dataesr/scientific-paragraphs-categorization huggingface dataset:
We focused on patients randomized to ustekinumab 90 mg SC every 12 weeks or every 8 weeks to test the association between baseline characteristics or induction mucosal improvement and week 44 outcomes. Because this analysis was conducted in patients who were responders to IV ustekinumab induction treatment, it may not be generalizable to other populations. In addition, the histological specimens in this study were evaluated by a single, qualified histopathologist; the use of a greater number of pathologists also could have improved generalizability. Our definition for histologic improvement (Geboes 3. 1) is based on an unbiased analysis of histology data from 3 prospective, placebo-controlled trials in patients with moderate-to-severe UC activity. 17 This definition differs from a recently published European Crohn's and Colitis Association (ECCO) position paper on UC histopathology in which the absence of intraepithelial neutrophils, erosion, and ulceration are required for histological remission.
Acknowledgments The corresponding authors had full access to all data and take full responsibility for the veracity of the data and data analysis. James P. Barrett, BS of  Janssen Scientific Affairs, LLC. provided editorial and writing support. Some of the data displayed in this article were presented at ECCO 2019, Copenhagen, Denmark; DDW 2019,  San Diego , California; ACG 2019,  San Antonio , Texas; and  UEGW 2019, Barcelona, Spain. The authors disclose the following:  Katherine Li ,  Colleen Marano ,  Hongyan Zhang ,  Feifie Yang , and  Joshua R. Friedman  were all employees of  Janssen Research & Development, LLC  at the time of the study and own stock/stock options. Joshua R. Friedman  is currently an employee of  Alnylam Pharmaceuticals. William J. Sandborn  has received research grants from  Atlantic Healthcare Limited ,  Amgen ,  Genentech ,  Gilead Sciences, AbbVie ,  Janssen ,  Takeda ,  Lilly ,  Celgene/Receptos , Pfizer ,  Prometheus Laboratories (now Prometheus Biosciences ); consulting fees from  AbbVie, Allergan, Amgen, Arena Pharmaceuticals, Avexegen Therapeutics, BeiGene, Boehringer Ingelheim, Celgene, Celltrion, Conatus, Cosmo, Escalier Biosciences, Ferring, Forbion ,  Genentech ,  Gilead Sciences ,  Gossamer Bio ,  Incyte ,  Janssen ,  Kyowa Kirin Pharmaceutical Research, Landos Biopharma, Lilly ,  Oppilan Pharma ,  Otsuka ,  Pfizer ,  Progenity, Prometheus Biosciences  (merger of  Precision IBD  and  Prometheus Laboratories) ,  Reistone ,  Ritter Pharmaceuticals ,  Robarts Clinical Trials  (owned by  Health Academic Research Trust, HART) ,  Series Therapeutics, Shire ,  Sienna Biopharmaceuticals ,  Sigmoid Biotechnologies ,  Sterna Biologicals ,  Sublimity Therapeutics, Takeda, Theravance Biopharma, Tigenix, Tillotts Pharma, UCB Pharma, Ventyx Biosciences, Vimalan Biosciences, Vivelix Pharmaceuticals ; and stock or stock options from  BeiGene ,  Escalier Biosciences ,  Gossamer Bio ,  Oppilan Pharma ,  Prometheus Biosciences  (merger of  Precision IBD  and  Prometheus Laboratories) ,  Progenity ,  Ritter Pharmaceuticals ,  Ventyx Biosciences, Vimalan Biosciences.
Eight age groups were defined (3&4, 5&6, 7&8, 9, 10, 11, 12&13 and 14&15 years old). Date of birth, gender, pertussis immunization history obtained from record booklet from the child or from a copy from school (information required at school entry), and serology results were collected, filled in paper questionnaires and recorded in a computerized database. No information on recent history of pertussis or respiratory illness was collected.
Ethical consideration and safety of participants The study protocol was reviewed and approved by the Research Ethics Committee of Pasteur Institute of Iran. Informed written consent was obtained from all parents/legal guardians, and oral consent form was obtained from children aged over 7. Only participants who agreed to participate were included in the study. Authorization for data processing has been obtained from French legal authority (Commission Nationale Informatique et Liberté [CNIL]), and pseudonymization of names was performed assigning a code specific to the study to each participant.
Patients. All admitted patients classified in the diagnostic-related group (DRG) "heart failure or cardiogenic shock" (04M09) present in the claim data of our hospital in 2013 were assessed for inclusion by a senior physician. The causes for admission were confirmed with clinical data, and only the first hospitalization of the year for each patient was included. We did not consider a wash-out period because the concepts studied are relevant for any admission for acute heart failure, were they admission or readmission. We included only patients older than 65 years old because younger patients are rare in this DRG, and age introduces much heterogeneity regarding readmission risk  9. Lowering heterogeneity of confounding factors allows to produce more accurate estimates of the impact of in-hospital pathways Therefore, excluding young patients was expected to improve the accuracy of our analysis.
Variables. Regarding patient demographics and global health status, we collected patients' age, sex, body mass index, current tobacco use, and data related to the following set of comorbidities: high blood pressure (HBP), chronic obstructive pulmonary disease (COPD), diabetes, renal failure, and atrial fibrillation. We also collected their zip codes and identified the patients living in the Lariboisière's catchment area. All these factors are known predictors of HF patients' pronostic  [5] [6] [7] [8] [9]. Regarding cardiac decompensation, we collected the following data: syndrome (acute pulmonary edema, congestive heart failure, cardiogenic shock, or right heart failure), left ventricular ejection fraction (LVEF), and blood level of B-type natriuretic peptide (BNP). Indeed, these features lead to different diagnostic and therapeutic management  20  , and are therefore needed to analyse in-hospital care pathways.
The in-hospital pathway of group 5 (N = 63) typically included an admission from the ER to the SSU, followed by a passage through another ward, which was often geriatrics. The diagnostic management was often performed -with the particularity that the chest X-ray was more frequent than an echocardiograph -and an pharmacological treatment was prescribed to 70% of patients (ACE/ARB or BB). Patients in this group were older, more often females with more comorbidities, and presented often with congestive heart failure. The association between the in-hospital care pathway and readmission rate was assessed for the 207 patients for whom readmission data were available. Overall, at 30 days, 11% and 27% of patients were readmitted at least once for HF and for any cause, respectively. These rates increased to 38% and 75% at one year (Table  2 ).
Data Processing and Analysis Position and velocity of the robot handles were recorded at a sampling rate of 1000 Hz. Signals were filtered using a sixth-order double-pass Butterworth low-pass filter with a cutoff frequency of 10 Hz. All data were collected using the Dexterit-E software program (version 3. 6. 1, Kinarm, Kingston, ON, Canada). MDT task data were analyzed using custom scripts in MATLAB (version 2017a, Mathworks Inc. , Natick, Massachusetts).
Statistical analysis Each parameter was compared between Test 1 and Test 2 to quantify test-retest reliability. Intraclass Correlation Coefficient estimates and their 95% confident intervals were calculated using MATLAB based on a single-rating (k = 1), absolute-agreement, 2-way mixed-effects model  [49]. Levels of reliability followed guidelines set out by Koo and Li  [50]. We also calculated the within-subjects Standard Deviation (Sw,  [49] ) as a measure of the precision of the test. Sw was calculated using the equation: Sw ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi X T 1 -T 2 ð Þ 2 =2n q where n = number of subjects  [51]. The 'repeatability coefficient' (CR) was calculated as an index that quantifies absolute reliability.
Reduction of number of trials The number of trials necessary to accurately determine the sensory threshold was initially determined based on simulated data. We examined the possibility of reducing the number of trials (post-hoc) by calculating the Discrimination Threshold and Displacement Variability from a subset of the collected trials. We calculated each variable using 25, 30, 40, 50, 75, 100 and 125 trials (starting from the beginning of the task). The variables were calculated in the same way as above, using the actual arm displacement data after the first wrong answer was recorded. We used Spearman correlations to compare the calculated variables for each subset of trials with the original variables calculated from all trials. We also re-calculated the test-retest reliability for each subset of trials using ICC, Sw and CR.
Procedures At admission, patients were examined by medical practitioner who collected the following data: weight, body mass index (BMI) calculation, recent weight loss, daily oral intake self-reported during the week prior to hospitalization, self-reported factors in uencing oral intake reduction, NRS-2002 (Nutrition Risk Screening 2002). During the hospitalization, daily oral intake was recorded by specialist dietitian. Demographic characteristics, comorbidities, treatments and biologic data were collected from the patient medical record including C reactive protein (CRP), albuminemia, protidemia, serum creatinine, glomerular ltration rate (GFR) (CKD-EPI formula), lymphocyte count, D-dimers at admission. Malnutrition was diagnosed according to the Global Leadership Initiative on Malnutrition (GLIM) two-step approach de ned by the association of one phenotypic criterion (especially non-volitional weight loss, low BMI) and one etiologic criterion (reduced food intake or assimilation, disease burden/in ammatory condition)  [11].
It should be noted that the nutritional assessment was conducted at admission, with a median onset of COVID-19 symptom of 7 days. Thus, we highlighted an acute malnutrition. The concept of acute malnutrition is described and might need a speci c management  [22]. Concerning metabolic disorders, hyperlipidemia affects immune functions and could promote COVID-19 susceptibility  [23]. Hypercholesterolemia is associated with cholesterol accumulation in immune cells, which participate to in ammatory responses and may affect the response to infections  [24]. Our multivariate analysis found an association between malnutrition and dyslipidemia in this context of in ammatory disease. To our knowledge, there is no data that can speci cally explain this association. Pectoralis muscle index has been suggested as a prognostic marker in relation to muscularity in an oncology population  [25].
Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Metabolite Analyses LC-HRMS analysis and data processing were performed with a Q-Exactive™ Hybrid Quadrupole-Orbitrap™ mass spectrometer (Thermo Fisher Scientific, Les Ulis, France) coupled to a Transcend 1250 liquid chromatography system (Thermo Fisher Scientific, Les Ulis, France) equipped with an aSequant ZIC-pHILIC column (Merck). Samples were analyzed at high resolution (70,000 FWHM), with alternating positive and negative ionization modes. Raw data were processed with the XCMS software  [26] , and metabolites were annotated according to the exact mass and retention time in the Profilomic metabolite database. Annotated metabolites were compared with both the Human Metabolome Database (HMDB)  [27]  and the Chemical Entities of Biological Interest (ChEBI) database  [28] , to obtain accession numbers.
MS/MS validation of metabolite annotations was performed by Profilomic on a subset of metabolites. Figure  1. Animal model of preconception maternal weight trajectories. C57BL/6J F0 females were fed a control diet (CD, blue), a high-fat diet (HFD, red) or an HFD for two months and then switched onto the CD. On postnatal day 21 (PND21), half of the litter was weaned onto the HFD and the other half under the paired-CD diet, resulting in six groups: CD-CD, CD-HFD, OB-CD, OB-HFD, WL-CD and WL-HFD. All male offspring were followed until the age of six months. The number of animals studied (n) is indicated for each group. Here, CD-or HFD-fed F1 males only were considered, as their adult phenotype was related to maternal conditioning  [21]. From the whole cohort published in  [21] , six individuals of each group were selected by principal component analysis, as the closest to the barycenter of their own group (Supplementary Figure  S1 ), after having excluded those presenting macroscopically apparent lesions at the time of death (malformation or nodules, which concerns up to 10% of C57BL/6 mice, as described in Harlan data sheet).
Biostatistical Analysis Datasets for each tissue were analyzed separately. Statistical analyses were performed on the Workflow4Metabolomics online platform (W4M)  [29, 30]. Intensities were log10-transformed. Quality control was performed by using the Hotelling's T2 test on the first plane of principal component analysis and the z-score of intensity deciles (threshold p-value = 0. 001): one WL-CD sample and one CTRL-HFD sample were discarded from the LI dataset, one WL-CD sample from the HYP dataset and one WL-HFD sample from the WOB dataset. Partial least squares-discriminant analysis (PLS-DA) was performed for each tissue, with maternal group or post-weaning diet as a Y response. Transformed signals were mean-centered and divided by the standard deviation of each variable. For all validated models, the response variance explained (R2Y) and the predictive performance of the model (Q2Y) were shown significant by comparing the model built with the true response value and 10 3 models built with random permutations of the response values  [31].
Two-way analysis of variance (ANOVA) with interaction testing was performed for maternal group and post-weaning diet. We minimized the bias caused by the redundancy of metabolites detected in both ionization modes, by performing these analyses separately on metabolites detected in positive and negative ionization conditions. The p-values were adjusted for the false discovery rate (FDR) by the Benjamini-Hochberg procedure  [32]  and considered significant if ≤0. 05. For metabolites displaying a maternal group effect or an interaction between maternal group and post-weaning diet, a post hoc test was performed on the raw signal intensity from MS peaks, with R software (version 3. 4. 3), using the lsmeans package (version 2. 27-62) and adjustment by the Tukey method  [33]. Correlation analyses were performed with the cor.
Pathway Analyses Datasets from each tissue were analyzed separately, using MetaboAnalyst with HMDB accession numbers. The pathway library was the Mus musculus Kyoto Encyclopedia of Genes and Genomes (KEGG)  [36]  database. The selected pathway enrichment analysis method was "Global ANCOVA", and the pathway topology analysis was "relative betweenness centrality". Raw p-values were adjusted for FDR. We chose to exclude the ADP metabolite from the LI pathway analysis because of its key role in multiple metabolic pathways and enzymatic reactions. We also excluded metabolites with multiple annotations and metabolites for which the MS/MS signal did not correspond to any spectrum from the database. For the pathway analysis, we used the KEGG database  [36]  and the Small Molecule Pathway Database (SMPDB)  [37].
5. Upregulation was observed for 13 out of 16 metabolites in the HYP and 4 out of 11 metabolites in the WOB (Table  1 ). The post-weaning diet therefore had a significant effect on the abundance of metabolites in the LI and, to a lesser extent, in the HYP and WOB. glutamine and glutamate, the biosynthesis of ubiquinone and other terpenoids-quinone, the metabolism of taurine and hypotaurine, β-alanine and methane. The metabolism of amino acids, lipids, carbohydrates and vitamin B was also altered, but to a lesser extent (impact score 10% to 40%; Table  2 ). In the HYP, according to the KEGG database, arginine and proline metabolism, the urea cycle and aspartate metabolism were significantly affected by the post-weaning diet (Table  3 ). Lysine degradation, pantothenate and CoA biosynthesis, and histidine metabolism were also affected by the post-weaning diet, but to a lesser extent.
In the WOB, arginine and proline metabolism, pantothenate and CoA biosynthesis and lysine degradation were significantly affected by the post-weaning diet (Table  3 ). With the SMPDB database, the same pathways were retrieved in the HYP, and another affected pathway emerged in the WOB: alpha linolenic acid and linoleic acid metabolism (Table  3 ). Three metabolites were affected by the post-weaning diet in all three tissues (Figure  4a ): 1,5anhydroglucitol (1,5-AG), saccharopine (Sacc) and β-hydroxybutyrate (β-HB). The abundance of 1,5-AG and Sacc was significantly lower in mice fed an HFD, whereas β-HB levels were higher, and the magnitude of this effect was similar in all three tissues (Figure  4b-d ). Analyses of the correlation between the levels of each of these three metabolites in all tissues showed that the abundance of 1,5-AG was strongly and positively correlated between the three tissues, whereas the correlation of abundance between tissues was modest or weak for Sacc and β-HB (Supplementary Figure  S3 ).
Furthermore, analyses of the correlation between LI 1,5-AG and metabolic parameters, such as glycemia, insulinemia and area-under-curve for glycemia measured during an oral glucose tolerance test (OGTT), showed weak negative correlations in individuals fed a CD. In contrast, no correlation was observed in individuals under HFD (Supplementary Figure  S4 ). The MSEA-ORA analysis showed no significant enrichment in any of the tissues studied (data not shown). Pathway analyses were not, therefore, biased by an over-representation of metabolites from a specific class. Functional pathway enrichment analysis, according to the KEGG database, revealed a major impact of post-weaning diet (impact score > 40%) in the LI on the metabolism of glutamine and glutamate, the biosynthesis of ubiquinone and other terpenoids-quinone, the metabolism of taurine and hypotaurine, β-alanine and methane.
The metabolism of amino acids, lipids, carbohydrates and vitamin B was also altered, but to a lesser extent (impact score 10% to 40%; Table  2 ). In the HYP, according to the KEGG database, arginine and proline metabolism, the urea cycle and aspartate metabolism were significantly affected by the post-weaning diet (Table  3 ). Lysine degradation, pantothenate and CoA biosynthesis, and histidine metabolism were also affected by the post-weaning diet, but to a lesser extent. In the WOB, arginine and proline metabolism, pantothenate and CoA biosynthesis and lysine degradation were significantly affected by the post-weaning diet (Table  3 ). With the SMPDB database, the same pathways were retrieved in the HYP, and another affected pathway emerged in the WOB: alpha linolenic acid and linoleic acid metabolism (Table  3 ).
We propose that a hepatic defect in HFD-fed males may affect the production of 1,5-AG, decreasing its concentration in the blood delivered to other tissues, as observed here for HYP and WOB. Sacc is involved in gluconeogenesis, through its role in the propionyl-CoA synthesis pathway, and the TCA cycle, through its role in the acetyl-CoA production pathway (see KEGG, SMPDB and HMDB databases). The most over-represented pathways associated with metabolome variation in the three tissues belong to amino acid metabolism. The decrease in Sacc levels observed here may reflect an increase in amino acid degradation, affecting acetyl-CoA production and the TCA cycle, or the production of ketone bodies, such as β-HB, which has been implicated in energy production during fasting (see KEGG, SMPDB and HMDB).
The increase in γ-linolenic acid levels and decreases in dihomo-γ-linolenic acid (8,11,14-eicosatrienoic acid) and eicosapentaenoic acid levels suggest that the linolenic acid pathway is modified in the WOB of HFD-fed animals (SMPDB database). These variations may affect the production of DHA, an essential long-chain polyunsaturated fatty acid (PUFA) of the n-3 series, involved in both membrane fluidity and neuroprotective/inflammatory processes in the brain  [65, 66]. This is consistent with results showing that obesity alters the serum long-chain PUFA profile  [67] , that moderate gestational dietary restriction alters their levels in the HYP, hippocampus and WOB of newborns  [68]  and that n-3 PUFA deficiency is associated with a decrease in olfactory performance  [69].
Outstandingly, we observed that the abundance of two metabolites in the LI was differential between maternal groups: anserine and a pentose or hexose phosphate; the latter could not be identified by MS/MS, and is therefore not discussed here. Anserine is the substrate of carnosinases, which convert it into methylhistidine and β-alanine  [75, 76]. Since the intestinal absorption of anserine is very stable  [75] , the variation reported here must be due to changes in its conversion. The low abundance of anserine may result from an increase in the activity of the conversion enzymes, which is consistent with the lower levels of methylhistidine in OB offspring. Alternatively, it may be due to a lower availability of substrates essentials for anserine production: carnosine and the methyl-donor S-adenosyl-methionine (SAM) (see KEGG and SMPDB databases).
00586 0% 3-Methylhistidine ↑, Pantothenic acid ↑ Valine, leucine and isoleucine degradation 51 1 0. 02030 0% L-Valine ↑ Catecholamine biosynthesis 14 1 0. 03065 0% L-Tyrosine ↑ Tyrosine metabolism 55 1 0. 03065 0% L-Tyrosine ↑ Whole olfactory bulb (KEGG database) Arginine and proline metabolism 44 1 0. 00233 4% Hydroxyproline ↓ Pantothenate and CoA biosynthesis 15 1 0. 00430 2% Pantothenic acid ↑ Lysine degradation 23 2 0. 00015 1% L-Lysine ↓; Saccharopine ↓ 8,11,14-Eicosatrienoic acid Biosynthesis of unsaturated fatty acids 42 3 0. 00015 0% ↓; Gamma-linolenic acid ↑; Eicosapentaenoic acid ↓ Lysine biosynthesis 4 2 0. 00015 0% L-Lysine ↓; Saccharopine ↓ Biotin metabolism 5 1 0. 00430 0% L-Lysine ↓ Aminoacyl-tRNA biosynthesis 69 1 0. 00430 0% L-Lysine ↓ Whole olfactory bulb (SMPDB database) Eicosapentaenoic acid ↓; Alpha linolenic acid and linoleic acid metabolism 17 3 0.
The secondary channels consisted of brightfield images with adapted z offset to collect morphological information. The region of interest (ROI) consisted of an enlarged Voronoi diagram derived from the nuclear mask to include the cytoplasm, thus limiting the possible overlapping with neighboring cells. Cell aggregates and debris were excluded from analysis using area cutoffs. Image acquisition was performed using a 20x objective (0. 45 NA) and 80 images or fields were generated per well in a way to cover 90% of the well surface. We extracted intensity, texture and morphology related information from the region of interest and exported a dataset of 148 features for every cell.
c. Data analysis Developed algorithms described in this paper were performed on MRC5 cells infected with 3 strains of C. burnetii: CB NMII, CB 223 and CB 227. Cells infected with C. burnetii exhibit a particular phenotype due to vacuoles formation. Therefore, we used the extracted dataset to detect cytopathic effects and differentiate between infected and uninfected cells. The exported file was uploaded in a dedicated application developed in R Studio® using the user interface 8 ShinyR. We generated a database of labeled cell data coming from uninfected and infected cells. Outliers were removed from this dataset and 6000 cells were kept as training data. Using radar graphs and Principal Component Analysis (PCA), we screened all 148 features and identified key features that distinguish infected cells (positive control) and uninfected cells (negative control).