dataesr/scientific-paragraphs-categorization huggingface dataset:
The STRING network also suggested a potential involvement of the Clock genes system, notably Clock and Rev1, which may be regulated by EGR1  [46]. In addition to indications of the early development of insulin resistance, the livers of the IUGR-N mice also presented signs of early onset steatosis. However, our dataset did not indicate any significant differential enrichment in the H3K4me3 histone mark on the promoter of peroxisome proliferator-activated receptor (PPAR) genes in the young adult IUGR-N mice relative to the controls. The alpha, beta, delta, and gamma 1,3-PPAR genes are expressed in the liver and encode proteins acting as lipid sensors in metabolism  [65, 66]. PPAR gamma has also been implicated in the M1 to M2 transition of macrophages in response to fatty acids  [67] , thereby affecting hepatocyte metabolism by modulating local inflammatory processes.
Moreover, the potential regulation of PPAR gamma expression at the DNA levels (through methylation) and the direct role of this regulation in the placenta in fetal growth restriction both suggest a crucial role in the regulation of metabolism after IUGR  [68, 69]. However, although both the Ppara and Ppargc1b genes were identified in the raw dataset, both these genes were filtered out as they did not satisfy the applied threshold. Thus, despite the essential role of PPARs in liver metabolism, we were unable to associate changes in their expression with IUGR in our mouse model. Multiple high-throughput approaches combining RNASeq, MeDIPSeq, and various ChIPSeqs and MIRSeq techniques during fetal and post-natal development would be required to reveal the precise molecular mechanisms underlying these first alterations inducing the development of metabolic diseases during adulthood following IUGR.
DATA AVAILABILITY The datasets generated during and/or analyzed in this study are available from the figshare website repository (https://figshare. com) (DOI under embargo: 10. 6084/m9. figshare. 8015906; private link for Relative to the control conditions (A), perinatal changes in nutrition in mice induce multiple and coherent epigenetic alterations in hepatocytes (B), notably affecting key genes involved in insulin signaling: lower amounts of AKT are synthesized, potentially leading to a predisposition to developing insulin resistance with age. This effect is aggravated by complementary epigenetic alterations to the primiRNA17Hg and Dicer1 genes, leading to a decrease in miRNA19a-3p levels. The decrease in miRNA19a-3p leads to an increase in the translation of PTEN, a well-known inhibitor of AKT activation, and may also contribute to a predisposition to developing insulin resistance with age.
The red core indicates proteins related to pathways in cancer, the blue core denotes insulin signaling pathway proteins, the yellow core represents axon guidance proteins, and the green core demonstrates Fc epsilon RI-mediated signaling pathway proteins according to the KEGG pathway analysis with the STRING database. The green circles indicate greater enrichment in the H3K4me3 mark in the IUGR-N group and the red circles indicate higher enrichment in the control group.
ACKNOWLEDGMENTS The authors thank  T. Ledent  and the staff at the  SPF  experimental animal housing platform (PHEA) at the  Saint Antoine Research Center  for animal care,  S. Placier  and  C. Chatziantoniou  from the hemodynamic platform at  Tenon Hospital  for help during blood pressure measurements, the  P3S  platform staff at the  UMS 29 OMICS  for ChIP sequencing, and  Alex Edelman  and Associates for English corrections.
FINANCIAL DISCLOSURE This study was supported by grants from  Inserm  (L. K. and Y. L. B. ),  Sorbonne Université  (L. K. and Y. L. B. ),  IHU ICAN   ANR-10-IAHU-05  (L. K. ), the  Premup Foundation  (L. K. , L. M. , M. L, and Y. L. B. ), the  Fondation pour la Recherche Médicale FRM  (Y. L. B. ), and an  SFD-MSD  research grant (L. K. ). S. S. holds fellowships from  Premup/Sorbonne Université and SFEDP-Sandoz.
Microarray screening and data analysis An Affymetrix GeneChip 1 Rat Gene 2. 0 Array was used to analyze differential gene expression profiles of the control intestines at various stages of maturation (between ED15 and ED21, n = 16) and the segments proximal and distal to intestinal obstruction (ED21, n = 8). The samples were prepared according to the manufacturer's instructions and recommendations (Affymetrix, Santa Clara, CA, USA). Briefly, total RNA from each sample was reversetranscribed, converted into complementary RNA using the standard Affymetrix protocol and hybridized to the Affymetrix GeneChip Rat Gene 2. 0 Array at the genomic platform at the Cochin Institute. Data processing and analysis. Raw Affymetrix data (. cel files) from the arrays were transformed by the Robust Multiarray Analysis (RMA) method using Bioconductor in R software to obtain genome-level expression values.
Quantitative real-time PCR Complementary DNA was obtained by reverse transcription (RT) of the extracted total RNA (0. 5 μg) with a QuantiTec Reverse Transcription Kit following the manufacturer's instructions (Qiagen, Hilden, Germany). Two RTs were performed for each RNA sample. All of the cDNA samples were quantified using a Nanodrop ND-2000/2000c spectrophotometer (Thermo, Waltham, Massachusetts, USA) to determine the concentration and purity of each cDNA sample. Primers for the genes of interest and the reference genes were designed using PrimerBlast (NCBI) and Primer3 software, with melting temperatures ranging from 62 ˚C to 63 ˚C. The sequences of the forward and the reverse primers used for the real-time PCR amplifications are shown in Table  1. The cDNA was amplified by real-time PCR using a RotorGene SYBR Green PCR Kit (Qiagen, Hilden, Germany) in a total volume of 25 μL consisting of 1x RotorGene SYBR Green MasterMix, 600 nM of each primer, and 80 ng of cDNA.
The amplification was carried out using a Rotor-Gene 6000 multiplex system (Corbett Research) with the following qPCR conditions: 95 ˚C for 10 min, followed by 40 cycles of 95 ˚C for 30 s, and then 62 ˚C for 45 s. Each cDNA sample analysis was performed in duplicate for the genes of interest and in triplicate for the reference genes. Negative (water) controls were included in all of the qPCR runs. The data were analyzed using the corresponding software version 1. 7 (Corbett Research). Gene expression was quantified using the relative delta Ct method by comparing the number of cycles of target genes with the internal reference genes (HPRT and TBP). Target gene/reference gene ratios were calculated and expressed as 2 -ΔCt. This ratio was then used to evaluate the expression level of the target genes in each sample.
The experimental protocol was approved by the French animal care and use committee (Comite Ńational de Reflexion Ethique sur l'Experimentation Animale; reference number 05180. 04) and 75014 Paris) and it has been assigned accession number EMTAB-5981 in ArrayExpress. The files of the microarray analysis are publicly accessible in ArrayExpress. The accession number is: E-MTAB-5981 DOI:https://www. ebi. ac. uk/arrayexpress/ experiments/E-MTAB-5981/.
Acknowledgments We would like to dedicate this work to the memory of our beloved friend and colleague,  Martin Fortier-Davy. Funding D. D. was supported by a  Wellcome Trust  Grant ( WT108720 ). A. L. H. was supported by the  National Institute on Drug Abuse, USA  [grant number  5R01 DA041336 ]. Y. Z. and E. K. were supported by the  Internal Grant Agency of the Ministry of Health of the Czech Republic  [grant number  AZV 17-32957A ]. K. H. P was supported by the  Heffter Research Institute, USA  [grant number  2020-03-01 ]. T. N. was partially supported by  Junior Research Fellowship COFUNDed between Durham University  and the  European Union  [grant number  609412 ].
Patient selection and management This study was a multicenter, open-label, retrospective, observational cohort study assessing all SBS-IF patients who received TED for at least six months in France since its marketing authorization. The database was locked on September 1, 2017, meaning that the treatment had to be initiated between October 2015 and March 2017. According to the French good medical practice consensus, TED initiation was allowed in the 15 authorized expert adult IF centers. SBS-IF patients selected to receive treatment were stable SBS-IF patients who remained PS dependent despite the use of all other intestinal rehabilitation modalities. Treatment was initiated after a stabilization period to ensure adequate nutrition and hydration status as well as patient compliance with therapy.
Data collection Epidemiological and clinical variables were collected in each center, including age, gender, body weight, body mass index (BMI), SBS causes (mesenteric ischemia, Crohn's disease, volvulus, radiation enteritis), SBS anatomical features (remnant bowel length and colon in continuity, type of stoma, reverse intestinal interposition surgery), IF features (PS duration, number of weekly infusions and daily infused volume, and energy needs, oral intake, daily fecal volume and steatorrhea in a 72-h stool collection). SBS anatomy was classified into 3 groups (jejunostomy or ileostomy, group 1; !50% of colon in continuity, group 2; <50% of colon in continuity, group 3) according to Jeppesen et al. [12]. IF was classified according to energy needs (PN or only fluids and electrolytes) and the volume of IVF supplementation (class 1e4), according to ESPEN recommendations  [13].
Procedures At admission, patients were examined by medical practitioner according to current recommendations who collected the following data: weight, BMI calculation, recent weight loss, daily oral intake self-reported during the week prior to hospitalisation (the oral intake in the previous week was reported by the patient or his relatives when the patient was not able to express it (ICU, death)), self-reported factors influencing oral intake reduction and Nutrition Risk Screening 2002. For the food intakes, we used the self-evaluation food intake according to Bouette et al. (11)  which finds a correlation between undernutrition and selfreported food intakes according to a simple analogical visual scale. For anthropometric data (height and weight), patients were weighed on admission. The height was retrieved from previous data if these data were available.
During the hospitalisation, daily oral intake was recorded by specialist dietitian. Demographic characteristics, co-morbidities, ICU severity score (Simplified Acute Physiology Score II)  (12)  , treatments and biological data were collected from the patient medical record including C reactive protein, albuminaemia, protidaemia, serum creatinine, glomerular filtration rate (Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula), lymphocyte count and D-dimers at admission. Data collection was done prospectively by a third-party physician not involved in the patient's care. Malnutrition was diagnosed according to the Global Leadership Initiative on Malnutrition (GLIM) two-step approach defined by the association of one phenotypic criterion (especially non-volitional weight loss, low BMI) and one aetiological criterion (reduced food intake or assimilation, disease burden/ inflammatory condition)  (13).
Due to COVID-19, it was recommended not to use conventional methods for assessing lean body mass in patients because of the risk of contamination for caregivers. All patients undergoing chest CT scan were assessed for an association between undernutrition and pectoral muscle area assessment of muscle mass. This index is not used in nutritional assessment and we wanted to study this index in a pilot way in undernutrition in ICU. There are no data on validity or reliability for pectoral muscle area in nutritional assessment. To estimate muscle mass, the pectoralis muscle area was analysed; the pectoral muscle area is used in pneumology as a prognosis factor  (14). Based on chest CT scans which were routinely performed as part of COVID-19 management, pectoralis muscle area was measured by a trained radiologist.
To highlight covariates associated with malnutrition, a partitioning of the population was represented using a Classification and Regression Tree analysis. The advantage of this multivariate analysis approach is to describe the means of distribution of the population in homogeneous groups according to the existence of malnutrition and the covariates selected from the multidimensional analysis. Statistical analyses were conducted using SPSS® for Window version 24 (IBM Corporation). P ≤0•05 was considered statistically significant.
Acknowledgements The authors thank  Anne Galinier  from the  Department of Biochimy  and all the doctors and paramedics who helped to manage COVID-19 patients during the crisis. This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. J. -M. C. , A. R. , V. M. , P. L. : conceptualisation; A. R. , P. L. , O. M. , B. M. , O. L. : data curation; A. R. , F. V. -B. , J. -M. C. , V. M. : formal analysis; A. R. , P. L. : investigation; J. -M. C. , V. M. , A. R. , P. L. , L. B. : methodology; J. M. C. , V. M. , O. F. : supervision; A. R. , F. V. -B. : writingoriginal draft; V. M. , J. -M. C. : writingreview and V. M. , J. -M. C. , A. R. , F. V. -B. : editing. The authors declare that they have no competing interests. The authors had no ethics approval and consent to participate relevant to this article to disclose.
Assessing and describing genetic diversity in crop plants is a crucial first step towards their improvement. The European hazelnut, Corylus avellana, is one of the most economically important tree nut crops worldwide. It is primarily produced in Turkey where rural communities depend on it for their livelihoods. Despite this we know little about hazelnut's domestication history and the genetic diversity it holds. • We use double digest Restriction-site Associated DNA (ddRAD) sequencing to produce genome-wide dataset containing wild and domesticated hazelnut. We uncover patterns of population structure and diversity, determine levels of crop-wild gene flow and estimate the timing of key divergence events. • We find that genetic clusters of cultivars do not reflect their given names and that there is limited evidence for a reduction in genetic diversity in domesticated individuals.
, 2015). In this study we aim to lay the groundwork for a genomic perspective on hazelnut in Turkey. We conduct double digest restriction-site associated DNA sequencing  (Peterson et al. , 2012)  on more than 200 individuals, principally wild and cultivated C. avellana from the Black Sea region of Northern Turkey. To provide context in our genomic analyses we also include specimens from the UK, Georgia and the Campania region of Italy as well as samples from other members of the same genus, C. colurna and C. maxima. We use these genomic data to determine patterns of genetic diversity and structure among and within wild and cultivated populations. Domestication is thought to cause a rapid reduction in population size, when early farmers isolate a strain, followed by expansion. This 'domestication bottleneck' will drastically reduce levels of genetic diversity  (Meyer & Purugganan, 2013)  and was thought to be the norm for cultivated species.
Sample collection We sampled putatively wild Corylus avellana individuals from 12 sites across Turkey as well as four sites in Georgia and a single site in the UK. Samples of cultivated individuals were taken from locations on the north coast of Turkey and from two sites in southern Italy. A map of collection sites (providing location data were available) in Turkey is shown in Figure  1. Individuals previously identified as Corylus colurna and C. maxima were sampled from the arboretum at Royal Botanic Gardens, Kew. A full list of samples and their collection locations can be found in Table  S1.
Locus construction and SNP calling Loci were constructed using STACKS (v1. 46)  (Catchen et al. , 2011). We used the program process_radtags in to clean and demultiplex reads (options -c -q & -r). Paired-end reads were mapped to a new, draft reference genome for the Turkish cultivar 'Tombul' (European Nucleotide Archive (ENA): GCA_901000735) using the Burrows-Wheeler alignment tool (BWA) algorithm  (Li & Durbin, 2010 ) BWA-MEM with the default options keeping only those reads with a mapping quality of 40 or greater. We then used pstacks (default parameters) to extract aligned stacks and identify SNPs. We built a catalogue of consensus loci by merging alleles (cstacks) based on alignment positions (option -g) and with a maximum of three mismatches allowed between sample loci. We used sstacks to search against this catalogue to match loci from each individual to a catalogue locus, again based on alignment position.
Population diversity and structure We first performed a principal components analysis (PCA) on the SNP data generated from all individuals and then a discriminant analysis of principal components (DAPC) analysis  (Jombart et al. , 2010)  to cluster individuals. The appropriate number of clusters was inferred using Bayesian information criterion (BIC). The number of suitable PCs to retain was identified using the optim. a. score function in 'adegenet'  (Jombart, 2008). We then used an alternative clustering approach, fastSTRUCTURE  (Raj et al. , 2014)  on our SNP dataset. We ran fastSTRUCTURE with the default settings (which account for admixture) and the simple prior. We used the associated program 'chooseK. py' to identify the number of clusters that best explained the structure in the data and the number that maximized the marginal likelihood.
We ran analyses using all individuals and then just those identified as domesticated individuals from our DAPC analysis. Results were visualised using the R package 'pophelper'  (Francis, 2016). Finally, we ran fineRADSTRUCTURE  (Malinsky et al. , 2018) , which uses a different methodology that is based on the fineSTRUCTURE program  (Lawson et al. , 2012). Test runs indicated that including some individuals (e. g. distantly realted C. colurna (not including 'E16', 'HAO' or 'CK1') individuals and those with high levels of missing data would yield uninformative results and bias ancestry calculations. These were removed and popualtions was rerun, leaving 195 individuals for the final analysis. We filtered our input loci by removing those that had more than 10 SNPs and those that had more than 25% missing data.
Phylogenetic networks and trees To understand relationships and distances between samples we used SplitsTree4  (Huson & Bryant, 2005)  to infer a phylogenetic network with the neighbour-net algorithm. We used the program PGDSpider (v2. 1. 1. 5; (Lischer & Excoffier, 2012) ) to convert the VCF to phylip format, which was used as input. We estimated a network using all samples, include those from C. colurna and C. maxima. We also ran SNAPP  (Bouckaert et al. , 2014)  to infer a coalescent-based species tree based on binary SNP data. We used the clusters inferred using DAPC as the different taxa. The VCF file was filtered to remove monomorphic loci and only biallelic SNPs were retained. SNAPP is extremely computationally intensive, so to reduce the complexity of our dataset we thinned to SNPs to those with < 3% missing data, used a single SNP per locus and randomly selected five individuals from each of the inferred population clusters.
Assessing levels of gene flow among genetic clusters We used TreeMix to infer patterns of population splitting and mixing from allele frequency data. We calculated allele frequencies for each of the clusters that were identified using DAPC. We sequentially increased the number of migration events from zero to five (m0-m5) and examined changes in likelihood with each event added. We also used the '-se' option to calculate the significance of each migration event. We used two different block sizes (10, 100). We then examined levels of admixture between wild and domesticated clusters using the D statistic  (Patterson et al. , 2012)  implemented in the program popstats  (Skoglund et al. , 2015). Significance was calculated using Z scores (D/standard error).
Sequencing On average we recovered 8. 21 million retained reads (standard deviation 3. 72 million) per sample after processing and cleaning. After identifying and removing incorrectly identified samples our total dataset consisted of 210 individuals. The total SNPs dataset had 64,509 high quality SNPs with an average depth of 79. 1 and 13. 53% missing data. The large number of SNPs called may be, in part, because we had multiple species in our dataset. All sequences were deposited in the sequence read archive (ENA: PRJEB32239).
Phylogenetic networks Our phylogenetic network revealed a clear separation among wild and cultivated individuals (Fig. 2 ). Generally there was no clear separation among different Turkish cultivars. We were able to identify areas where two major Turkish cultivars, 'Palaz' and 'Tombul' clustered with other members of the same cultivar. The network revealed a reticulated pattern of branching that linked groups of domesticated individuals, which suggests there is a large amount of conflict in the dataset among cultivars when compared to wild samples. Distinct groups were more easily distinguishable in wild Turkish individuals. We recovered three major groups corresponding to three different areas of collection, Bolu, Giresun and Ordu (Fig. 2 ). Samples from Giresun and Ordu were each split into two different groups, indicating that there may be some fine scale genetic structure in these regions.
Phylogenetic relationships and timing of divergence events After pruning, our final dataset for phylogenetic tree inference consisted of 472 SNPs. Our SNAPP analysis reached convergence (all ESS > 200) after approximately 0. 5m generations. The second run converged at the same point after 1m generations, suggesting our results are robust to different starting states. Our SNAPP tree (Fig. 6a ) generally had very high support, all but a single node had posterior probability > 0. 95. Clusters of Turkish cultivars formed a monophyletic group. The placement of the branch leading to the Italian cultivars was unclear. It was most frequently placed sister to the wild cluster (posterior probability = 0. 49; Fig. 6a ) but the posterior distribution of trees revealed another relatively common topology in which the Italian cluster was sister to the cluster of wild individuals (Fig.
| Data collection and follow-up All children received standard nutritional and medical care for outpatient treatment of uncomplicated SAM (excluding the randomized study intervention), described previously  (Isanaka et al. , 2016)  and as per the Médecins Sans Frontières and Government of Niger protocol. Children were followed up on a weekly basis at the health centre until programme discharge (minimum 3 weeks and maximum 8 weeks) and additionally at 4, 8, and 12 weeks from admission according to the study protocol. Each assessment involved the collection of a medical history, physical assessment, and anthropometric assessment. Children who required inpatient care were transferred and censored from the study at the time of transfer.
Participants in this study were enrolled in a randomized controlled trial of routine amoxicillin versus placebo in the treatment of uncomplicated SAM (clinicaltrials. gov NCT01613547). The parent study was conducted in four rural health centres in the Madarounfa district of Maradi region, Niger, which is located in the south central part of the country bordering Nigeria. Typical of the rural Sahel, household food production is linked to rain-fed agriculture, where staple crops such as millet and sorghum are harvested once per year. Each year, the decrease in food quantity and quality experienced in the months preceding the harvest and the concurrent increase in infectious illness, including diarrhoea, pneumonia, and malaria, result in a seasonal increase in acute malnutrition among children <5 years of age.