Leptospiral Proteins As Potential Immunity Targets To Protect Individuals Against Reinfection

Leptospirosis is a disease caused by pathogenic species of spirochetes of the genus Leptospira. The bacteria are widespread globally and can survive in the environment for weeks after being excreted through the urine of infected animals. Humans get infected through contact with contaminated water or soil. Leptospirosis is a life-threatening disease with a wide range of symptoms. It is believed that after infection, individuals acquire natural immunity against the same infecting serovar. However, there are over 300 serovars of Leptospira that can cause disease in humans and animals, and reinfections are common. Nevertheless, recent studies have shown that reinfection caused by the same serovar is frequent in highly endemic areas, indicating that antibodies in different individuals may be diverse. Our research has been focusing on trying to better understand the natural immunity against Leptospira. We conducted experiments to verify if antibodies against specific leptospiral proteins could induce immunity against secondary infection. Using mutants and recombinant proteins of the identified targets, we evaluated the role of those protein candidates on the pathogenesis of the bacteria and on the immunity of individuals living in an endemic area for leptospirosis. We identified proteins that have a role as a virulence factor and confirmed the overall role of specific targets as an immunogenic marker for protection. Our preliminary results indicate that those targets can be explored as potential diagnostic and/or prevention candidates against this important neglected disease

Experimental study on mechanical performance of partially precast steel reinforced concrete beams

[EN] In order to exploit the potentials in mechanical and constructional performance of steel reinforced concrete structures and prefabricated structures, three innovative kinds of partially precast steel reinforced concrete beams, which are abbreviated here as PPSRC, HPSRC and PPCSRC beam, are presented in this paper. The PPSRC beam is composed of two parts, which are the precast outer shell with high-performance concrete and the cast-in-place inner part with common-strength concrete. Meanwhile, on the basis of PPSRC beam, the PPCSRC beam applies castellated steel shape and the HPSRC beam keeps the beam core hollow. With the aim to investigate the mechanical behavior, failure mode and bearing capacity of the PPSRC, PPCSRC and HPSRC beams, a static loading experiment with twenty four specimens was carried out. The effects of aspect ratio, construction method, section shape, concrete flange and strength of concrete were critically examined. Test results indicate that the HPSRC, PPCSRC and PPSRC beams both exhibit similar mechanical performance and bonding performance. The flexural capacity and shear capacity are seldom affected by the construction method and section shape, and increase with the increasing of the cast-in-place concrete strength. The shear strength of the specimens is significantly affected by the concrete flange and aspect ratio.Yang, Y.; Xue, Y.; Yu, Y.; Liu, R. (2018). Experimental study on mechanical performance of partially precast steel reinforced concrete beams. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 107-114. https://doi.org/10.4995/ASCCS2018.2018.6942OCS10711

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.Comment: The paper was presented at 2022 Annual Meeting of Transportation Research Boar

Deriving Weeklong Activity-Travel Dairy from Google Location History: Survey Tool Development and A Field Test in Toronto

This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research enhanced an existing travel survey designer, Travel Activity Internet Survey Interface (TRAISI), to make it capable of deriving travel diaries from the respondents' GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights.Comment: The manuscript has been accepted for presentation at the 103rd Transportation Research Board (TRB) Annual Meeting, January 7-11, 202