Graphical review: The redox dark side of e-cigarettes; exposure to oxidants and public health concerns.

Since the initial marketing in 2005, the use of e-cigarettes has increased exponentially. Nonetheless, accumulating evidence has demonstrated the ineffectiveness of e-cigarettes in leading to smoking cessation, and decreasing the adverse health impacts of cigarette smoking. The number of adolescents adapted to e-cigarettes has been increasing substantially each year, and this adaptation has promoted openness to tobacco smoking. The present review discusses controversies regarding the smoking cessation effects of e-cigarettes, recent governmental policies and regulations of e-cigarette use, toxic components and vaporization products of e-cigarettes, and the novel molecular mechanisms underlying the adverse health impacts of e-cigarettes leading to oxidative stress in target tissues, and consequent development of cardiopulmonary diseases (i.e. COPD), neurodegenerative disorders (i.e. Alzheimer's' disease), and cancer. Health warning signs on the packaging and professional consultation to avoid adaptation in risk groups might be helpful solutions to control negative impacts of e-cigarettes. It is also recommended to further expand basic and clinical investigations to reveal more detailed oxidative stress mechanisms of e-cigarette induced damages, which would ultimately result in more effective protective strategies

Epiphyllous Hepaticae of Kowloon Peninsula (Hong Kong)

Twenty-eight species of epiphyllous liverworts were identified from twenty-five sites in Kowloon Peninsula, Hong Kong. Twenty-four of these species are new records to Hong Kong, while four of them are new epiphyllous records to the Chinese mainland. The majority of the sites are located between 30-200 m above sea level, representing the lowest altitude of epiphyllous liverworts found in the Chinese mainland. Characteristics of the host plants bearing epiphyllous liverworts are briefly discussed. The following new combination is proposed: Cololejeunea pseudolatilobula (Chen & Wu) But & Gao comb. nov. (Pedinolejeunea pseudolatilobula Chen & Wu)

Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.

To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on the environmental implications of these emerging systems is often limited by using aggregated travel pattern data to characterize personal mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect several key factors that determine potential environmental impacts, including charging behaviors, connection needs between different transportation modes, and car/ride sharing potentials. Therefore, to better understand these systems and inform decision making, travel patterns at the individual level need to be considered. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefits of integrating individual travel patterns into environmental assessments through three case studies (vehicle electrification, charging station siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification study indicate that individual travel patterns can impact the environmental performance of fleet electrification. When battery cost exceeds $200/kWh, vehicles with greater battery range cannot continuously improve travel electrification and can reduce electrification rate. At the current battery cost of$400/kWh, targeting subsidies to vehicles with battery range around 90 miles can achieve higher electrification rate. The public charging station siting case demonstrates that individual travel patterns can better estimate charging demand and guide charging infrastructure development. Charging stations sited according to individual travel patterns can increase electrification rate by 59% to 88% compared to existing sites. Lastly, the ride sharing case shows that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides in Beijing can reduce total travel distance and air emissions by 33% with 10-minute travel time deviation tolerance. Only minimal tolerance to travel time change (4 minutes) is needed from the riders to enable significant ride sharing (sharing 60% of the trips and saving 20% of travel distance). In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems.PhDNatural Resources and Environment and Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113510/1/caih_1.pd

Speedy grass stomata: emerging molecular and evolutionary features

No abstract available