dataesr/scientific-paragraphs-categorization huggingface dataset:
The results we obtained clearly showed that the implementation of the four NI, HD, NN and NG RVDs led to an active nuclease and that, as hypothesized, optimal spacer sizes (13-15 and 23-25 bp) were similar to those of TALEN (Supplementary Fig. 3a ). To additionally get an insight in the capacity of BurrH based nuclease to accommodate mismatches in the targeted sequence (relative to the NI:A, HD:C, NN:G and NG:T code), we tested, using the yeast SSA assay, the bn18473 nuclease on a serie of 9 targets containing 2, 8, 14 or 16 mismatches (Supplementary Table  5 ). An engineered TALEN targeting the same sequence was also used as a reference for specificity (Supplementary Table  3 ). Although a larger in vivo data set would be desirable to precisely characterize the specificity of BurrH-based molecular tools.
The frequency of Targeted Gene Insertion (TGI) events was monitored 18 days post transfection using TGI specific PCRs using the Herculase II Fusion kit (Agilent). The oligonucleotides used for the PCRs were designed to be located within the heterologous insert of the donor DNA for the first and on the genomic sequence outside the homology arms for the second. The sequences of these oligonucleotides are presented in Supplementary Table  9. Computational methods. A survey of the different publicly available databases was performed by applying default parameters. The results were filtered and crossreferenced via manual inspection. Depending on the queried database different sources of inputs were used, for example sequences of known DNA binding proteins as well as super secondary structure elements reported as DNA binding motifs or simple short sequence motifs and also keywords.
The databases principally used were: SCOP  12  , CATH  13  , Prosite 36 Pfam 14 , Go  18  , KEGG  19  , InterPro 37 , TargetP1. 1  17  , PSORT  16  , NCBI (www. ncbi. nlm. nih. gov), HMMER  20. Rosetta homology modeling  38  (rosetta3. 4) was used to build a model of the BurrH module using the structure of a TALE as template (PDB code 3UGM  15  ).
The delta OD (OD450 nm-OD 690 nm) was then calculated. At least three independent experiments were realized for every point. Data were expressed as % of control ± SEM for each treatment concentration and compared using an ANOVA-LSD test (Fisher's least significant difference) (Statgraphics Centurion, Statpoint Technologies, USA). Cell counting for the comet assay was performed with a Coulter Z1 (Beckman Coulter, France) (data not shown). 2. 6. Free Radical Generation. 5 × 10 4 V79 or SHE cells were treated with 0. 27 to 2. 1 g/cm 2 of CNTs for 24 h. Thirty minutes before the end of treatment, 25 M of 2  ,7 dichlorodihydrofluorescin diacetate (H 2 DCF-DA, Invitrogen, France) was added to the cultures. Cells were trypsinized and then centrifuged and then placed in HBSS (Hank's buffer saline solution) with 50 g/mL of propidium iodide.
Fluorescence was measured using a Becton Dickinson FACStar PLUS flow cytometer. A sample of nanometric anatase TiO 2  [37]  was used for the positive control at 9. 2 g/cm 2. Two independent experiments (with duplicates) were realized for every point. Data were expressed as the mean fluorescence intensity of the two experiments ± SD. Statistical analysis was performed using an ANOVA-LSD test (Fisher's least significant difference) (Statgraphics Centurion, Statpoint Technologies, USA). Potential interference between CNTs and DCF was tested by acellular assays, mixing H 2 DCF (obtained by NaOH treatment of H 2 DCF-DA) or DCF fluorescent probe (Sigma-Aldrich, France) and CNTs at different concentrations (from 1 to 250 g/mL, equivalent to 0. 23 to 58 g/cm 2 of cell culture dish). No interference was shown for up to 25 g/mL (5.
Building maximum likelihood trees is itself a difficult problem because the number of possible trees increases exponentially with the number of sequences in the alignment  [1, 12]. A number of maximum likelihood tree search heuristics have been developed to reduce the computational burden  [13] [14] [15] [16] ; however, building maximum likelihood trees is still the most time consuming step in the estimation process. For example, in this study it took 98% (319 out of 324 hours) when estimating the amino acid replacement rate matrix from 100 large alignments in the HSSP (homology-derived structures of proteins) database  [17]. In this paper, we propose a new maximum likelihood estimation procedure, FastMG, to work with large data sets. The key idea is to split large alignments into multiple non-overlapping sub-alignments with fewer sequences (each sub-alignment contains at most k sequences) in order to substantially reduce the computational burden of building maximum likelihood trees.
Results and discussion We assessed the performance of the FastMG procedure on three large data sets: HSSP data set  [17] , Pfam data set  [18] , and our concatenated protein alignment of mitochondrial proteins from 299 mammalian species with 3062 amino acid sites. The FastMG procedure was examined with the random splitting algorithm, the tree-based splitting algorithm, and different k values targeting sub-alignment sizes of 8, 16, 24, 32, and 64 sequences. All matrices were estimated on a personal computer (Intel 2. 66 GHz, 8 GB RAM). The PhyML software version 3. 0  [14]  was used to build maximum likelihood trees from alignments with options: 4 gamma categories model, no invariant sites, no bootstrap, SPR tree improvement, and JTT model. The XRATE software version 0. 2  [19]  was used to estimate model parameters using information in the alignments and corresponding phylogenetic trees.
Let us denote: FastMG R : The FastMG estimation procedure with the random splitting algorithm. FastMG T : The FastMG estimation procedure with the tree-based splitting algorithm. M R k : Replacement rate matrix estimated from data set M using the FastMG R procedure and threshold k (e. g. , HSSP R 8 is the matrix estimated by the FastMG R procedure with k = 8). M T k : Replacement rate matrix estimated from data set M using the FastMG T procedure and threshold k (e. g. , HSSP T  8 is the matrix estimated by the FastMG T procedure with k = 8). M s : Replacement rate matrix estimated from data set M using the standard maximum likelihood estimation procedure (e. g. , HSSP S is the matrix estimated by the HSSP data set using the standard estimation procedure). We compared M R k ; M T k ; and M s matrices in terms of both quality and running time.
HSSP data set We selected 400 alignments from the HSSP database to assess the performance of estimation procedures. The 100 alignments with the largest number of sequences were used as the training alignment set for estimating matrices while the other 300 alignments were used as the testing alignment set. The 100 training alignments contained between 140 and 285 sequences (the mean and max pairwise distances between sequences were 0. 481 and 1. 286 respectively), for a total of 18325 sequences. The 300 testing alignments contained between 10 and 100 sequences (the mean and max pairwise distances between sequences were 0. 635 and 1. 846, respectively), for a total of 12854 sequences. We examined the correlation between matrices estimated from the standard and the FastMG procedures. Table  1  shows high correlations between the 190 exchangeability coefficients of these matrices.
Pfam data set We also examined different estimation procedures on alignments from the Pfam database. The training alignment set contained the 100 largest alignments from the Pfam database with in total 7640 sequences and a range of 46 to 202 sequences per alignment (the mean and max pairwise distances between sequences were 1. 341 and 62. 428, respectively). The testing alignment set consisted of 480 other alignments with in total 5434 sequences and a range of 5 to 41 sequences per alignment (the mean and max pairwise distances between sequences were 1. 174 and 63. 096, respectively). Note that the Pfam alignments tended to contain fewer sequences than the HSSP alignments. We observed similar values and trends as for the HSSP data set, with very high correlations between the Pfam S matrix and matrices estimated from the FastMG procedure (see Table  4 ).
The pairwise comparisons of tree likelihood values between the Pfam S matrix and matrices estimated from the FastMG procedure are represented in Table  5. Again, we observed similar trends as for the HSSP data set. The quality of matrices increased with an increase of k, however, FastMG R matrices were never as good as the Pfam S matrix. In contrast, FastMG T produced matrices that were in a majority of cases slightly better than the standard estimation procedure for all k values. For example, the Pfam T   16   matrix was better than the Pfam S matrix on 290 out of 480 testing alignments. Moreover, the Kishino-Hasegawa test showed that the Pfam T 16 matrix was significantly better than the Pfam S matrix on 104 testing alignments. The opposite was true only 41 times. The running time of the standard estimation procedure and the FastMG procedure with different splitting algorithms and k values for the Pfam data set are represented in Table  6.
FastMG T with k ≥ 16 was about an order of magnitude faster than the standard estimation procedure while it did not reduce the quality of estimated matrices. Experiments strongly suggest that k = 16 is a good choice for the FastMG T estimation procedure. Even the analysis of the 100 largest alignments of the HSSP database took less than a day with FastMG T and k = 16 on a typical desktop computer. Thus, our method now enables researchers to avoid generic pre-calculated matrices and instead estimate optimal amino acid replacement matrices for their particular needs from large data sets on their personal computers.
Step 1: Build phylogenetic tree T i and rate across site model ρ i for each alignment D i using maximum likelihood tree construction programs such as PhyML  [14]. Step 2: Estimate a new exchangeability matrix Q' using the approach described by Le and Gascuel  [8]  and the XRate software  [19]. Step 3: Compare Q' and Q, if they are nearly identical, return Q' as the optimal model. Otherwise, assign Q ← Q' and goto Step 1. Previous studies have showed that this estimation procedure usually stops after three iterations. The  procedure is called "standard maximum likelihood estimation procedure". Building maximum likelihood trees in Step 1 is a computationally expensive problem  [12]. Although a number of heuristics have been proposed for searching maximum likelihood trees  [13] [14] [15] [16] , Step 1 is still the bottleneck of the estimation process.
EBV infection of primary human B-cells in vitro leads to down-regulation of DOK1 expression Based on our previous results that showed the down-regulation of DOK1 expression in BL cell-lines  [9] , we evaluate whether this event was linked to infection with EBV, a key risk factor for this malignancy. Primary human B-cells, isolated from different healthy donors, were infected in independent experiments with recombinant EBV virus expressing the green fluorescent protein (GFP-EBV). The infection efficiency was evaluated by flow cytometry to monitor GFP expression (data not shown). The expression of EBV genes EBNA1 and LMP1, as well as DOK1 was determined by real-time PCR and western blot at different time points post-infection (Figure  1A  and B ). EBV infection resulted in a strong reduction of DOK1 mRNA and protein levels, which was evident at 16 hours post-infection (Figure  1A ).
The DOK1 promoter was amplified using real time PCR and data from IP 1 and IP 2 were calculated as percentage of total input. doi:10. 1371/journal. ppat. 1004125. g003 It has previously been shown that LMP1 is able to increase the expression and activity of DNA methyl-transferases (DNMT 1, 3a, and 3b), which could explain the increase of the DOK1 promoter methylation. Interestingly, DNA methylation was strongly enhanced in B-cells infected by the entire virus compared with cells expressing only LMP1. Thus, it is likely that additional viral products may cooperate with LMP1 in promoting DOK1 silencing via DNA methylation. No down-regulation of DOK1 was observed when EBNA1, 2, 3A, 3B, and 3C are expressed in RPMI cells. In addition, none of these viral proteins further stimulate DNA methylation at DOK1 promoter when co-expressed with LMP1 (data not shown).
Quantitative RT-PCR Total RNA was extracted using TRIzol reagent (Life Technologies). Reverse transcription was performed using the RevertAid H Minus First Strand cDNA synthesis kit (Fermentas) according to the manufacturer's protocol. Real-time PCR was performed using the following gene-specific primers: DOK1: Fw ATGGACGGAGCAGTGATGGA, Rev CCCAG-GTCTTCCTCCACCTC LMP1: Fw CCCCCTCTCCTCTTCCATAG, Rev GCCAAA-GATGAACAGCACAA EBNA1: Fw GGACCCGGCCCACAACCTG, Rev CTCCT-GCCCTTCCTCACCCTCATC GAPDH: Fw GAAGGTGAAGGTCGGAGTC, Rev AAGA-TGGTGATGGGATTTC. Data were analyzed using the DD CT method.
Low cell ChIP kit (Diagenode) was used for primary B cells and infected with EBV for 48 hours. 50 000 cells per reaction were processed according to the manufacturer's protocol. The input and immuno-precipitated DNA from both methods (standard and low cell) were then analyzed by real-time PCR using primers flanking the E2F-response element (2498/2486) of the DOK1 promoter: Fw GCCAAAACCGAGGACTTTCG, Rev CATCACTGCTCCGTCCATGG, or primers for GAPDH promoter: Fw GACGGCCGCATCTTCTTGT, Rev CCTGGTGA-CCAGGCGC. Data were calculated as a percentage of enrichment of input.
Flow cytometry analysis To determine cell cycle profile, cells were collected 48 hours post-transfection with empty pCDNA3 (Vector) or expression vector pCDNA3-Flag-DOK1, washed twice with PBS 16, and then cell pellets were re-suspended in 70% ethanol while vortexing, in order to prevent cell clumps. After ethanol fixation (30 minutes at 4uC) the cells were rewashed in PBS 16 and finally re-suspended in PBS 16+ 100 mg/mL RNAse (Roche) + 25 mg/ mL of Propidium iodide (Sigma). Apoptotic cells were detected using the PE Annexin V apoptosis detection kit I (BD Pharmingen) according to the manufacturer's instructions. Stained cells for cell cycle and for apoptosis were detected using the BD FACSCanto II flow cytometer (BD Biosciences) and analyzed using FACSDiva software.
The study was partially supported by the Grant No. FP7-HEALTH-2011-282562  from the  European Commission (HPV-AHEAD , to MT). The DNA methylation analysis was performed on the pyrosequencing platform ( IARC Epigenetics Group ) that is purchased by the grant from the  Association pour la Recherche sur le Cancer (ARC), France  (to ZH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cloud computing is still considered a disruptive technology in spite of being part of our lives for several years now. However, cloud computing is much more than a technology; it is also a business model. Many companies that have sold software in a traditional way are now attending to this revolution, wondering if that new technological and business shift is adequate for them, if they would be able to move their application towards the cloud, transforming alongside the company in a service oriented company and how they could do that. The European Project ARTIST aims to guide companies in this transition by providing them with methods, techniques, and tools, from when the migration is just a thought, until it can be provisioned as a service, taking into account technical, business and organizational aspects.
INTRODUCTION AND MOTIVATION Much more than the technology that supports it, cloud computing is the latest step in the evolution of the IT industrialization process. Most CIOs wish to be on the "cloud train", and therefore cloud adoption is growing in popularity for enterprises and independent software providers. One way to quickly move to the cloud is by developing new, cloud-ready software applications that can be delivered through PaaS solutions like Google App Engine and Azure. But when cloudifying existing software applications, significant re-engineering and adaptation is needed. Only after those steps have been performed, existing applications can be delivered and offered effectively in the software-as-a-service (SaaS) model. However, this transition can be complex, timeconsuming and expensive, especially when not all applications can be moved to cloud and each application has its own business specific migration requirements that derive on the necessity of different problem approximation strategies.
This does not only require adaptation of the software and integration of modern services of cloud solutions on a technical level (monitoring, security etc. ), but also changing the business processes and models based on which the application is offered to the customers so as to exploit the strategic advantages of clouds. The ARTIST Methodology consists of three major phases which are explained below. These phases are:  Pre-migration: In this phase, a study on the technical and economic feasibility will be conducted as a prelude to perform modernization of the legacy system.  Migration: This phase will perform the migration process itself, by using reverse as well as forward engineering techniques to deploy the legacy system in the cloud.  Post-migration: In this phase the modernized application will be deployed on the target environment and checked if both technical and business objectives established in the premigration phase have been achieved.
Pre-migration Phase and Supporting Tools The pre-migration takes place even before the migration starts. Software vendors need to check if CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience what they want to achieve with a specific migration project is actually feasible to them in terms of technology, processes and business  (ARTIST D5. 1. 1, 2013). Once a company has realized that their current product is not sustainable as-it-is any more, the first big challenge faced is therefore the decision of what is more convenient, to migrate or to start from scratch. ARTIST proposes a pre-migration phase that starts off with the characterization of the legacy application from two points of view: technical and business, and follows up with a technical feasibility analysis and totally intertwined, an economic and business feasibility analysis.
The result of this premigration phase is a Go / NoGo to the migration. The first step of the pre-migration is the Maturity assessment with the main objective of analysing how mature the application is in terms of technology (i. e. architecture, programming language, database, integration with 3rd party offerings, installation requirements, versioning, etc. ) and business (i. e. current business model, existence of SLA, maintenance and upgrades procedures, customer service, etc. ) and how the customer wants the application to be in those two axes once the application is migrated. The evaluation of the current situation and the ideal situation allows ARTIST to perform a gap analysis, described in terms of a technical feasibility analysis and the business feasibility analysis. The Maturity Assessment in ARTIST is supported by the Maturity Assessment Tool (MAT).
The support for decision making at this early premigration stage, can benefit from a detailed breakdown of the migration process into a set of technical tasks, to estimate their required efforts, and to identify other resources needed to accomplish every task, including the selection of the appropriate technical expertise or even the detection of dependencies among tasks or other technical intricacies. ARTIST supports the technical feasibility analysis through the Technical Feasibility Tool (TFT). The aim of the TFT is to estimate the efforts required to migrate a legacy application to a selected target cloud environment, fulfilling some migration goals and requirements and automating the process as much as possible. Our implementation of TFT extends the Cloud Migration Point  (Tran, 2011)  approach by automating some steps, using techniques such as Model Driven Reverse Engineering (MDRE), Software Metrics or DSLbased heuristics, notably to extract knowledge of the legacy system, propose migration strategies and estimate the component complexity.
It provides as a final result the estimated effort required to perform the migration project (based on previous based experiences). The current available prototype supports the extraction off software metrics and the proposition of components and migration strategies. In parallel to the Technical Feasibility Analysis a Business Feasibility Analysis is proposed. The need of the Business Feasibility Analysis stems the observation that, although cloud computing is a software deployment scheme expected to bring a number of advantages (e. g. elastic provisioning, cost savings) to its adopters the same advantages does not apply equally well to all potential users  (ARTIST D5. 1. 1, 2013). Hardware costs savings, as an example, does not equally apply to SME and large enterprises. Moreover there are also potential (often sunken) costs that have to be carefully assessed.
Reverse Engineering Reverse Engineering is the first step of any migration process, as it consists in performing a prior analysis of the existing system or software that is going to be later migrated (e. g. to the cloud)  (Canfora et al. , 2011). Within the ARTIST methodology and model-based framework, a global Model Driven Reverse Engineering (MDRE)  (Rugaber et al. , 2004)  approach is being designed and developed to support it. Model Discovery, cf. the Model Discovery Toolbox (MDT) is the initial activity of obtaining "raw" (i. e. low-level) models from the different artefacts composing a given software. This is realized (at least semi-)automatically thanks to software components called model discoverers. The main related challenge is to deal with the software artefacts heterogeneity, as they can take many different forms/formats (Java or C# source code, XML documents, databases, etc.
). Thus, we have been working on a taxonomy of legacy artefacts that helps better classifying them according to several dimensions (their technical space, internal structure, nature, size, environment, etc). The second objective of this taxonomy is to provide some kind of guidance in the process of deciding on the most suitable way to discover the required initial models. Model Understanding, cf. the Model Understanding Toolbox (MUT) is the activity of processing the initial models, computed from the Model Discovery, in order to identify and build higher-level views on the analyzed software. This is generally performed via (chains of) model transformations, usually refining the models iteratively. There are several issues related to this model understanding context: How to deal with both functional and non-functional properties of a system? How to allow the definition and computation of new views? How to generalize views independently from the nature of the system initially reverse engineered? Intending to answer these open questions, several tracks are being explored.
Target Platform Selection One of the most complex activities when migrating to a cloud based solution is the selection of the appropriate cloud platform. Here, several aspects come to play, such as the different services offered by the cloud platforms (in any of their delivery models IaaS, PaaS or SaaS) and the needs from the migrated software to be deployed in an efficient manner (in terms of costs) and fulfilling the required QoS parameters (without compromising the SLAs agreed by the service provider and the final user) in the selected provider. To ease this process ARTIST proposes  (Menychtas et al. , 2013)  to implement two phases in parallel: one in the application domain and one in the candidate target environment domain. In the application domain, following the analysis of the application features and the creation of models describing the legacy system using reverse engineering techniques, we examine and profile the performance aspects of each application element and feature (through the profiling tool).