No abstract

Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.

We demonstrated that LBs are dynamic organelles notably involved in the host response to acute T. cruzi infection, an event that may be important for pathogen control during innate immunity. Our findings highlight LBs as structural markers of the innate immune responses in phagocytic cells.

Ridesourcing has experienced exponential growth in recent years, yet its impact on individual travel are unclear and have not been adequately examined. Recently, an Austin-based ridesourcing company released a large dataset containing disaggregate trip-level information. In this research, we use this new dataset in tandem with several publicly available data sources to estimate two models: a spatially lagged multivariate count model, which is used to describe how many trips are generated in a specific zone on both weekdays and weekend days; and a fractional split model, which helps us identify the characteristics of zones that attract ridesourcing trips. Our results show spatial dependence in ridesourcing trips among proximally located zones, as well as correlation between weekday and weekend day trips originating in a zone. Another interesting finding is the identification of a possible substitution effect between ridesourcing and transit use for weekday trips. Moreover, our results suggest that different income segments in the population may use ridesourcing for different activity purposes. From a travel behavior researcher perspective, the results in this paper identify aggregate area-level variables impacting ridesourcing, which can guide future efforts to better understand the demand for ridesourcing as well as the demand for autonomous and connected vehicles.

The pathology of schistosomiasis mansoni, a neglected tropical disease of great clinical and socioeconomic importance, results from the parasite eggs that become trapped in host tissues, particularly in the liver and intestines. Continuous antigenic stimulation from these eggs leads to recruitment of inflammatory cells to the sites of infection with formation of periovular granulomas. These complex structures have variable size and composition and are the most striking histopathological feature of schistosomiasis mansoni. However, evaluation of granulomas by conventional microscopy methods is time-consuming and limited, especially in large-scale studies. Here, we used high resolution Whole Slide Imaging (WSI), which allows fast scanning of entire histological slides, and multiple morphometric evaluations, to assess the granulomatous response elicited in target organs (liver, small and large intestines) of two models of schistosomiasis mansoni. One of the advantages of WSI, also termed virtual microscopy, is that it generates images that simultaneously offer high resolution and a wide field of observation. By using a model of natural (Nectomys squamipes, a wild reservoir captured from endemic areas in Brazil) and experimental (Swiss mouse) infection with Schistosoma mansoni, we provided the first detailed WSI characterization of granulomas and other pathological aspects. WSI and quantitative analyses enabled a fast and reliable assessment of the number, evolutional types, frequency and areas of granulomas and inflammatory infiltrates and revealed that target organs are differentially impacted by inflammatory responses in the natural and experimental infections. Remarkably, high-resolution analysis of individual eosinophils, key cells elicited by this helminthic infection, showed a great difference in eosinophil numbers between the two infections. Moreover, features such as the intestinal egg path and confluent granulomas were uncovered. Thus, WSI may be a suitable tool for detailed and precise histological analysis of granulomas and other pathological aspects for clinical and research studies of schistosomiasis.