DETERMINING THE OPTIMAL SITE LOCATION OF GNSS BASE STATIONS

The relative positioning technique plays an essential role in Global Navigation Satellite System (GNSS) surveys. Simultaneous observation at base and rover stations eliminates the majority of error sources thus the quality of a positioning solution can be substantially improved. However, topographic obstruction is still a key issue affecting positioning quality. In this study, an integrated approach for analyzing the impact of topographic obstruction on GNSS relative positioning has been developed. By considering varied satellite geometry according to actual terrain variation, this approach can be used to realistically determine satellite visibility condition for a specific base station with respect to any rover station. Furthermore, a base station quality index (BSQI) is proposed as an explicit indication of the sufficiency in a relative positioning. By incorporating the proposed approach, one can immediately identify an optimal site location for a GNSS base station with subsequent GNSS field survey thus achieved in a more reliable and cost-efficient manner

T Oligo-Primed Polymerase Chain Reaction (TOP-PCR): A Robust Method for the Amplification of Minute DNA Fragments in Body Fluids.

Body fluid DNA sequencing is a powerful noninvasive approach for the diagnosis of genetic defects, infectious agents and diseases. The success relies on the quantity and quality of the DNA samples. However, numerous clinical samples are either at low quantity or of poor quality due to various reasons. To overcome these problems, we have developed T oligo-primed polymerase chain reaction (TOP-PCR) for full-length nonselective amplification of minute quantity of DNA fragments. TOP-PCR adopts homogeneous "half adaptor" (HA), generated by annealing P oligo (carrying a phosphate group at the 5' end) and T oligo (carrying a T-tail at the 3' end), for efficient ligation to target DNA and subsequent PCR amplification primed by the T oligo alone. Using DNA samples from body fluids, we demonstrate that TOP-PCR recovers minute DNA fragments and maintains the DNA size profile, while enhancing the major molecular populations. Our results also showed that TOP-PCR is a superior method for detecting apoptosis and outperforms the method adopted by Illumina for DNA amplification

The characteristics of heat transfer in plate phase change energy storage unit

Abstract : was used to simulate the heat storage and release process of the plate phase change materials (PCM) energy storage unit. The simulation results show that the main heat transfer zone will advance along the direction of the flat plate (air flow direction), and the speed of the main heat transfer zone is proportional to the air velocity with the continuous heat transfer process. In heat storage, the speed of the main heat transfer zone will accelerate with the increase of air velocities; while in the heat release process, the main heat transfer zone will accelerate with the increase of air velocities. Meanwhile, the liquid phase ratio of PCM material is also changing continuously which increases with time and is affected by the liquefaction or solidification speed. The faster the liquefaction and solidification speed are, the faster it will change. As the fluid inlet velocity increases, the heat storage exothermic efficiency also increases as the heat transfer fluid inlet velocity increases. Overall, the total heat transfer rate is gradually reduced compared with the initial heat transfer, and then tends to be consistent with the temperature of the heat transferfluid

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

α-Lactosylceramide Protects Against iNKT-Mediated Murine Airway Hyperreactivity and Liver Injury Through Competitive Inhibition of Cd1d Binding.

Invariant natural killer T (iNKT) cells, which are activated by T cell receptor (TCR)-dependent recognition of lipid-based antigens presented by the CD1d molecule, have been shown to participate in the pathogenesis of many diseases, including asthma and liver injury. Previous studies have shown the inhibition of iNKT cell activation using lipid antagonists can attenuate iNKT cell-induced disease pathogenesis. Hence, the development of iNKT cell-targeted glycolipids can facilitate the discovery of new therapeutics. In this study, we synthesized and evaluated α-lactosylceramide (α-LacCer), an α-galactosylceramide (α-GalCer) analog with lactose substitution for the galactose head and a shortened acyl chain in the ceramide tail, toward iNKT cell activation. We demonstrated that α-LacCer was a weak inducer for both mouse and human iNKT cell activation and cytokine production, and the iNKT induction by α-LacCer was CD1d-dependent. However, when co-administered with α-GalCer, α-LacCer inhibited α-GalCer-induced IL-4 and IFN-γ production from iNKT cells. Consequently, α-LacCer also ameliorated both α-GalCer and GSL-1-induced airway hyperreactivity and α-GalCer-induced neutrophilia when co-administered in vivo. Furthermore, we were able to inhibit the increases of ConA-induced AST, ALT and IFN-γ serum levels through α-LacCer pre-treatment, suggesting α-LacCer could protect against ConA-induced liver injury. Mechanistically, we discerned that α-LacCer suppressed α-GalCer-stimulated cytokine production through competing for CD1d binding. Since iNKT cells play a critical role in the development of AHR and liver injury, the inhibition of iNKT cell activation by α-LacCer present a possible new approach in treating iNKT cell-mediated diseases

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins

<p>Abstract</p> <p>Background</p> <p>Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.</p> <p>Results</p> <p>In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the <it>high-sensitivity </it>mode and the <it>high-specificity </it>mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the <it>high-sensitivity </it>mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the <it>high-specificity </it>mode.</p> <p>Conclusion</p> <p>Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.</p

Use of Chinese Herbal Medicine Was Related to Lower Risk of Osteoporotic Fracture in Sarcopenia Patients: Evidence from Population-Based Health Claims

Introduction: With population aging, sarcopenia and its accompanying risk of osteoporotic fracture has drawn increased attention. Nowadays, while Chinese herbal medicine (CHM) is often used as complementary therapy for many medical conditions, its effect against likelihood of osteoporotic fracture among sarcopenia subjects was not fully elucidated yet. We therefore conducted a population-level study to compare osteoporotic fracture risk for sarcopenia persons with or without CHM use. Methods: Using the patient record from a nationwide insurance database, we recruited persons with newly diagnosed sarcopenia and simultaneously free of osteoporotic fracture between 2000 and 2010. Propensity score matching was then applied to randomly select sets of CHM users and non-CHM users. All of them were tracked until end of 2013 to measure the incidence and adjusted hazard ratios (HRs) for new new-onset fracture in multivariable Cox proportional hazards model. Results: Compared to non-CHM users, the CHM users indeed had a lower incidence of osteoporotic fracture (121.22 vs 156.61 per 1000 person-years). Use of CHM correlated significantly with a lower fracture likelihood after adjusting for potential covariates, and those receiving CHM treatment for more than two years experienced a remarkably lower risk by 73%. Uses of several herbal formulae were correlated to reduced risk of osteoporotic fracture, such as Caulis Spatholobi, Xuduan, Duzhong, Danshen, Shu-Jing-Huo-Xue- Tang, Du-Huo-Ji-Sheng-Tang, Shao-Yao-Gan-Cao-Tang, and Shen-Tong-Zhu-Yu -Tang. Conclusion: Our study depicted that cumulative CHM exposure was inversely associated with osteoporotic fracture risk in a duration-dependent manner, implying that CHM treatment may be embraced as routine care in preventing incident osteoporotic fracture