dataesr/scientific-paragraphs-categorization huggingface dataset:
Core genomes could only be computed of ribotypes, which were represented by two or more strains. For those ribotypes, which were represented by merely one strain, the whole genome instead of a core genome was included in the genome comparisons. Core genome and whole genome comparisons allowed the search for genes which are specific for (i. e. exclusively shared by) all members of a given cluster or cluster group. The size of each core genome depends on the number and diversity of the considered genomes. The number of genes determined for the 10 different core genomes ranges from 2299 to 3617 genes. The most comprehensive cluster (10 of 48 strains) in our dataset combines the PCR-ribotype 027 strains. This group shares the smallest core genome comprising 2299 genes. The largest core genome with 3617 genes was found for the PCR ribotype 075 with the strains E15 and T11.
The gene for binary toxin A is annotated differently in the genomes of our data set. It is annotated as binary toxin A, C2 toxin component I and iota toxin component Ia. In accordance to our specifications these CDSs are orthologous. The same holds true for the CDSs for binary toxin B, C2 toxin component II and iota toxin component Ib. BLAST comparisons of all genes that are associated with toxin activity were performed against the National Center for Biotechnology Information (NCBI) non-redundant database to see if these genes are similar to genes where toxin activity was described. In addition to toxin A, toxin B and binary toxin no other gene for which association with toxin activity was reported could be detected. Differences in toxin genes of C. difficile strains as found by comparisons of the pathogenicity loci have been used for differentiation purposes (toxinotypes;  [28] ).
The four strains with PCRribotypes 002 and 017 have also different toxinotypes (0 and VIII). The computation of the genes specific for certain groups of PCRribotypes confirmed the clustering of all two groups. The PCRribotype groups 033/078 and 078/126 had only few specific genes meaning they have few differences in the gene content. In this context it is interesting to note that in practise 078 and 126 strains are not always easily differentiated and therefore are sometimes designated as ribotype 078/126. The genome database annotation of the gene virE as ''putative virulence-associated protein E'' is suggestive of a possible role in virulence. Despite the fact that the function of virE has not been determined in C. difficile and hence it is not clear if it is really associated with virulence, we have included the virE sequences in the comparative analysis of this study, yielding an interesting result.
Hence no gene associated with toxin activity could be correlated only with so called hypervirulent C. difficile strains. It has been reported recently that PCR ribotype 027 is very similar to the ribotypes 016, 036 and 176  [30]. In our analysis the strain with ribotype 036 is always allocated to the same node except in the dendrograms for the genes tcdB and gyrA. Strains with the ribotypes 016 and 176 are not contained in our dataset. In conclusion, this report demonstrates that the PCR-ribotype is correlated with differences in the sequences of conserved genes. Thus it appears that the PCR-ribotype is indicative not only of a specific pattern of the 16S-23S rRNA intergenic spacer sequences, but also reflects specific differences in the nucleotide sequences of numerous genes such as the genes studied here.
Membranes were blocked with 5% skim milk powder (Saliter, Obergru ¨nzburg, Germany) and 1% bovine serum   S1. doi:10. 1371/journal. ppat. 1003957. g009 albumin (BSA, Sigma-Aldrich) in PBS-T (PBS/0. 1% Tween-20) for 1 h at room temperature. Membranes were incubated with primary antibodies overnight at 4uC in PBS-T/5% skim milk powder/1% BSA, followed by incubation with the corresponding HRP-conjugated secondary antibody for 1 h at room temperature. Proteins were visualized using ECL Prime Western Blotting Detection Reagent (GE Healthcare, Buckinghamshire, UK). Images were monitored using Fusion SL Gel Detection System (Vilber Lourmat, Marne-la-Vallee, France). Band densities were determined by BioID image analysis software (Vilber Lourmat), relative to the respective loading controls. The following primary antibodies were used: mouse anti-a-Tubulin (Merck), mouse anti-b-Actin (Sigma-Aldrich), chicken-anti-HPV18 E7 (E7C)  [11] , mouse-anti-p53 (BD Biosciences, Heidelberg, Germany), rabbit-anti-LEDGFp75 (Bethyl Laboratories, Montgomery, TX), rabbit-anti-Flag (Sigma-Aldrich) and mouseanti-cH2AX (Ser139, Millipore).
Cell cycle analyses For blocking HeLa cells in different cell cycle phases, cells were treated with 400 mM mimosine, 2 mM thymidine or 0. 04 mg/ml nocodazole (all from Sigma-Aldrich), respectively, for 16 h. Cells were trypsinized, washed in ice-cold PBS and fixed in 70% cold ethanol overnight at 220uC. Subsequently cells were pelleted, resuspended in PBS containing 1 mg/ml RNase A (Roche Diagnostics) and 25 mg/ml propidium iodide (Sigma-Aldrich) and incubated for 30 min at room temperature. Cell cycle analyses were performed by fluorescence-activated cell sorting (FACS) using a FACSCalibur (BD Biosciences) with CellQuest Pro software provided by the manufacturer. Apoptotic cells were excluded and quantitation of the percentage of cells in the individual phases was performed using FlowJo software (Tree Star, Ashland, OR), applying the Watson model  [79].
Statistical analyses Statistical significance of differences in measured variables between controls and treated samples was evaluated by a twosided paired t-test using the Sigma Plot software (Systat Software Inc. , San Jose, CA). For immunohistochemical analyses statistical significance of differences in calculated scores between histologically normal and HPV-positive samples was determined by twosided t-test using the SPSS software version 21 (Armonk, NY: IBM Corp. ). p-values of #0. 05 (*), #0. 01 (**), or #0. 001 (***) were considered statistically significant.
Accession numbers Genebank accession numbers according to the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/ genbank) for genes and proteins discussed in this paper are as follows: PSIP1 (Gene ID 11168), LEDGF (NP_150091. 2), HPV16 E6 (Gene ID 1489078), HVP16 E6 (NP_041325. 1), HPV16 E7 (Gene ID 1489079), HPV16 E7 (NP_041326. 1), H2AX (Gene ID 3014), H2AX (NP_002096. 1).
Images were captured for each sample with a final ×15 magnification. A representative gastric isthmus cross-section corresponding to the narrowest area composed of smooth muscles deprived of proventricular gland was selected and quantified using the Image J (http://rsbweb. nih. gov/ ij/) image analysis software. The different tissue layers were manually delimited (Figure  1 ), and the area of the layers, shape (circularity index), and folding (solidity index) of contours were calculated. Circularity equals 1 for a circular contour and becomes progressively small- er (toward 0. 1) as the structure flattens. Solidity equals 1 when no folding occurs and tends toward 0. 1 as folding increases similarly; a solidity of 1. 0 was assigned when there was no folding of the tissue and approached 0. 1 as folding increased.
ACKNOWLEDGMENTS This research was funded by the  National Agency for Research in France ; contract  ANR PCS-09-GENM-004  CHIEF. We thank  K. Gérard  ( UE 1295 Pôle Expérimental Avicole de Tours, INRA, Nouzilly, France ) for bird management, members of the  UE 1295 Pôle Expérimental Avicole de Tours  and  UR083   Recherches Avicoles ,  INRA , who helped with the sample collection, and  Christophe Praud  ( UR083 Recherches Avicoles, INRA ) for helpful discussions on histological quantification.
This user guide describes the rationale behind, and the modus operandi of a Unix script-driven package for evolutionary searching of optimal Support Vector Machine model parameters as computed by the libsvm package, leading to support vector machine models of maximal predictive power and robustness. Unlike common libsvm parameterizing engines, the current distribution includes the key choice of best-suited sets of attributes/descriptors, in addition to the classical libsvm operational parameters (kernel choice, kernel parameters, cost, and so forth), allowing a unified search in an enlarged problem space. It relies on an aggressive, repeated cross-validation scheme to ensure a rigorous assessment of model quality. Primarily designed for chemoinformatics applications, it also supports the inclusion of decoy instances, for which the explained property (bioactivity) is, strictly speaking, unknown but presumably "inactive", thus additionally testing the robustness of a model to noise.
Installation Download the. tar. gz archive and extract files in a dedicated directory. It does not include the libsvm package, which you must install separately. Two environment variables must be added to your configuration: GACONF, being assigned the absolute path to the dedicated directory, and LIBSVM, set to the absolute path of the residence directory of libsvm tools. An example (for tcsh shells) is given in the file EnvVars. csh of the distribution, to be edited and added to your. cshrc configuration file. Adapt as needed (use export instead of setenv, in your. profile configuration file) if your default shell is other than (t)csh. Specific executables shipped with this distribution are compiled for x86_64 Linux machines. They are all regrouped in the subdirectory $GACONF/x86_64, since it is assumed that your machine type, as encoded by the environment variable $MACHTYPE, is "x86_64".
If this is not your case, you will have to create your own executable subdirectory $GACONF/$MACHTYPE and generate the corresponding executables in that directory. To this purpose, source codes are provided for all Fortran utilities. They can be straightforwardly recompiled: foreach f ($GACONF/ *. f) g77 -O3 -o $GACONF/$MACHTYPE/$f:r $f end (please adapt to your favorite Unix shell syntax). Note: gfortran, f77 or other Fortran compilers may do the job as well: those snippets of code are fairly robust, using standard commands. simScorer, the executable used to calculate mean Euclidean distance and dot product values of training set descriptor vectors is a full-blown, strategic virtual screening tool of our laboratory. Its FreePascal source code cannot be released. However, we might perhaps be able to generate, upon request, the kind of executable you require.
Deployment-Specific Technical Parameters As hinted in the Introduction, this distribution supports a parallel deployment of the fitness scoring of operational parameter combinations encoded by the chromosomes. Four scenarios are supported: 1. local: several CPUs of a workstation are used in parallel, each hosting an independent machine learning/XV attempt (the "slave" job) with parameters encoded by the current chromosome, and returning, upon completion, the fitness score associated to that chromosome to the master "pilot" script overseeing the process. Master (pilot) and slave (evaluators of the parameterization scheme quality) scripts run on the same machine; however, since the pilot sleeps for most of the time, waiting for slaves to return their results, the number of slave jobs may equal the number of CPU cores available.
3. slurm: unlike in torque-driven deployment, the pilot script running on the front-end of the slurm-operated batch system reserves entire multi-CPU nodes for the slave jobs, not individualCPUs. Since slave jobs were designed to run on a single CPU, the slurm pilot does not directly manage them, but deploys, on each reserved node, a slave manager mandated to start the actual slave jobs on each CPU node, then wait for their completion. When the last of the slaves finished and returned results to the front-end pilot, the manager completes as well, and frees the node. The front-end pilot observes this, and reschedules another session on a free node, until the user-defined maximal number of parameterization attempts is reached. A runtime limit per node (walltime) must be specified: when reached, the manager and all so-far incomplete slave jobs will be killed.
User Guide: Understanding and Using the libsvm Parameter Configurator The libsvm parameter configurator is run by invoking, at command line, the pilot script dedicated to the specific deployment scheme supported by your hardware: $GACONF/pilot_<scheme>. csh data_dir=<directory containing input data> workdir=<intended location of results> [option=value. ] >& logfile. msg where <scheme> is either of the above-mentioned local, torque, slurm, bsub. The script does not attempt to check whether it is actually run on a machine supporting the envisaged protocol, calling pilot_slurm. csh on a plain workstation will result in explicit errors, while calling pilot_local. csh on the front-end of a slurm cluster will charge this machine with machine learning jobs, much to the disarray of other users and the ire of the system manager.
In D1-datadir, this is entitled ref. smi_act_info. It is a list of SMILES strings (column #1) of 272 ligands for which experimental affinity constants (pK i , column #2) with respect to the D1 dopamine receptor were extracted from the ChEMBL database  [8] , forming the training set of a D1 affinity prediction model. Several alternative schemes to encode the molecular information under numeric form were considered, i. e. , these molecules were encoded as position vector in several DS. These DS include pharmacophore (PH)-and atom-symbol (SY) labeled sequences (seq) and circular fragment counts (tree,aab), as well as fuzzy pharmacophore triplets  [9, 10]. For each DS, a descriptor file encoding one molecule per line (in the order of the reference list) is provided: FPT1. svm, seqPH37. svm, seqSY37.
Therefore, the user should not manually complete the training set compounds in the data directory with decoy molecules. An extra directory, to be communicated to the pilot script using the decoy_dir = <path of decoy repository> command line option, should be set up, containing DS. svm files of an arbitrary large number of decoy compounds. Again, all the DS candidates added to the data directory must be represented in the decoy directory. The software will therefrom extract random subsets of size comparable to the actual training set, automatically inject a low activity value into the first column, and join these to the training instances, creating a momentarily expanded training set. In case of doubt concerning the utility of decoys in the learning process, the user is encouraged to run two simulations in parallel-one employing decoys, the other not (this is the default behavior unless decoy_dir= <path to decoy repository> is specified).
svm, one would first need to call: $GACONF/svmPreProc. pl ExternalDS. svm selfile = DS_pruned. pri scale=yes output=ReadyToUseExternalDS. svm where the required DS_pruned. pri can be found in the working directory. If original (not scaled) descriptors are to be used, drop the scale=yes directive. If pruning was not envisaged (or shown not to be a good idea, according to the undergone evolutionary process), use the DS. pri selection file instead. The output. svm is now ready to used for prediction by the generated models. 1. wait = yes instructs the pilot script to generate the new working directory and preprocess input data, as described above, and then stop rather than launching the GA. The GA simulation may be launched later, using the cont = yes option. 2. maxconfigs = <maximal number of parameter configurations to be explored> by default, it is set to 3000.
Defining the Parameter Phase Space As already mentioned, the model parameter space to be explored features both categorical and continuous variables. The former include DS and libsvm kernel type selection, the latter cover (for SVMreg, i. e. , -regression mode only), cost γ and coef f 0 values. In general, as well as in this case, GAs typically treat continuous variables like a user precision-dependent series of discrete values, within a user-defined range. The GA problem space is defined in two mode-dependent setup files in $GACONF: SVMreg. rng and SVMclass. rng, respectively. They feature one line for each of the considered problem space parameters, except for the possible choices of DS, which are not available by default. After the preprocessing stage, when standardized. svm files were created in the working directory, the script will make a list of all the available DS options, and write it out as the first line of a local copy (within the working directory) of the <mode>.
rng file. Then it will concatenate the default $GACONF/<mode>. rng to the latter. This local copy is the one piloting the GA simulation, and may be adjusted whenever the defaults from $GACONF/<mode>. rng seem inappropriate. To do so, first invoke the pilot script with the data repository, a new working directory name, desired mode and option wait = yes. This will create the working directory, uploading all files and generating the local <mode>. rng file, then stop. Edit this local file, then re-invoke the pilot on the working directory, with option cont = yes. The syntax of. rng files is straightforward. Column one designs the name of the degree of freedom defined on the current line. If this degree of freedom is categorical, the remaining fields on the line enumerate the values it may take.
For example, line #1 is a list of descriptor spaces (including their optimally generated "pruned" versions) found in the working directory. The parameter "scale" chooses whether those descriptors should be used in their original form, or after Min/Max scaling (in clear, if scale is "orig", then DS. orig. svm will be used instead of DS. scaled. svm). The "kernel" parameter picks the kernel type to be used with libsvm. Albeit a numeric code is used to define it on the svm-train command line (0-linear, 1-3rd order polynomial, 2-Radial Basis Function, 3-Sigmoid), on the. rng file line, the options were prefixed by "k" in order to let the soft handle this as a categoric option. The "ignore-remaining" keyword on the kernel line is a directive to the GA algorithm to ignore the further options γ and coef f 0 if the linear kernel choice k0 is selected.
If so, it will update the population of so-far best, non redundant results in the working directory file best_pop, by sorting done_so_far by its fitness score, then discarding all chromosomes that are very similar to slightly fitter essays and cutting the list off at fitness levels of 70% of the best-so-far encountered fitness score (this "Darwinian selection" is the job of awk script $GACONF/chromdiv. awk). A new slave job will propose a novel parameter configuration by generating offspring of these "elite" chromosomes in best_pop. In order to avoid reassessing an already seen, but not necessarily very fit, configuration, it will also read the entire done_so_far file, in order to verify that the intended configuration is not already in there. If so, genetic operators are again invoked, until a novel configuration emerges.
However, neither best_pop nor done_so_far do not yet include configurations that are currently under evaluation on remote nodes. The asynchronous character of the GA does not allow absolute guarantees that it will be a perfectly self-avoiding search, albeit measures have been taken in this sense. Upon lecture of best_pop and done_so_far, the parameter selector of the slave job (awk script $GACONF/make_childrenX. awk) may randomly decide (with predefined probability) to perform a "cross-over" between two randomly picked partners from best_pop, a "mutation" of a randomly picked single parent, or a "spontaneous generation" (random initialization, ignoring the "ancestors" in best_pop). Of course, if best_pop does not contain at least two individuals, the cross-over option is not available, and if best_pop is empty, at the beginning of the simulation, then the mutation option is unavailable as well: initially, all parameter configurations will be issued by "spontaneous generation".
This, and also the stripping off of the "k" prefix in the kernel choice parameter, are also performed at the chromosome interpretation stage, mode-dependently carried out by dedicated "decoder" awk scripts chromo2SVMreg. awk and chromo2SVMclass. awk, respectively. At the end, the tool creates, within the attempt folder, the explicit option line required to pilot svm-train according to the content of the chromosome. This option line is stored in a one-line file svm. pars; check out, for example, $GACONF/D1-workdir/attempt. 11118/svm. pars, the option set that produced the fittest models so-far. A singled-out property-descriptor matrix and a command-line option set for svm-train are the necessary and sufficient prerequisites to start XV. For each of the ntrials requested XV attempts, the order of lines in the property-descriptor matrix is randomly changes.
Reconstruction and Off-Package Use of Optimally Parameterized libsvm Models As soon as the number of processed attempts reaches maxconfigs, the pilot script stops rescheduling new slave jobs. Ongoing slave processes are allowed to complete; therefore, the final number of lines in d one_so_far may slightly exceed maxconfigs. Before exiting, the pilot will also clean the working directories, by deleting all the attempt sub-folders corresponding to the less successful parameterization schemes, which did not pass the selection hurdle and are not listed in best_pop. A trace of their statistical parameters is saved in a directory called "losers". The winning attempt sub-folders contain all the fitted and cross-validated/predicted property tables and statistics reports, but not the battery of local SVM models that served to generate them.
These were not kept, because they may be quite bulky files which may not be allowed to accumulate in large numbers on the disk (at the end of the run, before being able to decide which attempts can be safely discarded, there should have been maxconfigs × lo times ntrials of them). However, they, or, actually, equivalent (recall that local model files are tributary to the random regrouping of kept vs. left-out instances at each XV step), model files can be rebuilt (and kept) by rerunning the pilot script with the use_chromo option pointing to a file with chromosomes encoding the desired parameterization scheme(s). If use_chromo points to a multi-line file, the rebuilding process may be parallelized: as the slave jobs proceed, new attempt sub-folders (now each containing lo times ntrials libsvm model files) will appear in the working directory (but not necessarily in the listing order of use_chromo).
This fitness score is in our opinion a good estimator of the extrapolative predictive power of the model. Furthermore, the approach allows decoy instances to be added in order to expand the learned problem space zone, without however adding uncertainty to the reported statistics (statistics of decoy-free and decoy-based models are directly comparable, because they both focus on the actual training instances and their confirmed experimental properties, ignoring "presumed" inactive decoy property values). Last, but not least, the approach is versatile, covering both regression and classification problems and supporting a large variety of parallel deployment schemes. It was, for example, successfully used to generate very large (> 9000 instances) dataset-based chemogenomics models  [11].
Acknowledgments The authors thank the  High Performance Computing centers of the Universities of Strasbourg, France, and Cluj, Romania , for having hosted a part of the herein reported development work. This work was also supported in part by a  Grant-in-Aid for Young Scientists  from the  Japanese Society for the Promotion of Science (Kakenhi (B)   25870336 ). This research was additionally supported by the  Funding Program for Next Generation World-Leading Researchers , as well as the  CREST program  of the  Japan Science and Technology Agency.