). The rs4731702-T allele was also associated with a decreased risk of ASCVD with an OR of 0.78 (P meta-analysis < 5.43 · 10 )4). In addition, we found that a missense variant of KLF14, rs11140 0400 (Ser58Pro), was associated with MI. Conclusion: Genetic variants newly identified near/in the KLF14 gene were implicated in the aetiology of atherosclerotic-related phenotypes.

Characteristics of the chemical and optical properties of aerosols in urban Shanghai and their relationship were studied over a three-day period in October 2011. A suite of real-time instruments, including an Aerosol Time-Of-Flight Mass Spectrometer (ATOFMS), a Monitor for AeRosols and GAses (MARGA), a Cavity Ring Down Spectrometer (CRDS), a nephelometer and a Scanning Mobility Particle Sizer (SMPS), was employed to follow the quick changes of the aerosol properties within the 72 h sampling period. The origin of the air mass arriving in Shanghai during this period shifted from the East China Sea to the northwest area of China, offering a unique opportunity to observe the evolution of aerosols influenced by regional transport from the most polluted areas in China. According to the meteorological conditions and temporal characterizations of the chemical and optical properties, the sampling period was divided into three periods. During Period 1 (00:00–23:00 LT, 13 October), the aerosols in urban Shanghai were mainly fresh and the single scattering albedo varied negatively with the emission of elemental carbon, indicating that local sources dominated. Period 2 (23:00 LT on 13 October to 10:00 LT on 15 October) was impacted by regionally transported pollutants and had the highest particulate matter (PM) mass loading and the lowest particle acidity, characterized by large fractions of aged particles and high secondary ion (nitrate, sulfate and ammonium) mass concentrations. Comparison between ATOFMS particle acidity and quantitative particle acidity by MARGA indicated the significance of semi-quantitative calculation in ATOFMS. Two sub-periods were identified in Period 2 based on the scattering efficiency of PM₁ mass. Period 3 (from 10:00 LT on 15 October to 00:00 LT on 16 October) had a low PM₁/PM₁₀ ratio and a new particle formation event. The comparison of these sub-periods highlights the influence of particle mixing state on aerosol optical properties. We directly observed the influence of regionally transported pollutants on local aerosol properties and demonstrate that the PM mass extinction efficiency is largely determined by the mixing states of the aerosol

Abstract. Knowledge about the chemical composition of aerosol particles is essential to understand their formation and evolution in the atmosphere. Due to analytical limitations, however, relatively little information is available for sub-10 nm particles. We present the design of a nano-cloud condensation nuclei counter (nano-CCNC) for measuring size-resolved hygroscopicity and inferring chemical composition of sub-10 nm aerosol particles. We extend the use of counting efficiency spectra from a water-based condensation particle counter (CPC) and link it to the analysis of CCN activation spectra, which provides a theoretical basis for the application of a scanning supersaturation CPC (SS-CPC) as a nano-CCNC. Measurement procedures and data analysis methods are demonstrated through laboratory experiments with monodisperse particles of diameter down to 2.5 nm, where sodium chloride, ammonium sulfate, sucrose and tungsten oxide can be easily discriminated by different characteristic supersaturations of water droplet formation. A near-linear relationship between hygroscopicity parameter κ and organic mass fraction is also found for sucroseammonium sulfate mixtures. The design is not limited to the water CPC, but also applies to CPCs with other working fluids (e.g. butanol, perfluorotributylamine). We suggest that a combination of SS-CPCs with multiple working fluids may provide further insight into the chemical composition of nanoparticles and the role of organic and inorganic compounds in the initial steps of atmospheric new particle formation and growth.

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.