dataesr/scientific-paragraphs-categorization huggingface dataset:
RT-qPCR The minimum information for publication of quantitative realtime PCR experiments guidelines for qPCR assay design and reporting  [33]  were applied. Reverse transcription (RT) was performed with 500 ng of total RNA using the High Capacity RNA-to-cDNA kit (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions. Reaction volume was set to 20 mL and RT was performed at 37uC for 60 min prior to a stopping step for 5 min at 95uC. Negative RT control of RNasefree water and a no-reverse transcription control (replacement of reverse transcriptase by RNase-free water) were included for assessing respectively any external contamination and the absence of DNA occurring during RNA extraction. The produced cDNAs were stored at 220uC. The sequences of target genes were obtained from the National Center for Biotechnology Information GenBank sequence database (http://www.
25 ng cDNA. Negative quantitative PCR controls of RNase-free water were included in each run for contamination assessment. The thermal cycling conditions were 94uC for 7 min, followed by 40 cycles of denaturation at 95uC for 15 s, annealing at the determined temperature for 15 s, and polymerisation at 72uC for 15 s. Opticon Monitor software (version 3. 0; Bio-Rad) was used for the quantitative analysis, and melting curve analysis was used to check the specificity of each amplicon. Threshold C q s were calculated from a baseline subtracted curve fit. Calibration curves were established for each gene from a serial two-fold dilution of a reference sample (pool of cDNA samples). Using these calibration curves, for each sample, median relative amounts of mRNA of the target genes were calculated and then normalised to that of the reference gene, RPLP0.
Statistical analysis Statistical analyses were performed using GraphPad Prism software (version 5. 0; GraphPad Software Inc. , La Jolla, CA). For the cytotoxicity assay, data were analysed using the One-sample t test with ''100'' as the theoretical mean. Means were declared significantly different from 100 at P,0. 05, and tendencies (P,0. 1) were included. For the qPCR assay, for each treatment condition the variances were firstly compared using the Fisher test; when P. 0. 1, data were analysed using the unpaired Student t test, and when P,0. 1, data were analysed using the unpaired Student t test with Wlech's correction. Differences were declared significant at P,0. 05. For immunolabelling, data were analysed using the paired Student t test. Differences were declared significant at P, 0. 05.
The 50 down-regulated genes gave another list of enriched GO terms from which only 3 biological processes (''response to calcium ion'', ''response to metal ion'', ''response to inorganic ion'') and 1 cellular component (''membrane-bounded vesicle'') were identified, no molecular function being enriched (data not shown). However, no concordance between these GO terms was found. With the SAM method on the ''filtered data'' and considering an FDR lower than 0. 01, we identified a set of 2911 differentially expressed genes between CYN treated and control samples. Among these genes, 1711 had an FC greater than 1, and 1200 had an FC less than 1. This set of genes was used to identify biological functions affected by CYN exposure using IPA software. Firstly, we focused on the biological functions considering a P-value of less than 0.
The GoMiner application and IPA software were used respectively for determine biological and molecular processes affected by CYN, and indentify key genes involved in these processes. We first observed that CYN modulated biological functions related to DNA recombination and repair. Experiments conducted previously in vitro as well as in vivo demonstrated that CYN induced DNA damage  [26, 28, [36] [37] [38] [39] [40]. Using a targeted approach in human lymphocytes and HepG2 cells, the expression of some genes associated with growth arrest (GADD45A, CDKN1A, UHRF1) and DNA repair processes (ERCC4, RAD51 and BRCA2) was modified following CYN exposure  [40] [41] [42]. Although the expressions of GADD45A, ERCC4, RAD51 and BRCA2 were not significantly modulated in our experiment, data revealed down-regulation of CDKN1A and UHRF1, and expression modulation of other genes coding for proteins involved in damaged DNA repair, such as APTX and PMS2 (up-regulated), and BRCA1 (down-regulated).
Introduction: The combination antiretroviral therapy (cART) has led to decreased opportunistic infections and hospital admissions in human immunodeficiency virus (HIV)-infected patients, but the intensive care unit (ICU) admission rate remains constant (or even increased in some instances) during the cART era. Hepatitis C virus (HCV) infection is associated with an increased risk for hospital admission and/or mortality (particularly those related to severe liver disease) compared with the general population. The aim of this study was to assess the mortality among HIV-infected patients in ICU, and to evaluate the impact of HIV/HCV coinfection and severe sepsis on ICU mortality. Methods: We carried out a retrospective study based on patients admitted to ICU who were recorded in the Minimum Basic Data Set (2005 through 2010) in Spain.
Study design and data source We carried out a retrospective cohort study of all consecutive HIV-infected patients older than 18 years who were admitted to the ICU in Spanish hospitals from January 1, 2005, to December 31, 2010. We also selected a control group of HIV-uninfected patients admitted to the ICU in a proportion of 4:1 regarding to HIV-infected patients involved in the study. This method increases the statistical power and the accuracy of statistical tests  [24] [25] [26]  and is especially useful with a limited number of events. The control group (selective "cohort") was obtained by random selection among the patients older than 18 years with negative results for HIV, HCV, and HBV testing. To avoid confounding factors, patients were matched for age, gender, trauma, and surgical conditions.
More specifically, the selection of the control patients was performed by frequency  [27] , quartiles of age of HIV-infected patients, and assuming an approximate percentage of men and conditions influencing health status (surgical conditions and trauma). Data were obtained from the records of the Minimum Basic Data Set (MBDS) of the National Surveillance System for Hospital Data in Spain, provided by the Spanish Ministry of Health. The MBDS is a clinical and administrative database containing information obtained and recorded at time of hospital discharge, with an estimated coverage of 97. 7% and 25% of total hospital admissions to public and private hospitals, respectively  [28]. The MBDS provides the encrypted patient identification number, sex, date of birth, dates of hospital admission and discharge, medical institutions providing the services, the diagnosis and procedure codes according to the International Classification of Diseases, 9th ed, Clinical Modification (ICD-9-CM), and outcome at discharge  [29].
The data were treated with full confidentiality, according to Spanish legislation. MBDS is a nonpersonal data extract that is mandatory for hospitals to guarantee the epidemiologic knowledge necessary for driving national health system management. This database is regulated by an organic law that explains how institutions have to proceed with health-related personal data. In this setting, consent is not required because personal data are collected for the exercise of the functions proper to public administrations. The Spanish Ministry of Health confirmed that our study fulfilled all ethical considerations, according to Spanish legislation. The study was approved by the Research Ethics Committee (Comité de Ética de la Investigación y de Bienestar Animal) of the Instituto de Salud Carlos III (Madrid, Spain).
Statistical analysis Overall, results are presented as the median and interquartile range for continuous variables, and frequencies and percentages for categoric data. HIV-infected patients and HIV-uninfected patients were matched by frequencies, which allowed the use of statistical tests for independent groups. Categoric data and proportions were analyzed by using the χ2 test or Fisher Exact test, as required. A t test or Mann-Whitney U test was used to compare continuous variables. All tests were two-tailed with P values <0. 05 considered significant. The cumulative mortality rate at days 7, 30, and 90 in patients with ICU admission was calculated without considering censoring. This rate was estimated by dividing the number of deaths by the number of patients admitted to the ICU. Log-linear modeling for contingency tables was used to estimate main and interaction (moderator) effects independently.
This semiparametric bayesian model provides the survival probabilities and the hazard ratio (HR). When interpreting results on the basis of calculated survival probabilities, we took the presence of competing risks (severe sepsis and death) into account to prevent biased results  [33]. All analyses were performed by using R statistical package version 3. 0. 2 (GNU General Public License; available at  [34] ) and BayesX software version 2. 1 (GNU General Public License; available at  [35] ).
In our study, the time to death or discharge was calculated from hospital rather than ICU admission because the date of ICU admission was not recorded in the MBDS. An immortal time bias could have occurred  [60, 61] , because some patients may have been admitted directly to ICU, whereas other patients may have survived between hospital and ICU admission, and this time period was included in the observation time. However, this theoretic bias should be well balanced in all groups, and only affect the basic value of survival time. Another limitation due to the use of administrative databases is the inaccuracy in differentiating etiologies of diseases and the reporting of organ dysfunction, engendering confusion bias. In this context, grouping of ICD-9-CM codes into comorbidities, organ dysfunction, and site of infection (Additional file 1: Appendices 1 through 6) may have represented the best approach to solve this issue.
Health-related quality of life study One year after allo-HSCT, 46 patients were alive and disease-free. We received 106 of the expected 184 questionnaires from these patients (58% reply rate). The descriptive analysis of the HRQL data revealed that the lowest functioning scores were experienced one month after allo-HSCT. Similar results were observed for the symptom scores. Thereafter, the level of symptoms decreased and levels of functioning increased and showed trends toward returning to day -7 before allo-HSCT levels. Online Supplementary Figure  S2  presents results for three functioning scales (physical functioning, cognitive functioning, and quality of life) and one symptom (fatigue). We performed a descriptive analysis of patients' answers based on patients completing both the first and last selfreport questionnaires (n=16).
Alatrash et al. recently reported 79 AML or MDS patients with a median age of 58 years (range 55-76) undergoing allo-HSCT prepared with 4-day intravenous Bu. 18  When analyzing only the data from patients transplanted in CR1, they reported a 2year OS and PFS of 71% and 68%, respectively, which is comparable to our results. In addition to classical transplant outcomes, this clinical trial prospectively measured the HRQL in disease-free patients, which is a major issue, and one that is not frequently reported. Only 58% of the patients answered the HRQL study adequately. This rather low rate may be due to the age of the patient population studied; older patients are usually more reluctant to complete self-report questionnaires than younger patients. 19 Due to the small sample size, one can argue that the statistical analyses used could not reflect the real population scores.
Acknowledgments We thank the nursing staff under the supervision of  L Caymaris  for providing excellent care for our patients, and the following physicians for their dedicated patient care:  C Oudin ,  R Crocchiolo ,  A Granata  and  C Faucher. We thank  A Boyer-Chammard ,  MD , project leader in the clinical trial office for managing this study. We also thank the  "Association pour la Recherche contre le Cancer ".
8. 3  [25]. The whole dataset was first normalized using the normalization function of DESeq and dispersion was estimated with default parameters. The statistical test was then applied on pairs of strains. Resulting p-values were adjusted for multiple comparisons according to the BH method  [26]. Two significance thresholds (0. 05 and 0. 001) were applied on adjusted p-values in order to declare genes as differentially expressed. The mapped reads were formatted into graph files for visualization using COV2HTML  [27]  (https://mmonot. eu/COV2HTML) and GBrowse (http:// genopole. pasteur. fr/gbrowse/).
Quantitative Real-time PCR Quantitative real-time PCR was performed to verify the transcriptomic data using Applied Biosystems 7300 Real-Time PCR system. Total RNA was extracted from cells grown to stationary phase in LB as described above. The RNA (1 mg) was reverse-transcribed 2 hours at 37uC in 50 ml of reverse transcriptase buffer in the presence of 2 mM dNTPs, 1 ml of random hexamers (1 mg/ml p(dN) 6 , Roche) and 10 U of avian myeloblastosis virus (AMV) reverse transcriptase (Promega) and 40 U of recombinant ribonuclease inhibitor (Rnasin, Promega). The Relative expression level and s S -dependency of s S -dependent genes (p,0. 001). The x axis shows reads counts in the wild-type strain VF7969 normalized to the lenght of the gene. The y axis shows the fold change in the expression levels of the gene in the DrpoS strain VF9356 compared to the wild-type strain (as reported in Dataset S2).
Red and green dots represent genes negatively and positively controlled by s S respectively. (B) Functional categories of s S -controlled genes (p,0. 001). Genes controlled by s S are grouped according to their functional categories in the COG database (detailed COG assignments are given in Dataset S2). The relative occurrence of genes belonging to each category in the set of genes positively controlled by s S (left pie chart) and negatively controlled by s S (right pie chart) is shown. Some of the genes do not currently have a COG functional category assignment (here represented as not in COGs). Note that some genes have multiple COG category assignments (Dataset S2). doi:10. 1371/journal. pone. 0096918. g001 relative amounts of target mRNA were determined by real-time PCR using the Fast Start Universal SYBR Green Master following the manufacturer's instructions  (Roche).
A final dissociation curve analysis step from 60uC to 95uC was performed to confirm the amplification specificity. To check whether contaminating chromosomal DNA was present, each sample was tested in control reactions that did not contain reverse transcriptase. The real-time PCR was performed using 200 nM gene-specific primer pairs (Table  S2 ) designed in silico using Primer3 software (http:// primer3. ut. ee) to generate amplicons in the 100-150 bp range. A relative standard curve experiment using a ten-fold dilution series of genomic DNA was performed for each primer pair to determine the amplification efficiency. The efficiency of the amplification for all the genes tested was higher than 1. 8. Three biological replicates were analysed in duplicate each. rpoZ was used as reference gene as it displays little variation in the transcriptional studies performed in our lab using wild-type and DrpoS strains.
Global Gene Expression in Wild-type and DrpoS Salmonella Strains To assess the relative impact of s S at a global level, transcript levels of wild-type and DrpoS strains of Salmonella were measured by directional RNA-sequencing using three biological replicates of strains grown to stationary phase in LB (GEO GSE46380). s S is known to accumulate during entry to stationary phase in rich medium and to reach its maximum level of production in late stationary phase  [14]. Consistently, in a previous study using RNA sequencing and chromatin immunoprecipitation methods to evaluate transcription in S. Typhimurium in rich medium, s 70 was the main s factor at early stationary phase  [28] , suggesting that most s S -regulated genes are expressed in late stationary phase. We thus isolated total RNA from cells in late stationary phase.
We identified a total of 1071 genes differentially expressed in the wild-type strain and the DrpoS mutant (p,0. 05), of which 607 were highly significant (p,0. 001) (Dataset S1 and Figure  1 ). In general, genes up-regulated in the DrpoS mutant (145 genes, p, 0. 001) exhibited lower expression levels and fold-change values than down-regulated genes (462 genes, p,0. 001) (Figure  1A ). Some s S -dependent genes are likely directly regulated through binding of s S to promoters while others are likely regulated indirectly by s S. Physiological Functions of the s S Network in S. Typhimurium Among ATCC14028 genes that have been assigned to a category of orthologous genes (COG), the most prominent categories associated with s S regulation were metabolism, transcription, signal transduction mechanisms and membrane biogenesis and unknown functions (see detailed COG assignments in Dataset S2 and an overview in Figure  1B ).
s S had a substantial effect on expression of metabolic genes, primarily for energy production/conversion and transport/ metabolism of carbohydrates, amino acids and inorganic ions (Dataset S2). In some cases, most genes in a given pathway are controlled by s S , suggesting a role for this pathway in stationary phase physiology (see for instance pathways shown schematically in Figure  2  and Figures  S1-S2 , with genes activated and downregulated by s S in green and red, respectively). A number of genes involved in central energy metabolism exhibited positive s S control (phosphotransferase systems, glycolysis, the pentose phosphate pathway, mixed acid fermentation, and acetate metabolism) whereas genes encoding enzymes in the tricarboxylic acid (TCA) cycle and the operons encoding NADH dehydrogenase-I (nuo) and ATP synthase (atp) were down-regulated by s S (Dataset S2 and Figure  S1 ).
s S might thus play a role in transition from aerobic respiration towards more fermentative and/or anaerobic respiratory energy metabolism in stationary phase Salmonella. Since s S controls, either positively or negatively, a large number of genes, and it is required for complex phenotypes such as multiple stress resistance and biofilm formation, it is difficult to pinpoint specific genes directly involved in a particular physiological function of s S. However, it is likely that several s S -controlled genes contribute to prevent or repair oxidative damage. First, s S controls antioxydant pathways involving catalases, superoxide dismutase, glutaredoxins and glutathione-S-transferases (Dataset S2). In addition, s S activates genes encoding ferritins/ferrochelatase and Fe-S repair proteins (including dps, bfr, hemH, sufABCDSE, Dataset S2) and controls genes coding for manganese/iron acquisition functions (including activation of mntH, sitABCD, iroBCN and down-regulation of feoB, fepEC, Dataset S2).
These findings suggest that s S controls iron use and the concentration of free iron in the cell. This strategy would be consistent with the role of s S in preventing oxidative stress since iron can promote the formation of reactive oxygen species (ROS) during aerobic metabolism  [32]. In addition to activating the suf genes encoding an alternative system for iron-sulfur clusters assembly, s S downregulated the hscBA genes (Dataset S2) encoding chaperones with central roles in assembly of iron-sulfur clusters mediated by the housekeeping Isc system  [33]. This suggests that stationary phase cells relay upon the alternative Suf machinery, rather than the housekeeping Isc system, for Fe-S cluster assembly, an hypothesis consistent with the findings that Suf is more resistant to oxidation than Isc and is functional under iron-limiting conditions  [33].
s S appears to trigger switching between certain isozymes (see differential regulation of talA/talB, tktA/tktB, acnA/acnB, pykF/pykA, fumA/fumB/fumB, sodA/sodB and nrdAB/nrdEF Figure  S1  and Dataset S2). s S can modulate expression of isoenzymes more functional during stationary phase and/or that display key features increasing their activity or stability in these conditions. For instance, selection of isozymes resistant to oxidation and iron depletion  [34]  and replacement of iron-containing enzymes with iron-independent isoenzymes  [35]  may be key characteristics of the stationary phase physiology, consistent with the effect of s S in metal acquisition function mentioned above. s S controlled carbon storage genes for production and degradation of glycogen and the osmoprotectant trehalose (Figure  S2 )  [3]  and seems to play an important role in the transport and utilization of amino acids such as L-arginine (Figure  2 ).
Indeed, the levels of carbonylated proteins increased in stationary phase E. coli strains lacking s S likely as a result of increased endogeneous oxidative stress  [38]. In addition, whereas the s S -mediated induction of genes encoding proteases (including htrA, clpX, ptrB, yggB, tldD, hslV, Dataset S2) may favor the recycling in stationary phase of mis-folded proteins as nutrients, a low level of expression of these genes in the DrpoS mutant may contribute to accumulation of damaged proteins. Altogether, these data on the s S -dependent Salmonella transcription are in general agreement with previous studies in E. coli K-12  [3, [6] [7] [8] [9]. One major exception concerns the expression of genes associated with motility. Genes encoding the flagellar sigma factor FliA, flagellar proteins and motor components are downregulated by s S in E.
coli K-12  [3, 7]. In contrast, the flagellin genes fliC, and to a lesser extent fljB, were positively controlled by s S , even though transcription of the flhDC genes encoding the master regulator of flagellar synthesis was slightly up-regulated in the Salmonella DrpoS strain. In addition, the Salmonella DrpoS mutant showed a decrease in motility compared to the wild-type strain (Dataset S2, Figure  3 ). Given the complexity of regulatory controls affecting motility  [3] , it is not clear whether the positive regulation of fliC accounts for the effect of s S on motility or whether s S acts through other ways as well. s S also positively regulated flagellar gene expression in another strain of S. Typhimurium, SL1344, and in other pathogens such as Vibrio, Legionella and Pseudomonas  [39].
Negative Regulation by s S and Bacterial Fitness Whereas s S has a positive effect on a large number of genes that likely contribute to stress resistance, it has also a negative effect on the expression of several genes under the control of other sigma factors (Dataset S2)  [3, 7, 12, 40]. These negative effects of s S on gene expression likely drive the selection of non-functional rpoS alleles in environments with no stress, where reduced s S activity confers a growth advantage  [3, [11] [12] [13] [14] [15] 41]. These observations have led to the proposal that the acquisition of stress resistance mediated by s S comes at the expense of growth capabilities as a consequence of a regulatory antagonism between s S and other s, mainly s 70  [3, 41, 42]. The implications of negative regulation by s S in the stationary phase physiology have not been studied in detail.