dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and data sources We carried out a multi-country analysis of cross-sectional surveys in SSA countries, namely the DHS Global Positioning System coordinates were obtained from the DHS database. These coordinates were intentionally and randomly displaced to ensure confidentiality of the respondents. Urban clusters were displaced between 0 and 2 kilometers (km), while rural clusters were displaced between 0 and 5 km. The spatial boundaries were also obtained from the DHS spatial database repository (https://spatialdata. dhsprogram. com/home/). The DHS data were linked to these spatial data. For our analysis, we selected SSA countries with available DHS surveys between 2011 and 2019 that contained the variables of interest and spatial data. We selected the more recent survey (as of November 2021) in countries with more than one eligible survey.
For example, racehorses, which are for the most part kept indoors, are mainly found in north-western France (especially in Normandy, Pays de la Loire and, to a lesser extent, Brittany), whereas draught and ride-and-drive horses, which are most often kept outside, are mainly present in southern France (Auvergne-Rhône-Alpes, Burgundy-Franche-Comté, and Occitanie) and in Brittany  [49]. Unfortunately, due to a lack of individual information on horses in the dataset analysed in the present study, the clinical status of each horse was not known, making it impossible to relate seropositivity to disease expression. In the  Chevrier et al. studies (1978 Chevrier et al. studies ( , 1979)) , 8. 9% of the symptomatic horses were positive, but apparently healthy animals were positive in only 2% of cases  [17, 31].
Similarly,  Soulé et al. (1990 Soulé et al. ( , 1995) )  reported that 11. 8% of horses suspected of piroplasmosis were positive, but only 2. 7% of healthy animals were positive  [18, 19]. Our study being based on blood samples sent by practitioners, it is moreover impossible to know whether the number of sera received from each region is correlated to the number of horses residing there. To limit financial costs for the ownerespecially in highly impacted areas-veterinarians often use imidocarb when piroplasmosis is suspected, even without performing any diagnostic tests, leading to a potential underestimation of positive horses in our dataset. Therefore, although serological data are a good proxy for piroplasm circulation, the lack of knowledge on the clinical status of our studied population cumulated with the lack of reliable estimations of the horse population by region or by year in France makes it unlikely for the seroprevalences of our dataset to be reliable estimations of national seroprevalence.
, a simulated dataset was built as a presence/absence matrix with hosts in lines and antibody combinations in columns  [52]. Four statistical distributions corresponding to the four combinations of antibody occurrence were obtained from 5000 simulations. A 95% confidence interval was then estimated to obtain a profile that included all the combinations simultaneously. From this profile, two quantiles, Qinf and Qsup, were inferred for each combination. Global test was based on the 95% confidence interval. When H0 (i. e. , hypothesis of random distribution) was rejected, the local tests were based on the confidence intervals (Qinf -Qsup)  [52]. Statistics were performed with R 3. 6. 1 and maps were drawn using QGIS v. 3. 8. 3 software.
Operating room procedures OR procedures were performed by trainees under the supervision of experienced surgeons. These operations were conducted between January 2018 and November 2021 at the University Hospital of Strasbourg. Those who went through an additional procedure at the same time (e. g. , another type of angioplasty or stenting, femoral bifurcation endarterectomy, femorodistal or femorofemoral bypass) were excluded. Total procedure time as well as the total irradiation time were gathered. Data were extracted retrospectively from a software program containing all surgical procedures in the hospital. Only procedures coded to French social security by the references (1) EDFA003 (intraluminal dilatation of the common iliac artery and/or external iliac artery with stenting, by transcutaneous arterial approach) for iliac procedures and (2) DGLF001 (placement of covered bifurcated aorto-bi-iliac stent, by transcutaneous arterial approach) for EVAR procedures were included.
8 This can reassure a young practitioner before operating in the OR or even help a more experienced surgeon in difficult cases. This study has several limitations. First, data were collected retrospectively, although all assessments were done with a training curriculum. Second, it was not possible to differentiate the fluoroscopy from DSA times. As a consequence, all irradiating time is recorded in the study under the term "fluoroscopy". Finally, although the simulated procedures were performed in a realistic environment, there was no communication or advice from the senior in simulated procedures in contrast to the real procedures, which could theoretically influence the fluoroscopy time. In a future study, other information could be integrated such as the position of the C arm.
Colon Cohort This cohort comprised human participants recruited as part of the FLOROMIDIA cohort  [16] , which is a pilot study to investigate OMICS signatures in individuals with a BMI between 20 and 60 kg/m 2  [16]  (Table  S1 ). The colon transcriptomics data from n = 37 subjects from the FLOROMIDIA study are deposited in GEO with accession number GSE158237. Inclusion criteria included: Caucasian origin; stable body weight 3 months preceding the study, no systemic disease, and absence of any infections or use of antibiotics one month before the visit. The existence of liver illness, specifically an infection with HBV/HCV and tumor disease, thyroid dysfunction (based on a biochemical work-up), and alcohol consumption (>20 g/day) were all exclusion factors. The Policlinico Tor Vergata University of Rome's ethical commission (Comitato Etico Indipendente Policlinico Tor Vergata, approval number 113/15, 17 July 2015) validated and accepted the written informed consent provided by each subject.
Jejunum Transcriptomics During gastric by-pass surgery, intestinal epithelium from the jejunum was removed and stored in RNAlater (Thermo Fisher Scientific) to maintain RNA integrity. Following that, samples were sent immediately to the laboratory. Tissue was handled and stored in strict aseptic conditions at a temperature of -80 • C. RNA was purified using the RNeasy-Tissue Mini-Kit (QIAgen). Total RNA was quantified, the integrity was checked and the RNASeq libraries were prepared and sequenced as previously described  [18]. RNAseq reads were mapped against human reference genome (GRCh38) using STAR software version 2. 5. 3a [19]  with ENCODE parameters. Genes were quantified using RSEM version 1. 3. 0 [20]  with default parameters and using the annotation file from GENCODE version 29.
For acidic (solvent A: 0. 1% FA in water, solvent B: 0. 1% FA in methanol) and basic (solvent A: 6. 5 mM ammonium bicarbonate pH 8. 0, B: 6. 5 mM ammonium bicarbonate in 95% methanol) mobile phase conditions, optimized for positive and negative ESI mode, respectively, two separate columns (2. 1 100 mm Waters BEH C18 1. 7 m particle) were used. The columns were produced in a gradient of 99. 5% A to 98% B in 11 min at a flow rate of 350 µL/min following injection of the sample extracts. The LTQ XL mass spectrometer's ESI source was directly coupled to the eluent flow. Turn-by-turn recordings were made of full scan mass spectra (80-1000 m/z) and data-dependent MS/MS scans with dynamic exclusion. Using Metabolon hardware and software, raw data was retrieved, peaks were detected, and quality control was performed (Durham, NC, USA).
Utilizing retention index (RI), accurate mass (+/-10 ppm), and MS/MS, compounds were identified by comparison to Metabolon's library entries (Durham, NC, USA). Finally, to verify the consistency of peak identification across the multiple samples, chemicals were manually reviewed and corrected, as appropriate, by data analysts at Metabolon utilizing proprietary visualization and interpretation software. The data was provided with the value at its original scale being the area under the curve of the compound peak. Compounds were screened for defective groups for additional analysis. If a compound was absent from more than half of the samples, it was removed.
Gene Co-Expression Patterns We used Spearman's rank correlation adjusting p-values with the Benjamini-Hochberg correction for multiple testing to identify gene transcripts linked to the DPP9 expression from colon samples. For jejunal samples, gene transcripts associated with DPP9 were identified using the DESeq2 R package  [23]. Functional pathway enrichment analyses were performed on significant genes based on Reactome Gene sets using Metascape  [24]  with all measured genes as the gene background. Only terms with p-value < 0. 01, a minimum count of 2, and an enrichment factor > 1. 5 were considered. From these terms, only those with a q-value < 0. 05 after correcting for multiple testing were considered as significant. p-values were calculated based on the cumulative hypergeometric distribution.
Gene-Gene Interaction Networks Gene-gene interaction networks were constructed from significant gene transcripts with the OmniPath, InWeb_IM, and BioGrid databases using Metascape. The resultant network contained the subset of genes that interact with at least one other member of the input list. To simplify large networks and provide more interpretable results, we applied a mature complex identification algorithm (MCODE)  [26]  to extract densely connected relevant clusters embedded in the large network. Then, a functional enrichment analysis was applied to each cluster independently to annotate its putative biological roles based on the three most significantly enriched Reactome gene sets.
To gain a better insight and further characterize the relationships among DPP9associated genes we constructed a protein-protein association network from significant gene transcripts with the BioGrid, InWeb_IM, and OmniPath databases (Figure  3c ). To simplify the large network and provide more interpretable results, we applied a mature complex identification algorithm (MCODE) to extract densely connected relevant clusters embedded in the large network. Then, a functional enrichment analysis was applied to each cluster independently to annotate its putative biological roles based on the three most significantly enriched Reactome gene sets (Figure  3d , Table  S6 ). From 12 identified significant cluster, three contained genes encoding for thirty-six 40S and 60S ribosomal proteins, five mitochondrial ribosomal proteins (MRPL), and four eukaryotic initiation factor 3s (EIF3s).
Promising Drugs Targeting DPP9-Associated Genes Finally, to identify potential existing drugs targeting genes associate with DPP9, we performed gene-drug enrichment analyses based on both DrugBank and STITCH databases. DrugBank enrichment analyses highlighted artenimol, puromycin, anisomycin, and 3-phenyllactic acid, as the most significant drugs both in colon (Figure  6a ) and jejunum samples (Figure  6b ), while STITCH-based analyses also identified linezolid (Figure  6c, d ). Protein-drug interaction networks of puromycin, 3-phenyllactic acid and linezolid are shown in Figure  6e-g.
Supplementary Materials: The following supporting information can be downloaded at: https:// www. mdpi. com/article/10. 3390/antiox11112177/s1, Table  S1 : Clinical characteristics of the Colon cohort; Table  S2 : Clinical characteristics of the Jejunum cohort; Table  S3 : Spearman's rank correlations between gene transcripts and DPP9 from the colon transcriptomics; Table  S4 : Pathway enrichment analysis of the colon genes significantly associated with DPP9 (P-FDR < 0. 0001) using the Reactome gene sets; Table  S5 : Enrichment networks analysis from colon enrichment analysis using Metascape; Table  S6 : Clusters and genes identified from the protein-protein interaction network from the DPP9associated colon genes using the MCODE algorithm; Table  S7 : DESeq2 results for the gene transcripts associated with DPP9; Table  S8 : Pathway enrichment analysis of the jejunum genes significantly and negatively associated with DPP9 (P-FDR < 1 × 10 -7 ) using the Reactome gene sets; Table  S9 : Enrichment networks analysis from jejunum enrichment analysis using Metascape; Table  S10 : Clusters and genes identified from the protein-protein interaction network resulting from the DPP9-associated jejunal genes using the MCODE algorithm.
Contributions: Á. d. C. -I. and J. M. -P. researched the data, performed the statistical analysis and wrote the manuscript. J. M. M. -N. , performed the RNA. sequencing of jejunal samples. J. L. obtained and analysed the expression of DPP9 in plasma samples. M. A. -R. contributed to collect the data for the jejunum database. M. B. , G. M. (Geltrude Mingrone), G. M. (Giovanni Monteleone), O. A. P. , B. S. , P. L. and M. F. contributed with the colon database. J. P. , R. R. and J. G. -O. contributed with the metabolomics validation database. M. J. , R. P. , M. P. -O. and J. S. performed the metabolomics for the metabolomics validation database. M. F. contributed to the discussion and reviewed the manuscript. J. M-P. and J. M. F. -R. carried out the conception and coordination of the study. All authors participated in final approval of the version to be published.
Author Contributions: Á. d. C. -I. and J. M. -P. researched the data, performed the statistical analysis and wrote the manuscript. J. M. M. -N. , performed the RNA. sequencing of jejunal samples. J. L. obtained and analysed the expression of DPP9 in plasma samples. M. A. -R. contributed to collect the data for the jejunum database. M. B. , G. M. (Geltrude Mingrone), G. M. (Giovanni Monteleone), O. A. P. , B. S. , P. L. and M. F. contributed with the colon database. J. P. , R. R. and J. G. -O. contributed with the metabolomics validation database. M. J. , R. P. , M. P. -O. and J. S. performed the metabolomics for the metabolomics validation database. M. F. contributed to the discussion and reviewed the manuscript. J. M-P. and J. M. F. -R. carried out the conception and coordination of the study.
SARS-CoV-2 genome detection in wastewater Analyses of SARS-CoV-2 genome in raw wastewater were realised by the Obepine group  (Réseau Obepine, 2021). Obepine is a French consortium born at the start of the SARS-CoV-2 health crisis, using wastewater as an epidemiological surveillance tool. For many months, they follow the evolution of the contamination of wastewater by this virus at the level of around thirty WWTPs located in France. Data collected by Obepine are summarized in indices which are available on their web site (Reims WWTP: https://www. reseau-obepine. fr/reims/; and center Seine WWTP: https://www. reseau-obepine. fr/seine-centre/). Briefly, virus particles were concentrated by ultracentrifugation of 11 mL of wastewater sample and RNA genome were extracted according to  Wurtzer et al.
To this end, we have built a software tool (PEAKIT) which identifies statistically-significant peaks and calculates the peak height, area and statistical significance. The baseline, necessary for peak characterization, is estimated locally using a Gaussian Process regression model and is therefore minimally impacted by direct water saturation and NOE effects. Compared to Lorentzian fits or the more complex Deep Neural Networks regression, Gaussian Processes (GP) are non-parametric models, well suited for problems in which the training data is relatively small. By construction GP predictions are not subject to large gradient variations which occasionally cause parametric models to diverge. One drawback of the GP approach is the associated computation time, which increases strongly with the number of training points considered.
The PEAKIT software tool PEAKIT was developed with Python 3. 8 and provided with a user interface through the tkinter package (Fig. 1 ). The tool takes as input a spectrum consisting of saturation offsets on the x axis and the normalized signal intensities on the y axis, and it outputs the location (chemical shift offset) of each detected peak along with the peak's height, area and statistical significance (p-value). The user can specify whether the software should look for positive or negative peaks. By default, negative peaks are expected; in order to analyze positive peaks the box ''up" has to be checked (see Fig. 1 ). There are three steps to the PEAKIT algorithm, corresponding to (i) the peak detection, (ii) the baseline estimation, and (iii) the p-value computation, respectively. A detailed description of each of these three steps is given in the following sections.
Peak detection The peak detection process (Fig. 2 ) is based on an iterative approach exploiting the local properties of the spectrum  [16]. Specifically, for each point i of coordinates ðx i ; y i Þ , a slope a i is computed as follows: a i ¼ y iþ1 À y i x iþ1 À x i Furthermore, at any point i we compute the moving average of the r preceding slopes as follows: a i À ¼ 1 r X iÀ1 j¼iÀrÀ1 a j where r is the number of consecutive points preceding point i. The appropriate choice of r depends on the noise level as well as on the shape of the spectrum: a dataset with numerous small peaks requires small values of r as the slope varies rapidly. PEAKIT algorithm detects the apparition of a peak at point i b (henceforth referred to as the beginning of the peak) if the following two conditions are met simultaneously: a i b À a i b À > D ð1aÞ a i b þ1 À a i b À > D ð1bÞ where the threshold D is a free positive parameter which can be defined by the user.
Baseline estimation In this section, we describe the baseline estimation for each detected peak. In essence, the baseline level estimates the shape of the spectrum if the detected peak were absent. Under this hypothesis, the baseline profile fb i g should match the spectrum fy i g located just outside the peak. To ensure this matching, our technique uses a set of 2r ''training points" formed by the r points in the spectrum immediately preceding the peak, and the same number r of points immediately succeeding the peak. The baseline estimation process relies on a Gaussian Process (GP) regression implemented via the GPy Python package  [17]. An excellent introduction to Gaussian Processes can be found in Ref. [15] ; this reference notably includes a chapter dedicated to GP regression which contains the full mathematical details of the method employed in PEAKIT.
Without delving into these details, we mention that the GP technique produces, for all points {x i } within the peak region i b i i e , the predicted baseline levels {b i } along with their standard deviations {r i g (Fig. 3 ). The values fr i g form the uncertainty band around the predicted baseline shape. We note that the uncertainty band narrows as r increases. We also note that, for a given value of r, a noisier dataset fy i b Àr ; Á Á Á ; y i b À1 ; y ie þ1 ; Á Á Á ; y ieþr g will produce a wider uncertainty band. Once the baseline level is established, it is possible to quantify how much of the height and area of the peak can be attributed to signal. These contributions are inferred from the observed data by subtracting the baseline estimation. In addition, we re-compute the peak center location by identifying the point i in the peak region of the spectrum for which the difference (y i -b i ) is maximum.
Conclusion We report the development and testing of PEAKIT -a software tool for the detection and characterization of CEST peaks. The performance of the tool was evaluated through the analysis of in vitro and in vivo CEST data acquired at 17. 2 T using a linescan CEST pulse sequence. The tool is not intended for the detection of highly overlapping peaks for which other analysis approaches should be employed  [9, 11]. Compared to other existing tools used for the detection and characterization of CEST peaks, PEAKIT has the advantage that it does not require assumptions regarding the position and the shape of the peaks. The software is easy to use and of reduced computational cost. For each detected peak, PEAKIT returns its height, area, and statistical significance. While these are quantitative measures characterizing the signal, care must be in exerted when interpreting the analysis results.
Specifically, these variables as well as other similar CEST metrics  [23]  depend on the RF saturation power levels, the B 0 field strength, the exchange rates, and the local shape of the Zspectrum. As a result, one cannot directly compare results obtained from samples having very different molecular composition (e. g. metabolites in solutions vs tissue samples). Absolute quantification is possible, however, if an a priori calibration is available, under the same experimental conditions. We note that PEAKIT can be a helpful tool in the characterization of new CEST agents. For in vivo applications, PEAKIT can be used to monitor metabolic dynamic processes. The PEAKIT software is open-source, and freely available for download from github  [24].
01 for methotrexate and TNF-α inhibitors). These converging results from different research lines suggest that autoimmunity is a modifiable component in diseases causing dementia. https://clinicaltrials. gov/search?id=%22NCT00759200%22,https://clinicaltrials. gov/search?id=%22N CHEMBL2107842 ALBINTERFERON ALFA-2B Protein Interferon alpha/beta receptor agonist Agonist EFO_0004239 chronic hepatitis B virus infection 1 Terminated https://clinicaltrials. gov/search?id=%22NCT00964665%22 CHEMBL2108252 APOLIZUMAB Antibody Human leukocyte antigen DR beta chain inhibitor Inhibitor EFO_0000095 chronic lymphocytic leukemia 1 Terminated https://clinicaltrials. gov/search?id=%22NCT00022971%22 CHEMBL2108252 APOLIZUMAB Antibody Human leukocyte antigen DR beta chain inhibitor Inhibitor EFO_0000403 diffuse large B-cell lymphoma 1 Terminated https://clinicaltrials.
gov/search?id=%22NCT00022971%22 CHEMBL2108252 APOLIZUMAB Antibody Human leukocyte antigen DR beta chain inhibitor Inhibitor EFO_0000574 lymphoma 1 Completed https://clinicaltrials. gov/search?id=%22NCT00004101%22 CHEMBL2108252 APOLIZUMAB Antibody Human leukocyte antigen DR beta chain inhibitor Inhibitor EFO_1001469 Mantle cell lymphoma 1 Terminated https://clinicaltrials. gov/search?id=%22NCT00022971%22 CHEMBL2108252 APOLIZUMAB Antibody Human leukocyte antigen DR beta chain inhibitor Inhibitor MONDO_0018906 follicular lymphoma 1 Terminated https://clinicaltrials. gov/search?id=%22NCT00022971%22 CHEMBL4297888 APX-005M Unknown Tumor necrosis factor receptor superfamily member 5 agonist Agonist EFO_1000657 rectum cancer 2 Recruiting https://clinicaltrials. gov/search?id=%22NCT04130854%22 CHEMBL4297888 APX-005M Unknown Tumor necrosis factor receptor superfamily member 5 agonist Agonist EFO_1001968 soft tissue sarcoma 2 Recruiting https://clinicaltrials.
gov/search?id=%22NCT03719430%22 CHEMBL4297888 APX-005M Unknown Tumor necrosis factor receptor superfamily member 5 agonist Agonist MONDO_0007576 esophageal cancer 2 Recruiting https://clinicaltrials. gov/search?id=%22NCT03165994%22 CHEMBL4297888 APX-005M Unknown Tumor necrosis factor receptor superfamily member 5 agonist Agonist EFO_0000756 melanoma 1 Recruiting https://clinicaltrials. gov/search?id=%22NCT02706353%22 CHEMBL4297888 APX-005M Unknown Tumor necrosis factor receptor superfamily member 5 agonist Agonist EFO_0002617 metastatic melanoma 1 Terminated https://clinicaltrials.