Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.     

Characteristic of polysaccharides from Flammulina velutipes in vitro digestion under salivary,simulated gastric and small intestinal conditions and fermentation by human gut microbiota

In this study, in vitro digestion and fermentation of Flammulina velutipes -derived polysaccharides (FVP) were investigated. It was found that FVP mainly consisted of 48.45% glucose, 15.40% mannose, 14.60% xylose, 11.80% fucose and 9.90% galactose. The -human saliva, simulated gastric and small intestinal juices conditions did not break down the FVP. Based on in vitro fermentation tests, FVP modulated the composition of gut microbiota by elevating the amounts of Bifidobacteriaceae and Bacteroidaceae and reducing the numbers of genera Lachnospiraceae and Enterococcaceae. Meanwhile, FVP affected the synthesis of short-chain fatty acids derived from gut microbiota.     

Distributed De La Garza algorithm for load-balancing routing in wireless sensor networks

Large scale wireless sensor networks raise many challenges in the design of efficient and effective routing algorithm due to their complexity and hardware constraints. However, the scalability challenge may be mitigated from a macroscopic perspective. One example is the distributed De la Garza iteration (DDLGI) algorithm for global routing load-balancing, based on a set of partial differential equations iteratively solved by the De la Garza method. We theoretically analyze the parallelism of DDLGI and illustrate that the region of interest may impact the degree of parallelism and error. Furthermore, though DDLGI always converges, the slow convergence and long-range information exchange problems may lead to excess energy consumption in communication. Thus, we propose various enhanced De la Garza routing (E-DLGR) algorithms to alleviate the energy consumption problem by which nodes may exchange less information and only need to exchange information with closer nodes to complete each iteration. Our theoretical analysis and simulation results show that the proposed E-DLGR algorithms may have less transmission overhead, thus further reducing energy consumption, and converge faster while still maintaining adequate accuracy.

     

Study on the removal of olefins from naphtha by clay loaded with trifluoromethanesulfonic acid

Journal of Porous Materials - In this work, a trifluoromethanesulfonic acid (TFOH) modified clay (TFOH-Clay) was developed for the removal of trace olefins in heavy naphtha. 5%TFOH-Clay can...     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Use of heat-moisture treated maize starch to modify the properties of wheat flour and the quality of noodles

Waxy, normal and high-amylose maize starches were subjected to heat-moisture treatment (HMT) and then added to wheat flour (WF) in different ratios (1%, 5% and 10%). The properties of blends and their cooked noodles were studied to investigate the effects of HMT starches. The incorporation of HMT starch in WF led to an increase in swelling power, peak viscosity and breakdown and to a decrease in setback, thus inhibiting retrogradation, hence enhancing resultant noodle softness. Compared to the same addition ratio of native starch to WF, HMT starch led to higher tensile strength and extensibility in resultant noodles. WF with added HMT starch had higher resistant starch than with native starch. This study showed that addition of HMT maize starch has potential to bring nutritional benefits. However, it is necessary to select the proper blending ratio and amylose content of starch to add, in consideration of its effect on noodle quality.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.