dataesr/scientific-paragraphs-categorization huggingface dataset:
(D) A control competition assay showing similar fitness of the two strains ATCC14028 and VF7969 is consistent with our previous data  [14]. Equal cell numbers of the wild-type strain ATCC14028 and the mutant strain were mixed in LB medium to give a total of about 3000 cells ml-1 (time 0) and the mixtures were incubated at 37uC with shaking. Aliquots of bacteria were removed at timed intervals and numbers of viable cells of each strain were determined. Cells number of each strain is reported as a percentage of the total number of viable cells in the culture. The error bars represent standard deviations for three independent measurements. * and **, statistically significant competitive disadvantage of the mutant compared to the wild-type (*p,0. 05, **p,0. 0005). doi:10. 1371/journal. pone. 0096918.
g006 might contribute to bacterial fitness. s S controls mutagenesis induced by subinhibitory concentrations of antibiotics via the down-regulation of mutS, a gene involved in mismatch-repair  [46]. It is tempting to speculate that negative regulation of mutS expression by s S (Dataset S2) might contribute to the appearance of antibiotic resistant mutants, and consequently the survival of bacterial populations in environments containing antibiotics. Down-regulation of porins (for example encoded by ompC, ompF, ompD/nmpC and ompW, Dataset S2) might also confer resistance to antibiotics and other toxic compounds and bacteriophages  [47]. More generally, s S controls genes encoding membrane proteins and transporters, especially those belonging to the ATP-Binding Cassette transporter family, suggesting altered membrane composition and traffic in stationary phase (Dataset S2).
Hierarchical Regulation and Regulatory Loops in the s S -Network and Interplay with other Global Regulators Because the number of sigma factors exceeds that of the core RNAP, sigma factors compete for binding to the core RNAP available in the cell  [2, 3]. Many genes down-regulated by s S (Dataset S2) are transcribed in Salmonella from promoters showing characteristics of s 70 -dependent promoters  [28, 50]. Negative control by s S is likely in part an indirect effect. According to the current model of negative regulation by s S invoking s competition  [3, 40, 42] , s 70 -dependent genes are up-regulated in the absence of s S because they are expressed from promoters that are sensitive to the increase in the cellular concentration of Es 70 that might result from a lack of competition between s S and s 70 for E binding.
However, global regulation by s S may also involve intermediate regulators in the s S network, including repressor molecules. As a first step to explore indirect regulation by s S , the possible regulatory functions of the s S -controlled downstream network were examined. s S affected the transcript levels of numerous genes encoding known or putative signal transducing and/or DNAbinding proteins (Figure  1B  and COGs T and K, Dataset S2), suggesting that s S controls the transcription of many secondary transcription factors. Genes for global regulators (csrA, soxS, arcA and to a lesser extent ompR), abundant nucleoid-associated proteins (ihfAB, cbpA, hupAB and to a lesser extent stpA), and modulators of RNAP activity (dksA and to a lesser extent greA, nusG) appeared differentially expressed in the wild-type and DrpoS strains (Dataset S2).
s S -dependent transcription of cbpA, ifhAB and csrA has been reported in E. coli K-12  [51] [52] [53] [54]  and arcA appeared slightly downregulated in a E. coli DrpoS mutant  [8]. Consistent with the RNAseq data, quantitative reverse transcriptase-polymerase chain reaction showed that transcription of the regulatory genes csrA, dksA, ihfA and ihfB is positively controlled by s S whereas the hupA and hupB genes are down-regulated by s S (Figure  4 , Dataset S2). Also, the clpX gene, encoding a subunit of the ATP-dependent complex ClpXP protease, involved in proteolysis of many proteins including s S [2,3] is activated by s S (Figure  4 , Dataset S2). Although these data showed that transcripts levels for csrA, dksA, ihfAB, hupAB and clpX are modulated by s S in late stationary phase in Salmonella, additional experiments are required to determine whether wild-type and DrpoS cells differ in the global activity of the corresponding gene products.
Indeed, other factors capable of differentially influencing protein levels and activity of these regulators might compensate for the observed variations in their transcript levels in the absence of s S. As an example, interesting regulatory antagonisms were observed. csrA, encoding a post-transcriptional global regulator, and the small RNA CsrC are both positively controlled by s S (dataset S2, Figures  4 5 and see paragraph below). CsrA acts mostly negatively by binding and destabilizing mRNAs  [53] [54] [55] [56]. CsrC binds and sequesters CsrA, thereby inhibiting its activity  [53, 57]. The other components of the Csr system include the sRNA CsrB, that also binds and sequesters CsrA, and CsrD, a protein that participates in degradation of CsrC and CsrB  [53, 56]. In the conditions used, csrB and csrD were detected to low and similar levels in wild-type and DrpoS strains (data not shown).
The regulation of expression of the Csr system is complex  [53, 54]. CsrA indirectly activates its own transcription while repressing its own translation and also controls production of CsrC/CsrB/ CsrD. Transcription of csrA in E. coli is controlled by several promoters, two of which are s S -dependent  [54]. It is conceivable that s S modulates the fine-tuned balance of the Csr system and uses this system to indirectly regulate target genes at the posttranscriptional level. Also, transcript levels for s E and for its antisigma factor RseA and its coantisigma factor RseB, encoded by the same operon, are all reduced in the DrpoS strain, compared to the wild-type (3. 5 fold p,0. 001 for rseA and about 2 fold p, 0. 05 for rpoE and rseB, Dataset S2). Since s E has a positive effect on s S expression in stationary phase  [10] , a possible control of s E expression and/or activity by s S would not be unexpected.
s S may have several self-regulatory circuits by controlling the expression of numerous genes that modulate its expression  [2, 3] , for instance, clpX, rssB/hnr, arcA, hupAB, dksA, and dsrA (p,0. 001, Dataset S2). Since these regulators may work either cooperatively or independently, or even have opposing effects on s S expression  [2, 3] , s S self regulatory control may be important for maintaining a proper level of s S and for integration of signal inputs. Future experiments will assess whether these changes in expression of global regulators at the transcriptome level are transferred at the functional level and whether some of the s S -controlled secondary regulators are intermediate regulators in regulatory cascades and/ or contribute to Es S -mediated regulation in feedforward regulatory loops.
Besides the control of regulatory proteins, control of metabolic/ signaling enzymes by s S might lead to variations in levels of signalling molecules and affect protein modifications and/or gene expression at different levels. For instance, genes involved in the metabolism of the second messenger C-di-GMP  [3, 55, 58]  showed differential transcript levels in the wild-type and DrpoS strains (yaiC, ydiV, yegE, STM14_2408, STM14_2209, STM14_5555, STM14_4086, STM14_2047, Dataset S2). Also, putrescine affects global gene expression  [37]  and the control of its intracellular levels (Figure  2 ) might be a mechanism of indirect gene regulation by s S. Since s S plays a central role in metabolism, it may affect the levels of intermediate metabolites with signalling functions such as CoA derivatives, NADH/NADPH, glutamate, acetate and acetyl-phosphate.
The targets of most of these sRNA are unknown so far but some of them have pleiotropic effects in outer membrane protein synthesis, metabolism remodeling, motility and biofilm formation  [28, [59] [60] [61]. For instance, RyhB-1/RyhB-2, OmrA, MicA and SdsR down-regulate expression of many genes  [59] [60] [61] [64] [65] [66] [67] [68]  including genes negatively controlled by s S (sodB, sdh, acnB, ompD, mutS, Dataset S2). In E. coli RyhB inhibits the production of iron-storage and iron-using proteins during growth under iron-limiting conditions and it has been proposed that this regulation enables iron sparing for essential pathways  [68]. Experiments are underway to assess to which extent RyhB sRNAs might play a role in s S -dependent modulation of iron use in late stationary phase. During the preparation of this manuscript, SraL was shown to be controlled by s S and to down-regulate the expression of a chaperone encoded by the tig gene and involved in protein folding  [63].
Under the conditions used in our study, the s S -dependent control of SraL (Figure  5 ) did not significantly affect tig transcripts levels or this effect was masked by compensatory regulations in the network. A few sRNAs appeared down-regulated by s S (Table  1 ). Among those displaying the highest fold change in their expression levels between wild-type and DrpoS strains, RydC activates translation of the cfa mRNA produced from a s 70 -dependent promoter  [69]. cfa encodes a cyclopropane fatty acid synthase which modifies phospholipids and contributes to the stability of the bacterial membrane and acid resistance  [3, 69]. In stationary phase, s S activates expression of the cfa gene (Dataset S2)  [3]  and the s S -dependent promoter yields a shorter isoform of the cfa mRNA, insensitive to RydC regulation  [69].
Thus, in stationary phase, when cfa transcription relies on s S , RydC might be dispensible and possibly detrimental for expression of other stationary phase genes and its expression might be downregulated accordingly by s S. Alternatively, RydC might be up-regulated in the DrpoS strain to activate synthesis of cyclopropane fatty acid synthase and compensate for the absence of s S -induction of cfa transcription (i. e. RydC-mediated activation of cfa might be a backup mechanism). s S -dependent sRNAs might also target proteins instead of mRNAs  [59]. As mentioned above the finding that csrC and csrA were both positively controlled by s S (Figures  4 5 , Dataset S2) reveals an interesting regulatory antagonism and suggests that s S modulates the fine-tuned balance of the Csr system in late stationary phase.
Supporting Information Figure  S1  Central metabolic pathways controlled by s S in LB stationary phase cultures of Salmonella. Central metabolic pathways, including glycolysis and gluconeogenesis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, acetate and pyruvate metabolism are shown schematically. To assess the contribution of s S in the expression of the metabolic pathways indicated, genes differentially expressed with a p value of less than 0. 05 in the wild-type and DrpoS strains of Salmonella were considered (Dataset S2). Genes showing differential expression with p,0. 001 are indicated in bold face. Genes in red and green were negatively and positively controlled by s S respectively. Genes in black did not show differential expression in the wild-type and DrpoS strains.
Figure1. Directional RNAseq data analyses. (A) 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.
The annotated sRNA genes are indicated as grey arrows and open-reading frames annotated in ATCC14028 are shown as blue arrows. Openreading frames overlapping the sRNAs are small putative CDS of unknown function annotated in ATCC14028. STnc2080 is located upstream of STM14_3200, indicated in red, a gene directing the synthesis of a tRNA-Arg and activated by s S (Dataset S2). SdsR is complementary to a portion of the sRNA transcribed from the opposite strand, SraC. The scale for read counts on the y axis was 1-250 for STnc1110 and sRNA10, 1-1000 for OmrA and MicA, 1-2500 for STnc750 and RyhB-1, 1-5000 for STnc2080 and RyhB-2, 1-10000 for SraL, SdsR and CsrC. doi:10. 1371/journal. pone. 0096918. g005.
Acknowledgments We thank  Anthony Pugsley  and all members of the laboratory for their kind support. We are very grateful to  Stephen Lory  and  Bertil Gummesson  for helpful comments and to  Emilie Camiade  and  Nara Figueroa-Bossi  for their advices on q-PCR and handling sRNAs, respectively. We acknowledge persons from the Microscope platform for the equipment and analyses made available and  Emmanuel Quevillon  for his help in RNAseq analyses using GBrowse.
Methods Mind-controlled optogenetic components. Comprehensive design and construction details for all expression vectors are provided in Supplementary Table  1. The integrity of all relevant genetic components was confirmed by sequencing (Microsynth, Balgach, Switzerland). The key plasmids used were as follows: pSO3, which contains SEAP under the control of an optimized hIFN-b promoter (P IFN(ACD þ ) -SEAP-pA; GenBank ID: KM591199); pSO4 that encodes the constitutive expression of a human codon-optimized PDE domain-deficient NIR lightactivated DGCL derived from Rhodobacter sphaeroides BphG1 (P hCMV -DGCL-pA; N-terminal PAS-GAF-PHY-GGDEF portion of BphG1 (Q8VRN4_RHOSH), catalytic DGCL domain GGDEF photoactivated by its cognate PAS-GAF-PHY phytochrome; GenBank ID: (Genbank ID: KM591197)); and pSTING that mediates constitutive expression of mouse STING.
The collected data sets are transmitted via Bluetooth (raw data, ms À 1 ; processed data, s À 1 ) for storage, display on a screen or control of the optogenetic device and wireless-powered implant (see below). For all mind-control experiments, the subjects sat in a comfortable chair in front of an LCD computer screen wearing the BCI headset and keeping the eyes open at all times. The LCD screen was controlled by a Laptop computer connected to the headset through a bluetooth serial connection. The subjects were verbally instructed to generate three different mental states: biofeedback, concentration and meditation. To generate the biofeedback mental state, the subject was asked to watch the meditation-meter values displayed on a screen and self-train to keep the meditation-meter values above and below a desired threshold.
Acknowledgements We thank  Christian Zschokke  and  Giovanni Nisato  at the  Centre Suisse d'Electronique et Microtechnique (CSEM)  for generous advice,  Mark Gomelsky  for providing the DGCL sequence before publication,  Volkhard Kaever  for providing the c-di-GMP HPLC/MS analysis,  Patrice Del Carmine  for assistance with the mouse experiments,  Jo ¨rge Rothe  for assistance with BCI programming and  Elmar Hullinger  for BCI control. This work was supported by a  European Research Council (ERC)  advanced grant (No. 321381 ) and in part by the  Cantons of Basel  and the  Swiss Confederation  within the  INTERREG IV A. 20 tri-national research programme.
, 2009; McArt et al. , 2013; Suthar et al. , 2013). Most of these studies have determined the objective cow-level NEFA and BHBA thresholds that are associated with increased risk of disorder. These thresholds allow the producer to identify individual cows at risk for these outcomes during the transition period. A large proportion of these data were obtained in North America in the 2000s. Many of these studies used similar statistical methods and expressed their results in the same way, leading to a good overview of the NEB issue and allowing for the comparison of study results. However, 3 main factors have complicated the use of these results. First, data regarding certain outcomes have remained scarce. For instance, only one trial examined the link between SCK and lameness  (Suthar et al.
MATERIALS AND METHODS A literature search and screening process were conducted using the PubMed (http://www. ncbi. nlm. nih. gov/pubmed), CAB (www. cabi. org), and Google Scholar (http://scholar. google. com/) search engines to create a data set of papers using the key words "hyperketonemia," "ketosis," "cattle," and "cow," separately or in combination. New papers referenced by at least 1 of the papers identified in the previous step were also included. Because of the lack of published data on the risk of mastitis in cows with SCK, unpublished information from the Mastitis National Council Website (https://www. nmconline. org/) was also included.
Inclusion and Exclusion Criteria To be included in the data set, the papers must have examined the risks of various diseases, the changes in milk production, or the changes in reproductive performances (named outcomes) in peripartum dairy cows with and without subclinical ketosis. Only papers that calculated risks at the animal level were included, except one that addressed milk production. Thus, all review papers, herd-level analyses, and nontarget papers (such as research on the association between NEB and immunity or on the prevention or treatment of subclinical or clinical ketosis) were excluded. Publications through June 2013 were included. Twenty-three papers evaluating the association between SCK and the abovementioned outcomes in dairy cattle were available. Most of the papers studied several outcomes, and 131 different models published in the literature were included in the present study.
Statistical Analysis: Meta-Analysis A meta-analysis was conducted on the extracted outcomes using the metafor package  (Viechtbauer, 2010)  of R (version 3. 0. 2; R Foundation for Statistical Computing, Vienna, Austria). A random-effects model was first conducted for each metabolite to estimate the logarithmic effect size, its 95% confidence interval, and its statistical significance. The effect size estimate analysis was conducted using a standardized z statistic. The inconsistency of results among trials was quantified using both Cochran's Q test and the I 2 statistic  (Higgins et al. , 2003). The I 2 statistic describes the percentage of total variation across studies that was caused by heterogeneity rather than chance (I 2 lay between 0 and 100%). A value greater than 50% was considered to indicate substantial heterogeneity.
For the same number, data were gathered with a weight of one-third for L1 and two-thirds for L >1 for the mean calculation in the review. 9 NA = not available. 10 T = tested but P > 0. 05 and then removed from the model. 11 Corresponds to -4. 4%. 12 Corresponds to -6. 0%. 13 The association between SCK and milk production was analyzed at the herd level: the herd was considered to be affected with SCK if the percentage of cows above the BHBA or NEFA threshold was above 15 or 20%. 14,15 For the same number, data were gathered with a weight of one-third for L1 and two-thirds for L >1 for the mean calculation in the review. ± 12) scenarios, metritis (100 ± 7), placental retention low (0 ± 0) and high (206 ± 10) scenarios, and clinical mastitis (5% of the milk production) were calculated in a previous review  (Fourichon et al.
HdIV segment analysis We used the Blastn algorithm (http://www. ncbi. nlm. nih. gov/ BLAST/)  [28]  to compare all of the identified HdIV segments, to assess potential sequence similarities between segments. We then checked for sequence similarity to segments of other IVs (TrIV, HfIV and CsIV) by using the Blastn algorithm to search the NCBI public nucleotide database. Synteny between the HdIV and HfIV segments was visualized with the ARTEMIS Comparison Tool (ACT) interface (www. sanger. ac. uk). Protein sequences were aligned with the ClustalW online tool  [29].
HdIV genome annotation, read mapping and RPKM counting For each HdIV genome segment, the open reading frames (ORF) present were predicted with the ARTEMIS interface (www. sanger. ac. uk)  [30]. Only ORFs encoding proteins of more than 99 amino acids (a. a. ), with an initiator methionine, were initially considered as putative genes. In cases of overlapping ORFs, we considered only the ORF displaying sequence similarity to known PDV genes or with limits consistent with RNAseq mapping (see below) data. For all putative genes, we carried out public database searches for sequence similarities with the NCBI Blastn, Blastx and tBlastx algorithms (http://www. ncbi. nlm. nih. gov/BLAST/) [28]. Searches for specific regulatory motifs in the upstream gene region were performed with the SCOPE and MEME online tools.
Consequently, in cases in which the overlapping region contained an open reading frame (ORF), the gene was present twice in the encapsidated form of the HdIV genome (Figure  S1 ). Finally, sequencing also revealed that 11 of the HdIV segments (Hd1, Hd2ab, Hd3, Hd4, Hd5, Hd6, Hd7, Hd10 and Hd26ab) contained at least two internal direct repeat sequences (Table  S2 ), suggesting possible nesting, as previously described for CsIV  [17, 35, 36]. In total, the encapsidated HdIV genome contains at least 135 genes: 134 ORFs predicted from the sequenced viral genome, plus a previously described HdIV gene (GlyPro3 GenBank #AF132023. 1) [22, 27, [37] [38] [39] [40]  not identified in the newly acquired HdIV sequence. The absence of this sequence indicates that at least one HdIV segment was not sequenced in this study, for unknown reasons.
In addition, the HdIV genome contains 12 other gene families encoding proteins of unknown function (Table  1 ). These families have been named HdIV_F0 (corresponding to the previously described GlyPro family  [27] ) to HdIV_F11, and each of these families contains two to four gene copies (Table  1 ). Lastly, HdIV genome contains 37 single-copy genes (Table  1 ), including four that have already been described (D8_Hd50 (GenBank #AF464931. 1), K19_Hd29 (GenBank #AF241775. 1), SerThr_Hd7 (S6 mRNA, GenBank #AF464930. 1) and P30_Hd6 (Orf1, GenBank #AF479654. 1)) [38, 40]. Nineteen of the 135 HdIV genes contained at least one intron (Figure  1 ), a classical characteristic of PDV genes. These genes included the 9 Cys genes, the 3 members of the GlyPro family, P30_Hd6, U1_Hd6, SerThr_Hd7, D8_Hd45, K19_Hd29, U1_Hd41 and U1_Hd19.
As reported in previous studies  [27, 38] , the first exon, corresponding to a putative signal peptide, was found to be conserved in several of these genes (GlyPro family, P30_Hd6, U1_Hd6 and SerThr_Hd7). Blastn similarity searches against the NCBI public database revealed strong similarities between HdIV and the IV carried by the wasp Hyposoter fugitivus, HfIV: 44 of the 50 HdIV segments displayed nucleotide sequence identity to 42 of the 56 HfIV segments (Table  S2 ), in their gene or intergenic regions. By contrast, except for genes belonging to multigene families the number of Illumina reads mapping to the segment sequence; for the sake of simplicity, only data from one of the 3 ''72 h p. p. '' replicates were used to draw the curve). Provisional GenBank accession numbers: Hd45:KJ586284; Hd44:KJ586285; Hd43:KJ586286; Hd42:KJ586287; Hd41:KJ586288; Hd40:KJ586289; Hd39: KJ586290; Hd38:KJ586291; Hd37:KJ586292; Hd36:KJ586293; Hd35:KJ586294; Hd34:KJ586295; Hd33: KJ586296; Hd32:KJ586298, Hd31:KJ586299; Hd30:KJ586300; Hd29:KJ586303; Hd28:KJ586304; Hd27:KJ586305; Hd26b:KJ586306; Hd26a:KJ586301; Hd25:KJ586307; Hd24:KJ586308; Hd23:KJ586309; Hd22:KJ586310; Hd21:KJ586311; Hd20b:KJ586297; Hd20a:KJ586312; Hd19:KJ586313; Hd18:KJ586315; Hd17b:KJ586316; Hd17a:KJ586314; Hd16:KJ586317; Hd15:KJ586318; Hd14:KJ586319; Hd13:KJ586320; Hd12:KJ586321; Hd11b:KJ586302; Hd11a:KJ586322; Hd10:KJ586323; Hd9:KJ586324; Hd8:KJ586325; Hd7:KJ586326; Hd6:KJ586328; Hd5:KJ586329; Hd4:KJ586330; Hd3:KJ586331; Hd2b:KJ586327; Hd2a:KJ586332; Hd1:KJ586333.