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We here describe an algorithm based on an evolutionary strategy to find the prototype series of a set of time series, and we use Dynamic Time Warping (DTW) as a distance measure between series, and do not restrict the search space to the series in the set. The problem of calculating the centroid of a set of time series can be addressed as a minimization problem, using genetic algorithms. Our proposal may be considered among the set of non-classical approaches to genetic algorithms, where an individual gene is a candidate time series for being the centroid or representative of the whole set of series. The representation and operators of genetic algorithms are redesigned, in order to generate efficient summaries, the fitness function of each candidate series to be a prototype is approximated, comparing them only with a subset of randomly selected time series from the original dataset. Three areas are looked at in order to assess the goodness of our proposal: the performance of the prototype generated in terms of a fitness function, the consistency of the prototype generation for use in classical grouping algorithms, and its use in classification algorithms based on the nearest prototypes.

5547 Background: Copy number alterations (CNA) arise as a result of somatic changes to chromosome structure, resulting in gain or loss of genomic regions. A likely source of copy number variation is an incorrect repair of DNA damage that causes chromosome instability (CI). Hence, many tumors with a high degree of CNAs suffer CI. This is the case of high grade serous ovarian cancer (HGSOC), where deficiency in homologous recombination (HRD) is highly prevalent, and so are CI and CNAs. Clinically, HR status in HGSOC is a biomarker of iPARP response and required for proper patient management. Available HRD tests measure genomic loss of heterozygosity (LOH) features, closely related to CI. Such features are complex to acquire in samples with low tumor content; overall, 15% of HGSOC specimens fail HR testing. CNA profiling is technically more amenable to lower tumor cellularity and may be captured by a wider range of techniques and applications (gene panels, low pass WGS...). We present here the analysis and identification of CNA features in HGSOC as a novel biomarker of HR status. Methods: A cohort of 123 primary HGSOC tumors were analysed with a custom hybrid capture-based NGS panel (VHIO-300) that provides, along mutations in 450 genes, genome-wide CNA profiles. B-allele fractions, obtained from single nucleotide polymorphisms also in the panel, allowed HRD-LOH score calculation (Marquard et al., 2015). 41 CNA segments correlated to HRD-LOH scores (proportions test, FDR < 0.001) and selected as features to calculate a CNA-HGSOC score (range 0-100). Among them, 10 segments appeared altered almost exclusively in BRCA1/2 pathogenic/likely pathogenic mutant tumors. Results: The density plot of the CNA-HGSOC score showed a bimodal distribution with modes at 7 and 65. A cut-off value of 34 was selected based on the lowest density CNA-score value between the modes. Hence, high CNA-HGSOC was defined as tumors with scores ≥ 34. Our CNA-HGSOC score was strongly correlated as a continuous variable to HRD-LOH (Pearson correlation = 0.72; p < 0.0001) and ROC analysis (AUC = 0.84; CI 95% 0.77-0.91; p < 0.0001) demonstrated high predictability to classify tumors as if using an HRD-LOH test (HRD-LOH score; “very high”: 100-75, “high”: 74-50,”mid”: 49-25 and “low”: 24-0). BRCA mutation status was also accurately predicted using a subset of CNA features (AUC = 0.71; CI 95% 0.59-0.82; p < 0.0001). Conclusions: CNAs may provide a new powerful genomic resource to HRD determination. We identified 41 CNA features in tumors to inform HR status and a subset of 10 revealed mutation status of BRCA1/2 in HGSOC. Upon further validation, a CNA-HCSOC score would be easily implemented in routine analysis pipelines in clinical labs, allowing HR testing or even broaden its application to emerging fields, such as liquid biopsy.

Background There are limited data on influenza vaccine effectiveness (IVE) in preventing laboratory-confirmed influenza illness among healthcare personnel (HCP). Methods HCP with direct patient contact working full-time in hospitals were followed during three influenza seasons in Israel (2016–2017 to 2018–2019) and Peru (2016 to 2018). Trivalent influenza vaccines were available at all sites, except during 2018–2019 when Israel used quadrivalent vaccines; vaccination was documented by electronic medical records, vaccine registries, and/or self-report (for vaccinations outside the hospital). Twice-weekly active surveillance identified acute respiratory symptoms or febrile illness (ARFI); self-collected respiratory specimens were tested by real-time reverse transcription polymerase chain reaction (PCR) assay. IVE was 100 × 1-hazard ratio (adjusted for sex, age, occupation, and hospital). Results Among 5,489 HCP who contributed 10,041 person-seasons, influenza vaccination coverage was 47% in Israel and 32% in Peru. Of 3,056 ARFIs in Israel and 3,538 in Peru, A or B influenza virus infections were identified in 205 (7%) in Israel and 87 (2.5%) in Peru. IVE against all viruses across seasons was 1% (95% confidence interval [CI] = −30%, 25%) in Israel and 12% (95% CI = −61%, 52%) in Peru. Conclusion Estimates of IVE were null using person-time models during six study seasons in Israel and Peru.

Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building’s sustainability. In this work, we propose a framework from which we can define models that capture this casuistry, gathering a set of time series of electrical consumption. The objective of these models is to obtain a linguistic summary based on y is P protoforms that describes in natural language the consumption of a given building or group of buildings in a specific time period. The definition of these descriptions has been solved by means of fuzzy linguistic summaries. As a novelty in this field, we propose an extension that is able to capture situations where the membership of the fuzzy sets is not very marked, which obtains an enriched semantics. In addition, to support these models, the development of a software prototype has been carried out and a small applied study of actual consumption data from an educational organization based on the conclusions that can be drawn from the techniques that we have described, demonstrating its capabilities in summarizing consumption situations. Finally, it is intended that this work will be useful to managers of buildings or organizational managers because it will enable them to better understand consumptionin a brief and concise manner, allowing them to save costs derived from energy supply by establishing sustainable policies.

The induction of classifiers by means of supervised learning techniques is one of the most common and extended applications in the field of the intelligent systems. Multi-classifier systems obtain a set of basic classifiers and uses it to predict the class of a data instance. In this work, a new method to reduce a set of classifiers to their equivalent minimal set is presented. For this purpose, a new fuzzy classifier called Atomic Fuzzy Classifier is defined. Furthermore, two different definitions of similarity, structural similarity and functional similarity, are considered. The combination of both produces a novel definition of a similarity function between two classifiers. This relation of similarity is used to obtain classes of equivalence, where each element of this class represents a subset of similar classifiers. The original set of classifiers is reduced to a new set of classifiers where only one of them is related to an unique equivalence class. In the experimental part, an application for the classification of elements of the IRIS database is presented.

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. This work was supported by start up funds from the institute for Predictive and Personalized Medicine of Cancer and the Center for Genomic Regulation [core funding to L.S.]; by the Spanish Ministry of Science and Technology [grant number SAF2004-06976 to L.S., Juan de la Cierva researcher contract to F.L., and technician contracts for support of technological infrastructures to E.G. and A.F.]; and by excellence in research team recognitions by the Catalan government, Departament de Innovació, Universitats i Ensenyament, Generalitat de Catalunya [Singular Research Group award number SGR2005-404 to L.S and SGR2009-0366 to J.A.D.R., by the Instituto de Salud Carlos III Fondo de Investigaciones Sanitarias [grant number PI10/01154 to L.S.] and to J.A.D.R, by the Spanish Ministry of Science and Technology to F.L. and J.A.D.R

Background: Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression. Results: In this study, we have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs. Conclusions: We propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes. This work was supported by grants from Ministerio de Ciencia e Innovación [MICINN; BFU2012-32617] to JADR and FL, Generalitat de Catalunya [SGR2009-366] to JADR and FL, and the EU-FP7 PRIORITY to JADR. Start up funds from the Institute for Predictive and Personalized Medicine of Cancer and the Center for Genomic Regulation to LS; by the Spanish Ministry of Science and Technology [SAF2004-06976] to LS, Juan de la Cierva researcher contract to FL, and by excellence in research team recognitions by the Catalan government, Departament de Innovació, Universitats i Ensenyament, Generalitat de Catalunya [SGR2005-404] to LS, and by the Instituto de Salud Carlos III Fondo de Investigaciones Sanitarias to JADR and to LS [PI10/01154]

This paper is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike licence.-- et al., Chronic lymphocytic leukaemia (CLL), the most frequent leukaemia in adults in Western countries, is a heterogeneous disease with variable clinical presentation and evolution(1,2). Two major molecular subtypes can be distinguished, characterized respectively by a high or low number of somatic hypermutations in the variable region of immunoglobulin genes(3,4). The molecular changes leading to the pathogenesis of the disease are still poorly understood. Here we performed whole-genome sequencing of four cases of CLL and identified 46 somatic mutations that potentially affect gene function. Further analysis of these mutations in 363 patients with CLL identified four genes that are recurrently mutated: notch 1 (NOTCH1), exportin 1 (XPO1), myeloid differentiation primary response gene 88 (MYD88) and kelch-like 6 (KLHL6). Mutations in MYD88 and KLHL6 are predominant in cases of CLL with mutated immunoglobulin genes, whereas NOTCH1 and XPO1 mutations are mainly detected in patients with unmutated immunoglobulins. The patterns of somatic mutation, supported by functional and clinical analyses, strongly indicate that the recurrent NOTCH1, MYD88 and XPO1 mutations are oncogenic changes that contribute to the clinical evolution of the disease. To our knowledge, this is the first comprehensive analysis of CLL combining whole-genome sequencing with clinical characteristics and clinical outcomes. It highlights the usefulness of this approach for the identification of clinically relevant mutations in cancer., This work was funded by the Spanish Ministry of Science and Innovation (MICINN) through the Instituto de Salud Carlos III (ISCIII) and Red Temática de Investigación del Cáncer (RTICC) del ISCIII. C.L.-O. is an Investigator of the Botin Foundation and D.T., of the ICREA program. We thank E. Santos for his support of this project, A. Carracedo and J. Benítez for genotyping studies, C. Fortuny for the supply of samples and N. Villahoz and M. C. Muro for their work in the coordination of the CLL-ICGC Consortium.