dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset A benchmark point cloud dataset representing an urban environment has been released with the Oakland 3D Point Cloud Dataset 2   (Munoz et al. , 2009). The data have been collected in the vicinity of the CMU campus in Oakland, USA, with a mobile laser scanning system. This system captures the local 3D geometry with side looking SICK LMS laser scanners used in push-broom mode. After acquisition, the dataset has been split into a training set consisting of approximately 37k points and a test set with about 1. 3M points. The reference class labels were assigned to the points in a semi-automatic annotation process. Thus, the classification task consists of assigning each 3D point a semantic label from the set {wire (w), pole/trunk (p/t), facade (f ), ground (g), vegetation (v)}. The distribution of the classes in the test set is very inhomogeneous, with 70.
In both variants, a node is only split if it is reached by at least nmin = 20 training samples, and the number of features for each test (na) is set to the square root of the number of features, following the recommendations of the openCV implementation. The first setting is a standard one, whereas the second one is expected to lead to a slightly improved performance due to the larger number of training samples and to the larger number of trees, though at the cost of a higher computational effort. First, we apply a classification solely based on the association potentials to the dataset, i. e. on the results of the two variants of the Random Forest classifier; the respective classification variants are denoted by RF100 and RF200, respectively. After that, we apply the contrast-sensitive Potts model in a CRF-based classification.
CONCLUSIONS In this paper, we have presented a generic approach for automated 3D scene analysis. The novelty of this approach addresses the interrelated issues of (i) neighborhood selection, (ii) feature extraction and (iii) contextual classification, and it consists of using individual 3D neighborhoods of optimal size for the subsequent steps of feature extraction and contextual classification. The results derived on a standard benchmark dataset clearly indicate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. For future work, we want to carry out deeper investigations concerning the influence of the amount of training data as well as the influence of the number of different classes on the classification results for different datasets.
Methods: We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing. Results: The cross-validation accuracy was highest when all three types of information were used.
Somatic mutation training data We downloaded all somatic point mutation data (Cos-micMutantExport_v68. tsv. gz) from the COSMIC database version 68  [15, 16]  corresponding to 235,589 specimens. We removed 227,512 specimens not labeled as "Genome. wide. screen" and 5,064 specimens labeled as cell-line (in union 227,757 specimens). In ten cases, two sample IDs matched to the same tumor ID, meaning one tumor gave rise to two samples in the data set. In 105 cases, the same sample name matched to more than one tumor ID. Specimens were removed to leave only one sample per tumor ID. When deciding which specimen to keep, the following priorities were made: Surgery biopsy, primary, verified and exome seq had priority over xenograft, relapse, unverified and RNA-Seq, respectively. The resulting data set consisted of 7,769 specimens from 28 different primary sites.
Gene annotation was not entirely consistent and thus required additional curation. We mapped as many genes as possible to Ensembl gene IDs, by searching for gene information in the following columns: Accession. Number, HGNC. ID, and Gene. name, which in most cases contained the gene symbol, but was also found to hold Ensembl gene IDs and Swissprot accession numbers. We were able to annotate Ensembl gene IDs to 99. 4 % of the point mutations in COSMIC. Finally, point mutations in COSMIC are reported for all possible transcripts, so we filtered the mutation table so that each row corresponded to a single unique mutation identified by its genomic position. We also downloaded all available SCNA data (Cosmic-CompleteCNV_v68. tsv. gz) from the COSMIC v68 database  [16]  and mapped the genes overlapping with each SCNA segment.
Random forest classifiers  [18]  were trained using the randomForest  [19]  package v. 4. 6-7 in R, using the default parameters to grow 500 trees, and sample ffiffiffi p p features as candidates at each split within a tree, where p is the total number of features. Stratified sampling was used to draw equal numbers of cases and non-cases for each tree, with sample size equal to 0. 632 times the size of the smallest group. When applied to a new data sample, we define the "classification score" as the proportion of the trees that voted for the given primary site. All data matrices used for training, testing and validation are available in Additional file 2: Table  S2. Validation data SAFIR01 and MOSCATO trials Mutation calls based on whole exome sequencing data for a cohort of 91 metastatic breast cancers was obtained from the Department of Medical Oncology, Gustave Roussy, Villejuif, France from the trials SAFIR01 (NCT01414933)  [20]  and MOSCATO (NCT01566019).
COSMIC v70 We downloaded all somatic point mutation data from the COSMIC database version 70  [27]  and removed any mutations with a sample ID also found in version 68, which was used for training, or with a primary site different from the ten primary sites that were used to train our model, resulting in data from 1669 tumors. We curated the gene annotations as described for the training data. NSCLC cohort In the non-small cell lung cancer patient cohort study (UCLHRTB 10/H1306/42), tumor specimens were collected from patients who were eligible for surgical resection at the University College London Hospitals NHS Foundation Trust. For each tumor region and matched germ-line, exome capture was performed on 1-2 μg DNA using either the Agilent Human All Exome V4 kit or Illumina Nextera Exome Enrichment kit according to the relevant manufacturer's protocol.
Development of a classifier based on somatic point mutations We used the COSMIC version 68 Whole Genomes database to identify tumor specimens with genomewide or exome-wide somatic point mutation data, and focused on solid non-CNS tumors of the ten primary sites for which at least 200 unique specimens were available (Table  1 ). CNS tumors were not included because extraneural metastases of these tumors are rare  [33] , and 200 specimens were required to allow for a reasonable number of tumors of each primary site within each cross-validation training and test set. The resulting 4,975 specimens were split randomly, while retaining proportionality of each class, into a training set of 3,982 specimens used to derive the classifier, and a test set of 993 specimens that was not used except to evaluate the final classifier.
Development of a classifier based on somatic point mutations and copy number aberrations We next considered whether copy number profiles could improve classification performance. However, SCNA data is available from the COSMIC Whole Genomes database for only ~60 % of the specimens in our training data. Thus, we assessed the performance of classifiers using a set of SCNA features in a separate analysis, reducing the number of samples and thereby also the number of primary sites from ten to six (Table  1 ). This increases the expected accuracy of a random classifier from 1/10 = 10 % to 1/6 = 17 %, and so for proper comparison we repeated some of the previous analyses on the reduced data set. In this reduced data set, the point mutation feature set alone classified primary site with an accuracy of 69 % (Fig.
Clinical features influencing classifier performance To investigate whether the performance of the PM and PM + CN classifiers is biased by certain clinical features of tumors, we analysed the subset of tumors in our COSMIC-derived training data that originated from TCGA, and for which we could retrieve clinical annotations based on sample names directly from the TCGA repository. We split the tumors according to the validity of the predicted primary site during cross validation or + - + + + - - - - + - + + + - + - + - + + - - - + - + - + + + - - + + - + + - + - - + + - +.
Our classifiers were trained on data found in COS-MIC, much of which comes from larger studies of many tumors of the same primary site. This introduces the possibility of bias resulting from confounding factors such as experimental or analytical protocols, which may explain why we observed slightly reduced performance in two of three independent validation data sets relative to what would be expected based on training data performance. The effect of this possible bias will be reduced as more data from multiple studies becomes available. Our method does not use raw DNA sequence as input but instead relies on lists of point mutations, which are the output of algorithms designed to call mutations from sequence data. Several mutation calling algorithms exist, and there are extensive discrepancies between their output  [35].
Some clinical subgroups are associated with increased or decreased performance of the primary site classifiers PM and PM + CN Information on subtype, grade and stage were retrieved from TCGA, and are therefore not available for all tumors in the COSMIC database. ER estrogen receptor positive. HER2 human epidermal growth factor receptor 2 positive. TNBC triple negative breast cancer. MSI microsatellite instability. MSS microsatellite stable. mBRCA1 mutated BRCA1. mBRCA2 mutated BRCA2. wtBRCA wildtype BRCA1 and BRCA2. Acc. accuracy ie. the percentage of tumors correctly classified. N the number of tumors in subgroup. P p-value from Fisher's exact test comparing accuracy among samples in or not in each subgroup. *p < 0. 05. **p < 0. 01.
Acknowledgements This work was supported by the  European Commission 7th Framework Programme  [ HEALTH-2010-F2-259303 ]; the  Danish Council for Independent Research  [ 09-073053/FSS ]; the  Breast Cancer Research Foundation  [to ZS]; the  Villum Kann Rasmussen Foundation  [to NJB]; the  Danish Cancer Society  [to ACE] and the  Novo Nordisk Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank  Andrea Vala  for proofreading the manuscript. The mutation and SCNA data used to develop the classifiers was obtained from the  Sanger Institute Catalogue Of Somatic Mutations In Cancer  [15] web site, http://cancer. sanger. ac. uk/cosmic.
Description of the bidirectional SLE The Bidirectional SLE discussed here was proposed to improve the laboratory session and to support SL. The Bidirectional SLE was built upon the integration of two dimensions: the technological and the educational. The technological dimension consisted of a system based on mobile devices (students' smartphones, and also tablets provided by teachers for students who did not wish to use their own smartphones) and bidirectional QR codes generated with etiquetAR (P erez-Sanagustín,  Martínez, & Delgado Kloos, 2013). etiquetAR is an authoring tool that takes advantage of QR code technologies to support the generation of special "tags". When these tags are scanned with any QR code reader, they provide information (as do traditional QR codes), but they also collect user-generated content.
We ran tests on between-subjects effects as well as pairwise comparisons for the mixed ANOVAs for both correctness and completeness scores. In order to analyze H3, we observed whether there were differences between open and closed questions by running tests on within-subjects effects and pairwise comparisons for the mixed ANOVAs. Finally, we analyzed the combined effect of the type of group and the type of question on both correctness and completeness. In order to test H4. a, H4. b and H4. c, we considered student opinion data collected from the 12 statement forms. Firstly, each student had three different scores, one for each category T, C and M (and by extension for each hypothesis). Each score was calculated as the average of the values given to the statements in the category. We compared the values of the variables T, C and M in two groups (EG and CG) in order to know whether T, C and M were better in the EG or not.
In this case, the data collected came from the subjective opinions of students using a 5-Likert scale. Since we consider T, C and M categories ordinal variables, we ran a ManneWhitney test for each of them in order to know if there was a statistically significant difference between the EG and the CG. Finally, we analyzed teacher opinion qualitatively to complement the trends identified in the statistical analysis of student opinions. This final analysis provided insights into aspects of the Bidirectional SLE that could be improved, and aspects of it that improved upon laboratory sessions of previous years.
(2012 Fouesneau et al. ( , 2014) )  for M31, and by de  Meulenaer et al. (2013 Meulenaer et al. ( , 2014 Meulenaer et al. ( , 2015) )  for M31 and M33, but studies of systematics have mostly been limited to questions of isolated metallicity, evolutionary tracks, and extinction law. To our knowledge no previous work has covered the possible systematics thoroughly in the context of a single framework, and none at all have explored the issue of priors. Second, we wish to describe a portion of the LEGUS data pipeline, which will be used to produce star cluster catalogs based on cluster slug modeling for the entire LEGUS sample. While this paper focuses on three fields in two galaxies for early analysis, the methods we develop here will be applied to the entire LEGUS sample. Ultimately, we will provide a catalog of homogeneously-observed andanalyzed star clusters, whose properties have been derived with a fully stochastic treatment of stellar population synthesis, for 50 galaxies that sample across the star-forming part of the Hubble sequence.
For a third input, the kernel density estimation bandwidth, we use h = 0. 1 dex in the physical variables and h = 0. 1 mag in the photometric ones. The cluster slug code requires a library of simulated star clusters to act as a "training set". We produce these using the slug stochastic stellar population synthesis code. To generate a library, we must specify the stellar evolution tracks, metallicity, extinction curve, stellar initial mass function (IMF), and the fraction of the ionizing light that is reprocessed into nebular emission within the observational aperture. One of the goals of this paper is to study how these choices affect the de-  duced cluster properties, and for this reason we generate a range of libraries, summarized in Table  2. The parameters we vary in these libraries are as follows: for tracks we consider both those from the Padova group including TP-AGB stars  (Vassiliadis & Wood 1993; Girardi et al.
At small masses where the statistical uncertainties are largest, the Yggdrasil results appear to be closest to what one obtains using cluster slug with a prior distribution β = -2, γ = -1. For the age distributions there is more discrepancy between the Yggdrasil and cluster slug results. The Yggdrasil data show sharp drops in the number of clusters at around 10 7. 3 and 10 8. 1 yr, with excesses near 10 6. 8 and 10 7. 9 yr, though the prominence of these features depends on how one chooses to bin. The same structure in the Yggdrasil-determined ages is visible as the horizontal banding in Figure  14 , and appears to result from the choice to assign a single best fit even in parts of color space where colors provide relatively poor constraints on the true age. The cluster slug models correctly capture the uncertainty in such regions by spreading out the posterior probability, and show no corresponding features.
This pipeline is available at https://www. slugsps. com, and will form the basis of a full stochastic cluster catalog derived from all the star clusters in the LEGUS sample. These will be expanded to include Hα data from the Hα-LEGUS program (PI: R. Chandar) as they become available. The resulting data set will provide an unprecedented sample of cluster properties, with realistic uncertainty distributions, from across the star-forming part of the Hubble sequence.
We developed a database and browser interface, the Yeast Gene Order Browser (YGOB-http://ygob. ucd. ie), as a resource for exploring synteny relationships among Saccharomycetaceae species  (Byrne and Wolfe 2005). Synteny comparisons, both within genomes and between post-WGD and non-WGD genomes, provided the main evidence for WGD  (Wolfe and Shields 1997; Dietrich et al. 2004; Dujon et al. 2004; Kellis, Birren and Lander 2004). Synteny conservation also enabled our laboratory, in 2009, to infer the approximate gene content and genome organization of the ancestral yeast organism that underwent WGD, and hence to study the chromosomal rearrangements, gene losses and gains that occurred in S. cerevisiae and other post-WGD species in their hundred-million-year descent from this ancestor  (Gordon, Byrne and Wolfe 2009).
Outside this family  (Kurtzman 2003; Kurtzman and Robnett 2013) , we have relatively limited genomic data from the closest outgroup genera such as Hanseniaspora/Kloeckera  (Giorello et al. 2014) , Saccharomycodes, Wickerhamomyces  (Schneider et al. 2012a,b)  and Cyberlindnera  (Tomita et al. 2012; Freel et al. 2014). Although genome evolution in the sequenced Saccharomycetaceae species has largely been conservative, with similar genes being arranged in similar ways along the chromosomes of each species, the exceptions to this rule-the differences between the species-can often be of interest and can point to differences in the biology of the species that own the genomes. In this review, we focus on aspects that are unique to the genome of a particular species or genus. We focus in particular on seven genomes that we sequenced in 2011, from three post-WGD genera (Naumovozyma, Kazachstania and Tetrapisispora) and one non-WGD genus (Torulaspora).
Background: Construction and validation of a prognostic model for survival data in the clinical domain is still an active field of research. Nevertheless there is no consensus on how to develop routine prognostic tests based on a combination of RT-qPCR biomarkers and clinical or demographic variables. In particular, the estimation of the model performance requires to properly account for the RT-qPCR experimental design. Results: We present a strategy to build, select, and validate a prognostic model for survival data based on a combination of RT-qPCR biomarkers and clinical or demographic data and we provide an illustration on a real clinical dataset. First, we compare two cross-validation schemes: a classical outcome-stratified cross-validation scheme and an alternative one that accounts for the RT-qPCR plate design, especially when samples are processed by batches.
Prognostic model building and validation is an active field of research in biostatistics: for instance, Steyerberg  [2]  presented several successful applications for public health, clinical practice and medical research. Although many prognostic models have been published in the medical literature every year, such models are rarely implemented in clinical practice. This is mainly due to the lack of generalization of these models, meaning that the performance of the reported models is not as good on new independent datasets as they were on the dataset used to build the model  [3]. The three most frequent reasons for this lack of generalization are: i) deficiencies in the design or in the modeling methods used to derive the model ii) upward biases regarding the estimation of the performance, and iii) differences between training and test samples, including differences in health-care systems, methods of covariate measurements, and patient characteristics  [4] [5] [6] [7] [8].
The objective of this article is to present a strategy for prognostic survival model building, selection, and validation for the development of routine prognostic tests based on a combination of RT-qPCR biomarkers and clinical or demographic variables and to illustrate it on a real dataset. We will focus on three main issues: resampling strategy, performance indicator estimation, and criteria for model selection. First, accurate estimation of model performance is a crucial step in the model validation process. Performance estimated on the training samples used for model building, i. e. apparent performance, is very likely to be upwardly biased. Resampling methods, such as k-fold cross-validation and bootstrap  [9]  are the most common techniques to estimate the actual performance. Resampling methods should properly capture the intrinsic technical variability of the biomarker assay.
However, depending on the cross-validation strategy, and the performance indicator considered, the pooling method can lead to biased estimations  [10]. The advantage of both methods in the context of censored survival data will be discussed hereafter. Third, selecting the optimal model is generally a difficult decision-making step. Indeed, several models might present similar performances. In this case, model performance may not be the sole criterium to select the optimal model. Complementary criteria for model selection will be discussed. Finally, we will detail the construction of the selected model, and the subsequent validation steps; the complete strategy is then illustrated on a real dataset: the prognostic of mortality for septic shock patients in intensive care units.
Sample size The estimation of sample sizes for the training and test datasets is pivotal to drive patient inclusion and scale the corresponding efforts. Unfortunately, up to now there is no theoretical rational for training sample size estimation when p < n (i. e. the number of candidate predictors, namely p, is lower than the number of patients denoted n). A simulation study suggested that at least 10 events per candidate predictor (EPV) seems "prudent"  [13] , whereas Vittinghoff et al. [14]  found bias and coverage problems with 5-9 EPV. The limitation of this study is that they focused on one primary predictor and they treated other variables as adjustment variables, i. e. they did not study the bias and coverage of adjustment covariates. Recently, Dobbin and Song  [15]  developed a method for sample size estimation to train a survival risk predictor in highdimensional settings.