dataesr/scientific-paragraphs-categorization huggingface dataset:
Crucially, applying this sparse coding method on this database, we observed that some images were sparser than others but that globally they all fitted well a similar prototypical probability distribution function (pdf). The parameters of this pdf provide with a quantitative descriptor for sparseness and is characteristic for this property. However, it is largely unclear how this property inherent to the structure of natural images may be used in the early visual system or in computer vision. The different levels of sparseness observed in images are qualitatively linked to their textural content. Indeed, different levels of roughness in any image correspond to different distributions of the coefficients corresponding to its representation. Such an analysis was previously performed for instance by characterizing the Hurst parameter for two-dimensional fractional Brownian motion  [7].
More generally, the modulus of this complex number gives the energy response to the edge (comparable to the response of complex cells in area V1), while its argument gives the exact phase. This property further expands the richness of the representation. Overall, such a representation is implemented in the LogGabor package of Python scripts  1. Because this dictionary of edge filters is over-complete, the linear representation would give an inefficient representation of the distribution of edges (and thus of edge co-occurrences) due to a priori correlations between coefficients. Therefore, starting from this linear representation, we searched for the most sparse representation. Minimizing the 0 pseudo-norm (the number of non-zero coefficients) leads to an expensive combinatorial search with regard to the dimension of the dictionary (it is NP-hard).
As proposed first by  [13] , we may approximate a solution to this problem using a greedy approach, namely the Matching Pursuit algorithm. This class of algorithms gives a generic and efficient representation of edges, as illustrated by the example in Figure  1. For the simulations presented here, sparse coding of images is implemented in the SparseEdges package of Python scripts 2 which depend on packages NumPy (version 1. 11. 1) and SciPy (version 0. 18. 0) [10]  on a cluster of Linux computing nodes. Visualization was performed using Matplotlib (version 1. 5. 1) [6]. Our goal is to study how the statistics of edges' occurrences vary across a set of natural images. In order to perform that, wes first defined a database of 600 images  3. The performance of the algorithm can be measured quantitatively by reconstructing the image from the list of extracted edges.
We fitted each histogram using the Levenberg-Marquardt method from SciPy  [10]. In particular, this was validated by checking the goodness of fit for each image. While the mode did not vary significantly across the database of natural images, we observed that the bandwidth B of the distribution was significantly different across the database. This is shown as the histogram of the B values obtained for each image (see Figure  2 -B, inset). This demonstrates that natural images show a wide range of sparseness in their coefficients from very sparse (B ≈ 3. ) to more dense (B ≈ 1. ). Qualitatively, this corresponds to images which are containing respectively less to more dense textures. An interpretation for that class of distributions comes from the fact that such pdf emerges from the mixing of different sources with narrow bandwidths: a denser mixing produces a broader bandwidth.
One consequence of using α < 1 is that, when removing energy along contours, edges can overlap; even so, the correlation is invariably reduced. Higher and smaller values of α were also tested, and gave classification results similar to those presented here. In summary, the whole learning algorithm is given by the following nested loops in pseudo-code: 1. draw a signal I from the database; its energy is E = I 2 , 2. initialize sparse vector s to zero and linear coefficients ∀j, a j =< I, Φ j >,.
Excessive DNA resection can lead to loss of genomic integrity. ABRAXAS also directly interacts with the BRCA1 BRCT (BRCA1 carboxyl-terminal) repeats through its phosphorylated SPxF motif  [22] [23] [24]  and contributes to BRCA1-dependent DNA damage responses by localizing BRCA1 to DNA damage foci  [16]. Depletion of ABRAXAS impedes the recruitment of BRCA1 to DNA damage sites, leading to impairment of G2/M checkpoint control after ionizing radiation (IR) induction  [25]. Further attesting to the critical role of ABRAXAS in cellular response to DNA damage and potential involvement in cancer susceptibility,  Castillo et al. recently reported that both homozygous and heterozygous Abraxas knockout mice exhibited decreased survival and increased tumor incidence  [26]. Analysis of gene expression levels in human tumors in the TCGA database revealed reduced ABRAXAS gene expression in numerous cancers, including breast cancer.
For each amplicon, samples showing an aberrant melting curve were purified on silica filtration microplates (Whatman hydrophilic GF/C Filter, GE Healthcare Life Science, Piscataway, USA) prior to Sanger sequencing in order to identify the variation causative of the observed difference in melting curves. Sequencing was performed using Big Dye Terminator chemistry on an ABI Prism 3730xl automated sequencer from Applied Biosystems (Applied Biosystems, Foster City, CA, USA). All sequence variants that were either unreported or had an allele frequency of <1% in the large scale reference groups "Caucasian Americans", "African Americans" and "East Asians" based on Exome Variant Server (EVS)  [31]  and 1,000 genomes project (1000G) data (1000 Genomes) were confirmed by concordant variant sequencing calls of the two independent reamplified WGA reaction products.
Alignments and scoring of missense substitution The multiple sequence alignment software M-Coffee, which is a part of the T-coffee software (http://tcoffee. crg. cat) (Tree-based Consistency Objective Function For alignment Evaluation)  [32, 33]  was used to prepare a protein multiple sequence alignment with ABRAXAS orthologs. Ten protein sequences of ABRAXAS were used, ranging from Human (Homo sapiens, NP_620775. 2) to the most divergent zebrafish sequence (Danio rerio, NP_001005993. 1), and including chimpanzee (Pan troglodytes, JAA36615. 1), orangutan (Pongo abelii,   ). The impact of the missense substitutions identified during our ABRAXAS mutation screening was scored using our M-coffee alignment with the two prediction software programs Align-GVGD (http://agvgd. iarc. fr) and Sorting Intolerant From Tolerant (SIFT) (http://sift.
jcvi. org/). PolyPhen2. 1 (http://genetics. bwh. harvard. edu/pph2/) was also used with its precompiled alignment. Briefly, Align-GVGD classifies missense variants in a query sequence into 7 grades, from the most deleterious C65 to the least deleterious C0, with the intermediate grades C15, C25, C35, C45 and C55  [34]. The program is based on Grantham calculation  [35] , a combination of Grantham Variation (GV) which measures the amount of observed biochemical evolutionary variation at a specific position of the alignment, and Grantham Deviation (GD) which measures the biochemical difference between the missense residue and the range of variation observed at this position in the alignment. SIFT is a sequence homology-based tool that predicts variants in the query sequence as "tolerated" or "deleterious", calculating normalized probabilities for all possible substitutions in the multiple sequence alignment.
In silico analysis of variants Intronic and exonic variants located in the vicinity of splice junction consensus sites were scored for their potential impact on splicing using the Splicing Prediction Module of Alamut (Interactive Biosoftware) which computes the following prediction methods: SpliceSiteFinderlike  [38] , MaxEntScan  [39] , NNSPLICE  [40] , GeneSplicer  [41]  and Human Splicing Finder  [42]. Variants located near the beginning of the first exon were analyzed using the MatInspector software of Genomatix  [43]  to assess whether they altered transcription factor binding sites.
Statistical analysis To assess the relationship between ABRAXAS variants and breast cancer risk we compared frequency distribution of rare missense substitutions and in-frame deletions in cases and in controls. The case-control statistical analysis has been described in detail previously  [7]. Briefly, a single table with one entry per subject, zero or one rare sequence variant per subject, annotations for type of sequence variants, study center, case-control status, and race/ethnicity was constructed. Analyses were performed using the chi square test, the Fischer test and multivariable unconditional logistic regressions using STATA version 11 software (StataCorp, College Station, TX, USA). Differences in the case/control ratio between racial/ethnic groups and study center were accounted for by including categorical variables for each racial/ethnic group and each study center.
Quantitative RT-PCR Total RNA was extracted using TriReagent (Sigma-Aldrich) following manufacturer's instructions. Reverse transcription was performed with the M-MuLV Reverse Transcriptase (NEB) and a mix of random hexamers and anchored oligo(dT). Quantitative PCR reactions were performed using four ng of cDNA, analyzed in three replicates for each data point, in a quantitative PCR (CFX384 RealTimeSystem (Bio-Rad)) using the iTAQ Universal SYBR Green Supermix (Bio-Rad). The following forward and reverse primers were used: 5'-AGAGATAATTAAAGTTC TTGACAAAAC-3' and 5'-TCAAATAATGGGTAAGAAAGAATAC-3'. PCR volume was 10 μl and conditions were as follow: Initial activation at 95°C, 30s followed by 40 cycles at 95°C, 5s and 60°C, 30s; final cycle (melting curve) 65°C, 0. 05s and 95°C 0. 5s. Relative expressions were calculated using the CFX Manager Software v3.
The promoterless pGL3-basic vector was used to measure basal expression levels. Each experiment was performed three times. Luciferase activity was normalized to Renilla luciferase. Data from four replicates per construct were analyzed with a mixed model including the fixed effect of genotype and the random effects of experiment number and clone within experiment number. The analysis was done at the 0. 05 level of significance. The model was fitted using the lme function of the nlme R package  [44].
Analysis of silent substitutions, 5'-UTR and intronic variations The variant c. -4T>C located in the 5'-UTR of ABRAXAS and the two silent substitutions c. 21G>A (p. Ser7Ser) and c. 33G>C (p. Ser11Ser) located near the beginning of the first exon were analyzed using the MatInspector software of Genomatix  [43]  in order to predict whether they altered transcription factor consensus binding sites. These analyses showed that the variant c. 21G>A (p. Ser7Ser) abolished a consensus binding site for the Early Growth Response (EGR2) transcription factor, while the two other examined variants were not predicted to affect the binding sites of any transcription factors known to be involved in breast cancer. Gene reporter assays were performed in the breast cancer cell line MCF7 using a promoter fragment containing c.
Discussion To our knowledge, this study is the largest case-control mutation screening investigating whether rare sequence variations within ABRAXAS contribute to breast cancer susceptibility. This gene was an appealing candidate because of (i) its central role in the formation of the Acomplex and it's critical functions during HR repair, (ii) its implication in BRCA1 recruitment to DNA double-strand breaks, (iii) its interaction with the proteins encoded by the two known breast cancer susceptibility genes BRCA1 and BABAM1 (MERIT40) and (iv) its identification as a breast cancer susceptibility gene in Finnish breast cancer families. Our mutation screening identified 17 ABRAXAS variants: 11 were already reported in the public databases such as EVS (Exome Variant Server)  [31]  and 1,000G (1000 Genomes) and 6 are novel variants.
Acknowledgments J. S. is Chairholder of the  Canada Research Chair in Oncogenetics. JYM is a  FRQS Chercheur National. We thank  Roger Greenberg  for the ABRAXAS-HA-Flag DNA construct. We wish to thank all participants in the BCFR for their contribution to the study. The BCFR would like to thank  Drs. Saundra   Buys-Department of Medicine, Huntsman Cancer Institute, University of Utah School of Medicine  ( PI-Utah site of the BCFR), Mary Daly-Department of Clinical Genetics ,  Fox Chase Cancer Center, Philadelphia, PA, USA  (PI-PA site of the BCFR), and  Mary Beth Terry-Department of Epidemiology, Columbia University Mailman School of Public Health, Herbert Irving Comprehensive Cancer Center ,  Columbia University Medical Center, New York, NY, USA  (PI-New York site of the BCFR) and  Dr. Esther John - Cancer Prevention Institute of California, Fremont, USA ,  Stanford University School of Medicine and Stanford Cancer Institute, Stanford, USA  (contact PI for the  BCFR-Esther.
Partial and full evaluation modes Many function prediction methods apply only to certain types of proteins, such as proteins for which 3D structure data are available, proteins from certain taxa, or specific subcellular localizations. To accommodate these methods, CAFA2 provided predictors with an option of choosing a subset of the targets to predict on as long as they computationally annotated at least 5,000 targets, of which at least 10 accumulated experimental terms. We refer to the assessment mode in which the predictions were evaluated only on those benchmarks for which a model made at least one prediction at any threshold as partial evaluation mode. In contrast, the full evaluation mode corresponds to the same type of assessment performed in CAFA1 where all benchmark proteins were used for the evaluation and methods were penalized for not making predictions.
Data sets Protein function annotations for the Gene Ontology assessment were extracted, as a union, from three major protein databases that are available in the public domain: Swiss-Prot  [6] , UniProt-GOA  [30]  and the data from the GO consortium web site  [5]. We used evidence codes EXP, IDA, IMP, IGI, IEP, TAS and IC to build benchmark and ground-truth sets. Annotations for the HPO assessment were downloaded from the Human Phenotype Ontology database  [45]. Figure  2  summarizes the benchmarks we used in this study. The left panel shows the benchmark sizes for each of the ontologies and compares these numbers to CAFA1. All species that have at least 15 proteins in any of the benchmark categories are listed in the right panel.
Baseline models We built two baseline methods, Naïve and BLAST, and compared them with all participating methods. The Naïve method simply predicts the frequency of a term being annotated in a database  [11]. BLAST was based on search results using the Basic Local Alignment Search Tool (BLAST) software against the training database  [4]. A term will be predicted as the highest local alignment sequence identity among all BLAST hits annotated with the term. Both of these two methods were "trained" on the experimentally annotated proteins available in Swiss-Prot at time t 0 , except for HPO where the two baseline models were trained using the annotations from the t 0 release of the Human Phenotype Ontology.
For this purpose, we used a hypothetical ontology by taking the intersection of the two Gene Ontology snapshots (versions from January 2011 and June 2013) so as to mitigate the influence of ontology changes. We thus collected 356 benchmark proteins for MFO comparisons and 698 for BPO comparisons. The two baseline methods were trained on respective Swiss-Prot annotations for both ontologies so that they serve as controls for database change. In particular, SwissProt2011 (for CAFA1) contained 29,330 and 31,282 proteins for MFO and BPO, while SwissProt2014 (for CAFA2) contained 26,907 and 41,959 proteins for the two ontologies. To conduct a "head-to-head" analysis between any two methods, we generated B = 10, 000 bootstrap samples and let methods compete on each such benchmark set. The average performance metric as well as the number of wins were recorded.
where m 1 and m 2 stand for methods from CAFA1 and CAFA2, respectively, and F (i) max (•) represents the F max of a method evaluated on the i-th bootstrapped benchmark set. The selection of top methods for this study was based on their performance in each ontology on the entire benchmark sets. Panels B and C in Figure  8  show the comparison between baseline methods trained on different data sets. We see no improvements of these baselines except for BLAST on BPO where it is slightly better to use the newer version of Swiss-Prot as the reference database for the search. On the other hand, all top methods in CAFA2 outperformed their counterparts in CAFA1. For predicting molecular functions, even though transferring functions from BLAST hits does not give better results, the top models still managed to perform better.
The field has moved forward Three years ago, in CAFA1, we concluded that the top methods for function prediction outperform straightforward function transfer by homology. In CAFA2, we observe that the methods for function prediction have improved compared to those from CAFA1. As part of the CAFA1 experiment, we stored all predictions from all methods on 48,298 target proteins from 18 species. We used those stored predictions and compared them to the newly deposited predictions from CAFA2 on the overlapping set of benchmark proteins and CAFA1 targets. The head-to-head comparisons among top five CAFA1 methods against top five CAFA2 methods reveal that the top CAFA2 methods outperformed all top CAFA1 methods. Although it is difficult to disentangle the contributions of larger training sets from those of methodological novelties, the fact that the BLAST algorithm using the data from 2011 and data from 2014 showed little difference, led us to conclude that a larger share of the contribution likely belongs to the new methods.
Additional Files This submission contains three supplementary resources. (1)  The Supplementary Information below provides additional CAFA2 analyses that are equivalent to those provided for the CAFA1 experiment in the CAFA1 supplement; (2) the large data repository provides all additional data, analyses as well as full prediction results for every method; (3) the entire library of code used in CAFA2 is available at https://github. com/yuxjiang/CAFA2. Supplementary Figure  5  Precision-recall curves for the top-performing methods for (A) eukaryotic benchmark category and Molecular Function ontology, (B) prokaryotic benchmark category and Molecular Function ontology, (C) eukaryotic benchmark category and Biological Process ontology, (D) prokaryotic benchmark category and Biological Process ontology, (E) eukaryotic benchmark category and Cellular Component ontology and (F) prokaryotic benchmark category and Cellular Component ontology.
The legend provides the maximum weighted F-measure (F ) and coverage (C) for all methods. In cases where a Principal Investigator (PI) participated with multiple teams, only the results of the best scoring method are presented. Calculation of the weighted precision-recall curve. Each term f in the ontology was weighted according to the information content of that term. The information content of the term f was calculated as ic(f ) = log 2 1 Pr (f |P(f )) , where Pr (f |P(f )) is the probability that the term f in the ontology is associated to a protein given that all of its parents are associated. (probabilities were determined based on the union of Swiss-Prot, UniProt-GOA and GO Consortium databases). Weighted precisions and recalls are calculated as wpr(τ ) = 1 m(τ ) m(τ ) i=1 f ic(f ) • 1 (f ∈ P i (τ ) ∧ T i (τ )) f ic(f ) • 1 (f ∈ P i (τ )) , and wrc(τ ) = 1 n e ne i=1 f ic(f ) • 1 (f ∈ P i (τ ) ∧ T i (τ )) f ic(f ) • 1 (f ∈ T i (τ )) , where P i (τ ) is the set of predicted terms for protein i with score no less than threshold τ and T i is the set of true terms for protein i, m(τ ) is the number of sequences with at least one predicted score greater than or equal to τ , and n e is the number of proteins used in a particular mode of evaluation.
Acknowledgements We acknowledge the contributions by  Maximilian Hecht ,  Alexander Grün ,  Julia Krumhoff ,  My Nguyen Ly ,  Jonathan Boidol ,  Rene Schoeffel ,  Yann Spöri ,  Jessika Binder ,  Christoph Hamm  and  Karolina Worf. This work was partially supported by the following grants:  National Science Foundation (NSF)  grants  DBI-1458477  (PR),  DBI-1458443  (SDM),  DBI-1458390  (CSG),  DBI-1458359  (IF), IIS-1319551 (DK),  DBI-1262189 (DK)  and  DBI-1149224  (JC);  National Institutes of Health (NIH)  grants  R01GM093123  (JC), R01GM097528 (DK),  R01GM076990  (PP),  R01GM071749  (SEB),  R01LM009722  (SDM) and UL1TR000423 (SDM); the  National Natural Science Foundation of China   3147124  (WT) and 91231116 (WT); the  National Basic Research Program of China   2012CB316505  (WT);  NSERC   RGPIN 371348-11  (PP);  FP7 "infrastructures" project TransPLANT Award 283496  (ADJvD);  Microsoft Research/FAPESP  grant  2009/53161-6  and  FAPESP  fellowship  2010/50491-1  (DCAeS);  Biotechnology and Biological Sciences Research Council (BBSRC)  grants  BB/L020505/1  (DTJ),  BB/F020481/1  (MJES),  BB/K004131/1  (AP),  BB/F00964X/1  (AP) and  BB/L018241/1  (CD);  Spanish Ministry of Economics and Competiveness , grant number  BIO2012-40205  (MT);  KU Leuven CoE PFV/10/016  SymBioSys (YM); the  Newton International Fellowship Scheme of the Royal Society  grant  NF080750  (TN).
Potentially, these results might also generalize to visual-to-tactile SSDs as well (though with the additional requirement of their dedicated hardware). 1. 1. 3 The sonification process. In these experiments EyeMusic was run as software on the same computer the experiment was run on, similar to other accessibility tools such as screenreaders, and not from the smartphone setup mentioned above. At every scan, typically 1 per 2 seconds but dynamically controlled by the user, EyeMusic sampled the graphical information via a screenshot of the contents displayed on the screen and sonified it for the user. As the user moved in the virtual environment via the keyboard the contents of their surroundings, and correspondingly the information displayed on the screen unseen by the users, changed according to standard 1 st person view, and thus the next round of screen sonification matched the current viewpoint.
Experimental setup 1. 1. 4 Equipment and setup. The virtual environments in this experiment were created using Blender 2. 49, and Python 2. 6. 2. Participants were seated comfortably in front of a computer. Navigation was accomplished using the arrow keys on a standard keyboard ("up" arrow for moving 0. 4 m forward, "left/right" arrows for rotating the avatar 30 degrees left/right accordingly), and the auditory cues were delivered via standard headphones (see Fig  1C ). The environments were scaled such that each "virtual meter" matched a real world meter. The sighted participants of experiment 2 were blindfolded throughout the experiment using Mindfold blindfolds. 1. 1. 4. 1 Experiment 1 participants. The main experimental group consisted of eight congenitally blind participants who took part in Experiment1:tasks 1-2 and seven congenitally blind participants who took part in Experiment1:task 3.