dataesr/scientific-paragraphs-categorization huggingface dataset:
Docking studies on DNA and human serum albumin (HSA) Molecular docking is an extremely useful tool in drug discovery for understanding drug-biological target interactions and to investigate the potential binding mode and energy. In our docking study, to examine the binding of the ligand H8qasesc and complex 1 to DNA, the duplex of the sequence dĲCGCGAATTCGCG) 2 (PDB ID 3U2N)  38  was used. In silico molecular docking experiments revealed that the docked ligand H8qasesc showed absolute preference for minor groove binding (Fig. 6A ), with binding energies in the range -27. 2 to -29. 3 kJ mol -1. No intercalation positions were found. The established interactions between H8qasesc and DNA nucleotides are hydrogen bonds (Fig. 6B ). This is expected since H8qasesc consists of groups that are hydrogen bond donors (particularly NH and NH 2 groups) as well as hydrogen bond acceptors (the quinoline nitrogen atom).
Immunoblot analysis Expression level quantification of VKORC1 and VKORC1L1 proteins in microsomal fractions were determined by western blotting as described previously  [6]. Microsomal proteins were separated on 12% SDS-polyacrylamide gel electrophoresis, transferred onto Immobilon-P membranes and probed with anti-c-myc antibodies (Invitrogen, Cergy Pontoise, France). The resulting immunocomplexes were visualized using alkaline phosphataseconjugated anti-mouse immunoglobulins as secondary antibodies and a BCIP/NBT solution. Quantification of the stained bands was performed by densitometry using the Scion Image software. The relative intensity (RI) of the signal was correlated to the quantity of microsomal proteins. The relation was linear from 0 to 50 µg for microsomal proteins. To evaluate the expression level of VKORC1 and VKORC1L1 proteins, the expression of wild type human VKORC1 was designated as the basal expression.
The dry residue was immediately dissolved in 0. 2 mL of isopropanol and reaction product was analyzed by liquid chromatography-mass spectrometry. The LC-APCI/MS/MS used was a 1100 Series LC/MSD ion Trap VL with an Atmospheric Pressure Chemical Ionisation (APCI) interface and a LCMS Chemstation software from Agilent Technologies (Palo Alto, CA, USA). Chromatographic separation was performed using a SunFire reverse phase C8 column (4. 6 mm × 150 mm, 5 µm, Waters, Milford, MA, USA) with a mobile phase of methanol, 0. 1% acetic acid (95:5) in isochratic conditions. The column temperature was 45°C. The flow rate in the LC column was 1 ml/min. The injection volume was 20µl. The temperature of the autosampler tray was set to 5°C and the samples were protected from the daylight. Detection was by MS/MS with APCI source in positive mode.
Enzyme Kinetic Studies for CAPA (1) The mechanism of inhibition and the inhibition constant (Ki) of compound 1 (CAPA) was assessed by enzyme-kinetic studies. The kinetic data were used to generate the Lineweaver-Burk, Dixon and Cornish-Bowden plots (Figure  3B-D ), showing that CAPA is a competitive inhibitor of arginase  [41]. The Ki values for CAPA, CGA and nor-NOHA could be calculated on the basis of the Cheng-Prusoff equation [Ki = IC50/(1 + [S]/Km)]  [42] , with [S] = 14. 3 mM of L-arginine and the Michaelis-Menten constant of b-ARG I Km = 55. 5 ± 10. 5 µM  [34]  (Table  2 ). The in vitro affinity of CAPA was better than that of CGA (Ki 5. 5 ± 1. 0 vs. 8. 4 ± 1. 2 µM, respectively). In the last few years, several chemical groups that target the bimanganese cluster have been identified.
Enzyme Kinetic Studies for CAPA (1) The mechanism of inhibition and the inhibition constant (K i ) of compound 1 (CAPA) was assessed by enzyme-kinetic studies. The kinetic data were used to generate the Lineweaver-Burk, Dixon and Cornish-Bowden plots (Figure  3B-D ), showing that CAPA is a competitive inhibitor of arginase  [41]. The K i values for CAPA, CGA and nor-NOHA could be calculated on the basis of the Cheng-Prusoff equation [K i = IC 50 /(1 + [S]/K m )]  [42] , with [S] = 14. 3 mM of L-arginine and the Michaelis-Menten constant of b-ARG I K m = 55. 5 ± 10. 5 µM  [34]  (Table  2 ). The in vitro affinity of CAPA was better than that of CGA (K i 5. 5 ± 1. 0 vs. 8. 4 ± 1. 2 µM, respectively). In the last few years, several chemical groups that target the bimanganese cluster have been identified.
Molecular Docking Studies In order to gain further insight into the inhibitory mechanism, the binding modes of CAPA (1) and CGA were determined by docking simulations on h-ARG I (pdb id: 3kv2). The structure of b-ARG I is not available in the Protein Data Bank but present 100% homology with h-ARG I in the active site. In addition, the alignment of the b-ARG I and h-ARG I sequences using the stand-alone Java Web Start application, accessible from the PDB server (http://www. rcsb. org), revealed a high level of similarity between the two sequences (91% and 95%, respectively). A few residues were different, but not involved in the active site of the two arginases, whose residues are strictly conserved. The most active compound 1 (CAPA), CGA and nor-NOHA were successfully docked into the active pocket of h-ARG I by using a FlexX docking program implemented in LeadIt 2.
Chemistry All reagents were purchased from Sigma-Aldrich and used without further purification, except for the purified liver bovine arginase I (b-ARG I) which was from MP Biomedicals (One unit (1 U) of b-ARG I is defined by this manufacturer as the amount of enzyme that converted 1 µmole of L-arginine to urea and L-ornithine per minute at pH 9. 5 and 37 • C), and for the recombinant human arginase I (h-ARG I) (BXC572/n P0408M10) which was from Interchim (Number of units is not defined by the manufacturer and thus a preliminary study (data not shown) allowed us to choose the concentration of enzyme leading to an absorbance of about 1). Solvents (methanol, ethyl acetate, cyclohexane and dichloromethane) were supplied by Carlo Erba Reagents and VWR Chemicals companies. Reactions were monitored by thin-layer chromatography (TLC) on pre-coated silica gel aluminum plates (Macherey-Nagel) and visualized under UV light (254 and 365 nm).
Flash column chromatography was carried out using EasyVarioFlash ® DL cartridges (Merck, Fontenay-sous-Bois, France) for dry loading, eluting with CH 2 Cl 2 and MeOH. 1 H-NMR at 300 MHz and 13 C-NMR at 75 MHz (only for compounds lacking analytical data from literature) were acquired using a Bruker AC300 spectrometer (Bruker BioSpin, Wissembourg, France). Chemical shifts (δ) were reported in parts per million (ppm) relative to the residual solvent signals. Coupling constants (J) were reported in hertz (Hz). Data were presented as follows: chemical shift (δ, ppm), multiplicity (s, singlet; bs, broad singlet; d, doublet; dd, doublet of doublet; t, triplet; q, quadruplet; m, multiplet), coupling constant (J, Hz), and integration. High-resolution electrospray ionisation mass spectra (HRMS-ESI) were analyzed by using the SCA Illkirch QToF instrument.
Preparation of Target Protein The 3D crystal structure of h-ARG I complexed with co-ligand nor-NOHA (pdb id: 3kv2) was retrieved from Protein Data Bank (http://www. rcsb. org). The receptor was prepared following the standard procedure of LeadIt 2. 0. 2 software. The binding site was defined as all the amino acid residues enclosed within 16. 0 Å radius sphere centered by the co-ligand, nor-NOHA. All water molecules were removed.
Preparation of Ligands Ligands were built and prepared by using Sybyl-X 2. 0 software  [60]. Hydrogen were added to the 3D structure, which was optimized by energy minimization using Conj Grad Method with convergence criterion set at 0. 0001 kcal/(Åmol), max iterations (10,000) and with Gasteiger Hükel charge assigned in Tripos force field; other parameter values were kept at default. Then, the structures were subjected to a simulated annealing run and further optimized to obtain the lowest-energy conformers, which were stored in the database for the docking simulation.
Docking Simulation The docking simulations were performed using the FlexX docking program  [44, 61]  implemented in LeadIt 2. 0. 2 software with number of poses set at 10 for analyzing, maximum number of solutions per iteration set at 1000 and maximum number of solutions per fragmentation set at 200, other docking parameters were kept at default setting. The molecular docking technique used in this study was a flexible one in which the ligand binding process was an induced-fit process. The 3D and 2D binding modes of docking solutions were generated by using MOE 2008. 10 software [62].
This is particularly important to flies, because unlike humans they don't develop resistance to future infections with the same bacteria. Future studies are needed to determine if flies use their sense of taste to detect other chemicals that are signs of infections. Additionally, studies are needed to determine if the activated bitter-sensing nerves alert the fly's immune system to a potential infection. dTRPA1 is expressed in the mouthpart in a subset of gustatory neurons in the esophagus  (Figure 3A,B and Kang et al. , 2010)  and in a few neurons in the labellum that also express Gr66a (data not shown and  Kang et al. , 2011). However, no co-localization between dTrpA1 and Gr66a was observed in the leg (Figure  3 -figure supplement 1). To test whether avoidance of LPS is mediated by labellar or esophageal chemosensors we performed a proboscis extension reflex (PER) assay in wild type animals.
The preference index (PI) for the control-containing solutions was calculated as: (n Control -n Test )/(n Con- trol + n Test ), where n Control and n Test are the number of flies that ate the control solution (in red or blue color) and the test solution (containing control or LPS, both in red or blue color), respectively. The test solutions were colored with food dyes that showed comparable results: red (food dye or Sulforhodamine B sodium salt, 1 mg/ml) and blue (food dye or Erioglaucine disodium salt, 0. 16 mg/ ml). Preference data was always represented in box charts in which lines and the dots inside the boxes represent the median and the mean of the data, respectively.
The organs were removed with forceps and the preparation was immediately used for imaging. Intracellular Ca 2+ imaging was performed using a monochromator-based system consisting of a Polychrome V monochromator (TILL Photonics GmbH, Germany), an upright microscope (Olympus, U-TV1X-2, Japan) and a 10X water immersion objective. Images were obtained with an iXon3 888 (Andor, Germany) camera controlled by LiveAcquisition software (TILL Photonics GmbH, Germany). The bath temperature was controlled by a SC-20 dual in-line heater/cooler (Warner Instruments, USA) and an Objective HeaterÒ System (Bioptech, USA). The data were classified semi-automatically using a function programmed in MATLAB (MathWorks, MA) and analyzed with Origin 7. 0 (OriginLab Corporation, Northamptom, MA, USA).
30 software. In experiments on cells isolated from wild type flies we used Fura2 as Ca 2+ indicator. Neurons were functionally identified at the end of each experiment by their responsiveness to the application of an extracellular solution containing high K + concentration. The data were classified semi-automatically using a function programmed in MATLAB (MathWorks, MA) and analyzed with Origin 7. 0 (OriginLab Corporation, Northamptom, MA, USA).
Patch-clamp experiments Whole-cell membrane currents were measured at 23 ˚C with an EPC-10 patch-clamp amplifier and the softwares Pulse (HEKA, Lambrecht/Pfalz, Germany) and Clampex (Axon Instruments, Sunnyvale CA, US). Currents were digitally filtered at 2. 9 kHz, acquired 20 kHz and stored for off-line analysis on a personal computer. Cells were recorded in an extracellular solution containing (in mM): 140 NaCl, 5 KCl, 10 HEPES, 2 CaCl 2 , 2 MgCl 2 , 10 glucose, pH titrated to 7. 4 with NaOH. The pipette solution contained (in mM): 120 Cs-Aspartate, 5 EGTA, 10 HEPES, 1 MgCl 2 , pH titrated to 7. 4 with CsOH. Non-transfected HEK cells were used as control. Whole-cell currents were elicited using a 200 ms voltage ramp from -110 mV to +110 mV every 2 s from a holding potential of -40 mV. NMDG + (N-methyl-D-glucamine) was used to monitor the size of the leak currents during the patchclamp recordings  (Meseguer et al.
Acknowledgements We are very grateful to  Paul Garrity  for reagents and discussion of the data during the initial part of the project and to  Patrik Verstreken  for the NSyB-Gal4; UAS-GCaMP3  flies. We thank  Zeynep Okray  for her help with larval dissections,  Koenraad Philippaert  for his help with in vivo Ca 2+ imaging experiments, and members of the  laboratories of Ion Channel Research (KU Leuven) and Neurogenetics (VIB)  for helpful discussions. This work was funded by grants from the  Belgian Federal Government (Belspo ;  IUAP P7/13 ), the  Research Council of the KU Leuven  ( GOA/14/011 ,  OT/12/091  and  PF-TRPLe ), VIB and the  Research Foundation-Flanders (FWO)  ( G. 0702. 12,G. 0C77. 15 , G. 0680. 10, G. 0681. 10, G. 0503. 12, G. 0654. 15, G. 0761. 10N, G. 0596. 12  and  G. 0565.
07 ). AS and YAA hold Postdoctoral Mandates from  KU Leuven. BB is funded by a  Ph. D. grant of the Agency for Innovation by Science and Technology (IWT, Flanders, Belgium) , GL by the  FliAct Marie-Curie Initial Training Network , and LF is by a fellowship from the  VIB International Ph. D. Program. BAH is an Allen Distinguished Investigator and an  Einstein Visiting Fellow  of the  Berlin Institute of Health.
Effect estimates in each study-specific strata were pooled, and the statistical significance of differences between subgroups of potential sources of heterogeneity was assessed using meta-regression. Inameta-analysis 13 of 7 studies with available genetic data, we also examined potential interaction by variants in FADS (OMIM208150) desaturase genes (rs174546, rs968567), pooling study-specific estimates using an additive genetic model (eMethods in the Supplement). Within each study, interaction terms for each singlenucleotide polymorphism (SNP) were constructed as across-product term of the ω-3 PUFA biomarker (continuous) by the SNP (ordinal: 0,1, or 2 T alleles), including the main effects in the model; these interaction terms were pooled using meta-analysis. Meta-analyses were performed using STATA statistical software, version 12 (StataCorp).
0% and 100%, respectively). Across all studies, the median BMI was 26, and generally up to 30% of participants were current smokers, except for higher rates in the Scottish Heart Health Extended Cohort 32 (44. 6%) and Uppsala Longitudinal Study of Adult Men 35 50 (51. 0%). Alcohol intake was modest, with most participants consuming up to 1 drink per day. Fish oil supplementation was infrequently assessed across studies; use was low (0%-4% of participants) in 5 of 6 studies with these data (eTable 6 in the Supplement); only the European Prospective Investigation of Cancer (Norfolk) (EPIC-Norfolk)  21  had a higher prevalence (33%).
Conclusions Our pooled collaboration of global studies using biomarkers of ω-3 PUFAs in participants without prevalent CHD reveals that habitual consumption of seafood and plant-based ω-3 PUFAs is associated with a lower risk of fatal CHD. Cohort Study recruitment was funded by VicHealth and Cancer Council Victoria and was further supported by grants 209057, 251553, and 504711 from Australia's National Health and Medical Research Council and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The Multi-Ethnic Study of Atherosclerosis (MESA) and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with MESA investigators.
The national studies were financed by the  Dutch Ministry of Health. Ulster Cancer Foundation and Milk Intervention Board , grant  AKT76  from  Cancer Research Switzerland , grant  32-9257-87  from the  Swiss National Science Foundation , The  Spanish Fondo de Investigaciones Sanitarias and Ministry of Science and Education, and German Federal Health Office. EPIC-Norfolk  was funded by grants from  Medical Research Council and Cancer Research UK. Dr Imamura  received support from grant  MC_UU_12015/5  from the  Medical Research Council Epidemiology Unit. The Health Professionals Follow-up Study (HPFS) was supported by grants  UM1 CA167552 ,  R01 HL35464 ,  AA11181 ,  HL35464 ,  CA55075 ,  HL60712 , and  P30 DK46200  from the  National Institutes of Health. The InChianti study was supported by grant  ICS 110.
The  Nurses Health Study  was supported by research grants  UM1 CA186107 ,  R01 CA49449 ,  R01 HL034594 ,  P01CA87969 ,  R01HL034594 , and  R01HL088521  from the  National Institutes of Health. The Physician's  Health Study  was supported by grants  R21 HL088081 ,  CA-34944 ,  CA-40360 , and  CA-097193  from the  National Cancer Institute  and grants  HL-26490  and  HL-34595  from the  National Heart, Lung, and Blood Institute. The Three-City study was conducted under a partnership agreement between the  Institut National de la Santé et de la Recherche Médicale , the  University Bordeaux 2 Victor Segalen , and  Sanofi. The  Fondation pour la Recherche Médicale  funded the preparation and initiation of the study. The  Three-City study  was also supported by the  Caisse Nationale Maladie des Travailleurs Salariés ,  Direction Générale de la Santé, MGEN, Institut de la Longévité, Conseils Régionaux d'Aquitaine et Bourgogne, Fondation de France, Ministry of Research-Institut National de la Santé et de la Recherche Médicale Programme Cohortes  et collections de données biologiques, grant  COGINUT ANR-06-PNRA-005  from the  Agence Nationale de la Recherche , grant  FCS 2009-2012  from the  Fondation Plan Alzheimer , and the  Caisse Nationale pour la Solidarité et l'Autonomie.
Dr Samieri  was sponsored by a grant from the  Fondation Plan Alzheimer. The Scottish Heart Health Extended Cohort study was funded by the  Scottish Health Department Chief Scientist Organization ,  British Heart Foundation , and  FP Fleming Trust. The  Singapore Chinese Health Study  was supported by grant  NMRC 1270/2010  from the  Singapore National Medical Research Council  and grants  R01CA 144034  and  UM1 CA182876  from the  National Institutes of Health. The  Uppsala Longitudinal Studies of Adult Men 50  and  70  were funded by the  Swedish Research Council for Health, Working Life and Welfare, Uppsala City Council , and  Swedish Research Council.
4, 5 Given the diversity and complexity of intestinal mucin glycan structures, strategies for deconstructing these molecules rely on the cooperative action of a number of proteases, sulfatases, and glycosidases encoded by the genome of mucin-degrading bacteria. So far, only a limited number of bacterial species/strains from the Bacteroidetes, Firmicutes, Actinobacteria, and Verrucomicrobia phyla have been studied for their ability to consume mucins and it is becoming apparent that the strategy used by these mucin-degraders is strain and species-dependent. 5 ased on these studies, a number of glycoside hydrolases (GH) families as classified in the CAZy database (www. cazy. org)  6 have been involved in mucin-degradation. 5 These include neuraminidases/sialidases (GH33), fucosidases (GH29 and GH95), exo-and endo-b-N-acetylglucosaminidases (GH84 and GH85), b-galactosidases (GH2, GH20 and GH42), a-N-acetylglucosaminidases (GH89), endo-b1-4-galactosidases (GH98) and a-N-acetylgalactosaminidases (GH101, GH129) (www.
Comparative CAZome analysis The translated protein sequences of R. gnavus E1, ATCC 29149 and ATCC 35913 were compared to the full length sequences derived from the Carbohydrate-Active enZymes (CAZy) database (www. cazy. org)  15 sing BLAST. 16 The sequences that had an e-value >0. 1 were assigned to glycoside hydrolases (GH), glycoside transferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), and carbohydrate binding module (CBM) families using a parallel procedure involving a BLAST search against partial sequences corresponding to individual GH, GT, PL, CE and CBM modules and a HMMer search  17  using hidden Markov models built for each CAZy module family. 15 he counts for each CAZy family of each strain were then compared and the putative function of the proteins of interest was evaluated by alignment with the sequences of biochemically characterized enzymes.
A portion (120 mL) of the diluted library pool was then transferred into a 200 mL strip tube, spiked with 1% PhiX Control v3 and placed on ice before loading onto the Illumina cBot. The flow cell was clustered using Tru-Seq Single End Cluster Generation Kit v3, following the Illumina SE_amplification_Linearization_Block-ing_PrimerHyb_v8 recipe. Following the clustering procedure, the flow cell was loaded onto the Illumina HiSeq2000 instrument following the manufacturer's instructions. The sequencing chemistry used was Tru-Seq SBS Kit v3-HS using HiSeq Control Software 1. 4. 8 and RTA 1. 12. 4. 2. The library pool was run in a single lane for 100 cycles. Reads in bcl format were demultiplexed based on the 6 bp Illumina index by CASAVA 1. 8, allowing for a one base-pair mismatch per library, and converted to FASTQ format by bcl2fastq.
rRNA depletion levels were assessed following read mapping. To carry this out, rRNA genes were initially manually curated on the ATCC 29149 genome. To compare the transcript expression levels across all samples, the RNA-seq reads were mapped onto the R. gnavus ATCC 29149 genome with the open source tool Bowtie v0. 12. 9 18 using default parameters. Differentially expressed genes were identified using the DESeq2 R package  19  with raw counts normalized to the effective library size. Results were also assessed using DeSeq R package,  20  edgeR  21  and simple RPKM normalization methods to check the DESeq2 analysis. A QC check was performed using a sample correlation matrix and plotting it as a tree. The significance of differential expression was determined by the Benjamini-Hochberg  22  corrected p-values of the Wald test for the negative binomial between two conditions.
The purified product was analyzed by electrospray ionization mass spectrometry (ESI-MS) and H 1 NMR. MS spectra were acquired on Expression CMS (Advion) with ESI ionization using direct injection operated in a negative ion mode. Advion Data Express (version 2. 2. 29. 2) software package was used to evaluate the MS data. The 1 H NMR spectrum was recorded in D 2 O at 400 MHz on a Bruker Avance spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) running Topspin 3. 2 software, and was fitted with a cryoprobe. 1 H NMR spectrum was acquired with 16 scans, a spectral width of 8,223. 6 Hz, an acquisition time of 3. 98 s and a relaxation delay of 4. 0 s. The spectrum was transformed with a 0. 3-Hz line broadening, manually phased, baseline corrected and referenced by setting the water signal to 4.
on mucins, we analyzed the transcriptional activity of ATCC 29149 (Fig. 3A ) and ATCC 35913 (Fig. 3B ) by RNAseq analysis. The level of expression was compared to a reference dataset of the strains grown in minimal medium with Glc as the sole carbon source. These data confirmed the increased transcription of the GH33 sialidase gene (nanH) when ATCC 29149 was grown with mucin, previously observed by qPCR, and are in agreement with the implication of this extracellular enzyme in enabling R. gnavus ATCC 29149 to grow on mucin. 10 Similarly the gene encoding GH33 sialidase from R. gnavus ATCC 35913 was highly upregulated in presence of mucins, the highest of all GHs (Fig. 3B ). The GH98 endo-b1-4-galactosidase gene expression was also induced in presence of mucin for both strains. Other mucin-specific upregulated genes included some GH29 and GH95 fucosidases (Fig.