Neuroevolutionary learning in nonstationary environments

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour

DetectA: abrupt concept drift detection in non-stationary environments

Almost all drift detection mechanisms designed for classification problems work reactively: after receiving the complete data set (input patterns and class labels) they apply a sequence of procedures to identify some change in the class-conditional distribution – a concept drift. However, detecting changes after its occurrence can be in some situations harmful to the process under analysis. This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift). Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test. Based on the results of the hypothesis tests, we attempt to detect the drift on the test set, before the real labels are obtained. A procedure for creating datasets with abrupt drift has been proposed to perform a sensitivity analysis of the DetectA model. The result of the sensitivity analysis suggests that the detector is efficient and suitable for datasets of high-dimensionality, blocks with any proportion of drifts, and datasets with class imbalance. The performance of the DetectA method, with different configurations, was also evaluated on real and artificial datasets, using an MLP as a classifier. The best results were obtained using one of the detection methods, being the proactive manner a top contender regarding improving the underlying base classifier accuracy

f(R) theories

Over the past decade, f(R) theories have been extensively studied as one of the simplest modifications to General Relativity. In this article we review various applications of f(R) theories to cosmology and gravity - such as inflation, dark energy, local gravity constraints, cosmological perturbations, and spherically symmetric solutions in weak and strong gravitational backgrounds. We present a number of ways to distinguish those theories from General Relativity observationally and experimentally. We also discuss the extension to other modified gravity theories such as Brans-Dicke theory and Gauss-Bonnet gravity, and address models that can satisfy both cosmological and local gravity constraints.Comment: 156 pages, 14 figures, Invited review article in Living Reviews in Relativity, Published version, Comments are welcom

Sarcopenic obesity and risk of new onset depressive symptoms in older adults: English Longitudinal Study of Ageing

We examined the role of sarcopenic obesity as a risk factor for new-onset depressive symptoms over 6-year follow-up in a large sample of older adults

An intriguing shift occurs in the novel protein phosphatase 1 binding partner, TCTEX1D4: evidence of positive selection in a pika model

T-complex testis expressed protein 1 domain containing 4 (TCTEX1D4) contains the canonical phosphoprotein phosphatase 1 (PPP1) binding motif, composed by the amino acid sequence RVSF. We identified and validated the binding of TCTEX1D4 to PPP1 and demonstrated that indeed this protein is a novel PPP1 interacting protein. Analyses of twenty-one mammalian species available in public databases and seven Lagomorpha sequences obtained in this work showed that the PPP1 binding motif 90RVSF93 is present in all of them and is flanked by a palindromic sequence, PLGS, except in three species of pikas (Ochotona princeps, O. dauurica and O. pusilla). Furthermore, for the Ochotona species an extra glycosylation site, motif 96NLS98, and the loss of the palindromic sequence were observed. Comparison with other lagomorphs suggests that this event happened before the Ochotona radiation. The dN/dS for the sequence region comprising the PPP1 binding motif and the flanking palindrome highly supports the hypothesis that for Ochotona species this region has been evolving under positive selection. In addition, mutational screening shows that the ability of pikas TCTEX1D4 to bind to PPP1 is maintained, although the PPP1 binding motif is disrupted, and the N- and C-terminal surrounding residues are also abrogated. These observations suggest pika as an ideal model to study novel PPP1 complexes regulatory mechanisms.publishe

Impact of COVID-19 pandemic on asthma symptoms and management: A prospective analysis of asthmatic children in Ecuador

Background Asthma affects up to 33% of children in Latin American settings. The ongoing COVID-19 pandemic has had a significant impact on access to and use of health services. We aimed to evaluate the impact of the COVID-19 lockdown on asthma exacerbations, medical facility visits, and use of asthma medications in children. Methods We used data from a prospective cohort of 213 children aged 5–17 years in 3 Ecuadorian cities and analysed the impact of the COVID-19 lockdown on asthma. Outcomes (asthma exacerbations, emergency room [ER] visits, planned and unplanned outpatient visits, and use of inhaled corticosteroids and Beta-2 agonists) were analysed using repeated Poisson counts (ie, number of events per participant before and during the COVID-19 lockdown). Results During compared to before lockdown: a) the number of asthma exacerbations remained constant (IRR, 0.87; 95% CI: 0.72–1.05; p = 0.152); b) outpatient visits (IRR 0.26, 95% CI 0.14–0.47, p < 0.001) declined 74% while ER visits declined 89% (IRR 0.11, 95% CI 0.04–0.32, p < 0.001); and c) there was no change in inhaled corticosteroids use (IRR 1.03, 95% CI 0.90–1.16, P = 0.699) while Beta-2 agonist use increased (IRR 1.32, 95% CI 1.10–1.58, P = 0.003). Conclusions In a cohort of Ecuadorian children with asthma, health services attendance decreased dramatically after COVID-19 lockdown, but asthma exacerbations and use of inhaled corticosteroids were unchanged. Future analyses will address the question of the effect of SARS-CoV-2 infection on asthma exacerbations and control in this paediatric population

Synonymous Genes Explore Different Evolutionary Landscapes

The evolutionary potential of a gene is constrained not only by the amino acid sequence of its product, but by its DNA sequence as well. The topology of the genetic code is such that half of the amino acids exhibit synonymous codons that can reach different subsets of amino acids from each other through single mutation. Thus, synonymous DNA sequences should access different regions of the protein sequence space through a limited number of mutations, and this may deeply influence the evolution of natural proteins. Here, we demonstrate that this feature can be of value for manipulating protein evolvability. We designed an algorithm that, starting from an input gene, constructs a synonymous sequence that systematically includes the codons with the most different evolutionary perspectives; i.e., codons that maximize accessibility to amino acids previously unreachable from the template by point mutation. A synonymous version of a bacterial antibiotic resistance gene was computed and synthesized. When concurrently submitted to identical directed evolution protocols, both the wild type and the recoded sequence led to the isolation of specific, advantageous phenotypic variants. Simulations based on a mutation isolated only from the synthetic gene libraries were conducted to assess the impact of sub-functional selective constraints, such as codon usage, on natural adaptation. Our data demonstrate that rational design of synonymous synthetic genes stands as an affordable improvement to any directed evolution protocol. We show that using two synonymous DNA sequences improves the overall yield of the procedure by increasing the diversity of mutants generated. These results provide conclusive evidence that synonymous coding sequences do experience different areas of the corresponding protein adaptive landscape, and that a sequence's codon usage effectively constrains the evolution of the encoded protein