dataesr/scientific-paragraphs-categorization huggingface dataset:
The best feature selection accuracy is theoretically obtained by removing one feature at a time (SVM-RFE1), however this is computationally expensive. Therefore, following Loo et al. 15  , we used SVM-RFE2, which removes the 10% of the measurements with the lowest weight at each iteration. We selected the resulting model that obtained the best prediction accuracy. In order to eliminate more measurements, Loo et al. 15  eliminated measurements until the prediction accuracy fell below 0. 9 × ((C max -C min ) + C min ), where C max is the maximum prediction accuracy and C min the minimal prediction accuracy over the full range of a selected number of measurements. Gaussian mixture (GM) modeling-To build GM profiles, 10% of the data were subsampled uniformly across all samples. This selection was mean-centered, after which the data were transformed using principal-component analysis, retaining enough principal components to explain 80% of the variance (~54 for our dataset).
Available data To facilitate the development and evaluation of additional profiling methods, we provide our ground-truth annotations (Table  S2 ) and the measurements of each of the ~450,000 cells whose treatments are annotated. The data are supplied as comma-delimited files together with scripts for loading them into a MySQL database (Data S2). The data schemas are described (Text S1). The images and metadata have been deposited with the Broad Bioimage Benchmark Collection (http://www. broadinstiute. org/bbbc/)  16 , accession number BBBC021. [Reviewers and editors: http://jbs:awesome@www. broadinstitute. org/bbbc/BBBC021/ to access while under embargo. ].
Software implementations The profiling methods are implemented as part of the open-source image data-analysis software CellProfiler Analyst (http://cellprofiler. org/). The implementations do not make assumptions that are particular to our experiment, and can be readily applied to measurement data from the widely used image-analysis software CellProfiler  [11] [12]  or data from other sources that can be imported into CellProfiler Analyst or otherwise converted to CellProfiler's database schema. The implementations contain support for parallel processing on a cluster of computers. The profiling methods can be executed as scripts from the Unix command line or used in Python programs as a module (Text S2).
Results We implemented five proposed methods  [13] [14] [15] [18] [19]  for constructing per-sample profiles from per-cell measurements in a common computational framework. We ben hmarked the five methods on images we had previously collected of MCF-7 breast cancer cells treated for 24 h with a collection of 113 small molecules at eight concentrations (Table  S3 ). The cells were fixed, labeled for DNA, F-actin, and β-tubulin, and imaged by fluorescent microscopy. For this study, we measured 453 standard cytometric measurements (Table  S1 ) of each cell using CellProfiler  [11] [12]  and applied each of the five profiling methods. In order to be able to evaluate the performance of the profiling methods, we limited our attention to a subset of the data (our "ground-truth" dataset) for which we were confident that the primary MOA of compounds was achieved at the concentration tested during the course of the experiment.
(The term "mechanism-of-action" is used rather loosely here and refers to a sharing of similar phenotypic outcomes among different compound treatments, rather than referring strictly to modulation of a particular target or target class. ) The mechanistic classes were selected so as to represent a wide cross-section of cellular morphological phenotypes. The differences between phenotypes were in some cases very subtle: we were only able to identify 6 of the 12 mechanisms visually; the remainder were defined based on the literature. This carefully collected ground-truth dataset consisted of 38 compounds at active concentrations. Some compounds were active at only one concentration and some at up to seven concentrations, for a total of 103 treatments (active compound concentrations) spanning 12 mechanistic classes (Table  S2 , Figure  S1 ).
The mock treatment dimethyl sulfoxide (DMSO) was included as a negative control. Using the cosine distance as measure of profile dissimilarity, we classified the 103 treatments into mechanisms of action by assigning to each profile the MOA of the most similar profile (Figure  1 ; top panel). When classifying a treatment, all concentrations of the same compound were held out from the training set in order to prevent overtraining. The samples were prepared and imaged in 10 batches, but classes and replicates were distributed across batches and plates, respectively, so as to avoid biasing the classification (Text S4)  20. Using this experimental dataset, we tested five profiling methods (Figure  1A-E ), as detailed below.
Discussion We compared five methods  [13] [14] [15] [18] [19]  for generating per-sample profiles from image-based cell data in the context of classifying small molecules into 12 mechanisms of action based on cellular morphology. All me hods had previously been demonstrated in distinct experiments, mostly proof-of-principle studies, with some yielding biological discovery. However, these methods had never before been directly compared on a common dataset. Each method was previously proposed as part of a larger methodology, sometimes including strategies for particular contexts, such as making use of information from multiple cell lines or multiple concentrations. These strategies can be applied independently of the core profiling method; here, we compared only the computational cores of the profiling methods.
We did not evaluate the underlying statistical methods (KS, SVM, GM, FA), which have solid theoretical foundations and an excellent record of solving analysis problems of many kinds. On our dataset, the simplest method, which profiles compounds by the population means of the measurements of the treated cells, performed better than expected, achieving 83% accuracy in predicting MOA. Because many of the measurements are non-Gaussian, we expected non-parametric KS statistics to be superior, but that was not the case. Describing a compound by the decision boundary of a linear SVM trained to distinguish compoundtreated cells from mock-treated cells did not offer improvement either (83%), and adding a feature-reduction step reduced performance (64%). A GM method that tries to model subpopulations of cells with a mixture model might be expected to have an advantage in experiments where the perturbations lead to shifts between a small number of discernible cell states (e.
The choice of assay and optimal timepoint for profiling will likely depend on the scientific questions being asked. The chemical compounds we tested are commonly studied bioactive compounds. Therefore, the present study is valuable in providing a comparative analysis of methods in the context of one particular (but representative) profiling experiment. Creating and annotating a ground-truth set of compounds with known MOA is not trivial; we hope this work provides a template for future creation of ground-truth datasets. With the emergence of image-based high-content screening across more complex and diverse assay formats incorporating co-cultures, stem cells, and model organisms, future studies may demonstrate that particular profiling methods perform better on specific assays, cell types, or even focused compound or siRNA libraries.
Thus, we foresee additional value in providing an analysis framework and a ground-truth dataset to facilitate further comparisons in the field using alternate datasets or methods. We have implemented all five methods and offer the source code (Text S2), along with our entire set of cellular measurements for our ground-truth dataset (Data S2) so that they can aid in the future application, development, and comparison of image-based phenotypic profiling approaches. Distributions of classification accuracies for 20 runs of the factor analysis method for each possible choice of the number of factors from 2 to 100. The performance was similar to the other methods when using ~25 factors, but the accuracy increased gradually with the number of factors, reaching a plateau at ~50 factors.
Acknowledgments We would like to thank  Christopher Denz ,  Lisa Drew ,  Tom Houslay ,  Zhongwu Lai ,  David Logan ,  Melissa Passino ,  Tejas Shah , and  J. Anthony Wilson  for helpful input. This work was supported in part by the  National Institutes of Health  [grant number  U54-HG005032 ]; the  National Science Foundation  [grant number  CAREER DBI 1148823 ]; and  AstraZeneca Pharmaceuticals.
Medulloblastoma (MB) is a malignant pediatric brain tumor arising in the cerebellum consisting of four distinct subgroups: WNT, SHH, Group 3 and Group 4, which exhibit different molecular phenotypes. We studied the expression of Dickkopf (DKK) 1-4 family genes, inhibitors of the Wnt signaling cascade, in MB by screening 355 expression profiles derived from four independent datasets. Upregulation of DKK1, DKK2 and DKK4 mRNA was observed in the WNT subgroup, whereas DKK3 was downregulated in 80% MBs across subgroups with respect to the normal cerebellum (p < 0. 001). Since copy number aberrations targeting the DKK3 locus (11p15. 3) are rare events, we hypothesized that epigenetic factors could play a role in DKK3 regulation. Accordingly, we studied 77 miRNAs predicting to repress DKK3; however, no significant inverse correlation between miRNA/mRNA expression was observed.
Results and Discussion We evaluated the expression of DKK family members (DKK1, DKK2, DKK3 and DKK4), by screening 355 expression profiling data, including 333 MB tumors and 22 normal cerebella from four independent datasets. This analysis showed the significant DKK1, DKK2 and DKK4 upregulation (p < 0. 01) in WNT subgroup tumors (Figures  S1-S3 ), whereas DKK1, DKK2 and DKK4 were either not expressed or expressed at only very low levels in non-WNT tumors or normal cerebellum. Only a subset of SHH tumors shows some DKK2 expression. Our findings are in agreement with Northcott et al. [3] , who showed the overexpression of DKK1 mRNA and protein in the WNT subgroup. The WNT-specific overexpression of DKK1, DKK2 and DKK4 suggests the activation of a negative feedback loop in this peculiar subgroup of tumors.
The X-axis indicates the four molecular subgroups according to the current consensus (  [7] ; WNT, SHH, Group 3 (GRP3) and Group 4 (GRP4)); the dataset A was not divided in the subgroups, due to a low number of samples. Y-axis: DKK3 mRNA expression value in the (A), (B) and (C) dataset, respectively. The dataset (D) reports the relative expression between MB samples and NC, and the dotted grey line delineates the expression level in a pool of NC samples. Abbreviations: NC, normal cerebellum; MB, medulloblastoma; GRP3, Group 3; GRP4, Group 4. We then explored different genetic and epigenetic mechanisms, alone or in cooperation between themselves, that could explain the DKK3 downregulation. First, we investigated if the somatic copy number changes, as well as the 11p loss and the focal event affecting the DKK3 locus may contribute to drive its down-modulation.
We analyzed both the status of chromosome 11 in 77 MBs (17 from dataset A and 60 from dataset D) and the focal aberration targeting DKK3 by SNPs study  [21]  in 1,087 MBs. We found that 11 monosomy (10/77) and structural 11p loss (3/77) copy number aberrations were rare events and, in addition to the total absence to focal aberrations targeting DKK3, suggest that other regulatory mechanisms may exist. Deregulation of miRNAs have been found in several cancers; thus, miRNAs may play a role in gene expression modulation. Therefore, we studied the miRNAs as a possible mechanism of DKK3 epigenetic silencing. We interrogated five miRNA target prediction programs-miRWalk, Diana-microT, miRanda, miRDB and TargetScan-and selected 147 miRNAs found in at least three out of five predictive tools. Hence, we investigated the inverse correlation integrating data between miRNAs and DKK3 expression by in silico analysis on the available 25 MB tumors from dataset D, containing 77 out of 147 predicted miRNAs.
Gene Expression Profiling, Array-CGH Data and miRNA Gene expression profiling was performed on 19 MB samples and two pools of normal cerebellum, as reference samples using the 44 k whole genome oligonucleotides microarray (Agilent Technologies; Santa Clara, CA, USA) (Dataset A). Labeling and hybridization of samples was performed according to the "one-color microarray-based gene expression analysis" protocol. The reference samples, including a pool of normal cerebellum from children and a commercial pool from adults, were hybridized in triplicate and duplicate, respectively. The data were extracted using Feature Extraction software (v. 9. 5; Agilent Technologies; Santa Clara, CA, USA, 2009); probe intensities were log base 2-transformed and normalized across arrays with the scale normalization method implemented in R package "limma" version 3.
10. 2. All raw data were deposited in Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/; GSE39182). We also included the data obtained from three independent public datasets of gene expression: (1) B, including 188 MBs and 11 normal cerebellum data  [4] ; (2) C, including 62 MB data  [2]  and 9 normal cerebellum  [20] ; (3) D, including 64 MB data  [5]. Data are accessible through the open access database R2 for visualization and analysis of microarray data (http://r2. amc. nl). Differences between the comparisons of MBs versus normal cerebellum were studied using the one-way analysis of variance (ANOVA), while differences between four subgroups were calculated by two-tailed Student's t-test; p values < 0. 01 were considered to be statistically significant. Array-CGH data were available for 77 MBs already published by Coco et al.
[31]  (17 MBs, 244 k Agilent Technologies; GEO accession number GSE23005) and Pfister et al. [32]  (60 MBs, 6 k BAC array; GEO accession number GSE8634). The raw data were analyzed by Agilent Genomics Workbench Lite Edition software (v. 6. 5; Agilent Technologies, Santa Clara, CA, USA, 2010) using the z-score algorithm. SNPs data of 1,087 MBs were by Nothcott et al. [21]. The microRNAs targeting DKK3 was determined interrogating five target prediction programs: miRWalk, Diana-microT (v. 3. 0); miRanda (August 2010); miRDB (April 2009); and TargetScan (v. 5. 1). Statistical analysis was performed calculating the level of inverse correlation between miRNA/mRNA combined miRNAs and DKK3 expression values from 25 tumors from dataset D by respective Pearson's rank correlation coefficient; -1 < r 2 > 1; and a significant negative correlation was defined as r 2 ≤ -0.
Methylation Analysis The methylation status of the three promoters of DKK3 gene was performed on 32 tumor samples MB tumors and five cell lines by pyrosequencing analysis. Briefly, genomic DNA (1 µg) was modified with sodium bisulfite, which converts the unmethylated C into U, using the EZ DNA Methylation Gold kit (Zymo Research, Irvine, CA, USA), according to the manufacturer's recommendations. The pyrosequencing analysis was carried out as described by Banelli et al. [33]  with a SPQ 96MA instrument (Qiagen, Hilden, Germany) and conducted with the Pyro Q-CpG software (version 1. 0. 9; Qiagen Technologies, Hilden, Germany, 2006). The primers were designed with the Pyrosequencing Assay Design Software (Qiagen Technologies, Hilden, Germany). The sequence of the primers used pyrosequencing and the PCR conditions are indicated in the Table  S4.
Acknowledgments The work has been supported by:  Italian Neuroblastoma Foundation ,  Associazione Italiana per la Ricerca sul Cancro (AIRC), Ministero dell'Istruzione dell'Università e della Ricerca (MIUR). We are grateful to  NICHD Brain  and  Tissue Bank ,  University of Maryland, Department of Pediatrics (Baltimore, USA ), for sharing healthy control cerebellum tissues. B. B. is the recipient of the 'Young Investigators' Grant  GR-2008-1143408  from the  Italian Ministry of Health.
Since then, other improved SAS processing versions became available, and in order to have up-to-date and homogeneous X-ray parameters we cross-matched our source lists with the 2XMMi-DR3 catalogue 2. To evaluate the success rate of our visual screening we performed the cross-match for all the 2353 Xray sources and found 1319 sources in the 2XMMi-DR3 within a 20 arcsec radius (1126 with the best XMM-Newton quality, i. e. sum flag = 0 3  ). Extending the radius to larger values did not yield many more matches. In Fig. 2  we show the distribution of the distance between the original XMM-Newton sources and the 2XMMi-DR3 sources. The majority of the matches are found to be within 3-5 ′′. About 50% of the unrecovered sources in the 2XMMi-DR3 catalogue had been flagged by us as being spurious or dubious based on our visual screening.
The XMM-Newton error circle is the combination of two errors. The first one is the statistical uncertainty on the centroid of the PSF determined by the detection algorithm, σ radec typically ∼ 1 -2 ′′. The second one is the systematic error introduced by the uncertainty of the satellite's attitude, with values σ systematic ∼ 1 ′′. In the less crowded fields, systematic errors were reduced to ∼ 0. 35 ′′ by matching the XMM-Newton positions with the USNO-B1. 0  (Monet et al. 2003 ) catalogue making use of the SAS task eposcorr. Assuming a two-dimensional Gaussian distribution, the 90% confidence-level radius is given by: r 90 = 2. 15 × σ 2 radec + σ 2 systematic (1) In many cases the X-ray source position error circle contained more than one optical candidate brighter than our limiting magnitude.
Effective area of the survey To estimate the effective area of our survey, we first built sensitivity maps following the method described by  Carrera et al. (2007). For each band and field, we created: i) exposure maps, that contain information on exposure times in each detector pixel taking into account the mirror vignetting, the detector efficiency, bad pixels and CCD gaps, and the field of view; and ii) background maps, which hold information on the counts in source free regions. We used the SAS tasks eexpmap and esplinemap respectively. We calculated the count rate in the soft and the hard bands by adding the tabulated count rates in the bands 0. 5 -1. 0 and 1. 0 -2. 0 keV, and 2. 0 -4. 5 and 4. 5 -12. 0 keV respectively. We derived maximum likelihood (ML) values in the soft and hard energy bands making use of the listed individual ML, and using the formula given by  Watson et al.
Luft und  Raumfhart (Germany) DLR , under grant numbers  FKZ 50 OX 0201  and  50 OX 0801. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg , France This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation  This research has made use of the  USNO Image  and  Catalogue Archive  operated by the  United States Naval Observatory, Flagstaff Station  (http://www. nofs. navy. mil/data/fchpix/). The  Guide Star Catalogue-II  is a joint project of the  Space Telescope Science Institute  and the  Osservatorio Astronomico di Torino.
Acknowledgments We thank  Y. Yang  and  Dr. B. Dynlacht  for providing C2C12 cells;  Dr. S. Orkin  for Jarid2 fl/fl and Jarid2 knockout ES cells;  Dr. S. Kaneko ,  Dr. G. Li , and  Dr. S. Tu  for reagents and helpful discussions;  Dr. W-W. Tee  and  Dr. R. Bonasio  for valuable comments on the manuscript; and  Dr. S. Gamblin  for great advice. We are also grateful to  Dr. L. Vales  for insightful discussion and comments on the manuscript. This work was supported by a grant from the  National Institutes of Health  ( GM-64844 ) and the  Howard Hughes Medical Institute  to D. R.
Microarrays The hybridization was performed on five 4X44K microarray slides where both the host O. tauri and the virus OtV5 genes were spotted. Four 60-mer oligonucleotide probes were designed for each gene on the microarray slide. A total of 33,093 probes were thus designed for 7,552 O. tauri genes and 1,056 probes for the 261 OtV5 genes. After 17 h at 65 C of hybridization, microarrays were scanned on an Agilent G2505B scanner. Raw data were obtained by Agilent Feature Extraction Software version 9131 (FE), and the quality was monitored with Agilent QC Tool (v1. 0) with the metric set GE2-v5_95. Five hundred nanograms of RNA was amplified and labeled by Low input Quick Amp Labeling kit (Agilent). For the first experiment, the control T0 cRNA was labeled with cyanine-3, and T2, T5, and T10 cRNA were labeled with dye cyanine-5.
Total RNA from each postinfection sampling time was hybridized with the control (T0) for 17 h at 65 C. After microarray disassembly and a wash procedure according to the manufacturer's instructions (Agilent), microarrays were scanned with the Agilent G2505B scanner. Raw data processing was carried out with the Agilent Feature Extraction Software version 9. 1. 3. 1 (FE), and the quality was monitored with Agilent QC Tool (v1. 0) with the metric set GE2-v5_95. The complete data set has been submitted to the ArrayExpress public database at EBI  (Parkinson et al. 2009)  under the accession number: E-MEXP-3520.
tRNA Data The available annotated tRNAs did not allow the translation of all codons in both Ostreococcus genomes. We therefore used tRNA-scanSE  (Lowe and Eddy 1997)  with default parameters to identify tRNAs and completed these predictions with recent findings on permuted tRNAs in Ostreococcus and Micromonas  (Maruyama et al. 2010 ). We curated these predictions manually to provide a complete annotation of 47 nuclear tRNA genes in O. tauri (supplementary table  S4 , Supplementary Material online). We used the software SPLITS  (Sugahara et al. 2006 ) to detect additional permuted tRNA genes that cannot be detected with tRNA-scanSE in the six viral genomes and the recently sequenced B. prasinos genome.
Acknowledgments The authors thank two anonymous referees for constructive comments and the  Genomics of phytoplankton team  for stimulating discussions, especially  Romain Blanc-Mathieu  and  Rozenn Thomas. They also thank  Adam Eyre-Walker  for insightful suggestions on a previous version of the manuscript. This work was funded by  Agence Nationale de la Recherche  grants PHYTADAPT no. NT09_567009 , REVIREC no. 12-BSV7-0006-01 , and  European Community  7th  Framework Program FP7  under grant agreement no. 254619.
MOTIVATION Semantic annotation of visual content is a process that requires linking pixels within an image with the semantic concepts associated to each group of pixels. This is often done in a user-assisted way. There exist several levels of interaction between users and visual content, ranging from an intentional and accurate annotation targeted at generating high-quality labels (e. g. , LabelMe  [11] ), to a completely unintentional process which is more bound to be noisy (e. g. , textual contents surrounding images on web pages and multimedia documents). This paper proposes a method for semantic object segmentation, at a pixel level, based on high-quality annotation data collected from an online game: Ask'nSeek  [1]. Ask'nSeek is a two-player game where one player (the master ) places a small square on the image to be segmented, a square which is invisible to the other player (the seeker ).