bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of imbalanced bioinformatics data by using boundary movement-based ELM
Authors: Cheng Ke , Chen Qingfang , Yang Xibei , Gao Shang , Yu Hualong 
Published: August 2015

Abstract To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transformed space, to find the optimal moving distance of the classification hyperplane by estimating the probability density distributions of the instances in different classes. Experimental results on four real imbalanced bioinformatics classification data sets indicated that the proposed BM-ELM algorithm outperforms some traditional bias correction algorithms due to it can greatly improve the sensitivity of the classification results with small loss of specificity as possible. Also, BM-ELM algorithm has presented better performance than the widely used support vector machine (SVM) classifier. The algorithm can be widely popularized in various large-scale bioinformatics applications.
An effective fuzzy kernel clustering analysis approach for gene expression data
Authors: Sun Lin , Xu Jiucheng , Yin Jiaojiao 
Published: August 2015

Abstract Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.
Analysis of vascular endothelial dysfunction genes and related pathways in obesity through systematic bioinformatics
Authors: Zhang Hui , Wang Jing , Sun Ling , Xu Qiuqin , Hou Miao , Ding Yueyue , Huang Jie , Chen Ye , Cao Lei , Zhang Jianmin , Qian Weiguo , Lv Haitao 
Published: August 2015

Abstract Obesity has become an increasingly serious health problem and popular research topic. It is associated with many diseases, especially cardiovascular disease (CVD)-related endothelial dysfunction. This study analyzed genes related to endothelial dysfunction and obesity and then summarized their most significant signaling pathways. Genes related to vascular endothelial dysfunction and obesity were extracted from a PubMed database, and analyzed by STRING, DAVID, and Gene-Go Meta-Core software. 142 genes associated with obesity were found to play a role in endothelial dysfunction in PubMed. A significant pathway (Angiotensin system maturation in protein folding and maturation) associated with obesity and endothelial dysfunction was explored. The genes and the pathway explored may play an important role in obesity. Further studies about preventing vascular endothelial dysfunction obesity should be conducted through targeting these loci and pathways.
Preliminary testing for the Markov property of the fifteen chromatin states of the Broad Histone Track
Authors: Lee Kyung-Eun , Park Hyun-Seok 
Published: August 2015

Abstract Epigenetic computational analyses based on Markov chains can integrate dependencies between regions in the genome that are directly adjacent. In this paper, the BED files of fifteen chromatin states of the Broad Histone Track of the ENCODE project are parsed, and comparative nucleotide frequencies of regional chromatin blocks are thoroughly analyzed to detect the Markov property in them. We perform various tests to examine the Markov property embedded in a frequency domain by checking for the presence of the Markov property in the various chromatin states. We apply these tests to each region of the fifteen chromatin states. The results of our simulation indicate that some of the chromatin states possess a stronger Markov property than others. We discuss the significance of our findings in statistical models of nucleotide sequences that are necessary for the computational analysis of functional units in noncoding DNA.
A hybrid ensemble method based on double disturbance for classifying microarray data
Authors: Chen Tao , Xue Huifeng , Hong Zenglin , Cui Man , Zhao Hui 
Published: August 2015

Abstract Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.
A genetic filter for cancer classification on gene expression data
Authors: Kim Yong-Hyuk , Yoon Yourim 
Published: August 2015

Abstract We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-correlation among selected genes. The proposed genetic filter was tested on well-known leukemia datasets, and significant improvement over previous work was obtained.
Multisite protein subcellular localization prediction based on entropy density
Authors: Zhao Qing , Wang Dong , Chen Yuehui , Qu Xumi 
Published: August 2015

Abstract Protein subcellular localization prediction is currently receiving much attention in the field of protein research. Many researchers make great efforts to study single-site protein subcellular localization, but the experimental data shows that many proteins can be found in two or more sub-cellular locations, prompting the study of multisite protein sub-cellular localization. This study utilized a Gpos-mPLOC data set and pseudo amino acid compositions, physicochemical properties of amino acid composition, and entropy density as three effective feature extraction methods. Then, these features were then placed in a multi-label k nearest neighbor classifier to predict subcellular protein locations. Experimental results verified that this approach provides a localization precision of 66.73% through the Jack-knife test.
Hematocrit estimation using online sequential extreme learning machine
Authors: Huynh Hieu Trung , Won Yonggwan , Kim Jinsul 
Published: August 2015

Abstract Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient points of this method are that (1) the neural network is trained by the online sequential extreme learning machine (OS-ELM) in which the devices can be still trained with new samples during the using process and (2) the extended features are used to reduce the number of current points which can save the battery power of devices and speed up the measurement process.
Inhibition of RhoA expression by adenovirus-mediated siRNA combined with TNF-α induced apoptosis of hepatocarcinoma cells
Authors: Yin Kun , Zhao Guihua , Huang Xiaodan , Gao Ge , Sun Hui , Wei Qingkuan , Liu Qiaoqiao , Li Mian , Xu Chao , Zhu Song , Ba Zaihua , Yan Ge 
Published: August 2015

Abstract Tumor necrosis factor-alpha (TNF- α) has been used as an effective treatment for Hepatocellular Carcinoma, however, inducing tumor cell apoptosis by TNF- α alone is still unsatisfactory. RhoA is highly expressed in hepatocarcinoma cells and can be activated by TNF- α. The activation of RhoA directly leads to a poor prognosis of HCC. Therefore, we propose to investigate the therapeutic effect of TNF- α together with RhoA siRNA. RhoA inhibition was accomplished by constructing a recombinant adenovirus that can efficiently express RhoA siRNA in HepG2 cells. The recombinant adenovirus AdshRNA-RhoA and AdU6-control were generated by adenovirus-mediated siRNA expression system. The inhibition effects were detected by RT-PCR in addition to immunoblot to quantify the decreased levels of RhoA expression, and the therapeutic effect for HCC was demonstrated by the proliferation and apoptosis ratios of HepG2 cells. The inhibition effects of RhoA by AdshRNA-RhoA were significant at both mRNA and protein levels: the transcription of RhoA mRNA decreased by 74.46%, and the expression of protein decreased by 76.48%. The proliferation rate of HepG2 cells detected by MTT showed that a treatment of AdshRNA-RhoA and TNF- α together could strengthen the suppression ability of TNF- α to HepG2 cells, resulting in approximately 14.2% more than those treated with only TNF- α. FCA and TUNEL assays results revealed that the combined treatment can induce apoptosis in approximately 52.14%-65% of the HepG2 cells, whereas this ratio in the TNF- α-alone group was only 21.91%-32%. Our results showed that AdshRNA-RhoA can efficiently enhance the TNF- α-induced apoptosis of hepatocarcinoma cells. This method might be a useful therapeutic route in HCC and other tumors.
PGBL inhibits the RAW 264.7 cells to express inflammatory factor
Authors: Zhang Chunmei , Lin Linghui , Li Guirong , Ma Jianan , Han Xu , Fei Rui 
Published: August 2015

Abstract Polysaccharides derived from Ginkgo biloba leaf (PGBL) is a kind of active ingredient came out from ginkgo biloba leaf extractions. Previous studies have shown that PGBL has a good anti-inflammatory effect. However, the mechanism is not clear. This study is to investigate the modulated immunity effect of PGBL on RAW264.7 cells. Here we showed that lipopolysaccharide (LPS) induces the expression of tumor necrosis factor- α (TNF- α) and interleukin-6 (IL-6), and this induction can be repressed by PGBL treatment both in protein level and mRNA level, and PGBL strongly reduced the translocation of nuclear factor (NF)- κB to the cell nucleus. These findings demonstrate that PGBL can decrease the sensitivity of monocytes to LPS, and PGBL has applications in systemic inflammation and immune diseases.
Noise induced calcium oscillations in a cell exposed to electromagnetic fields
Authors: Zhang Yuhong , Zhao Yongli , Chen Yafei , Yuan Changqing , Zhan Yong 
Published: August 2015

Abstract The effects of noise on the calcium oscillations in a cell exposed to electromagnetic fields are described by a dynamic model. Noise is a very important factor to be considered in the dynamic research on the calcium oscillations in a cell exposed to electromagnetic fields. Some meaningful results have been obtained here based on the discussion. The results show that the pattern of intracellular calcium oscillations exposure to electromagnetic fields can be influenced by noise. Furthermore, the intracellular calcium oscillations exposure to electromagnetic fields can also be induced by noise. And the work has also studied the relationships between the voltage sensitive calcium channel’s open probability and electromagnetic field. The result can provide new insights into constructive roles and potential applications of selecting appropriate electromagnetic field frequency during the research of biological effect of electromagnetic field.
Antibacterial, anti-inflammatory, and antioxidant effects of Yinzhihuang injection
Authors: Liu Juan , Qiu Hong , Zhu Zhaorong , Zou Tangbin 
Published: August 2015

Abstract The Yinzhihuang injection, a traditional Chinese medicine, has been the recent target of increasing interest due to its anti-inflammatory properties. The molecular basis by which Yinzhihuang injection could cure Riemerella anatipestifer (RA) serositis in ducks is unclear. This study evaluated the antibacterial, anti-inflammatory and antioxidant effects of Yinzhihuang injection, using disease models of RA-induced infectious serositis in ducks and heptane-induced inflammation in mice and rats. The duck mortality rate was reduced from 60% to 20% and both the inflammatory response and histological damage were ameliorated by treatment with Yinzhihuang injection (0.02 g/kg). Further studies indicated that superoxide dismutase (SOD), nitric oxide synthase (NOS), and inducible nitric oxide synthase (iNOS) were elevated while malondialdehyde (MDA), nitric oxide (NO) and RA growth were inhibited when the ducks were treated by Yinzhihuang injection. In addition, Yinzhihuang injection (0.04 g/ml) effectively inhibited xylene-induced auricle swelling in mice, (demonstrating an inhibition rate of 35.21%), egg albumen-induced paw metatarsus swelling in rats, (demonstrating an inhibition rate of 22.30%), and agar-induced formation of granulation tissue. These results suggest that Yinzhihuang injection ameliorates RA-induced infectious serositis in ducks by modulation of inflammatory mediators and antioxidation.
Aberrant Wnt-1/beta-catenin signaling and WIF-1 deficiency are important events which promote tumor cell invasion and metastasis in salivary gland adenoid cystic carcinoma
Authors: Wang Ruinan , Geng Ning , Zhou Yuqiao , Zhang Dunfang , Li Longjiang , Li Jing , Ji Ning , Zhou Min , Chen Yu , Chen Qianming 
Published: August 2015

Abstract This study investigates whether Wnt components play a role in carcinogenesis, or the invasion and metastasis of salivary glands, also referred to as adenoid cystic carcinoma (sAdCC). Several sAdCC cell lines with low invasive potential (ACC-2), high metastatic potential (ACC-M), and higher invasive potential (T-ACC-M) were examined to determine whether Wnt components correlate with tumors’ invasive and metastatic behavior. Immunohistochemistry was performed in a sAdCC tissue array. ACC-M expressed higher levels of Wnt-1, beta-catenin and lower WIF-1 compared to ACC-2 (P<0.05). T-ACC-M exhibited increased mRNA of Wnt-1 and beta-catenin, and decreased WIF-1 compared to ACC-2 and ACC-M. Immuno-histochemistry showed up-regulation of Wnt-1 and down-regulation of WIF-1 in sAdCC compared with normal salivary glands. Beta-catenin was found in the cytoplasm and nuclei of sAdCC. Dislocation of E-cadherin in sAdCC was observed. These results suggest that sAdCC exhibits diverse expressions of Wnt components. It has an important relationship with the invasive phenotype of these cells.
Analysis of nervous fiber, muscle, and blood vessels using their ulraviolet near infrared reflectance characteristics
Authors: Tufan Kadir , Belli Ahmet Korkut 
Published: August 2015

Abstract Injury to the nervous system can lead to irreversible problems as nervous tissues have limited regenerative capability. Therefore it is imperative to find an objective, reliable, cheap, and easy-to-apply method that separates nervous fibers from muscles and blood vessels. The aim of this study is to determine structural differences that can aid in easy and reliable identification of nervous fibers. We analyzed light reflectance from these tissues from 230 nm to 1000 nm and found that in the range of 400 nm-600 nm nervous fibers have higher reflectance in comparison to others. Therefore, we generated distinct features in this range and utilized support vector machine to automatically classify samples. Classification performance demonstrated that light reflectance is a good candidate feature that can help to classify nervous tissue.
Peroxynitrite induced fibrinogen site identification
Authors: Luo Yunjing , Shi Jianlong , Li Jingjing 
Published: August 2015

Abstract An increasing number of peroxynitrite-mediated fibrinogen nitrifications have been associated with thrombotic diseases. However, few reports related to priority nitrified fibrinogen injury sites exist. In this paper, an improved method, which simulated the environment in vivo, was used to inspect the structural changes of fibrinogen treated with peroxynitrite and LC-MS/MS in order to investigate the fibrinogen injury sites. The SDS-PAGE results indicated that γ subunits of Fg were vulnerable to oxidative/nitrative modifications induced by peroxynitrite. An in-depth analysis of fibrinogen γ chain (Fg I) nitration site identification and susceptibility to peroxynitrite-utilizing LC-MS/MS strategy was performed. Based on a large dataset, the results indicated the priority injury sites during database searching were Y96, Y262, Y274, Y348, and Y363; these results could be applied to biomedical studies.
Regression models for the quantification of Parkinsonian bradykinesia
Authors: Kim Ji-Won , Kwon Yuri , Yun Ju-Seok , Heo Jae-Hoon , Eom Gwang-Moon , Tack Gye-Rae , Lim Tae-Hong , Koh Seong-Beom 
Published: August 2015

Abstract The aim of this study was to develop regression models for the quantification of parkinsonian bradykinesia. Forty patients with Parkinson’s disease participated in this study. Angular velocity was measured using gyro sensor during finger tapping, forearm-rotation, and toe tapping tasks and the severity of bradykinesia was rated by two independent neurologists. Various characteristic variables were derived from the sensor signal. Stepwise multiple linear regression analysis was performed to develop models predicting the bradykinesia score with the characteristic variables as input. To evaluate the ability of the regression models to discriminate different bradykinesia scores, ANOVA and post hoc test were performed. Major determinants of the bradykinesia score differed among clinical tasks and between raters. The regression models were better than any single characteristic variable in terms of the ability to differentiate bradykinesia scores. Specifically, the regression models could differentiate all pairs of the bradykinesia scores (p<0.05) except for one pair in the finger tapping task and one pair in the toe tapping task. In contrast, any single characteristic variable was found not sensitive enough to discriminate many of the pairs, especially in case of the toe tapping task. The results suggest that the multiple regression models reflecting these differences would be beneficial for the quantification of bradykinesia because the cardinal features included in the determination of bradykinesia score differ among tasks as well as among the raters.
Down-regulation of AKT combined with radiation-induced autophagy and apoptosis roles in MCF-7 cells
Authors: Zhang Jing , Zhao Dali , Xie Zhongwei , Qi Yali 
Published: August 2015

Abstract Autophagy is an evolutionarily conservation process whereby cytoplasm and cellular organelles are degraded in lysosomes for amino acid and energy recycling. Autophagy concurrent with radiotherapy has emerged as a novel approach in breast cancer treatment. Our studies conclude that autophagy and apoptosis can be induced by radiation and inhibition of autophagy can increase apoptosis. In addition, Akt is a molecule that down-regulates autophagy and apoptosis; blocking Akt can enhance autophagy and apoptosis induced by radiation in MCF-7 cells. Akt could become a new focus in breast cancer radiotherapy.
Synthesis, spectroscopic studies of novel N-substituted phthalimides and evaluation of their antibacterial, antioxidant, DNA binding and molecular docking studies
Authors: Nayab Pattan Sirajuddin , Pulaganti Madhusudana , Chitta Suresh Kumar , Oves Mohammad , . Rahisuddin 
Published: August 2015

<p class="Abstract">A new series of N-substituted phthalimide derivatives were prepared by condensation of appropriate amount of n-amino tetrachlorophthalimide with respective aldehyde in glacial acetic acid. The structural investigation of the synthesized compounds was done by spectroscopic methods (UV-Vis., IR, <sup>1</sup>H and <sup>13</sup>C NMR) and elemental analysis. The antibacterial screening of these compounds was performed against Escherichia coli and Staphylococcus mutans. The synthesized compounds were evaluated for their antioxidant potential using 2,2-diphenyl-1-picrylhydrazyl (DPPH) as a scavenging agent. The interaction ability of most promising compounds (3a and 3b) with native calf thymus DNA (Ct-DNA) was also studied by means of UV-Vis., circular dichroism (CD), viscosity measurements and thermal studies. The intrinsic binding constants (K<sub>b</sub>) of 3a and 3b with Ct-DNA obtained from UV-Vis. absorption studies were 8 × 10<sup>4</sup> and 1 × 10<sup>5</sup>, respectively. Molecular docking of target compounds (3a and 3b) against DNA dodecamer d(CGCGAATTCGCG)<sub>2</sub> has been carried out. The test compounds exhibited remarkable antibacterial, antioxidant and DNA binding activities.</p><p> </p>
Microsatellite markers for studies with the carnivorous plant Philcoxia minensis (Plantaginaceae)
Authors: Scatigna André V. , Oliveira Fernanda A. , Mantello Camila C. , Francisco Patrícia M. , Souza Anete P. , Simões André O. 
Published: August 2015

Premise of the study:Microsatellite markers were developed for the critically endangered carnivorous species Philcoxia minensis (Plantaginaceae) for further population genetic studies aiming at its conservation.Methods and Results:We identified 29 clones containing 40 microsatellites from a genomic enriched library. A total of 27 primer pairs were developed and evaluated in 30 individuals of a natural P. minensis population. Seventeen markers successfully presented amplification products within the expected size range, of which 12 were polymorphic. The expected and observed heterozygosities ranged from 0.03 to 0.65 and from 0.00 to 0.77, respectively. Positive transferability with the related species P. bahiensis was observed for the same 17 markers.Conclusions:The 12 polymorphic microsatellite markers are suitable for studies in genetic diversity and structure, mating system, and gene flow in P. minensis and also may be useful for similar issues regarding the related species P. bahiensis.