dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike many exome sequencing data analysis tools, ONCOCNV does not take into account B allele frequencies. Because of this limitation, ONCOCNV is not able to evaluate the level of the contamination by normal cells or improve the accuracy of CNA calling by simultaneously processing copy number and B allele frequency profiles  (Boeva et al. , 2012; Sathirapongsasuti et al. , 2011). Our analysis of biases intrinsic to amplicon sequencing revealed two factors influencing the RCs: amplicon length and GC content. As LOESS correction is possible only for the amplicon sizes frequently present in the dataset (from 70 to 140 bp in our case), we advise using as narrow a range of amplicon lengths as possible in the future design of amplicon regions. Similarly, for the GC-content normalization, the CNA prediction may be inaccurate for amplicons with extremely high or low GC content.
CONCLUSIONS We present ONCOCNV, a computational method and software tool to detect CNAs in tumor amplicon sequencing data. Our method includes normalization of RCs for library-specific biases (library size, GC content and amplicon length) and for technology-specific bias. To normalize for the latter, we use a set of control samples. We demonstrate that ONCOCNV significantly outperforms methods such as ADTEx  (Amarasinghe et al. , 2013)  and NextGENe developed for the analysis of exome sequencing data. To make ONCOCNV useful for both bioinformaticians and clinicians, we output both detailed and summarized results, as well as a visualization of the annotated copy number profile (Fig. 4B ). We believe that the use of our method on large panels of targeted genes could potentially replace CGH or SNP arrays in the future.
Funding: The SHIVA trial is supported by the grant  ANR-10-EQPX-03  from the  Agence Nationale de le Recherche  (Investissements d'avenir) and  SiRIC (Site de Recherche Int egr e sur le Cancer). High-throughput sequencing was performed by the  NGS platform of the Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de la Recherche  (Investissements d'avenir) and by the  Canceropôle Ile-de-France.
API module contributes as an open platform to extract useful information from inputs and store it in the database. Information extraction module extracts necessary information from database as well as receives the precision limitation from applications then provides them to the next module. Calculating module does the main processing function and produces user localization result. Depending on applications and their required precisions, results would be different. Afterwards, the outputs including user location, precision, and probability are sent to applications and fed back to be stored in database for next calculation tasks.
GPS, RFID, and Pedometer. For GPS technology, we use the built-in GPS API from Android platform with extraction information including location (latitude, longitude, and altitude) and its precision. The pedometer is implemented using built-in sensors from Android smartphones. Using derivative applied to the smoothened signal from accelerometer, when the derivative is greater than threshold value, we detect a step  [12, 13]. Direction of users is determined by geometry sensor with azimuth, roll, and pitch angle information. After a high precision localization from RFID, the pedometer can make a good localization in a short time. With characteristics of RFID radio signal, it can be assured that at the moment user's RFID tag is recognized by a reader, the user is within 2 meters around it. The RFID reader position is stored in database and every time a RFID tag is read from a reader, information including user ID and RFID reader ID will be sent to system API.
Experiment Results Experimental scenario is taken in the 8th floor of an 11-floor building, as shown in Figure  4. A user holds a smartphone, walks starting from Point 1 straight to Point 2, Point respectively, and then returns to Point 2 and Point 1 at the same path, with the total distance of 160 m. In this experiment, 3 RFID readers, 16 WiFi access points are set up for RFID and WiFi technologies. First, data are collected to tune system parameters, as discussed in Section 2. 2. A person holding the first smartphone moves slowly at a constant speed along the corridors on the three floors to measure the WiFi signal strengths. There are totally 672 measurements carried out for the training data. The training process using the GA is set up with configuration provided in Table  2. Using these data, the produced optimal parameter values are given in Table  3.
Devising the questionnaire The questionnaire was based on the analysis of the scientific literature related to the criteria reported in association with HS. Using the Medline database (via Pubmed), on September 17, 2012, we selected papers published in English or French with any of the following terms: (hemophagocytic syndrome) OR (macrophage activation syndrome) OR (hemophagocytic lymphohistiocytosis) OR (hemophagocytosis). Focusing on adult cases, we excluded all publications related to pediatry literature. Papers focusing on basic science research were also excluded. The remaining publications were screened by two of us (LF and LG) and led to the selection of 26 clinical, biological or cytological criteria reported to be associated with the diagnosis of HS (Table  1 ). The questionnaire explored experts' opinions on the helpfulness of these 26 criteria in the positive diagnosis of a reactive HS in adults.
Acknowledgments The authors are indebted to the following collaborative investigators (expert panel of the  HScriteria Delphi survey ) for completing the Delphi survey questionnaire:  A Amiot ,  JB Arlet ,  E Azoulay ,  C Bloch-Queyrat ,  D Dierickx ,  U Emmenegger ,  O Fain ,  M Faurschou ,  DN Fisman ,  A Karras ,  T Kelesidis ,  S Kumakura ,  C Larroche ,  JM Michot ,  K Neftel ,  C Ponticelli ,  S Rajagopala ,  L Sailler ,  R Strauss ,  AG Tristano ,  H Tsuda ,  KF Wong ,  CD Yang ,  S Yasuda.
In order to implement the steps S1 (Receive Order), S4 (Confirm Order), S5 (Reject Order) of the TaxiOrdering-CSBA process, TaxiTilburg has developed an in-house SMS software component that is able to receive new orders by sms, and send order confirmations or rejections to customers. Unfortunately, due to lack of expertise, TaxiTilburg has to rely on third-party SaaS offerings for managing its taxi fleet and for calculating routes, which are the two necessary functionalities to implement step S2 (Allocate Taxi) and step 3 (Calculate Route) in its TaxiOrdering-CSBA process. These two required functionalities are captured as the two functional requirements Fleet Management Service and Map Service. Figure  1  also shows the Quality-of-Service (QoS) offerings (responseTime(s) ≤ The QoS requirement of minimum throughput 100 req/s remains same for all steps, as shown in Figure  1.
CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience It is worth noting in Figure  2  that there could be alternative matchmaking results for fulfilling a requirement, e. g. for fulfilling the requirement Map Service of the TaxiOrdering-CSBA blueprint, and for fulfilling the requirement SQL Database of the FleetMgt-SaaS blueprint. The QoS properties of the alternative matchmaking results are different, which implies that there might exist alternative configurations for the TaxiOrdering-CSBA that could trigger a reconfiguration process in case there are some changes in the QoS requirements of the TaxiOrdering-CSBA. The next step after configuring an CSBA is to select among the alternatives the optimum blueprint configuration that will be deployed on the physical cloud infrastructure.
Deploying the CSBA In the next phase, a deployment architecture has to be developed based on the selected optimum blueprint configuration. The purpose of this deployment architecture is to serve as a cookbook for cloud administrator to deploy and maintain the CSBA on the cloud infrastructure. In this architecture, the blueprints will be transformed into a set of concrete, multi-layered cloud service instances and network links that collaboratively form the deployment topology of the CSBA. Figure  3  illustrates the deployment architecture for the TaxiOrdering-CSBA, in which: • Two instances of the MySQL-PaaS should be deployed • Two instances of the FleetMgt-SaaS blueprint should be deployed on two instances of the JEE-Linux-PaaS/IaaS. • A network link should be deployed to con- nect the two MySQL-PaaS instances with the two JEE-Linux-PaaS/IaaS instances.
• One instance of the MapA-SaaS should be deployed • A network link should be deployed to connect the two FleetMgt-SaaS instances with the Map-SaaS instance and with all the prospective TaxiOrdering-CSBA instances. Maintaining the QoS levels that have been specified in the SLAs is indispensable for all the service providers providing platform service, software services, and physical resource services that are used for building the TaxiOrdering-CSBA application that will underpin the process. At runtime, a framework is required for monitoring the execution of TaxiOrdering-CSBA process steps, detecting the occurrence of violations or potential violations of QoS constraints, and preventing these violations. PAEAN4CLOUD is proposed for addressing these requirements as an efficient solution is missing.
Before defining the PSLAVs, the concepts SLA and violation of SLA are defined. Definition 1. Let 'Σ' be the SLA that contains a list of quality constraints 'Q C ', security constraints 'S C ', privacy constraints 'P C ', and regulatory constraints 'R C ' such that, Σ := (Q C ∧ S C ∧ P C ∧ R C ) Since CSBA relies on third party cloud service providers, an SLA involves a 'consumer' and one to CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience many 'providers'. For example, a client "X" buys infrastructure services from Amazon 1  , platform service from google 2  , and software service from SalesForce 3. These providers must satisfy the obligations they promise to the client "X". Definition 2. A violation 'Γ' indicates either a cloud service consumer or a provider fails to satisfy one or more constraints contained in the agreement.
CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience In this section, the detection of violations is discussed using the TaxiOrdering-CSBA scenario (described in section 2). The TaxiOrdering-CSBA is configured by integrating a business process service, software services, platform services, and infrastructure service that are provided by different vendors. The configuration of CSBA is discussed in Section 2. 1. The central focus of TaxiOrdering-CSBA scenario is the SLA between the client TaxiTilburg and the cloud service providers include BestHosting, Tele-comProvider A and B, Auto Inc. , and MapProvider A and B. As TaxiTilburg is yet another service provider for the end-users, the quality of the services promised by the service providers is critical to TaxiTilburg. Besides, the data security is always crucial for the client TaxiTilburg.
The response time violation of Taxi Ordering Process results in end-client dissatisfaction that influences the customer in choosing another taxi service provider. In order to avoid such consequence, the SLA manager acts proactively based on history of completed activities. Furthermore, for each process instance that is completed successfully, the data is saved in the database permanently. The DatabaseProviderA grantees data security in particular, the durability of the data and consistency of the database to TaxiTilburg. The SLA monitor can detect if data is lost or the database is inconsistent. Algorithm 5. 2 is used for automated reasoning with TaxiOrdering-CSBA. After data of an end-client (the client who ordered for a taxi) is stored in the TaxiOrdering database, the application manager of TaxiOrdering-CSBA access the database remotely for retrieving detail of a customer.
EXPRSS sequence tags can be reliably assigned to transcripts EXPRSS library preparation uses total RNA and unlike NlaIII-DGE no prior selection for poly(A) RNA was performed. Most of the total RNA is ribosomal RNA (rRNA), and the number of reads mapped to Arabidopsis rRNA sequences  [46, 47]  was examined to rule out nonspecific reads from rRNA. Reads mapping to rRNA from the eight sequenced EXPRSS libraries accounted to 0. 64 %. A similar analysis on NlaIII-DGE reads indicate about ~0. 13 % of reads map to rRNA. Most of the rRNA reads in EXPRSS are from transcripts downstream of the 5. 8S rRNA-encoding loci (AT2G01020 and AT3G41979) with cDNA evidence in TAIR database (see Additional file 1, Figure  S2 ). Although NlaIII-DGE has less reads from rRNA, there were no reads from the transcripts downstream of 5.
8 s rRNA-encoding loci, as they lack NlaIII restriction sites, exemplifying one of the caveats for restriction enzyme based methods. Individual libraries from multiplexed samples were aligned to TAIR10 genome sequences [48] using Bowtie short read alignment software  [49]. Three main groups of tag sequences were observed from the uniquely mapped reads of EXPRSS data sets (Figure  2 ). Type A tags (the majority) align within ~300 bp from the 3′ end of existing gene models. Type B tags align upstream of 300 bp from the 3′ end, revealing genes with early polyadenylation sites. Type C tags align to antisense strands of existing gene models. In the following three sub-sections, we explain various sequence tag groups detected and their assignment to gene expression analysis. i EXPRSS Tag-seq generates cDNA tags at a controlled distance from polyadenylation site The EXPRSS method is mainly intended to produce a tag sequence from the 3′ end of each cDNA.
Uniquely matched tags to the sense strand of genes were included in this analysis and the distance for the longest splice variant was taken into consideration for tags that match to multiple splice variants of a gene. Figure  2A  shows the plot of pooled frequency of tags aligned against the distance from 3′ ends in bp from the start position of the tag. As expected for random shearing, ~90 % of the tags aligned within the ~300 bp distance from 3′ end with a clear peak at ~150 bp. Tags derived from transcript variants, with either premature or alternative polyadenylation, contribute to the long tail of distribution reaching the 5′ end (Figure  2A ). Tags aligning to distinct long and short transcripts of FCA (cDNA clones are ~6. 5 kb apart -Figure  2B ) support this interpretation. Identification of alternative polyadenylation sites in EXPRSS data sets is only reliable when these sites are >200 bp apart so that tag peaks can be identified and distinguished.
In the present study, ~17% of the transcripts that provide tags (mean expression of ≥1TPM [tags per million] from at least 4 replicates) were found to be derived from the antisense strand of genes. Antisense transcription observed in this study is not due to second strand cDNA synthesis by reverse transcriptase, which was shown to compromise strand specificity of microarray based techniques  [58]. Such artefacts would not influence EXPRSS data sets, since the strand synthesized from mRNA using oligo-dT primer acts as a template for sequencing, thus providing the same strand information as that of the mRNA. One hypothesis for the high amounts of antisense transcription observed is gene looping during transcription  [59]  which might lead to antisense transcription by jumping of the transcriptional machinery to the other strand.
Further instances of novel transcription were identified when all the un-assigned reads from eight libraries were pooled (see Additional file 1, Figure  S4C  & D ), highlighting the need for increased depth of sequencing to investigate such phenomena. To assign tags to genes, reads were initially mapped to Arabidopsis genome sequences [48] and unaligned reads were mapped to transcript sequences  [61]. Details of tag to gene association and alignment parameters are explained in the methods section. We have created modules of our analysis pipeline and implemented them in Galaxy, an open web-based research platform  [62]  and modules are provided in Additional file 3. EXPRSS Tag-seq can generate long (36 bp to 150 bp) reads. With EXPRSS reads, we found that ~94% of reads aligned uniquely, ~5% mapped to multiple positions and 1% were unmapped.
Rectangle selection is shown as inset picture. (B) Example of alternative polyadenylation. A distinct cluster of tags from both full length (blue circle) and short (red circle) sense transcripts of FCA (AT4G16280), which are about 6. 5 kb apart. Evidence from cDNA sequences (green arrows) in the TAIR database corroborates short transcripts. Reads from such alternative polyadenylation transcripts result in a long tail in tag alignment frequency distribution presented in A. (C) Frequency distribution of antisense tags aligning to 10 selected transcripts plotted against mapping position as a distance from 5′ end of annotated genes. Each individual abundantly expressing an antisense transcript has a distinct peak of tag alignment suggesting a defined polyadenylation site for antisense transcripts identified through EXPRSS method.
Illumina quality filter were obtained of which ~10% were discarded as reads with "N", below quality threshold, adapter only and shorter than 14 bp tag sequence. Sequenced libraries were computationally assigned to sublibraries based on perfect match to barcode for EXPRSS data and up to 1 mismatch allowed for NlaIII-DGE data. For EXPRSS, each sub-library received ~12% reads, while 2. 1% have no distinct barcode. For NlaIII-DGE, each library received ~11. 1% reads, and 1. 4% reads with no distinct barcode were discarded. Library distributions are presented in Table  2. Artefact filtering using FASTX-toolkit  [64]  resulted in loss of 7. 7% and 4% of reads in each sub-library for EXPRSS and NlaIII-DGE respectively. Most of these reads were found to be due to poly(A) sequences, initially assumed to be resulting from the poly(A) tail.
Comparison of differential expression between EXPRSS, NlaIII-DGE and microarray Differential gene expression analysis was carried out using the baySeq R package  [65]  for both EXPRSS and NlaIII-DGE data; genes with FDR <0. 01 were classified as differentially expressed. An example of differential expression from EXPRSS data is provided in Figure  3D-E. It depicts the number of tags observed for WRKY22, a flg22-inducible gene encoding a WRKY transcription factor, in control and flg22-treatment data sets (Figure  3D-E , respectively). Tags observed upon flg22 treatment are ~7 times more compared to tags observed with water treatment, in line with previously published microarray analysis  [41]. It is apparent that there are multiple polyadenylation sites in WRKY22 and all of them are induced upon flg22 treatment.
The distribution of tags aligning to WRKY22 at the 3′ end is wider than expected (~150 bp). This observation is in agreement with varying lengths of cDNA, resulting from alternative polyadenylation, in the TAIR database (Figure  3E  cDNA lengths). Full-length transcripts of WRKY22 have most of their tags aligned and thus provide an example that not only illustrates the dynamics of expression differences but also supports the choice of summation of tags from alternative transcripts for each gene to identify differential expression at the gene level. Once differential expression is validated, further investigations can be carried out on alternative transcripts. Microarrays have been widely used for gene expression analyses. We therefore compared differential expression results derived from EXPRSS data, microarray data and NlaIII-DGE data.
EXPRSS and NlaIII-DGE data sets were generated from the same RNA sample, while microarray data is from similar RNA samples from a previous study,  hybridised to Affymetrix ATH1 chip  [41]. Analysis of EXPRSS data identified differential expression in 2883 transcripts; 2504 of which were derived from sense transcripts (1066 down-regulated and 1438 up-regulated) and 379 from antisense transcripts (72 down-regulated and 307 up-regulated) (details provided in Additional file 4). A similar analysis on NlaIII-DGE data revealed differential expression in 462 transcripts (379 sense [75 down-regulated and 304 up-regulated] and 83 antisense [19 down-regulated and 64 up-regulated] transcripts) (details provided in Additional file 5). Microarray analysis using the Rankproducts method  [66]  revealed differential expression in 874 probe sets (< 0.
05 FDR) associated with 899 genes (370 down-regulated and 529 up-regulated) (details provided in Additional file 6). Comparisons were performed between these three methods, using only the sense gene list. Figure  4A  shows the Venn diagram representing the results from a 3-way comparison. Not only does EXPRSS have a higher overlap with NlaIII-DGE [~85% -391 out of 462 (338 sensehyper geometric probability 0. 00 and 53 antisensehyper geometric probability 3. 51E-83)] but it also has a similar overlap with microarray data [~80% -723 out of 899 (hypergeometric probability 0. 00)], while the overlap of NlaIII-DGE with microarray is 23% (207 out of 899). As mentioned in the introduction, one of the main advantages of Tag-seq methods is the opportunity to identify all genes expressed in a cell, unlike detecting only spotted genes on a microarray.
Genes not present on the ATH1 microarray chip that are detected by EXPRSS and NlaIII-DGE are highlighted in red in the respective section of Venn diagram (Figure  4A ). To find the similarities with respect to expression changes detected between the methods compared, fold-changes of genes commonly found from each pairwise comparisons are used for correlation analysis. Correlation of fold changes from genes commonly found by EXPRSS & microarray is 0. 96, EXPRSS & NlaIII-DGE is 0. 95 and microarray and NlaIII-DGE is 0. 90 (see Additional file 1, Figure  S7 ). Correlations from expression fold-changes indicate that there is generally a good agreement between platforms and more so for EXPRSS with ATH1 microarray data and NlaIII-DGE. changes, and increased differential expression detection, highlights the potential of EXPRSS Tag-seq as an expression profiling method.
The data showing higher overlap of EXPRSS data with microarray and NlaIII-DGE, better agreement of expression One key observation coming from the differential expression analysis is that additional genes are identified in EXPRSS compared to the other two methods. Validation of differential expression was carried out to verify some of the genes identified from EXPRSS Tag-seq through qPCR. A set of genes spotted on the microarray or not spotted on microarray that were differentially expressed in EXPRSS was chosen for qPCR verification. Details about all the genes and their expression levels in EXPRSS and qPCR are provided in Additional file 7. Double stranded cDNA samples subjected to EXPRSS library preparation were also used for qPCR verification. Figure  4C  and D verify that differential expression of selected genes could be confirmed with qPCR.
Evidence from qPCR supports differential expression from the selected genes identified by EXPRSS method. However, there are 176 genes that are not differentially expressed in EXPRSS but found to be differentially expressed using microarray data. Further analysis revealed that 167 genes of these were identified in EXPRSS data and about one third of the genes had FDR between 0. 01 and 0. 05, suggesting that they are near the statistical threshold (Additional file 8). Most of the remaining genes had mean expression levels less than 10 TPM and high variability among replicates. Such discrepancies can thus be associated with depth of sampling and statistical techniques employed.