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111.
Colussi C Banfi C Brioschi M Tremoli E Straino S Spallotta F Mai A Rotili D Capogrossi MC Gaetano C 《Proteomics. Clinical applications》2010,4(1):71-83
Purpose: Histone Deacetylase Inhibitors (DI) ameliorates dystrophic muscle regeneration restoring muscular strength in the mdx mouse model of Duchenne muscular dystrophy (DMD). The further development of these compounds as drugs for DMD treatment is currently hampered by the lack of knowledge about DIs effect in large dystrophic animal models and that of suitable biomarkers to monitor their efficacy. Experimental design: In this study we applied proteomic analysis to identify differentially expressed proteins present in plasma samples from mdx mice treated with the Suberoylanilide hydroxamic acid (SAHA) and relative normal controls (WT). Results: Several differentially expressed proteins were identified between untreated wild type and mdx mice. Among these, fibrinogen, epidermal growth factor 2 receptor, major urinary protein and glutathione peroxidase 3 (GPX3) were constitutively up‐regulated in mdx, while complement C3, complement C6, gelsolin, leukaemia inhibitory factor receptor (LIFr), and alpha 2 macroglobulin were down‐regulated compared to WT mice. SAHA determined the normalization of LIFr and GPX3 protein level while apoliprotein E was de novo up‐regulated in comparison to vehicle‐treated mdx mice. Conclusions and clinical relevance: Collectively, these data unravel potential serological disease biomarkers of mdx that could be useful to monitor muscular dystrophy response to DI treatment. 相似文献
112.
A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling. 相似文献
113.
Donggang Yu Author Vitae Tuan D. Pham Author Vitae Author Vitae Stephen T.C. Wong Author Vitae 《Pattern recognition》2009,42(4):498-508
Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method. 相似文献
114.
In this paper, we describe a formal framework for conformance testing of continuous and hybrid systems, using the international
standard ‘Formal Methods in Conformance Testing’ FMCT. We propose a novel test coverage measure for these systems, which is
defined using the star discrepancy notion. This coverage measure is used to quantify the validation ‘completeness’. It is
also used to guide input stimulus generation by identifying the portions of the system behaviors that are not adequately examined.
We then propose a test generation method, which is based on a robotic motion planning algorithm and is guided by the coverage
measure. This method was implemented in a prototype tool that can handle high dimensional systems (up to 100 dimensions). 相似文献
115.
Optimal assembly plan generation: a simplifying approach 总被引:3,自引:0,他引:3
The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble
a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods
to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the
approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept,
the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance
criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show
a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly
sequence modeling allows to handle more complex products with a large number of parts. 相似文献
116.
Lena Cibulski Denis Gračanin Alexandra Diehl Rainer Splechtna Mai Elshehaly Claudio Delrieux Krešimir Matković 《The Visual computer》2016,32(6-8):847-857
Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach. 相似文献
117.
Vo Ngoc Phu Vo Thi Ngoc Chau Vo Thi Ngoc Tran 《International Journal of Speech Technology》2017,20(3):487-508
Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set. 相似文献
118.
Vo Ngoc Phu Vo Thi Ngoc Tran Vo Thi Ngoc Chau Nguyen Duy Dat Khanh Ly Doan Duy 《International Journal of Speech Technology》2017,20(3):593-613
Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results. 相似文献
119.
Tuan D. Pham Author Vitae 《Pattern recognition》2011,44(5):1023-1030
The paradigm of the permanence of updating ratios, which is a well-proven concept in experimental engineering approximation, has recently been utilized to construct a probabilistic fusion approach for combining knowledge from multiple sources. This ratio-based probabilistic fusion, however, assumes the equal contribution of attributes of diverse evidences. This paper introduces a new framework of a fuzzy probabilistic data fusion using the principles of the permanence of ratios paradigm, and the theories of fuzzy measures and fuzzy integrals. The fuzzy sub-fusion of the proposed approach allows an effective model for incorporating evidence importance and interaction. 相似文献
120.
Tich Phuoc Tran Thi Thanh Sang Nguyen Poshiang Tsai Xiaoying Kong 《Artificial Intelligence Review》2011,35(4):369-382
In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast
growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend
without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning
technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models
are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics
of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy
and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace
Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative
diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy
with relatively small computational complexity compared with other conventional detection methods. 相似文献