A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system.

OBJECTIVE: Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model. METHODS: Data from twenty-nine subjects under normo and hypercapnic (5% CO2 in air) conditions was used. Cerebrovascular resistance and vessel compliance models with ABP as input and CF velocity as output were fitted using a MO approach, considering fitting Pearson's correlation and error. RESULTS: MO approach finds better model configurations than the single-objective (SO) approach, especially for hypercapnic conditions. In addition, the Pareto-optimal front from the multi-objective approach enables new information on dCA, reflecting a higher contribution of myogenic mechanism for explaining dCA impairment.

Single nucleotide polymorphisms associated with wine fermentation and adaptation to nitrogen limitation in wild and domesticated yeast strains.

For more than 20 years, Saccharomyces cerevisiae has served as a model organism for genetic studies and molecular biology, as well as a platform for biotechnology (e.g., wine production). One of the important ecological niches of this yeast that has been extensively studied is wine fermentation, a complex microbiological process in which S. cerevisiae faces various stresses such as limited availability of nitrogen. Nitrogen deficiencies in grape juice impair fermentation rate and yeast biomass production, leading to sluggish or stuck fermentations, resulting in considerable economic losses for the wine industry. In the present work, we took advantage of the "1002 Yeast Genomes Project" population, the most complete catalogue of the genetic variation in the species and a powerful resource for genotype-phenotype correlations, to study the adaptation to nitrogen limitation in wild and domesticated yeast strains in the context of wine fermentation. We found that wild and domesticated yeast strains have different adaptations to nitrogen limitation, corroborating their different evolutionary trajectories. Using a combination of state-of-the-art bioinformatic (GWAS) and molecular biology (CRISPR-Cas9) methodologies, we validated that PNP1, RRT5 and PDR12 are implicated in wine fermentation, where RRT5 and PDR12 are also involved in yeast adaptation to nitrogen limitation. In addition, we validated SNPs in these genes leading to differences in fermentative capacities and adaptation to nitrogen limitation. Altogether, the mapped genetic variants have potential applications for the genetic improvement of industrial yeast strains.

A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference.

The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the diversity of solutions while improving or keeping the quality of solutions in terms of hypervolume.

Total evidence or taxonomic congruence? A comparison of methods for combining biological evidence.

Phylogenetic inference proposes an evolutionary hypothesis for a group of taxa which is usually represented as a phylogenetic tree. The use of several distinct biological evidence has shown to produce more resolved phylogenies than single evidence approaches. Currently, two conflicting paradigms are applied to combine biological evidence: taxonomic congruence (TC) and total evidence (TE). Although the literature recommends the application of these paradigms depending on the congruence of the input data, the resultant evolutionary hypotheses could vary according to the strategy used to combine the biological evidence biasing the resultant topologies of the trees. In this work, we evaluate the ability of different strategies associated with both paradigms to produce integrated evolutionary hypotheses by considering different features of the data: missing biological evidence, diversity among sequences, complexity, and congruence. Using datasets from the literature, we compare the resultant trees with reference hypotheses obtained by applying two inference criteria: maximum parsimony and likelihood. The results show that methods associated with TE paradigm are more robust compared to TC methods, obtaining trees with more similar topologies in relation to reference trees. These results are obtained regardless of (1) the features of the data, (2) the estimated evolutionary rates, and (3) the criteria used to infer the reference evolutionary hypotheses.

Comparison of Phylogenetic Tree Topologies for Nitrogen Associated Genes Partially Reconstruct the Evolutionary History of Saccharomyces cerevisiae.

Massive sequencing projects executed in Saccharomyces cerevisiae have revealed in detail its population structure. The recent "1002 yeast genomes project" has become the most complete catalogue of yeast genetic diversity and a powerful resource to analyse the evolutionary history of genes affecting specific phenotypes. In this work, we selected 22 nitrogen associated genes and analysed the sequence information from the 1011 strains of the "1002 yeast genomes project". We constructed a total evidence (TE) phylogenetic tree using concatenated information, which showed a 27% topology similarity with the reference (REF) tree of the "1002 yeast genomes project". We also generated individual phylogenetic trees for each gene and compared their topologies, identifying genes with similar topologies (suggesting a shared evolutionary history). Furthermore, we pruned the constructed phylogenetic trees to compare the REF tree topology versus the TE tree and the individual genes trees, considering each phylogenetic cluster/subcluster within the population, observing genes with cluster/subcluster topologies of high similarity to the REF tree. Finally, we used the pruned versions of the phylogenetic trees to compare four strains considered as representatives of S. cerevisiae clean lineages, observing for 15 genes that its cluster topologies match 100% the REF tree, supporting that these strains represent main lineages of yeast population. Altogether, our results showed the potential of tree topologies comparison for exploring the evolutionary history of a specific group of genes.

A New Strategy to Evaluate Technical Efficiency in Hospitals Using Homogeneous Groups of Casemix : How to Evaluate When There is Not DRGs?

The public health system has restricted economic resources. Because of that, it is necessary to know how the resources are being used and if they are properly distributed. Several works have applied classical approaches based in Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for this purpose. However, if we have hospitals with different casemix, this is not the best approach. In order to avoid biases in the comparisons, other works have recommended the use of hospital production data corrected by the weights from Diagnosis Related Groups (DRGs), to adjust the casemix of hospitals. However, not all countries have this tool fully implemented, which limits the efficiency evaluation. This paper proposes a new approach for evaluating the efficiency of hospitals. It uses a graph-based clustering algorithm to find groups of hospitals that have similar production profiles. Then, DEA is used to evaluate the technical efficiency of each group. The proposed approach is tested using the production data from 2014 of 193 Chilean public hospitals. The results allowed to identify different performance profiles of each group, that differs from other studies that employs data from partially implemented DRGs. Our results are able to deliver a better description of the resource management of the different groups of hospitals. We have created a website with the results ( bioinformatic.diinf.usach.cl/publichealth ). Data can be requested to the authors.