dataesr/scientific-paragraphs-categorization huggingface dataset:
Beyond their robustness in T. versatilis, the suitability of these reference genes for RT-qPCR analysis within the filamentous fungal kingdom was assessed, by collecting RNA-seq based transcriptomic data from 18 phylogenetically distant fungal species. The representative set of organisms and experimental conditions that was collected, confirmed that most of the classic "housekeeping" genes such as g6pdh, β-tub and act did not appear as the most stable genes, even if the latter has been classified among the best reference genes in few specific studies  [35, 41]. Other genes involved in central metabolism, e. g. pfk, gapdh, aspC or glk, also showed the highest occurrence of significant down-or up-regulation, which, together with their probable risk of co-regulation, strongly discouraged their further use as reference gene for RT-qPCR gene expression analysis.
Acknowledgements This work was supported in part by the  CINABio-Adisseo (Adisseo SAS, Antony, France)  to JMF & JLP and by  CIFRE , grant number:  1558/2010  to AL. We are grateful to  Christophe Roux  ( LRSV, Toulouse, France ) and  Thierry Rouxel  ( INRA-Bioger, Thiverval-Grignon, France ) for providing access to their unpublished RNA-seq data from Rhizophagus irregularis and Leptosphaeria maculans, respectively,  Jolanda Van Munster  ( University of Nottingham, UK ) for providing RNA-seq data analysis from T. reesei and A. niger, and  David Archer ,  Steve Pullan ,  Stéphane Delmas  and  Martin Blyth  ( University of Nottingham, UK  ) for contribution in the production of T. versatilis RNA-seq results (unpublished results). We also thank  David Archer  ( University of Nottingham, UK ) for scientific support during this work and proofreading of the manuscript.
Plant material and DNA extraction A total of 62 accessions of strawberry (F. × ananassa) were used for DArT marker development in this study, including the parental lines of the '232' × '1392' mapping population and 4 progenies. They were obtained from the IFAPA strawberry germplasm collection (ESP138) located at Centro IFAPA Churriana Málaga Spain or from the CIREF strawberry germplasm collection (FRA207) located at Douville France. Cultivar names, their year of release, pedigree and geographical origin are shown in Table  1. The chosen cultivars represent a wide range of variation based on agronomic traits, different geographical origins and pedigrees. The cultivars we studied were included in the European project GENBERRY collection and detailed information about each accession is publicly available at the European GENBERRY database (https://www.
Genomic representations were labeled with fluorescent dyes (Cy3 and Cy5). Labeled targets were then hybridized to printed DArT arrays for 16 hours at 62°C in a water bath. Slides were processed as described in  [10]  and scanned using Tecan LS300 scanner (Tecan Group Ltd, Männedorf, Switzerland) generating three images per array: one image scanned at 488 nm for reference signal measures the amount of DNA within the spot based on hybridization signal of FAM-labelled fragment of a TOPO vector multiple cloning site fragment and two images for "target" signal measurement. Signal intensities were extracted from images using DArTsoft 7. 4. 7 software (http://www. diversityarrays. com/software. html). DArTsoft was also used to convert signal intensities to presence/absence (binary) scores used in the downstream analysis.
DArTseq Platform Development Similarly to the DArT microarray, the DArTseq technology was optimized for strawberry by selecting the most appropriate complexity reduction method (both the size of the representation and the fraction of a genome selected for assays). Four methods of complexity reduction were tested in strawberry (data not presented) and the PstI-MseI method was selected. DNA samples are processed in digestion/ligation reactions principally as per  [10]  but replacing a single PstI-compatible adaptor with two different adaptors corresponding to two different Restriction Enzyme (RE) overhangs. The PstI-compatible adapter was designed to include Illumina flowcell attachment sequence, sequencing primer sequence and "staggered", varying length barcode region, similar to the sequence reported previously  [9].
In that way the assignments of the sequences to specific samples carried in the "barcode split" step are very reliable. Approximately 600,000 (+/-7%) sequences per barcode/sample were used in marker calling. Finally, identical sequences are collapsed into FASTQCOL. The propriety software package DArT-soft14 is used for marker discovery and scoring from FASTQCOL files. The FASTQCOL files from the samples of '232' × '1392' population were analyzed using DArTsoft14 to output candidate SNP and silicoDArT markers which are polymorphic within the set of samples (Silico-DArT markers are sequences with presence/absence variation in the DArTseq genomic representation). All unique sequences from the set of FASTQCOL files are identified, and clustered by sequence similarity at a distance threshold of 3 base variations.
Statistical analysis of genetic relationships among accessions DArTs were scored as 0/1 and they were used as different inputs for the RESTDIST and NEIGHBOR programs of the PHYLIP 3. 6 software package to construct Neighbor-Join phylograms, based on Felsenstein's modification of the Nei and Li restriction fragment distance  [61]. Phylograms were rooted with 'Pink Panda' (hybrid between F. × ananassa and Comarum palustre, formerly Potentilla palustris). Clade strength was tested by 1,000 bootstrap analyses performed with the SEQBOOT program  [62]. The genetic structure of the germplasm collection was analyzed performing Principal Coordinate Analysis (PCoA) implemented in the program GenAlex 6. 41  [63]  and by using STRUC-TURE 2. 1 software  [64, 65]. PCoA was based on standardized covariance of genetic distances calculated for DArTs markers.
STRUCTURE software applies a Bayesian clustering algorithm to organize genetically similar individuals into clusters using multilocus genotype data. STRUCTURE sorts individuals into K clusters, according to their genetic similarity. The best K is chosen based on the estimated membership coefficients (Q) for each individual in each cluster. Twenty independent runs for K values ranging from 1 to 10 were performed with a burn-in length of 50,000 followed by 500,000 iterations. The admixture model was applied and no prior population information was used. The log-probability of the data, given for each value of K, was calculated and compared across the range of K. The software CLUMPP 1. 1. 2 [66]  was used to find optimal alignments of independent runs and the output was used directly as input into a program for cluster visualization DISTRUCT 1.
To overcome the limited number of SSR markers, recently, a database listing a high number of SSRs in the cultivated strawberry was reported  [45]  (http://marker. kazusa. or. jp/strawberry/). However, high throughput platforms offer the advantage of cost and time efficient whole genome coverage. After this work, two complementary platforms are now available for high throughput genotyping of the octoploid strawberry: the DArTseq here developed and the 90K Axiom 1 SNP array  [52]. The first one offers a cost-effective genotyping approach, yielding a large number of markers with easy interpretation as dominant markers. The DArTseq derived SNP markers can alternatively been used as codominant markers. However, caution should be taken that both the reference and the SNP segregate as single dose markers in the same sub-genome.
Analysis of genetic diversity highlighted the pedigree in strawberry Results obtained using the DArT data set were highly consistent throughout the three statistical tools used in this work and with the geographical, historical and pedigree data of the samples. The groups clustered varieties genetically related and these groups were also highlighted using STRUCTURE and PCoA. As an example, the three French varieties 'Charlotte', 'Cirafine' and 'Cijosée' illustrate the relationship between genotypes, arranged in the same cluster with the variety 'Mara des Bois', their maternal parent. This is extensible to 'Pajaro', 'Sweet Charlie', 'Betty' and CF1116 or to genotypes from our segregating population, the parents '1392' and '232' and their progeny 93-04, 93-54, 93-85 and 93-88 (Fig  1 ). Interestingly, some genotypes were clearly close to one of their parents but far from the other.
This work was supported by the  Spanish Ministry of Economy and Competitivity and FEDER  (grant number  AGL2012-40066 ), the  EUBerry  Project ( EU FP7 KBBE-2010-4  Grant Agreement number  265942 ) and by a  Marie Curie International Outgoing Fellowship  within the 7th European Community Framework Programme to IA ( IOF Flavor 328052 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We aim for our model to accomplish this in a cognitively and neurophysiologically plausible manner. HAC+M vectors are an example of very sparse representations in a very high-dimensional space. For high-dimensional sparse representations there are several methods that can be used for simultaneously learning primitives and the way in which complex phenomena are constructed as a sum of the primitives. In our model we chose Non-Negative Matrix Factorization (NMF)  [47]  as a computational analogue of a cognitive process that updates and modifies internal memory representations based on the experience with perceived stimuli. NMF was originally developed as a batch learning procedure, i. e. , a procedure that must iterate over a large database of learning tokens, a procedure that is not cognitively plausible.
Driesen, ten Bosch, and Van hamme  [48]  developed a version of NMF that can be used for incremental and causal learning. Accordingly, our model encounters each utterance in the database of learning materials exactly once, and the primitives are updated after each input utterance. The algorithms for NMF learning that are available do not allow to learn how many primitives are necessary to represent the learning data. Instead, the number of primitives, in other words the size of the model's long-term memory, must be specified in advance. It is our experience that this number is not a very important parameter, as long as it is four to five times as large as the number of acoustic-keyword associations that must be learned. Increasing the number of primitives in the memory beyond that number has only marginal effects on the eventual outcome of a learning process.
MapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have great impact on MapReduce applications running over the Internet. To address this, an availability and network-aware MapReduce framework over the Internet is proposed. Simulation results show that the MapReduce job response time could be decreased by 27. 15%, thanks to Naive Bayes Classifier-based availability prediction and landmark-based network estimation.
Introduction In the past decade, Desktop Grid and Volunteer Computing Systems (DGVC-S's) have been proved an effective solution to provide scientists with tens of TeraFLOPS from hundreds of thousands of resources  [1]. DGVCS's utilize free computing, network and storage resources of idle desktop PCs distributed over Intranet or Internet environments for supporting large-scale computation and storage. DGVCS's have been one of the largest and most powerful distributed computing systems in the world, offering a high return on investment for applications from a wide range of scientific domains, including computational biology, climate prediction, and high-energy physics. Through donating the idle CPU cycles or unused disk space of their desktop PCs, volunteers could participate in scientific computing or data analysis.
For example, transferring data to a more closer node may save some time. Therefore, network distance and resource availability have great impact on MapReduce jobs running over Internet. To address these problems, on the basis of our previous work BitDew-MapReduce, we propose a new network and availability-aware MapReduce framework on Internet. Given this need, our goal in this paper is to determine and evaluate predictive methods that ensure the availability of a collection of resources. In order to achieve long-term and sustained high throughput, tasks should be scheduled to high available resources. We presents how to improve job scheduling for MapReduce running on Internet, through taking advantages of resource availability prediction. In this paper, Naive Bayes Classifier (NBC) based prediction approach is applied for the MapReduce task scheduler.
Map/Reduce Algorithm and Implementation Our previous work introduced the architecture, runtime, and performance evaluation of BitDew-MapReduce  [19]. In this section, we first give a detailed description of the regular BitDew-MapReduce, then present how to improve it through availability prediction and network distance estimation. When the user aims to achieve a MapReduce application, the master splits all files into chunks, registers and uploads all chunks to the BitDew services, which will be scheduled and distributed to a set of workers as input data for Map task. When a worker receives data from the BitDew service node, a data scheduled event is raised. At this time it determines whether the received data is treated as Map or Reduce input, and then the appropriate Map or Reduce function is called.
As mentioned previously, BitDew allows the programmer to develop datadriven applications. That is, components of the system react when Data are scheduled or deleted. Data are created by nodes either as simple communication messages (without "payload") or associated with a file. In the later case, the respective file can be sent to the BitDew data repository service and scheduled as input data for computational tasks. The way the Data is scheduled depends on the Attribute associated to it. An attribute contains several (key, value) pairs which could influence how the BitDew scheduler will schedule the Data. The BitDew API provides a schedule(Data, Attribute) function by which, a client requires a particular behavior for Data, according to the associated Attribute. The initial MapReduce input files are handled by the Master (see Algorithm 1).
Availability Prediction Availability traces We evaluate our predictive methods using real availability traces gathered from the SETI@home project. In our simulation, we used 60 days' data from real SETI@home availability traces. The SETI@home traces contain the node availability information (online or offline) of dynamic largescale nodes over Internet (around 110K nodes for the full traces)  [9]. We generate a trace subset for our simulations by randomly selecting 1,000 nodes from the full SETI@home traces, then we perform a statistic analysis on those selected nodes, and the characteristics are presented as follows: up to around 350 nodes (approximately 35%) are online simultaneously; and around 50% of nodes whose availability are less than 0. 7.
The training period is used to fit the models. The test period is used to validate the prediction effect of different methods. In our evaluation, the size of training data is 30 days (the length of the 01 training string is 720). The key parameters used for evaluation are as follows: in the EMA method, the value of α is 0. 90. In terms of evaluating prediction accuracy, we use two metrics. First, we measure the mean squared error (MSE) between the predicted values and the true values in the prediction interval. Second, we measure success rate, which is defined as the ratio of the number of accurate predictions to total number of predictions. In general, the higher the success rate, the better, and the lower the MSE. Fig. 4 (a) and 4(b) shows the cumulative distribution function (CDF) of the success rate and MSE for different prediction methods, respectively.
Network estimation only Because network distance has impact on data transfer time, we compared Scenario I with Scenario III in this evaluation, and the result is presented by Fig. 5(b). Intermediate file size and the number of landmark are two important factors for MapReduce job completion time. If the landmark-based network estimation is not used, the server doesn't consider any information or attributes of nodes and all nodes are treated equally, which may cause the problem that transferring intermediate data to a far desktop PCs. For Model A and Model C, it takes more time to transfer large intermediate file, and the job completion time is decreased by 21. 93% and 14. 68% respectively, when the network estimation method is used in Scenario III. We also evaluated job completion time when configuring different number of landmark, and we adopted the Model A for evaluation.
Acknowledgement This work is supported by the  "100 Talents Project" of Computer Network Information Center of Chinese Academy of Sciences  under grant no. 1101002001 , and the  Natural Science Foundation of Hunan Province  under grant no. 2015JJ3071 , and  Scientific Research Fund of Hunan Provincial Education Department  under grant no. 12C0121 ,  11C0689  and  11C0535.
Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. Nav-iCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping largescale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls.
INTRODUCTION Biology is a scientific discipline deeply grounded in visual representations serving for communicating results and ideas. Nowadays, there is a strong incentive to provide interactive web-based visual representations, that users can easily adapt to address a biological question. Modern molecular biology is particularly demanding for new tools that can represent numerous 'omics' data in a meaningful way. Visualizing quantitative 'omics' data in the context of bio-logical networks provides insights into the molecular mechanisms in healthy tissues and in diseases  (1, 2). This is one of the most demanded features of existing pathway databases such as KEGG, Reactome and BioCyc  (3) (4) (5) ). To answer this need, many tools have been developed allowing mapping 'omics' data on top of biological networks  (6) (7) (8) (9).
These tools use the content of existing pathway databases to retrieve the pathway information through dedicated application programming interface (API). In addition, some pathway databases provide graphical user interfaces (GUI) to perform data visualization, on interactive pathway maps. When a user faces the necessity to visualize the 'omics' data on biological networks, there are currently only two options available. The first is to use a graphical interface of a suitable pathway database, which can be a tedious manual work. The second is to use one of the available standalone or web-based tools for creating static images of colored pathways which are not interactive anymore, and do not allow browsing the molecular interactions. Currently, there is a lack of well-developed APIs that allow applying data visualization programmatically on top of biological networks, such that the 'omics' data can be browsed simultaneously with the pathway information.
Available network-based methods for data visualization have several common limitations. First, most of them do not provide any possibility of data abstraction and do not provide a possibility of visualizing coarse-grained trends in the 'omics' data. This is needed if, for example, there is a wish to visualize the whole transcriptome of a cell or a group of samples on a large network, representing an important part of the cellular interactome. Current methods usually attach some elements of the standard scientific graphics (gradient color, heatmaps, barplots) to the individual elements of pathway maps, which makes the visualization hardly readable at higher levels of zooming as well as the map content itself. Second, most of data visualization tools are specific to a particular pathway database structure, format and graphical pathway representation.
This limits the use of data visualization on user-defined maps. Third, APIs for program-Nucleic Acids Research, 2015, Vol. 43, Web Server issue W561 matic web-based data visualization are rudimentary or do not exist. To overcome some of these limitations, we have developed NaviCell Web Service tool. It allows visualizing 'omics' data via GUI and flexible API using standard and advanced methods of data visualization, including a possibility to perceive the mapping of data at different network scales. The NaviCell Web Service can exploit pathway maps created by users or from existing databases, including large network maps with thousands of elements. The NaviCell Web Service for Network-based Data Visualization is a combination of (i) a user-friendly Navi-Cell JavaScript-based web interface  (10) , allowing browsing large maps of biological networks using Google Maps API and semantic zooming principle, and visualizing 'omics' data on top of them (https://navicell.
curie. fr); (ii) a collection of maps available for data visualization including the Atlas of Cancer Signaling Network (ACSN) (https://acsn. curie. fr) and other detailed network maps created by research groups for particular topics (Toll-like receptor signalling, EGFR and mTOR pathways, mast cell activation, dendritic cells, iron metabolism, Alzheimer disease and others); (iii) a REST API allowing programmatic use of all data visualization functions and manipulating the web interface. A quick 'live example' which automatically uploads a sample dataset from TCGA database and illustrates the data visualization capabilities of NaviCell Web Service is provided at http://navicell. curie. fr/pages/nav web service. html. Sample Python code for quick start on using Navi-Cell Web Service API is available from the same link in the 'Python API and data files' section.
Implementation We conceived the NaviCell Web Service for Network-based Data Visualization as a significant development of NaviCell network browser  (10). New functionalities include possibility of visualizing and analyzing different types of 'omics' data, from within NaviCell interface, and a set of programmatic APIs allowing to manipulate all NaviCell features. The web service is implemented at two interconnected functional levels. The first level corresponds to the interactive, dialog-based use of the web interface to load and visualize data. This level is implemented as a specific set of JavaScript functions linked to the menu located in the righthand panel of the NaviCell web interface. The second level corresponds to a programmatic usage of the service, which allows users to write the code that will communicate with the NaviCell server in order to automate all the visualization operations.