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Abstract Background The bacterial genus Listeria contains pathogenic and non-pathogenic species, including the pathogens L. monocytogenes and L. ivanovii, both of which carry homologous virulence gene clusters such as the prfA cluster and clusters of internalin genes. Initial evidence for multiple deletions of the prfA cluster during the evolution of Listeria indicates that this genus provides an interesting model for studying the evolution of virulence and also presents practical challenges with regard to definition of pathogenic strains. Results To better understand genome evolution and evolution of virulence characteristics in Listeria, we used a next generation sequencing approach to generate draft genomes for seven strains representing Listeria species or clades for which genome sequences were not available. Comparative analyses of these draft genomes and six publicly available genomes, which together represent the main Listeria species, showed evidence for (i) a pangenome with 2,032 core and 2,918 accessory genes identified to date, (ii) a critical role of gene loss events in transition of Listeria species from facultative pathogen to saprotroph, even though a consistent pattern of gene loss seemed to be absent, and a number of isolates representing non-pathogenic species still carried some virulence associated genes, and (iii) divergence of modern pathogenic and non-pathogenic Listeria species and strains, most likely circa 47 million years ago, from a pathogenic common ancestor that contained key virulence genes. Conclusions Genome evolution in Listeria involved limited gene loss and acquisition as supported by (i) a relatively high coverage of the predicted pan-genome by the observed pan-genome, (ii) conserved genome size (between 2.8 and 3.2 Mb), and (iii) a highly syntenic genome. Limited gene loss in Listeria did include loss of virulence associated genes, likely associated with multiple transitions to a saprotrophic lifestyle. The genus Listeria thus provides an example of a group of bacteria that appears to evolve through a loss of virulence rather than acquisition of virulence characteristics. While Listeria includes a number of species-like clades, many of these putative species include clades or strains with atypical virulence associated characteristics. This information will allow for the development of genetic and genomic criteria for pathogenic strains, including development of assays that specifically detect pathogenic Listeria strains.

Introduction: Drug information (DI) services should work toward efficiency by identifying knowledge gaps and actively creating resources to address those needs. The aim was to identify training needs and active information opportunities in primary care by analyzing DI requests and to calculate labor cost associated with DI requests addressable with training or active information. Methods: DI requests received in 2016 and 2017 by ambulatory care pharmacists were independently classified by 2 authors into: training (i.e., delivery of content meant to be retained as knowledge and used when needed); active information (i.e., resources created preemptively and consulted when needed); or passive information (i.e., not addressable with training or active information). Inter-rater reliability was calculated using Cohen's Kappa. Median time spent by category and across practice settings/professional types was compared using bivariate analysis. Thematic analysis categorized specific training and active DI requests and labor costs were calculated. Results: Of 2,041 DI requests, 330 (16.2%) were classified as training, 454 (22.2%) active information, and 1257 (61.6%) passive information (kappa = 0.769). Median (IQR) time to resolve requests was 5 (2-10) mins for training, 5 (3-11) active information, and 10 (4-15) passive information. Pharmacists spent 132.1 hrs = $8,956.98 answering questions addressable with training or active information. Areas warranting training or active information included: controlled substances, immunizations, patient assistance programs, policy/regulations, medication preparation/administration, storage/stability, disposal, availability/ordering medications, and patient-related resources. Conclusion: Several opportunities for training and active information were identified. Despite the single-institution nature, the method described can serve as an example for other institutions. [ABSTRACT FROM AUTHOR]

A multicenter study was conducted to evaluate the potential of conversion analysis compared with DNA sequencing alone to detect heterogeneous germline mutations in MLH1, MSH2, and MSH6 in colorectal cancer patients. The data confirmed the heterogeneity of mismatch repair mutations and revealed that many mutations in colorectal cancer cases would be missed using conventional genomic DNA sequencing alone.

Based on an in-depth survey of the literature, the purpose of the paper is to explore cyber-physical system-based manufacturing. Using and replicating data from Capgemini, CompTIA, EY, Microsoft, PAC, and PwC, we performed analyses and made estimates regarding the relationship between Internet of Things sensing networks, deep learning-enabled smart process planning, and big data-driven innovation. Data were analyzed using structural equation modeling. [ABSTRACT FROM AUTHOR]

Examines nonverbal expressions of power and control in the daily lives of old people from a life-span perspective. Because of societal devaluation of the aged and later life declines in power resources, old people experience loss of status and social control during everyday nonverbal rituals of dominance and deference. (Author)

This research synthesizes existing studies and investigates how automation will transform the labor market. Building our argument by drawing on various empirical data, we performed analyses and made estimates regarding the proportion of U.S. adults who think it is not at all/not very/somewhat/very likely that the following jobs will be replaced by robots or computers in their lifetimes, share of current work hours with potential for automation by 2030 in selected countries, probability of job automation by race, ethnicity and gender, and percentage of adults who think it is likely that people would have a hard time finding jobs/the inequality between the rich and poor would be much worse than it is today/economy would be more efficient/there would be new, better-paying jobs if robots and computers were able to do much of the work currently being done by humans. [ABSTRACT FROM AUTHOR]

I draw on a substantial body of theoretical and empirical research on advanced automation technology, labor market insecurity, and collective joblessness, and using and replicating data from OECD/PIAAC, Pew Research Center, PwC, and The Economist, I performed analyses and made estimates regarding percentage who says automation will lead to new, better-paying jobs, jobs that will likely be eliminated by robots in the future, automation risk by job type (%), automation risk (50% or higher) and GDP per person (selected countries), and percentage of existing jobs at potential risk of automation. [ABSTRACT FROM AUTHOR]