dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was supported by I-site E2S:  Energy and Environment Solutions  from the  University of Pau and Pays de l'Adour ,  UPPA  contract  2020-32  ( M. B. , PhD  fellowship). The authors thank the  Agence Nationale de la Recherche  ( MERSEL Project  no. ANR-18-CE34-0004 ) and  The Institute of Marine Research, Norway  (TuneSel, Project no. 15745 ) for their financial support. This project has also received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie Grant  Agreement no. 101007962. The authors wish to thank  Marine Biotechnology  Products (Port-Louis, Mauritius) and  Orffa (Breda, The Netherlands)  for the kind supply of tuna meal and selenomethionine, respectively. They are also very grateful to  F.
This important finding, together with i) the homology of TMEM165 with other members of the UPF0016 family transporting Mn and possibly also for some of them Ca 2+ and/or H + in plants (PAM71, CMT1, PML3-5 from Arabidopsis thaliana)  [14] [15] [16] , cyanobacteria (SynPAM71/Mnx from Synechocystis)  [17] , and bacteria (MneA from Vibrio cholerae)  [18] , ii) the evidence that TMEM165 deficiency alters Golgi Mn homeostasis, as observed with the resistance of the Golgi Mn-sensitive GPP130 protein in TMEM165-deficient mammalian cells  [19] , iii) the sensitivity of TMEM165 itself to increased cytosolic Mn concentrations and its subsequent degradation in lysosomes  [19]  and iv) the measurement of the Mn transport activity in Lactococcus lactis of both a truncated form of TMEM165 and Gdt1p, the yeast ortholog of TMEM165  [20] , strongly support the assertion that TMEM165 is a Mn Golgi importer playing a key role in regulating cellular Mn homeostasis.
After 3 washings with PBS, cells were incubated for 1 h with Alexa Fluor 488-, or Alexa Fluor 568-conjugated secondary antibody (Fisher Scientific) diluted at 1:600 in BSA-Block or blocking buffer. After 3 washings with PBS, nuclei were labelled with DAPI 1:200 for 10 min and then mounted on glass slides with Mowiol. Fluorescence was detected by an inverted Zeiss LSM700 confocal microscope. Acquisitions were done using ZEN Pro2. 1 Software (Zeiss, Oberkochen, Germany).
Materials and methods Utilising a cross-sectional design for this pilot study, we recruited children and adolescents aged 0-15 years visiting either the inpatient or outpatient setting at KCH in Kathmandu, Nepal, during a 1-month period (April 2022). After written consent was obtained in the parent's primary language, nutritional anthropometrics were assessed, and socio-demographic questionnaires were administered. Clinical data (e. g. reason for admission or visit to outpatient clinic) were abstracted from the medical chart. The study was approved by KCH's Ethical Review Committee (reference number 963).
Nutritional assessment Anthropometric data were obtained and recorded by a study investigator. Height was measured to the nearest 0•1 cm using a portable stadiometer for children, and weight was measured to the nearest 0•05 kg using a calibrated digital scale. BMI and height Z scores for age and sex were classified based on the WHO standard growth charts  (13). A child with a height-for-age Z score of <-2 was classified as stunted  (14). A BMI-for-age Z score <-2 indicated wasting and >þ2 indicated overweight  (14, 15). Mid-upper arm circumference (MUAC) was measured to the nearest 1 millimeter (mm) using a non-stretch tape around the midpoint between the olecranon process of the ulna and the acromial process of the scapula. For children 6 to 59 months old, a value <125 mm indicated wasting  (15).
Statistical analysis Demographic, clinical and nutritional data were input into a REDCap database and summarised using frequencies and proportions for categorical variables. For continuous variables with a normal distribution, mean and SD were reported, and for categorical values, number of patients (n) and percentages were reported. Individual wealth indices were derived utilising a multistep process that has been previously described  (18). Briefly, the dichotomous responses (yes or no) for patients' assets (electricity, radio, etc. ) were transformed into zero if negative or one if positive. Principal component analysis was then performed to obtain the relative contribution of each asset in the model  (11, 19). The asset with the largest contribution in differentiating the variance in households' wealth was considered the first component (PC1).
Variables with a significant association (P < 0•05) in a univariate model were included in the multivariate binary logistic regression model, with resulting variables yielding OR and 95 % CI. Statistical significance was set at P < 0•05. Data were analysed using Statistical Package for the Social Sciences (SPSS, version 23. 0) and to perform the principal component analysis, PAST4 Software Package for Education and Data Analysis. WHO Anthro (version 2. 0) and AnthroPlus (Geneva, Switzerland) software were used to graph nutritional indicators.
Results Our cross-sectional sample consisted of 370 participants. Similar to regional data  (22, 23)  , 65•1 % were males and represented families from several provinces across Nepal (Table  1 , Fig. 1 ). The mean age of the children was 3•9 years (SD ± 3•4 years). Children were recruited from the outpatient (n 165) and inpatient (n 205) settings. More than half of the participants (n 208) were being treated for an acute condition, while 43•8 % (n 162) were undergoing treatment for a chronic medical condition. The most common acute conditions included fever and pneumonia, while the most common chronic conditions were oncologic/ haematologic (n 21), neurologic (n 15) and nephrology (n 10). Almost one-third of families (30•3 %) reported moderate to severe food insecurity and 41•6 % of participants reported affiliation with the Brahmin/Chhetri ethnicity, followed by the Janajati ethnicity (37•4 %) (Table  2 ).
Nutritional indices The prevalence of children classified as stunted was 25•9 % (Table  3 ). The prevalence of stunting was higher among children under 5 years of age (29•2 %) compared with children and adolescents 5 years of age and older (17•9 %; P = 0•026). To understand the growth of Nepalese children in relation to the WHO growth charts, Z score distributions from our dataset were compared with the WHO reference values (Fig. 2(a-d )). For both height-and BMI-for-age Z scores, the mean Z scores for Nepalese children were below the reference indicators. For both indicators, there was a left shift of the growth curve suggesting that Nepalese children are not developing as per global standards set forth by WHO. We then analysed growth curves by sociodemographic factors. Family self-reported ethnicity revealed poor growth was particularly evident for the Madhesi ethnicity followed by the Dalit ethnicity (see online Supplemental Fig.
As with previous successfully implemented nutrition programmes in Nepal  (4)  , our data suggest that national, multisector initiatives are needed to further achieve reductions in stunting among children and adolescence. Compared with national data and other hospital-based surveys, we found a higher prevalence of wasting among our sample (8 % and 9•2 % (on admission) v. 17•3 %, respectively)  (6, 25). Several factors may account for this discrepancy. First, the children in our study represent children suffering from either an acute or chronic medical condition, both of which can cause wasting. Additionally, KCH serves as a national referral hospital especially for children with severe disease, refractory conditions or rare diseases. Thus, the children served by KCH may be especially vulnerable to wasting due to the complexity of their condition.
Our study collected data over a single month and variations in nutritional status over time (e. g. summer v. winter months) was not obtained. Due to personnel limitations, several outpatient clinics were not included in the study including the psychiatric department, burn unit and neonatal intensive care unit. Children from the inpatient setting were recruited at any point during their hospital admission; therefore, duration of hospital stay was also not collected. Finally, we were limited in our analyses due to the relatively small sample size. Our pilot study aimed to establish an initial snapshot of nutritional morbidities in the largest, government-supported children's hospital in the country and was not intended to establish country-wide indicators on the nutritional health Hospital-based nutrition programmes improve the nutritional status of children  (51).
Pangenomic annotations were performed by Roary (Supplementary Data S1). Raw reads can be accessed on NCBI under the reference number PRJNA903792. From pangenomic annotations, strain specificity, using the NCBI BLAST tool and conventional PCRs, was verified for many genes. Among these genes, two genes unique to G2008b (LpsA and DmsB) and two genes unique to D2008b (McrBC and RimP) were selected as candidates for qPCR/PCR targets for the quantification/identification of each strain in the samples (Supplementary Figure  S1 ).
Statistical analyzes Figures and statistical analyzes were performed with GraphPad Prism 9. 2. 0 (GraphPad Software Inc. , La Jolla, CA). A Shapiro-Wilk test was used to verify data normality. To analyze statistical differences between groups according to the time point and room for the in vivo trial and co-culture assays, the specific statistical tests used are indicated in the figure legends. The limit of detection was set to 10 2 CFU/g of cecal or ileal content for total C. jejuni counts and to 10 4 gene copies/g of cecal or ileal content for qPCR results. Thus, in absence of detection, 9 × 10 1 CFU/g of cecal or ileal contents were used to compare total C. jejuni counts and 9 × 10 3 CFU/g to compare qPCR results between colonized chicken groups. Differences were statistically different when *p < 0.
Acknowledgments We would like to thank all members of the Research Chair in Meat Safety for their help during necropsies and the staff of the  Centre de Recherche Avicole of the Faculté de Médecine Vétérinaire  for their rigorous work and assistance. We are also grateful to  JEFO Nutrition Inc. ( Saint-Hyacinthe, QC, Canada ) for providing the chicken feed, to  Eduardo N. Taboada  for performing the whole genome sequencing, and to  Neda Barjesteh  for providing scientific advice.
Transcriptomic analysis Total RNA was isolated with the RNA Now kit (Ozyme) with an overnight isopropanol precipitation from duodenum biopsies of 5 pups fed WT milk and 5 with miR-30b milk at PND-5 and PND-15. The concentration and integrity of the RNA were assessed by spectrophotometry (Nanodrop, ND-1000) and using the Total RNA Nano kit on a BioAnalyser 2100 (Agilent Technologies). The RNA samples were stored at À80 C until further processing. Total RNA (2. 5 μg) was reverse transcribed using SuperScript IV Vilo Master Mix (Fisher Scientific) supplemented with 10 μL ) and on a no-template control. Efficiency was calculated with the slope of the standard curve as follows: E ¼ 10 (-1/slope) -1. Primers were validated with an efficiency of 80% to 110%. After normalization with GAPDH to calculate the relative expression of the genes, the data were analyzed using the Delta-Delta Ct method (2 -ΔΔCt )  [40].
Ionization (1. 5 kV ionization potential) was performed with a glass needle (noncoated capillary silica tips, 360/20-10; New Objective). Peptide ions were analyzed using Xcalibur 2. 07 with the following data-dependent acquisition steps: 1) full MS scan (mass-to-charge ratio [m/z], 400 to 1400; profile mode) with a resolution of 70,000 and 2) MS/MS (isolation window ¼ 3 m/z, automatic gain control (AGC) target ¼ 5e4, max ion time ¼ 120 ms, collision energy ¼ 27%; profile mode, resolution ¼ 17,500). Step 2 was repeated for the 8 major ions detected in step 1. Dynamic exclusion was set to 40 s. Only the doubly and triply charged precursor ions were subjected to MS/MS fragmentation. Xcalibur raw data were transformed to mzXML open source format and centroided using the msconvert software in the ProteoWizard 3.
0. 4601 package  [41]. Protein identification was performed using the X! Tandem Piledriver (version 2015. 04. 01. 1; www. thegpm. org) using the Mus musculus Uni-Prot/SwissProt database  [42]  (v. 20105. 10. 14; 25,248 entries). Proteins identified were filtered and grouped using X! Tandem Pipeline  [43]  (v3. 3. 4 and v. 3. 3. 5/2) (http://pappso. inrae. fr/ bioinfo/xtandempipeline/news/2019_xtpcpp_0236). Data filtering was achieved according to a peptide E-value < 0. 01, protein log (E-value) less than À4 and a minimum of 2 identified peptides per protein. MS peaks were detected and aligned using MassChroQ and MassChroqR  [44]. The relative quantification of protein abundances was performed using the method of spectral counting defined as the number of MS spectra assigned to a protein  [45].
Oxygenation and a temperature of 37 C were constantly maintained. Two 200-μL samples were collected on the serosal side every 15 min for 1. 5 h, and the volume of 10 mM Krebs glucose was readjusted to 2 mL at each collection. At each time point, 200 μL (100 μL in duplicate) were placed in a black 96-well plate (Greiner Bio One #655076) for FSA quantification, and 200 μL (100 μL in duplicate) were added to a transparent 96-well plate (Greiner BioOne #655101) for HRP assay. FSA fluorescence measurement was performed on a Tecan instrument (Infinite M200 Pro, Tecan) with Infinite 200Pro software at an excitation wavelength of 488 nm and emission of 520 nm. The HRP assay was performed after reaction with 160 μL/well of a solution consisting of 1. 8 mg/mL Na 2 HPO 4 (Merck S9763), 13. 7 mg/mL NaH 2 PO 4 (Merck S0751), 0.
1% FA in 80% ACN. Peptide separation was achieved at 300 nL/min with a linear gradient from 1% to 45% buffer B for 120 min. Peptide ions were analyzed using Xcalibur 3. 1. 66. 10 with the following machine setup in CID (collision induced dissociation) mode: 1) full MS scan in Orbitrap (scan range [m/z] ¼ 400-1500) and 2) MS/MS using CID (35% collision energy) in Ion Trap (AGC target ¼ 4. 0 Â 10 3 , maximum injection time ¼ 300 ms, data type ¼ centroid). The analyzed charge states were set to 2 to 4, the dynamic exclusion was set to 60 s, and the intensity threshold was fixed at 5. 0 Â 10 3. Protein identification was performed using X! Tandem Piledriver (version 2015. 04. 01. 1), X! Tandem Pipeline (version 3. 4. 2), and the Mus musculus UniProt/SwissProt database (version January 2017; 35,208 entries).
Data filtering was achieved according to a peptide E-value < 0. 01, protein log (Evalue) less than À4 and a minimum of 2 identified peptides per protein. MS peaks were detected and aligned using MassChroQ and MassChroqR. The relative quantification of protein abundance was performed using the method of spectral counting defined as the number of MS spectra assigned to a protein. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037629.
04 to 20 mmol/L for Cer, S1P, and SM, respectively. Standard solutions and milk samples (10 μL) were then extracted with 500 mL of methanol/chloroform mixture (2:1, v/v) containing exogenous ISs [Cer (d18:1/17:0) 500 nmol/L; S1P (d17:1) 500 nmol/L; and SM (d18:1/17:0) 5 mmol/L]. Cer and SM profiles were analyzed using reversed-phase LC-MS/MS (Xevo TQD mass spectrometer coupled to an ACQUITY H-Class UPLC System, Waters Corporation) in MRM mode. Data acquisition and analyses were performed with MassLynx1 and TargetLynx1 version 4. 1 software, respectively (Waters Corporation). Recoveries were assessed with ISs and were >91%.
Statistical analysis For duodenal transcriptomics analysis and milk metabolic, lactose, and hormone analysis, data are presented as the mean AE SEM, and significant differences between the WT and miR-30b groups were assessed using nonparametric Mann-Whitney tests. Test results were considered statistically significant when P values were less than 0. 05. For duodenal and milk proteomics analysis, a principal component analysis (PCA) of the data was performed using R software v3. 2. 4 (R Development Core Team, 2018, http://www. Rproject. org) with the ade4 package  [50]. Differential expression analysis between proteomic spectra counts was performed according to the WT or miR-30b milk with the Limma package  [51]  after normalization by a voom transformation of the data (mean-variance modeling at the observational level).
In Limma, rowwise linear models were applied using an empirical Bayes shrinkage procedure. The P values were adjusted for multiple testing using the Benjamini and Hochberg method  [52] , and those with an adjusted P value less than 0. 05 were considered significant. Growth curve data statistical analyses were performed using R software v3. 2. 5 with a linear mixed-effects model, considering maternal milk, time, sex, sex:time, and milk:time as fixed effects and animals as random effects and with an autoregressive correlation structure of order 1. P values less than 0. 05 were considered significant.
miR-30b milk displays minor protein, FAA, and total lipid changes To determine which components in the milk may be associated with early intestinal maturation in the pups, the composition of the miR-30b milk was studied deeply at LD-15 around the lactation peak. Lactose and total protein levels did not differ between miR-30b and WT milk (data not shown); the composition of casein, whey acidic protein, and fatty acid synthase was also consistent (Supplemental Table  5 ). In contrast, the PCA score plot performed on the LC-MS/MS proteomic data showed a clear separation between miR-30b milk and WT milk at LD-15 (Figure  5 ); out of 787 detected proteins, 9 were more abundant in the miR-30b milk (P < 0. 10; Table  3 ; Supplemental Table  5 ). These proteins were tenascin (Tena), osteopontin (Ospn), apolipoprotein C-I (ApoC1), cathepsin Z (Catz), pulmonary surfactantassociated protein D (SP-D), major urinary protein 2 (Mup2),  glycosylation-dependent cell adhesion molecule 1 (Glcm1), and serum amyloid proteins A-1 and A-2 (Saa1 and Saa2).
Acknowledgments The authors are grateful to  N. Brun ,  C. Baly ,  M. Duval , and  A. Saint Albin  for their contribution to the experimental analysis. The authors acknowledge the Mass Spectrometry Core Facility of CNRH-O ( Centre de Recherche en Nutrition Humaine Ouest)  and Biogenouest (https://www. biogenouest. org/), particularly the Corsaire network of analytical platforms, for their support of this project and the @BRIDGe Platform (http://abridge. inra. fr/) for histology technical services. Proteomics analyses were performed on the PAPPSO platform (http://pappso. inrae. fr), which is supported by INRAE (http://www. inrae. fr), the Ile-de-France regional council (https://www. iledefrance. fr/education-recherche), IBiSA (https://www. ibisa. net), and  CNRS  (http://www. cnrs. fr).
Funding This work was supported by the  "Institut national de la santé et de la recherche médicale (Inserm) , " the  Nantes Université , the  SantéDige Foundation  and the cluster IBD NExt from  Nantes Excellence Trajectory  ( NExT Health  and Engineering). MM benefits a doctoral fellowship managed by  Bba Milk Valley , dairy industrial association. We are grateful to  Regional Councils of BRetagne  (grant no. 19008213 ) and  Pays de la Loire  (grant no. 2019-013227 ) for their financial support through the interregional project  PROLIFIC. MR-D is supported by the  Centre National pour la Recherche Scientifique (CNRS).
actDC core transcriptome and the expression profile was almost identical. To better understand the nature of RORgt + APC, we first compared the six clusters identified in Figure  1E  with the gene expression profiles of cells available on ImmGen (Figure  1F ). Of all the queried ImmGen cell types, the RORgt + APC transcriptional profile showed the greatest similarity to DCs and particularly to the intestinal lamina propria cDC2s. The correlation profile of RORgt + APC and qDC2 in our dataset to the ImmGen transcriptomes was very similar indicating their transcriptional similarity. RORgt + MHCII + antigen-presenting TCs were suggested to be enriched in the gut draining lymph node of young mice and to be distinct from type 3 ILCs (ILC3s). 24  We compared our six cell clusters to the published scRNA-seq data of RORgt +- MHCII + cells representing Thetis I-IV subsets and ILC3 subsets isolated from PND14 MLN.
R848 (Figures  6J, S6D,  and S6E ). Further, we analyzed genes and corresponding GO terms that were differentially expressed in both, adult vs. neonatal and PND11 PBS vs. R848 comparisons (Figure  S7 ). Very few genes were co-regulated in both datasets. Even within common GO terms, different genes contributed to GO term enrichment, illustrating that the IFN-I-driven activation in the neonate did not recapitulate the neonatal-to-adult transition. Adult mice deficient in the IFN type I receptor in CD11c + cells (IFNAR DIEC ) exhibited no decrease in PP cDC2 maturation (Figure  6K ). Thus, the type-I-IFN-inducer R848 stimulated a strong systemic cytokine response in neonates and was able to overcome the maturational arrest of neonatal DC in respect to CD4 + T cell priming. However, despite its potent activity, IFN I appear not to represent the physiological stimulus that drives PP APC maturation in steady state.
Comparative GO term enrichment was further carried out using the REVIGO pipeline. 109  Pseudobulk analysis against the ImmGen clusters was carried out as previously described. 110  Raw data were deposited under GEO accession number GSE188714. Shotgun metagenomic sequencing DNA for the metagenomic analysis was isolated using a modified protocol according to Godon et al. 111  Snap frozen samples were mixed with 600 ml stool DNA stabilizer (Stratec Biomedical), thawed, and transferred into sterile 2-mL screw-cap tubes containing 500 mg 0. 1 mm (diameter) silica/zirconia beads. 250 ml 4 M guanidine thiocyanate in 0. 1 M Tris (pH 7. 5) and 500 ml 5 % N-lauroyl sarcosine in 0. 1 M PBS (pH 8. 0) were added. Samples were incubated at 70 C and 700 rpm for 60 min. A FastPrepÒ instrument (MP Biomedicals) cooled with dry ice was used for cell disruption (3 x 40 s at 6.
ACKNOWLEDGMENTS We would like to thank  A. Schwentker ,  D. P. P. Branco ,  D. Wylensek  ( RWTH Aachen ), and  C. Wagner  ( CIML Marseille ) for help with animal experiments/data analysis;  V. Cerovic  ( RWTH Aachen ) for critical discussion,  E. Slack  ( ETH Z€ urich ) for advice on Salmonella vaccination,  S. Jennings  ( RWTH Aachen ) for gnotobiotic animals,  A. Dupont  ( RWTH Aachen ) for mouse breeding management,  C. Ruedl  ( Nanyang Technological University Singapore ) for the Plet1 antibody, and  Nelson Gekara  and  Kyaw Min Aung  (both  Stockholm University ) for generation of E. coli MVs and the genomics and flow cytometry core facilities of the  Interdisciplinary Center for Clinical Research (IZKF) Aachen  within the  Faculty of Medicine at RWTH Aachen University. This work was supported by  TO1052/1-1  (to N.
Torow) and the  Collaborative Research Center   CRC1382  (project  ID 403224013-SFB 1382  projects rising star,  B01 , Q01, and Q02 to N. Torow, M. W. H. , M. v. B. , and T. C. ), Clinical  Research Unit 344  ( KFO344 , project  ID 417911533  to N. Torow),  CRC/TRR 359  (project  ID 491676693  to S. P. R. and M. W. H. ),  Emmy Noether-Programm   RO 6247/1-1  (project  ID 446316360  to S. P. R. ) and  SFB1160-2 IMPATH  (to S. P. R. ) from the  German Research Foundation (DFG)  as well as the  ERC Advanced Grant EarlyLife  (project ID:  101019157  to M. W. H. ). N. Torow was supported by the  START-Program   of the Faculty of Medicine, RWTH Aachen  (to N. Torow). H. L. was supported by the  French National Research Agency   ANR-20-CE15-0016  and used the  PICSL imaging facility  of the  CIML (ImagImm) , member of the national infrastructure France-BioImaging, supported by the  French National Research Agency  ( ANR-10-INBS-04 ).