dataesr/scientific-paragraphs-categorization huggingface dataset:
cDNA manipulation The synthetic gene corresponding to the CgrAlcOx sequence (Accession number: EFQ30446. 1, see the sequence in Fig. 2 ) was designed with a C-terminal His 6 -tagencoding sequence, synthesized by Genscript (Piscataway, NJ, USA) and cloned into the Pichia pastoris expression vector pPICZaC (Invitrogen, Carlsbad, CA, USA) flush with the Saccharomyces cerevisiae a-factor secretion signal peptide sequence.
Molecular modelling We used the all-atom Monte Carlo (MC) software PELE (Protein Energy Landscape exploration), to map the enzyme-substrate intermolecular interactions  [50, 51]. PELE  follows a heuristic MC approach, generating new conformational proposals using vibration modes of the proteins with translations and rotations of the substrate and relaxing the system with structure prediction methods, including side chain sampling and minimization, so that the probability of acceptance in the Metropolis criterion remains high  [50]. CgrAlcOx and FgrGalOx were prepared from the 5C92 and 1GOF PDB files, respectively, and refined with the Protein Preparation Wizard from Schrodinger  [35]. D-Gal substrate was created with MAESTRO 3D Builder and parameterized with the OPLS2005 all-atom force field, where atomic charges were obtained from an electrostatic potential on a quantum mechanical minimized structure at DFT MO6 level of theory and implicit solvent  [52].
To generate initial positions for the PELE simulation, D-Gal was then docked on the prepared enzymes using the GLIDE software  [53]. Then, we performed a PELE-induced fit sampling consisting of 24 h using 256 CPUs. Each PELE step consisted of a random perturbation (a rotation of 0. 02-0. 15 rad and a translation of 0. 5-1. 0 A), using a restrained search space in the center of mass of the ligand of 9 A. The perturbation step is then followed by a relaxation phase including side chain prediction (all side chains within 6 A from the ligand) and a full system minimization.
Acknowledgements We are grateful to  Pom Geslin-Vinck  for her assistance in constructing some of the CgrAlcOx loop variants. This study was supported by (a) the  'Centre National de la Recherche Scientifique' (CNRS) , (b) the  French 'Agence Nationale de la Recherche' (ANR)  and the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through the  joint 'Projet de Recherche Collaboratif International'/'Strategic Partnership Grants for Projects' program , supporting the project entitled ' FUNTASTIC-Fungal copper radical oxidases as new biocatalysts for the valorization of biomass carbohydrates and alcohols'  ( ANR-17-CE07-0047 ,  NSERC   STPGP 493781-16 ) and (c) the  Spanish Ministry of Innovation and Sciences  ( PID2019-106370RB-I00 ).
Pyrosequencing and sample preparation One μg of DNA was first linearized using BamHI (fast digest, Thermo Scientific) at 37°C for 1 h. 500 ng of DNA was then bisulfite-converted using the EZ DNA methylation Gold Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer's instructions. For pyrosequencing, bisulfite PCR of the CSBII/III, COX1, D-loop, HSP1, LSP, and ND6 regions was conducted using bisulfite-specific primers (Supplementary Table  S3 ). Primers were designed using the PyroMark Assay Design 2. 0 software (Qiagen). To avoid amplification of NUMTs, primers designed for pyrosequencing analysis were evaluated via sequence homology analysis using BLASTn 1 against the nuclear reference genome (Human or Mouse). In case of a highly probable nDNA hit, adjacent 1 https://blast. ncbi.
nlm. nih. gov/Blast sequences (Additive sliding windows by 25 nucleotides up to 150) were separately run (BLASTn) and aligned against reference mtDNA and the nuclear genome to discard NUMTs. Only primers without strong hits in nuclear DNA were used. The PCR product was then run on a 2% agarose gel to confirm amplification and predicted amplicon length. The PCR product was then sequenced using the Q48 automated pyrosequencing machine (Qiagen) according to the manufacturer's instructions. The percentage methylation at each CpN site was determined using the PyroMark Q48 Autoprep 2. 4. 2 software (Qiagen).
Statistics Cell line data are expressed as the mean ± SEM of at least 3 independent experiments, unless indicated differently, with three sets of the three transgenic cell lines created at three independent time points. Statistical analysis was performed using Graph-pad Prism 7 software. Single group comparisons were performed with the two-tailed unpaired student's t-test. Human and mouse data were analyzed using a two-tailed Mann Whitney U test. Correlation analysis was conducted using Spearman's correlation test and p values ≤0. 05 were considered significant. As in this study, our comparative statistical analysis of cytosine methylation percentages to evaluate pyrosequencing data was hypothesis-driven, we did not adjust our significance levels for multiple testing, as was also previously suggested by others  (51).
mtDNA methylation impairs lipid metabolism To assess whether the altered mtDNA methylation status caused impaired lipid metabolism, we measured lipid accumulation in the transgenic cell lines after 48 h exposure to free fatty acids. Interestingly, without additional lipids, HepG2 mtM. CviPI and mtM. SssI cells showed significant increased lipid accumulation as measured by Oil red O staining (Figures  3C, E  vs. Figure  3A , and quantification in Figure  3G ). After treatment with palmitic acid and oleic acid (1 mmol/L), HepG2-mtM. SssI showed significant increased lipid accumulation against HepG2 mtM. CviPI and HepG2 mtM. CviPI-Mut transgenic lines (Figure  3F , vs. Figures  3B, D , and quantification in Figure  3H ). Taken together, these data indicate that increased methylation of mtDNA associates with increased lipid accumulation in HepG2 cells.
Human liver samples Next, we analyzed steatotic liver samples from morbid obese individuals who underwent bariatric surgery and compared those to non-steatotic human liver tissue. While hepatic mRNA levels of PNPLA3, a biomarker of MAFLD, were significantly enhanced in steatotic liver tissue when compared to non-steatotic human liver, expression of inflammatory (TNFα, IL1β) and fibrotic markers (COL1A1, ACTA2) in these patients was not increased (data not shown). Interestingly, similar to the mice, ND6 expression was significantly higher in steatotic livers (p < 0. 01) compared to the healthy controls (Figures  9A, B ). Other mitochondrial genes, such as CYTB, COX1, 12S and 16S, were also significantly elevated (p < 0. 05) compared to the healthy controls (Figures  9C-E ). Intriguingly, the overall increase in gene expression was not associated with higher mtDNA content between steatotic and non-steatotic human livers (Figure  9F ).
Acknowledgments The authors would like to thank  Ali Saeed  and  Danial Afsharzadeh , both from the  UMCG , for their assistance with the patient and mouse liver material. Authors also thank to the  De Cock-Hadders initiative  for supporting this project. The ONT sequencing, including conceptualizing the sequencing experiment and assistance with data analysis was performed in Neuromics Support Facility at  VIB-UAntwerp Center for Molecular Neurology (Belgium). K. Lunnon  and  D. Soanes  ( University of Exeter, UK ) are acknowledged for their helpful discussions regarding analyses of nanopore data. We thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the  Hábrók (formerly Peregrine ) high-performance computing cluster and its data storage environment RDMS (Research Data Management System).
Differential and double-differential distributions of kinematic variables of leptons from decays of top-quark pairs (t t) are measured using the full LHC Run 2 data sample collected with the ATLAS detector. The data were collected at a pp collision energy of √ s = 13 TeV and correspond to an integrated luminosity of 140 fb -1. The measurements use events containing an oppositely charged eµ pair and b-tagged jets. The results are compared with predictions from several Monte Carlo generators. While no prediction is found to be consistent with all distributions, a better agreement with measurements of the lepton p T distributions is obtained by reweighting the t t sample so as to reproduce the topquark p T distribution from an NNLO calculation. The inclusive top-quark pair production cross-section is measured as well, both in a fiducial region and in the full phase-space.
The latter measurement was based on data collected during 2015-16, corresponding to an integrated luminosity of 36 fb - 1. The same analysis technique is applied here to the entire 13 TeV data sample from LHC Run 2, corresponding to an integrated luminosity of 140 fb -1. Similar measurements have also been performed by the CMS Collaboration at √ s = 13 TeV  [7] [8] [9]. The large integrated luminosity of the Run 2 data sample allows the lepton differential distributions to be measured over a wider range and with finer granularity than in ref. [6]. The differential distributions of eight kinematic variables of the two leptons are studied: • p T , the single-lepton transverse momentum 1 ( = e or µ); • |η |, the single-lepton pseudorapidity; • m eµ , the eµ system invariant mass; • p eµ T , the eµ system transverse momentum; • |y eµ |, the eµ system rapidity; • E e + E µ , the sum of lepton energies; • p e T + p µ T , the scalar sum of lepton transverse momenta; • |∆φ eµ |, the azimuthal angular separation of the leptons.
JHEP07(2023)141 The effect of multiple interactions in the same and neighbouring bunch crossings (pileup) was modelled by overlaying each hard-scattering event with inelastic pp collisions generated with Pythia 8. 186  [19]  using the NNPDF2. 3 set of PDFs  [20]  and the A3 set of tuned parameters  [21]. The EvtGen 1. 6. 0 program  [22]  is used for properties of the bottom and charm hadron decays.
Differential fiducial cross-sections The events used in the analysis are required to contain either exactly one or exactly two b-tagged jets, thereby reducing backgrounds to the 11% or 4% level in the two regions, respectively, and allowing the simultaneous determination of the t t cross-section and the combined jet selection and b-tagging efficiency. In each bin i of the lepton kinematic variables listed in section 1, these parameters are evaluated by solving the equations N i 1 = Lσ i t tG i eµ 2 i b (1 -i b C i b ) + N i 1,bkg N i 2 = Lσ i t tG i eµ ( i b ) 2 C i b + N i 2,bkg (6. 1) where • N i 1 and N i 2 are the numbers of selected data events with either one b-tagged jet or two b-tagged jets in the reconstructed bin i, • N i 1,bkg and N i 2,bkg are the numbers of predicted background events with either one b-tagged jet or two b-tagged jets in reconstructed bin i, • L is the integrated luminosity of the data, • σ i t t is the cross-section for t t production resulting in an opposite-sign eµ pair in the MC generator-level (i.
The two unknown variables in these equations, the cross-section and the combined selection and b-tagging efficiency, are determined with a log-likelihood fit, as in refs. [4, 6]. The binning is chosen so that more than 90% of eµ pairs originating in bin i at particle level are usually measured in bin i at detector level. A bin-by-bin unfolding procedure is used where the G i eµ efficiency accounts for both the accuracy of lepton reconstruction and the impact of bin migration, which refers to the situation where events categorized in bin JHEP07(2023)141 j at the particle level are observed in a different bin i = j during reconstruction. The large LHC Run 2 dataset allows the chosen binning to be finer than in the previous 13 TeV analysis  [6]  and the measurements to be made in an extended energy range.
Systematic and statistical uncertainties Uncertainties due to theoretical assumptions and detector modelling affect the parameters L, G i eµ , C i b , N i 1,bkg and N i 2,bkg of eq. (6. 1). Uncertainties associated with generators are evaluated by changing the values of parameters in the simulations or by using alternative generators. Some background uncertainties are evaluated by using data-driven uncertainty estimates, as explained in section 7. 3. For each variation, eq. (6. 1) is solved and the change with respect to the baseline sample is assigned as the impact of the uncertainty on the cross-section measurement. Effects of finite data and MC sample sizes are evaluated by the bootstrapping method described in section 6. 4 and summarised in a covariance matrix covering all measured kinematic bins.
Total inclusive cross-section The cross-section for the fiducial region is measured with the full Run 2 dataset to be σ fid t t = 10. 53 ± 0. 02 (stat) ± 0. 13 (syst) ± 0. 10 (lumi) ± 0. 02 (beam) pb and the total inclusive cross-section is σ t t = 829 ± 1 (stat) ± 13 (syst) ± 8 (lumi) ± 2 (beam) pb, where the uncertainties are due to statistics, theoretical and experimental systematic effects, the integrated luminosity and the beam energy. The fiducial region is defined as p T > 27  (25)  GeV for the leading (sub-leading) lepton and |η | < 2. 5 after applying the overlap removal procedure described in section 4 for both leptons from t t decays producing an eµ pair. The total relative uncertainties in σ fid t t and σ t t are 1. 6% and 1. 8%, respectively, where the breakdown of the various sources is shown in table  3.
Conclusion The production of t t pairs in pp collisions at √ s = 13 TeV is measured using opposite-sign eµ events in association with one or two b-tagged jets in the LHC Run 2 (2015-2018) data collected by the ATLAS experiment with an integrated luminosity of 140 fb -1. The inclusive fiducial and total cross-sections for t t production are measured. The total inclusive cross-section is measured to be σ t t = 829 ± 1 (stat) ± 13 (syst) ± 8 (lumi) ± 2 (beam) pb, where the uncertainties are due to statistics, theoretical and experimental systematic effects, the integrated luminosity and the beam energy. This result is in excellent agreement with theoretical expectations. It is the most precise measurement of the inclusive t t crosssection to date. The The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.
The measurements are compared with a wide range of models for t t production in pp collisions. No model can describe all measured distributions within their uncertainties. Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC.
The precision of the measurements is typically 2% for the absolute differential crosssections and at the 1% level for the normalised differential cross-sections, except in the highest energy bins where the t t/W t interference uncertainty contribution increases. The measurements are compared with a wide range of models for t t production in pp collisions. No model can describe all measured distributions within their uncertainties. Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Standardized questionnaires were used to interview consenting participants at three points: the day of the delivery of the antenatal HIV test result, three months later, and three months after ART initiation. The data collected were entered into KoboCollect and analyzed using SPSS V23. 0 software. The Chi-square test was used to compare proportions, Kaplan-Meier techniques and Cox proportional hazards regression was used to estimate retention in ART and identify factors associated with ART non-retention, respectively. Results and discussion: A total of 85 ART-naïve pregnant women living with HIV were enrolled in the study. The median age and gestational age at the first antenatal care visit were 29 years (interquartile range (IQR), 2333. 5) and 28 weeks of amenorrhea (IQR, 2032), respectively.
Data collection Three standardized questionnaires were developed to interview women: T0, T1, and T2. The T0 captured data on sociodemographics, parity, relationship duration with an intimate partner, history of HIV testing, intention to share HIV results with a partner, and selfefficacy perception to initiate ART. The T1 questionnaire was used to collect information on the initiation of ART, reasons for non-initiation, and place of ART supply. Lastly, T2 assessed retention at 3 months post ART initiation and reasons for non-retention. Participants were interviewed at three time points: on inclusionthe day of the delivery of the antenatal HIV test result (T0); 3 months after the delivery of the antenatal HIV test result (T1); and 3 months after ART initiation (T2). The T1 and T2 were administered on the same day for participants whose dates of ART initiation and delivery of HIV results were similar, as well as for those who missed the T1 but were caught up on their T2 interview visit.
Data analysis The descriptive statistics performed included frequencies and percentages for categorical variables and median and interquartile ranges (IQRs) for continuous variables. Proportions were used to calculate ART uptake and retention. The chi-square test was used to compare the proportions of initiation of ART and retention among PWLHIV between women enrolled in IHCs with no ART and those enrolled in IHCs where ANC integrated ART (p < 5%). Estimates of retention in ART were determined using Kaplan-Meier. Cox proportional hazards regression was used to identify associated factors to ART non-retention. Covariables that were significant in the bivariate analysis (p < 10%) were adjusted in the final multivariate model at a level of 10% significance. The covariates used for this purpose were PMTCT management, age group, study level, marital status, income-generating activity, relationship duration, pregnancy willingness, awareness of HIV status of their male partner, knowledge of MTCT of HIV before ANC, and perceived self-efficacy to initiate ART.
Introduction Since the discovery of the atomic nucleus, technological applications of nuclear energy have flourished. The design and modeling of such devices rely on technological properties (geometry, compositions, etc. ) but also on fundamental constants describing the properties of spontaneous or induced nuclear reactions. These constants, called nuclear data, are however not known up to an arbitrarily high precision and special efforts must be brought to quantify related uncertainties, generally understood as covariance data. Nuclear data are obtained in a process called evaluation that combines experimental data and theoretical models. Once evaluated, these are usually further stored in files in ENDF format  [1] , these latter are then processed to create libraries specific to application codes, for instance to produce effective cross sections for medium at given temperature.
Introduction Soft-tissue sarcomas (STSs) are heterogeneous subtypes of tumors with distinct origins and genomic profiles  (Fletcher, 2014). To stratify them and define prognostic markers, large-scale approaches have revealed how histio-and genotypes are associated with distinct epigenetic, metabolic or immune traits  (Cancer Genome Atlas Research Network et al, 2017). A bioinformatics analysis of TCGA database centered on metabolic signatures showed that, whereas enhanced glycolysis was a hallmark of sarcomas, the degree of oxidative phosphorylation varied significantly amongst histiotypes  (Miallot et al, 2021). Mitochondrial activity conditions metabolic plasticity  (Masoud et al, 2020) , adaptation to stress, and progression towards aggressive phenotypes  (Senyilmaz & Teleman, 2015). Along this line, Vanin1 pantetheinase that regulates the generation of the coenzyme A (CoA) precursor pantothenate (Pan), also known as vitamin B5 (VitB5)  (Naquet et al, 2020; Millet et al, 2022) , acts as a negative regulator of spontaneous sarcoma development in a p16/ p19-deficient tumor-prone mouse model  (Giessner et al, 2018).
LC-MS Two independent metabolomics analyses were performed from tumor samples. Metabolomics experiments to detect VitB5-related metabolites were performed by Metabolon (the complete metabolite data are available in Supplemental Data 1). In this experiment, nine samples were obtained from pantothenate-treated mice and 10 from control tumors. To remove batch variability, for each metabolite, the values in the experimental samples are divided by the median of those samples in each instrument batch, giving each batch, and thus the metabolite, a median of one. For each metabolite, the minimum value across all batches in the median-scaled data is imputed for the missing values. The Batch-norm-Imputed Data are transformed using the natural log. Metabolomics data typically display a log-normal distribution; therefore, the log-transformed data are used for statistical analyses.