bluuebunny/crossref_metadata_2025 huggingface dataset:
Osteoprotegerin and TRAIL in Acute Onset of Atrial Fibrillation
Authors: Rewiuk Krzysztof , Grodzicki Tomasz
Published: October 2015

Background. There is a growing amount of evidence that inflammatory processes are involved in the development of atrial fibrillation (AF) and its complications. We decided to investigate the behavior of osteoprotegerin (OPG) and TNF-related apoptosis inducing ligand (TRAIL) in terms of acute onset of AF. Methods and Results. We included 60 patients with acute onset of AF, candidates for pharmacological cardioversion. The presence of cardiovascular comorbidities was connected with higher concentration of OPG and lower level of TRAIL right from the first hours of AF paroxysm. The initial TRAIL level correlated also positively with left ventricle ejection fraction and negatively with left atrium diameter. We found subsequent increase of OPG in subgroups selected on the basis of CHA2DS2-VASc scoring. Although basal concentrations of studied markers did not allow prediction of the restoration of sinus rhythm, we observed important increase of TRAIL concentration in subgroup with sinus rhythm maintenance (94.11 ± 29.46 versus 111.39 ± 30.23 pg/mL; p=0.002). Conclusions. OPG and TRAIL are associated with the underlying cardiovascular damage in AF, but their balance is modulated by the fact of sinus rhythm restoration. Determining the suitability of OPG and TRAIL as predictive markers in AF requires further prospective studies.
A Fundamental Wave Amplitude Prediction Algorithm Based on Fuzzy Neural Network for Harmonic Elimination of Electric Arc Furnace Current
Authors: Lei Wanjun , Wang Yanxia , Wang Lu , Cao Hui
Published: October 2015

Electric arc furnace (EAF) causes the harmonics to impact on the supply network greatly and harmonic elimination is a very important research work for the power quality associated with EAF. In the paper, a fundamental wave amplitude prediction algorithm based on fuzzy neural network for harmonic elimination of EAF current is proposed. The proposed algorithm uses the learning ability of the neural network to refine Takagi-Sugeno type fuzzy rules and the inputs are the average of the current measured value in different time intervals. To verify the effectiveness of the proposed algorithm, some experiments are performed to compare the proposed algorithm with the back-propagation neural networks, and the field data collected at an EAF are used in the experiments. Moreover, the measured amplitudes of fundamental waves of field data are obtained by the sliding-window-based discrete Fourier transform on the field data. The experiments results show that the proposed algorithm has higher precision. The real curves also verify that the amplitude of fundamental wave current could be predicted accurately and the harmonic elimination of EAF would be realized based on the proposed algorithm.
Dynamics Analysis of a Delayed Rumor Propagation Model in an Emergency-Affected Environment
Authors: Li Chunru , Ma Zujun
Published: October 2015

Rumors influence people’s decisions in an emergency-affected environment. How to describe the spreading mechanism is significant. In this paper, we propose a delayed rumor propagation model in emergencies. By taking the delay as the bifurcation parameter, the local stability of the boundary equilibrium point and the positive equilibrium point is investigated and the conditions of Hopf bifurcation are obtained. Furthermore, formulas for determining the direction of Hopf bifurcation and the stability of the bifurcating periodic solutions are derived by applying the normal form method and center manifold theorem. Finally, some numerical simulations are also given to illustrate our theoretical results.
Big Data Visualization Collaborative Filtering Algorithm Based on RHadoop
Authors: Cai Lijun , Guan Xiangqing , Chi Peng , Chen Lei , Luo Jianting
Published: October 2015

With the rapid growth of various data, it is becoming increasingly important to extract useful information from big data. While the analysis tools of big data visualization is very rare, in this paper, we propose a new big data visualization algorithm analysis integrated model. The model integrates the processing of big data and the visualization of data as a whole. It is a good analysis tool of timely big data visualization. We use hadoop_1.X as the data storage and use R as the compiler environment in the model. If you are skilled in R, it is easy to design kinds of paralleling algorithms, and analyze and process the kinds of big data. Secondly we design and implement a paralleled collaborative filtering algorithm with the model. Finally we analyze the various performance indicators with kinds of experiments. The indicators show that the model has good scalability and easy operability, and contains all the advantages of Map Reduce. In conclusion, the big data visualization algorithm analysis integrated model has high performance to process and visualize the big data.
Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks
Authors: Xu Bin , Qi Jin , Zhou Chunxia , Hu Xiaoxuan , Xu Bianjia , Sun Yanfei
Published: October 2015

The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernelk-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.
Investigation of Theoretical Models for the Elastic Stiffness of Nanoparticle‐Modified Polymer Composites
Authors: Thorvaldsen T. , Johnsen B. B. , Olsen T. , Hansen F. K.
Published: October 2015

Mathematical models are investigated and suggested for the calculation of the elastic stiffness of polymer nanocomposites. Particular emphasis is placed on the effect on the elastic stiffness from agglomerates and the particle interphase properties. The multiphase Mori‐Tanaka model and an interphase model are considered as two relevant models. These models only include and require the designation of a few system independent parameters with a clear physical meaning. Extensions of the models are also presented. The model calculations are compared to results from other models, as well as experimental data for different nanocomposites. For nanocomposites with spherical particles and with fiber‐like particles, the suggested models are found to be the most flexible ones and are applicable to estimate the stiffness increase of nanocomposites for both low and high particle volume fractions. The suggested theoretical models can hence be considered as a general multiscale “model toolbox” for analysis of various nanocomposites.
Untapped Resources: Biotechnological Potential of Peptides and Secondary Metabolites in Archaea
Authors: Charlesworth James C. , Burns Brendan P.
Published: October 2015

Archaea are an understudied domain of life often found in “extreme” environments in terms of temperature, salinity, and a range of other factors. Archaeal proteins, such as a wide range of enzymes, have adapted to function under these extreme conditions, providing biotechnology with interesting activities to exploit. In addition to producing structural and enzymatic proteins, archaea also produce a range of small peptide molecules (such as archaeocins) and other novel secondary metabolites such as those putatively involved in cell communication (acyl homoserine lactones), which can be exploited for biotechnological purposes. Due to the wide array of metabolites produced there is a great deal of biotechnological potential from antimicrobials such as diketopiperazines and archaeocins, as well as roles in the cosmetics and food industry. In this review we will discuss the diversity of small molecules, both peptide and nonpeptide, produced by archaea and their potential biotechnological applications.
Dynamic Generation and Editing System for Wrongly Written Chinese Characters Font
Authors: Li Qingsheng , Li Xiao
Published: October 2015

The uniqueness of Chinese makes Chinese language a hotspot in language learning. In view of the problem of wrongly written character teaching in Chinese language teaching, it provides a simple, convenient, and efficient input method of wrongly written characters and realizes a dynamic generation and editing system for wrongly written Chinese character font, which solves the problems of real-time edit, coding, and input of wrongly written character in editing process using dynamic editing technology, and provides a convenient input method of wrongly written character in editing, printing, typesetting, and the research of digital Chinese language teaching. This method can also be used in dynamic editing, generation and processing of ancient variants, Oracle bone inscriptions, Bronze inscription, folk combined characters, and other fonts.
An Adaptive Aggregation Scheduling Algorithm Based on the Grid Partition in Large-Scale Wireless Sensor Networks
Authors: Qi Xiaogang , Liu Lifang , Zheng Gengzhong , Xie Mande
Published: October 2015

Data aggregation algorithm aims to reduce the redundant information by gathering the sensed data, save energy, and prolong the lifetime of the network. However, the data aggregation technology will increase the network transmission delay of wireless sensor networks. Minimum-latency aggregation scheduling is designed to minimize the number of scheduled time slots to perform an aggregation. In this paper, we present an Adaptive Aggregation Scheduling Algorithm based on the Grid Partition (AASA-GP) in large-scale wireless sensor networks. By dividing the network into grids based on the geographical information, we allocate the channels according to the grid coordinates. Nodes with the same grid coordinates use the same channel and the adjacent grids use the different channels, so we can effectively avoid the wireless media transmission interference, increase the parallel transfer rate, and reduce the aggregation latency. Our extensive evaluation results demonstrate the superiority of the AASA-GP. For small-scale networks, the resultant latency is comparable with the best practice, and it is more suitable for large-scale wireless sensor networks.
Machine Vision Based Automatic Detection Method of Indicating Values of a Pointer Gauge
Authors: Chi Jiannan , Liu Lei , Liu Jiwei , Jiang Zhaoxuan , Zhang Guosheng
Published: October 2015

This study proposes an automatic reading approach for a pointer gauge based on computer vision. Moreover, the study aims to highlight the defects of the current automatic-recognition method of the pointer gauge and introduces a method that uses a coarse-to-fine scheme and has superior performance in the accuracy and stability of its reading identification. First, it uses the region growing method to locate the dial region and its center. Second, it uses an improved central projection method to determine the circular scale region under the polar coordinate system and detect the scale marks. Then, the border detection is implemented in the dial image, and the Hough transform method is used to obtain the pointer direction by means of pointer contour fitting. Finally, the reading of the gauge is obtained by comparing the location of the pointer with the scale marks. The experimental results demonstrate the effectiveness of the proposed approach. This approach is applicable for reading gauges whose scale marks are either evenly or unevenly distributed.
Missed Opportunities for the Diagnosis of Colorectal Cancer
Authors: Siminoff Laura A. , Rogers Heather L. , Harris-Haywood Sonja
Published: October 2015

Objective. To examine patient and medical characteristics which predict a missed diagnostic opportunity (MDO) for colorectal cancer (CRC).Methods. The sample consisted of 252 patients diagnosed with Stages 1–4 CRC who were diagnosed in the prior six months, had experienced symptoms prior to diagnosis, and were not diagnosed through routine screening. Systematic review of all medical records prior to patients’ diagnosis was conducted. An MDO was defined as a clinical encounter where, even in the presence of presumptive CRC symptoms, the CRC diagnostic process is not started.Results. 92 patients (36.5%) experienced an MDO. Almost 80% of alternate diagnoses were other GI-GU diseases, including hemorrhoids and diverticulitis. Stomach pain, anemia, and constipation were the most common symptoms experienced by the MDO group. These symptoms, and weight loss and vomiting, were more likely to be noted in the charts of the MDO patients (P<0.04). Independent risk factors for MDO included age (<50) [OR = 2.29 (1.14–4.60),P=0.02] and female sex [OR = 2.19 (1.16–4.16),P=0.03]. Each additional physician seen, more than doubled the MDO risk [OR = 2.05 (1.53–2.74),P<0.001].Conclusions. Females, younger patients, and those consulting more physicians were all more likely to experience an MDO. Continued increased training of physicians to enhance knowledge of who is vulnerable to CRC is needed in addition to an increased focus to adherence to screening recommendations.
A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction
Authors: Zhang Daqing , Xiao Jianfeng , Zhou Nannan , Zheng Mingyue , Luo Xiaomin , Jiang Hualiang , Chen Kaixian
Published: October 2015

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available logBBmodels. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our logBBmodel suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.
System Optimization for Temporal Correlated Cognitive Radar with EBPSK-Based MCPC Signal
Authors: Chen Peng , Wu Lenan
Published: October 2015

The system optimization is considered in cognitive radar system (CRS) with extended binary phase shift keying- (EBPSK-) based multicarrier phase-coded (MCPC) signal. A novel radar working scheme is proposed to consider both target detection and estimation. At the detection stage, the generalized likelihood ratio test (GLRT) threshold is deduced, and the GLRT detection probability is given. At the estimation stage, an approach based on Kalman filtering (KF) is proposed to estimate target scattering coefficients (TSC), and the estimation performance is improved significantly by exploiting the TSC temporal correlation. Additionally, the optimal waveform is obtained to minimize the mean square error (MSE) of KF estimation. For the practical consideration, iteration algorithms are proposed to optimize the EBPSK-based MCPC signal in terms of power allocation and coding matrix. Simulation results demonstrate that the KF estimation approach can improve the estimation performance by 25% compared with maximum a posteriori MAP (MAP) method, and the KF estimation performance can be further improved by 90% by optimizing the transmitted waveform spectrum. Moreover, by optimizing the power allocation and coding matrix of the EBPSK-based MCPC signal, the KF estimation performances are, respectively, improved by 7% and 8%.
Vision-Based Faint Vibration Extraction Using Singular Value Decomposition
Authors: Lei Xiujun , Guo Jie , Zhu Chang’an
Published: October 2015

Vibration measurement is important for understanding the behavior of engineering structures. Unlike conventional contact-type measurements, vision-based methodologies have attracted a great deal of attention because of the advantages of remote measurement, nonintrusive characteristic, and no mass introduction. It is a new type of displacement sensor which is convenient and reliable. This study introduces the singular value decomposition (SVD) methods for video image processing and presents a vibration-extracted algorithm. The algorithms can successfully realize noncontact displacement measurements without undesirable influence to the structure behavior. SVD-based algorithm decomposes a matrix combined with the former frames to obtain a set of orthonormal image bases while the projections of all video frames on the basis describe the vibration information. By means of simulation, the parameters selection of SVD-based algorithm is discussed in detail. To validate the algorithm performance in practice, sinusoidal motion tests are performed. Results indicate that the proposed technique can provide fairly accurate displacement measurement. Moreover, a sound barrier experiment showing how the high-speed rail trains affect the sound barrier nearby is carried out. It is for the first time to be realized at home and abroad due to the challenge of measuring environment.
Integrated Variable Speed Limits Control and Ramp Metering for Bottleneck Regions on Freeway
Authors: Ma Ming-hui , Yang Qing-fang , Liang Shi-dong , Li Zhi-lin
Published: October 2015

To enhance the efficiency of the existing freeway system and therefore to mitigate traffic congestion and related problems on the freeway mainline lane-drop bottleneck region, the advanced strategy for bottleneck control is essential. This paper proposes a method that integrates variable speed limits and ramp metering for freeway bottleneck region control to relieve the chaos in bottleneck region. To this end, based on the analyses of spatial-temporal patterns of traffic flow, a macroscopic traffic flow model is extended to describe the traffic flow operating characteristic by considering the impacts of variable speed limits in mainstream bottleneck region. In addition, to achieve the goal of balancing the priority of the vehicles on mainline and on-ramp, increasing capacity, and reducing travel delay on bottleneck region, an improved control model, as well as an advanced control strategy that integrates variable speed limits and ramp metering, is developed. The proposed method is tested in simulation for a real freeway infrastructure feed and calibrates real traffic variables. The results demonstrate that the proposed method can substantially improve the traffic flow efficiency of mainline and on-ramp and enhance the quality of traffic flow at the investigated freeway mainline bottleneck.
Increased NHC Cells in the Peritoneal Cavity of Plasmacytoma Susceptible BALB/c Mouse
Authors: Sánchez-González Berenice , García-Vázquez Francisco Javier , Farfán-Morales José Eduardo , Jiménez-Zamudio Luis Antonio
Published: October 2015

BALB/c strain mice are unique in that they develop murine plasmacytoma (MPC) as a consequence of the inflammation induced by pristane oil injection in the peritoneal cavity. In this work the Treg, Th17, B1, B2, and NHC lymphocyte populations from the peritoneal environment of BALB/c, the susceptible strain, and C57BL/6 mice, which do not develop MPC after oil treatment, were studied. Both oil‐treated strains showed decreased levels of Th17 lymphocytes, no significant variation in Treg lymphocytes, and a drastic decrease of all B lymphocyte populations. However, only oil‐induced BALB/c showed increased levels of natural helper cells (NHC) which could be important in the myeloma induction.
Mycobacterium aviumSubspeciesparatuberculosisand Bovine Leukemia Virus Seroprevalence and Associated Risk Factors in Commercial Dairy and Beef Cattle in Northern and Northeastern China
Authors: Sun Wu-Wen , Lv Wen-Fa , Cong Wei , Meng Qing-Feng , Wang Chun-Feng , Shan Xiao-Feng , Qian Ai-Dong
Published: October 2015

Mycobacterium aviumsubspeciesparatuberculosis(MAP) and bovine leukemia virus (BLV) are important pathogens, commonly responsible for economical loss to cattle farms all over the world, yet their epidemiology in commercial dairy and beef cattle in China is still unknown. Thus, from September 2013 to December 2014, a large-scale seroprevalence study was conducted to determine the seroprevalence and identify herd-level risk factors associated with MAP and BLV infection. The source sample was 3674 cattle from 113 herds in northern and northeastern China. Antibodies against MAP and BLV were detected using ELISA tests. At animal-level, the seroprevalence of antibodies against MAP and BLV was 11.79% (433/3674) and 18.29% (672/3674), respectively. At herd-level, the seroprevalence of antibodies against MAP and BLV was 20.35% and 21.24% (24/113), respectively. Herd size was identified to be associated with MAP infection while herd size and presence of cattle introduced from other farms were significantly associated with BLV infection. Further research is needed to confirm these findings and improve the knowledge of the epidemiology of these two pathogens in these regions and elsewhere in China.
Initial Self-Alignment for Marine Rotary SINS Using Novel Adaptive Kalman Filter
Authors: Pei Fujun , Zhu Li , Zhao Jian
Published: October 2015

The accurate initial attitude is essential to affect the navigation result of Rotary Strapdown Inertial Navigation System (SINS), which is usually calculated by initial alignment. But marine mooring Rotary SINS has to withstand dynamic disturbance, such as the interference angular velocities and accelerations caused by surge and sway. In order to overcome the limit of dynamic disturbance under the marine mooring condition, an alignment method using novel adaptive Kalman filter for marine mooring Rotary SINS is developed in this paper. This alignment method using the gravity in the inertial frame as a reference is discussed to deal with the lineal and angular disturbances. Secondly, the system error model for fine alignment in the inertial frame as a reference is established. Thirdly, PWCS and SVD are used to analyze the observability of the system error model for fine alignment. Finally, a novel adaptive Kalman filter with measurement residual to estimate measurement noise variance is designed. The simulation results demonstrate that the proposed method can achieve better accuracy and stability for marine Rotary SINS.
Agent-Based Supernetworks Model of University Knowledge System
Authors: Zhao Ye-Qing
Published: October 2015

A novel supernetworks framework of knowledge system based on complex networks is proposed for presenting the multidimensional, multilevel, dynamic complexity characteristics and initiative, adaptability, and purposefulness of knowledge mainbody in the university. This model differs from the existing knowledge diffusion method. In order to make good use of abundant information of the mainbody and improve the performance of knowledge diffusion, agent-based model and supernetworks method are adopted to exploit the complex correlation among the knowledge mainbody, knowledge carrier, and knowledge element. Firstly, the agent-based supernetworks model of knowledge diffusion was established by use of complex adaptive system theory and supernetworks analysis method. Secondly, the multilevel structure definition of supernetworks and knowledge networks of mainbody, element, and carrier in the knowledge system were described in detail. Thirdly, for verifying the validity of the model, a local search operator was designed, which can improve the effectiveness and efficiency of knowledge diffusion. Experimental studies based on synthetic datasets show that the proposed method can exhibit good performance, especially providing theoretical and practical guidance for establishing harmonious relationship between students and teachers.
Tuning Electronic Structures of BN and C Double‐Wall Hetero‐Nanotubes
Authors: Liu Xueran , Han Meijun , Zhang Xinjiang , Hou Haijun , Pang Shaoping , Wu Qisheng
Published: October 2015

First principle calculations based on density functional theory with the generalized gradient approximation were carried out to investigate the energetic and electronic properties of carbon and boron nitride double‐wall hetero‐nanotubes (C/BN‐DWHNTs) with different chirality and size, including an armchair (n, n) carbon nanotube (CNT) enclosed in (m, m) boron nitride nanotube (BNNT) and a zigzag (n, 0) CNT enclosed in (m, 0) BNNT. The electronic structure of these DWHNTs under a transverse electric field was also investigated. The ability to tune the band gap with changing the intertube distance (di) and imposing an external electric field (F) of zigzag DWHNTs provides the possibility for future electronic and electrooptic nanodevice applications.
In VivoOsseointegration Performance of Titanium Dioxide Coating Modified Polyetheretherketone Using Arc Ion Plating for Spinal Implant Application
Authors: Tsou Hsi-Kai , Chi Meng-Hui , Hung Yi-Wen , Chung Chi-Jen , He Ju-Liang
Published: October 2015

Polyetheretherketone (PEEK), which has biomechanical performance similar to that of human cancellous bone, is used widely as a spinal implant material. However, its bioinertness and hydrophobic surface properties result in poor osseointegration. This study applies a novel modification method, arc ion plating (AIP), that produces a highly osteoblast compatible titanium dioxide (TiO2) coatings on a PEEK substrate. This PEEK with TiO2coating (TiO2/PEEK) was implanted into the femurs of New Zealand white male rabbits to evaluate itsin vivoperformance by the push-out test and histological observation. Analytical results show that AIP can prepare TiO2coatings on bullet-shaped PEEK substrates as implant materials. After prolonged implantation in rabbits, no signs of inflammation existed. Newly regenerated bone formed more prominently with the TiO2/PEEK implant by histological observation. The shear strength of the bone/implant interface increases as implantation period increases. Most importantly, bone bonding performance of the TiO2/PEEK implant was superior to that of bare PEEK. The rutile-TiO2coatings achieved better osseointegration than the anatase-TiO2coatings. Therefore, AIP-TiO2can serve as a novel surface modification method on PEEK for spinal interbody fusion cages.
Efficient and Enhanced Diffusion of Vector Field for Active Contour Model
Authors: Liu Guoqi , Sun Lin , Liu Shangwang
Published: October 2015

Gradient vector flow (GVF) is an important external force field for active contour models. Various vector fields based on GVF have been proposed. However, these vector fields are obtained with many iterations and have difficulty in capturing the whole image area. On the other hand, the ability to converge to deep and complex concavity with these vector fields is also needed to improve. In this paper, by analyzing the diffusion equation of GVF, a normalized set is defined and a dynamically normalized constraint of vector fields is used for efficient diffusion, which makes the edge vector diffusing rapidly to the entire image region. In order to improve the ability to converge to concavity, an enhanced diffusion term is integrated into the original energy functional. With the dynamically normalized constraint and enhanced diffusion term, new vector fields of EDGVF (efficient and enhanced diffusion for GVF) and EDNGVF (efficient and enhanced diffusion of NGVF) are obtained. Experimental results demonstrate that vector fields with proposed method capture the entire image and are obtained with less iterations and computational times. In particular, EDNGVF greatly improves the ability to converge to concavity.
Priority Control Strategy of VSC-MTDC System for Integrating Wind Power
Authors: Yan Wen-ning , Li Ke-jun , Wang Zhuo-di , Meng Xin-han , Zhao Jianguo
Published: October 2015

For the obvious advantages in integrating wind power, multiterminal HVDC transmission system (VSC-MTDC) is widely used. The priority control strategy is proposed in this paper considering the penetration rate of wind power for the AC grid. The strategy aims to solve the problems of power allocation and DC voltage control of the DC system. The main advantage of this strategy is that the demands for wind power of different areas can be satisfied and a power reference for the wind power trade can also be provided when wind farms transmit active power to several AC grids through the DC network. The objective is that power is well distributed according to the output power of wind farm with the demand of AC system and satisfactory control performance of DC voltage is obtained.
Toxicopathological Evaluation of Hydroethanol Extract ofDianthus basuticusin Wistar Rats
Authors: Ashafa Anofi Omotayo Tom , Kazeem Mutiu Idowu
Published: October 2015

Background. Dianthus basuticusis a commonly used medicinal plant in Basotho traditional medicine for the treatment of diabetes, but there is no report on its safety or toxicity. Therefore, we evaluated the toxicity profile of the hydroethanol whole plant extract ofDianthus basuticusin Wistar rats.Methods.Acute toxicity test was performed with single oral administration of 100–3200 mg/kg body weight ofD. basuticusextract to rats and the animals were observed for 14 days for signs of toxicity. The subacute toxicity experiment was conducted by oral administration of graded doses (200, 400, and 800 mg/kg) ofD. basuticusextract daily for 28 days. Behavioural changes as well as haematological, biochemical, and histological parameters were then evaluated.Results.There was no observable sign of toxicity in the acute toxicity test. There were significant decreases (P<0.05) in the feed and water intake as well as total cholesterol and triglycerides of theD. basuticusextract-treated rats in subacute toxicity study. There were no treatment related differences in the haematological, biochemical, and histopathological evaluations.Conclusions.Administration of hydroethanol extract ofD. basuticusmay be safe at the dosages tested in this study but its continuous usage can cause anorexia.
Novel Protocols for Improving the Performance of ODMRP and EODMRP over Mobile Ad Hoc Networks
Authors: Darabkh Khalid A. , Alsukour Ola A.
Published: October 2015

Due to its simplicity, efficiency, and robustness to mobility, the On-Demand Multicast Routing Protocol (ODMRP) becomes a standout amongst the most broadly utilized multicast routing protocols in mobile ad hoc networks (MANETs). However, the robustness of ODMRP comes at the expense of incurring a high control overhead to the network. The Enhanced ODMRP (EODMRP) proposed a dynamic refresh interval for the multicast mesh based on the network feedback on real disconnections experienced by the multicast network members. Veritably, EODMRP decreased the network control overhead at the cost of obtaining a low packet delivery ratio especially in high mobility conditions of the network. In this paper, two protocols, as improvements to both ODMRP and EODMRP, are proposed where the refresh interval is basically adapted based on the source moving speed and the number of disconnections reported by multicast members. Furthermore, we proposed an impressive local recovery to be employed in both protocols, which includes new setup and failure mechanisms that contribute effectively to boosting the performance of our proposed protocols. Since the majority of nodes in MANET rely on batteries, the main contribution of this research is to limit the amount of control information that is passed between nodes (i.e., reducing the control overhead over that in ODMRP) while maintaining a better packet delivery ratio than EODMRP.
Comprehensive Screening of Gene Function and Networks by DNA Microarray Analysis in Japanese Patients with Idiopathic Portal Hypertension
Authors: Kotani Kohei , Kawabe Joji , Morikawa Hiroyasu , Akahoshi Tomohiko , Hashizume Makoto , Shiomi Susumu
Published: October 2015

The functions of genes involved in idiopathic portal hypertension (IPH) remain unidentified.
The present study was undertaken to identify the functions of genes expressed in blood
samples from patients with IPH through comprehensive analysis of gene expression using
DNA microarrays. The data were compared with data from healthy individuals to explore the
functions of genes showing increased or decreased expression in patients with IPH. In cluster
analysis, no dominant probe group was shown to differ between patients with IPH and healthy
controls. In functional annotation analysis using the Database for Annotation Visualization
and Integrated Discovery tool, clusters showing dysfunction in patients with IPH involved
gene terms related to the immune system. Analysis using network‐based pathways revealed
decreased expression of adenosine deaminase, ectonucleoside triphosphate
diphosphohydrolase 4, ATP‐binding cassette, subfamily C, member 1, transforming growth
factor‐β, and prostaglandin E receptor 2; increased expression of cytochrome P450, family 4,
subfamily F, polypeptide 3, and glutathione peroxidase 3; and abnormalities in the immune
system, nucleic acid metabolism, arachidonic acid/leukotriene pathways, and biological
processes. These results suggested that IPH involved compromised function of
immunocompetent cells and that such dysfunction may be associated with abnormalities in
nucleic acid metabolism and arachidonic acid/leukotriene‐related synthesis/metabolism.
Characteristics and Outcomes of Patients with Acute Myocardial Infarction at Non-PCI Capable Hospitals in 2007 and in 2014
Authors: Kalinauskiene Egle , Gerviene Dalia , Sabeckyte Inga , Naudziunas Albinas
Published: October 2015

Background. There is little known about whether characteristics and outcomes of patients with acute myocardial infarction (AMI) have changed over the years in non-PCI capable hospitals in real-life. Our aim was to assess them between 2007 and 2014.Methods. It was a retrospective cohort study. Characteristics and in-hospital mortality (standardized in cases of different characteristics between the groups by original simple method) were assessed for all patients with non-ST elevation myocardial infarction (NSTEMI) and ST elevation myocardial infarction (STEMI) at two non-PCI capable hospitals: one in 2007 (n=104) and another in 2014 (n=58).Results. In 2014, females were older than in 2007 (80.18 ± 7.54 versus 76.15 ± 8.77,p=0.011), males were younger (71.61 ± 11.22 versus 79.20 ± 7.63,p=0.019), less had renal failure (RF) (19% versus 34.6%,p<0.0001) and reinfarction (13.8% versus 35.6%,p<0.0001), and the proportion of males (31% versus 43.3%,p=0.001) and the proportion of NSTEMI (60.3 versus 69.2,p<0.0001) decreased. In cases of STEMI there were no differences in patient characteristics. STEMI (18.8% versus 21.7%) and standardized mortalities by gender, RF, and reinfarction NSTEMI (19.47%, 15.34%, and 17.5%, resp., versus 17.1%) showed no differences between 2007 and 2014.Conclusions. There were some differences in patient characteristics but not in mortality for AMI at non-PCI capable hospitals between 2007 and 2014.
Prognostic Value of Homotypic Cell Internalization by Nonprofessional Phagocytic Cancer Cells
Authors: Schwegler Manuela , Wirsing Anna M. , Schenker Hannah M. , Ott Laura , Ries Johannes M. , Büttner-Herold Maike , Fietkau Rainer , Putz Florian , Distel Luitpold V.
Published: October 2015

Background. In this study, we investigated the prognostic role of homotypic tumor cell cannibalism in different cancer types.Methods. The phenomenon of one cell being internalized into another, which we refer to as “cell-in-cell event,” was assessed in 416 cases from five head and neck cancer cohorts, as well as one anal and one rectal cancer cohort. The samples were processed into tissue microarrays and immunohistochemically stained for E-cadherin and cleaved caspase-3 to visualize cell membranes and apoptotic cell death.Results. Cell-in-cell events were found in all of the cohorts. The frequency ranged from 0.7 to 17.3 cell-in-cell events per mm2. Hardly any apoptotic cells were found within the cell-in-cell structures, although apoptotic cell rates were about 1.6 to two times as high as cell-in-cell rates of the same tissue sample. High numbers of cell-in-cell events showed adverse effects on patients’ survival in the head and neck and in the rectal cancer cohorts. In multivariate analysis, high frequency was an adverse prognostic factor for overall survival in patients with head and neck cancer (p=0.008).Conclusion. Cell-in-cell events were found to predict patient outcomes in various types of cancer better than apoptosis and proliferation and might therefore be used to guide treatment strategies.
How to Use SNP_TATA_Comparator to Find a Significant Change in Gene Expression Caused by the Regulatory SNP of This Gene’s Promoter via a Change in Affinity of the TATA-Binding Protein for This Promoter
Authors: Ponomarenko Mikhail , Rasskazov Dmitry , Arkova Olga , Ponomarenko Petr , Suslov Valentin , Savinkova Ludmila , Kolchanov Nikolay
Published: October 2015

The use of biomedical SNP markers of diseases can improve effectiveness of treatment. Genotyping of patients with subsequent searching for SNPs more frequent than in norm is the only commonly accepted method for identification of SNP markers within the framework of translational research. The bioinformatics applications aimed at millions of unannotated SNPs of the “1000 Genomes” can make this search for SNP markers more focused and less expensive. We used our Web service involving Fisher’sZ-score for candidate SNP markers to find a significant change in a gene’s expression. Here we analyzed the change caused by SNPs in the gene’s promoter via a change in affinity of the TATA-binding protein for this promoter. We provide examples and discuss how to use this bioinformatics application in the course of practical analysis of unannotated SNPs from the “1000 Genomes” project. Using known biomedical SNP markers, we identified 17 novel candidate SNP markers nearby: rs549858786 (rheumatoid arthritis); rs72661131 (cardiovascular events in rheumatoid arthritis); rs562962093 (stroke); rs563558831 (cyclophosphamide bioactivation); rs55878706 (malaria resistance, leukopenia), rs572527200 (asthma, systemic sclerosis, and psoriasis), rs371045754 (hemophilia B), rs587745372 (cardiovascular events); rs372329931, rs200209906, rs367732974, and rs549591993 (all four: cancer); rs17231520 and rs569033466 (both: atherosclerosis); rs63750953, rs281864525, and rs34166473 (all three: malaria resistance, thalassemia).
Photonic Wannier-Stark Ladder from Coupled Electromagnetic Cavities
Authors: Anwar Shahzad , Li Sucheng , Lu Weixin , Hou Bo
Published: October 2015

We have investigated the photonic Wannier-Stark ladder in the system of coupled electromagnetic cavities, which consists of a stack of metallic plates structured with subwavelength apertures and where the tilted potential effect is mimicked by imposing the gradient variation of refractive index. Making an analogy to its quantum counterpart and assuming the translational property of its solutions, we have shown the photonic ladder has the eigenenergies, that is, frequencies, in a geometrical series. Within the approximation of small gradient, the ladder states manifest the equidistant frequency spacing in the spectrum. By both analytical derivation and numerical simulation, we have illustrated the geometrically progressed energies of the photonic Wannier-Stark ladder.
Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype
Authors: Huang Ru , Chu Xiaoli , Zhang Jie , Hu Yu Hen
Published: October 2015

Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS.
One-Tier versus Two-Tier Wireless Sensor Networks: Coverage Problem
Authors: Sheltami Tarek
Published: October 2015

This paper tackles the coverage problem in homogenous and heterogeneous wireless sensor networks. The homogenous sensor network consists of sensor nodes and relays; however, the heterogeneous sensor network consists of sensor nodes, super nodes, and satellite nodes. In the latter network, super nodes and satellite nodes are utilized to demonstrate different scenarios. Super nodes consume huge amount of energy, compared to sensor nodes. To address this problem, the aim of this paper is to find the baseline when super nodes are used efficiently, despite the inherited high power consumption. Wolfram Mathematica is used to compare random independent deployment circular analytical model against a much simpler square analytical model. The achieved results showed that the simple square model is very close to circular model whenK-coverage is≤2.
An Image Based Mathematical Model for the Propagation of Fan Noise in a Plenum with Large Side Openings
Authors: Panza Michael J.
Published: October 2015

This paper presents another application of an images group model for a special enclosure geometry and source orientation. A previous work outlined the concept via application to a special tight-fitting enclosure. Application of the concept to a fan plenum requires different mathematical descriptions for the image groups. This paper describes the sound reverberation inside a sound enclosure with mostly open sides where the primary noise sources are the air inlets and exhausts of axial type fans located at the top of the enclosure, the sound transmission through the air inlet openings, and the radiation to wayside positions. The main reverberation between the floor and ceiling is determined with an image based mathematical model. The model considers how the main reverberant part image group is amplified by its images from two parallel bulkheads and any side wall frame members. The method of images approach allows the hard surfaces of an untreated plenum to be represented by perfectly reflecting surfaces with zero sound absorption coefficients, thus not requiring any estimate or measurement for these surfaces. Numerical results show excellent comparison to experimental results for an actual plenum. The image model is also shown to be significantly more accurate than the standard large room diffuse field reverberant model.
Stability of Three-Dimensional Slurry Trenches with Inclined Ground Surface: A Theoretical Study
Authors: Jin Xiao-Fei , Liang Shu-Ting , Zhu Xiao-Jun
Published: October 2015

Stability of slurry trenches is an important issue during the construction of the groundwater cutoff walls and diaphragm walls, and thus gradually draws attention. In this paper, a theoretical method for a three-dimensional trench model with an inclined ground was proposed. Based on the Coulomb-type force equilibrium, a safety factor assessing the stability was derived. The results showed that the existing two-dimensional model was conservative compared to the present three-dimensional model; concretely, a greater inclined angle of the inclined ground and trench length decreased the safety factor. This work could be used to assess the trench stability for both 2D and 3D cases with inclined ground surfaces.
miR-125b Suppresses Proliferation and Invasion by Targeting MCL1 in Gastric Cancer
Authors: Wu Shihua , Liu Feng , Xie Liming , Peng Yaling , Lv Xiaoyuan , Zhu Yaping , Zhang Zhiwei , He Xiusheng
Published: October 2015

Understanding the molecular mechanisms underlying gastric cancer progression contributes to the development of novel targeted therapies. In this study, we found that the expression levels of miR-125b were strongly downregulated in gastric cancer and associated with clinical stage and the presence of lymph node metastases. Additionally, miR-125b could independently predict OS and DFS in gastric cancer. We further found that upregulation of miR-125b inhibited the proliferation and metastasis of gastric cancer cells in vitro and in vivo. miR-125b elicits these responses by directly targeting MCL1 (myeloid cell leukemia 1), which results in a marked reduction in MCL1 expression. Transfection of miR-125b sensitizes gastric cancer cells to 5-FU-induced apoptosis. By understanding the function and molecular mechanisms of miR-125b in gastric cancer, we may learn that miR-125b has the therapeutic potential to suppress gastric cancer progression and increase drug sensitivity to gastric cancer.
Cancer Cachexia and MicroRNAs
Authors: Camargo Rodolfo Gonzalez , Quintas Teixeira Ribeiro Henrique , Geraldo Murilo Vieira , Matos-Neto Emídio , Neves Rodrigo Xavier , Carlos Carnevali Luiz , Donatto Felipe Fedrizzi , Alcântara Paulo S. M. , Ottoch José P. , Seelaender Marília
Published: October 2015

Cancer cachexia is a paraneoplastic syndrome compromising quality of life and survival, mainly characterized by involuntary weight loss, fatigue, and systemic inflammation. The syndrome is described as a result of tumor‐host interactions characterized by an inflammatory response by the host to the presence of the tumor. Indeed, systemic inflammation is considered a pivotal feature in cachexia progression and maintenance. Cytokines are intimately related to chronic systemic inflammation and the mechanisms underlying the release of these factors are not totally elucidated, the etiology of cachexia being still not fully understood. Therefore, the understanding of cachexia‐related mechanisms, as well as the establishment of markers for the syndrome, is very relevant. MicroRNAs (miRNAs) are a class of noncoding RNAs interfering with gene regulation. Different miRNA expression profiles are associated with different diseases and inflammatory processes. miRNAs modulate adipose and skeletal muscle tissue metabolism in cancer cachexia and also tumor and tissue derived inflammation. Therefore, we propose a possible role for miRNAs in the modulation of the host inflammatory response during cachexia. Moreover, the establishment of a robust body of evidence in regard to miRNAs and the mechanisms underlying cachexia is mandatory, and shall contribute to the improvement of its diagnosis and treatment.