dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Fifteen healthy male volunteers with normal lung function tests were included in the study, but two of them did not tolerate nasogastric tube insertion. All the recordings in spontaneous non-assisted breathing were successfully performed and analyzed. Seventy-one of the 78 (91%) planned recordings under assisted ventilation could be recorded and stored. Due to poor comfort associated with the corresponding conditions, four of the 78 (5. 1%) planned sequences under assisted ventilation could not be recorded. Due to automatic switch to the backup PS mode, NAVA could not be applied in one of the participants. For technical reasons, one NAVA recording and two esophageal pressure-time curves (during nonassisted breathing) could not be saved. Finally, in two subjects, the quality of the esophageal pressure curves recorded was not good enough for offline data analyses.
, spontaneous non-assisted breathing without resistance) due to RR decrease. Tin increased, while VT remained stable. This is in line with previous data reported by Calabrese et al. [33]  except for VT, which did not significantly increase in our study in the presence of the resistance. Peak Eadi values  (23 [16-28]  μV) were higher compared to baseline in the presence of the resistance. They can be compared to peak Eadi recorded during non-assisted breathing in patients suffering from various respiratory disease. Jolley Patients for whom one or more conditions could not be recorded are represented as gray triangles. Only the subjects represented as white circles were considered for median calculations and Friedman analysis et al. [18]  reported mean peak Eadi values in stable not ventilated COPD patients at 53 ± 29 μV.
Hence the title is a misnomer. The negative correlation between development time and body size needs to be tested properly with ANCOVA, entering ALL main factors (medium, bacteria, sex, plus interactions; cf. Table  1 ), such that the remaining correlation is indeed tested WITHIN treatment combinations. Fig. 3  clearly indicates that the negative regression line largely results from the two medium treatments (red vs. blue dots; lab vs. grape). I don't think this is what the authors are saying or testing. Overall, this descriptive study is worth publishing after major revision as indicated by the reviewers and myself above. Better still, adding further treatment levels would make this work more interesting and meaningful. Additional requirements of the managing board: As indicated in the 'How does it work?' section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad (to pay) or some other institutional repository.
Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e. g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused. -Details on experimental procedures are available to readers in the text or as appendices. -Authors have no financial conflict of interest relating to the article. The article must contain a "Conflict of interest disclosure" paragraph before the reference section containing this sentence: "The authors of this preprint declare that they have no financial conflict of interest with the content of this article.
CirSeq analysis of in vitro-transcribed pgRNA CirSeq of in vitro-transcribed pgRNA of HBV genotype A was performed as previously described  (18). Briefly, fragmented pgRNA was circularized and converted to DNA by rollingcircle reverse transcription, yielding tandemly repeated cDNAs. Two replicates were prepared and 325-cycle single-end sequencing of both replicates was performed on an Illumina MiSeq at the Rockefeller University Genomics Resource Center. Tandem repeat reads were converted to consensus sequences, filtering out random errors generated during library preparation and sequencing, and then mapped to the reference. The error rate of T7 polymerase was determined as the frequency of mismatches with respect to the reference sequence for bases with average quality score >= 20. Sequences are deposited in the Sequence Read Archive, accession number XXX.
Briefly, samples were sheared, ligated to Illumina compatible adapters with unique barcodes, and sequenced from both ends on the MiSeq platform with 2x150 run parameters. DNA sequencing data analysis Amplicon sequences were mapped to the reference sequence using tophat2 with a maximum of two mismatches allowed. Mapped sequences were read using the RSamtools package  (19)  and split into codons. Codons having one or more base with quality score < 13 were ignored. For paired sample sets (one sample generated in the absence of LAM and one generated in the presence of 400 μM LAM), we performed one-sided Fisher's exact tests for each possible substitution to obtain p-values for enrichment in the presence of LAM. P-values for substitutions from multiple, independent paired sample sets were combined using weighted z-method (20), applied using the metap package  (21).
github. com/ashleyacevedo/HBVseq_sub_enrichment. Sequences are deposited in the Sequence Read Archive, accession number XXX. cccDNA is (2) transcribed by cellular Pol II to produce all viral mRNAs, which are then exported to the cytoplasm and (3) translated into viral proteins. Of these mRNAs, pgRNA, serves as the template for reverse transcription and also encodes the core and polymerase proteins. The encapsidated pgRNA (4) is reverse transcribed (5) by HBV polymerase and can either (6a) associate with the viral surface antigens, HBsAg, and be exported from the cell, or (6b) "recycle" to the nucleus and replenish cccDNA. (B) HBV genome and mRNA transcripts. Left, the highly compact, 3. 2 kb double stranded HBV DNA genome encodes multiple overlapping open reading frames (ORFs). Colored boxes indicate ORFs and arrows indicate Pol II transcriptional start sites.
Acknowledgements: We thank  Dr. Haitao Guo  ( Indiana University ) for providing the HepDE19 cell line and  Yosef Shaul  for the 1. 3x HBV plasmid. We thank  Kuanhui Xiang ,  Jessica Sonnabend ,  Alex Kaufman ,  Ankit Bhatta , and  Jonathan Pabon  for technical assistance and  Hans-Heinrich Hoffmann  for helpful discussion. We also thank the  Rockefeller University High-Throughput and Spectroscopy, Genomics , and  Flow Cytometry Resource Centers. Funding: this work was supported by the  National Institutes of Health  Grant  R01AI091707  (to C. M. R. ),  NIH  fellowship ( 5 F32DK107164 ) (to E. M. ),  NIH  fellowship ( 5 F32AI126892 ) (to A. A. ), a grant from the  Binational Science Foundation  (to A. S. and C. M. R. ) and the  German Center for infectious research (DZIF) ,  TTU Hepatitis , Project  5.
Results: After applying genome-wide false discovery rate significance thresholds, we identified 1,173 and 1,021 eQTLs in samples of OD and PPP, respectively. Among the strongest eQTLs shared between OD and PPP were CHURC1 (OD p-value¼1. 71 Â 10 -24 ; PPP p-value ¼ 3. 64 Â 10 e24 ) and PSPH (OD p-value ¼ 3. 92 Â 10 À26 ; PPP p-value ¼ 3. 64 Â 10 À24 ). We identified eQTLs in linkage-disequilibrium with GWAS loci T2D and associated traits, including TTLL6, MLX and KIF9 loci, which do not implicate the nearest gene. We found in the PPP datasets 11 eQTL genes, which were differentially expressed in T2D and two genes (CYP4V2 and TSEN2) associated with HbA1c but none in the OD samples. Conclusions: eQTL analysis of LCM islets from PPP led us to identify novel genes which had not been previously linked to islet biology and T2D.
Expression microarray RNA was analysed for gene expression profiling using the Affymetrix Human Genome U133 Plus 2. 0 Array (deposited under the accession number GSE76896), according to the manufacturer's protocol. Data from Gene Expression Omnibus (GEO) and annotation data have been imported using R package affy. The array data include 54,210 probe sets covering over 47,000 transcripts and splice variants. Expression data was normalised using the Robust Multichip Analysis (RMA) method as implemented in R package affy  [15]. Possible batch effect was corrected for using the "ComBat" approach implement in R package sva  [16] , using only the batch information. Differential expression analysis to compare non-diabetic versus T2D samples was performed using Limma (Bioconductor R package) with age and gender as covariates.
DNA extraction and genotyping A total of 203 DNA samples were extracted from either blood or pancreatic tissue and isolated using the DNeasy Blood & Tissue kit (Qiagen, Germany). All samples were genotyped using the Illumina Omni2. 5M array and run on the Illumina iScan platform at the Imperial College London Centre (Hammersmith Campus, Imperial College London, UK). Genotypes were called using the Genome-studio software. Quality-control was performed and SNPs were kept for further analysis according to the following thresholds: 1/maf >0. 05, 2/Hardy-Weinberg equilibrium >0. 001 and 3/call rate >0. 95. Prephasing was performed with ShapeIt (v2. r790) and imputation with Impute2 (v2. 3. 2) using 1,000 genomes panel (phase 3). This lead to a dataset of 1,233,520 SNPs and imputation lead to a further 7,574,416 SNPs, for a total of >8.
Bioinformatics The Database for Annotation, Visualisation and Integrated Discovery (DAVID) is a software tool that clusters proteins into functional annotations to facilitate understanding of complex datasets  [19]  (https:// david. ncifcrf. gov/). The DAVID tool was used to cluster genes to identify enriched gene ontological functions. P-values were calculated using Fisher's exact test to score the enriched gene ontology terms. In addition, the OD and PPP eQTL datasets were exported to Ingenuity Pathways Analysis (IPA) Software (Ingenuity Systems, Incorporated, California, USA; www. ingenuity. com). A core analysis was performed to identify pathways. The pathways were organised according to significance using a Fisher's exact test, with a cut-off of a elog (pvalue) < 1. 3. A z-score was used to predict downstream regulator activity (activation or inhibition) patterns.
Phosphoserine phosphatase (PSPH) catalyses the last step of serine biosynthesis from carbohydrates  [22]. Other strong eQTLs unique in either dataset were ACO1 in PPP (p ¼ 1. 47 Â 10 À25 ) and ELP5 (p-value ¼ 5. 71 Â 10 À24 ) in OD samples. Aconitase 1 (ACO1) is an essential enzyme involved in the TCA cycle and cellular iron homeostasis  [23]. ELP5 is a member of the RNA polymerase II elongator complex, which plays a role in chromatin remodelling and histone acetylation  [24]. Of the identified eQTLs in OD and PPP, 42 were transcription factors. This was determined using the TFcheckpoint, a resource for transcription factors that provides a comprehensive list of transcription factor annotations with evidence from the literature  [25]. In addition, a total of 46 in OD and 46 in PPP non-coding RNAs, including long noncoding and non-protein coding RNAs, were found to be significantly affected by nearby SNPs.
Of these, 26 were shared between the OD and PPP datasets. We compared our analysis to published eQTLs in islets from OD: one study in 89 OD identified 616 eQTLs  [11] ; another study in 118 OD found 2,341 eQTLs  [12]. While the reproducibility rate between the two studies was 43%  [12] , we found a 28% (OD) and 29% (PPP) shared genes with van de Bunt et al. [12]  and 23% (OD) and 18% (PPP) with Fadista et al. [11]. Of these, 24 genes were shared between our OD and PPP samples and those in these previous datasets, consistent in direction of effect (Table  1 ; Figure  2B ). This low number is likely due to substantial differences in RNA extraction and transcriptomic methods among these studies. For instance, we used microarrays for transcriptomic analysis, whereas the other studies relied on RNA sequencing.
Regulatory and biological interpretations of eQTL genes Consistent with these regions being regulatory, we annotated the eQTL datasets with active open chromatin maps in islets (promoters, active enhancers and CTCF regions)  [15]  and found 102 and 93 sites overlapping with open chromatin regions in OD and PPP, respectively (Figure  2C , Supplementary Tables 2e3). Of these, 15 were shared between the two datasets, with the majority being in active enhancer sites and clusters. One shared gene relevant for beta-cell biology was RPH3AL/NOC2, which has been shown to be crucial for exocytosis of insulin in pancreatic beta cells  [26]. The gene ontology (GO) tool was used to make inferences on the biological function of the eQTL genes. GO identified ontologies that relate to beta cell function included cell junction, positive regulation of GTPase activity and glutathione metabolism (Supplementary Fig.
Of these genes, AP3S2 and IGF1R were represented in both our OD and PPP datasets. AP3S2, encoding for an adaptor protein, was the only gene replicated in both our datasets in addition to reported eQTLs in islets  [11, 12]. ADCY5 is a member of the adenylate cyclase family, which has previously been shown in a knockout study to be involved in glucose stimulation and insulin secretion  [27]. Interestingly, we have previously demonstrated that SSR1 expression is enriched in human islets and isolated beta cells  [28]. In addition, for both OD and PPP datasets, we found strong associations with UBE2Z, a ubiquitin-conjugating enzyme. Although this gene has not been previously been studied for its role in beta cells, UBE2Z was also reported to be associated with coronary artery disease in different populations [29e31].
UHRF1BP1 was the only gene that was annotated to an active enhancer in pancreatic islets. We and others from the GIANT, MAGIC and ExomeBP consortia, recently identified 40 additional loci associated with T2D  [6]  and found that TPCN2, a gene encoding for a transmembrane ion channel with an effect on insulin action, also has an eQTL in both PPP and OD. TPCN2 À/À mice display improved insulin sensitivity, which is in agreement with our findings that an increased expression of TPCN2 associated with the rs72928978 reported SNP  [6]. We then assessed whether our eQTL SNPs were in linkage disequilibrium with GWAS reported SNPs for T2D and associated traits using the GWAS catalogue ((https://www. ebi. ac. uk/gwas/) and a recently published GWAS  [6]. The most significant loci in the PPP and OD  datasets are shown in Table  3.
eQTL genes differentially expressed with T2D and HbA1C levels Our eQTL analysis further identified in the PPP dataset only 21 genes (Table  4 ), which were also differentially expressed, 11 of which also consistently in their directionality. Among them was SCTR, which encodes for the secretin receptor and is the most potent regulator for bicarbonate in the pancreas. Although early studies showed that SCTR is linked to insulin release in humans (Lerner and Porte 1970), this association has not since been re-investigated. eQTLs in the mitochondrial Acid Phosphatase 6 Lysophosphatidic ACP6 have been associated with T2D and BMI  [34]. Three loci, namely KLHDC10, VAMP1, and CFLAR lie within active islet enhancer regions. Among them, however, only KLHDC10, which is involved in the activation of oxidative stress  [35] , was consistent in its directional effect between eQTL and differential expression.
It would be interesting to further investigate whether DNA methylation changes within this gene has a role in the cell biology of adult beta cells. In our comparison of GWAS loci and eQTLs, we were able to provide an up-to-date analysis of approximately 300 identified genes linked to T2D and associated traits. An intriguing finding was that long noncoding RNAs were found to be the most significantly associated with GWAS SNPs, which includes the long non-coding RNA LOC101927636 locus, which in GWAS studies was previously assigned to FREM3  [33]. This is in addition to approximately 66 non-coding RNAs identified between the OD and PPP datasets. Accumulating literature has shown that SNPs in long non-coding RNAs are associated with human diseases, including T2D, hence highlighting their role as master regulators  [41, 42].
Figure4: Summary of eQTL genes with relevance to T2D. A schematic of a representative pancreatic beta cell, with identified genes annotated to their known sub-cellular localization using gene ontology. These genes include overlapping GWAS genes (black), eQTLs in LD with GWAS loci (asterisks), eQTL genes from the PPP dataset that were differentially expressed in T2D (red) or associated with HbA1c (pink), consistent in directional effect with eQTLs. (Figure was illustrated using app. biorender. io).
ACKNOWLEDGEMENTS This study was supported by non-profit organizations and public bodies for funding of scientific research conducted in France and within the  European Union :  "Centre National de la Recherche Scientifique", "Université de Lille 2", "Institut Pasteur de Lille", "Société Francophone du Diabète", "Contrat de Plan Etat-Région", "Agence Nationale de la Recherche ",  ANR-10-LABX-46 ,  ANR EQUIPEX Ligan MP :  ANR-10-EQPX-07-01 ,  European Research Council  GEPIDIAB e 294785; the  German Center for Diabetes Research  (DZD e. V. ), which is supported by the  German Ministry for Education and Research (BMBF) ;  Innovative Medicines Initiative Joint Undertaking  (grant no. 155005 ) for IMIDIA, which received financial contributions from the  European Union 's  Seventh Framework Program  ( FP7/ 2007e2013 ); companies belonging to the  European Federation of Pharmaceutical Industries and Associations ;  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreements n  155005  (IMIDIA), n  115881  (RHAPSODY) and n 115797 (INNODIA), which include financial contributions from  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  Horizon 2020 research and innovation programme and EFPIA.
The fate and effects of copper in the environment are governed by a complex set of environmental processes that include binding to inorganic and organic ligands in water, soil, and sediments. In natural waters, these interactions can limit copper bioavailability and result in copper transport from the water column to the sediment. In the present study, data on the fate of copper added to lakes, microcosms, and mesocosms were compiled and analyzed to determine copper removal rates from the water column. Studies on copper behavior in sediment were also reviewed to assess the potential for remobilization. A previously developed, screening-level fate and transport model (tableau input coupled kinetic equilibrium transport-unit world model [TICKET-UWM]) was parameterized and applied to quantify copper removal rates and remobilization in a standardized lake setting.
Their work focused on analysis of field data, and they called for the development of a standard set of laboratory conditions to assess partitioning half-times, although no details were offered as to the nature and setup of such bench-scale tests. A logical extension of the  Skeaff et al. (2002)  approach is to consider 70% removal times (in addition to half-times) relative to 28 d because GHS degradability assessments consider this removal extent and time frame  (United Nations 2017). The goal of the present review was to assess the fate of copper added to surface water. The intrinsic chemical properties of copper were examined in the context of environmental fate. Similar to  Skeaff et al. (2002) , data on the fate of copper added to lakes, microcosms, and mesocosms were compiled and analyzed to determine copper removal rates from the water column.
Studies on removal of copper from the water column When we assembled field and laboratory studies examining the loss of copper from the water column, emphasis was placed on studies in which copper was added to the water column in a "spike" load. Data from these types of studies are most useful in the context of degradability and hazard assessment. For a spike load of a particle reactive metal such as copper, the general response is an instantaneous increase in concentration followed by a decline in concentration as copper is lost from the water column via adsorption/precipitation/sedimentation processes, diffusion into the sediment, and washout. A single field study with a continuous load of copper was assessed. For a continuous copper load into a flow-through system, the expected water column response is an increase of copper from the initial concentration to some steady-state concentration.
First-order loss fits to the data (lines in Figure  1 ) indicated that the time required for 50% removal (i. e. , half-time) was approximately 9 d for Lake Courtille and approximately 4. 1 d for Saint Germain les Belles Reservoir. The contribution of dilution to the water column half-time was assessed using the hydraulic detention time because this parameter was available for the 2 lakes. The hydraulic detention times for Lake Courtille and Saint Germain les Belles Reservoir are more than 1 yr and 0. 25 yr, respectively. The calculated half-times associated with dilution are approximately 250 d for the lake and 63 d for the reservoir. These values are considerably larger than the half-times observed from the data. This finding indicates that dilution from inflow was not a major metal removal process in these systems.
The lines are model results reproduced from Di  Toro et al. (2001b)  with and without a first-order loss process included. through bioturbation (where the action of filter-feeding and surface-scavenging organisms mixes porewater and resuspends sediment). Studies on copper remobilization were reviewed by  Skeaff et al. (2002; Supplemental Data, Table SI-1). Although some remobilization of copper was indicated in certain studies,  Skeaff et al. (2002)  concluded that the net flux of metals was generally directed toward the sediment. Data from additional studies described in the Acid-volatile sulfide oxidation, Changes in redox conditions and the effect on bioavailability, and Resuspension events sections provide more insight into the potential remobilization of copper from sediments. Metal concentrations (including copper) were assessed using bulk sediment measurements of acid-volatile sulfide (AVS) and simultaneously extracted metal (SEM).
The wileyonlinelibrary. com/ETC © 2019 The Authors   (2001b) ;  Gächter (1979) ; TICKET-UWM: present study. b Numbers in parentheses denote the 95% confidence interval of 50 or 70% removal times. This interval was developed from the 95% confidence interval of the first-order loss rate constant fit to the copper concentration data for each study. For the catfish pond dissolved copper data and Cazenovia Lake total copper data, application of a first-order model was not supported. Removal time values were interpolated from plots of fraction remaining versus time. Removal times for MELIMEX were calculated from the first-order loss rate constant of 0. 0091 day -1 determined by Di  Toro et al. (2001b). No information was provided on the uncertainty associated with this rate constant. c Removal times were determined for each nominal copper dosing concentration in the IME microcosms.
Metal sorption to SPM was described in the EUSES Model Lake simulations using 2 separate methods. For the first method, water and sediment distribution coefficient (K D ) values were specified based on empirical data from a copper partition coefficient review  (Heijerick and Van Sprang 2005). These empirical K D values are 10 4. 48 L/kg for the water and 10 4. 39 L/kg for the sediment. For the second method, separate water and sediment K D values were calculated with Windermere humic aqueous model (WHAM) VII at each time step in TICKET-UWM simulations (calculated K D values). This second method allows one to consider the impact of water chemistry and metal loading on K D and copper fate. Simulation results for both methods are shown in Figure  4. For the simulations with the empirical K D values, total and dissolved copper concentrations decreased by 50% approximately 2.