dataesr/scientific-paragraphs-categorization huggingface dataset:
31e-6M, 0. 62e-6M, 1. 25e-6M, 2. 5e-6M and 5. 0e-6M and injected over the flow cells at a flow rate equal to 30 µl/h for 2 min, followed by a dissociation phase of 4 min. The sensor chip surface was regenerated with 1 mM HCl. The specific binding profiles were obtained after subtracting the response signal from the control empty channel and from blank-buffer injection. The specific binding was calculated using Biaeval 3 software (Biacore, GE Healthcare) with the postulate of 1:1 ligand/analyte interaction. Based on the Chi 2 value in regard to the R max (less than 10%) and the difference interval ±2. 5 RU theoretic fitting compared to experimental results, best fitting algorithm between Langmuir or drifting base line was applied. In silico modeling of the KH176(m)-Peroxiredoxin 4 interaction.
In silico modelling was performed using Accelrys discovery studio 2. 1 software. Two PDB files for KH176 and KH176m, generated using the same program, and the PDB for Peroxiredoxin 4 (PDB 2PN8) were subjected to CHARMM force field. Thereafter, using the Libdock module within the Discovery Studio 3. 5 software (DS3. 5), KH176 or KH176m were tested for their interactions with the Prdx4 dimers. All generated files were subjected to standard dynamic cascade (Accelrys discovery studio module Simulation) and minimized with Steepest descents (Max = 500, RMS gradient = 0. 1) and conjugate gradient (Max = 500, RMS gradient = 0. 0001) as well as heating (steps 2000 kCal, time step = 0. 001 sec, initial temperature = 50 kCal, target temperature = 300 kCal, adjusted velocity frequency = 50 Hz, saved results frequency = 100 Hz).
Normalization was performed on the basal condition (no exogenous oxidant). Compounds effects were compared to vehicle; ***p < 0. 001; n. s. = nonsignificant. (d) Cellular superoxide levels were detected using HEt. Basal levels were decreased in the presence of KH176m (EC 50 KH176m = 1. 7 × 10 -6 M). KH176 had no effect. Results are the average of two independent experiments, SD is indicated. (e) Mitochondrial superoxide levels, detected with mitoSOX, could be decreased by KH176m (IC 50 = 1. 4 × 10 -6 M). KH176 was much less potent. (f) CumOOH induced lipid peroxidation measured with bodipy 581/591 C11 could be inhibited by KH176 (IC 50 = 6. 4 × 10 -5 M) and KH176m (IC 50 = 7. 1 × 10 -8 M), KH176i had no effect. For all panels: unless otherwise indicated the data points represent the average of triplicate measurements and are normalized on the untreated condition, SD is indicated.
Acknowledgements Dr. Werner Koopman ,  Dr. Peter Willems ,  Prof. Pedro Hermkens  and the  MercaChem Team  are acknowledged for scientific advice during the lead optimization process. JS has been/is supported by the  PreSeed IOP-Genomics Program  ( IGE05003 ) and a  ZonMW PM-Rare  Grant (113302003). Part of this work has been supported by the  Foundations Energy4All ,  Join4Energy ,  Ride4Kids ,  Road4Energy  and the  Tim Foundation.
3,3-Diaminobenzidine (DAB) Stain Imaging and Signal Quantification Each TMA slide was imaged on an AxioScan Z1 slide scanner (Zeiss, UK) at 40× magnification and an image of each TMA core was exported for analysis using ImageJ software (NIH, Bethesda, MD, USA). A random selection of tissue cores (n = 10 HR, n = 10 LR) was used as a training set to select two-pixel value thresholds based on a linear regression model. The first threshold encompassed the quantity of tissue (AmtT) in the image by measuring any non-background pixels; the second threshold encompassed the amount of DAB signal (AmtS) in the image. The quantity of tissue present in each image was calculated using a macro written in ImageJ. Once selected, the same threshold values were applied to every image stained with a single antibody.
The amount of DAB signal expressed in each tissue core was calculated as AmtS/AmtT and is used for statistical tests of significance of difference using a Mann-Whitney-U test. Mountain plots were constructed using Origin (OriginLab, Northampton, MA, USA) to illustrate differences in expression between HR and LR cores. Receiver operating characteristics (ROC) curves were constructed in Medcalc software to evaluate the propensity of the protein as a biomarker of malignancy.
The positive post-test probability ranged from 0. 67 to 1. 82% (LR+ = 1. 463-3. 950) and the negative post-test probability ranged between 0. 12-0. 31% (LR-= 0. 2630-0. 678). The area under the curve (AUC) of the ROC curve was highest in ATP5A1 (0. 806 ± 0. 026) and lowest in MYL6 (0. 61 ± 0. 034) with all other biomarkers demonstrating a range of AUC between these two values. The AUC indicates a greater likelihood of being able to distinguish between disease than other proteins tested, and a higher AUC would generally make a protein a better candidate for putative biomarker. A logistic regression model, performed using MedCalc software, shows an increase in diagnostic performance can be achieved by incorporating data from both ATP5A1 and HSP60. The use of both sets of data resulted in 71.
85% of cases being correctly classified (AUC = 0. 822 ± 0. 025, data not shown in Table  3 ). both ATP5A1 and HSP60. The use of both sets of data resulted in 71. 85% of cases being correctly classified (AUC = 0. 822 ± 0. 025, data not shown in Table  3 ). Predictive ability to distinguish between HR and LR tissue cores. Proteins are sorted by descending under the curve (AUC) (area under the receiver operating characteristics (ROC) curve) revealed that ATP5A1 had the highest AUC value (0. 806 ± 0. 026), followed by HSP60 (0. 800 ± 0. 026) and PABPC3 (0. 740 ± 0. 030). Sensitivity and specificity are measured using the Youden index to  Predictive ability to distinguish between HR and LR tissue cores. Proteins are sorted by descending under the curve (AUC) (area under the receiver operating characteristics (ROC) curve) revealed that ATP5A1 had the highest AUC value (0.
806 ± 0. 026), followed by HSP60 (0. 800 ± 0. 026) and PABPC3 (0. 740 ± 0. 030). Sensitivity and specificity are measured using the Youden index to show the potential effectiveness of the marker. Values calculated from data in Figure  3. show the potential effectiveness of the marker. Values calculated from data in Figure  3. NB: Sensitivity and specificity are listed along with respective cut-off values and likelihood ratios in Supplementary Table  S1. 3. Sensitivity and specificity are listed along with respective cut-off values and likelihood ratios in Supplementary Table  S1.
Study Population After ethics committee approval, we retrospectively evaluated all distal radius fractures (n=391) from 2007 to 2015 that were treated in the British Hospital of Montevideo, Uruguay. These were identified through an ongoing procedure database kept by the traumatology department, that collects patient and treatment data prospectively. Patients with a non-anatomic fracture position were operatively treated using a volar approach with volar variable angle plate. The scapholunate joint was not surgically treated in any patient. All patients underwent a postoperative Computer Tomography (CT) scan. We excluded one patient with a bilateral distal radius fracture that had a SLD of one of the fractured sides. Patients (n=14) with a scapholunate interval (SLI) of ≥3mm in the injured wrist underwent a CT scan of the contralateral wrist.
All these strains grew well and displayed the normal blue green color for more than 7 days (Fig  3 ). During this time, the hydrogenase activity of the WT and CE5 strains did not vary (data not shown). After 14 days of cultivation on urea (5 mM, sole N source) and Ni (2. 5 μM) cells reaching the stationary phase of growth (OD 750 > 5. 0) became invariably yellowish (Fig  3 ). Once the chlorosis process became visibly detectable, a large decrease in cell viability was observed by plating assay on solid standard growth medium (it contains nitrate, not urea). A similar urea-induced chlorosis was observed in the cyanobacteria Anabaena cylindrica and Synechococcus PCC7002, distantly related to Synechocystis PCC6803, when they were cultivated on urea as the sole nitrogen source  [28, 29]. The urea-mediated chlorosis and cell death were found not to occur in a urease defective mutant harboring an inactivated allele of ureC, the gene normally encoding the large urease subunit.
This study was supported by the  CEA  and  ANR  ( ANR-09-BIOE-002-01 ; project EngineeringH2cyano). Theo Veaudor and  Marcia Ortega-Ramos  received a phD fellowship from  CEA  and  ANR , respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Procedures Lifestyle factors were based on self-reported information, except for height and weight in one of the studies (Whitehall II). Smoking status included categories of current smoker and non-smoker. We defined high alcohol consumption as a weekly consumption exceeding 112 g of absolute alcohol for both men and women, according to 2016 guidelines in the UK. 24 Moderate alcohol consumption referred to a weekly consumption of more than 0 g and 112 g or fewer. Heavy episodic drinking was defined as a participant reporting having passed out at least once because of heavy drinking during the past 12 months (data available in FPS and HeSSup). BMI was categorised as lower than 18•5 kg/m² (underweight), 18•5-24•9 kg/m² (normal weight), 25•0-29•9 kg/m² (overweight), and 30•0 kg/m² or higher (obesity).
Smokers were compared with non-smokers; participants with high alcohol consumption were compared with those with moderate alcohol consumption; participants in the overweight and obesity categories were each compared with those in the normal weight category; and participants with low physical activity were compared with those with high or moderate physical activity. The minimally adjusted models included age and sex as covariates. The multi variable models were also adjusted for socioeconomic status, chronic disease, and other lifestyle factors. In subgroup analyses, we stratified the analyses by prevalent chronic disease at baseline to assess whether the associations varied by chronic disease status. Studyspecific results were analysed with SAS 9. 4 software. At the second stage, we pooled study-specific rate ratio (RR) estimates in a fixed effects meta-analysis, which is suitable for studies with few cohorts,  25  with Stata 15 software.
We did not assess smoking intensity, which might have yielded stronger associations for smokers who smoked more. 42 ecause people tend to under-report their alcohol use, some of the risky drinkers might have been wrongly assigned to the moderate drinkers group, which might have diluted the estimates. The study populations consisted of employed men and women, among whom the prevalence of severe alcohol use disorders is likely to be low, as was shown in a Finnish population-based study (5•2%). 11 oreover, the sickness absence data differed between countries. In Finland (FPS 20 and HeSSup 21 studies), diagnosis-specific sickness absence episodes are only registered for absences lasting longer than 9 days, including absences with very long duration (rehabilitation and disability allowances). In the GAZEL  23  and Whitehall II  22  cohorts, sickness absence registers also included short-term absences.
However, in GAZEL, the coverage of diagnoses in absences shorter than 7 days was about 50%. 8 Shorter absences are often caused by respiratory diseases (common cold and other acute upper respiratory infections), many of which were missed when monitoring longer absence episodes alone. In FPS, HeSSup, and GAZEL cohorts, sickness absence data included medically certified data, whereas in the Whitehall II cohort, it was a mixture of records from self-certified absences lasting 1 to 7 days and medicallycertified absences exceeding 7 days. Further differences included those related to the diagnostic systems used (International Classification of Diseases [ICD] 10 in FPS, HeSSup, and GAZEL, but ICD-8 in Whitehall II). However, in a sensitivity analysis excluding the Whitehall II and GAZEL cohorts one at a time, the results remained largely unchanged, suggesting that variation in the data was not a major source of bias.
Metformin, the first drug chosen to be tested in a clinical trial aimed to target the biology of aging per se, has been clinically exploited for decades in the absence of a complete understanding of its therapeutic targets or chemical determinants. We here outline a systematic chemoinformatics approach to computationally predict biomolecular targets of metformin. Using several structure-and ligand-based software tools and reference databases containing 1,300,000 chemical compounds and more than 9,000 binding sites protein cavities, we identified 41 putative metformin targets including several epigenetic modifiers such as the member of the H3K27me3-specific demethylase subfamily, KDM6A/UTX. AlphaScreen and AlphaLISA assays confirmed the ability of metformin to inhibit the demethylation activity of purified KDM6A/UTX enzyme.
| New possible indications and biomolecular targets for metformin New possible indications of metformin were evaluated by crossing the ligand-and structure-based metformin targets obtained after VP with DisGeNET, a database containing 429,036 associations between 17,381 genes and 15,093 diseases, disorders, and clinical or abnormal human phenotypes  (Piñero et al. , 2017). A graphical summary of the diseases in which the predicted metformin targets are involved suggested an enrichment of the most prevalent aging-related diseases, including neoplasms/carcinoma, cardiovascular, and neurodegenerative disorders (Figure  S1B ). Using the WEB-based Gene SeT AnaLysis Toolkit to statistically evaluate the DisGeNet findings, we confirmed the nonrandom and unbiased distribution of such metformin targetassociated morbidities (Table  S4 ).
Gene Ontology (GO) was extracted from UniProtKB database to provide a detailed description of the molecular functions and biological processes of the ligand-and structure-based metformin targets (Table  S5 ). We then performed a Protein ANalysis THrough Evolutionary Relationship (PANTHER) overrepresentation test using as a reference list the comprehensive GO annotations directly imported from the GO database that includes 20,972 mapped identifiers. Fourteen different classes of molecular functions were found significantly enriched in the list of metformin targets (Table  S6 ). Beyond the expected overrepresentation of molecular functions related to transmembrane transporter activity, most of the molecular functions were related to metal ion binding and peptidase activity. Our computational de-orphanization of metformin further revealed that DNA/histone-interacting proteins might be viewed as unforeseen targets for anti-aging metformin.
| Molecular dynamics calculations Short (1 ns) MD simulations were performed using NAMD version 2. 10 over the best-docked complexes, which were selected based on the interaction energy. The Ambers99SB-ILDN and the GAFF forcefield set of parameters were employed for receptors and ligands, respectively. The ligand parameters were obtained using Acpype software, whereas the receptor structures were modeled using the leap module of Amber Tools. Simulations were carried out in explicit solvent using the TIP3P water model with the imposition of periodic boundary conditions via a cubic box. Electrostatic interactions were calculated by the particle-mesh Ewald method using constant pressure and temperature conditions. Each complex was solvated with a minimum distance of 10 A from the surface of the complex to the edge of the simulation box.
, F I G U R E 2 Metformin effects on the demethylation activities of KDM4C/JMJD2C, KDM6B/JMJD3, and KDM6A/UTX. (a) A dose-response curve of KDM4C activity was created with Graphpad Prism by plotting AlphaScreen signals as the function of metformin concentration. Overall structure and views of the interaction between metformin and KDM4C (PDB code 5KR7). The coordinating residues are numbered. Metformin (MET) is placed over the bottom part of the cavity defined by 6X9 (6-ethyl-2,5-dimethyl-7-oxidanylidene-4~{H}-pyrazolo[1,5-a] pyrimidine-3-carbonitrile), the crystallographic ligand of PDB ID 5KR7. 6X9 coordinates Fe 2+ , suggesting that metformin might share a metalinteracting role. Metformin is hydrogen bonded to K243, H190, E192, S198, N292, and Y179. Metformin additionally shows a p-cation interaction with F187 as well as a hydrophobic interaction with H278.
Of note, metformin shares all the residues involved in the binding mode of 6X9 at the bottom part of the cavity. (b) Dose-response curves of KDM6B/JMJD3 (top) and KDM6A/UTX (bottom) demethylation activity were created with Graphpad Prism by plotting AlphaScreen signals as the function of metformin concentration. Overall structures and views of the interaction between metformin (red) and the crystallographic ligands AKG and NOG (blue) in the catalytic pockets of KDM6B/JMJD3 (PDB code 2XUE) and KDM6B/UTX (PDSB code 3AVS), respectively 2012). The metformin-binding mode to KDM6A/UTX employed the crystal structure 3AVS in complex with N-oxalylglycine (NOG, a nonreactive analog of AKG and Ni 2+ , which coordinates the Fe 2+ -binding site but does not support catalysis). Despite their structural similarity, metformin accommodates in a slightly different manner to NOG by occupying the bottom-left corner of the Ni 2+ -coordinated binding site (Figure 3a,b).
Virtual profilingVirtual profiling was performed with the ligand-and structure-based software tools (www. mindthebyte. com) Cabrakan (2D), Hurakan (3D), and Ixchel (3D target-based), using the chemical structure of metformin as a seed. Cabrakan is a 2D ligand-based VP tool that compares molecules through the use of 2D fingerprints and the assignment of biological activity with an internal algorithm. Hurakan is a 3D VP tool that compares a query molecule with the structures present in the reference database using CoMSIA fields on a 3D grid. Hurakan can compare molecules according to their relationship with their environment, thus obtaining biomimetic compounds with different structures. Cabrakan and Hurakan employ an in-house modified version of CHEMBL as reference database, which contains around 1,300,000 chemical compounds with detailed information including target data.
Cabrakan and Hurakan can therefore predict the biological profile of a query molecule based on 2D and 3D similarity. A target was counted once when it appeared as both 2D and 3D hit during ligand-based VP experiments. Ixchel is a structure-based VP tool that performs docking calculations of a molecule (SDF or SMILE file) against an in-house developed database comprising almost 9,000 binding sites protein cavities curated from RSCB PDB according to UniProtKB human entries. Ixchel returns the binding energy of every possible interaction, which allows the classification and prediction of the targets.
Decays of hadronic particles are described by EvtGen  [17] , in which final-state radiation is generated using Photos  [18]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit  [19]  as described in Ref. [20]. Other sources of background, such as those from b → ψ(2S) transitions, where the ψ(2S) decays radiatively to a χ cJ meson, are studied using the RapidSim fast simulation package  [21].
Mass fit The energy resolution of the LHCb calorimeter results in an invariant-mass resolution for the χ c1 and χ c2 states of about 50 MeV/c 2. This makes it difficult to separate the two states based on the J/ψ γ invariant mass alone. To improve the mass resolution, the approach used in previous LHCb analyses  [4, 6]  is followed. Two kinematic fits are made to the dataset in which constraints are applied to ensure the pointing of the candidate to the associated primary vertex, on the J/ψ mass and either on the χ c2 or χ c1 mass. Owing to the small radiative branching fraction any contribution from the B 0 s → χ c0 K + K -decay mode can be ignored. As can be seen in Fig. 4  the two components are then separable from the B 0 s invariant mass calculated from this fit. A mass model for the B 0 s → χ c1,c2 K + K - signal is developed using the simulation.
A sequence search of the predicted single large ORF encoding a polyprotein of 235kDa in the Pfam database resulted in identification of five domains, characteristic of the genus Marafivirus, family Tymoviridae. The AVF genome also contains a conserved "marafibox", a 16-nt consensus sequence present in all known marafiviruses. Phylogenetic analysis of the complete nucleotide sequences of AVF and other viruses of the family Tymoviridae grouped AVF in the same cluster with MsMV1. In addition to 5' and 3' terminal extensions, the identity of the virus was confirmed by RT-PCRs with primers derived from VANA-contigs, transmission electron microscopy with virus-infected tissues and transient expression of the viral coat protein gene using a heterologous virusbased vector. Based on the criteria demarcating species in the genus Marafivirus that include overall sequence identity less than 80% and coat protein identity less than 90%, we propose that AVF represents a distinct viral species in the genus Marafivirus, family Tymoviridae.
Virion-associated nucleic acids (VANA) metagenomics-based approach Thirty-three alfalfa samples were processed using the VANA-based 454 pyrosequencing approach exactly as described in Francois et al. [13]. PCR amplicon libraries were sequenced on an Illumina MiSeq platform as 2x300 bp paired-end reads (Beckman Coulter Cogenics, Morrisville, NC, USA). Bioinformatics analyses were performed as described in Francois et al. [13]. Briefly, read cleanups and corrections were performed using the CutAdapt version 1. 9 program (https://cutadapt. readthedocs. io/en/stable/) and de-novo assemblies were generated using SPAdes 3. 6. 2 software (http://cab. spbu. ru/software/spades/).
Nucleotide sequence and genome organization To confirm the identity of the sequenced sample as Medicago sativa species, all transcripts generated by the VANA metagenomics approach  [13]  were analyzed by BLASTn. Most of the eukaryotic transcripts with the length > 300 nucleotides (nt) and identity >90% aligned to the species in the subfamily Papilionoideae, genus Medicago thus validating the host as Medicago sativa. Assembly of the raw data reads resulted in an incomplete viral genome lacking 5' and 3'-end sequences of the mRNA, including the coat protein. After missing sequences were generated using the Takara Smarter 5'/3' RACE protocol and the ThermoFisher Scientific 3'RACE system, respectively, the full-length monopartite viral genome consisting of 6,818 nts was assembled and functionally annotated.
On the nucleotide level, AVF has 78% identity with the newly identified and proposed alfalfa marafivirus MsMV1  [11] , (BLASTN query coverage 80%, E-value = 0. 0, accession MF443260. 1). The next closest species from the genus Marafivirus, per PASC tool  [16] , is oat blue dwarf virus isolate OBDV2r (50. 2% identities, accession GU396990), a member of the genus Marafivirus. Multiple nucleotide sequence alignment using the MegaAlign tool and ClustalW algorithm of the DNAStar package (DNASTAR Inc. , WI, USA) generated even lower identity between the complete genomes of AVF and MsMV1 (75. 3%); identity with other viruses of the genus was at the 50% level or less (Fig  1 ). Therefore, based on the nucleotide sequence alignment of the compete AVF genome and criteria demarcating species in the genus Marafivirus that include overall sequence identity less than 80%  [8] , AVF represents a distinct viral species in the genus Marafivirus, family Tymoviridae.