Biophysical and Computational Approaches to Unravel pH-Dependent Conformational Change of PspA Assist PspA-PspF Complex Formation in Yersinia enterocolitica

The Protein Journal - In enteropathogen, Yersinia enterocolitica, the genes encoding phage shock proteins are organized in an operon (pspA-E), which is activated at the various types of cellular...     

The Fate of Chromosomes and Alleles in an Allohexaploid Brassica Population

Production of allohexaploid Brassica (2n = AABBCC) is a promising goal for plant breeders due to the potential for hybrid heterosis and useful allelic contributions from all three of the Brassica genomes present in the cultivated diploid (2n = AA, 2n = BB, 2n = CC) and allotetraploid (2n = AABB, 2n = AACC, and 2n = BBCC) crop species (canola, cabbages, mustards). We used high-throughput SNP molecular marker assays, flow cytometry, and fluorescent in situ hybridization (FISH) to characterize a population of putative allohexaploids derived from self-pollination of a hybrid from the novel cross (B. napus × B. carinata) × B. juncea to investigate whether fertile, stable allohexaploid Brassica can be produced. Allelic segregation in the A and C genomes generally followed Mendelian expectations for an F₂ population, with minimal nonhomologous chromosome pairing. However, we detected no strong selection for complete 2n = AABBCC chromosome complements, with weak correlations between DNA content and fertility (r² = 0.11) and no correlation between missing chromosomes or chromosome segments and fertility. Investigation of next-generation progeny resulting from one highly fertile F₂ plant using FISH revealed general maintenance of high chromosome numbers but severe distortions in karyotype, as evidenced by recombinant chromosomes and putative loss/duplication of A- and C-genome chromosome pairs. Our results show promise for the development of meiotically stable allohexaploid lines, but highlight the necessity of selection for 2n = AABBCC karyotypes.     

Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.