dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality assessment of included studies Assessment of the study quality was performed independently by two authors using the validated Effective Public Health Practice Project (EPHPP) quality assessment tool  [16]. This instrument covers any quantitative study design (randomized or not, controlled or not), and is based on the evaluation of six types of bias: selection bias, study design, confounders, blinding, data collection method, and withdrawals and dropouts. An overall evaluation was performed and a strong rating assigned to a study if there was no weak component score. A moderate rating was assigned if there was only one weak component score, and a weak rating if there were two or more weak component scores.
Results of long-term studies evaluating BP fluctuations and cardiovascular outcomes De Brito-Ashurst et al. [22]  studied the impact of chronic exposure (two years) to oral sodium bicarbonate on BP fluctuations, admission rates for congestive heart failure, worsening hypertension requiring more intensive therapy, and worsening oedema requiring increased use of loop diuretics in patients with chronic kidney disease. They found no significant impact of chronic exposure on any of these parameters. The study of George et al. [11]  was a nested case-control study, based on the UK Clinical Practice Research Datalink (CRPD) clinical database. The study population was comprised of 1,292,337 patients over the age of 18 years. The authors found that patients who survived a stroke, myocardial infarction, or who died of a cardiovascular condition had a higher risk of being prescribed an effervescent, soluble, or dispersible medicine (considered to be an HSCD) than patients who did not experience one of these events (odds ratio 1.
Most trials were monocentric studies, introducing a high risk of selection bias. Only one study had two inclusion centres  [18]. The last study was a nested case-control study which used the CRPD database  [11]. This database aggregates information from more than 6. 5% of the UK population, and is considered to be representative of the general UK population  [25]. Generally, confounders were poorly taken into account due to the large number of such covariates: classical cardiovascular risk factors (diabetes mellitus, hypertension, etc. ), risk factors associated with SSBP (black ethnicity, obesity, genetic variations, etc. ), and confounders associated with concomitant treatments and comorbidities. Some drugs interfere with the control of hypertension, including corticosteroids, non-steroidal anti-inflammatory drugs, immunosuppressant, and oestrogen and progestin.
In this context, the exploitation of clinical databases is a potentially powerful approach to assess the cardiovascular consequences of exposure to HSCD under real-life conditions, especially in high cardiovascular risk patients. Clinical databases typically allow the design of case-control studies with large populations, resulting in reasonable statistical power. The main challenge to such an approach is the difficulty to control for all confounders. This problem may be overcome by using relevant and validated proxy and/or instrumental variables. Another challenge is that exposure to HSCD in real life may be discontinuous, leading to misclassification of person exposed/not exposed in case-control studies using classical statistical models, such as conditional logistic regression models. The use of dynamic models, such as structural marginal models, could help to solve this problem.
Conclusion Despite numerous study limitations, this systematic review suggests that exposure to HSCD may be associated with an increase in blood pressure, with a consequently higher risk of cardiovascular events, especially strokes. As expected, this iatrogenic risk may be higher for high drug-associated sodium intake, maintained over a long period in patients with comorbidities. The present review highlights the paucity of evidence in this field of investigation. This may be critical because of the very large population of patients exposed to such drugs. Further studies, especially those based on clinical databases, are required to identify high-risk populations and risk factors under real-life conditions, requiring the use of sophisticated statistical models.
, 2013). The HMM-based algorithm Glimmer (version 3. 02)  (Majoros et al. , 2004)  was trained for gene finding using the genome of Penicillium oxalicum  (Liu et al. , 2013). Furthermore, an evidence-based method of gene finding was performed using the CodingQuarry  (Testa et al. , 2015)  software tool and the mapped mRNA-Seq reads. The output of both methods was combined into a single gene model of 14,188 genes. The CAZyme gene content of P. subrubescens, and of the compared fungal species, was determined as follows. All encoded protein models were compared using BLASTp  (Altschul et al. , 1997)  to the proteins listed in the CAZy database (www. cazy. org, Lombard et al. , 2014). All hits that gave an e-value >0. 001 were kept for further analysis. Each model with >50% identity over the entire length of an entry in CAZy was directly assigned to the same family (or families in the case of modular proteins).
Proteins with <50% identify to a protein in CAZy were manually aligned to known family members and searched for conserved features as the catalytic residues whenever known. Because a given percentage of sequence identity can result in widely different e-values (from non-significant to highly significant) depending on the length of the query sequence, no fixed e-value threshold was followed. In case of multimodular CAZymes, sequence alignments with isolated functional domains were performed  (Cantarel et al. , 2009). Related known activities information from derived from the CAZy database, crossed with literature data, were used to identify families likely involved in plant biomass degradation (Table  2 , Suppl. Table  1 ). This analysis revealed a significantly higher number of CAZy genes for P.
Most of these families are related to hemicellulose or pectin degradation, with the exception of GH32, which contains inulin degrading enzymes. This fits well with the high inulinase activity that was previously reported for P. subrubescens  (Mansouri et al. , 2013). Recently, a comparison of P. subrubescens and Aspergillus niger during growth on wheat bran and sugar beet pulp revealed the ability of P. subrubescens to produce a set of plant biomass degrading enzymes with similar efficiency in plant biomass saccharification as A. niger  (Mäkelä et al. , 2016). The CAZy analysis of the P. subrubescens genome described here supports the high potential of this species as a producer of industrial enzyme mixtures. This draft genome sequence of P. subrubescens FBCC1632/CBS132785 has been deposited at GenBank under accession number MNBE00000000.
The version described in the paper is MNBE00000000. The BioProject in GenBank is PRJNA343459. The strain is available from the HAMBI-Fungal Biotechnology Culture Collection, University of Helsinki (e-mail: fbcc@helsinki. fi) and from the CBS collection (www. cbs. knaw. nl). The genome is also available through the JGI fungal genome portal MycoCosm  (Grigoriev et al. , 2014). This portal also provides more detailed information on the genome, such as orthologous groups, signal analysis and KEGG pathways.
Acknowledgements We thank  Ataúlfo Martínez Torres ,  Xarubet Ruiz Cabrera  and  Yazmín Macotela Guzmán  for technical advice for MACS,  Dulce María Soría Lara  for TEER measurements,  Nicole Marilu Hernández-Godínez  and  Ana Patricia Juárez Mercado  for help with Trpv4 -/-mouse genotyping, and  Liora Shoshani  for providing us ARPE-19 cells. D. Arredondo Zamarripa  is a Doctoral student from the  Programa de Posgrado en Ciencias ,  Universidad Nacional Autónoma de México (UNAM)  and received fellowships from  National Council of Science and Technology of Mexico (CONACYT). We thank  E. Espino ,  M. Ramírez Romero ,  M. García ,  A. Castilla , and  E. N. Hernández Ríos  for their technical assistance, and  D. D. Pless  and  Jessica Norris  for critically editing the manuscript. This study was supported by the  UNAM  grant  IN201814  (ST), the  CONACYT  grant  247246  (ST), the  Ministère de l'Education Nationale , the  Institut National de la Santé et de la Recherche Medicale (INSERM) , and by the  Lille I University.
Measurements Clinical and laboratory data were recorded during a 5-h in-person visit at enrollment and during follow-up. They included demographics, renal diagnosis, medical history, height and weight, resting blood pressure, and medications. We collected blood and urine samples to determine levels of P K , venous CO2, HbA1c, and albumin, as well as urinary creatinine, albumin, and potassium. P K status was studied in three categories: < 4 mmol/L (low P K ), 4-5 mmol/L (normal P K ), and >5 mmol/L (high P K ). Diabetes was defined as either fasting glycemia ≥7 mmol/L or HbA1c ≥6. 5% or antidiabetic treatment. At each visit, GFR was measured by 51 Cr-EDTA renal clearance. Briefly, 1. 8-3. 5 MBq of 51 Cr-EDTA (GE Healthcare, Velizy, France) was injected intravenously as a single bolus. An hour was allowed for distribution of the tracer in the extracellular fluid, and then the average renal 51 Cr-EDTA clearance was determined for five to six consecutive 30-min clearance periods.
Outcomes The primary endpoints were ESKD, defined by dialysis start or preemptive kidney transplantation, and pre-ESKD all-cause mortality. The secondary endpoints were pre-ESKD cardiovascular (CV) mortality and all-cause death, regardless of ESKD. Events were identified either from patients' medical records or through record linkage with the national REIN (Renal Epidemiology and Information Network) registry of treated ESKD and the national death registry. All survival data were right-censored on December 31, 2013, or to the date of last visit for patients not identified in registries. Cardiovascular causes of death included ischemic heart disease, cerebrovascular disease, HF, dysrhythmia, peripheral arterial disease, sudden death, and valvular disease. Patients were followed up through December 31, 2013.
In the time-dependent analysis, medications were also updated at each visit. Finally, penalized splines were used in fully adjusted time-dependent Cox models to represent the functional relation between P K measurements and the risk of each outcome. Third, we described changes in P K status between the first and the second visit in the subpopulation of patients with at least two visits as well as changes in medication between the visits for patients with low or high P K at baseline that normalized at the second visit. Changes were tested with McNemar's test. Statistical analyses were performed with SAS version 9. 4 (SAS Institute, Cary, NC) and R version 3. 0. 2.
Acknowledgements Collaborators in the NephroTest Study Group contributing to data collection are  Emmanuel Letavernier ,  Pierre Ronco ,  Hafedh Fessi  ( Hôpital Tenon );  Eric Daugas ,  Caroline du Halgouet (Hôpital Bichat );  Renaud de La Faille ,  Christian d'Auzac ,  Gerard Maruani ,  Marion Vallet ,  Laurence Nicolet-Barousse ,  Mélanie Roland , and  Christian Jacquot  ( Hôpital Européen G. Pompidou ). We thank  Jo Ann Cahn  for revising the English of this manuscript.
454-pyrosequencing Bacterial tag-encoded FLX-titanium amplicon pyrosequencing was performed using the primers 530F (5'-GTG CCA GCM GCN GCG G) and 1100R (5'-GGGTTN CGN TCG TTG) which were used to target the 600 base-pair region of the 16S rRNA genes  [22]. Raw sequence data was screened, trimmed, de-noised, filtered, and depleted of chimeras using the QIIME (Quantitative Insights Into Microbial Ecology) open-source pipeline  [23]. Operational taxonomic units (OTU) were assigned based on at least 97% sequence similarity using QIIME and open OTU picking was used. The sequences were deposited in the Sequence Read Archive under the following accession number: SRP055383.
The OTU table for input used in PICRUSt utilized closed OTU picking from the Green Genes 13_5 data files. All samples which met a cutoff of having 1400 sequences after OTU picking was performed using the Green Genes 13_5 data files per sample were included. The results in PICRUSt are represented on three levels, similar to the way in which taxonomic lineage is utilized: level 1 being the broadest category, level 2 being slightly more specific, and level 3 being the most specific to the KEGG orthologs (KO) that make up that gene family. Linear discriminant analysis effect size (LEfSe) was used to elucidate taxa and genes associated with different periods during pre-weaning development. LEfSe is freely available online in the Galaxy workflow framework  [27, 28]. Generally, an LDA score of greater than 2.
Transcutaneous measurement of GFR (mGFR) in conscious mice For GFR measurements, mice were anaesthetized with isoflurane, and a miniaturized imager device built from two light-emitting diodes, a photodiode and a battery (Mannheim Pharma and Diagnostics, Mannheim, Germany) was mounted via double-sided adhesive tape onto the shaved animal's neck. For the duration of the recording (1 h), each animal was conscious and kept in a single cage. Before the intravenous injection of 150 mg kg -1 fluorescein isothiocyanate (FITC)-sinistrin (Mannheim Pharma and Diagnostics), the skin's background signal was recorded for 5 min. After removal of the imaging device, the data were analysed using MPD Lab software (Mannheim Pharma and Diagnostics). mGFR (μl min -1 ) was calculated from the decrease in fluorescence intensity over time (i.
Funding This work was supported by the  National Centre of Competence in Research Kidney  control of homeostasis and a  Swiss National Science Foundation  grant  31003A 156736/1  to EF,  310030 143929/1  to JL and  31003A 155959  to CAW. KU received funding within the framework of IKPP2 from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 608847.
Immunoautoradiographic labeling of VGLUT3 Immunoautoradiography experiments were performed on fresh frozen mouse brain sections (12 µm) as previously described  (Amilhon et al. , 2010; Vigneault et al. , 2015). Brain slices were incubated with VGLUT3 rabbit polyclonal antiserum (1:20,000, Synaptic Systems) and then with anti-rabbit [ 125 I]-IgG (Perkin Elmer). The sections were then washed in PBS, rapidly rinsed in water, dried and exposed to X-ray films (Biomax MR, Kodak) for five days. Standard radioactive microscales were exposed to each film to ensure that labeling densities were in the linear range. Densitometry measurements were performed with MCID TM analysis software on 4-6 sections for each region per mouse (8 mice per genotype).
Sections were observed using a SP8 gated-STED microscope (Leica Microsystems) equipped with a 592 nm depletion laser. BD HorizonTM V500 and Oregon Green 488 were excited at 470 nm and 514 nm, respectively. All acquisitions were performed using the same excitation laser power (50%). Alternatively, we compared the effect of increased laser power on the number of spots (50, 75, 100%). Images were submitted to deconvolution (Huygens software, Scientific Volume Imaging), which permits the recovery of objects that are degraded by blurring and noise. Finally, the images were analyzed using ImageJ and Adobe Photoshop. The number of VGLUT3-positive puncta per varicosity surface was quantified in each genotype  (80, 76, 76, 90, 105  varicosities per animals were quantified in 6 WT, 6 VGLUT3 A224V/+ , 6 VGLUT3 A224V/A224V , 6 VGLUT3 A224V/-and 5 VGLUT3 -/-mice, respectively).
5 Airy units to enhance signal detection. A bleaching protocol was used to prevent spontaneous recovery of venus fluorescence, as previously reported  (McAnaney et al. , 2005). Fluorescence recovery was monitored every 30 s during the first 5 min and then every 5 min for the next 70 min for FRAP and every 5 s for 10 min for Fast FRAP. The entire FRAP and Fast FRAP procedures were automated using SP5 live data mode software. Image processing was automated using ImageJ macro commands. Integrated fluorescence intensities were extracted from six bleached and eight control boutons, as well as one background area. The background signal was subtracted.
Quantitative reverse transcription-PCR analysis The expression of VGLUT3 transcript in stable BON cells was estimated by quantitative reverse transcription PCR (RT-PCR), as previously described  (Gras et al. , 2002). Nucleic acids were extracted from 65 x 10 4 BON cells (RNAeasy Mini Kit, Qiagen). Reverse transcription was performed with a SuperScript Reverse Transcriptase Kit (Life Technologies) using 2 µg of nucleic-acid extract. cDNA amplification was performed with Taq TM DNA polymerase (Sigma-Aldrich), and the following primers: 5'-ACTCTGAACATGTTTATTCCC-3' and 5'-CTTAGACTAACCACGTTGGC-3' (3 min. at 94°C followed by 30 sec. at 94°C, 30 sec. at 55°C and 40 sec. at 72°C for 40 cycles). The RT-PCR products were separated on 1% agarose gel and viewed under UV light. Intensity quantification was performed with ImageJ software (Macbiophotonics plugins).
VGLUT3 3D models were built from these alignments and from the crystallographic atomic coordinates of GlpT (PDB ID: 1PW4) using the automated comparative modeling tool MODELER 9. 0 (Discovery Studio 4. 1, Accelrys Software Inc. ). A three-dimensional model of the VGLUT3-p. A211V mutant was generated using a Build mutant protocol  (Feyfant et al. , 2007). The mutant model was minimized using the Adopted Basis Newton-Raphson (NR) algorithm, with a maximum step of 500 and a "generalized Born with Implicit Membrane (GBIM)" as an implicit solvent model. A 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) membrane of 100 x 80 angstroms was added using software from Visual Molecular Dynamic (VMD 1. 9. 2, http://www. ks. uiuc. edu/Research/vmd/vmd-1. 9. 2/). Proteins were solvated, and ions were added using the solvation and ionization package from VMD1.
Glutamate vesicular uptake and release are minimally altered by the p. A211V mutation We then wondered whether p. A211V altered the 3D structure of VGLUT3. Theoretical models (Fig. 3A-H ) were obtained using the crystal structure of the Glycerol-3-Phosphate Transporter (GlpT) from E. coli (PDB ID: 1PW4, 19% identity, 39% similarity with VGLUT3) as a template  (Almqvist et al. , 2007). As shown in Fig. 3 , in these putative 3D models, alanine 211 is strategically located on a small cytoplasmic loop (cytoplasmic loop 4, CL4) and at the top of the transporter pore. Switching a valine for an alanine in this position only minimally modified the 3D structure of the loop. In particular, in the WT isoform, A211 interacted with the N-terminal domain through cysteine 67 hydrophobic binding (Fig. 3C, D ), as well as with leucine 214 in CL4.
Yet their microbial degradation is essential for the release of fermentation products like organic acids, gases and short-chain fatty acids (SCFAs) and hence to provide benefits for the host  26. Dietary fibres are metabolized by the intestinal bacteria using a large number of degradative enzymes such as glycoside hydrolases, polysaccharide lyases and carbohydrate esterases collectively termed carbohydrate-active enzymes (CAZymes) and listed in the CAZy database (http://www. cazy. org). CAZymes are classified in different families based on amino acid sequence similarities, protein folds and hydrolytic mechanisms  27. In Bacteroidetes, CAZymes that are necessary to the digestion of a particular glycan structure are often part of Polysaccharide Utilization Loci (PULs), characterized by genes encoding the outer membrane SusCD-like proteins involved in polysaccharide binding and oligosaccharide import  [28] [29] [30].
2 ). Contig coverage ranging from 29 to 118X allowed accurate assemblies with a predicted coding DNA sequence (CDS) number ranging from 19 to 43 depending on the clone (Fig. 2 ). Predicted proteins of the eleven clones were assigned to 18 Non-supervised Orthologous Groups, named egg-NOG categories (Fig. 3  and Table  S1 ). The carbohydrate transport and metabolism [G] category is a major assignment in the radial plot. Each clone follows individually the same trend, except the clone IL_B5 (Fig. S1 ). Many proteins were assigned to the unknown function [S] category, suggesting a lack of knowledge and reliable annotations regarding these proteins, or/and missing representative categorized proteins in the eggNOG database. To evaluate the encoding enzymatic equipment of the positive clones, genetic maps of the eleven metagenomic inserts were built (Fig.
The IL_D9 and IL_C4 clones have an overlapping metagenomic sequence of 19. 6 kb, sharing SusCD-like proteins and three putative GH related to GH9 and GH31 (Fig. 4 ). This region is homologous to the predicted PUL 52 (BACUNI_04175-BACUNI_04186) of B. uniformis ATCC 8492 (Fig. 5A  and Table  S2 ) with unknown function, and includes a member of the hybrid two-component system (HTCS) for carbohydrate sensing and signal transduction. IL_C4 bears additional GH9 and CE4 sequences which could be involved in cellulose degradation and xylan deacetylation, respectively. Among the three GH9 enzymes, a family which mainly consists of endoglucanases, two sequences are more remotely related to the classical GH9 profile with only 22% amino acid (aa) identity with endoglucanases from Ruminiclostridium thermocellum (accession number CAA28255.
The GHs from families 2, 3, 5, 9, 16, 94 and 127 are directly involved in complex PCW polysaccharide breakdown; the GH13, GH31 and GH97 in disruption of starch and starch-derivatives; and the GH32 and GH91 in degradation of inulin. Whilst all of these enzymes have sequence homologs in the publicly available metagenomic databases, their actual enzymatic properties and substrate specificities remain uncharacterized.
None of these genes bear GH, encoding proteins involved in general metabolism, possibly shared by luminal and mucosa-associated bacteria from the ileum. Secondly, fifty-two non-redundant CDS encoded proteins from the eleven hit clones comprising CAZymes and other proteins involved in carbohydrate metabolism and transport, were searched in the MetaHit microbial gene catalogue, built from 1,267 fecal metagenomic samples  6. All the proteins had almost one homologous sequence, except the short IL_F5 CDS 29 protein (Table  S5 ). We thus examined the prevalence of these homologs in individuals. Two clusters of genes exhibited an extremely low prevalence in fecal samples, being detected in less than 4% (IL_C5 CDS 15 to 19 and IL_B3 CDS 1 to 5 on heat map; Fig. 7  and Table  S5 ). These genes encode GH and other uncharacterized proteins assigned to carbohydrate metabolism.
The full set of metagenomic insert sequences has been deposited in the European Nucleotide Archive (ENA) under the accession numbers LT674122-LT674132 (http://www. ebi. ac. uk/ena/data/ view/LT674122-LT674132). The taxonomic assignment of full metagenomic insert sequences was based on nucleic sequence similarity using the microbial nucleotide Basic Local Alignment and Search Tool (Blast) at NCBI facilities  58  with either representative genomes or all complete genomes, as well as by performing classic BlastN using the non-redundant nucleotide database nt (identity percentage > 97%). Coding DNA sequences (CDS) were predicted using FramePlot  59  and PROKKA 60 packages. Most of CDS were identical except that PROKKA did not detect the two incomplete CDS at the 5' extremity of the IL_C4 and IL_B3 clones.