27 research outputs found

    Comparison of phylogenetic trees through alignment of embedded evolutionary distances

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    <p>Abstract</p> <p>Background</p> <p>The understanding of evolutionary relationships is a fundamental aspect of modern biology, with the phylogenetic tree being a primary tool for describing these associations. However, comparison of trees for the purpose of assessing similarity and the quantification of various biological processes remains a significant challenge.</p> <p>Results</p> <p>We describe a novel approach for the comparison of phylogenetic distance information based on the alignment of representative high-dimensional embeddings (xCEED: Comparison of Embedded Evolutionary Distances). The xCEED methodology, which utilizes multidimensional scaling and Procrustes-related superimposition approaches, provides the ability to measure the global similarity between trees as well as incongruities between them. We demonstrate the application of this approach to the prediction of coevolving protein interactions and demonstrate its improved performance over the mirrortree, tol-mirrortree, phylogenetic vector projection, and partial correlation approaches. Furthermore, we show its applicability to both the detection of horizontal gene transfer events as well as its potential use in the prediction of interaction specificity between a pair of multigene families.</p> <p>Conclusions</p> <p>These approaches provide additional tools for the study of phylogenetic trees and associated evolutionary processes. Source code is available at <url>http://gomezlab.bme.unc.edu/tools</url>.</p

    The evolution of diabetic chronic complications after pancreas transplantation

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    Pancreas transplantation is an invasive procedure that can restore and maintain normoglycemic level very successfully and for a prolonged period in DM1 patients. The procedure elevates the morbimortality rates in the first few months following the surgery if compared to kidney transplants with living donors, but it offers a better quality of life to patients

    Epidemiological and cohort study finds no association between COVID-19 and Guillain-Barré syndrome

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    Reports of Guillain-Barré syndrome (GBS) have emerged during the Coronavirus disease 2019 (COVID-19) pandemic. This epidemiological and cohort study sought to investigate any causative association between COVID-19 infection and GBS. The epidemiology of GBS cases reported to the UK National Immunoglobulin Database was studied from 2016 to 2019 and compared to cases reported during the COVID-19 pandemic. Data were stratified by hospital trust and region, with numbers of reported cases per month. UK population data for COVID-19 infection were collated from UK public health bodies. In parallel, but separately, members of the British Peripheral Nerve Society prospectively reported incident cases of GBS during the pandemic at their hospitals to a central register. The clinical features, investigation findings and outcomes of COVID-19 (definite or probable) and non-COVID-19 associated GBS cases in this cohort were compared. The incidence of GBS treated in UK hospitals from 2016 to 2019 was 1.65–1.88 per 100 000 individuals per year. GBS incidence fell between March and May 2020 compared to the same months of 2016–19. GBS and COVID-19 incidences during the pandemic also varied between regions and did not correlate with one another (r = 0.06, 95% confidence interval: −0.56 to 0.63, P = 0.86). In the independent cohort study, 47 GBS cases were reported (COVID-19 status: 13 definite, 12 probable, 22 non-COVID-19). There were no significant differences in the pattern of weakness, time to nadir, neurophysiology, CSF findings or outcome between these groups. Intubation was more frequent in the COVID-19 affected cohort (7/13, 54% versus 5/22, 23% in COVID-19-negative) attributed to COVID-19 pulmonary involvement. Although it is not possible to entirely rule out the possibility of a link, this study finds no epidemiological or phenotypic clues of SARS-CoV-2 being causative of GBS. GBS incidence has fallen during the pandemic, which may be the influence of lockdown measures reducing transmission of GBS inducing pathogens such as Campylobacter jejuni and respiratory viruses

    Microbial degradation of furanic compounds: biochemistry, genetics, and impact

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    Microbial metabolism of furanic compounds, especially furfural and 5-hydroxymethylfurfural (HMF), is rapidly gaining interest in the scientific community. This interest can largely be attributed to the occurrence of toxic furanic aldehydes in lignocellulosic hydrolysates. However, these compounds are also widespread in nature and in human processed foods, and are produced in industry. Although several microorganisms are known to degrade furanic compounds, the variety of species is limited mostly to Gram-negative aerobic bacteria, with a few notable exceptions. Furanic aldehydes are highly toxic to microorganisms, which have evolved a wide variety of defense mechanisms, such as the oxidation and/or reduction to the furanic alcohol and acid forms. These oxidation/reduction reactions constitute the initial steps of the biological pathways for furfural and HMF degradation. Furfural degradation proceeds via 2-furoic acid, which is metabolized to the primary intermediate 2-oxoglutarate. HMF is converted, via 2,5-furandicarboxylic acid, into 2-furoic acid. The enzymes in these HMF/furfural degradation pathways are encoded by eight hmf genes, organized in two distinct clusters in Cupriavidus basilensis HMF14. The organization of the five genes of the furfural degradation cluster is highly conserved among microorganisms capable of degrading furfural, while the three genes constituting the initial HMF degradation route are organized in a highly diverse manner. The genetic and biochemical characterization of the microbial metabolism of furanic compounds holds great promises for industrial applications such as the biodetoxifcation of lignocellulosic hydrolysates and the production of value-added compounds such as 2,5-furandicarboxylic acid

    Mud Lobsters (Thalassinoidea)

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