Health and economic benefits of building ventilation interventions for reducing indoor PM2.5 exposure from both indoor and outdoor origins in urban Beijing, China

China is confronted with serious PM2.5 pollution, especially in the capital city of Beijing. Exposure to PM2.5 could lead to various negative health impacts including premature mortality. As people spend most of their time indoors, the indoor exposure to PM2.5 from both indoor and outdoor origins constitutes the majority of personal exposure to PM2.5 pollution. Different building interventions have been introduced to mitigate indoor PM2.5 exposure, but always at the cost of energy expenditure. In this study, the health and economic benefits of different ventilation intervention strategies for reducing indoor PM2.5 exposure are modelled using a representative urban residence in Beijing, with consideration of different indoor PM2.5 emission strengths and outdoor pollution. Our modelling results show that the increase of envelope air-tightness can achieve significant economic benefits when indoor PM2.5 emissions are absent; however, if an indoor PM2.5 source is present, the benefits only increase slightly in mechanically ventilated buildings, but may show negative benefit without mechanical ventilation. Installing mechanical ventilation in Beijing can achieve annual economic benefits ranging from 200yuan/capita to 800yuan/capita if indoor PM2.5 sources exist. If there is no indoor emission, the annual benefits above 200yuan/capita can be achieved only when the PM2.5 filtration efficiency is no less than 90% and the envelope air-tightness is above Chinese National Standard Level 7. Introducing mechanical ventilation with low PM2.5 filtration efficiency to current residences in urban Beijing will increase the indoor PM2.5 exposure and result in excess costs to the resident

Neuroprotective and anti-inflammatory effects of myricetin 3-glucoside in a rat model of cerebral ischemia

Purpose: To investigate the effect of myricetin 3-glucoside (M3GLS) on middle cerebral artery occlusion (MCAO)-induced cerebral ischemia in a rat model, and the mechanism of action involved.Methods: A cerebral ischemia rat model was established using MCAO under 10 % chloral hydrate anesthesia. Neurological severity score was determined by analyzing reflex, motor and sensory functions, as well as balancing potential. Infarction volume was determined using triphenyl tetrazolium chloride dye, while counting of Nissl bodies was done after toluidine blue staining. The protein expression levels of Bax and Bcl-2 were assayed using western blotting, while cytokine levels were determined by enzyme-linked immunosorbent assay (ELISA).Results: Treatment of cerebral ischemia rats with M3GLS effectively reduced infarct volume, when compared to vehicle-treated group (p < 0.05). Moreover, M3GLS treatment significantly increased the population of Nissl bodies and effectively improved neurologic scores (p < 0.05). In M3GLS-pretreated rats, cerebral ischemia-induced elevation of protein expressions of TNF-α, IL-6 and IL-1β were significantly suppressed. M3GL treatment significantly reversed cerebral ischemia-mediated downregulation of Bcl-2 protein level, but markedly reduced cerebral ischemia-induced upregulation of Bax protein level (p < 0.05).Conclusion: M3GLS exerts protective effect against cerebral ischemia-induced brain injury in rats via downregulation of inflammatory cytokines. It reduces infarction volume in the brain of cerebral ischemia rats, and regulates Bcl-2/Bax protein ratio. Thus, M3GLS has a potential for use in the clinical management of cerebral ischemia. Keywords: Myricetin, Neuroprotection, Anti-inflammation, Cerebral ischemia, Cytokines, Infarctio

Distribution-Based Trajectory Clustering

Trajectory clustering enables the discovery of common patterns in trajectory data. Current methods of trajectory clustering rely on a distance measure between two points in order to measure the dissimilarity between two trajectories. The distance measures employed have two challenges: high computational cost and low fidelity. Independent of the distance measure employed, existing clustering algorithms have another challenge: either effectiveness issues or high time complexity. In this paper, we propose to use a recent Isolation Distributional Kernel (IDK) as the main tool to meet all three challenges. The new IDK-based clustering algorithm, called TIDKC, makes full use of the distributional kernel for trajectory similarity measuring and clustering. TIDKC identifies non-linearly separable clusters with irregular shapes and varied densities in linear time. It does not rely on random initialisation and is robust to outliers. An extensive evaluation on 7 large real-world trajectory datasets confirms that IDK is more effective in capturing complex structures in trajectories than traditional and deep learning-based distance measures. Furthermore, the proposed TIDKC has superior clustering performance and efficiency to existing trajectory clustering algorithms