A Novel Communication Platform to Enable the Collaboration of Autonomous Underwater Vehicles

Abstract -A novel communication platform is introduced to enable the collaboration of a set of Autonomous Underwater Vehicles (AUV´

Machine learning-based ensemble recursive feature selection of circulating miRNAs for cancer tumor classification

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selectin

MadQCI: a heterogeneous and scalable SDN QKD network deployed in production facilities

Current quantum key distribution (QKD) networks focus almost exclusively on transporting secret keys with the highest possible rate. Consequently, they are built as mostly fixed, ad hoc, logically, and physically isolated infrastructures designed to avoid any penalty to the quantum channel. This architecture is neither scalable nor cost-effective and future, real-world deployments will differ considerably. The structure of the MadQCI QKD network presented here is based on disaggregated components and modern paradigms especially designed for flexibility, upgradability, and facilitating the integration of QKD in the security and telecommunications-networks ecosystem. These underlying ideas have been tested by deploying many QKD systems from several manufacturers in a real-world, multi-tenant telecommunications network, installed in production facilities and sharing the infrastructure with commercial traffic. Different technologies have been used in different links to address the variety of situations and needs that arise in real networks, exploring a wide range of possibilities. Finally, a set of realistic use cases have been implemented to demonstrate the validity and performance of the network. The testing took place during a period close to three years, where most of the nodes were continuously active

Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic disorder characterized by disabling fatigue. Several studies have sought to identify diagnostic biomarkers, with varying results. Here, we innovate this process by combining both mRNA expression and DNA methylation data. We performed recursive ensemble feature selection (REFS) on publicly available mRNA expression data in peripheral blood mononuclear cells (PBMCs) of 93 ME/CFS patients and 25 healthy controls, and found a signature of 23 genes capable of distinguishing cases and controls. REFS highly outperformed other methods, with an AUC of 0.92. We validated the results on a different platform (AUC of 0.95) and in DNA methylation data obtained from four public studies on ME/CFS (99 patients and 50 controls), identifying 48 gene-associated CpGs that predicted disease status as well (AUC of 0.97). Finally, ten of the 23 genes could be interpreted in the context of the derailed immune system of ME/CFS

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics

N-1-methylnicotinamide is a signalling molecule produced in skeletal muscle coordinating energy metabolism

Obesity is a major health problem, and although caloric restriction and exercise are successful strategies to lose adipose tissue in obese individuals, a simultaneous decrease in skeletal muscle mass, negatively effects metabolism and muscle function. To deeper understand molecular events occurring in muscle during weight-loss, we measured the expressional change in human skeletal muscle following a combination of severe caloric restriction and exercise over 4 days in 15 Swedish men. Key metabolic genes were regulated after the intervention, indicating a shift from carbohydrate to fat metabolism. Nicotinamide N-methyltransferase (NNMT) was the most consistently upregulated gene following the energy-deficit exercise. Circulating levels of N-1-methylnicotinamide (MNA), the product of NNMT activity, were doubled after the intervention. The fasting-fed state was an important determinant of plasma MNA levels, peaking at similar to 18 h of fasting and being lowest similar to 3 h after a meal. In culture, MNA was secreted by isolated human myotubes and stimulated lipolysis directly, with no effect on glucagon or insulin secretion. We propose that MNA is a novel myokine that enhances the utilization of energy stores in response to low muscle energy availability. Future research should focus on applying MNA as a biomarker to identify individuals with metabolic disturbances at an early stage.Peer reviewe

Incidence of and factors associated with SARS-CoV-2 infection among people living with HIV in Southern Spain after one year of pandemic

Whether people living with HIV (PLWH) are at greater risk of acquiring SARS-CoV-2 infection is currently unknown. Prospective serologic studies may allow seroincidence analyses, where all infections are accurately identified. Because of this, we evaluated the incidence of associated factors with and the clinical outcome of SARS-CoV-2 infection in PLWH in Southern Spain. This prospective cohort study included PLWH from a Tertiary University Hospital in Southern Spain. Patients were enrolled in the study if (1) they had attended as outpatients our Unit from 1 August 2019 to 8 February 2020 and (2) had two subsequent evaluations from 9 February 2020 to 4 March 2021. SARS-CoV-2 infections were diagnosed by PCR, antigen detection or serology. Seven hundred and nine PLWH were included in the study. Of them, 55 [7.8%, 95% confidence interval (95% CI) 5.9%-9.9%] patients developed SARS-CoV-2 infection. Between 18 May and 29 November 2020, the rate of seroconversion was 5.3% (95% CI: 3.1%-9.0%) for the general population in the area of Seville and 2.3% (95% CI: 1.3%-2.6%) for PLWH in this study (p = .001). After multivariable analysis, adjusted by age, sex, and risk factors for HIV infection, active tobacco use and CDC stage, active tobacco smoking was the only factor independently associated with lower risk of SARS-Cov-2 infection [Incidence rate ratio: 0.29 (95% CI 0.16-0.55) p < .001]. In conclusion, the incidence of SARS-CoV-2 infection among PLWH in Southern Spain during the ongoing pandemic was lower than that reported for the general population in the same area.This work was supported in part by the Instituto de Salud CarlosIII (Project ‘PI16/01443’), integrated in the national I+D+i 2013–2016andco-fundedbytheEuropeanUnion(ERDF/ESF,‘Investinginyour future’), by the Spanish Network for AIDS investigation (RIS)(www.red.es/redes/inicio)(RD16/0025/0010,RD16/0025/0040),asapartoftheNacionalI+D+I,ISCIIISubdirecciónGeneraldeEvaluaciónand the European Fund for Development of Regions (FEDER). JuanA.PinedareceivedaresearchextensiongrantfromtheProgramadeIntensificacióndelaActividaddeInvestigacióndelServicioNacionaldeSaludCarlosIII(I3SNS).FedericoGarciareceivedaresearchextensiongrantfromtheProgramadeIntensificacióndelaActividaddeInves-tigacióndelServicioAndaluzdeSalud.AnaïsCorma-GomezreceivedaRíoHortegagrantfromtheInstitutodeSaludCarlosIII(grantnum-ber CM19/00251). Funding for open access charge: Universidad deMálaga/CBU