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Search Results: 1 - 10 of 23980 matches for " Tianzi Jiang "
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Geometric Primitive Extraction by the Combination of Tabu Search and Subpixel Accuracy
JIANG Tianzi,

计算机科学技术学报 , 1999,
Abstract: In this paper, a novel method for extracting the geometric primitives from geometric data is proposed. Specifically, tabu search is combined with subpixel accuracy to improve detection accuracy and convergent speed. On the one hand, this new shape detection method not only has TS's ability to find the global optimum, but also keeps all advantages of tabu search. On the other hand, it has subpixel accuracy ability to match the local optimum.
Contour matching using wavelet transform and multigrid methods
Contour Matching Using Wavelet Transform and Multigrid Methods

Jiang Tianzi,and Ma Songde,
Jiang Tianzi
,Ma Songde

计算机科学技术学报 , 1997,
Abstract: In this paper, wavelet transform and multigrid method are combined to make the method more practical. It is known that Gaussian filtering causes shrinkage of data. To overcome this disadvantage, Gaussian filtering is replaced with wavelet transform. This method introduces no curve shrinkage. Then, the linearized form of objective equation is proposed. This makes contour matching easier to implement. Finally, the multigrid method is used to speed up the convergence.
Is Payoff Necessarily Weighted by Probability When Making a Risky Choice? Evidence from Functional Connectivity Analysis
Li-Lin Rao, Shu Li, Tianzi Jiang, Yuan Zhou
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0041048
Abstract: How people make decisions under risk remains an as-yet-unresolved but fundamental question. Mainstream theories about risky decision making assume that the core processes involved in reaching a risky decision include weighting each payoff or reward magnitude by its probability and then summing the outcomes. However, recently developed theories question whether payoffs are necessarily weighted by probability when making a risky choice. Using functional connectivity analysis, we aimed to provide neural evidence to answer whether this key assumption of computing expectations holds when making a risky choice. We contrasted a trade-off instruction choice that required participants to integrate probability and payoff information with a preferential choice that did not. Based on the functional connectivity patterns between regions in which activity was detected during both of the decision-making tasks, we classified the regions into two networks. One network includes primarily the left and right lateral prefrontal cortices and posterior parietal cortices, which were found to be related to probability in previous reports, and the other network is composed of the bilateral basal ganglia, which have been implicated in payoff. We also found that connectivity between the payoff network and some regions in the probability network (including the left lateral prefrontal cortices and bilateral inferior parietal lobes) were stronger during the trade-off instruction choice task than during the preferential choice task. This indicates that the functional integration between the probability and payoff networks during preferential choice was not as strong as the integration during trade-off instruction choice. Our results provide neural evidence that the weighting process uniformly predicted by the mainstream theory is unnecessary during preferential choice. Thus, our functional integration findings can provide a new direction for the investigation of the principles of risky decision making.
Increased Cortical Thickness in Sports Experts: A Comparison of Diving Players with the Controls
Gaoxia Wei,Yuanchao Zhang,Tianzi Jiang,Jing Luo
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0017112
Abstract: Sports experts represent a population of people who have acquired expertise in sports training and competition. Recently, the number of studies on sports experts has increased; however, neuroanatomical changes following extensive training are not fully understood. In this study, we used cortical thickness measurement to investigate the brain anatomical characteristics of professional divers with extensive training experience. A comparison of the brain anatomical characteristics of the non-athlete group with those of the athlete group revealed three regions with significantly increased cortical thickness in the athlete group. These regions included the left superior temporal sulcus, the right orbitofrontal cortex and the right parahippocampal gyrus. Moreover, a significant positive correlation between the mean cortical thickness of the right parahippocampal gyrus and the training experience was detected, which might indicate the effect of extensive training on diving players' brain structure.
Sex-Dependent Correlations between the Personality Dimension of Harm Avoidance and the Resting-State Functional Connectivity of Amygdala Subregions
Ying Li, Wen Qin, Tianzi Jiang, Yunting Zhang, Chunshui Yu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0035925
Abstract: Harm avoidance (HA) is a personality dimension involving the tendency to respond intensely to signals of aversive stimuli. Many previous neuroimaging studies have associated HA scores with the structural and functional organization of the amygdala, but none of these studies have evaluated the correlation between HA score and amygdala resting-state functional connectivity (rsFC). Moreover, the amygdala is not a homogeneous structure, and it has been divided into several structurally and functionally distinct subregions. Investigating the associations between HA score and properties of subregions of the amygdala could greatly improve our understanding of HA. In the present study, using a large sample of 291 healthy young adults, we aimed to uncover correlations between HA scores and the rsFCs of each amygdala subregion and to uncover possible sex-based differences in these correlations. We found that subregions of the amygdala showed different rsFC patterns, which contributed differently to individual HA scores. More specifically, HA scores were correlated with rsFCs between the laterobasal amygdala subregion and temporal and occipital cortices related to emotional information input, between the centromedial subregion and the frontal cortices associated with emotional output control, and between the superficial subregion and the frontal and temporal areas involved in both functions. Moreover, significant gender-based differences were uncovered in these correlations. Our findings provide a more detailed model of association between HA scores and amygdala rsFC, extend our understanding of the connectivity of subregions of the amygdala, and confirm sex-based differences in HA associations.
The Neuronal Correlates of Digits Backward Are Revealed by Voxel-Based Morphometry and Resting-State Functional Connectivity Analyses
Rui Li, Wen Qin, Yunting Zhang, Tianzi Jiang, Chunshui Yu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0031877
Abstract: Digits backward (DB) is a widely used neuropsychological measure that is believed to be a simple and effective index of the capacity of the verbal working memory. However, its neural correlates remain elusive. The aim of this study is to investigate the neural correlates of DB in 299 healthy young adults by combining voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analyses. The VBM analysis showed positive correlations between the DB scores and the gray matter volumes in the right anterior superior temporal gyrus (STG), the right posterior STG, the left inferior frontal gyrus and the left Rolandic operculum, which are four critical areas in the auditory phonological loop of the verbal working memory. Voxel-based correlation analysis was then performed between the positive rsFCs of these four clusters and the DB scores. We found that the DB scores were positively correlated with the rsFCs within the salience network (SN), that is, between the right anterior STG, the dorsal anterior cingulate cortex and the right fronto-insular cortex. We also found that the DB scores were negatively correlated with the rsFC within an anti-correlation network of the SN, between the right posterior STG and the left posterior insula. Our findings suggest that DB performance is related to the structural and functional organizations of the brain areas that are involved in the auditory phonological loop and the SN.
A combinational feature selection and ensemble neural network method for classification of gene expression data
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-136
Abstract: We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets.Thus, we conclude that our methods can obtain more information in microarray data to get more accurate classification and also can help to extract the latent marker genes of the diseases for better diagnosis and treatment.With the successful completion of the Human Genome Project (HGP), we are entering the post genomic era. Facing mass amounts of data, traditional biological experiments and data analysis techniques encounter great challenges. In this situation, cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies as global (genome-wide or system-wide) experimental approaches that are effectively used in systematical analysis of large-scale genome data. In recent years, with its ability to measure simultaneously the activities and interactions of thousands of genes, microarray promises new insights into the mechanisms of living systems and is attracting more and more interest for solving scientific problems and in industrial applications. Meanwhile, further biological and medical research also promoted the development and application of microarray.Typical issues addressed by microarray experiments include two main aspects: finding co-regulated genes for classification based on different cell-type [1], stage-specific [2,3], disease-related [4-6], or treatment-related [6-8] patterns of gene expression and understanding gene regulatory networks by analyzing functional roles of genes in cellular processes [9,10]. Here we focus on the former, especially on tumor classification using gene expression data, which is a hot topic in recent years and has received general attention by many biological and medical researchers [11-19]. A rel
Esub8: A novel tool to predict protein subcellular localizations in eukaryotic organisms
Qinghua Cui, Tianzi Jiang, Bing Liu, Songde Ma
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-66
Abstract: Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test.Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms.Over recent years the number of new genomes and protein sequences has increased dramatically. Therefore, reliable and efficient sequence analysis tools are urgently needed. The native subcellular localization of a protein is important for understanding gene/protein function. Aberrant subcellular localization of proteins has been observed in the cells of several diseases, such as cancer and Alzheimer's disease [1]. Therefore, knowing the protein's localization will be one important step identifying its function. Even if we already know a protein's function, information about protein localization may provide us ins
The Development of Visual Areas Depends Differently on Visual Experience
Wen Qin, Yong Liu, Tianzi Jiang, Chunshui Yu
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0053784
Abstract: Visual experience plays an important role in the development of the visual cortex; however, recent functional imaging studies have shown that the functional organization is preserved in several higher-tier visual areas in congenitally blind subjects, indicating that maturation of visual areas depend unequally on visual experience. In this study, we aim to validate this hypothesis using a multimodality MRI approach. We found increased cortical thickness in the congenitally blind was present in the early visual areas and absent in the higher-tier ones, suggesting that the structural development of the visual cortex depends hierarchically on visual experience. In congenitally blind subjects, the decreased resting-state functional connectivity with the primary somatosensory cortex was more prominent in the early visual areas than in the higher-tier ones and were more pronounced in the ventral stream than in the dorsal one, suggesting that the development of functional organization of the visual cortex also depends differently on visual experience. Moreover, congenitally blind subjects showed normal or increased functional connectivity between ipsilateral higher-tier and early visual areas, suggesting an indirect corticocortical pathway through which somatosenroy information can reach the early visual areas. These findings support our hypothesis that the development of visual areas depends differently on visual experience.
Graph Theoretical Approaches in Brain Networks
Fabrizio De Vico Fallani,Danielle Bassett,Tianzi Jiang
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/590483
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