dataesr/scientific-paragraphs-categorization huggingface dataset:
The newest iteration of the underlying toolbox is available both as compiled stand-alone versions on http://www. thevirtualbrain. org/tvb/ after registration as well as Python based source code on at https://github. com/the-virtual-brain/ tvb-library. Data of the simulation as run and reported here as well as the Matlab based analysis code can be made available on request, by contacting the corresponding authors (PR and RB) or Simon Rothmeier.
Patients and eligibility criteria This study was a multicenter, randomized, non-comparative, open phase II trial, conducted in French centers. Inclusion criteria were: histologically or cytologically proven mPC, no previous chemotherapy (adjuvant chemotherapy with gemcitabine was allowed if administered more than 12 months before inclusion) or radiotherapy (unless at least one measurable target lesion was present outside the irradiated area) and WHO performance status <2. Exclusion criteria were bile ducts adenocarcinoma, ampulloma and a history of another cancer. All patients were fully informed of the study and provided signed written informed consent (see S1 Informed Consent). The protocol was approved by the ethics committees ("Comité de Protection des Personnes"). This study FIRGEM was registered with EudraCT (https://eudract.
The analysis was performed on intent-to-treat principle (all randomized patients irrespective of treatment received and eligibility criteria). Analyses of primary endpoint were done on the first randomized 46 patients with available PFS data (to match with Fleming criteria decision rules) while all other analyses were done on all randomized patients. Tumour responses were defined using RECIST (version 1. 1)  [26]  and determined by investigators. Population. Randomized patients whatever eligibility criteria with at least one HRQoL score were included in the QFS analysis (modified intent to treat analysis). Pre-specified targeted HRQoL dimensions were GHS (mITT1), physical (mITT2) and emotional functioning (mITT3), fatigue (mITT4) and pain (mITT5). Since this is a non comparative randomized phase II trial and HRQoL was an exploratory secondary endpoint, no p-value was provided while effect size was presented using hazard ratio with 95% confidence interval (CI95%).
A five-point difference in HRQoL scores was considered as the Minimal Clinically Important Difference  [27]. Descriptive analysis. Baseline variables were described using means and standard deviations for continuous variables and percentages for qualitative variables. Baseline HRQoL scores were described by treatment arm. The number of HRQoL questionnaires completed at each measurement time was reported. The Most Common Grade 3 or 4 Adverse Events occurring during the study according to the National Cancer Institute Common Terminology Criteria for Adverse Events (version 3. 0)  [28]  were reported by treatment arm. Missing data analysis. The missing data patterns were patients with at least one missing HRQoL score during the follow-up (partial responders) versus patients with all available scores until their drop-out of the study or death (complete responders).
Financial support for this research was provided by  Pfizer ,  AGEO  and  AROLD (Association pour la Recherche en Oncologie Digestive). This study was also supported by a grant from the  French Public Health Research Institute  (http://www. iresp. net) under the 2012 call for projects as part of the 2009-2013  Cancer Plan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This article presents GRAPP&S (Grid APPlication & Services), a specification o f a n E -learning architecture for the decentralized sharing of educational resources. By dealing with different resources such as files, data streams (video, audio, VoIP), queries on databases but also access to remote services (web services on a server, on a cloud, etc. ), GRAPP&S groups the resources of each institution in the form of a community and allows sharing among different communities. Educational resources are managed on a transparent manner through proxies specific t o e ach t ype o f r esources. T he t ransparency p rovided b y p roxies c oncerns t he l ocation of sources of educational data, the processing of queries, the composition of the results and the management of educational data consistency.
) in a "Community", which can foster the aims of reusing and sharing educational resources without costly duplicating them into local learning repositories. Through the use of communities, we can promote the goals of reuse and sharing educational resources. By extending the coverage to different educational resources (files, videos, data in a database, even a video stream for example when subscribing to TV channel for learning languages), we can integrate all tools to improve education in a single infrastructure, with a smaller cost. Following this approach, two major research challenges must be considered to ensure interoperability across the Web: 1. Compatibility between systems: if today most APIs (Dropbox, Google Drive, etc. ) allow to handle simple files, it is less clear how to integrate complex data such as queries on a database, data streams or web services.
2. Decentralized data management: most platforms are migrating to the cloud, but at the expense of losing its proximity to the consumers, as well as poor access speed and privacy threats. From these elements, we present GRAPP&S, an architecture designed to connect institutes interested on sharing educational data sources through the network. GRAPP&S brings therefore: • a decentralized solution for sharing all types of educational resources, not only files (text/xml) but also databases, streams (video, audio), and resources from remote services such as web services and distributed computing, all integrated through the use of data proxies; • a simpler way to connect a large number of institutes interested into resource sharing. This is obtained through the aggregation of resources from each educational institute in the form of a "community".
This will allow quick access to resources due to the proximity to consumers, unlike most of the E-learning solutions based on cloud storage when consumers are penalized because of the slow access speed; • an access to resources GRAPP&S independent from direct interconnection between nodes. In GRAPP&S, it is always possible to route data transfer by the inverse of the path used during the search. • the possibility to define security policies for the protection of educational resources within a community, and access policies between different communities through the establishment of Service-Level Agreements (SLAs); The remaining sections of the paper cover: Section 2 discusses the related work. Section 3 presents an overview of GRAPP&S architecture and describes the elements of this architecture, while Section 4 describes the lookup algorithm used to locate resources inside GRAPP&S.
E-learning Systems Because GRAPP&S aims at the decentralized management of resources, it seems natural to identify peerto-peer (P2P) works in this area. Indeed, an architecture for sharing educational resources among different learning institutions is proposed in  [4]. This architecture, called LOP2P, aims at helping different educational institutions to create course material by using shared educational resource repositories. Nonetheless, resources of different formats cannot be easily integrated in this platform. Similarly,  [9]  develops a P2P based E-learning system that uses the video for learning. This system divides multimedia data into fragments managed by assigned agents, and this system allows the sharing of multimedia data, but it cannot deal with other data types. Several E-learning systems based on cloud are being proposed, like  [12]  or  [2].
The GRAPP&S Architecture The GRAPP&S framework is an E-learning solution for the decentralized sharing all types of resources. As stated before, our main objective is to propose a resource sharing middleware to support E-learning tools and applications that allies both flexibility, security and performance on remote locations. We observe indeed that current sharing tools tend to externalize data (on a dedicated server or in the cloud), but this solution is not adapted to remote locations whose computational resources are limited and the access speeds are reduced. To circumvent these limitations we to propose an alternative architecture inspired on the grid environments, where individual resources are gathered and shared among the grid members. By relying on the GRAPP&S framework, we allow pooling of data from each institution in the form of community, and allow different communities to share resources based on predefined access rules.
For this reason, GRAPP&S can also be extended to other areas of the school, by creating a community in the administrative part, separated from the education part, with safety rules and resource protection. GRAPP&S also allows a flexible deployment of its components, adapting therefore to the capacity of the computing nodes. Finally, the data proxy abstraction that composes the framework leafs (see the Data Manager component below) allow the integration of different data sources, making therefore GRAPP&S an unified resource sharing platform. In the following sections, we present the different elements of our framework GRAPP&S for the decentralized sharing of educational resources.
The communicator also defines the security rules (access) for the protection of educational resources inside the community (for example the administration community can see the data on the educational system, but not the reverse, thanks to this access rules defined by the communicator). Resource Manager(RM). processes ensure indexing and organization of educational resources in the community. The RM i processes are involved in the search and indexing of data in the community c i , and by receiving queries from its neighbors communities. Given the important role of RM processes in research and indexing of resources, we choose a RM among ordinary nodes that have good performance levels in both CPU, memory size and communication speed. Data Manager(DM). processes interact with sources of educational data such as databases, file, email servers, WebDAV servers, FTP servers, disks, or cloud services.
Research Data in GRAPP&S Among main functionalities on GRAPP&S there exists research and data access. This section describes our data search algorithm in a community GRAPP&S. The nodes that intervenes in this research has a few primitives: • Route_request allows a source node to send a request to a destination node. Route_request uses the function Route() (cf. Algorithm 1) to send the message m so its signature is Route_request(m, source, destination). • Route_reply allows to send a reply to m that follows the reverse path to the initiating node Message Route_request. The method Route_reply has almost the same structure as the Route_request function except that it does not use the same message in Route_request. The structure of the method Route_reply is Route_reply(m , source, destination).
• Route_RequestDirect, enables a source node to send a message directly to the recipient, if the communication mechanism allows it. • Save() allows a node receiving a message Route_reply to store it on a non-volatile support(for example a file on the hard-drive, on Dropbox, on Gmail etc. ). When a customer searches any data on GRAPP&S, it contacts a DM i proxy that sends a request Y containing the characteristics of the data. This research is conducted in steps so as to respect the hierarchical organization of the network, as follows: • DM i ∈ C i sends its request Y to node RM i ∈ C i through Route_request() (cf. Algorithm 1); • RM i uses the method V erif _of _RM i () (cf. Algorithm 2) to check on its index if among his neighbors there is at least a DM that contains the requested information, thanks to the Local_Search() function (cf.
Data Transfer in GRAPP&S If successful in the search, the customer gets the node identifier DM x responsible for the data. In this case, there are two possibilities for contacting DM x and retrieve the data: using a direct connection or using a routed connection. In the case of direct connections, the customer sends the request directly to DM x by a simple Send() call (cf. Algorithm 6) in order to access the data. The customer can view or store the data with the save() function. In case this direct connection is not possible, then the data transfer is done by hierarchical routing in GRAPP&S, based on the prefixes of identifiers of nodes to back up the hierarchy. The steps in this routing prefix are as follows: 1. the customer sends a request Y (m, id (DM i ), id(DM x )) to its RM_i, which forwards the request to the node communicator c i ; 2.
Conclusions In this article we present an E-learning architecture specification named GRAPP&S, which is a decentralized solution for managing and sharing educational resources. GRAPP&S has been constructed as an hierarchical network to allows a pooling resources of each institute under the concept of "community", and to take advantage of the proximity of data and the users (students, teachers, administrative, etc. ). One can even extend the use of a community of GRAPP&S to other sectors of an institute such as a community just for the administrative departments, separated from the educational users. In addition, GRAPP&S uses security rules to protect the resources of each community, and defines access policies between different community. The use of proxies for each specific type of data allows sharing a transparent manner not only files but also databases, streams (video, audio, VoIP), resources from the web services, cloud services, distributed computing.
This analysis identified 627 differentially expressed genes at a 5% threshold, of which 274 were more strongly expressed in the JN10 strain than in the JN17 strain and 353 were more strongly expressed in the JN17 strain than in the JN10 strain (see Gene Expression Omnibus with the accession number GSE55083 for a complete dataset). The expression of 72 genes was at least two fold higher in the JN10 strain than in the JN17 strain whereas the expression of 111 genes was at least two fold higher in the JN17 strain than in the JN10 strain. We used gene ontology analysis to identify groups of genes or pathways among these differentially expressed genes, which revealed that genes involved in sulfur metabolism were differentially expressed between the two strains (Figures  2  and 3 ). The differential expression of a large number of genes is consistent with the coordinated regulation of all the genes of this pathway.
Furthermore, a linkage analysis based on an extrapolation of the qualitative evaluation of H 2 S production into a binary phenotype (production/absence of production of H 2 S) revealed two peaks of QTL overlapping those regions and therefore reinforced the results (data not shown). We used the Saccharomyces genome database (www. yeastgenome. org) to explore the QTL regions and identify genes potentially associated with  our phenotypes of interest. The first region was about 50 kb long and contained 35 ORF. Among them, we found a relevant candidate gene, SKP2, which encodes an F-box protein predicted to be part of an SCF ubiquitin protease complex that is involved in regulating the abundance of sulfur metabolism enzymes. The deletion of SKP2 is associated with high H 2 S and SO 2 production phenotype  [17, 45].
cerevisiae available in databases and in other species of the Saccharomyces genus. However, the V 350 residue, which is present in JN17, seems to be more common than the I 350 and is thus probably the ancestral allele. We also found a SNP in the MET2 coding sequence at position 560 pb. This SNP leads to a C > G transversion resulting in the replacement of arginine with glycine at position 301 (R301G) in the JN17 strain. The G 301 residue corresponds to that of the S288C reference strain sequence, whereas the R 301 residue is present in several other S. cerevisiae strains and in other species of the Saccharomyces genus, and thus appears to be the ancestral allele. We carried out reciprocal hemizygosity analysis  [46]  of SKP2 and allelic replacement of MET2 to evaluate the effect of these genetic variants on phenotype and we examined SO 2 , H 2 S, acetaldehyde and propanol production in the resulting strains.
Determination of phenotypic variables CO 2 release was monitored by weight loss, which was assessed either automatically for the 1. 2 L unit (one acquisition every 20 minutes) or manually for the 0. 3 L unit. The rate of CO 2 production was calculated with a method of polynomial smoothing from the weight loss data of the 1. 2 L fermentation units. Cell number was determined with an electronic particle counter (Coulter, Beckman). Sulfite production was determined in small fermentation units at 16°C on a nitrogen rich media after 90% of the fermentation process was complete. The media contained 425 mg/L of assimilable nitrogen because these conditions were determined to be optimal for the assessment of differences in sulfite production between strains (data not shown). SO 2 concentration was measured with an enzymatic UV assay (r-Biopharm) according to the manufacturer's instructions.
Analysis of gene expression Gene expression was analyzed with microarrays spotted with the 6308 oligonucleotides (70 mer) of the S. cerevisiae Oligoset (Operon) in duplicate on UltraGap chips (Biochip Platform, Toulouse, France). RNA was extracted with Trizol reagent with a method adapted from Chomczynski and Sacchi  [54]. Reverse transcription and labeling were performed with a ChipShot direct labeling and clean-up system kit (Promega) according to the manufacturer's instructions. Microarray hybridization was carried out with a Pronto Universal Microarray kit (Corning) according to the manufacturer's instructions. Two biological replicates were included according to a dye swap design. Microarrays were scanned with a Gen-ePix pro 3 scanner (Axon Instruments). Data were processed with the R software (R2.
9. 2) and the Limma package  [55] [56] [57] [58] [59]. Intra-array normalization was carried out with the print-tip loess method and inter-array normalization with the quantile method. Differentially expressed genes were identified through a linear model approach and a Benjamini-Hochberg method was used to adjust the p-values  [60]. A gene was considered as differentially expressed at a significance level of 5% if its adjusted p-value was less than 0. 05. The complete data set is available at Gene Expression Omnibus with the accession number GSE55083. Statistical analysis to determine functional groups of genes that were over-represented in the data set was performed with the web-based tool FunSpec  [61]  (available online at http://funspec. med. utoronto. ca/, p-value <0. 05, and Bonferroni correction) and genes were classified with the GO database.
Genotyping with high-density oligonucleotide microarrays Genomic DNA was extracted with the Genomic Tip 100G kit (Qiagen) according to the manufacturer's instructions. Three independent extractions were performed for each parental strain and one for each meiotic segregant. Genomic DNA was fragmented, labeled and hybridized onto Yeast Genome S98 arrays (Affymetrix) by the genomic platform ProfilExpert (IFR Neuroscience, Lyon). Microarrays were scanned by an Affymetrix scanner. Raw data were submitted to multiple filters and statistical analyses with the R software as previously described  [43]  to identify informative markers. Molecular markers were positioned on a physical map and on a genetic map, using a conversion factor of 3000 bp for 1 cM.
QTL mapping Linkage analysis between phenotypic and genotypic datasets was performed by an interval mapping method  [62]  implemented in the R/qtl package  [63]. A nonparametric model was applied to all the studied phenotypes, except for H 2 S production, which was analyzed with a binary model because of its semi-quantitative character  [64]. The values 1 and 2 of the arbitrary color scale were combined into one single value. The significance level was determined through permutation tests (1000 permutations). The confidence interval for the location of each QTL was defined as 1-LOD support interval: the region in which the LOD score is within 1 unit of the linkage peak.
such as Schistosoma mansoni. They are classified as members of family C13 in the MEROPS protease database  (Rawlings et al. , 2008;  http://merops. sanger. ac. uk/) that belongs to the CD clan, which also contains caspases (family C14A) and metacaspases (family C14B)  (Misas-Villamil et al. , 2013). Caspases are the main players in the regulation of programmed cell death (PCD) in animals, whereas metacaspases are involved in the same process in plants and fungi  (Hara-Nishimura and Hatsugai, 2011; Tsiatsiani et al. , 2011 Tsiatsiani et al. , , 2012)). Clan CD proteases contain a His-Cys catalytic dyad and have strict substrate requirements for the amino acid preceding the cleavable bond (P1 position)  (Chen et al. , 2004; Dall and Brandstetter, 2013; Misas-Villamil et al. , 2013). Plant VPEs are classified into vegetative and seed-expressed types  (Gruis et al.
, 2003; Nakaune et al. , 2005; Julián et al. , 2013; Kang et al. , 2013; Christoff et al. , 2014; Pierre et al. , 2014). To gain further insight in plant VPEs and benefiting from the present Amborella sequences, we reconstructed a phylogeny of VPE proteins based on the amino acid sequences of VPEs from a wide range of embryophytes (land plants). By using a comparative approach, combined with the principle of parsimony, data from this uniquely-placed angiosperm can help defining the condition of any character in the most recent common ancestor (MRCA) of the living angiosperms, and we have applied this method to the structural and functional evolution of the VPE family. Another way to evaluate the functional relevance of genes is to examine the levels of naturally occurring genomic variations therein, i.
d. , 150 mm; NanoSeparations). Buffers used were 0. 1% HCOOH in water (A) and 0. 1% HCOOH in ACN (B). Peptide separation was achieved with a linear gradient from 5 to 30% B for 30 min at 300 nl/min -1. Including the regeneration step at 95% B and the equilibration step at 95% A, one run took 45 min. Eluted peptides were analyzed on-line with a Q-Exactive mass spectrometer (Thermo Electron) using a nano-electrospray interface (noncoated capillary probe, 10 µ i. d. ; New Objective). Xcalibur 2. 1 interface was used to monitor data-dependent acquisition of peptide ions (http://www. thermoscientific. com/content/tfs/ en/product/xcalibur-software. html). This acquisition included a full MS scan covering 300 to 1400 range of mass-to-charge ratio (m/z) with a resolution of 70,000 and a MS/MS step (normalized collision energy: 30%; resolution: 17,500).
MS/MS step was reiterated for the eight major ions detected during full MS scan. Dynamic exclusion was set to 45 s. A database search was performed with XTandem (version 2011. 12. 01. 1)  (Bjornson et al. , 2008 ; http://www. thegpm. org/ TANDEM/) for protein identification. Enzymatic cleavage was declared as a trypsin digestion with one possible miscleavage. Cys carbamidomethylation and Met oxidation were declared, respectively as fixed and variable modifications. Precursor mass and fragment mass tolerance were 10 ppm and 0. 02 Th, respectively. The Amborella Genome database (http://www. amborella. org/) and a contaminant database (trypsin, keratins) were used. Identified proteins were analyzed using XTandem Pipeline (http://pappso. inra. fr/bioinfo/xtandempipeline/, version 3. 3). Only peptides with an E-value smaller than 0.