PCR Identification and Phylogenetic Analysis of Trichomonas gallinae from Domestic Pigeons in Guangzhou, China

Shen-Ben Qiu,Meng-Na Lv,Xi He,Ya-Biao Weng,Shang-Shu Zou,Xin-Qiu Wang,Rui-Qing Lin 대한기생충학열대의학회 2017 The Korean Journal of Parasitology Vol.55 No.3

Study on the internal irreversible losses and process exponent of single screw expanders

Lili Shen,Yuting Wu,Wei Wang,Biao Lei,Wei Duan,Ruiping Zhi 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.3

The irreversible losses including intake pressure, leakage, heat transfer, friction and over-expansion losses have great influence on the expander performance. In this paper, a thermodynamic model is presented to predict the real expansion process exponent and analyze the under-expansion or over-expansion under designed and off-designed operation conditions. The model verified by experimental results has a good agreement. Results showed that the real expansion process exponent of air is higher than the ideal adiabatic index of 1.4 and decreases from 1.716 to 1.644 with the internal volume ratio changing from 1.8 to 6.5. The real expansion process exponent of R123 is close to 1.00 under different internal volume ratio. Compared to the intake pressure, the variation of back pressure has greater influence on the large internal volume ratio than the small one. Thus, to adjust the back pressure is more effective to match the designed condition for the expander with a large internal volume ratio.

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease

Jie, Biao,Liu, Mingxia,Liu, Jun,Zhang, Daoqiang,Shen, Dinggang IEEE 2017 IEEE Transactions on Biomedical Engineering Vol.64 No.1

<P>Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment. However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper, we propose a novel temporallyconstrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term thatrequires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term thatrequires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers.</P>

Pricing Weather Derivatives using a Predicting Power Time Series Process

Jun-Biao Lin,Chung-Chang Chang,Wen-Min Shen 한국증권학회 2009 Asia-Pacific Journal of Financial Studies Vol.38 No.6

This paper extended the Cao-Wei (2004, JFM) model to construct a theoretical model for pricing weather derivatives in two significant ways. One adopted a time series model developed by Campbell and Diebold (2005, JASA) to describe the dynamics of temperature. The advantage of using Campbell and Diebold’s time series model to describe the temperature dynamics is that it can not only take the conditional mean of temperature coming from trend, seasonal, and cyclical components but also allow for the conditional variance dynamics. The other purpose of this paper is to use an extended power utility function, instead of Cao and Wei’s constant proportional risk aversion (CPRA) utility function. The extended power utility function could exhibit decreasing, constant, and increasing relative risk aversion. Eventually, we find that the prices of weather derivatives can be determined by weather conditions, discount factors, and forward premiums. Additionally, these sources have close relations with some risk aversion parameters. Furthermore, the results are consistent with Cao and Wei’s condition under some specific parameter assumptions

Increased Cognition Connectivity Network in Major Depression Disorder: A fMRI Study

Ting Shen,Cao Li,Biao Wang,Wei-min Yang,Chen Zhang,Zhiguo Wu,Mei-hui Qiu,Jun Liu,Yi-feng Xu,Dai-hui Peng 대한신경정신의학회 2015 PSYCHIATRY INVESTIGATION Vol.12 No.2

ObjectiveaaEvidence of the brain network involved in cognitive dysfunction has been inconsistent for major depressive disorder (MDD), especially during early stage of MDD. This study seeks to examine abnormal cognition connectivity network (CCN) in MDD within the whole brain. MethodsaaSixteen patients with MDD and 16 health controls were scanned during resting-state using 3.0 T functional magnetic resonance imaging (fMRI). All patients were first episode without any history of antidepressant treatment. Both the left and right dorsolateral prefrontal cortex (DLPFC) were used as individual seeds to identify CCN by the seed-target correlation analysis. Two sample t test was used to calculate between-group differences in CCN using fisher z-transformed correlation maps. ResultsaaThe CCN was constructed by bilateral seed DLPFC in two groups separately. Depressed subjects exhibited significantly increased functional connectivity (FC) by left DLPFC in one cluster, overlapping middle frontal gyrus, BA7, BA43, precuneus, BA6, BA40, superior temporal gyrus, BA22, inferior parietal lobule, precentral gyrus, BA4 and cingulate gyrus in left cerebrum. Health controls did not show any cluster with significantly greater FC compared to depressed subjects in left DLPFC network. There was no significant difference of FC in right DLPFC network between depressed subjects and the health controls. ConclusionaaThere are differences in CCN during early stage of MDD, as identified by increased FCs among part of frontal gyrus, parietal cortex, cingulate cortex, and BA43, BA22, BA4 with left DLPFC. These brain areas might be involved in the underlying mechanisms of cognitive dysfunction in MDD.

Integration of Network Topological and Connectivity Properties for Neuroimaging Classification

Jie, Biao,Zhang, Daoqiang,Gao, Wei,Wang, Qian,Wee, Chong-Yaw,Shen, Dinggang IEEE 2014 IEEE Transactions on Biomedical Engineering Vol.61 No.2

<P>Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results.</P>

Stability of the m/n=1/1 mode during the ramp phase of the sawtooth oscillations at different phase angles of the lower hybrid wave on the HT-7 tokamak

youwen Sun,Baonian Wan,Biao Shen,Bojiang Ding,Haiqing Liu,Jinping Qian,Liqun Hu,Zhongyong Chen 한국물리학회 2006 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.49 No.III

The stability of the m/n = 1/1 mode (named “mid-oscillation”) during the ramp phase of the sawtooth oscillations in different current profiles has been investigated in a recent experiment. Modification of the local current density profile in the central region was obtained by changing the spectrum of the parallel refractive index Nk of the lower hybrid wave (LHW) with other parameters fixed. This shows that the amplitude and the growth rate of the mid-oscillation decrease with increasing peak value of the parallel refractive index Npeak k and the mode disappears during most of the sawtooth rampphases when Npeak k 3.15. This result directly demonstrates that the mode is mainly destabilized by the local current density gradient

Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment

Zu, Chen,Jie, Biao,Liu, Mingxia,Chen, Songcan,Shen, Dinggang,Zhang, Daoqiang SPRINGER SCIENCE AND BUSINESS MEDIA 2016 BRAIN IMAGING AND BEHAVIOR Vol.10 No.4

<P>Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.</P>

Effect of Bimodal Grain Structure on the Yielding Behavior of Commercial Purity Titanium Under Quasi-static Tension

Xianzhe Shi,Xiuxia Wang,Biao Chen,Junko Umeda,Katsuyoshi Kondoh,Jianghua Shen 대한금속·재료학회 2023 METALS AND MATERIALS International Vol.29 No.8

In this work, a bimodal grain structure was developed for commercial purity Ti(CP-Ti) via powder metallurgy processing,followed by free hot forging and then heat treatment. The bi-modal grains were characterized with electron backscatterdiffraction. The mechanical tests showed that in comparison to the uniform and equiaxed grain structure, the bimodalgrains improved the yield strength of CP-Ti significantly, while it maintains a merely changed ultimate tensile strength andelongation to failure. In addition, an interesting yield plateau was observed in the bimodal CP-Ti. To explore underlyingmechanisms behind the phenomenon, the microstructures of the samples before and after testing were carefully examined. The results revealed that geometrically necessary dislocations accumulating at the interface between coarse and fine grainsinduced back stress hardening, which together with the statistically stored dislocations also accounted for the yield plateauin the bimodal CP-Ti.

Mathematical modeling of torque for single screw expanders

Yuting Wu,Ruiping Zhi,Wei Wang,Lili Shen,Yeqiang Zhang,Biao Lei,Jingfu Wang,Chongfang Ma 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.1

This paper presents a mathematical model of torque for Single screw expanders (SSEs). Instantaneous torque and torque ratio were analyzed and discussed. The periodic variation of instantaneous torque is the same for different inlet pressure levels. The torque ratio, with its value close to 1, is independent of the inlet pressure of SSE. An experimental system was established to measure the torque, power and shaft efficiency of the self-developed SSE prototype, and results were used to validate the model. Comparison shows that the difference between calculated and experimental torque values is small (6.58 N.m to 7.55 N.m). The calculated and experimental output power is similar, with a difference of 2.07 kW to 2.37 kW. Therefore, the proposed model can be used to estimate the torque and output power of SSEs.