Interpreting XML keyword query using hidden Markov model

Pretraživanje ključne riječi na XML bazi podataka privuklo je prilično zanimanja. Kako se XML dokumenti vrlo razlikuju od plošnih (flat) dokumenata, učinkovita pretraga XML dokumenata zahtijeva posebno razmatranje. Tradicionalni model vreće riječi (bag-of-words) ne uzima u obzir uloge ključnih riječi i odnos između ključnih riječi pa prema tome nije pogodan za XML pretragu ključne riječi. U ovom radu predstavljamo novi model, nazvan polu-strukturno pretraživanje ključne riječi (SSQ), koji podrazumijeva pretraživanje ključne riječi na različit način; to se pretraživanje sastoji od nekoliko cjelina pretrage i svaka cjelina predstavlja stanje pretrage (query condition). Za interpretaciju pretrage po tom modelu, potrebna su dva koraka. Prvo, predlažemo probabilistički pristup zasnovan na skrivenom Markovljevom modelu za izračunavanje najboljeg uklapanja traženih ključnih riječi u termine baze podataka, tj. elemenata, atributa i vrijednosti. Drugo, generiramo konstrukcije ključnih riječi (SSQs) na osnovu uklapanja. Eksperimentalni rezultati potvrđuju učinkovitost naših metoda.Keyword search on XML database has attracted a lot of research interests. As XML documents are very different from flat documents, effective search of XML documents needs special considerations. Traditional bag-of-words model does not take the roles of keywords and the relationship between keywords into consideration, and thus is not suited for XML keyword search. In this paper, we present a novel model, called semi-structured keyword query (SSQ), which understands a keyword query in a different way: a keyword query is composed of several query units, where each unit represents query condition. To interpret a keyword query under this model, we take two steps. First, we propose a probabilistic approach based on a Hidden Markov Model for computing the best mapping of the query keywords into the database terms, i.e., elements, attributes and values. Second, we generate SSQs based on the mapping. Experimental results verify the effectiveness of our methods

Overexpression of Testes-Specific Protease 50 (TSP50) Predicts Poor Prognosis in Patients with Gastric Cancer

Purpose. To investigate the expression of TSP50 protein in human gastric cancers and its correlation with clinical/prognostic significance. Methods. Immunohistochemistry (IHC) analysis of TSP50 was performed on a tissue microarray (TMA) containing 334 primary gastric cancers. Western blot was carried out to confirm the expression of TSP50 in gastric cancers. Results. IHC analysis revealed high expression of TSP50 in 57.2% human gastric cancer samples (191 out of 334). However, it was poorly expressed in all of the 20 adjacent nontumor tissues. This was confirmed by western blot, which showed significantly higher levels of TSP50 expression in gastric cancer tissues than adjacent nontumor tissues. A significant association was found between high levels of TSP50 and clinicopathological characteristics including junior age at surgery (P=0.001), later TNM stage (P=0.000), and present lymph node metastases (P=0.003). The survival of gastric cancer patients with high expression of TSP50 was significantly shorter than that of the patients with low levels of TSP50 (P=0.021). Multivariate Cox regression analysis indicated that TSP50 overexpression was an independent prognostic factor for gastric cancer patients (P=0.017). Conclusions. Our data demonstrate that elevated TSP50 protein expression could be a potential predictor of poor prognosis in gastric cancer patients

Reduction of Na/K-ATPase potentiates marinobufagenin-induced cardiac dysfunction and myocyte apoptosis

Background: Na/K-ATPase decrease has been reported in patients with heart failure and is related to cardiac dysfunction. Results: Reducing Na/K-ATPase activates caspase 9 and induces cardiac dilation when treated with marinobufagenin. Conclusion: Reduction of Na/K-ATPase potentiates marinobufagenin-induced cardiac myocyte apoptosis. Significance: Decreased Na/K-ATPase content together with increased cardiotonic steroids levels is a novel mechanism that may account for cardiac dysfunction

The invasion of tobacco mosaic virus RNA induces endoplasmic reticulum stress-related autophagy in HeLa cells

The ability of human cells to defend against viruses originating from distant species has long been ignored. Owing to the pressure of natural evolution and human exploration, some of these viruses may be able to invade human beings. If their ‘fresh’ host had no defences, the viruses could cause a serious pandemic, as seen with HIV, SARS (severe acute respiratory syndrome) and avian influenza virus that originated from chimpanzees, the common palm civet and birds, respectively. It is unknown whether the human immune system could tolerate invasion with a plant virus. To model such an alien virus invasion, we chose TMV (tobacco mosaic virus) and used human epithelial carcinoma cells (HeLa cells) as its ‘fresh’ host. We established a reliable system for transfecting TMV-RNA into HeLa cells and found that TMV-RNA triggered autophagy in HeLa cells as shown by the appearance of autophagic vacuoles, the conversion of LC3-I (light chain protein 3-I) to LC3-II, the up-regulated expression of Beclin1 and the accumulation of TMV protein on autophagosomal membranes. We observed suspected TMV virions in HeLa cells by TEM (transmission electron microscopy). Furthermore, we found that TMV-RNA was translated into CP (coat protein) in the ER (endoplasmic reticulum) and that TMV-positive RNA translocated from the cytoplasm to the nucleolus. Finally, we detected greatly increased expression of GRP78 (78 kDa glucose-regulated protein), a typical marker of ERS (ER stress) and found that the formation of autophagosomes was closely related to the expanded ER membrane. Taken together, our data indicate that HeLa cells used ERS and ERS-related autophagy to defend against TMV-RNA

Returning Clustered Results for Keyword Search on XML Documents

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. In this paper, we address the problem of returning clustered results for keyword search on XML documents. We first propose a novel semantics for answers to an XML keyword query. The core of the semantics is the conceptually related relationship between keyword matches, which is based on the conceptual relationship between nodes in XML trees. Then, we propose a new clustering methodology for XML search results, which clusters results according to the way they match the given query. Two approaches to implement the methodology are discussed. The first approach is a conventional one which does clustering after search results are retrieved; the second one clusters search results actively, which has characteristics of clustering on the fly. The generated clusters are then organized into a cluster hierarchy with different granularities to enable users locate the results of interest easily and precisely. Experimental results demonstrate the meaningfulness of the proposed semantics as well as the efficiency of the proposed methods

ARMA-Based Segmentation of Human Limb Motion Sequences

With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing

Distributed Multi-Target Search and Surveillance Mission Planning for Unmanned Aerial Vehicles in Uncertain Environments

In this paper, a distributed, autonomous, cooperative mission-planning (DACMP) approach was proposed to focus on the problem of the real-time cooperative searching and surveillance of multiple unmanned aerial vehicles (multi-UAVs) with threats in uncertain and highly dynamic environments. To deal with this problem, a time-varying probabilistic grid graph was designed to represent the perception of a target based on its a priori dynamics. A heuristic search strategy based on pyramidal maps was also proposed. Using map information at different scales makes it easier to integrate local and global information, thereby improving the search capability of UAVs, which has not been previously considered. Moreover, we proposed an adaptive distributed task assignment method for cooperative search and surveillance tasks by considering the UAV motion environment as a potential field and modeling the effects of uncertain maps and targets on candidate solutions through potential field values. The results highlight the advantages of search task execution efficiency. In addition, simulations of different scenarios show that the proposed approach can provide a feasible solution for multiple UAVs in different situations and is flexible and stable in time-sensitive environments

Weighting tags and paths in XML documents according to their topic generalization

Text-centric (or document-centric) XML document retrieval aims to rank search results according to their relevance to a given query. To do this, most existing methods mainly rely on content terms and often ignore an important factor - the XML tags and paths, which are useful in determining the important contents of a document. In some previous studies, each unique tag/path is assigned a weight based on domain (expert) knowledge. However, such a manual assignment is both inefficient and subjective. In this paper, we propose an automatic method to infer the weights of tags/paths according to the topical relationship between the corresponding elements and the whole documents. The more the corresponding element can generalize the document's topic, the more the tag/path is considered to be important. We define a model based on Average Topic Generalization (ATG), which integrates several features used in previous studies. We evaluate the performance of the ATG-based model on two real data sets, the IEEECS collection and the Wikipedia collection, from two different perspectives: the correlation between the weights generated by ATG and those set by experts, and the performance of XML retrieval based on ATG. Experimental results show that the tag/path weights generated by ATG are highly correlated with the manually assigned weights, and the ATG model significantly improves XML retrieval effectiveness. (C) 2013 Elsevier Inc. All rights reserved

Human Action Recognition Using Key-Frame Attention-Based LSTM Networks

Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference