dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation In order to evaluate the runtime performance and analyze the quality of samples generated by WAPS, we implemented a prototype in Python. For d-DNNF compilation, our prototype makes use of Dsharp  [43]  when sampling set is available else we use D4  [30]. We would have preferred to use state-of-the-art d-DNNF compiler D4 but owing to its closed source implementation, we could not modify it as per our customized compilation procedure PCompile. Therefore, for projected compilation, we have modified Dsharp which has an open-source implementation. We have conducted our experiments on a wide range of publicly available benchmarks. In all, our benchmark suite consisted of 773 benchmarks arising from a wide range of real-world applications. Specifically, we used constraints arising from DQMR networks, bit-blasted versions of SMT-LIB (SMT) benchmarks, and ISCAS89 circuits  [8]  with parity conditions on randomly chosen subsets of outputs and nextstate variables  [34, 48].
Simulated data were analyzed using Scilab to compute productive and reproductive performance statistics by reproductive cycle (from birth to first parturition for parity 0 and from parturition n to n + 1 for parity n > 0). Within each cow, reproductive intervals were calculated for each reproductive cycle between event times: estrous cycle length (i. e. ovulation without conception to next ovulation), parturition to first ovulation, parturition to conception with completed gestation, calving interval and gestation length. Within-cow reproductive rates were calculated for each reproductive cycle using ratios of event counts: insemination rate is the number of inseminations divided by the number of ovulations; non-fertilization rate is the number of fertilization failures divided by the number of inseminations; early and late embryo and fetal death rates are the number of early and late embryo and fetal deaths divided by the number of inseminations; and parturition rate is the number of parturitions divided by the number of inseminations.
Acknowledgments The authors would like to acknowledge  Henri Woelders ,  Nicola Dennis  and  Susanne Meier  for sharing their expertise on genotype-environment effects on reproduction, and the  INRA PHASE Cowplaner group  for initial discussions on reproductive physiology. The authors would also like to acknowledge the  European Union  for funding this project. This study was performed as a part of the European Project  Pluridisciplinary study for a RObust and sustainabLe Improvement of Fertility In Cows (PROLIFIC , grant no. FP7. KBBE. 2012. 6 ,grant agreement no. 311776 ).
1(b )), in agreement with previous reports  [20]. To best mimic the structure of a deformed sample, we used the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)  [21]  software package to perform molecular dynamic simulations of pure Ni highly deformed under uniaxial compression. The resulting atomic positions were used an input for image simulations of APT. Using dislocation analysis (DXA)  [22]  in OVITO  [23] , all dislocations were identified in the input file. About 2% of Re atoms were randomly placed Ni sites, and a 20 at. % concentration of Re (20%) were positioned on stacking faults (more details are in Suppl. Material). Using this approach, a local enrichment of Re close to dislocations is generated. A subset of the original input data is shown in Fig  1(c ). This virtual specimen was field evaporated using the Robin-Rolland Model, described in retail in Ref.
The gas pressure was adjusted so as to maintain a pressure in the range of 1. 3±0. 5 10 - 7 mbar. Time-of-flight FIM was performed on the same instrument, with high-voltage pulses and a pulse fraction of 20%. A similar approach with laser pulses was recently discussed in Ref. [40]. The APT data reconstruction and post-processing was done in the commercial software IVAS 3. 8. 2. FIM data extraction in the form of a. epos file came from the same software package, and further processing was done via in-house built routines in MATLAB. A first pass filter is applied following the idea that ions generated at a higher electric field during the pulse will tend to arrive on higher order of hit multiplicity  [30]. These correlated field-evaporative events are also known to be spatially correlated  [29] , and hence a second pass filter is applied whereby only ions arriving within a radius of 2mm of each other are considered.
DFT for FIM contrast interpretation We used DFT in the local-density approximation using the projector-augmented wave approach  [41]  as implemented in the SPHInX code  [42]. The surfaces were modelled in the repeated slab approach, with a thickness of six layers along the (011) normal (approx. 1. 4 nm) and a vacuum separation of 1. 5 nm. The slabs were subject to a field of 50 V/nm on one side using the generalized dipole correction. Owing to the difficulty of convergence in DFT for high fields, a field of 50 V/nm was chosen. Test calculations show that variations in the field strength by over 10% do not qualitatively change results. The imaging plane in the vacuum for the DOS calculation was chosen such that the average electrostatic potential on that plane corresponds to 20. 8eV above the Fermi energy, i.
At a constant field evaporation rate, Re atoms will tend to preferentially be in retention at the specimen's surface, in order to enhance the local electric field sufficiently to cause field evaporation. To best mimic the deformed structure, we used the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)  [21]  software package to perform molecular dynamic simulations of a Ni sample highly deformed by uniaxial compression. The resulting atomic positions were used an input for image simulations of FIM and APT. The deformed volume was replicated using periodic boundary conditions, and then reduced into a tip with an initial radius of 12 nm. The input contains 1. 4 millions of atoms. Using dislocation analysis (DXA)  [22]  in OVITO  [23] , all dislocations were identified in the input file.
The central considerations in these studies have been the predictive ability of available statistical models and the composition and size of the training population  (Heslot et al. , 2012; Daetwyler et al. , 2013; de los Campos et al. , 2013). Using eight datasets from four plant species including wheat and barley,  Heslot et al. (2012)  tested 11 GS models and found predictive abilities to be equivalent for many of the methods but with differences in computational times. Ridge regression best linear unbiased prediction (RR-BLUP), is computationally efficient  (Endelman and Jannink, 2012; Lipka et al. , 2015)  and is used in the present study to assess predictive variation between genetic marker sets. In this study, a previously described panel of 376 elite winter wheat varieties released or commercialized in the  UK, France, and Germany between 1946 and 2007 (Bentley et al.
All trait data are summarized in Supplementary Table  S1  and available from Figshare DOI: 10. 6084/m9. figshare. 7350284. For each trait, best linear unbiased estimates (BLUEs) were generated in GenStat  (Payne, 2009)  for variety performance at each site and over all sites for use in association analysis and genomic prediction. Marker-trait association for FT and GY was calculated on BLUEs for each site per year and overall values from all sites. Association for PH and LR was calculated from overall site BLUEs.
Genotyping, Variant sNP Calling, and Imputation Genomic DNA was isolated from 2-week-old seedlings of each line using a modified Tanksley extraction protocol  (Fulton et al. , 1995). GBS was conducted as described by  Poland et al. (2012b). To ensure adequate sequencing coverage and enhance accuracy each line was replicated four times with each replicate identified by a unique barcode. GBS libraries were sequenced in 96-plex across four flow cell lanes in Illumina HiSeq. Fastq sequence files were processed in the TASSEL GBS pipeline version 5. 2. 31  (Glaubitz et al. , 2014). Reads were trimmed to 64 base pairs and filtered based on sequence quality score to obtain only good quality reads with a barcode sequence, five nucleotides of PstI restriction site fragment and no unreadable bases (N) in between.
Population structure A subset of 7,865 uncorrelated SNPs derived from thinning the full set of SNPs based on physical distance (minimum distance 100,000 bp) was used for evaluation of population structure. Principal coordinate analysis (PCoA) was conducted in the R package "ade4"  (Dray and Dufour, 2007). The first two principal coordinates accounting for the largest proportion of variation were used to visualize patterns of population structure within the panel. Population structure was also evaluated using the Bayesian clustering approach implemented in the software STRUCTURE 2. 3. 4  (Pritchard et al. , 2000). A burn-in of 100,000 iterations followed by a Markov Chain Monte Carlo (MCMC) of 100,000 iterations was executed to estimate the number of subpopulations. An admixture model was applied for two to ten putative populations (K) and six independent runs were conducted for each K.
The optimal K value was inferred based on the rate of change in log probability of data between successive K values using the ad hoc statistic, DeltaK  (Evanno et al. , 2005). The program CLUMPP was used to assign results from separate STRUCTURE runs to common populations  (Jakobsson and Rosenberg, 2007). The panel had been previously genotyped with 2,012 polymorphic dominant Diversity Array Technology (DArT; www. diversityarrays. com) array markers and 1,804 markers retained for analyses. In this study, a subset of 1,117 unlinked DArT markers were reselected and used to re-estimate PCoA based on DArT.
Genomic Prediction RR-BLUP as implemented in the R package "rrBLUP"  (Endelman, 2011)  was used to predict genomic estimated breeding values (GEBVs). The predictive ability of GBS and DArT markers were compared across the panel for all 12 traits using tenfold crossvalidation. The panel was also split by country of origin (FRA, DEU, and GBR) and prediction accuracy assessed within each group by tenfold cross-validation. Training populations were assembled separately from FRA, DEU, and GBR with 192, 82, and 70 varieties, respectively and with each used to predict the phenotypes of varieties from the two remaining countries combined and separately. In all cases prediction accuracy was evaluated as the average Pearson's correlation between the predicted GEBVs and the true phenotype value across 10 runs.
After filtering, a total of 42,795 SNPs and 350 individuals were retained for subsequent analyses. The proportion of SNP markers across the three wheat genomes was highest on the B genome (52%) followed by the A genome (32%) and the D genome (10%), which was lowest, as expected. Chromosome 1B had the highest number of SNPs (4,878) while 4D had the least (240). Unmapped SNPs comprised 2% of the SNP dataset (Supplementary Table  S2 ). MAF were slightly skewed in favor of lower values. MAFs for 22. 5% of SNPs were within the range 5%-10% (Supplementary Figure  S1 ).
Accuracy was highest when FRA was used as the training population to predict GY in DEU, GBR and the combined DEU and GBR dataset and lowest when the GBR set was used as the training population to predict performance of the FRA and DEU sets (Supplementary Table  S5 ). FT and Awns could only be predicted within the FRA varieties; while ears/m² was only predicted when the FRA varieties were used to train the prediction model. Using GBS markers, prediction accuracies by tenfold crossvalidation on the whole panel were higher than predictions in FRA, DEU, and GBR subsets for FT, PH, MAT, Gpt, LR, and GSW. A similar trend was observed using dart markers and crossvalidation on the whole germplasm panel for predicting FT, PH, Wkill, MAT, and GSW (Figure  6 ). Overall predictions made using GBS by tenfold cross-validation for the full dataset resulted in higher genomic prediction accuracy for GY (0.
Objective The purpose was to analyse the effectiveness of high-fidelity patient simulation (HFPS) based on life-threatening clinical condition scenarios on undergraduate and postgraduate nursing students' learning outcomes. Design A systematic review and meta-analysis were conducted based on the Cochrane Handbook for Systematic Reviews of Interventions and its reporting was checked against the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Data sources PubMed, Scopus, CINAHL with Full Text, Wiley Online Library and Web of Science were searched until July 2017.
9 10 Unfortunately, the organisational issues and short rotations in clinical settings do strengths and limitations of this study ► This meta-analysis provides data on the impact of high-fidelity patient simulation sessions based on life-threatening clinical scenarios on knowledge, performance, satisfaction, self-confidence and self-efficacy in undergraduate and postgraduate nursing students. ► A structured search strategy was used across multiple databases. ► Data heterogeneity and limited amount of high-quality primary studies limit the generalisability of results in nursing education practice. Open access not always allow nursing students to train in an interactive way especially in high-risk, low-incidence clinical events. 11 ll these reasons have generated the need for integrative teaching methods, such as high-fidelity patient simulation (HFPS).
eligibility and inclusion criteria In order to be included in this analysis, the abstract had to clearly indicate the study (a) was experimental or quasi-experimental, (b) had used HFPS and (c) had involved nursing students (undergraduate or postgraduate). Available full-texts had to be published in English, French, Spanish or Italian language and studies had to include (a) HFPS based on critical care scenarios, (b) control groups not tested on the HFPS before the intervention, (c) data on the learning outcomes of performance, knowledge, self-confidence, self-efficacy or satisfaction measured just after the simulation session, and (d) data for meta-analytic synthesis. For the purpose of this systematic review, the concept of knowledge was intended as delivery of the theoretical basis of caring,  28  self-confidence is defined as trusting the soundness of one's own judgement and performance,  23  satisfaction is considered the fulfilment of student's expectations during the simulation experience,  29  self-efficacy consists of the way students perceive, think and motivate themselves when learning and performing clinical training  30  and finally, performance is the student's ability to demonstrate clinical skills.
31 formation sources and search A pilot search was performed to identify keywords and Medical Subject Headings relevant for the electronic research. PubMed, Scopus, CINAHL with Full Text, Wiley Online Library and Web of Science were searched until July 2017 using the search strategies listed in the box of the online supplementary file. To perform an exhaustive search, reference and citation lists from included studies were checked for other relevant references. Thomson Reuters EndNote X7 was used for the management of the retrieved studies and references. study selection Titles and abstracts were screened by three raters (CLC, AD and VC) for eligibility according to the listed criteria and, for each eligible study, full-texts were retrieved by using online databases and faculty interlibrary service as well as by contacting the authors.
Data items and quality appraisal of individual studies Data related to year of publication, study design, country, sample size, participants characteristics, simulator features, control conditions, scenarios, outcomes and measurement tools and time of exposure to scenarios were extracted independently by two authors (AD and Open access CLC). Krippendorff's alpha coefficient was used to calculate inter-rater reliability and any disagreement about data extraction was resolved by discussing with a third author (LL) to gain consensus. 32 The study designs were checked with 'List of study design features'. 26 he included studies were screened for their methodological quality through the Quality Appraisal Checklist for Quantitative Intervention Studies designed by the National Institute for Health and Care Excellence  33  shown in the table A of the online supplementary file.
synthesis of results and summary measures For each study, the outcome data were synthesised through meta-analytic procedures using the software ProMeta V. 3. 0. The random-effect model was used for all studies as a conservative approach to account for different sources of variation among studies (between-studies and within-study variance). 34 35 As Cohen's d (standardised mean difference) permits meta-analysis even when studies have used different original measures, it was directly computed or derived. 36 37 In this regard, standardisation has been the only way to carry out a meta-analysis, considering multiple measurement instruments found in included studies. 37 Effect sizes were pooled across studies to obtain an overall effect size with the inverse-variance method. For each effect size, the corresponding 95% CI, weight and statistical significance were calculated.
The pooled effect size significantly favoured the HFPS when Cohen's d was higher than '0' and its 95% CI did not overlap the 0line. Values of Cohen's d can be interpreted as a small effect (0. 2), medium effect (0. 5) and large effect (0. 8). 37  In order to assess the significance of the difference between the means of HFPS and the other teaching methods, a Z-test was performed for each meta-analysed outcome. The historical trends from the searched databases were graphed.
results study selection The search produced 2603 references from databases and 1857 studies from reference and citation searching, all published until July 2017. After removing duplicates, 2130 abstracts were screened for relevance. Consequently, 492 full-texts were analysed and 459 studies were excluded for not meeting the inclusion criteria (figure  1 ). Inter-rater reliability among the authors for abstracts and full-texts was 0. 84 and 1. 00 (Krippendorff's alpha coefficient), respectively, before consensus among authors was reached. The final sample of 33 studies originating 44 comparisons was included in this systematic review, as shown in the table B of the online supplementary file. It should be noted that a significant increase in the general number of studies (R 2 =0. 835; p<0. 001) occurred over the last 30 years about HFPS (figure  1  of the online supplementary file).
COnClusIOns Results of this systematic review demonstrate HFPS is superior to other teaching methods in improving knowledge and performance of nursing students when exposed to life-threatening clinical condition scenarios, corroborating the importance of HFPS into the academic educational programmes especially for the management of clinically acute events. However, more studies are still necessary to explore the potential use of the HFPS as an effective tool to increase nursing students' competence levels and to better understand its impact on patient outcomes. Contributors All authors developed the protocol, interpreted the results, and approved the final version. CLC, AD, IF, EG, and VC completed the search, screened articles for inclusion, and synthesised the findings. CLC and AD extracted data and drafted the manuscript.
The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i. e. , the biomarkers (e. g. , gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets.
)  technologies, which include microarrays, mass spectrometry, and whole exome/genome and RNA sequencing. Specific patterns associated with a clinical outcome of interest (e. g. , disease diagnostic, prognostic), called biomarker signatures, can be derived from these high-dimensional technologies outputs (e. g. , gene expression, polymorphisms)  (Lin et al. , 2017). These signatures, which are measurable indicators for predicting a biological phenomenon, are usually identified using machine learning  (Pasolli et al. , 2016)  or statistical multivariate analysis approaches  (Rohart et al. , 2017c). Biomarker signature identification from disease-derived omics datasets is a challenging task involving many pitfalls. First, the datasets are generally highly unbalanced, where the features (e. g.
, 2013) , canonical correlation analysis  (Hotelling, 1936) , Kmeans clustering  (Hartigan and Wong, 1979) , and associated visualizations. Recently, other research teams have proposed approaches in machine learning (ML)  (Janevski et al. , 2009; Cun and Fröhlich, 2013; Lagani et al. , 2013; Swan et al. , 2013 Swan et al. , , 2015;; Butti et al. , 2014; Kong et al. , 2014; Kourou et al. , 2015) , a branch of artificial intelligence that holds a great potential for pattern recognition in complex diseases datasets. ML has already shown its ability to identify key features (markers) and modeling predictive biomarker signature in a variety of fields, including cancer research  (Matsumura et al. , 2010; Cima et al. , 2011; Cui et al. , 2011; Roth et al. , 2011; Fröhlich and Cun, 2012; Kourou et al.
g. , disease diagnosis, prognosis, therapeutics), thus helping to understand the biology behind a measured outcome. Considering the complexity of the ML approach, we present in this paper a software called BioDiscML (Biomarker Discovery by Machine Learning), which aims to greatly facilitate the work required for biomarker signature identification from highdimensional data, such as gene expression, by automating the ML approach. Some non-commercial automatic software already exists to facilitate the choice of learning algorithms and perform hyper-parameter optimization, such as Auto-weka  (Thornton et al. , 2013) , auto-Sklearn  (Feurer et al. , 2015) , autoML  (Feurer et al. , 2015) , and preconfigured pipelines in Orange canvas  (Demšar et al. , 2013). But they are not explicitly designed to answer biological problems, lack of user-friendly experience for non-ML experts, some focusing only on hyperparameter optimization, and may be complex to parallelize to decrease calculation time.
MATERIALS AND METHODS BioDiscML is a tool that automates main ML steps by implementing methods for feature and model selection. In this section, we describe the program procedures separated in three main components: preprocessing, feature selection and model selection. We also present all supported models (see Supplementary Materials), evaluation metrics, feature search methods, best model selection and correlated features search approaches. Finally, we have summarized the real-life datasets we used to compare BioDiscML against various existing tools.
BioDiscML Software BioDiscML is a biomarker discovery software that supports classification (categorical class) and regression (numerical class) problems. It is written in JAVA 8 language  (Fischer, 2015)  and use Weka 3. 8 machine learning library  (Holmes et al. , 1994; Hall et al. , 2009; Witten et al. , 2016). It automates several machine learning steps aiming to identify predictive models. To this purpose, BioDiscML can routinely perform data preprocessing, features dimension reduction, a combined feature and model selection strategy, identify best models, and search correlated features. All machine learning generated models are evaluated by various cross validation procedures. All steps are configured with editable default parameters. Advanced parameters can also be modified by the user.