dataesr/scientific-paragraphs-categorization huggingface dataset:
HKC-SCs were not resistant to axitinib in hypoxia and thus remained sensitive (sAH). HKCSCs exhibited the same relationship with sorafenib in normoxia (sSN). Sorafenib and axitinib effectively inhibited the growth of primary and metastatic clear-cell renal cell carcinoma cell lines in normoxia and hypoxia, but pRCC stem-like cells' growth was inhibited in an oxygen-dependent manner (Fig. 3d ). Stem cell colony formation assay was performed only with sorafenib and axitinib to confirm the impact of oxygen concentration on renal cancer cells' response to tyrosine kinase inhibitors. Regardless of oxygen concentration, HKCSCs, 769-P and Caki-1 formed colonies if no TKI treatment was applied, however, hypoxic colonies were in general smaller and did not develop necrosis. Cells inside the colonies were living cells based on Neutral Red uptake (data not shown).
Computational prediction of coding sequences (CDS) and other genome features (RNA encoding genes, ribosome binding sites, etc. ), together with functional assignments were performed using the annotation pipeline implemented in the MicroScope platform 6   (Vallenet et al. , 2017). The genes discussed in the text were checked and manually annotated. Accession numbers from the European Nucleotide Archive (ENA 7  ) of the updated At. ferrivorans CF27 genome sequence are LT841305 for chromosome and LT841306 for the plasmid. Genomic islands (GIs) in the At. ferrivorans CF27 genome were detected using the web server IslandViewer3 8   (Dhillon et al. , 2015)  using the GI prediction methods IslandPath-DIMOB  (Hsiao et al. , 2005) , SIGI-HMM  (Waack et al. , 2006) , and IslandPick  (Langille et al.
, 2008) , with default parameters. A DNA region was considered as a GI if predicted by at least one of the three prediction methods. GC content and GC skew were calculated using infoseq (from EMBOSS package 9  ) and in-house perl scripts (Supplementary Text), respectively. Circos software version 0. 69  (Krzywinski et al. , 2009)  were used to produce circular maps representations of the chromosome and plasmid.
Genome Datasets Three distinct genome datasets were used in this study. Dataset 1 was composed of seven annotated genomes of Acidithiobacillus spp. [At. ferrivorans CF27 (this study), At. ferrivorans SS3  (Liljeqvist et al. , 2011) , At. ferrooxidans ATCC 23270 T  (Valdes et al. , 2008) , At. ferrooxidans ATCC 53993 (NC_011206), At. thiooxidans ATCC 19377 T  (Valdes et al. , 2011) , and At. caldus strain SM-1  (You et al. , 2011 ) (all downloaded from the MicroScope website), and At. caldus ATCC 51756 T  (Valdes et al. , 2009)  retrieved from the NCBI database]. Dataset 2, downloaded from the NCBI ftp site 10 (In March 2015), consisted of 2,770 complete prokaryotic genomes, excluding those of Acidithiobacillus spp. but including 2,605 bacterial and 165 archaeal genomes as well as their taxonomy lineages.
Comparative Genomic Analysis Taxonomic affiliations from top-scoring BLAST hits were performed as follows. Ribosomal protein sequences were first extracted from Acidithiobacillus spp. and from representative proteobacterial proteomes using the extractseq (EMBOSS package). Next, each protein was searched against the complete prokaryotic dataset 2 proteins (without Acidithiobacillus proteins) using the Blastp program  (Zhaxybayeva et al. , 2009). The highest-ranking match of each protein consists of the best blast hit from an organism that is different from the studied species and having a BLASTP alignment E-value less than 10 -5. The taxonomic assignment of the highest-ranking match was done using the NCBI taxonomy database  (Federhen, 2012). The same procedure was applied to each protein of Acidithiobacillus spp.
and representative proteobacterial proteomes. Protein classification into gene families included the seven Acidithiobacillus spp. (see above). Protein-CDS from Acidithiobacillus spp. dataset were clustered in orthologous groups (OG) using OrthoMCL 1. 4  (Li et al. , 2003)  with E-value less than 10 -5 , and a default MCL inflation parameter of 1. 5. Following this, core OG families (i. e. , conserved proteins in all of the seven strains), dispensable OG families (i. e. , common proteins present in two or more, but less than seven genomes) and specific OG proteins families (i. e. , specific protein groups found in only one genome) were defined. In order to identify cluster regions of specific genes, At. ferrivorans CF27-specific OG proteins were searched against the nr prokaryotic database using the Blastp program with a threshold E-value of 10 -5 , leading to a final set of At.
The proportion of At. ferrivorans CF27 (24. 3% of the overall genome) or SS3 (24. 4%) strain-specific proteins is higher when compared to those found for other Acidithiobacillus genomes (8. 9-20. 3%). Among the At. ferrivorans CF27 strain-specific proteins, 82. 3% of proteins are either hypothetical proteins or proteins of unknown function. In order to identify At. ferrivorans CF27 proteins that are absent in other prokaryotic species, we first performed a blast comparison between the set of 947 At. ferrivorans CF27-specific proteins and the nr prokaryotic database. This resulted in the identification of 434 (11. 2% of the overall CF27 genome) proteins only detected in At. ferrivorans CF27 (Figure  2 ). The At. ferrivorans CF27 specific proteins have in average 91 amino acids (271 aa for the overall genome), an isoelectric point of 8.
4 index from NOAA's Climate Prediction Center (CPC), and analyze correlations to drivers for years immediately following El Niño/La Niña years as well as for all years. In doing so, we examine contrasts in response between post El Niño versus post La Niña conditions. We also analyze the correlations based on global land sink as simulated by an ensemble of terrestrial biosphere models participating in the Multi-scale Synthesis and   S1 ). The use of November to February for estimating the AGR response to ENSO is preferable for separating the impacts of different ENSO events to using other months closer to the middle of the year (e. g. May, June etc) because ENSO events typically peak during the winter season. We test whether our results are sensitive to the specific definition of AGR described above by calculating alternate detrended AGR time series using atmospheric CO 2 data collected at Mauna Loa (NOAA/ESRL) (Ballantyne et al 2012) and at South Pole (SIO) (Keeling et al 2005), and basing the calculation on single-month differences of consecutive Januaries and Februaries.
5°resolution from the Climatic Research Unit-National Centers for Environmental Prediction (CRU-NCEP) (Huntzinger et al 2013, Wei et al 2014a, Wei et al 2014b). Those gridded monthly data are aggregated temporally from January to December to gridded annual data for a given calendar year. Annual mean tropical surface air temperature and precipitation time series are aggregated over land surface area between latitudes 30°N and 30°S to Tropical Mean Annual Temperature (TMAT) and Tropical Mean Annual Precipitation (TMAP). The time series of TMAT and TMAP are detrended for signals at the interannual scale (figure S1). This approach is comparable to those used in earlier studies (Cox et al 2013, Wang et al 2013, Wang et al 2014, Wenzel et al 2014). We choose the CRU-NCEP data set not only because it has been applied in a number of earlier studies on ENSO responses of global land carbon cycle (Piao et al 2014, Wang et al 2013, Wang et al 2014), but also because this dataset is used as driver data input for all the MsTMIP terrestrial biosphere models analyzed in this study (Huntzinger et al 2013, Wei et al 2014b).
We also test alternate datasets (e. g. precipitation data from the Global Precipitation Climatology Centre, GPCC (Schneider et al 2015, Schneider et al 2013), temperature data from NCEP Reanalysis data provided by the NOAA/ OAR/ESRL PSD (http://www. esrl. noaa. gov/psd/) given the previously noted differences in reanalysis products (Trenberth et al 2015), and find our conclusions to be robust to the choice of precipitation and temperature dataset. Terrestrial biosphere models We use monthly carbon fluxes (including Net Ecosystem Exchange-NEE, Gross Primary Production-GPP and total Ecosystem Respiration-TRE) during 1959-2010 as simulated from ten global terrestrial biosphere models (TBMs) participating the MsTMIP project (Huntzinger et al 2013, Wei et al 2014b). Those models (table S1) are driven by a standard protocol, including environmental drivers in climate, atmospheric CO 2 , biome map, nitrogen deposition, soil conditions, land use change and phenology (Wei et al 2014b).
Those gridded monthly carbon fluxes are first aggregated temporally to gridded annual carbon fluxes. Annual mean global carbon budget time series are aggregated spatially from the gridded annual carbon fluxes over all land surface area, while annual mean tropical carbon budget time series are aggregated spatially over all land surface area between 30°N and 30°S (Huntzinger et al 2014). The model output analyzed here is taken from the Version 1 release of the MsTMIP data (Huntzinger et al 2014, Huntzinger et al 2013). This dataset includes 15 models, 10 of which are used here. The CLASS-CTEM-N and TRIPLEX-GHG models are excluded due to the quality control issues. The SiB3 model is excluded to avoid possible issues in the continuity of its simulated annual global/tropical NEE time series, because it features unrealistic massive pulses of carbon release in January of 1959.
Transmission electron microscopy Strains were grown in the BHI broth, until they reached an OD 620nm = 0. 5-0. 7. Preparation and examination of S. aureus cells by transmission electron microscopy (TEM) were performed as described previously. 15 Ultrathin sections of the samples were examined using a Zeiss 10C electron microscope and images analyzed with Image J 1. 46r software (http://imagej. nih. gov/ij/). Cell wall thickness at nearly equatorially cut surfaces was measured (30 cells for each strain) using a magnification of 50,000 • , and the results for each strain were expressed as median and range.
Characterization of the peptidoglycan Peptidoglycan was isolated as previously described  16  and digested with mutanolysin (Sigma-Aldrich). Reaction mixture was incubated overnight at 37°C, and the soluble muropeptides were separated by reversed phase-high performance liquid Chromatography using a 3 mm ODS-Hypersil column (4. 6-250 mm; Thermo Scientific), at 25°C. The compounds were detected at 210 nm. HPLC elution patterns are included in Supplementary Data 1; (Supplementary Data are available online at www. liebertpub. com/mdr) The areas of the peaks were added together, and individual peaks were expressed as a percentage of the total. Relevant peaks were collected and lyophilized. Samples were hydrolyzed in 6 M HCl at 95°C for 16 hr. After evaporation, the dry residues were dissolved in 67 mM trisodium citrate-HCl (pH = 2.
Autolysis assay Strains were grown in the BHI broth until they reached an OD 620nm = 0. 5-0. 7, chilled on ice, harvested by centrifugation (3,000 g, for 10 min at 4°C), and washed with ice-cold water. Cells were then suspended to an OD 620nm = 1. 0 either in the lysis buffer (50 mM glycine buffer, pH = 8. 00, containing 0. 01% Triton X-100) as previously described  19  or in 50 mM phosphate buffer, pH = 7. 00, and incubated at 37°C with agitation. Autolysis was evaluated by following OD 620nm. The transcription of PBP2 and PBP4 coding genes was evaluated by reverse transcription quantitative real-time polymerase chain reaction (RTqPCR) using primers described in Supplementary Data 2. The effect of vancomycin at subinhibitory concentration (¼ MIC) on the pbp2 and pbp4 transcription was further analyzed.
Statistical analysis The NRQ values of pbp2 and pbp4 genes were converted into logarithmic values to obtain symmetrical data and compared by two-way analysis of variance (ANOVA). Multiple comparisons were performed with the Duncan post-test using Infostat Software. 24 p Values of <0. 05 were considered statistically significant. Cell wall thickness values were compared with the nonparametrical Kruskal-Wallis test and Dunn post-test using Infostat Software. p Values of <0. 05 were considered statistically significant.
Results Previously, we reported that strains isolated from the same patient (D1 and D2) and the laboratory-selected mutants (D23C9 and D2P11) displayed distinct antibiotic susceptibility FIG. 1. HPLC muropeptide profile analysis. Area under curve of relevant peaks (expressed as percentage of the total area), and proposed structures for muropeptides corresponding to peaks: (A) eluted at RT 47 min and (B) eluted at RT 64 min, after MS amino acid analysis. HPLC, high performance liquid chromatography; MS, mass spectrometry; RT, retention time. FIG. 2. Autolysis assay performed using (A) 50 mM phosphate buffer pH = 7. 00 and (B) lysis buffer (0. 01% Triton X-100, 5 mM glycine pH = 8. 00). Data were expressed as the percent loss of OD 620nm at the indicated times compared to the zero time point.
Each data point represents the mean and standard deviation from three independent experiments. patterns (Table  1 ). Daptomycin susceptibility was additionally explored in this study. Daptomycin prediffusion method was positive for D2P11, suggesting the potential reduction in daptomycin susceptibility for this strain. Daptomycin MIC values were higher for the derived mutants D2P11 and D23C9 compared to the parental strain, but all of them are classified as susceptible according to CLSI breakpoint (Table  1 ). The stability of the hVISA/VISA phenotype was explored by the determination of vancomycin MIC after daily passages in the drug-free medium. While vancomycin MIC value remained unchanged in D2 and D23C9 mutant, the MIC of mutant D2P11 decreased twice (to 2 mg/ml) after 60 daily passages in the drug-free medium, suggesting some instability in its phenotype.
Acknowledgments This work was supported, in part, by grants from  University of Buenos Aires, Argentina  ( UBACYT 2014-2017 20020130100381BA ),  Agencia Nacional de Promocion Cientı ´fica y Tecnologica  ( PICT 2362 ), and  Consejo Nacional de Investigaciones Cientı ´ficas y Tecnicas (CONICET)  ( PIP CONICET N°: 11220110100707CO ) to M. M. ; and  BELSPO ,  IAP 7/44  iPROS to B. J. The bilateral cooperation was supported by  scientific agreement between the Belgian Fund for Scientific Research and the Ministry of Science, Technology and Productive Innovation (MINCyT/FRS-FNRS ,  BE 0907 ). M. M. is member of '' Carrera del Investigador '' of  CONICET. SDiG is a postdoctoral fellow of CONICET.
Aims. There is a need of more quantitative standardised data to compare local Mental Health Systems (MHSs) across international jurisdictions. Problems related to terminological variability and commensurability in the evaluation of services hamper like-with-like comparisons and hinder the development of work in this area. This study was aimed to provide standard assessment and comparison of MHS in selected local areas in Europe, contributing to a better understanding of MHS and related allocation of resources at local level and to lessen the scarcity in standard service comparison in Europe. This study is part of the Seventh Framework programme REFINEMENT (Research on Financing Systems' Effect on the Quality of Mental Health Care in Europe) project. Methods. A total of eight study areas from European countries with different systems of care (Austria, England, Finland, France, Italy, Norway, Romania, Spain) were analysed using a standard open-access classification system (Description and Evaluation of Services for Long Term Care in Europe, DESDE-LTC).
); these problems hamper like-with-like comparisons. This study was part of the REFINEMENT project (Research on Financing Systems' Effect on the Quality of Mental Health Care in Europe) (http:// www. refinementproject. eu/), which was funded by the European Seventh Framework Programme (FP7) and implemented in order to better understand the patterns of mental health care provision, and the balance between hospital and community care (e. g. between residential and day care) in Europe. This specific study was aimed at:   Carulla et al. 2000 Carulla et al. , 2011a Carulla et al. , b, 2013;; Poole, 2004; Johnson et al. 2011).
Sample Services with public coverage and universal access, providing health and social care to adults aged ≥18 years with a psychiatric disorder (F2-F6 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) diagnostic (World Health Organization, 2010)) available during the year 2010 were gathered in an ad hoc document. Services not intended exclusively for mental health care users were excluded from the study. After completing the database in August 2012, various reviews and updates followed to create the final dataset in July 2013 with 857 BSICs and 1018 MTCs.
First, there was a high disparity in the data sources and the availability of information across the different local areas. Figures regarding sociodemographic characteristics were collected from the best available local or national source, and should therefore be considered only for orientation. Second, generic services available for the general population (e. g. primary care) or for persons with mental disorders, were not included in this study. Third, these findings are only applicable to the areas under study and cannot be generalised to the whole country. Fourth, this paper does not provide a full description of every area; further information is available on the REFINEMENT Decision Support Toolkit (DST) Appendix  (Kalseth et al. 2013). The information on service availability, capacity and diversity has to be completed with information on the financing, organisational structures and management and quality of the local MHS.
Data analysis Custom R (v3. 03) scripts were used for annotation of PLFAs. Statistical analysis of chitin and PLFA biomass was done with t tests in SPSS Statistics 22 software. Changes of laccase, chitin, and PLFA over time were analysed using ANOVA with Bonferroni or Dunnet T3 post hoc correction in SPSS Statistics 22 software. In all cases p was <0. 05.
Discussion Fungal and bacterial biomass was quantified during a 26-day-incubation of PII-end compost in the presence  and absence of A. bisporus. Chitin was used to quantify total fungal biomass, laccase and C18:2ω6 for the living fraction of the fungal biomass, and bacterial PLFAs for living bacterial biomass. Laccase activity was absent in PII-end compost during the incubation period in the absence of A. bisporus, while chitin and C18:2ω6 content increased by 26% and dropped by 50%, respectively. In contrast, laccase activity and C18:2ω6 levels increased during the first 19 days of A. bisporus colonization, while chitin content already reached its maximum at day 13. These data confirm that laccase is a good marker for vegetative growth of A. bisporus in compost as reported previously  (Wood 1979 ).
We present the discovery and spectroscopic confirmation with the ESO NTT and Gemini South telescopes of eight new 6. 0 < z < 6. 5 quasars with z AB < 21. 0. These quasars were photometrically selected without any star-galaxy morphological criteria from 1533 deg 2 using SED model fitting to photometric data from the Dark Energy Survey (g, r, i, z, Y), the VISTA Hemisphere Survey (J, H, K) and the Wide-Field Infrared Survey Explorer (W1, W2). The photometric data was fitted with a grid of quasar model SEDs with redshift dependent Lyman-α forest absorption and a range of intrinsic reddening as well as a series of low mass cool star models. Candidates were ranked using on a SED-model based χ 2 -statistic, which is extendable to other future imaging surveys (e. g. LSST, Euclid). Our spectral confirmation success rate is 100% without the need for follow-up photometric observations as used in other studies of this type.
In this paper we present the results of our search for high-redshift quasars in the first year of DES data. DES magnitudes, near infrared (NIR) VISTA magnitudes and WISE magnitudes are quoted on the AB system. The conversions from Vega to AB that have been used for the VISTA data are: JAB = JVega + 0. 937 and KsAB = KsVega + 1. 839, these are taken from the Cambridge Astronomical Survey Unit's website 1. The conversions used for the ALLWISE data are W 1AB = W 1Vega + 2. 699 and W 2AB = W 2Vega + 3. 339 which are given in  Jarrett et al. (2011)  and in the ALLWISE explanatory supplement. 2 When required, a flat cosmology with Ωm0 = 0. 3 and H0 = 70. 0 km/s/Mpc was used. The code used in this analysis makes use of the astropy python package  (Astropy Collaboration et al. 2013.
The data are then reduced using the DES data management process  (Desai et al. 2012; Mohr et al. 2012). DECam is particularly suited to high redshift survey work due to its large field of view and red sensitivity. The data contained in the Y1A1 release were taken between 2013 August 15 th and 2014 February 9 th. The Y1A1 release is shallower than the final survey depth and consists of 3707 coadded tiles covering two contiguous regions one overlapping the Stripe 82 area imaged by the SDSS and one overlapping with the area covered by the South Pole Telescope (SPT). The tiles are coadd images made up of between 1 and 5 exposures in each of the 5 wavebands with an average coverage of 3. 5 exposures making up each tile. A magnitude limit of zPSF 21. 0 was used in this work; this corresponds to an area of 1835 deg 2.
In this paper DES magnitudes quoted are point spread function (PSF) magnitudes derived from PSF fluxes calculated using PSFs for each coadd tile measured as part of the DES reduction using PSFex  (Bertin 2011). When other magnitude or flux measurements (e. g. aperture) are used this is explicitly stated. All magnitudes are given in the AB system. Aperture magnitudes and fluxes from DES are given for a 2" diameter aperture with an aperture correction applied based on the point spread function to compensate for missing Figure  1. Cumulative area versus 10σ z-band depth in a 2 diameter aperture (solid line), PSF magnitudes (dotted line) and auto magnitudes (dashed line) for the DES Y1A1 data. The aperture magnitudes were converted to PSF and auto magnitudes using the median offset between PSF (or auto) magnitude and aperture magnitude for point sources from the whole Y1A1 dataset.
VISTA Hemisphere Survey Data The VISTA Hemisphere Survey (VHS)  (McMahon et al. 2013)  aims to carry out a near infra red (NIR) survey of ∼ 18,000 degrees 2 of the southern hemisphere to a depth 30 times fainter than the Two Micron All Sky Survey (2MASS) in two wavebands J and Ks. The survey uses the 4m VISTA telescope at ESO's Cerro Paranal Observatory in Chile. In the Southern Galactic Cap ∼5000 degrees 2 , which will overlap the DES area, is being imaged more deeply (JAB = 21. 2, Ks;AB = 20. 4; 5σ point source depths) with partial coverage in H. This gives data in three bands (J, H and Ks) in the near infrared at ∼1-2µm. H band data is not being taken over the full DES and some of the area used in this project does not have H band imaging. The VHS data used in this work were taken between 2009 November 4 th and 2014 February 1 st.
Wide Infrared Survey Explorer Data Longer wavelength data at 3. 4, 4. 6, 12 and 22µm (known as W1, W2, W3 and W4 respectively) were used from the all-sky Wide Infrared Survey Explorer (WISE)  (Wright et al. 2010). The WISE satellite uses a 40cm telescope with a camera containing four 1024 x 1024 arrays with a median pixel size of 2. 757" and a field of view of 47 x 47 arcminutes. The telescope scanned the sky and took multiple images giving coadd 5σ point source depths of W1AB = 19. 3, W2AB = 18. 9, W3AB = 16. 5 and W4AB = 14. 6. In W1, W2 and W3 these coadd images had a full width at half maximum of 6. 1" and in W4 6. 4". Once the cryogenic fuel was exhausted in 2010 the telescope continued to survey the sky in the two shortest bands as part of the post-cryogenic NEOWISE mission phase. The two datasets were combined into the 2013 WISE AllWISE Data Release.