No abstract

Streptococcus dysgalactiae subsp. equisimilis (SDSE) causes cellulitis, bacteremia, and invasive diseases, such as streptococcal toxic shock syndrome. Although SDSE infection is more prevalent among elderly individuals and those with diabetes mellitus than infections with Streptococcus pyogenes (Group A streptococci; GAS) and Streptococcus agalactiae (Group B streptococci; GBS), the mechanisms underlying the pathogenicity of SDSE remain unknown. SDSE possesses a gene hylD encoding a hyaluronate lyase (HylD), whose homologue (HylB) is involved in pathogenicity of GBS, while the role of HylD has not been characterized. In this study, we focused on the enzyme HylD produced by SDSE; HylD cleaves hyaluronate (HA) and generates unsaturated disaccharides via a β-elimination reaction. Hyaluronate-agar plate assays revealed that SDSE promoted dramatic HA degradation. SDSE expresses both HylD and an unsaturated glucuronyl hydrolase (UGL) that catalyzes the degradation of HA-derived oligosaccharides; as such, SDSE was more effective at HA degradation than other β-hemolytic streptococci, including GAS and GBS. Although HylD shows some homology to HylB, a similar enzyme produced by GBS, HylD exhibited significantly higher enzymatic activity than HylB at pH 6.0, conditions that are detected in the skin of both elderly individuals and those with diabetes mellitus. We also detected upregulation of transcripts from hylD and ugl genes from SDSE wild-type collected from the mouse peritoneal cavity; upregulated expression of ugl was not observed in ΔhylD SDSE mutants. These results suggested that disaccharides produced by the actions of HylD are capable of triggering downstream pathways that catalyze their destruction. Furthermore, we determined that infection with SDSEΔhylD was significantly less lethal than infection with the parent strain. When mouse skin wounds were infected for 2 days, intensive infiltration of neutrophils was observed around the wound areas Nguyen et al.

The travel demand prediction of an activity-based travel demand model (ABM) is based on a hierarchical structure of multiple choices related to an individual’s activity scheduling. This structure has, however, not been investigated for motorcycle-based cities. The coarseness of the traffic analysis zoning system combined with mixed land use results in a large proportion of intrazonal trips, which demands model enhancement in ABMs for these cities. Using large-scale household travel survey data from Ho Chi Minh City, a major motorcycle-based city in Vietnam, this study investigated the hierarchical structure for non-work activity scheduling, with consideration of three dimensions: (1) activity starting time, (2) travel mode, and (3) destination choices at the tour level with attention given to the impacts of intrazonal tours. Multinomial logit and nested logit models were adopted for model development. Results showed that work durations in the schedule strongly affected the scheduling of non-work activities. The estimated logsum parameters showed empirical evidence that hierarchy could be different for different activity types. Our findings also suggested a significant impact of intrazonal tours on the structuring and modeling of activity scheduling choices. The validation result indicated that our proposed models’ predictive capability is acceptable.

It is difficult to control the SARS-CoV-2 virus with many complicated strains with a fast- spreading speed. In Vietnam, the number of new infections gradually shows signs of increasing, potentially posing many future diseases outbreak risks. The SIR model is a model that provides a practical approach to current and future epidemics; the SIR model is a classical, simple model of community infection. The model can add or change relevant components in the community, such as mortality, immigration or birth rates, resilience, and immunity. In this paper, we focus on COVID-19 data from Vietnam and model it is using the SIR epidemiological model to analyze the spread of the disease and forecast the future disease situation. The results include an assessment of the fit or not of the model through the prediction over the periods.