dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonresponders were patients showing progressive disease (PD) within the first 3 months of therapy (usually at first restaging), without marked toxicity leading to treatment discontinuation. All patients were treated with standard dosage of rapalogs: temsirolimus (n=41 at 25 mg IV weekly) or everolimus (n=38 at 10 mg PO daily). Clinical-pathological data was obtained either from Pfizer through a data transfer agreement, or collected retrospectively from the institutions at which treatment was given, and included treatment received and best response to rapalog. Uniform data collection templates were used for all subjects. Institutional Review Board approval was obtained locally before tissue acquisition, processing, and provision of clinical information.
Sequencing libraries were prepared, as previously described, starting from 200 ng of genomic DNA with inclusion of a unique bar-code for each sample to enable pooling  (29). Libraries were quantified using qPCR (Kapa Biosystems, Inc. , Woburn, MA), pooled in equimass amounts to 500 ng total, and captured using the Oncopanel_v3 baitset using the Agilent SureSelect hybrid capture kit. The captured libraries were again quantified using qPCR, and sequenced on a Hiseq 2500 sequencer (Illumina Inc. , San Diego, CA) in 2× 100 nucleotide (nt) paired end read mode. Primary sequence data were deconvoluted using index sequences to individual sample files and converted to FASTQ format using Picard tools. Reads were aligned to the human genome using bwa-0. 5. 8c (Burrows-Wheeler Alignment)  (30)  and filtered to eliminate reads of low quality and duplicates.
The data were then analyzed for sequence variants using tools from the Genome Analysis Toolkit (GATK)  (31) , including IndelGenotyperV2 and UnifiedGenotyper, to identify indels and single-nucleotide variants. A second approach was used in parallel to analyze the sequence data, with capture of read calls at all positions using SAMtools Pileup  (32)  followed by custom processing in Python and Matlab to determine base call frequency at each position in each read orientation for AKT1, AKT2, AKT3, MTOR, PIK3CA, PTEN, RHEB, TSC1, and TSC2. These data were then filtered to eliminate variant calls observed in only a single read orientation, or seen in multiple samples to exclude artifacts derived from the sequencing process. All variants observed at a frequency of ≥ 2% were directly reviewed using the Integrative Genomics Viewer  (33)  to identify bona fide variant calls and exclude sequencing or alignment artifacts.
Amplicon NGS was performed by NGS analysis of individual amplicons, leading to the generation of 10,000 -1,000,000 sequencing reads. These read files as well as the primary original read files were interrogated using Unix grep with a 20 nt sequence matching the wild-type allele and the mutant allele, to determine the precise frequency of mutant and wild-type reads. Missense and potential splice site variants in TSC1/TSC2 were compared to findings in the LOVD Tuberous Sclerosis mutation database to help assess pathogenicity. Missense variants were also assessed using PolyPhen2  (37)  and SIFT  (38). Missense variants in MTOR were assessed by comparison with the set of missense variants reported to cause mTORC1 activation  (39). Thus, only variants in TSC1/TSC2 that were thought to be inactivating, and those in MTOR that were thought to be activating were included in the subsequent analyses.
Assessment of copy number variation from exome sequencing data Copy number variants were identified using RobustCNV, a tool developed by the CCGD at DFCI (Paul Van Hummelen, personal communication). RobustCNV includes a normalization step in which systematic bias in mapping depth is reduced or removed using a two-step process. First, robust regression is used to fit tumor mapping depth values against mapping depths from a panel of normals (PON) sampled with the same capture bait set. Observed values are normalized against predicted values calculated from the fitted model and expressed as log2 ratios. In the second step, remaining GC bias is removed using a loess fit  (40). Normalized coverage data is then segmented using Circular Binary Segmentation  (41)  with the DNAcopy Bioconductor package and default settings.
Comparisons between mutational status according to response to mTOR inhibitors were evaluated using a one-sided Fisher exact test for each mutation category with hypothesis that a mutation category was positively associated with treatment response. Similarly exploratory analysis assessed non-mTOR gene mutations in ARID1A, ATM, BAP1, KDM5C, PBRM1, SEDT3, TP53 and VHL with treatment response. An exploratory analysis was also performed to assess the association of copy number (CN) alterations with response. All statistical analyses were performed using SAS v. 9. 2 (SAS Institute Inc. , Cary, NC, USA) and R 3•2•1 (The R Foundation for Statistical Computing, Vienna, Austria).
Methodology This paper is based on qualitative data collected from the fieldwork for the PhD research project "Woman and Rock Music in Portugal". 2 From 2012 to 2014, I conducted 59 in -depth interviews with white Portuguese women 3 who self -identified as rock music fans and/or musicians and DJs. The participants were mainly recruited using snowball sampling and by identifying DJ and musician pages online. The oldest interviewee was 50 years old and the youngest 18, but the majority interviewees were aged between the late twenties and forties. They all live in urban areas of Portugal. The majority had at least one degree (only 10 did not attend university, 8 of whom had completed secondary education). 4 Most of the participants were currently musicians (3 were professional musicians, living exclusively from music), and/or DJs.
Acrylamide gels were run at a constant voltage of 150 V in Tris/Gly buffer at pH 8. 3 for 30 min. Agarose gels were run at 100 V in 20 mM KPi at pH 6. 0 for 20 min. Visualization of the protein migration was achieved with Coomassie staining and a flatbed scanner. NMR characterization. 1  H-15 N TROSY-HSQC  43, 44  spectra were acquired at 303 K on an Agilent 600 MHz NMR spectrometer equipped with a HCN cold probe. The samples contained 0. 25 or 0. 5 mM 15 N-RSL in the sugar-free form or in the presence of 1-12 equivalents of Fuc-PEG, methyl-α-L-fucoside or L-galactose. The buffer (20 mM potassium phosphate, 50 mM NaCl, pH 6. 0) was identical for all samples. The data were processed in NMRPipe. 45 Differences in chemical shifts (∆δ) between the sugar-free and -bound forms of RSL were measured in CCPN  46  and the average perturbations were calculated as ∆δ avg = {[∆δ H 2 + (0.
2 × ∆δ N 2 )]/2} ½. 35, 47 othermal titration calorimetry. Experiments were performed at 25° C using a Nano ITC (TA Instruments) and conditions similar to those previously published. 32 Samples of protein (24 µM)   and ligands (1-2 mM) were prepared in identical buffer (0. 1 M Tris-HCl, pH 7. 5). Ligand solutions were added in 30 injections of 3 µl at intervals of 3 min while stirring at 300 rpm. A control experiment was performed by injecting Fuc-PEG into buffer, which yielded negligible heats of dilution. The data analysis was performed in NanoAnalyze.
SAXS characterization. Small-angle X-ray scattering experiments were performed at the ESRF BioSAXS beamline BM29 equipped with a Pilatus 1M detector (Dectris). 48, 49 RSL bound to Mefuc or to Fuc-PEG was characterized at four different protein concentrations (10, 5, 2 and 1 mg/ml) using the automated sample changer. 50 Data were collected also on a sample of the conjugate that was pre-treated via an online SEC at BM29. 51  All of the samples were prepared in the same buffer, identical to that used for the NMR experiments. 55 (automated sample changer) or 100 (SEC) µl volumes were exposed to X-rays for 10 individual frames, each 1 second in duration. Individual frames were processed with the automatic processing pipeline  52  , to yield individual radially-averaged curves of normalized intensity versus scattering angle s = 4πsinθ/λ.
Additional data reduction in EDNA utilized the automatic data processing tools of the EMBL-Hamburg ATSAS package  53, 54  to combine timeframes (excluding any data points affected by radiation damage), yielding the average scattering curve for each exposure series. Matched buffer measurements taken before and after every sample were averaged and used for background subtraction. Merging of separate concentrations and further analysis were performed manually using the ATSAS package. 55 he forward scattering I(0) and radius of gyration R g were calculated from the Guinier approximation. 56 The hydrated particle volume was computed using the Porod invariant 57 and the maximum particle size D max was determined from the pair distribution function computed by GNOM  58  in PRIMUS. 59 An estimate of the molecular mass (MM) of each construct was made from the I(0), with the partial specific volumes of RSL and PEG taken as 0.
In contrast the spectrum of RSL bound to Fuc-PEG was completely unchanged by the addition of the weaker binding L-gal. These experiments proved the noncovalent / reversible nature of the interaction between RSL and Fuc-PEG. ITC was used to further characterize the complex of RSL with Fuc-PEG (Figure  S8 ). The fit parameters matched previously published data for Mefuc,  32, 34  and the measured K d (1. 3 ±0. 3 µM) was consistent with the results of the competition experiments. Small angle X-ray scattering. RSL samples, at a maximum concentration of ~10 mg/mL, bound to Me-fuc or Fuc-PEG were characterised by SAXS. Data collection was performed using both the automated sample changer and the online SEC at BM29. The online SEC was used to remove high molecular weight aggregates (>150 kDa, Figure  S4 ) immediately prior to data collection.
Acknowledgements This research was supported by  Teagasc  ( Walsh fellowship  to PMA),  NUI Galway (NMR infrastructure support and Millennium Fund  to PBC), the  Leverhulme Trust  (grant  F/00128/BO  to AME), the  EU COST Action  ( TD1003   Bioinspired Nanotechnologies ) and  Science Foundation Ireland  (grants  10/RFP/BIC2807  and  13/CDA/2168  to PBC). We thank  M. L. Rennie ( NUI Galway ) for assistance with ITC data analysis,  R. Doohan  ( NUI Galway ) for maintaining the NMR facility, and  M. Wimmerová  ( Masaryk University ) for providing the pET25rsl plasmid. The  ESRF  is gratefully acknowledged for the provision of beam time and experimental support.
Microcanonical Annealing and Threshold Accepting for Parameter Determination and Feature Selection of Support Vector Machines Support vector machine (SVM) is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM) and Threshold Accepting (TA-SVM) to determine the optimal value parameter and the relevant features subset, without reducing SVM classification accuracy. In order to evaluate the performance of MA-SVM and TA-SVM, several public datasets are employed to compute the classification accuracy rate. The proposed approaches were tested in the context of medical diagnosis.
Chapelle et al. [11]  presented an automatically tuning multiple parameters and used the principal components to obtain features for the SVM technique. In  [12]  the authors adopt the accuracy rate of the classifier as the performance measure. Shon et al. [13] , use genetic algorithms in screening the dataset features. Seyyid Ahmed Medjahed et al. [14]  have proposed a new feature selection approach based on Gray Wolf Optimizer and a new objective function for band selection in hyperspectral image. In  [15] , the authors have used Binary Cuckoo Search which is a new optimization approach for hyperspectral band selection. In this study, the problem of parameter determination and feature selection is defined as a combinatorial optimization problem. The goal is to reduce the number of features by eliminating the irrelevant features and to determine the optimal value of SVM parameters.
We propose two novel approaches called: MA-SVM (Microcanonical Annealing -Support Vector Machine) and TA-SVM (Threshold Accepting -Support Vector Machine). The first approach is based on Microcanonical Annealing (MA) and the second uses Threshold Accepting (TA). These algorithms are stochastic optimization algorithms which have never been tested in the context of feature selection and parameter determination. We slightly modified the MA and TA algorithms to be adapted for our objective. Performance assessment was conducted on five medical datasets taken from the UCI Machine Learning Repository and two DNA microarray datasets largely used for cancer diagnosis (colon cancer and leukemia). The first contribution of our approach is to combine the problems of parameter determination and feature selection in a single problem which interestingly obtains good results and establishes a relationship with SVM parameters and the selected features.
Parameter Determination and Feature Selection Hyper-parameters of SVM (Parameter C and kernel function parameter) must be chosen determination in a single model. This combining improves the quality of SVM model and the generalization ability. Therefore, combining feature selection with parameter determination provides the values of SVM parameter C and kernel parameter in function of the relevant subset of selected features. In the proposed approaches, we reformulate the problems of parameter determination and feature selection as a single combinatorial problem which can be defined as follows: Considering the dataset D = {f 1 ,. , f t } where f i represents the feature of the dataset ∀i = 1,. , t. We define the set V = {V 1 ,. , V t }, V i = {0, 1} ∀i = 1,. , t, such as: if V i = 1 The feature f i is selected and participates in the construction of the model else if V i = 0 The feature f i is not selected and does not participate in the construction of the model In this study, the objective function E (X) of the proposed approaches is the classification error rate computed by using SVM.
Datasets In this study, to evaluate performances of the proposed MA-SVM and TA-SVM approaches, two experimentations are done. The first experimentation is conducted on five UCI Machine Learning Repository datasets (Breast Cancer, Cardiotocography, ILPD (Indian Liver Patient Dataset), Mammographic Mass and Vertebral Column) widely used in the literature. These datasets are taken from: http://archive. ics. uci. edu/ml/. The format of UCI Machine Learning Repository datasets is arranged as shown in Table  1. The second experimentation is done to support and validate our results. We have applied both approaches on two DNA microarray datasets: Colon Cancer and Leukemia. These datasets are widely used in the literature  [38] ,  [39] ,  [40] ,  [41] ,  [42]. The colon cancer dataset contains expression levels of 2000 genes taken from over 62 different samples (40 negative and 22 positive)  [43].
The leukemia dataset contains expression levels of 7129 genes taken from over 72 samples. Labels indicate which of the two variants of leukemia is present in the sample (AML: 25 samples and ALL: 47 samples)  [44]. The colon cancer and leukemia datasets used for this experimentation are characterized by the large number of features (genes expression) and the analysis of all these genes is impossible. Nevertheless, the selection of a relevant feature subset has an important role.
Parameters setting To conduct our experimentations and demonstrate the effectiveness of our approaches, the parameters of the proposed approaches are configured as follows: For MA-SVM, the total energy E t is set to 1000. This value will decrease slowly by following a geometric law with a ration α = 0. 99. The dataset is randomly divided, by using the holdout method, into three sets: 60% of instances constitute the training set, 20% of instances constitute the testing set and 20% of instances are used for the validation.
Results and discussion The results obtained for UCI Machine Learning Repository datasets by using the proposed MA-SVM and TA-SVM approaches are summarized in the Table  2. Table  2  describes the classification accuracy rate obtained by the proposed approaches for each dataset. The classification accuracy is computed by using the SVM classifier with Linear, Polynomial and Gaussian kernel functions. The first column in Table  2  represents the datasets. The second column is the classification accuracy rate obtained by TA-SVM and the third column is the classification accuracy rate obtained by MA-SVM. For breast cancer dataset, the high accuracy is achieved by using MA-SVM with Polynomial and Gaussian kernel (100% of accuracy). The same remark is observed for cardiotocography dataset, MA-SVM achieved 100%.
For ILPD dataset, MA-SVM using Gaussian kernel has reached 75. 47% of accuracy and we noted 75. 33% of accuracy obtained by TA-SVM with Gaussian kernel. Also, for Mammographic mass and Vertebral column datasets, MA-SVM using Gaussian kernel has achieved slightly higher accuracy, 91. 98%, than TA-SVM with Gaussian kernel which has achieved 91. 80%. As seen in Table  2 , both proposed approaches have produced a high classification accuracy rate with some advantage for MA-SVM. The Gaussian kernel and Polynomial kernel have produced quite similar results for breast cancer and cardiotocography datasets (100% using MA-SVM). For the rest of the datasets, the Gaussian kernel has achieved good accuracy compared to Polynomial kernel. Low classification accuracy is noted for the Linear kernel. The analysis of the results has demonstrated that high accuracy is observed for the Gaussian kernel.
This is why we focused the rest of our experimentations on the Gaussian kernel. The number of iterations and computational time of MA-SVM and TA-SVM using the Gaussian kernel is described in Table  3. Table  3  shows the number of iterations and the computational time of MA-SVM and TA-SVM. The first column contains the datasets. The second column contains the number of iterations and the last column contains the computational time in seconds. From Table  3 , we clearly observe that MA-SVM is much faster that TA-SVM. The number of iterations performed by MA-SVM is smaller than that performed by TA-SVM. The analysis of the results described in Tables  2  and 3 , allows us to highlight, on the one hand, that the MA-SVM approach provides better results compared to those of the TA-SVM, and, on the other hand, that both approaches, MA-SVM and TA-SVM, gave us a better classification accuracy rate and provided the most appropriate C and σ values for all the datasets used in the experimentation.
From Table  5  and for leukemia dataset, we noticed 99. 98% of accuracy obtained by MA-SVM and 66. 67% of accuracy obtained by TA-SVM. Also, the MA-SVM achieved 95. 65% of accuracy on colon cancer dataset and 65. 22% accuracy achieved by TA-SVM. In this experimentation, the results obtained by the MA-SVM are quite appropriate and very satisfying compared to those obtained by the TA-SVM in terms of classification accuracy rate and computation time. It is clear that the number of selected features is much smaller than the initial one. TA-SVM is not very effective on large datasets (large number of features) such as the microarray gene expression dataset. This is due to the fact that TA-SVM must compute the threshold set in each round, which is very intense in computation time. The results obtained with the two datasets corroborate the conclusion that we have reached.
Conclusion In this paper, we present two novel approaches in feature selection and parameter determination applied for medical diagnosis. The approaches are called MA-SVM and TA-SVM. The first one is based on the Microcanonical Annealing algorithm, while the second one is based on the Threshold Accepting algorithm. The objective function to minimize is the classification error rate. The aims of this study are to determine the optimal parameter of the SVM and its kernel function. In addition, we attempt to select the optimal subset of features by removing the irrelevant and redundant features. The experimentation is done under five UCI Machine Learning Repository datasets and two DNA microarray datasets widely used in the literature. The results obtained by both approaches demonstrate that MA-SVM improves the classification accuracy rates by removing trivial or insignificant features and effectively finds the better parameters values.
Participants came to a common approximation of the English term for mental illness, termed as "Rawani" but specifying a focus on 'severe psychological problems' and drawing a distinction between intellectual and mental disability. The specification of terms within the modeling session allowed us to develop a model of a complex concept that did not have a clear Dari or Pashto language analog. Between sessions and after the conclusion of the workshop, the researchers iterated a further series of revised model using Vensim PLE software. Revisions primarily focused on closing implicit feedback loops and taking decisions about how constructs could be aggregated or disaggregated to clarify meaning. All revisions were grounded in the model itself and based on notes taken by the facilitation team during the GMB session.
H3K27ac marks hundreds of SEs in zebrafish To assess characteristic features of vertebrate SEs, we identified enhancer regions in zebrafish (Danio rerio), mouse, and human brain, heart, intestine, testis, and pluripotent cells. For zebrafish, we used the early embryonic dome stage as a comparative stage to the pluripotent state of mouse and human ESCs  (Schier and Talbot 2005). All mouse and human enhancer annotations, as well as zebrafish pluripotent state enhancer annotations, were based on publicly available data sets of the H3K27ac mark, whereas those of the zebrafish adult brain, heart, intestine, and testis were performed using in-house generated H3K27ac ChIP-seq data sets (Fig. 1A ; Supplemental Table  S1 ;  Bernstein et al. 2010; Rada-Iglesias et al. 2011; Bogdanovicet al. 2012; Chadwick et al.
2012; Mouse ENCODE Consortium 2012; Nord et al. 2013; Yue et al. 2014). To identify typical enhancers and SEs, H3K27ac-enriched regions were identified with SICER  (Zang et al. 2009) , filtered to discard active promoters, and stitched by the ROSE software (Fig. 1A ;  Lovén et al. 2013; Whyte et al. 2013). We identified an average of 743 and 1183 SEs for zebrafish and mammals, respectively (Fig. 1B ; Supplemental Table  S1 ; Supplemental Data set S1). Similar to mammalian SEs, most zebrafish SEs were longer than typical enhancers, although the length parameter was not explicitly considered for their identification (Supplemental Fig. S1A-C ; examples of typical enhancers and SEs are shown in Supplemental Fig. S2A ).
Computational analyses The calculation of typical enhancer and SE distributions around TSSs was performed using Nebula  (Boeva et al. 2012). Typical enhancer and SE enrichments over gene bodies were calculated with a customized script (Supplemental File S3) and control enrich-ments were obtained by bootstrap resampling with 100 iterations. To calculate the percentage of typical enhancer and SE sequences overlapping with genomic features, typical enhancer and SE annotations were compared to RefSeq Gene annotations  (Rosenbloom et al. 2015)  using BEDTools intersect function  (Quinlan and Hall 2010). Sequence conservation scores were calculated based on the vertebrate conservation PhastCons tracks from UCSC  (Siepel and Haussler 2005; Siepel et al. 2005 ) associated with each of the genome versions used for read mapping using hgWiggle  (Kent et al.