dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, a polyphasic approach is currently proposed in our laboratory to describe new bacterial taxa, including their genome sequence, MALDI-TOF spectrum, and major phenotypic characteristics such as Gram staining, culture conditions, metabolic characteristics, habitat and, if applicable, pathogenicity  [9]. Here we present a summary classification and a set of features for Dermabacter indicis sp. nov. , together with a description of the complete genome sequencing and annotation. These characteristics support the circumscription of the Dermabacter indicis species. identify the strain FF11 T , MALDI-TOF protein analysis was performed using a Microflex LT (Bruker Daltonics, Leipzig, Germany), as previously reported  [17, 18]. The scores previously established by Bruker to identify or validate species compared to the instrument's database were applied.
In short, a score of 2. 000 with a species with a validly published name allows identification at the species level; scores of 1. 700 and <2. 000 allow identification at the genus level; and a score of <1. 700 does not allow any identification to be made. We performed 12 distinct deposits from 12 isolated colonies of strain FF11 T. They were then imported into MALDI Biotyper software (version 2. 0, Bruker) and analysed by standard pattern matching (with default parameter settings) against the main spectra. Scores ranging from 1. 315 to 1. 511 were obtained for FF11 T , suggesting that this strain was not a member of any known species. The reference mass spectrum from strain FF11 T was incremented in our database (Fig. 1 ). Moreover, strain FF11 T exhibited 97. 9% 16S rRNA sequence similarity with Dermabacter hominis  [1]  (GenBank accession no.
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position, 16S rRNA similarity and phenotypic differences with other members of the Dermabacteraceae family. Here we present the first Dermabacter indicis sp. nov. genome. The EMBL/EBI accession number is CYUG00000000. Table  3  shows the project information and its association with MIGS (minimum information about a genome sequence) version 2. 0 compliance  [23].
1 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 39-hour run at a 2 × 251 bp read length. Total information of 5. 9 GB was obtained from a 624K/mm 2 cluster density with cluster passing quality control filters of 96. 33% (12 040 000 clusters). Within this run, the index representation for Dermabacter indicis FF11 T was determined at 16. 54%. The 1 918 640 paired reads were filtered according to the read qualities. These reads were trimmed and then assembled using the CLC genomicsWB4 software.
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [24]  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  [25]  and the Clusters of Orthologous Groups (COGs) database using BLASTP. The tRNAScan-SE tool  [26]  was used to find tRNA genes, while ribosomal RNAs were found using RNAmmer  [27]  and BLASTn against the GenBank database. Transmembrane topology and signal peptide predictors were provided using the Phobius server  [28]. ORFans were identified if their BLASTP E value was lower than 1e-03 for an alignment length greater than 80 aa. If alignment lengths were smaller than 80 aa, we used an E value of 1e-05. Such parameter thresholds have been used in previous works to define ORFans.
Artemis  [29]  was used for data management and DNA Plotter  [30]  for the visualization of genomic features. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [31]. Briefly, this software combines the Proteinortho software  [32]  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. Annotation and comparison processes were performed in the Multi-Agent Software System DAGOBAH  [33] , including Figenix  [34]  libraries that provide pipeline analyses. Genome-to-Genome Distance Calculator (GGDC) analysis was also performed using the GGDC web server as previously reported  [35, 36].
Dermabacter indicis strain FF11 T is susceptible to ciprofloxacin, amoxicillin/ clavulanic acid, ticarcillin, ceftriaxone, imipenem, doxycycline, gentamicin and cefalotin but resistant to colistin, trimethoprim/ sulfamethoxazole, erythromycin and nitrofurantoin. The G+C content of the genome is 63. 2%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers LN810502 and CYUG00000000, respectively. The type strain FF11 T (= CSUR P1488 = DSM 100283) was isolated from a human finger wound in Dakar, Senegal.
08 Nucleotide transport and metabolism H 72 3. 47 Coenzyme transport and metabolism I 42 2. 02 Lipid transport and metabolism P 106 5. 11 Inorganic ion transport and metabolism Q 22 1. 06 Secondary metabolites biosynthesis, transport and catabolism R 198 9. 54 General function prediction only S 105 5. 16 Function unknown - 1183 35. 23 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome. FIG. 6. Distribution of functional classes of predicted genes in genomes of indicated chromosomes according to clusters of orthologous groups of proteins. BF, Brachybacterium faecium; BM, Brachybacterium muris; BP, Brachybacterium paraconglomeratum; BS, Brachybacterium squillarum; DH, Dermabacter hominis; DI, Dermabacter indicis.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
In contrast, the experimental APs recorded from real cardiac cells are variable, with changes from beat-to-beat in a single cell (termed intrinsic variability), and from one cell to another (extrinsic variability). Intrinsic variability may be caused by random processes such as stochastic ion channel gating, non-linear dynamics such as alternans of action potential duration (APD), or more complex behaviour. Extrinsic variability is considered to be caused by quantities that genuinely vary from cell to cell, e. g. cell size or ion channel expression. In practice it can be difficult to distinguish these sources of variability; in what follows we model variability as extrinsic only, although some of our data may also capture intrinsic variability. In addition, variability can be compounded by measurement errors when data from experiments are used to generate parameters for use in AP models.
Synthetic data We used noisy simulated data as our voltage recordings, which provide a best-case scenario of the recordings we would observe if the AP model was 'correct' (eliminating the need to consider structural uncertainty). We refer to these simulated traces with added noise as synthetic data. Synthetic data were generated by solving equations for a particular AP model, given one of our experimental protocols, with the conductances that feature in the published model. We sampled the simulation output at 0. 2 ms intervals and then added random noise to each point of this synthetic voltage trace to simulate any noise in the system or the observations. The noise was randomly sampled from a Normal distribution with mean zero and standard deviation of σ = 0. 25 mV, chosen as this described the noise at resting potential on a repolarised canine ventricular AP experimental recording (as described below).
Time resolution of the recordings was 250 μs (4 kHz), while voltage resolution was 31. 2 μV. Recordings in the dataset (available to download with the simulation code) cover ≈ 800 ms of each pace, when the action potential occurs, in order to give processing time to log these data continuously. The bath solution was composed of (concentrations in mM) NaCl 130, KCl 4, CaCl 2 1. 8, MgCl 2 1. 2, NaHCO 3 18, HEPES 10 and glucose 10, and pH was set at 7. 4 with NaOH. Pipettes were filled with (in mM) NaCl 10, KCl 130, MgCl 2 0. 5, HEPES 10, MgATP 5, and pH was set at 7. 2 with KOH. After 200 s of pacing 10 μM of Moxifloxacin was introduced to the bath, and at 240 s the bath concentration of KCl was increased to 5. 4 mM. The APD of each experimental pace is shown in Supplementary Material Figure  B2 , along with an indication of the paces that were selected as approximately steady-state for training and validation purposes.
Implementation of the TP06 model The TP06 model for epicardial cells was implemented in MatLab (Mathworks), with code automatically generated from the Physiome Repository (http://models. physiomeproject. org, [48] ). The model was configured to run for 20 beats at a cycle length of 1000 ms using the initial conditions specified in CellML, and was solved with the Matlab time adaptive solver for stiff systems of ordinary differential equations ode15s. Each beat was elicited with a stimulus of strength -52 pA pF -1 applied for 1 ms. This protocol enabled the model to approach a steady state, and in the multiple runs undertaken for the design data (see below) the greatest absolute difference in APD between the 19th and 20th beats was less than 0. 5 ms.
Inputs and outputs We selected as model inputs the 12 maximal conductances in the TP06 model, along with a multiplier of the time constant for slow inactivation of the L-type Ca 2 + channel τ f. The rationale for selecting these parameters as inputs was that the maximal conductances provide a clear link to cellular physiology as they represent the density of ion channel proteins in the cell membrane. As outputs, we selected six descriptors of AP shape based on membrane voltage (V m ) measurements used in recent studies (e. g. [6] ); maximum dV m /dt, maximum V m , dome V m , APD 90 , resting V m , and APD 50. For the design and test data (see below) these outputs were obtained from the 20th AP in each run.
Design data and emulator construction We built separate emulators for each model output. The GP parameters for each emulator were determined by fitting to design data, comprising a set of input parameters and corresponding model outputs obtained from multiple runs of the model. For each model run, we selected the input parameters using Latin hypercube sampling from uniform distributions centred on the input parameter value specified in the original model, and with a range of half this value (see Table  2 ). We chose this range so that the emulator could be trained over a region of parameter space that was large enough to examine the effects of uncertainty in the input parameters. For clarity and convenience, each of these input parameter ranges were transformed into normalised units, so that the minimum value of the input parameter became 0, the maximum became 1, and the central value 0.
Conclusions Uncertainties in cardiac action potential models and their parameters need to be carefully quantified, and that results from simulation studies should be examined in a probabilistic framework to establish the robustness of their predictions to uncertainties. Such uncertainty quantification is crucial to the development of robust, reliable and trustworthy electrophysiological models, since cardiac myocytes are associated with much observational uncertainty and natural variability, and UQ allows these to be formally and rigorously integrated into the models. While the statistical techniques applied in this paper are advanced, we wish to emphasise that in some cases the simplest approach to UQ may be sufficient (fitting a distribution for a parameter to a dataset, and then using repeated sampling to estimate the distribution of a model output, see  [10]  for an example of this).
Sources of Funding RHJ is supported by a  Systems Approaches to Biomedical Science Industrial Doctorate Centre studentship  ( EP/G037280/1 ) and  F. Hoffmann-La Roche AG. RHC and ETYC gratefully acknowledge support from the  UK Engineering and Physical Sciences Research Council  (www. epsrc. ac. uk) grant number  EP/K037145/1. GRM gratefully acknowledges support from a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  (Grant Number  101222/ Z/13/Z ).
The HRSV strain 1856 was sequenced using the Ion Torrent platform. A single-end library was generated, resulting in 143,843 reads with an average length of 153 bp (22,064,342 bases). The whole-genome sequence of 15,276 nucleotides was assembled with TMAP version 4. 2. 18 using the Netherlands strain 03-034613 (accession no. JX576759. 1) as a mapping reference sequence. The average coverage was 1,321ϫ and only one contig was obtained, annotated, and submitted using the NCBI BankIt tool. The sequence contains the 5= and 3= untranslated regions and 11 open reading frames that encode NS1, NS2, N, P, M, SH, G, F, M2-1, M2-2, and L proteins. Nucleotide sequence accession number. The whole-genome sequence of strain 1856 has been deposited in GenBank under the accession number KR350475. and do not necessarily represent the official opinion of the Ministry of Health in Mexico.
Spectroscopy. To monitor gel uptake and retention of the DOX, acellular spectroscopic studies were performed on collagen gels. The gels were prepared in an identical manner as that in the cellular studies and dosed with media containing the required concentration as in the cellular exposure protocol. The DOX content was monitored, in situ in the well plates, with the aid of a Molecular Devices Spectramax M3 spectrometer by means of both absorbance measurements at 480nm Statistical Analysis. At least three independent experiments were conducted for all experiments. Test results for each assay were expressed as percentage of the unexposed control ± standard deviation (SD). Control values were set as 100%. Differences between samples and the control were evaluated using the statistical analysis package Minitab14.
Patients were randomized (1:1) with a minimization technique stratifying them by chemotherapy timing: perioperative versus postoperative, local intervention: surgery versus radiofrequency ablation (RFA) with/without surgery, and Fong's score: 0-1 versus 2-3 versus 4-5. Patients received either 12 FOLFOX4 cycles (oxaliplatin dose: 85 mg/m 2 ) or 6 FOLFOX7 cycles (oxaliplatin dose: 130 mg/m 2 ), followed by 6 FOLFIRI cycles (irinotecan dose: 180 mg/m 2 ), 1 cycle every 2 weeks. All patients were fully informed of the study and provided signed written informed consent. The protocol was approved by the ethics committees of Lille, France ("Comité de Protection des Personnes"). This study MIROX (Combination Chemotherapy in Treating Patients with Colorectal Cancer and Resectable Metastases) was registered on ClinicalTrials.
gov (Identifier: NCT00268398). The protocol for this trial (including the written informed consent form and the list of Ethics Committees) and supporting CONSORT checklist are available as supporting information (S1 and S2 Protocols and S1 Checklist). The primary endpoint was 2-year disease-free survival (DFS) and analysis was conducted by intend to treat (ITT). Secondary endpoints were overall survival (OS), objective response rate (ORR), resection type (R0-R2), toxicity, and HRQoL. TUDD approach was used for HRQoL longitudinal analysis. Between May 2004 and June 2010, 284 patients were enrolled.
Analysis of HRQoL TUDD was defined as the interval between randomization and the first decrease in HRQoL score 5 points compared to baseline HRQoL score with no further improvement or in case of patient who dropped out after a 5 points decrease, resulting in missing data or death  [10, 19]. Alive patients were censored at the last HRQoL follow-up if a 5 points deterioration from baseline was not observed or if a 5 points decrease was present, but was followed by secondary 5 points improvement  [10]. All randomly assigned patients with a baseline and at least one post-baseline HRQoL assessment were included in TUDD analyses. TUDD was calculated using the Kaplan-Meier method and compared with the log-rank test. TUDD was described using medians with 95% confidence interval (CI). The univariate Cox model was used to calculate HR with 95% CI.
Some limitations of the analysis presented here should be noted. Firstly, although the MIROX study represents a large dataset, not all patients completed questionnaires, which could have an impact on the validation of results. Although the non-responders rate was high (39. 8%), the patient characteristics were similar to responders. Nevertheless, a 60. 2% HRQL response rate is considerable for advance-disease population study. Secondly, as we did not correct for multiple comparisons it may have introduced possible inflation of Type I error and in turn resulted in the lack of significant finding or findings with particularly low significance  [26, 27]. In conclusion, the present HRQoL results support our recent findings study about the lack of a clinical and statistical significant difference between FOLFOX4 and FOLFOX7-FOLFIRI in the main dimension of the QLQ-C30 scores in CRC patients with advanced disease.
This work was funded by the  Group Coopérateur Multidisciplinaire en Oncologie Results gov NCT00268398 (GERCOR) (France ) and the Ligue nationale contre le cancer. This work was also supported by the  National clinical research Platform for Quality of life in Oncology  labelled by the  National Cancer Ligue  and the  French National Cancer, Institute , and by  Sanofi-Aventis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
For all subsequent time points, tissues from at least three animals were collected and analyzed separately. As the A/J strain has an extra day of gestation relative to the other inbred strains used in this study, embryos were collected at E19. 5 only for this strain. Only male mice were used in this study. The sex of every embryo and pup less than 21 days of age was determined by PCR of DNA extracted from tail fragments using primers designed to amplify the male-specific Sry gene (MGI:98660) (Data S1). All animal work was performed in accordance with Jackson Laboratory Animal Care and Use Committee (ACUC) protocol 101011 (CJB). Total RNA was extracted using mirVana RNA isolation kits (Ambion) following manufacturer's directions. An additional DNase I (Promega) digestion step was added before organic extraction.
Data processing Gene expression was assayed using the Affymetrix Mouse Gene 1. 0 ST array platform (Affymetrix). The. CEL files from the arrays were deposited in the Gene Expression Omnibus (GEO) archive at NCBI  (Barrett et al. , 2013)  (Accession GSE74243). The array data were processed using the DeSNPer pipeline developed at the Center for Genome Dynamics (CGD) at The Jackson Laboratory (https://github. com/jaxcs/DeSNP). The DeSNPer algorithm removed probes with single nucleotide polymorphisms (SNPs)  (Yalcin et al. , 2012)  among the three strains from further analysis to ensure that gene expression differences detected among the strains were not due to hybridization biases resulting from strain-specific sequence variation. Following probe removal, the data were log2-transformed, then probe-level hybridization intensities were quantile normalized and median polished to generate summarized gene-level expression values  (Irizarry et al.
, 2003). A Z-score transformation was then applied, standardizing each gene to an average of zero and standard deviation of one across all samples. Finally, to focus data interpretation on the dominant patterns of gene expression changes across the developmental time series, the dataset was filtered to retain the top 30% genes with the largest pre-Z-transform standard deviation (referred to as the ''filtered dataset''; Data S2). The temporal expression patterns for all of the genes measured on the arrays can be visualized using the web interface implemented for this project (http://lungdevelopment. jax. org/).
Principal Component Analysis (PCA) The filtered dataset described above (Data S2) was analyzed using Principal Component Analysis (PCA), a data transformation method to reduce the dimensionality of a dataset to those orthogonal components that account for the maximum variance in the data with the fewest number of observations. PCA was performed using JMP R v11. 1. 1 (SAS Institute) using the principal component toolset within the multivariate library. All default settings were used and no weighted bias was applied. PCA was first used to identify any systematic biases between samples, specifically among batches of arrays that were processed together (i. e. , batch effects). This process identified a subset of 42 arrays that were strongly correlated with each other independent of strain or time point.
The batches containing these arrays corresponded with changes in reagent chemistry used for array hybridization and were removed from further analysis. PCA was then performed on the final set of 216 arrays to identify global patterns of expression variation between samples. This analysis generated two data matrices: gene loading values (Data S3), which represent the correlation between each gene and the expression patterns captured by each principal component (PC), and sample scores (Data S4), which reflect the relationship between each sample and each principal component. The gene loading values are either positive or negative, with positive (negative) values reflecting correlation (anti-correlation) between the gene expression and the sample score plot (Fig. S1 ). Characteristic gene sets for each component were created using the most highly correlated genes (positively and negatively) from each PC.