dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability NBench lists all the detailed information of the 41 datasets: number of protein, resolution, sequence identity, structural similarity and year of publication and PDB ID list. It provides all the PDB IDs, curated PDB files, sequence files in FASTA format and binding sites definition based on RBscore criterion which considers both distance cutoff and accessible surface area change. Besides, NBench stores all the assessment results of the programs and exhibit them in terms of 2D heat map, as shown in Figure  2. Users of the database can select their interested program, dataset, distance cutoff to define binding sites, assessment criterion and plot the 2D heat map accordingly. In this way, the comparison can be more specific and concrete. Finally, users are allowed to export these heat maps in different formats.
Potential benchmarking Many predictors targeting the nucleic acid binding site prediction problem are being developed every year, NBench makes the validation of the new predictors easier and straightforward: on one hand, new predictor developers can download the datasets from NBench to run their predictor and compare with the results of other predictors obtained from NBench. Developers can perform their assessments on all the results for comparison, it avoids repeated calling of the other web servers. On the other hand, developers are also encouraged to submit their prediction results to NBench, so new predictions can be benchmarked in a more systematic way by NBench during the maintenance of the database. New data is added to NBench during regular maintenance of the database or upon request.
DISCUSSION RBscore highlights the point that a nucleic acid binding site prediction is not a binary classifier but is to find the potential binding region to help understanding the underlying essence of protein-nucleic acid interaction, as well as to find the potential binding energy funnel. It is the first automated web server reported to predict DNA-and RNAbinding sites within the same prediction model. Besides, it directly combine feature values into a probability score of nucleic acid binding without complexion and achieves high level accuracy on all datasets regardless of binding site definition bias. Nucleic acid binding site prediction is an active field of work, no fewer than eight papers  (24) (25) (26) (27) (28) (29) (30) (31)  targeting this problem were published in 2014. Validation of new predictors is a crucial necessity but prone to bias.
Viral Constructs, Virus Stocks and Plant Infections The TEV genome used to generate the virus constructs, was originally isolated from Nicotiana tabacum plants  (Carrington et al. 1993). In this study five different variants of TEV were used containing single gene duplications. Two of these virus variants, TEV-NIb 1 -NIb 9 and TEV-NIb 2 -NIb 9 were generated in a previous study  (Willemsen et al. 2016). The other three variants were generated in this study: TEV-HCPro 2 -HCPro 3 , TEV-NIaPro 2 -NIaPro 8 , and TEV-CP 10 -CP 11. The TEV-HCPro 2 -HCPro 3 and TEV-CP 10 -CP 11 viruses contain gene duplications in tandem. TEV-HCPro 2 -HCPro 3 , TEV-NIaPro 2 -NIaPro 8 , and TEV-CP 10 -CP 11 were generated from cDNA clones constructed using plasmid pMTEVa, which consists of a TEV infectious cDNA (accession: DQ986288, including two silent mutations, G273A and A1119G) flanked by SP6 phage RNA promoter derived from pTEV7DA (GenBank: DQ986288).
Fitness Assays The genome equivalents per 100 mg of tissue of the ancestral virus stocks and all evolved lineages were determined for subsequent fitness assays. The InviTrap Spin Plant RNA Mini Kit (Stratec Molecular) was used to isolate total RNA from 100 mg homogenized infected tissue. Real-time quantitative RT-PCR (RT-qPCR) was performed using the One Step SYBR PrimeScript RT-PCR Kit II (Takara), in accordance with manufacturer instructions, in a StepOnePlus Real-Time PCR System (Applied Biosystems). Specific primers for the CP gene were used; forward 5 0 -TTGGTCTTGATGGCAACGTG-3 0 and reverse 5 0 -TGTGCCGTTCAGTGTCTTCCT-3 0. The StepOne Software v. 2. 2. 2 (Applied Biosystems) was used to analyze the data. The concentration of genome equivalents per 100 mg of tissue was then normalized to that of the sample with the lowest concentration, using phosphate buffer.
The ratio (R) of the evolved and ancestral lineages to TEV-eGFP is then R ¼ n CP À n eGFP ð Þ =n eGFP , where n CP and n eGFP are the RT-qPCR measured copy numbers of CP and eGFP, respectively. Within-host competitive fitness can then be estimated as W ¼ ffiffiffiffiffiffiffiffiffiffiffi ffi R t =R 0 t p , where R 0 is the ratio at the start of the experiment and R t the ratio after t days of competition  (Carrasco et al. 2007). Note that the method for determining R only works well when the frequency of the common is below ~0. 75. This limitation was not problematic though, because in these experiments the fitness of the evolved virus populations remained the same or increased. The statistical analyses comparing the fitness between lineages were performed using R v. 3. 2. 2 (R Core Team 2014) and IBM SPSS Statistics version 23.
Libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina Inc. ). Sequencing quality control was performed by GenoScreen, based on PhiX error rate and Q30 values. Read artifact filtering and quality trimming (3 0 minimum Q28 and minimum read length of 50 bp) was done using FASTX-Toolkit v. 0. 0. 14 (http://hannonlab. cshl. edu/fastx_ toolkit/index. html, last accessed April 10, 2016). De-replication of the reads and 5 0 quality trimming requiring a minimum of Q28 was done using PRINSEQ-lite v. 0. 20. 4  (Schmieder and Edwards 2011). Reads containing undefined nucleotides (N) were discarded. As an initial mapping step, the evolved sequences were mapped using Bowtie v. 2. 2. 6  (Langmead and Salzberg 2012)  against their corresponding ancestral sequence: TEV (GenBank accession number KX137149), TEV-HCPro 2 -HCPro 3 ancestral (GenBank accession number KX137150), TEV-NIaPro 2 -NIaPro 8 ancestral (GenBank accession number KX137151), TEV-NIb 1 -NIb 9 ancestral (GenBank accession number KT203712), TEV-NIb 2 -NIb 9 ancestral (GenBank accession number KT203713), and against the evolved lineages including the corresponding deletions in the lineages where they are present.
The value of b used is 1. 344, and a values are 1. 175, 1. 234, 1. 185, and 1. 134 for TEV-HCPro 2 -HCPro 3 , TEV-NIaPro 2 -NIaPro 8 , TEV-NIb 1 -NIb 9 , and TEV-NIb 2 -NIb 9 , respectively. The only model parameter that needed to be estimated from the data is. To obtain an estimate of this parameter, we implemented the model as described in equations (  1 ) and (2) in R 3. 1. 0. For each dataset to which we wanted to fit the model, we first simply ran the model for a wide range of recombination rates: considering all values of log() between À20 and À0. 1, with intervals of 0. 1. Onethousand simulations were run for each parameter value. To fit the model to the data, we considered model predictions of the frequency of three kinds of virus populations over time: (i) those populations containing only the full-length ancestral virus with a gene duplication (X 1 ), (ii) those populations containing only variants with a genomic deletion removing the artificially introduced second gene copy (X 2 ), and (iii) those populations containing a mixture of both variants (X 3 ).
The multinomial probability of a particular realization (x 1 , x 2 , and x 3 ) is given by: P X 1 ¼ x 1 ; X 2 ¼ x 2 ; X 3 ¼ x 3 ð Þ ¼ P 3 i¼1 x i ! Å 3 i¼1 x i ! Å 3 i¼1 p x i i : The estimate of is then simply the value that corresponds to the lowest negative log-likelihood (NLL), for the entire range of values tested. We first fitted the model with a single value of to all the data (Model 1; 1 parameter). Next, we fitted the model with a virus-dependent value of , but one which is independent of passage duration (Model 2; 4 parameters). We then fitted the model with value dependent on passage duration, but the same for each virus (Model 3; 2 parameters). Finally, we fit the model to each experimental treatment separately (Model 4; 8 parameters). For all these different model fittings, 95% fiducial estimates of were obtained by fitting the model to 1000 bootstrapped datasets.
Acknowledgments We thank  Francisca de la Iglesia  and  Paula Agudo  for excellent technical assistance. This work was supported by the  John Templeton Foundation  [grant number  22371  to S. F. E. ]; the  European Commission   7 th Framework Program EvoEvo Project  [grant number  ICT-610427  to S. F. E. ]; the  Spanish Ministerio de Economı ´a y Competitividad (MINECO)  [grant numbers  BFU2012-30805  and  BFU2015-65037-P  to S. F. E. ]; the  Botı ´n Foundation from Banco Santander  through its  Santander Universities Global Division  [J. S. ]; the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya  [J. S. ]; and the  European Molecular Biology Organization  [grant number  ASTF 625-2015  to A. W]. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation.
However, the active pixels give no signal when the pre-set threshold level (THL) is not crossed. The 14-bit counter of each pixel operates only when the Timepix shutter is open (see section 2. 1. 1) and has an overflow control that stops at 11810 counts  [9]. All the Timepix parameters are easily configured via Pixelman, a cross-platform and modular software tool developed by the Czech Technical University in Prague  [14]. Figure  2  compares the energy dependence of photon absorption probability, which is directly related to the photon detection efficiency, for the three substrates (Si, CdTe and GaAs) that can be currently chosen for the Timepix chip. According to this figure, CdTe has globally a better photon absorption probability due to its high atomic number and density (Z = 48. 52 and ρ = 5.
2). Another aspect to highlight is that the parallax correction is no longer possible at source-to-detector distances less than 50 cm (see the mismatch between the actual location of the considered radioactive sources and that of the associated colored hotspots as determined by iPIX in figures 28 and 29). 1Under which iPIX is able to autonomously select, among other parameters, the appropriate shutter-time and the minimum number of frames needed for every measurement environment. -23 - -26 -The minimum times needed, considering data collection only, to localize the above low-activity point sources are summarized in table  4. In all cases, the mask R7e8 mask always gives the fastest localization time. Moreover, iPIX was able to localize the 412 kBq 241 Am point source, providing a dose rate at the measurement point of only 2 nSv/h, with a minimum of 70 frames of 1 sec shutter-time.
Acknowledgments This study has been partially funded by the  European Union Seventh Framework Programme ,  Marie Curie Actions , through the  EDUSAFE Initial Training Network  ( EU FP7-PEOPLE-2012-ITN-316919 ). The authors would also like to acknowledge the staff of both  CANBERRA  and  KIT  (in particular  T. Liedtke  for providing figure 10) irradiation facilities for their valuable technical support.
Recently, a large number of systems have been developed over the years to solve this kind of problems and perform tasks in Information Retrieval; many of these systems perform specific tasks such as word counter and text summarization, however they are not in the level to satisfy the growing need of users to extract the useful information from documents using Text OLAP approaches. In this paper we describe a software platform for keywords extraction and aggregation in an OLAP context. The platform implements a new way for extracting keywords from a corpus of document based on the Microsoft academia research web site and six algorithms for keyword aggregation which process a corpus of textual data to discover aggregated keywords. The rest of the paper is organized as follows: Section 2 introduces related works in keywords extraction and aggregation in OLAP context.
The first one uses statistical meth-ods; the second one is based on linguistic knowledge; the third one is based on graphs; while the last uses external knowledge. The approaches based on statistical methods use the occurrence frequencies of terms and the correlation between terms to extract the keywords. Hady et al. (Hady et al. , 2007)  proposed an approach called TUBE (Text-cUBE). They adopted a relational database to textual data based on the cube design, each cell contains keywords, and they attached to each keyword an interestingness value. Zhang et al. (Zhang et al. , 2009)  proposed an approach called Topic Cube. The main idea of a Topic Cube is to use the hierarchical topic tree as the hierarchy for the text dimension. This structure allows users to drill-down and roll-up along this tree.
Their aggregation approach is called TAG (Textual Aggregation by Graph). It aims at extracting from a set of terms a set of the most representative keywords for the corpus of textual document using a graph. The function takes as input the set of all extracted terms from a corpus, and outputs an ordered set, containing the aggregated keywords. The process of aggregation goes through three steps: (1) Extrac-tion of keywords with their frequencies, (2) Construction of the affinity matrix and the affinity graph, and (3) Cycle construction and aggregated keywords selection. The software system developed in this domain consists of two main components; Text Pre-processor and Topics Extractor. Text pre-processor, offers learning and inference functionalities. The learning functionality pre-processes a document collection by exploiting a stop words list and a general purpose to obtain the word-document matrix according to the bagof-words model.
The user can choose the number of words to be used for document indexing. The inference functionality processes a document to obtain one of the following bag-of-words representations; binary, term frequencies and the inverse term document frequency. Topic extractor implements a customized version of the Latent Dirichlet Allocation (LDA) model  (Blei and Andrew, 2003). The solution of the LDA learning is obtained by using the Expected Maximization and the Gibbs Sampling algorithms which have been implemented in the C++ programming language on a single processor machine. Each topic is summarized through the estimate its prior probability, a sorted list of its most frequent words together with the estimate of their conditional probabilities. Semantria 1 is a text analytical tool that offers an API that performs sentiment analysis and analytic text.
In addition it shows the students how the recall, precision and F-measure change their values according to number of aggregated keywords k introduced by the user. Instructors may ask their students to do experiments with a real corpus using OLAP-TAS, write applications that use the Java classes, extend an existing approaches, or contribute in implementing a new algorithm to integrate in OLAP-TAS. Research: OLAP-TAS contains implementations for several algorithms and approaches that solve common problems, such as textual aggregation in an OLAP context. It also comes with two corpora and annotated datasets. The implementation of other algorithms as well as other corpora, can be integrated into the plat-form. This makes it a good resource for researchers to build systems and conduct experiments.
Background Non-Negative Matrix Factorization (NMF) is a very efficient approach to feature extraction in machine learning when the data is naturaly non-negative. It has been applied to an extremely large range of situations such as clustering  [1] , email surveillance  [2] , hyperspectral image analysis  [3] , face recognition  [4] , blind source separation  [5] , etc. It has recently also been applied to microarray data analysis  [6]  and biomedicine  [7]. Given a dataset consisting of n vectors x 1 ,. , x n in R d , the NMF approach builds a matrix M whose columns are x 1 ,. , x n and then factorizes this matrix as M = UV t + E, where E is an error term, U and V are componentwise non-negative, and U has a small number of columns. The features are the columns of U. They are often interpretable and summarize the data in an efficient manner since each data then consists of a mixture of these columns.
For many real datasets, the rank of the obtained matrix, i. e. the number of features extracted, is usually small and the NMF thus provides a compact representation of the data. The method was first explored by Lee and Seung  [8]  in the late 90's and it then enjoyed a significant growth of interest in many application fields and especially machine learning. There exists a wide variety of methods for computing the NMF. One most employed strategy is the famous alternating minimization scheme, which consists in successively minimizing in U and then in V. Notice that minimization in U (resp. V ) is a convex and easy optimization problem. Furthermore, it has been observed as quite efficient in practice. The main drawback of this approach however, is that no convergence garantee towards a global minimizer has been proved so far.
Back to the not necessarily separable case,  [12, 13]  proposed Bregman divergence based iterative methods for NMF. Bregman-divergence based proximal approaches have been the subject of great interest recently due to good practical performances and connection with mirror descent type algorithms (see for instance the survey  [14] ). In the present article we devise a Bregman-proximal method for NMF naturally extends to the case where some data may be missing and/or corrupted by the occurrence of outliers. Missing data and outliers are very frequent in gene expression data. Our approach also borrows ideas from robust PCA  [15] , where the matrix we want to approximate is assumed to be splittable into a low rank part and a sparse part: M = L + S. The outliers are represented by the matrix S. Notice that the noise was not taken into account in the original article  [15] , whereas gene expression dataset are often corrupted by very large noise.
This is easily overcome by performing least squares penalized estimation as in e. g. the code GoDec  [16]. In the present work, an efficient method is proposed that denoises the data, estimates the missing values, and identifies the outliers in M via nonnegative low-rank + sparse + noise matrix factorization. Our algorithm is inspired by the recent work  [17] , which presents a clear interpretation of the ADMM in terms of proximal method-type iterations. In our approach, a Bregman divergence is chosen for the proximal scheme which allows to easily take into account the nonnegativity constraints. In the next section, the Bregman proximal scheme is presented and in the subsequent Section, a version taking into account potential outliers and/or missing data is described in full details. The choice of the relaxation parameter is also addressed.
A toy numerical experiment We start with a simple random example programmed in Matlab. Let U 0 be a random matrix in R 50×8 with i. i. d. components having the uniform distribution on [ 0, 1]. Let V 0 be a random matrix in R 70×8 with components having the same distribution. Take M = U 0 V t 0 , ρ = 100, and random initial matrices. Figure  1  shows that the method converges to M in the sense that it produces a sequence of matrices U (k) and V (k) whose product U (k) V (k) t converges to M. At this point, we can see on a toy example that the method converges, but it is obviously not proven in the general case. However, the present state of knowledge in the analysis of numerical algorithms for NMF is still lacunary and the case of outliers is therefore even more out of reach for the moment.
For instance, the usually very efficient sparse principal component analysis returns contradictory number of features when the alpha sparsity controlling parameter ranges from 0 to 0. 5, see Fig. 2. Our approach in this section is to use Non-negative Matrix Factorization in order to jointly take into account the data's intrinsic non-negative nature and the necessity of clustering the data by performing efficient feature extraction. One of the main challenges in the study of such data sets is to take into account possible outliers. For the bladder cancer dataset, some outliers have been observed by using standard PCA visualization, thus enforcing the need to automatically detect such phenomena in order to avoid subsequent misinterpretations of the genes' respective influences on the tumor state.
Conclusion In this article, a new way to find a relevant dictionary for extracting the relevant features in a given dataset has been presented, in an original context of missing values and outliers. The well-known Non-negative Matrix Factorization (NMF) method has been extended on denoised data, where missing values have been guessed and outliers have been detected, leading to a mixture of Bregman proximal methods and the of Augmented Lagrangian scheme. Finally, an application to the analysis of gene expression data of patients with bladder cancer has been provided for illustration purpose.
Acknowledgements The first and second authors were funded by the grant Eléments Transposables from the  Région Franche-Comté. The first author would like to acknowledge the help of  Haokun Li  for the preparation of the manuscript. The work of  Regis Delage Mouroux  and  Michele Jouvenot  was supported by the  Région Franche-Comté  and by the  Ligue Contre le Cancer  ( CCIR-GE ). P. Huetz acknowledges the Montbéliard and  Besançon Leagues against Cancer  for financial support.
The air-sea flux of greenhouse gases [e. g. , carbon dioxide (CO 2 )] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air-sea CO 2 flux processing toolbox called the ''FluxEngine,'' designed for use on a cloudcomputing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO 2 flux data from model, in situ, and Earth observation data, and its air-sea gas flux calculation is user configurable.
2004 ). The North Atlantic sink in particular has been shown to be highly variable  (Watson et al. 2009 ) and the mechanisms driving this variability are not well understood. Therefore, isolating and reducing the uncertainties in the estimates of the oceanic sink of CO 2 is a crucial goal of climate science (Le  Quéré et al. 2009 ). In the last decade there has been an explosion in the availability of large (. 1 TB) high-quality, wellcharacterized, and often multisensor cross-calibrated Earth observation (EO) datasets. For example, the European Space Agency (ESA) GlobWave project (http:// globwave. ifremer. fr/) produced a 20-plus-year time series of global coverage multisensor cross-calibrated wave and wind data. Similar efforts in the United States resulted in the National Aeronautics and Space Administration (NASA) project Making Earth System Data Records for Use in Research Environments (MEaSURES), which produced a 13-plus-year time series of global coverage multisensor surface biology datasets  (Maritorena et al.