Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterize the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems

Eradication of unresectable liver metastasis through induction of tumour specific energy depletion.

Treatment of liver metastasis experiences slow progress owing to the severe side effects. In this study, we demonstrate a strategy capable of eliminating metastatic cancer cells in a selective manner. Nucleus-targeting W18O49 nanoparticles (WONPs) are conjugated to mitochondria-selective mesoporous silica nanoparticles (MSNs) containing photosensitizer (Ce6) through a Cathepsin B-cleavable peptide. In hepatocytes, upon the laser irradiation, the generated singlet oxygen species are consumed by WONPs, in turn leading to the loss of their photothermally heating capacity, thereby sparing hepatocyte from thermal damage induced by the laser illumination. By contrast, in cancer cells, the cleaved peptide linker allows WONPs and MSNs to respectively target nucleus and mitochondria, where the therapeutic powers could be unleashed, both photodynamically and photothermally. This ensures the energy production of cancer cells can be abolished. We further assess the underlying molecular mechanism at both gene and protein levels to better understand the therapeutic outcome

Chemiluminescent Nanomicelles for Imaging Hydrogen Peroxide and Self-Therapy in Photodynamic Therapy

Hydrogen peroxide is a signal molecule of the tumor, and its overproduction makes a higher concentration in tumor tissue compared to normal tissue. Based on the fact that peroxalates can make chemiluminescence with a high efficiency in the presence of hydrogen peroxide, we developed nanomicelles composed of peroxalate ester oligomers and fluorescent dyes, called peroxalate nanomicelles (POMs), which could image hydrogen peroxide with high sensitivity and stability. The potential application of the POMs in photodynamic therapy (PDT) for cancer was also investigated. It was found that the PDT-drug-loaded POMs were sensitive to hydrogen peroxide, and the PDT drug could be stimulated by the chemiluminescence from the reaction between POMs and hydrogen peroxide, which carried on a self-therapy of the tumor without the additional laser light resource

Bimolecular Fluorescence Complementation of Alpha-synuclein Demonstrates its Oligomerization with Dopaminergic Phenotype in Mice

Alpha-synuclein (αSyn) is encoded by the first causal gene identified in Parkinson's disease (PD) and is the main component of Lewy bodies, a pathological hallmark of PD. aSyn-based animal models have contributed to our understanding of PD pathophysiology and to the development of therapeutics. Overexpression of human wildtype αSyn by viral vectors in rodents recapitulates the loss of dopaminergic neurons from the substantia nigra, another defining pathological feature of the disease. The development of a rat model exhibiting bimolecular fluorescence complementation (BiFC) of αSyn by recombinant adeno-associated virus facilitates detection of the toxic αSyn oligomers species. We report here neurochemical, neuropathological and behavioral characterization of BiFC of αSyn in mice. Overexpression and oligomerization of αSyn through BiFC is detected by conjugated fluorescence. Reduced striatal dopamine and loss of nigral dopaminergic neurons are accompanied neuroinflammation and abnormal motor activities. Our mouse model may provide a valuable tool to study the role of αSyn in PD and to explore therapeutic approaches

Coordinating supply and demand on an on-demand service platform with impatient customers

We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers with heterogeneous valuation of the service. As such, the supply and demand are “endogenously” dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time. We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights