countries of origin. In the whole group, the female to male ratio was 2:1, SBS-J was the prevalent type of SBS, MI and CD were the more frequent underlying diseases, BMI was normal in most patients, IVS dependency was longer than 3 years in around one-half of the cases, IVS was infused 5 days or more per week in three-fourth and consisted of FE in around 10%. The highest percentage of females was also observed in all three SBS-types, as well as in the individual country cohorts, a finding supported by older investigations noting that normal human small intestinal length tends to be shorter in women  [1].
The observed highest number of days of IVS per week, of IVS volume and of FE type in SBS-J are, of course, consistent with the greatest intestinal fluid and electrolyte losses in SBS without a colon in continuity  [1, 2, 4]. The patient cohorts included by countries differed significantly in a number of characteristics. CD was the main underlying disease in UK (35. 4%), USA (48. 0%), Denmark (29. 3%) and Poland (24. 1%). The same countries had the highest percentages of SBS-J (UK, 75. 3%; USA, 59. 7%; Denmark, 82. 8%; Poland, 56. 4%), thus confirming the association between the underlying disease and type of SBS. The difference in CD percentages is perhaps most relevant; assuming that the prevalence and the severity of CD were the same in all the countries participating in the survey, the differences in the percentages of patients with SBS-IF due to CD could be determined by the disease treatment strategies adopted by different countries and/or centers contributing to the ESPEN database.
These limitations may imply that, notwithstanding the fact that the participating HPN centres were among the largest of their countries, they may not be representative of the entire country's experience, and that the epidemiology of SBS-IF in 2021 may differ from that of 2016, even though a significant change in SBS-IF pathogenesis seems unlikely within a 6-year timeframe. In conclusion, the analysis of the SBS-IF cohorts of patients included in the ESPEN CIF database provides useful information for planning and managing both clinical activity and research studies on SBS. Overall, SBS-IF was twice as frequent in females and SBS-J was the most frequent type of SBS. Significant variations among countries were observed in underlying diseases and IVS characteristics, which may be determined by differences in clinical practice, as well as in service organization for managing CIF and HPN.
Jian Wu, Trevor Smith, Charlotte Pither, Michael Stroud; University Hospital Southampton NHS Foundation Trust, Southampton Nicola Wyer, Reena Parmar, Nicola Burch; University Hospital, Coventry Sarah Zeraschi; Leeds Teaching Hospitals NHS Trust, Leeds United States of America Charlene Compher; Hospital of the University of Pennsylvania, Philadelphia, PA Manpreet Mundi; Mayo Clinic College of Medicine, Rochester, MN Denise Jezerski, Ezra Steiger; Cleveland Clinic Foundation, Cleveland, OH ESPEN CIF database managers and statisticians: Elena Nardi, Department of Experimental, Diagnostic and Specialty Medicine; Giorgia Brillanti, Department of Medical and Surgical Sciences; University of Bologna, Italy.
Thermal and crystalline properties The thermal properties during starch gelatinization were determined using a Multi-Cell Differential Scanning Calorimeter (MC-DSC) -(TA Instruments, Lindon, Utah, USA). The starch samples were weighed and hydrated directly into the ampoules (10 g starch / 100 g suspension). An ampoule with deionized water was used as a reference and three runs for each sample were analyzed. The MC-DSC heating program consisted of going from 20 to 100°C at a rate of 2°C/min. The onset temperature (To), the peak temperature (Tp), the final temperature (Tf), and the gelatinization enthalpy (ΔH) associated with the starch gelatinization interval were calculated using the Universal Analyser software (TA Instruments). The relative crystallinity of starch powder was determined by X-ray diffraction (XRD) Inel X-ray equipment (Inel, France) operated at 40 kV and 30 mA.
3D printing process The starch hydrogels, produced as described in Section 2. 3, were transferred into the printer syringes (60 mL), cooled to room temperature, and stored under refrigerated conditions (5 ± 2°C) for 24 h before printing. Afterward, the printing process was carried out in a 3D printer Stampante 3D (3DRAG V1. 2, Futura Elettronica, Italy). A nozzle (0. 8 mm diameter x 18 mm height) was coupled to the syringe, the robotic arm showed a velocity of 5 mm/s, and the extrusion rate was 4. 5 mg/s at room temperature (20 ± 2°C). Three different physical shapes (heart, star, and cylinder) were printed using the Repetier Host V2. 0. 1 and Slic3r software (Hot-World GmbH & Co. KG, Willich, Germany). The dimensions of the heart shape: 5 cm × 6 cm × 2 mm (Length × Width × Height), star shape: 2.
Acknowledgments The authors are grateful to: -the  Région Pays de la Loire (France)  /  RFI "FOOD 4 TOMORROW"  for funding the  Post-doctoral fellowship  " STARCH-3D " of  BC Maniglia ; -the  National Council for Scientific and Technological Development  ( CNPq, Brazil ) for the productivity grant of  PED Augusto  ( 306557/2017-7 ); - Dr. Manoel D. da Matta Junior , for assistance with the GPC analysis.
Mass spectrometric conditions Identi cation of cPOA and tPOA in serum samples was conducted using an AB 5500 Q-trap LC-MS/MS (ABSCIEX, Framingham, MA, USA) equipped with electro spray ionization (ESI). Quantitative analysis of cPOA and tPOA were also performed by UPLC-MS/MS. Detection was performed in negative ion mode under the following conditions: curtain gas at 35. 0 L/h, collision gas medium, ion spray voltage at 4500 V, 550 ℃, and ion source gases at 50 L/h. AB Analyst 1. 6. 0 software (ABSCIEX, Framingham, MA, USA) was used for system control and data acquisition. ESI-MS/MS parameters are shown in Table  1. Detection was performed in the negative ion mode and conditions for cPOA and tPOA detection optimized using standards. Daughter ions obtained from deprotonated molecular ions of cPOA and tPOA included three main ions from each compound at m/z 235.
In the third group, rats were administered i. g. of tPOA with 75 mg/kg body weight. Blood samples were collected at the time points of 0, 10, 20, 30, 40, 60 min, and 2 h, 3 h, 6 h, 12 h, 24 h. Serum was separated and stored frozen at -80℃ until analysis. The following main pharmacokinetic parameters were analyzed using the noncompartmental pharmacokinetics data analysis soft-ware of PK solution 2TM (Summit ResearchService, Montrose, CO, USA): area under curve from zero to the last measurable serum concentration point (AUC 0 - t , t = 24h), maximum concentration (Cmax), time-to-maximum concen-tration (Tmax).
| Functional imaging analysis To analyze the two photon calcium imaging data, the Calcium Imaging Analysis (CaImAn) package  16  and Non-Rigid Motion Correction (NoRMCorre)  17  package were used in MATLAB R2018A (The MathWorks Inc). First, imaging recordings were motion corrected using the NoRMCorre code. Next, using the CaImAn package's pipeline, regions of interest (ROI) were determined in the recordings using a constrained nonnegative matric factorization (CNMF) approach that finds the best spatial and temporal components explaining the observed fluorescence. This algorithm deals with heavily overlapping and neuropil contaminated movies, resulting in a background-corrected fluorescence signal per ROI. Recordings were manually aligned to ensure that the same ROI recorded throughout several days was noted to avoid artificially increasing the number of ROIs.
To improve the signal to noise ratio, the extracted fluorescence signal was preprocessed by smoothing using a moving mean. To determine the moving mean period, a moving mean of 1, 10, 20 and 50 frames was tested, and 20 frames resulted in optimal signal to noise ratio and was therefore used in preprocessing (Figure  S1B ). After preprocessing the data, data were extracted from each delivery. A 5-frame window before the delivery was used as a baseline to calculate ΔF/F, and the response was measured in the 20 frames subsequent to the delivery. Deliveries with a negative baseline value were excluded from analysis. ROIs with any ΔF/F value >5 (for LH VGLUT2 neurons) at any time point were consider outliers, and removed for subsequent analysis, eliminating ~1% of measurements. Data from multiple testing days were pooled.
| Statistical analysis In order to analyze the interacting effects of the diet and the solution drunk during imaging, a mixed effects model was exploited using RStudio (https://rstud io. com/), running on R version 3. 5. 3 (the R Foundation), using the lme4 and lmeTest packages. The average fluorescence was taken as the dependent variable, and stimuli (water, sucrose, sucralose) were set as an independent fixed factor. Sex was included as a fixed factor to correct for male/female differences. To prevent artificially increasing the number of measurements, ROI number was set as a random effect to account for repeated measurements of the same ROI throughout multiple imaging sessions. This model was then ran on data from either chow-fed animals, or fcHFD-fed animals. To compare the positive or negative responses, the same mixed effects model was used, but only on the subset of data that included the positive or negative responses.
The number of consistent neurons, or positive or negative responses was compared using a chi-square test in Prism 8. 3. 0 (GraphPad Software). To compare the number of positive or negative responses between stimuli, a chi-square including only 2 stimuli was performed. For both the mixed effects model, and the chi-square testing, a Bonferroni correction was applied to account for comparing the three stimuli and only P values below. 0167 were accepted. For the daily caloric intake, a student's t test was performed in Prism. For the body weight development, a repeated-measures ANOVA was performed in Prism. For the preference test, a two-way ANOVA was performed in Prism. All bar and line graphs were created in Prism, while heatmaps were created in MATLAB. All data are plotted as mean ± SEM.
ACKNOWLEDGEMENTS We thank  Dr Y. Aponte  for kindly providing the infrastructure for this study and  S. Sarsfield  for assistance with histology and imaging. We thank  C. Cover  for his assistance with the building and use of the lickometer. We thank  Dr. R. Holman  for her assistance with the statistical analysis. Lastly, we thank prof. D. Small  for her critical reading of the manuscript. This work was supported by an  AMC   PhD fellowship grant  awarded by the  AMC Executive Board  and by the  Netherlands Organization of Scientific Research (NWO-VICI  grant  016. 160. 617 ).
SPM was calculated as the difference between the dry weights after and before filtering divided by the volume of filtered water. E. coli and enterococci counts were determined using the standardized microplate method (NF EN ISO 9308-3 and NF EN ISO 7899-1 for E. coli and enterococci counts, respectively). Each sample was incubated at four dilution rates (i. e. 1:2, 1:20, 1:200 and 1:2000 with a volume of 150 µl in well) in a 96-well microplate (MUG/EC and MUD/SF, BIOKAR DIAGNOSTICS) and incubated for 48h at 44°C. Ringers' lactate solution was used for the dilutions and one plate was used per sample. The number of positive wells for each microplate was noted and the Most Probable Number (MPN) was determined using the Poisson distribution. We used the "MPN CALCULATOR" software to perform the conversion between the number of wells detected and the MPN / 100 ml E.
Epidemiological data Health data for diarrheal diseases were collected at Garango, which collects all the health data from the health district, with the agreement of the Ministry of Health. This study focused on Data from several Annual Action Plans were also collected. They contain information on the area covered by the HSPC (geography, socio-economic and cultural aspects), the distribution of the population across the HSPC area, dominant pathologies, resources (infrastructure, material resources and equipment, means of communication, energy sources, human resources, financial resources), curative activities, and planning of strategic activities for the following year.
Satellite data High frequency satellite-derived rainfall data were averaged over the Kapore area, including the two main tributaries located upstream of the sampling point (Fig  1A ). We used the GPM IMERGHHV6 data product (30mins), which is suited for monitoring rainfall in tropical regions  [72] [73] [74]. Rainfall data were cumulated per day and also over 8 day periods to be consistent with the timing of the lake water sampling. Data were collected through the Giovanni website (https://giovanni. gsfc. nasa. gov/giovanni/). Altimetry data from Jason-3 were used to monitor the water level of the Bagre Reservoir from 01/03/2018 to 01/04/2019. Jason-3 provides 10-day data for the track that crosses the Bagre Reservoir (Fig  1A ). They were collected from the Dahiti website  [75] , https://dahiti.
dgfi. tum. de/en/943/water-level-altimetry/). Sentinel-2 surface reflectance products were used to observe the dynamics of surface water reflectance and area at Kapore. Data were obtained from the THEIA data gateway (theia. cnes. fr). The revisit time is 5 days when Sentinel 2A and 2B are combined. To detect surface water and estimate water areas, we used a threshold on the Modified Normalized Difference Water Index (MNDWI which is calculated as the normalized difference between the green and the SWIR bands). Robert et al. [76]  applied a 0. 2 threshold for the Agoufou lake in Mali. Environmental conditions for Bagre led us to retain a lower MNDWI threshold, namely 0. 075, above which a pixel is considered to be water. Although images are corrected for atmospheric effects using the MAJA software  [77] [78] , we added additional thresholds to avoid undetected residual clouds and retained only blue reflectance values lower than 0.
Statistical analysis and software FIB (E. coli and enterococci) data were log 10 transformed to achieve a normal distribution. We calculated correlations and regressions to identify explanatory variables predicting E. coli and cases of diarrhea. First, standard Pearson analysis was performed for correlations between log 10 transformed E. coli and enterococci, and between in-situ SPM and the Near Infrared (NIR) band. Pearson's correlation (r) is preferred to study these relationships, because a linear relationship can be expected and tested. Spearman's rank correlation was used for E. coli concentrations and environmental parameters (SPM, water level, rainfall, NDVI, number of water pixels) and cases of diarrhea (incidence). Spearman's rank correlation (r s ) is preferred in these cases because these datasets do not have normal distributions and linear relations are not expected.
In 2018, non-bloody diarrhea (NBD) disease and dysentery constituted the majority of admissions with 62. 3% and 34. 4% respectively. There were a few cases of typhoid fever and bloody diarrhea. Whereas the HPSC of Dango reported mainly cases of dysentery, the HPSC of Lengha reported mainly cases of NBD disease as well as cases of dysentery. Similarly, the HPSC of Dierma reported mainly cases of NBD disease, although fewer than those recorded in Lengha. When the data from the 3 HPSCs for 2018 was pooled, a first peak was observed in February, this was followed by a decreasing trend in March and April that thereafter increased to reach a maximum in August after which the values decreased to a minimum in December-January (  Fig 10). When observed individually by site, the data from the HPSC of Dango differed slightly from this bulk trend: the first peak occurs in April instead of February, and its minimum is in October.
Acknowledgements The authors thank  Ali Niaoné  for the field measurements,  Marielle Gosset  for advice on precipitation datasets,  Bruno Lartiges  for discussion on bacteria-particle characteristics,  Kpeke Nestor Kambiré  and  Yasmina Karambiri  for collecting part of the health data and part of the location of village concessions, boreholes and wells, and the regional health office of  Tenkodogo  for the use of its premises.
The mode of preparation (quoted ILS = "Ionic Liquid + Starch" for the rest of the paper) consisted in adding the choline chloride under the form of ionic liquid  [15]  (previous dissolution of Cc and water for one hour before starting the experiments); in that case, the script that was used was "(Cc + W)", and the ionic liquid was then added to the flour. These systems were written with the following script: F x [Cc y W z ]. All analyses were repeated in triplicate. A statistical analysis was performed with variance analysis (p < 0. 005) to detect significant differences.
The data were monitored by a VANTEC 500 2D detector (Bruker, Karlsruhe, Germany) for 10 min and normalized. The sample was placed in a capillary with a diameter of 1. 5 mm, and a second capillary was introduced into the first to avoid loss of water during the rise in temperature; the two capillaries were sealed. The as-prepared capillary was placed in a heating stage HFS91 (Linkam, Tadworth, UK). The detector was positioned at a focusing distance of 8. 6 cm from the sample surface. It was in direct beam position  [17]. The heating kinetic applied to the sample was 1 • C/min from 20 to 120 • C. Every 10 • C, a plateau of 10 min was introduced to enable the acquisition of the diffraction spectrum.
We explore the transcriptomic adaptations in each lineage using DNA microarray in VAT and bioinformatic exploration of biological regulations using online databases. Transgenerational intake of EPA led to a reduced activation of inflammatory processes, perturbation in metabolic homeostasis, cholesterol metabolism, and mitochondrial functions in response to the obesogenic diet as compared to control mice from a control lineage. This suggests that the continuous intake of long chain n-3 PUFA could be preventive in situations of oversupply of energy-dense, nutrient-poor foods.
Comparison of Cell Type Distribution in VAT The effect of the intervention on VAT cell-type composition was explored using CIBERSORT. According to the in silico strategy developed with this online software  [26] , the gene expression data were used to estimate the relative abundance of the different cells types found in adipose tissue. As illustrated in Figure  2 , the representation of the main cell types did not differ between samples suggesting that neither the obesogenic challenge, nor the previous transgenerational supplementation with n-3 PUFA, had an effect on the proportion of adipocytes (43%), immune cells (41%), adipose stem cells (6%), and chondrocytes (5. 5%).
Transgenerational Supplementation with EPA-Induced Differential Regulation of Biological Pathways and Processes in VAT Gene Set Enrichment Analysis (GSEA) was used to explore how the transgenerational intake of omega 3 FA impacted the regulation of biological processes and pathways induced by the obesogenic challenge. GSEA applies a statistical analysis to identify concordant variation in expression value of set of genes between two biological states with adjustment for multiple testing. Consequently, we explored the regulation of biological processes and pathways using the different databases implemented in GSEA pathways (WIKI, KEGG, Biocarta, and Reactome) on all differentially expressed mRNA in the three pairwise comparisons: HFoleic vs. Ref (unique transcripts), HFepa vs. Ref (6574 unique transcripts), and HFepa vs.
HFoleic (6633 unique transcripts) according to the statistical analysis of microarray data before FDR adjustment as described in Section 4. 4. In these conditions, the representation of the numbers of regulated transcripts in each comparison showed that 5867 out of the 12,062 transcripts differentially regulated between HFoleic and Ref groups also exhibited a differential expression level between HFepa and HFoleic groups (Figure  1c ). Measurements of mRNA levels by RT-qPCR were performed on a subset of genes and validated the differential expression between groups (Table  1 ), notably between HFoleic and HFepa. The exploration using GSEA of regulated processes and reactions showed that the obesogenic diet induced in HFoleic group a depletion in electron transport chain (ETC), oxidative phosphorylation genes, and enrichment in genes related to inflammation, cholesterol synthesis, extracellular matrix remodeling, and eicosanoid metabolism as compared to the Ref group (Figure  3a ).
As illustrated in Figure  3c , only few pathways were overrepresented in HFepa group compared to HFoleic group. It concerned mRNA processing and G protein-coupled receptors (GPCRs). Many reactions extracted from the GeneTrail's reactome database, which were depleted in HFepa group, concerned extracellular matrix, and concerned genes such as Mmp13 and Timp3. The heatmap representation of correlation between expression data related to the genes involved in cholesterol metabolism and chemokine signaling showed a good sepa- As illustrated in Figure  3c , only few pathways were overrepresented in HFepa group compared to HFoleic group. It concerned mRNA processing and G protein-coupled receptors (GPCRs). Many reactions extracted from the GeneTrail's reactome database, which were depleted in HFepa group, concerned extracellular matrix, and concerned genes such as Mmp13 and Timp3.
Differential Regulation of Mitochondrial Processes in VAT between EPA and Control Lineages As the obesogenic diet induced an enrichment in several pathways related to mitochondrial function or processes, we further extracted information using the MitoMiner database of mitochondrial targeted genes. We independently used the list of up and down regulated transcripts identified at p < 0. 05 after FDR correction, for the HFoleic vs. Ref and HFepa vs. Ref comparisons. In agreement with the authors of MitoMiner  [27] , we extracted the list of genes related to mitochondria with a IMPI score confidence > 0. 8. As compared to reference group, we identified 317 up-and 383 down-regulated genes in HFoleic, 28 up-and 42 down-regulated genes in HFepa (Supplementary Table  S2 ). Details about the roles of these genes were obtained by an analysis of the related ontology to identify over represented KEGG pathways (Supplementary Table  S3 ).
Biological Pathway Analyses Molecular signatures in gene lists were explored using GeneTrail2  [58]  and GSEA to identify significant enrichment in biological pathways, processes. The identification of potential transcription factors (TF), and TF families or complexes involved in gene expression changes was performed using an over-representation analysis in GeneTrail2 following the default statistical recommendations. The CIBERSORT software was used to calculate the abundance of the different celltypes composing adipose tissue based on the dataset establish by Lenz et al. [26, 57]. The identification of genes related to mitochondria was performed using the MitoMiner online database  [27]. Analysis of gene ontology of mitochondria targeted genes was performed using the David database  [59, 60].