The infection dynamics of PaV1 in the Caribbean spiny lobster Panulirus argus

Panulirus argus Virus 1 (PaV1) is an emerging disease in Caribbean spiny lobster Panulirus argus. It is considered a threat to the lobster industry in the Florida Keys. In order to understand the infection dynamics of the PaV1 virus in the lobster host, a sensitive and specific fluorescence in situ hybridization (FISH) assay was developed for diagnosis of PaV1 in lobsters. The lower limit of detection using the 110-bp DNA probe in a dot-blot hybridization for PaV1 was 10 pg of cloned DNA template and 10 ng of genomic DNA extracted from hemolymph of diseased spiny lobster. The probe specifically hybridized to PaV1-infected cells in all the tissues tested. The probe did not hybridize with host tissues of uninfected spiny lobsters, nor did it cross-react with other virus samples tested. A primary culture of hemocytes was developed for in vitro study of PaV1. The modified Leibovitz L-15 medium supported the best survival of hemocytes in cultures. Hyalinocytes and semigranulocytes maintained higher viability (∼ 80%) after 18 days when cultured separately. Hyalinocytes and semigranulocytes were susceptible to PaV1 in vitro. Cytopathic effects (CPE) were observed as early as 12 h post-inoculation, followed by cell debris and cellular exudates in inoculated cultures. This assay was further developed to assess viral load in hemolymph of diseased lobsters using a 50 tissue culture infectious dose assay (TCID50) based on CPE. The histopathology and hematology of the spiny lobster infected with PaV1 were studied over time courses of experimental infection. The fixed phagocytes in the hepatopancreas were the primary site of PaV1 infection in spiny lobsters. Infection was subsequently observed in the hepatopancreas, gill, heart, hindgut, glial cells around the ventral nerves, as well as in the cuticular epidermis and foregut. as the disease progressed, the hepatopancreas became significantly altered, with hemal sinuses filled with massive amounts of cellular aggregates, including infected circulating hemocytes and a proliferation of infected spongy connective tissues. The virus caused significant decreases in total hemocyte density in later stages of infection and significantly altered several constituents in the hemolymph serum of diseased lobsters, including: glucose, phosphorus, triglycerides, and lipase

Rail-induced Traffic in China

The rapid development of China’s railway has exerted an enormous influence on the intercity passenger transport structure in recent years. However, it has not satisfied the passengers’ travel demand due to induced traffic. This paper is committed to solving such issue, with the aim of satisfying the current travel demand, and of anticipating the demand of the predicted traffic growth over the next 20 to 30 years. The paper has considered the increase in rail passenger kilometres caused by the growth of rail kilometres as rail-induced traffic. Based on the concept and former research of induced traffic, the panel data of 26 provinces and 3 municipalities of China between the year 2000 and 2014 were collected, and the elasticity models (including elasticity-based model, distributed lag model, high-speed rail (HSR) elasticity model and rail efficiency model) have been constructed. The results show the importance of model formation incorporation of rail-induced traffic. It is better to get the correct value in divided zones with different train frequencies or incorporation rail efficiency in cities or provinces. The lag time and rail types also need to be considered. In summary, the results analysis not only confirms the existence of rail-induced traffic, but also provides substantial recommendations to train operation planning.</p

Uncertainty Quantification of Collaborative Detection for Self-Driving

Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double-M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double-M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4X improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github.io/double-m-quantification.Comment: 6 pages, 3 figure