Unlabelled: To gain insights into the diversity of sensu lato affecting sweet cherry in California, we sequenced and analyzed the phylogenomic and genomic architecture of 86 fluorescent pseudomonads isolated from symptomatic and asymptomatic cherry tissues. Fifty-eight isolates were phylogenetically placed within the species complex and taxonomically classified into five genomospecies: pv. , , , , and .
View Article and Find Full Text PDFAim: This study aims to examine the association between wearing wearable devices and physical activity levels among people living with diabetes.
Methods: 1298 wearable device users and nonusers living with diabetes from eight states of the 2017 Behavioral Risk Factors Surveillance System were included in the analysis. Unadjusted and adjusted linear regression was performed to determine the association between self-reported physical activity per week (min) and wearable device usage (users and nonusers) among people living with diabetes using survey analysis.
Influenza A viruses of the H2 subtype represent a zoonotic and pandemic threat to humans due to a lack of widespread specific immunity. Although A(H2) viruses that circulate in wild bird reservoirs are distinct from the 1957 pandemic A(H2N2) viruses, there is concern that they could impact animal and public health. There is limited information on AIVs in Latin America, and next to nothing about H2 subtypes in Brazil.
View Article and Find Full Text PDFWe sequenced and comprehensively analysed the genomic architecture of 98 fluorescent pseudomonads isolated from different symptomatic and asymptomatic tissues of almond and a few other Prunus spp. Phylogenomic analyses, genome mining, field pathogenicity tests, and in vitro ice nucleation and antibiotic sensitivity tests were integrated to improve knowledge of the biology and management of bacterial blast and bacterial canker of almond. We identified Pseudomonas syringae pv.
View Article and Find Full Text PDFThe search for existing non-animal alternative methods for use in experiments is currently challenging because of the lack of both comprehensive structured databases and balanced keyword-based search strategies to mine unstructured textual databases. In this paper we describe 3Ranker, which is a fast, keyword-independent algorithm for finding non-animal alternative methods for use in biomedical research. The 3Ranker algorithm was created by using a machine learning approach, consisting of a Random Forest model built on a dataset of 35 million abstracts and constructed with weak supervision, followed by iterative model improvement with expert curated data.
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