No abstract

Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.

Water-soluble cupric oxide nanoparticles are fabricated via a quick-precipitation method and used as peroxidase mimetics for ultrasensitive detection of hydrogen peroxide and glucose. The water-soluble CuO nanoparticles show much higher catalytic activity than that of commercial CuO nanoparticles due to their higher affinity to hydrogen peroxide. In addition, the as-prepared CuO nanoparticles are stable over a wide range of pH and temperature. This excellent stability in the form of aqueous colloidal suspensions makes the application of the water-soluble CuO nanoparticles easier in aqueous systems. A colorimetric assay for hydrogen peroxide and glucose has been established based on the catalytic oxidation of phenol coupled with 4-amino-atipyrine by the action of hydrogen peroxide. This analytical platform not only confirms the intrinsic peroxidase-like activity of the water-soluble cupric oxide nanoparticles, but also shows its great potential applications in environmental chemistry, biotechnology and medicine.

The purpose of this study was to assess the metabolic profile of plasma samples from cows with clinical and subclinical ketosis. According to clinical signs and 3-hydroxybutyrate plasma levels, 81 multiparous Holstein cows were selected from a dairy farm 7 to 21 d after calving. The cows were divided into 3 groups: cows with clinical ketosis, cows with subclinical ketosis, and healthy control cows. (1)H-Nuclear magnetic resonance-based metabolomics was used to assess the plasma metabolic profiles of the 3 groups. The data were analyzed by principal component analysis, partial least squares discriminant analysis, and orthogonal partial least-squares discriminant analysis. The differences in metabolites among the 3 groups were assessed. The orthogonal partial least-squares discriminant analysis model differentiated the 3 groups of plasma samples. The model predicted clinical ketosis with a sensitivity of 100% and a specificity of 100%. In the case of subclinical ketosis, the model had a sensitivity of 97.0% and specificity of 95.7%. Twenty-five metabolites, including acetoacetate, acetone, lactate, glucose, choline, glutamic acid, and glutamine, were different among the 3 groups. Among the 25 metabolites, 4 were upregulated, 7 were downregulated, and 14 were both upregulated and downregulated. The results indicated that plasma (1)H-nuclear magnetic resonance-based metabolomics, coupled with pattern recognition analytical methods, not only has the sensitivity and specificity to distinguish cows with clinical and subclinical ketosis from healthy controls, but also has the potential to be developed into a clinically useful diagnostic tool that could contribute to a further understanding of the disease mechanisms.

We recently showed that forkhead-box class O1 (FoxO1) activation protects against high glucose-induced injury by preventing mitochondrial dysfunction in the rat kidney cortex. In addition, FoxO1 has been reported to mediate putative kinase 1 (PINK1) transcription and promote autophagy in response to mitochondrial oxidative stress in murine cardiomyocytes. In this study, we ascertained whether overexpressing FoxO1 in the kidney cortex reverses preestablished diabetic nephropathy in animal models. The effect of FoxO1 on mitophagy signaling pathways was evaluated in mouse podocytes. In vivo experiments were performed in male KM mice. A mouse model of streptozotocin (STZ)-induced type 1 diabetes (T1D) was used, and lentiviral vectors were injected into the kidney cortex to overexpress FoxO1. A mouse podocyte cell line was treated with high concentrations of glucose and genetically modified using lentiviral vectors. We found aberrant mitochondrial morphology and reduced adenosine triphosphate production. These mitochondrial abnormalities were due to decreased mitophagy via reduced phosphatase/tensin homolog on chromosome 10-induced PINK1/Parkin-dependent signaling. FoxO1 upregulation and PINK1/Parkin pathway activation can individually restore injured podocytes in STZ-induced T1D mice. Our results link the antioxidative activity of FoxO1 with PINK1/Parkin-induced mitophagy, indicating a novel role of FoxO1 in diabetic nephropathy.

No abstract

A privacy-preserving video subscription scheme with the limitation of expire date SCIENCE CHINA Information Sciences 60, 098101 (2017); A privacy-preserving data collection model for digital community

With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.