dataesr/scientific-paragraphs-categorization huggingface dataset:
The reference mass spectrum from strain FF12 T was incremented in our database (Fig. 1 ). Colonies that remained unidentified with MALDI-TOF after three tests are used for amplifying and sequencing the 16S rRNA sequence, as previously described elsewhere  [32, 33]. Chryseobacterium senegalense sp. nov. exhibited a 97. 97% 16S rRNA sequence similarity with Kaistella flava  [34] , the phylogenetically closest bacterial species with standing in the nomenclature (Fig. 2 ). These values were lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Meier-Kolthoff et al. [37]  in 2013 to delineate a new species within the phylum Bacteroidetes without carrying out DNA-DNA hybridization. Different growth temperatures (25, 28, 37, 45 and 56°C) were tested. Growth was obtained between 25 and 37°C, with optimal growth at 28 and 37°C.
9 nmol/L. The libraries were normalized at 2 nM and pooled. After a denaturation step and dilution at 15 pM, the pool of libraries was loaded onto the reagent cartridge and then onto the instrument along with the flow cell. Automated cluster generation and sequencing runs were performed in a single 27-hour run at 2 × 251 bp. A total of 8. 6 Gb of information was obtained from a 950K/mm 2 cluster density, with a cluster passing quality control filters of 93. 2% (18 182 000 clusters). Within this run, the index representation for Chryseobacterium senegalense was determined to be 8. 35%. The 1 414 815 paired reads were filtered according to the read qualities. These reads were trimmed and then assembled using the CLC genomics WB4 software.
Genome annotation and comparison Open reading frames (ORFs) were predicted using Prodigal  [42]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [43]  and the Clusters of Orthologous Groups (COGs) databases using BLASTP. The tRNAscan-SE tool  [44]  was used to find tRNA genes, whereas ribosomal RNAs were found using RNAmmer  [45]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [46]  and TMHMM  [47]  respectively. ORFans were identified if their BLASTP E value was lower than 1e-03 for an alignment length greater than 80 amino acids. If the alignment lengths.
NMNI were smaller than 80 amino acids, we used an E value of 1e-05. Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [48]  was used for data management and DNA Plotter  [49]  to visualize genomic features. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [50]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used the MAGI homemade software to calculate the average genomic identity of gene sequences (AGIOS) among compared genomes  [18]. Briefly, this software combines the Proteinortho software  [51]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Genomes from the genus Chryseobacterium and closely related genera were used to calculate AGIOS values. The genome of Chryseobacterium senegalense strain FF12 T (GenBank accession no. CYUH01000001-CYUH0100 0015CYUH01000001CYUH01000002CYUH01000003-CYUH01000004CYUH01000005CYUH01000006CYUH010 00007CYUH01000008CYUH01000009CYUH01000010CYUH 01000011CYUH01000012CYUH01000013CYUH01000014-CYUH01000015) was compared to Chryseobacterium haifense strain DSM 19056 T (GenBank accession no. JASZ00000000), Chryseobacterium indologenes strain NBRC 14944 T (GenBank accession no. BAVL01000000), Chryseobacterium formosense strain LMG 24722 (GenBank accession no. JPRP00000000) and Elizabethkingia miricola strain ATCC 33958 (GenBank accession no. JRFN00000000).
Genome properties The GenBank BioProject number is PRJEB10923. The draft genome of C. senegalense FF12 T consists of 68 contigs and generated a 4 397 629 bp long genome with a 35. 1% G+C content (Fig. 5 ). Of the 4056 predicted genes, 4001 were protein-coding genes, three were RNAs (one 5S rRNA gene,   one 16S rRNA gene, one 23S rRNA gene) and 52 were tRNA genes assigned a putative function. A total of 56 genes were identified as ORFans (1. 38%). The remaining genes were annotated as hypothetical proteins. The genome properties and statistics are summarized in Table  3. The distribution of genes into COGs functional categories is presented in Table  4.
75 μm (range 0. 5-1 μm) and a mean length of 2. 25 μm (range 1. 5-3 μm). Positive reactions were observed for alkaline phosphatase, phosphatase acid, esterase, lipase, leucine arylamidase, α-glucosidase, β-glucosidase, naphthol-AS-BIphosphohydrolase, α-fucosidase, β-galactosidase, α-galactosidase, D-glucose, D-maltose and starch. Chryseobacterium senegalense strain FF12 T is susceptible to amoxicillin, amoxicillin/ clavulanic acid, ceftriaxone, trimethoprim/sulfamethoxazole, erythromycin, ciprofloxacin, nitrofurantoin, doxycycline, rifampicin and imipenem but resistant to gentamicin and metronidazole. The G+C content of the genome is 35. 1%. The 16S rRNA and genome sequences of C. senegalense strain FF12 T (CSUR = P1490, DSM 100279) are deposited in GenBank under accession numbers LN810503 and CYUH01000001-CYUH01000015 respectively.
1 FIG. 2. FIG. 3. FIG. 4. 1234 FIG. 2. Phylogenetic tree highlighting position of Chryseobacterium senegalense strain FF12 T (LN810503) relative to other type strains within Flavobacteriaceae family. GenBank accession numbers are indicated in parentheses. Sequences were aligned using MUSCLE [35], and phylogenetic tree was inferred by Maximum Likelihood method with Kimura two-parameter model from MEGA6 software [36]. Numbers at nodes are percentages of bootstrap values obtained by repeating analysis 1000 times to generate majority consensus tree. Riemerella columbina was used as outgroup. Scale bar = 0. 1% nucleotide sequence divergence.
37 Nucleotide transport and metabolism H 91 2. 27 Coenzyme transport and metabolism I 72 1. 79 Lipid transport and metabolism P 187 4. 67 Inorganic ion transport and metabolism Q 84 2. 09 Secondary metabolites biosynthesis, transport and catabolism R 254 6. 34 General function prediction only S 333 8. 32 Function unknown - 267 6. 67 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome.
Image and data acquisition was performed using the ScanImage and Ephus softwares (Janelia Farms). Simultaneous LFP and whole-cell/cell-attached recordings were performed using glass micropipettes filled respectively with ACSF (in mM: 135 NaCl, 5 KCl, 5 HEPES, 1 MgCl 2 , 1. 8 CaCl 2 , 0. 01 Alexa-488 [adjusted to pH 7. 3 with NaOH]) and internal solution (in mM: 135 potassium gluconate, 4 KCl, 10 HEPES, 10 phosphocreatine, 4 MgATP, 0. 3 Na 3 GTP, 0. 01 Alexa-488 [adjusted to pH 7. 3 with KOH; osmolarity adjusted to 300 mOsmol]). Unless otherwise noted, no current was injected during current clamp recording. On a subset of current clamp recordings, Vm was maintained under spike initiation threshold by negative current injections. Vm was not corrected for liquid junction potentials (Vj). Vj was, nevertheless, estimated as described previously  [65]  using extracellular ion concentration measured in vivo  [66]  (in mM: 153.
Visual Stimulation Visual stimuli were generated using the psychtoolbox Matlab extension and displayed on a small LCD screen (19 x 12. 5 cm) placed ~11 cm in front of the eye of the animal in the contralateral hemifield. Stimulation appeared on a grey isoluminant background and consisted of sinusoidal drifting gratings (Spatial frequency: 0. 04 cycles/degree, Temporal frequency: 1 cycles/s) displayed at full contrast on a circular area (radius: 15 degree) centered on the receptive field of recorded neurons. Gratings were displayed at one of eight possible directions for a duration 3 s, starting 2 s after the onset of 7 s long trials. A total of 40 to 80 trials were acquired for each recording (5-10 stimulus set repetitions). Grating direction was randomized across each stimulus set repetition.
Data Preprocessing Data processing and analyses were performed offline in the Matlab environment (Mathworks). Signals were notch-filtered at 50 Hz in order to remove spurious line noise. LFP signals were de-trended by subtracting a linear fit to traces on individual trials, low-pass filtered at 200 Hz and expressed as z-scores. Cell-attached recordings were high-passed filtered at 100 Hz. Spikes were detected on whole-cell and cell-attached recordings, using a threshold based procedure. The time corresponding to action potential threshold was taken as the first peak of the third derivative of the trace. For the analysis of membrane potentials (Vm), spikes were removed by interpolating trace segments from -1 ms to +3 ms around AP peaks with cubic splines.
Gamma centered Vm averages were derived by first sorting gamma cycles per quintile as a function of gamma amplitude at cycle trough. Then, 50 ms Vm segments centered on the trough of each cycle were aligned and averaged. A linear fit was computed on each 50 ms Vm average to derive its slope. Then, this fit was subtracted from the Vm average and the amplitude of the gamma entrained Vm fluctuation was calculated as the maximum of the resulting trace. For the assessment of the phase locking of spike and Vm to the LFP, a continuous spectrotemporal representation of LFPs and membrane potentials was derived from 2 to 120 Hz (step 2 Hz) with a Continuous Wavelet Transform using a Complex Morlet Wavelet having 9 cycles (bandwidth parameter: 1, center frequency: 2, wavelet name: "cmor1-2" in the Matlab Wavelet toolbox).
PPC values were considered significantly different if there was no overlap between their 95% confidence intervals. Non-uniformities in the phase distribution of spikes were tested with Rayleigh's tests. As Rayleigh's test tends to be permissive, the threshold for significance was set to α = 10 -5. All values are presented as mean ± s. e. m. unless otherwise stated. Data deposited in the Dryad repository: http://dx. doi. org/10. 5061/dryad. 4754j [72].
Acknowledgements We thank  Francisco Salinas  for technical help with large-scale crossing,  Martin Zackrisson  for much appreciated technical assistance with extraction and analysis of growth estimates, and  Oliver Stegle ,  Cornelis Albers  and  Daniel Gaffney  for comments on the text. K. M. was supported by the  European Regional Development Fund  through the  BioMedIT  project, J. H. by the  Labex SIGNALIFE  ( ANR-11-LABX-0028-01 ),  Swedish Research Council  (grant numbers  325-2014-6547  and  621-2014-4605 ) and the  Research Council of Norway  (grant number  222364/F20 ), J. H. and G. L. by  ATIP-Avenir (CNRS/INSERM) ,  ARC  (grant number  SFI20111203947 ),  FP7-PEOPLE-2012-CIG  (grant number  322035 ),  ANR  ( ANR-13-BSV6-0006-01 ) and  Canceropole PACA (AAP emergence) ,  Labex SIGNALIFE  ( ANR-11-LABX-0028-01 ), and L.
4a ). Specifically, nucleosome levels at both Hsf1-binding sites (Fig. 3a ) and Hsf1-regulated promoters (1 kb upstream of ATG) (Supplementary Fig. 4b ) did not significantly differ upon heat shock, suggesting that Hsf1 activation and heat shock do not invoke significant nucleosome eviction at these heat shock-induced promoters. Heat shock increases virulence of C. albicans in vivo. Our data demonstrate that Hsf1 binds to genes involved in virulence, adhesion and biofilms during heat shock, but these genes may play an as yet undetermined role in thermal adaptation. To assess this, als3D/als3D and rob1D/rob1D mutants, along with their wild-type counterparts, were heat shocked and cell viability was determined by colony forming units (CFUs). The mutants displayed no significant heat shock sensitivity compared with their respective wild-type controls (Supplementary Fig.
Next, we assayed epithelial cell damage by measuring the release of lactate dehydrogenase (LDH) from TR146 cells. After 20 h co-incubation, damage increased when C. albicans had received a prior heat shock (Po0. 05) (Fig. 4b ). These results cannot be attributed to morphological changes during heat shock, as cells remain in the yeast form up to 120 min post 30-42 °C heat shock  18. Our data suggest that a drastic heat shock activates a C. albicans program associated with increased host cell adhesion and damage. To evaluate the effect of heat shock on virulence, we utilized two complementary infection models, the greater wax moth (Galleria mellonella) and zebrafish (Danio rerio). G. mellonella larvae infected with heat shocked C. albicans exhibited increased mortality rates compared with controls (Po0.
RNA was extracted with Triazol (GibcoBRL: Grand Island, NY), as described previously  54. RNA integrity was assayed on an Agilent Bioanalyser 2,100 (Stockport, UK). TruSeq RNA-seq libraries were prepared according to the manufacturer's instructions (Illumina, Cambridge, UK), and sequenced on the Illumina HiSeq2000 platform. Over 80% of raw sequences aligned to the C. albicans SC5314 genome sequence (Candida Genome Database)  55. Gene expression analysis was performed using Partek Genomics Suite software, version 6. 6; 2015. Approximately 20 million reads were generated per sample, over 80% of which mapped to C. albicans ORFs. DESeq  56  was used to normalize the data sets and calculate the fold changes and their statistical significance on the basis of three independent biological replicates for each condition (Supplementary Data 5).
5 h, followed by incubating the mixture with B15 ml packed Protein A Sepharose (GE Healthcare) for another 1. 5 h at room temperature on an end-to-end rotator. Subsequently, IgG or Protein A Sepharose matrix was transferred to a Corning Costar SpinX centrifuge tube filter and washed according to Fan and colleagues  58. Immuno-precipitated chromatin DNA was decrosslinked at 65 °C overnight and was purified using a QIAGEN PCR clean up purification column. Library preparation and multiplex Illumina Sequencing was carried out as described previously  59, 60. Libraries were sequenced by the Illumina HiSeq2500 platform. The nuclear and mitochondrial genome sequences and General Feature File for C. albicans isolate SC5314 v. A21-s02-m08-r01 were downloaded from www. candidagenome. org (GenBank project accession number PRJNA10701).
Illumina reads were aligned to the genome using Bowtie with this parameter: À 5 10 À k 1, --best  61. Identification of Hsf1 targets. Binding sites of Hsf1-TAP were identified using MACS  62. MACS-called peaks with a P values o0. 05 were manually inspected and dubious binding sites (peaks with low signal in both untreated and heat conditions and/or portraying a strange shape) were removed from further analysis. Mapping the genes by their ATG position located closest to confident Hsf1-TAP ChIP-seq peaks, identifying direct Hsf1 target genes. A total of 103 genes were identified from 104 MACS peaks from data sets of untreated and heat shock conditions (Supplementary Data 1). De novo motif discovery. Sequences (200 bp) spanning the summit of the Hsf1-TAP ChIP-seq peaks were subjected to de novo motif analysis using MEME, DREME and HOMER.
For extended analysis in identifying additional motifs, peaks with a canonical Hsf1-binding site (GAAnnTTC) 100 bp each side of the summit were removed and the same de novo motif analysis was carried out on the remaining peaks (Supplementary Data 4). Hsf1 binding and nucleosome density (histone H3) analysis. Hsf1 and histone H3 ChIP-seq signals were calculated by counting the number of sequencing read fragments that overlapped within 20 bp sliding windows across a 1 kb region spanning the summit of each peak or across the 1 kb promoter and the first 1 kb coding region of each target gene. The read count was then normalized to the total number of mapped reads in the respective data set. A median was taken for each 20 bp window, and the median (multiplied by a factor of 1,000,000 for y axis scaling for plotting purposes) was used in the ChIP-seq signal plot for the analysed peaks, promoter and coding regions ( À 1,000 to þ 1,000 with respect to ATG).
Nucleosome free regions were systematically identified using the wild-type untreated histone H3 ChIP-seq data by scanning for the lowest Histone H3 signals across 20 bp windows within 500 bp upstream of ATG. If the identified window had zero histone H3 signal, the first 20 bp neighbouring window with a positive signal is set as the boundary of the nucleosome free region. Histone H3 signals (normalized to total number of mapped reads) at the identified regions were measured and compared between different samples and expressed as log 2 fold change. To rule out that the observed effect of HSP90 depletion (the increased H3 levels) is not due to a technical artefact, comparative analysis was performed between biological replicates. Two-sample Kolmogorov-Smirnov test was performed between untreated and heat shocked samples (Supplementary Fig.
7d ; P values ¼ 7. 723e À 07 and 5. 791e À 09 for untreated and heat shock comparisons, respectively). Pol II ChIP-seq analysis. ChIP-seq against RNA polymerase II (Pol II) was used to determine active transcription activities. Transcription level was measured by the number of ChIP-seq fragments (sequencing reads) across the entire open reading frame of a gene normalized first to the length of the gene followed by the total number of mapped reads of a data set. The normalized number, which is similar to RPKM in the RNA-seq analysis, was used in expression comparisons between untreated and heat shock conditions as well as between wild-type and tetO-HSP90/hsp90D strains. Classification of Hsp90 targets. Gene expression (measured by PolII ChIP-seq) before and after heat shock in strains with or without HSP90 depletion was compared.
All reactions were done in triplicate using primers listed in Supplementary Table  4. Transcript levels were normalized to ACT1. Data were analysed using the BioRad CFX Manager software, version 3. 1 (Biorad). Heat shock survival assays. To test heat shock survival in different mutants, C. albicans rob1D/rob1D (CaLC2739) and als3D/als3D (CaLC3115) mutants, along with their respective wild-type SN152 (CaLC2740) and BWP17 (CaLC3665) (Supplementary Table  3 ) were grown to mid-log phase in YPD at a starting OD 600 of 0. 1. Upon reaching mid-log phase, cells were left untreated or were stressed for one hour with a 30-42 °C heat shock, and CFUs determined by plating onto YPD plates. Viability was calculated as a percentage of survival after a heat shock compared with untreated cells. Experiment was replicated on three separate occasions.
01 (ref. 34) were added to TR146 monolayers and incubated at 37 °C and 5% CO 2 for 20 h. Control wells contained PBS alone. Following incubation, culture supernatant was collected and assayed for LDH using the Cytox 96 Non-Radioactive Cytotoxicity Assay kit (Promega) according to manufacturer's instructions. Replicate LDH measurements were made on the single well, and the assay was repeated on separate days. All data were analysed using a two-tailed t-test with Po0. 05 considered significant. Galleria mellonella killing assay. Larvae in their final instar (Next Millennium Farms) were injected with C. albicans wild-type (SN95, CaLC239) cells grown at 30 °C or subject to a 15 min 30-42 °C heat shock. Briefly, overnight cultures were adjusted to an OD 600 0. 1 in YPD. Cells were grown to mid-log phase before being subjected to the heat shock.
, Frendale, WA). For each independent experiment, 30 larvae were microinjected with 4 nl of either PBS or 1 Â 10 7 cells per ml of C. albicans suspension through the otic vesicle into the hindbrain ventricle to achieve a dose of B15-20 yeast/larva. Larvae were then screened by microscopy immediately post-infection to ensure correct inoculum sizes and injection location. Data from three independent experiments were plotted. Kill curves were plotted and analysed by the Kaplan-Meier method (GraphPad Prism). Log-rank test was used to determine significance.
Acknowledgements We thank  Karim Gharbi  and  Urmi Trivedi  for their assistance with RNA sequencing, carried out in the  GenePool genomics facility (University of Edinburgh ). We also thank  Susan Fairley  and  Eduardo De Paiva Alves  ( Centre for Genome Enabled Biology and Medicine, University of Aberdeen ) for help with the initial bioinformatics analysis. We thank  Aaron Mitchell  for kindly providing the ALS3 mutant,  Julian Naglik  for the gift of TR146 cells, and  Jon Richardson  for technical assistance. We thank the  Genomics and Bioinformatics core of the Faculty of Health Sciences for Next Generation Sequencing and Bioinformatics  support, the  Information and Communication Technology Office at the University of Macau  for providing access to a High Performance Computer and  Jacky Chan  and  William Pang  for their expert support on the  High Performance Computer.
Data availability. RNA-sequencing data sets are available at ArrayExpress (www. ebi. ac. uk) under accession code E-MTAB-4075. ChIP-seq data sets are available at the NCBI SRA database (http://www. ncbi. nlm. nih. gov) under accession code SRP071687. The authors declare that all other data supporting the findings of this study are available within the article and its supplementary information files, or from the corresponding author upon request.
Recent evidence suggests that global semi-arid ecosystems provide an important contribution to the global land carbon sink and can dominate inter-annual variability and the trend of global terrestrial carbon cycle  8, 9. Previous studies of the semi-arid carbon sink primarily relied on model outputs  8, 9. The results can be subject to uncertainties in input variables such as the assimilation of datasets that are sparse in many regions of the world, and may be further confounded by model assumptions, as suggested by a previous study finding that models can differ substantially in predicting inter-annual variability of the terrestrial carbon cycle  10, 11. More importantly, these studies do not fully utilise ground measurements of carbon fluxes (e. g. , from eddy-covariance flux towers). An observation-based study of the spatial attribution and temporal evolution of extreme wet-year driven carbon sinks over semi-arid regions has yet to be conducted to date.
To characterize the hydroclimatic variations, we used the total water storage change (TWSC) from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites and standardized precipitation-evapotranspiration drought index (SPEI) computed from long-term ground meteorological datasets. For SPEI, we used data computed at 6-month time scale considering the fact that vegetation across Australia primarily respond to drought at time-scales around 6-months or less. Lastly, as the most direct measure of ecosystem-atmosphere carbon exchange, we used NEP measured by in-situ eddy-covariance flux towers from two semi-arid ecosystems in Australia. Furthermore, to infer the contribution from fire emission to the semi-arid carbon-source dynamics, we employed the fire emission data from the fourth generation global fire emission database (GFED4.
This drying trend as indicated by GRACE TWS imply that vegetation over these regions were stressed during the 2002-2009 period and may have lost resilience, thus unable to respond strongly to the 2010-11 wet year. Future studies, with the incorporation of more field observations, are needed to investigate the exact mechanisms that explain the lack of response of ecosystems over Western Australia to the wet period. These patterns of varying rain-use-efficiency, as derived from observational datasets, are informative and reflect the differences in ecosystem-level carbon-water relationship. Therefore, Earth system models may consider using these patterns as observational test. Specifically, models may test their capability of simulating the spatial nature of the response of terrestrial carbon uptake per unit change in rainfall amount during the extreme wet years, i.