Space-division multiplexing for fiber-wireless communications

We envision the application of optical Space-division Multiplexing (SDM) to the next generation fiber-wireless communications as a firm candidate to increase the end user capacity and provide adaptive radiofrequency-photonic interfaces. This approach relies on the concept of fiber-distributed signal processing, where the SDM fiber provides not only radio access distribution but also broadband microwave photonics signal processing. In particular, we present two different SDM fiber technologies: dispersion-engineered heterogeneous multicore fiber links and multicavity devices built upon the selective inscription of gratings in homogeneous multicore fibers.Comment: 4 pages, 20th International Conference on Transparent Optical Networks (ICTON), Girona (Spain), 2017. arXiv admin note: text overlap with arXiv:1810.1213

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

On the acoustic environment of a neonatal intensive care unit: initial description, and detection of equipment alarms

The acoustic environment of a typical neonatal intensive care unit (NICU) is very rich and may contain a large number of different sounds, which come either from the equipment or from the human activities taking place in it. There exists a medical concern about the effect of that acoustical environment on preterm infants, since loud sounds or particular sounds may be harmful for their further neurological development. In this work, first of all, an initial description of the acoustic characteristics of the NICU has been carried out using a set of diverse recordings produced with microphones placed both inside and outside an incubator. Then, the work has focused on detection of the most relevant types of sounds. In this paper, after describing the recorded database and the acoustic environment, preliminary experiments for detection of the acoustic alarms of devices are reported. The proposed detection system is based on Deep Belief Networks (DBN). The experimental results show that the DBN-based system is able to achieve better results than a baseline GMM-based system.Peer Reviewe

On the acoustic environment of a neonatal intensive care unit: initial description, and detection of equipment alarms

The acoustic environment of a typical neonatal intensive care unit (NICU) is very rich and may contain a large number of different sounds, which come either from the equipment or from the human activities taking place in it. There exists a medical concern about the effect of that acoustical environment on preterm infants, since loud sounds or particular sounds may be harmful for their further neurological development. In this work, first of all, an initial description of the acoustic characteristics of the NICU has been carried out using a set of diverse recordings produced with microphones placed both inside and outside an incubator. Then, the work has focused on detection of the most relevant types of sounds. In this paper, after describing the recorded database and the acoustic environment, preliminary experiments for detection of the acoustic alarms of devices are reported. The proposed detection system is based on Deep Belief Networks (DBN). The experimental results show that the DBN-based system is able to achieve better results than a baseline GMM-based system.Peer ReviewedPostprint (published version

On the acoustic environment of a neonatal intensive care unit: initial description, and detection of equipment alarms

The acoustic environment of a typical neonatal intensive care unit (NICU) is very rich and may contain a large number of different sounds, which come either from the equipment or from the human activities taking place in it. There exists a medical concern about the effect of that acoustical environment on preterm infants, since loud sounds or particular sounds may be harmful for their further neurological development. In this work, first of all, an initial description of the acoustic characteristics of the NICU has been carried out using a set of diverse recordings produced with microphones placed both inside and outside an incubator. Then, the work has focused on detection of the most relevant types of sounds. In this paper, after describing the recorded database and the acoustic environment, preliminary experiments for detection of the acoustic alarms of devices are reported. The proposed detection system is based on Deep Belief Networks (DBN). The experimental results show that the DBN-based system is able to achieve better results than a baseline GMM-based system.Peer Reviewe

PopHuman: the human population genomics browser

The 1000 Genomes Project (1000GP) represents the most comprehensive world-wide nucleotide variation data set so far in humans, providing the sequencing and analysis of 2504 genomes from 26 populations and reporting >84 million variants. The availability of this sequence data provides the human lineage with an invaluable resource for population genomics studies, allowing the testing of molecular population genetics hypotheses and eventually the understanding of the evolutionary dynamics of genetic variation in human populations. Here we present PopHuman, a new population genomics-oriented genome browser based on JBrowse that allows the interactive visualization and retrieval of an extensive inventory of population genetics metrics. Efficient and reliable parameter estimates have been computed using a novel pipeline that faces the unique features and limitations of the 1000GP data, and include a battery of nucleotide variation measures, divergence and linkage disequilibrium parameters, as well as different tests of neutrality, estimated in non-overlapping windows along the chromosomes and in annotated genes for all 26 populations of the 1000GP. PopHuman is open and freely available at http://pophuman.uab.cat.Ministerio de Economía y Competitividad/European Regional Development Fund [grant numbers BFU2013-42649-P to A.B., BFU2016-77961-P to J.B.]; Generalitat de Catalunya [2014-SGR-1346, 2014-SGR-866]; Departament de Genètica i de Microbiologia of the Universitat Autònoma de Barcelona [12ª PIPF to S.H.]; Youth Employment Initiative and European Social Fund [PEJ-2014 to E.S]. Funding for open access charge: Ministerio de Economía y Competitividad/European Regional Development Fund [BFU2013-42649-P to A.B., BFU2016-77961-P to J.B.]