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Most archaea divide by binary fission using an FtsZ-based system similar to that of bacteria, but they lack many of the divisome components described in model bacterial organisms. Notably, among the multiple factors that tether FtsZ to the membrane during bacterial cell constriction, archaea only possess SepF-like homologs. Here, we combine structural, cellular, and evolutionary analyses to demonstrate that SepF is the FtsZ anchor in the human-associated archaeon Methanobrevibacter smithii. 3D super-resolution microscopy and quantitative analysis of immunolabeled cells show that SepF transiently co-localizes with FtsZ at the septum and possibly primes the future division plane. M. smithii SepF binds to membranes and to FtsZ, inducing filament bundling. High-resolution crystal structures of archaeal SepF alone and in complex with the FtsZ C-terminal domain (FtsZ CTD ) reveal that SepF forms a dimer with a homodimerization interface driving a binding mode that is different from that previously reported in bacteria. Phylogenetic analyses of SepF and FtsZ from bacteria and archaea indicate that the two proteins may date back to the Last Universal Common Ancestor (LUCA), and we speculate that the archaeal mode of SepF/FtsZ interaction might reflect an ancestral feature. Our results provide insights into the mechanisms of archaeal cell division and pave the way for a better understanding of the processes underlying the divide between the two prokaryotic domains.

Background: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery (PGS) and the improvement in the preoperative radiological assessment, facial nerve injury (FNI) remains the most severe complication after PGS. Until now, no studies have been published regarding the application of machine learning (ML) for predicting FNI after PGS. We hypothesize that ML would improve the prediction of patients at risk., Methods: Patients who underwent PGS for benign tumors between June 2010 and June 2019 were included., Results: Regarding prediction accuracy and performance of each ML algorithm, the K-nearest neighbor and the random forest achieved the highest sensitivity, specificity, positive predictive value, negative predictive value F-score, receiver operating characteristic (ROC)-area under the ROC curve, and accuracy globally. The K-nearest neighbor algorithm achieved performance values above 0.9 for specificity, negative predictive value, F-score and ROC-area under the ROC curve, and the highest sensitivity and positive predictive value., Conclusions: This study demonstrates that ML prediction models can provide evidence-based predictions about the risk of FNI to otolaryngologists and patients. It is hoped that such algorithms, which use clinical, radiological, histological, and cytological information, can improve the information given to patients before surgery so that they can be better informed of any potential complications., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Uruguay is one of the few countries in the Americas that successfully contained the coronavirus disease 19 (COVID-19) epidemic during the first half of 2020. Nevertheless, the intensive human mobility across the dry border with Brazil is a major challenge for public health authorities. We aimed to investigate the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains detected in Uruguayan localities bordering Brazil as well as to measure the viral flux across this ∼1,100 km uninterrupted dry frontier. Using complete SARS-CoV-2 genomes from the Uruguayan-Brazilian bordering region and phylogeographic analyses, we inferred the virus dissemination frequency between Brazil and Uruguay and characterized local outbreak dynamics during the first months (May-July) of the pandemic. Phylogenetic analyses revealed multiple introductions of SARS-CoV-2 Brazilian lineages B.1.1.28 and B.1.1.33 into Uruguayan localities at the bordering region. The most probable sources of viral strains introduced to Uruguay were the Southeast Brazilian region and the state of Rio Grande do Sul. Some of the viral strains introduced in Uruguayan border localities between early May and mid-July were able to locally spread and originated the first outbreaks detected outside the metropolitan region. The viral lineages responsible for Uruguayan urban outbreaks were defined by a set of between four and 11 mutations (synonymous and non-synonymous) with respect to the ancestral B.1.1.28 and B.1.1.33 viruses that arose in Brazil, supporting the notion of a rapid genetic differentiation between SARS-CoV-2 subpopulations spreading in South America. Although Uruguayan borders have remained essentially closed to non-Uruguayan citizens, the inevitable flow of people across the dry border with Brazil allowed the repeated entry of the virus into Uruguay and the subsequent emergence of local outbreaks in Uruguayan border localities. Implementation of coordinated bi-national surveillance systems is crucial to achieve an efficient control of the SARS-CoV-2 spread across this kind of highly permeable borderland regions around the world., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mir, Rego, Resende, Tort, Fernández-Calero, Noya, Brandes, Possi, Arleo, Reyes, Victoria, Lizasoain, Castells, Maya, Salvo, Schäffer Gregianini, Mar da Rosa, Garay Martins, Alonso, Vega, Salazar, Ferrés, Smircich, Sotelo Silveira, Fort, Mathó, Arantes, Appolinario, Mendonça, Benítez-Galeano, Simoes, Graña, Motta, Siqueira, Bello, Colina and Spangenberg.)

In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

Background: Rare diseases are pathologies that affect less than 1 in 2000 people. They are difficult to diagnose due to their low frequency and their often highly heterogeneous symptoms. Rare diseases have in general a high impact on the quality of life and life expectancy of patients, which are in general children or young people. The advent of high-throughput sequencing techniques has improved diagnosis in several different areas, from pediatrics, achieving a diagnostic rate of 41% with whole genome sequencing (WGS) and 36% with whole exome sequencing, to neurology, achieving a diagnostic rate between 47 and 48.5% with WGS. This evidence has encouraged our group to pursue a molecular diagnosis using WGS for this and several other patients with rare diseases., Results: We used whole genome sequencing to achieve a molecular diagnosis of a 7-year-old girl with a severe panvascular artery disease that remained for several years undiagnosed. We found a frameshift variant in one copy and a large deletion involving two exons in the other copy of a gene called YY1AP1. This gene is related to Grange syndrome, a recessive rare disease, whose symptoms include stenosis or occlusion of multiple arteries, congenital heart defects, brachydactyly, syndactyly, bone fragility, and learning disabilities. Bioinformatic analyses propose these mutations as the most likely cause of the disease, according to its frequency, in silico predictors, conservation analyses, and effect on the protein product. Additionally, we confirmed one mutation in each parent, supporting a compound heterozygous status in the child., Conclusions: In general, we think that this finding can contribute to the use of whole genome sequencing as a diagnosis tool of rare diseases, and in particular, it can enhance the set of known mutations associated with different diseases.

The purpose of our research was to analyze the relationship among motivation, burnout, and engagement in sports. Five hundred athletes of both sexes from multiple sports modalities took part, with a mean age of 17.39 years (SD = 4.60). The instruments applied were as follows: Spanish versions of the Sport Motivation Scale (SMS), the Athlete Engagement Questionnaire (AEQ) and the Athlete Burnout Questionnaire (ABQ). Pearson correlations showed that motivation is negatively related to burnout and positively to engagement, while burnout and engagement are inversely related to each other. Through structural equation modeling, it was shown that engagement has a mediating role between motivation and burnout. Furthermore, there are no gender differences in this relationship, although there are differences between athletes who practice individual sports and those who practice collective sports. Encouraging high levels of self-determined motivation can help to increase athletes' degree of engagement and protect them against burnout and sport withdrawal.

Background: The etiology of many genetic diseases is challenging. This is especially true for developmental disorders of the central nervous system, since several genes can be involved. Many of such pathologies are considered rare diseases, since they affect less than 1 in 2000 people. Due to their low frequency, they present several difficulties for patients, from the delay in the diagnosis to the lack of treatments. Next-generation sequencing techniques have improved the search for diagnosis in several pathologies. Many studies have shown that the use of whole-exome/genome sequencing in rare Mendelian diseases has a diagnostic yield between 30% and 50% depending on the disease., Methods: Here, we present the case of an undiagnosed 6-year-old boy with severe encephalopathy of unclear cause, whose etiological diagnosis was achieved by whole-genome sequencing., Results: We found a novel variant that has not been previously reported in patients nor it has been described in GnomAD. Segregation analysis supports a de novo mutation, since it is not present in healthy parents. The change is predicted to be harmful to protein function, since it falls in the first quarter of the protein producing an altered reading frame and generating a premature stop codon. Additionally, the variant is classified as pathogenic according to ACMG criteria (PVS1, PM2, and PP3). Furthermore, there are several reported frameshift mutations in nearby codons as well as nonsense mutations that are predicted as pathogenic in other studies., Conclusion: We found a novel de novo frameshift mutation in the PURA gene (MIM number 600473), c.151_161del, with sufficient evidence of its pathogenicity., (© 2021 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space-time modeling and prediction of precipitation on Crete island in Greece. The proposed space-time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10-2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space-time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction, and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this paper, we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines. Specifically, we consider six brain parcellation definitions, five methods for functional connectivity matrix construction, six feature extraction/selection approaches, and nine classifier building algorithms. We report the prediction performance sensitivity to each of these choices, as well as the best results that are comparable with the state of the art.

Objective: Introducing a new, simple and inexpensive portable equipment for lifeguards, consisting of a pre-assembled full-size plastic blanket with a mask and HEPA filter, which could offer significant time-saving advantages to reduce COVID-19 risk transmission in the first few minutes of CPR after water rescue, avoiding the negative impact of delayed ventilation., Method: A pilot study was carried out to determine the feasibility of the pre-assembled kit of face-mask and HEPA filter adapted on a pre-set plastic-blanket. The first step consisted of washing hands, putting on safety glasses and gloves as the first personal protection equipment (PPE) and then covering the victim with an assembled plastic blanket. The second step consisted of 10 min of cardiopulmonary resuscitation (CPR) with PPE and plastic blanket, following the technical recommendations for ventilation during COVID-19., Results: Ten rescuers took part in the pilot study. The average time to wear PPE and place the pre-assembly kit on the victim was 82 s [IC 58-105]. After 10 min the quality of the resuscitation (QCPR) was 91% [87-94]. Quality chest compressions (CC) were 22% better than ventilations (V). Most of the rescuers (60%) thought that placing the plastic blanket on the victim on the beach was somewhat simple or very simple., Conclusions: Resuscitation techniques in COVID-19 era at the beach have added complexities for the correct use of PPE. Plastic blanket plus basic ventilations equipment resource could be a new alternative to be considered for lifeguards to keep ventilation on use while reducing risk transmission., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

Background: Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area., Results: We have tested our methodology in the Revista Médica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds., Conclusions: Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.

Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction.

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user's behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

The mechanisms of Z-ring assembly and regulation in bacteria are poorly understood, particularly in non-model organisms. Actinobacteria, a large bacterial phylum that includes the pathogen Mycobacterium tuberculosis, lack the canonical FtsZ-membrane anchors and Z-ring regulators described for E. coli. Here we investigate the physiological function of Corynebacterium glutamicum SepF, the only cell division-associated protein from Actinobacteria known to interact with the conserved C-terminal tail of FtsZ. We show an essential interdependence of FtsZ and SepF for formation of a functional Z-ring in C. glutamicum. The crystal structure of the SepF-FtsZ complex reveals a hydrophobic FtsZ-binding pocket, which defines the SepF homodimer as the functional unit, and suggests a reversible oligomerization interface. FtsZ filaments and lipid membranes have opposing effects on SepF polymerization, indicating that SepF has multiple roles at the cell division site, involving FtsZ bundling, Z-ring tethering and membrane reshaping activities that are needed for proper Z-ring assembly and function.

Indoor crowd localization and counting in big public buildings pose problems of infrastructure deployment, signal processing, and privacy. Conventional approaches based on optical cameras, either in the visible or infrared range, received signal strength in wireless networks, sound or chemical sensing in sensor networks need careful calibration, noise removal, and sophisticated data processing to achieve results in limited scenarios. Moreover, personal data protection is a growing concern, so that detection methods that preserve the privacy of people are highly desirable. The aim of this paper is to provide a technique that may generate estimations of the localization of people in a big public building using anonymous data from already-deployed Wi-Fi infrastructure. We present a method applying geostatistical techniques to the access data acquired from Access Points (AP) in an open Wi-Fi network. Specifically, only the time series of the number of accesses per AP is required. Geostatistical methods produce a 3D high-quality spatial distribution representation of the people inside the building based on the interaction of their mobile devices with the APs. We report encouraging results obtained from data acquired at a building of Wroclaw University of Science and Technology.

Radiomics and texture analysis represent a new option in our biomarkers arsenal. These techniques extract a large number of quantitative features, analyzing their properties to incorporate them in clinical decision-making. Laryngeal cancer represents one of the most frequent cancers in the head and neck area. We hypothesized that radiomics features can be included as a laryngeal cancer precision medicine tool, as it is able to non-invasively characterize the overall tumor accounting for heterogeneity, being a prognostic and/or predictive biomarker derived from routine, standard of care, imaging data, and providing support during the follow up of the patient, in some cases avoiding the need for biopsies. The larynx represents a unique diagnostic and therapeutic challenge for clinicians due to its complex tridimensional anatomical structure. Its complex regional and functional anatomy makes it necessary to enhance our diagnostic tools in order to improve decision-making protocols, aimed at better survival and functional results. For this reason, this technique can be an option for monitoring the evolution of the disease, especially in surgical and non-surgical organ preservation treatments. This concise review article will explain basic concepts about radiomics and discuss recent progress and results related to laryngeal cancer.

Invasive meningococcal disease (IMD), caused by the bacterium Neisseria meningitidis, entails significant mortality and morbidity. Disease incidence is highest in infants <1 year and young children globally. In Europe, N. meningitidis serogroup B is responsible for over 50% of overall IMD cases, whereas the majority of IMD cases in Latin America is caused either by serogroup B or C. The development of an effective vaccine against serogroup B has challenged the researchers for over half a century. Serogroup B capsular polysaccharide was an inappropriate vaccine antigen, and the success of outer membrane vesicle (OMV) vaccines was restricted to homologous bacterial strains. Reverse vaccinology led to the development of a 4-component meningococcal vaccine including three novel antigens, and OMVs (4CMenB). Each vaccine component has a different target. 4CMenB has been authorised based on its immunogenicity and safety data because the low disease incidence precluded formal clinical efficacy studies. Human serum bactericidal antibody (hSBA) assay tests functional antibodies in the serum of vaccinated individuals (i.e. the vaccine immunogenicity), and is the accepted correlate of protection. Vaccine strain coverage has been assessed both through hSBA assays and a more conservative method named Meningococcal Antigen Typing System (MATS). Effectiveness data of 4CMenB have been collected in the field since 2013. The vaccine proved effective in outbreak control in North America, and recent data from the introduction of the vaccine in the United Kingdom infant national immunisation programme reveal a vaccine effectiveness of 82.9% for the first two doses, with an acceptable safety profile., (©The Author 2019. Published by Sociedad Española de Quimioterapia. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/).)

Mitochondrial diseases (MD) are a group of diseases that can be caused by either mutations in the mitochondrial genome or nuclear DNA. MD may be difficult to diagnose since very often they are highly heterogeneous and with overlapping phenotypes. Molecular genomics approaches, especially NGS have helped in this sense. In this study we have sequenced the mitochondrial genome of a girl with an unspecific neurological disorder and her mother. The later, while neurologically unaffected, suffers from a myopathy without clear cause. We were able to detect two non-synonymous mutations in the MT-ATP6 gene, which we propose are strong candidates for causative agents. 9017C as the main candidate present at high heteroplasmy frequency in the patient (83,2%) and moderate in the mother (45,4%) while it has a low frequency in the general population. It might act alone or in conjunction with 9010A as an accessory mutation. Evolutionary analysis showed that both mutations were located in a critical position in the F 0 a subunit, from F 0 -F 1 ATPase. Functional studies showed that carriers of those mutations in comparison to an unaffected individual (father) presented a decrease in the basal and ATP-dependent oxygen consumption rate and a decrease in the maximum respiration rate., (Copyright © 2018 Elsevier B.V. and Mitochondria Research Society. All rights reserved.)

Background: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions., Objective: To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile., Materials: An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost., Methods: Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size., Results: Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms., Conclusions: We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Objectives: Hospital readmission risk prediction facilitates the identification of patients potentially at high risk so that resources can be used more efficiently in terms of cost-benefit. In this context, several models for readmission risk prediction have been proposed in recent years. The goal of this review is to give an overview of prediction models for hospital readmission, describe the data analysis methods and algorithms used for building the models, and synthesize their results., Methods: Studies that reported the predictive performance of a model for hospital readmission risk were included. We defined the scope of the review and accordingly built a search query to select the candidate papers. This query string was used as input for the chosen search engines, namely PubMed and Google Scholar. For each study, we recorded the population, feature selection method, classification algorithm, sample size, readmission threshold, readmission rate and predictive performance of the model., Results: We identified 77 studies that met the inclusion criteria, out of 265 citations. In 68% of the studies (n = 52) logistic regression or other regression techniques were utilized as the main method. Ten (13%) studies used survival analysis for model construction, while 14 (18%) used machine learning techniques for classification, of which decision tree-based methods and SVM were the most utilized algorithms. Among these, only four studies reported the use of any class imbalance addressing technique, of which resampling is the most frequent (75%). The performance of the models varied significantly among studies, with Area Under the ROC Curve (AUC) values in the ranges between 0.54 and 0.92., Conclusion: Logistic regression and survival analysis have been traditionally the most widely used techniques for model building. Nevertheless, machine learning techniques are becoming increasingly popular in recent years. Recent comparative studies suggest that machine learning techniques can improve prediction ability over traditional statistical approaches. Regardless, the lack of an appropriate benchmark dataset of hospital readmissions makes a comparison of models' performance across different studies difficult., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: The global pandemic of obesity has led to an increased risk for prediabetes and type-2 diabetes (T2D). The aims of the current project are: (1) to evaluate the effect of a 22-week family based intervention program, including supervised exercise, on insulin resistance syndrome (IRS) risk in children with a high risk of developing T2D and (2) to identify the profile of microRNA in circulating exosomes and in peripheral blood mononuclear cells in children with a high risk of developing T2D and its response to a multidisciplinary intervention program including exercise., Methods: A total of 84 children, aged 8-12 years, with a high risk of T2D will be included and randomly assigned to control (N = 42) or intervention (N = 42) groups. The control group will receive a family based lifestyle education and psycho-educational program (2 days/month), while the intervention group will attend the same lifestyle education and psycho-educational program plus the exercise program (3 days/week, 90 min per session including warm-up, moderate to vigorous aerobic activities, and strength exercises). The following measurements will be evaluated at baseline prior to randomization and after the intervention: fasting insulin, glucose and hemoglobin A1c; body composition (dual-energy X-ray absorptiometry); ectopic fat (magnetic resonance imaging); microRNA expression in circulating exosomes and in peripheral blood mononuclear cells (MiSeq; Illumina); cardiorespiratory fitness (cardiopulmonary exercise testing); dietary habits and physical activity (accelerometry)., Discussion: Prevention and identification of children with a high risk of developing T2D could help to improve their cardiovascular health and to reduce the comorbidities associated with obesity., Trial Registration: ClinicalTrials.gov, ID: NCT03027726 . Registered on 16 January 2017.

Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.

Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms. Objectives: We look for WM tract voxel clusters showing significant differences when comparing of AD vs. LOBD, and its correlations with systemic blood plasma biomarkers (inflammatory, neurotrophic factors, and oxidative stress). Materials: A sample of healthy controls (HC) ( n = 19), AD patients ( n = 35), and LOBD patients ( n = 24) was recruited at the Alava University Hospital. Blood plasma samples were obtained at recruitment time and analyzed to extract the inflammatory, oxidative stress, and neurotrophic factors. Several modalities of MRI were acquired for each subject, Methods: Fractional anisotropy (FA) coefficients are obtained from diffusion weighted imaging (DWI). Tract based spatial statistics (TBSS) finds FA skeleton clusters of WM tract voxels showing significant differences for all possible contrasts between HC, AD, and LOBD. An ANOVA F -test over all contrasts is carried out. Results of F -test are used to mask TBSS detected clusters for the AD > LOBD and LOBD > AD contrast to select the image clusters used for correlation analysis. Finally, Pearson's correlation coefficients between FA values at cluster sites and systemic blood plasma biomarker values are computed. Results: The TBSS contrasts with by ANOVA F -test has identified strongly significant clusters in the forceps minor, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum gyrus. The correlation analysis of these tract clusters found strong negative correlation of AD with the nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) blood biomarkers. Negative correlation of AD and positive correlation of LOBD with inflammation biomarker IL6 was also found. Conclusion: TBSS voxel clusters tract atlas localizations are consistent with greater behavioral impairment and mood disorders in LOBD than in AD. Correlation analysis confirms that neurotrophic factors (i.e., NGF, BDNF) play a great role in AD while are absent in LOBD pathophysiology. Also, correlation results of IL1 and IL6 suggest stronger inflammatory effects in LOBD than in AD.

Bacterial kinesin light chain is a TPR domain-containing protein encoded by the bklc gene, which co-localizes with the bacterial tubulin (btub) genes in a conserved operon in Prosthecobacter. Btub heterodimers show high structural homology with eukaryotic tubulin and assemble into head-to-tail protofilaments. Intriguingly, Bklc is homologous to the light chain of the microtubule motor kinesin and could thus represent an additional eukaryotic-like cytoskeletal element in bacteria. Using biochemical characterization as well as cryo-electron tomography we show here that Bklc interacts specifically with Btub protofilaments, as well as lipid vesicles and could thus play a role in anchoring the Btub filaments to the membrane protrusions in Prosthecobacter where they specifically localize in vivo. This work sheds new light into possible ways in which the microtubule cytoskeleton may have evolved linking precursors of microtubules to the membrane via the kinesin moiety that in today's eukaryotic cytoskeleton links vesicle-packaged cargo to microtubules.

Cell-cell fusion is inherent to sexual reproduction. Loss of HAPLESS 2/GENERATIVE CELL SPECIFIC 1 (HAP2/GCS1) proteins results in gamete fusion failure in diverse organisms, but their exact role is unclear. In this study, we show that Arabidopsis thaliana HAP2/GCS1 is sufficient to promote mammalian cell-cell fusion. Hemifusion and complete fusion depend on HAP2/GCS1 presence in both fusing cells. Furthermore, expression of HAP2 on the surface of pseudotyped vesicular stomatitis virus results in homotypic virus-cell fusion. We demonstrate that the Caenorhabditis elegans Epithelial Fusion Failure 1 (EFF-1) somatic cell fusogen can replace HAP2/GCS1 in one of the fusing membranes, indicating that HAP2/GCS1 and EFF-1 share a similar fusion mechanism. Structural modeling of the HAP2/GCS1 protein family predicts that they are homologous to EFF-1 and viral class II fusion proteins (e.g., Zika virus). We name this superfamily Fusexins: fusion proteins essential for sexual reproduction and exoplasmic merger of plasma membranes. We suggest a common origin and evolution of sexual reproduction, enveloped virus entry into cells, and somatic cell fusion., (© 2017 Valansi et al.)

We previously reported a multigene family of monodomain Kunitz proteins from Echinococcus granulosus (EgKU-1-EgKU-8), and provided evidence that some EgKUs are secreted by larval worms to the host interface. In addition, functional studies and homology modeling suggested that, similar to monodomain Kunitz families present in animal venoms, the E. granulosus family could include peptidase inhibitors as well as channel blockers. Using enzyme kinetics and whole-cell patch-clamp, we now demonstrate that the EgKUs are indeed functionally diverse. In fact, most of them behaved as high affinity inhibitors of either chymotrypsin (EgKU-2-EgKU-3) or trypsin (EgKU-5-EgKU-8). In contrast, the close paralogs EgKU-1 and EgKU-4 blocked voltage-dependent potassium channels (Kv); and also pH-dependent sodium channels (ASICs), while showing null (EgKU-1) or marginal (EgKU-4) peptidase inhibitory activity. We also confirmed the presence of EgKUs in secretions from other parasite stages, notably from adult worms and metacestodes. Interestingly, data from genome projects reveal that at least eight additional monodomain Kunitz proteins are encoded in the genome; that particular EgKUs are up-regulated in various stages; and that analogous Kunitz families exist in other medically important cestodes, but not in trematodes. Members of this expanded family of secreted cestode proteins thus have the potential to block, through high affinity interactions, the function of host counterparts (either peptidases or cation channels) and contribute to the establishment and persistence of infection. From a more general perspective, our results confirm that multigene families of Kunitz inhibitors from parasite secretions and animal venoms display a similar functional diversity and thus, that host-parasite co-evolution may also drive the emergence of a new function associated with the Kunitz scaffold.

Gli2 is the primary transcriptional activator of Hedgehog signalling in mammals. Upon stimulation of the pathway, Gli2 moves into the cilium before reaching the nucleus. However, the mechanisms underlying its entry into the cilium are not completely understood. Since several similarities have been reported between nuclear and ciliary import, we investigated if the nuclear import machinery participates in Gli2 ciliary entry. Here we show that while two conserved classical nuclear localization signals mediate Gli2 nuclear localization via importin (Imp)-α/β1, these sequences are not required for Gli2 ciliary import. However, blocking Imp-mediated transport through overexpression of GTP-locked Ran reduced the percentage of Gli2 positive cilia, an effect that was not explained by increased CRM1-dependent export of Gli2 from the cilium. We explored the participation of Imp-β2 in Gli2 ciliary traffic and observed that this transporter is involved in moving Gli2 into the cilium, as has been described for other ciliary proteins. In addition, our data indicate that Imp-β2 might also collaborate in Gli2 nuclear entry. How does Imp-β2 determine the final destination of a protein that can localize to two distinct subcellular compartments remains an open question. Therefore, our data shows that the nuclear-cytoplasmic shuttling machinery plays a critical role mediating the subcellular distribution of Gli2 and the activation of the pathway, but distinct importins likely play a differential role mediating its ciliary and nuclear translocation., Competing Interests: The authors have declared that no competing interests exist.

An abdominal aortic aneurysm (AAA) is a pathological dilation of the abdominal aorta that may lead to a rupture with fatal consequences. Endovascular aneurysm repair (EVAR) is a minimally invasive surgical procedure consisting of the deployment and fixation of a stent-graft that isolates the damaged vessel wall from blood circulation. The technique requires adequate endovascular device sizing, which may be performed by vascular analysis and quantification on Computerized Tomography Angiography (CTA) scans. This paper presents a novel 3D CTA image-based software for AAA inspection and EVAR sizing, eVida Vascular, which allows fast and accurate 3D endograft sizing for standard and fenestrated endografts. We provide a description of the system and its innovations, including the underlying vascular image analysis and visualization technology, functional modules and user interaction. Furthermore, an experimental validation of the tool is described, assessing the degree of agreement with a commercial, clinically validated software, when comparing measurements obtained for standard endograft sizing in a group of 14 patients., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Mitochondrial diseases are a group of clinically heterogeneous disorders that can be difficult to diagnose. We report a two and a half year old girl with clinical symptoms compatible with Leigh disease but with no definitive diagnosis. Using next generation sequencing we found that mutation 3697G>A was responsible for the patient's clinical symptoms. Corroboration was performed via segregation analysis in mother and sister and by evolutionary analysis that showed that the mutation is located in a highly conserved region across a wide range of species. Functional analyses corroborated the mutation effect and indicated that the pathophysiological alterations were partially restored by Coenzyme Q10. In addition, we proposed that the presence of the mutation at high frequencies causes the phenotype in the patient, while other family members with intermediate levels of heteroplasmy are symptoms-free., (Copyright © 2016 Elsevier B.V. and Mitochondria Research Society. All rights reserved.)

A common way to obtain standard-model-like Lagrangians in string theory is to place D3-branes inside flux compactifications. The bosonic and fermionic masses and couplings of the resulting gauge theory are determined by the ten-dimensional metric and the fluxes, respectively, and the breaking of supersymmetry is soft. However, not any soft-supersymmetry-breaking Lagrangian can be obtained this way since the string theory equations of motion impose certain relations between the soft couplings. We show that for D3-branes in background fluxes, these relations imply that the sums of the squares of the boson and of the fermion masses are equal and that, furthermore, one- and two-loop quantum corrections do not spoil this equality. This makes the use of D3-branes for constructing computationally controllable models for physics beyond the standard model problematic.

The genus Leptospira is composed of pathogenic and saprophytic spirochetes. Pathogenic Leptospira is the etiological agent of leptospirosis, a globally spread neglected disease. A key ecological feature of some pathogenic species is their ability to survive both within and outside the host. For most leptospires, the ability to persist outside the host is associated with biofilm formation, a most important bacterial strategy to face and overcome hostile environmental conditions. The architecture and biochemistry of leptospiral biofilms are rather well understood; however, the genetic program underpinning biofilm formation remains mostly unknown. In this work, we used the saprophyte Leptospira biflexa as a model organism to assess over- and underrepresented transcripts during the biofilm state, using transcriptome sequencing (RNA-seq) technology. Our results showed that some basal biological processes like DNA replication and cell division are downregulated in the mature biofilm. Additionally, we identified significant expression reprogramming for genes involved in motility, sugar/lipid metabolism, and iron scavenging, as well as for outer membrane-encoding genes. A careful manual annotation process allowed us to assign molecular functions to many previously uncharacterized genes that are probably involved in biofilm metabolism. We also provided evidence for the presence of small regulatory RNAs in this species. Finally, coexpression networks were reconstructed to pinpoint functionally related gene clusters that may explain how biofilm maintenance is regulated. Beyond elucidating some genetic aspects of biofilm formation, this work reveals a number of pathways whose functional dissection may impact our understanding of leptospiral biology, in particular how these organisms adapt to environmental changes. IMPORTANCE In this work, we describe the first transcriptome based on RNA-seq technology focused on studying transcriptional changes associated with biofilm growth in a member of the genus Leptospira. As many pathogenic species of this genus can survive inside the host but also persist in environmental water, mostly forming biofilms, identifying the molecular basis of this capacity can impact the understanding of how leptospires are able to fulfill a complete life cycle that alternates between adaptation to the host and adaptation to hostile external environmental conditions. We identified several genes and regulatory networks that can be the kickoff for deepening understanding of the molecular mechanisms involving bacterial persistence via biofilm formation; understanding this is important for the future development of tools for controlling leptospirosis.

Aims: Members of the thioredoxin (Trx) protein family participate mainly in redox pathways and have not been associated with Fe/S binding, in contrast to some closely related glutaredoxins (Grxs). Cestode parasites possess an unusual diversity of Trxs and Trx-related proteins with unexplored functions. In this study, we addressed the biochemical characterization of a new class of Trx-related protein (IsTRP) and a classical monothiol Grx (EgGrx5) from the human pathogen Echinococcus granulosus., Results: The dimeric form of IsTRP coordinates Fe 2 S 2 in a glutathione-independent manner; instead, Fe/S binding relies on the CXXC motif conserved among Trxs. This novel binding mechanism allows holo-IsTRP to be highly resistant to oxidation. IsTRP lacks canonical reductase activities. Mitochondrially targeted IsTRP aids growth of a Grx5 null yeast strain. Similar complementation assays performed with EgGrx5 revealed functional conservation for class II Grxs, despite the presence of nonconserved structural elements. IsTRP is a cestode lineage-specific protein highly expressed in the gravid adult worm, which releases the infective stage critical for dissemination., Innovation: IsTRP is the first member from the Trx family to be reported to bind Fe/S. We disclose a novel mechanism of Fe/S coordination within the Trx folding unit, which renders the cluster highly resistant to oxidation-mediated disassembly., Conclusion: We demonstrate that IsTRP defines a new protein family within the Trx superfamily, confirm the conservation of function for class II Grx from nonphylogenetically related species, and highlight the versatility of the Trx folding unit to acquire Fe/S binding as a recurrent emergent function. Antioxid. Redox Signal. 00, 000-000.

Background: Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment., Objective: We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD., Materials: A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject., Methods: DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters., Results: Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata., Conclusion: It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Background: Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment., Objective: The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables., Materials: A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time., Methods: We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance., Results: Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%., Conclusion: It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.

Dendrotoxins are a group of peptide toxins purified from the venom of several mamba snakes. α-Dendrotoxin (α-DTx, from the Eastern green mamba Dendroaspis angusticeps) is a well-known blocker of voltage-gated K(+) channels and specifically of K(v)1.1, K(v)1.2 and K(v)1.6. In this work we show that α-DTx inhibited the ASIC currents in DRG neurons (IC50=0.8 μM) when continuously perfused during 25 s (including a 5 s pulse to pH 6.1), but not when co-applied with the pH drop. Additionally, we show that α-DTx abolished a transient component of the outward current that, in some experiments, appeared immediately after the end of the acid pulse. Our data indicate that α-DTx inhibits ASICs in the high nM range while some Kv are inhibited in the low nM range. The α-DTx selectivity and its potential interaction with ASICs should be taken in consideration when DTx is used in the high nM range., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions such as schizophrenia. This paper deals with the application of ensembles of Extreme Learning Machines (ELM) to build Computer Aided Diagnosis systems on the basis of features extracted from the activity measures computed over rs-fMRI data. The power of ELM to provide quick but near optimal solutions to the training of Single Layer Feedforward Networks (SLFN) allows extensive exploration of discriminative power of feature spaces in affordable time with off-the-shelf computational resources. Exploration is performed in this paper by an evolutionary search approach that has found functional activity map features allowing to achieve quite successful classification experiments, providing biologically plausible voxel-site localizations., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length.

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.

Oxidative stress and iron limitation represent the grim side of life in an oxygen-rich atmosphere. The versatile electron transfer shuttle ferredoxin, an iron-sulfur protein, is particularly sensitive to these hardships, and its downregulation under adverse conditions severely compromises survival of phototrophs. Replacement of ferredoxin by a stress-resistant isofunctional carrier, flavin-containing flavodoxin, is a widespread strategy employed by photosynthetic microorganisms to overcome environmental adversities. The flavodoxin gene was lost in the course of plant evolution, but its reintroduction in transgenic plants confers increased tolerance to environmental stress and iron starvation, raising the question as to why a genetic asset with obvious adaptive value was not kept by natural selection. Phylogenetic analyses reveal that the evolutionary history of flavodoxin is intricate, with several horizontal gene transfer events between distant organisms, including Eukarya, Bacteria, and Archaea. The flavodoxin gene is unevenly distributed in most algal lineages, with flavodoxin-containing species being overrepresented in iron-limited regions and scarce or absent in iron-rich environments. Evaluation of cyanobacterial genomic and metagenomic data yielded essentially the same results, indicating that there was little selection pressure to retain flavodoxin in iron-rich coastal/freshwater phototrophs. Our results show a highly dynamic evolution pattern of flavodoxin tightly connected to the bioavailability of iron. Evidence presented here also indicates that the high concentration of iron in coastal and freshwater habitats may have facilitated the loss of flavodoxin in the freshwater ancestor of modern plants during the transition of photosynthetic organisms from the open oceans to the firm land., (© The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Background: Emergency department (ED) readmissions are considered an indicator of healthcare quality that is particularly relevant in older adults. The primary objective of this study was to identify key factors for predicting patients returning to the ED within 30 days of being discharged., Methods: We analysed patients who attended our ED in June 2014, stratified into four groups based on the Kaiser pyramid. We collected data on more than 100 variables per case including demographic and clinical characteristics and drug treatments. We identified the variables with the highest discriminating power to predict ED readmission and constructed classifiers using machine learning methods to provide predictions., Results: Classifier performance distinguishing between patients who were and were not readmitted (within 30 days), in terms of average accuracy (AC). The variables with the greatest discriminating power were age, comorbidity, reasons for consultation, social factors, and drug treatments., Conclusions: It is possible to predict readmissions in stratified groups with high accuracy and to identify the most important factors influencing the event. Therefore, it will be possible to develop interventions to improve the quality of care provided to ED patients.

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.