Search

A novel parallel mechanism of two translations and one rotation freedom degrees two-prismatic joint-cylindrical joint-one-cylindrical joint-prismatic joint-revolute joint (2PC-CPR) is proposed. The mechanism can be applied to write Chinese characters and to classify productions with the appropriated control. In this article, the kinematics and dynamics analysis are systematically conducted with the following procedure. First of all, the 2PC-CPR parallel mechanism is designed by GF set and the freedom degree of the mechanism is calculated using screw theory. Then the formula for solving the inverse/forward displacement, velocity, and acceleration is derived based on the geometrical constraints. The dynamics model is established by using virtual work principle. Finally, kinematics SimMechanics model is created by the co-simulation of SolidWorks and MATLAB software, and its workspace is analyzed.

Objective HJURP (Holliday Junction-Recognizing Protein) plays dual roles in DNA repair and in accurate chromosome segregation during mitosis. We examined whether the single nucleotide polymorphisms (SNPs) of HJURP were associated with the risk of occurrence of hepatocellular carcinoma (HCC) among chronic hepatitis B virus (HBV) carriers from well-known high-risk regions for HCC in China. Methods Twenty-four haplotype-tagging SNPs across HJURP were selected from HapMap data using the Haploview software. We genotyped these 24 SNPs using the using Sequenom's iPLEX assay in the Fusui population, consisting of 348 patients with HCC and 359 cancer-free controls, and further investigated the significantly associated SNP using the TaqMan assay in the Haimen population, consisting of 100 cases and 103 controls. The genetic associations with the risk of HCC were analyzed by logistic regression. Results We observed an increased occurrence of HCC consistently associated with A/C or C/C genotypes of the non-synonymous SNP rs3771333 compared with the A/A genotype in both the Fusui and Haimen populations, with a pooled odds ratio 1.82 (95% confidence interval, 1.33–2.49; P = 1.9 × 10−4). Case-only analysis further indicated that carriers of the at-risk C allele were younger than those carrying the A/A genotype (P = 0.0016). In addition, the expression levels of HJURP in C allele carriers were lower than that in A/A genotype carriers (P = 0.0078 and 0.0010, for mRNA and protein levels, respectively). Conclusion Our findings suggest that rs3771333 in HJURP may play a role in mediating the susceptibility to HCC among Chinese.

A Kretschmann (KR) and reverse Kretschmann (RK) dual-mode surface plasmon coupled emission (SPCE) imaging apparatus based on prism coupling was built up. Highly directional and polarized fluorescence images for both RK and KR configurations were obtained. Besides, surface plasmon field-enhanced fluorescence and free space imaging can also be measured conveniently from this apparatus. Combining the high sensitivity of KR mode and the simplicity of RK mode, the multifunctional imaging system is flexible to provide different configurations for imaging applications. Compared to the free space imaging, SPCE imaging provides enhanced fluorescence, especially large enhancement up to about 50 fold in KR configuration. Additionally, the degree of evanescent field enhancement effect was easily estimated experimentally using the apparatus to compare the different imaging configurations. We believed that the dual-mode SPCE imaging apparatus will be useful in fundamental study of plasmon-controlled fluorescence and be a powerful tool for optical imaging, especially for microarray and biological applications.

Sepsis represents a systemic inflammatory response to infection and its sequelae include severe sepsis, septic shock, multiple organ dysfunction syndrome (MODS) and death. Studies in mice and humans indicate that the inducible nitric oxide synthase (iNOS, NOS2) plays an important role in the development of sepsis and its sequelae. It was reported that several single nucleotide polymorphisms (SNPs) within NOS2 could influence the production or activity of NOS2. In this study, we assessed whether SNPs within NOS2 gene were associated with severity of sepsis in Chinese populations. A case–control study was conducted, which included 299 and 280 unrelated patients with sepsis recruited from Liaoning and Jiangsu provinces in China, respectively. Six SNPs within NOS2 were genotyped using Sequenom MassARRAY system. The associations between the SNPs and risk of sepsis complications were estimated by a binary logistic regression model adjusted for confounding factors. Functional assay was performed to assess the biological significance. The GA + AA genotype of a non-synonymous SNP in the exon 16 of NOS2 (rs2297518: G>A) was significantly associated with increased susceptibility to septic shock compared with GG genotype in Liaoning population (OR = 3.29, 95 % CI = 1.40–7.72, P = 0.0047). This association was confirmed in the Jiangsu population (OR = 3.49, 95 % CI = 1.57–7.79, P = 0.0019). Furthermore, the functional assay performed in the immortalized lymphocyte cell lines indicated that the at-risk GA genotype had a tendency of higher NOS2 activity than the GG genotype (P = 0.32). Our findings suggest that the NOS2 rs2297518 may play a role in mediating the susceptibility to septic shock in patients with sepsis in Chinese populations.

This study was designed to investigate the effects of a cyclooxygenase (COX)-1 inhibitor, SC-560, administered in combination with taxol, on the molecular mechanisms of antitumor efficacy in a SKOV-3 human ovarian carcinoma cell xenograft-bearing mouse model. The mice were treated with 6 mg/kg/day SC-560 by gavage twice every other day and 20 mg/kg taxol by intraperitoneal injection once a week for three weeks. Microvessel density (MVD) and vascular endothelial growth factor (VEGF) mRNA levels of ovarian cancer were detected in the tumor tissues using immunohistochemistry and reverse transcription-polymerase chain reaction (RT-PCR), respectively. The index of proliferating and apoptotic cells in the tumor tissues was determined by staining for Ki-67 and using the terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) method, respectively. On day 7 after the end of administration, the tumor volume of mice in the combination group was reduced by 55.35% compared with that of the control mice, and the difference was statistically significant (P

The exact etiology and pathogenesis of knee osteoarthritis (KOA) are still unknown and it is hard to treat the disease fundamentally. With new therapeutic methods and techniques appearing, the present situation of treating the disease will be changed in the near future. Basic research of knee osteoarthritis will contribute to clarifying the pathogenesis and exploring the therapeutic methods. This article makes a brief review on the up-to-date basic researches of knee osteoarthritis by reviewing literature concerned in recent years.

China launched formal negotiations to join the GPA in the December 28, 2007; On July 19, 2010 the WTO Government Procurement Committee submitted a revised bid list, the door of China’s government procurement market will be open. Tianjin is one of four municipalities in China, the economy is large in scale, rapid development, and the government procurement market potential is also large. In Joining GPA, Tianjin government procurement will face opportunities and challenges. Therefore, this research needs to adapt to the situation and development, provide reference information for Tianjin Joining GPA.

Background Increases in human telomerase reverse transcriptase (TERT) expression and telomerase activity are frequently seen in nasopharyngeal carcinoma (NPC). Recently, a variable tandem-repeats polymorphism, MNS16A, located in the downstream region of the TERT gene, was identified and reported to have an effect on TERT expression and telomerase activity. We examined whether the functional MNS16A was related to the risk of occurrence or progression of NPC in the Chinese population. Methods We genotyped the MNS16A polymorphism in a case-control study of 855 patients with NPC and 1036 cancer-free controls using PCR, and determined genotype by classifying the DNA band of 243 or 272 base pairs (bp) as the short (S) allele and 302 or 333 bp as the long (L) allele. The genetic associations with the risk of NPC were analyzed by logistic regression. Results The MNS16A genotype was not associated with the progression of NPC. However, individuals carrying the S alleles (SL + SS genotype) had a significantly reduced risk of NPC occurrence compared with those carrying the LL genotype (odds ratio (OR) = 0. 71, 95% confidence interval (CI) = 0. 52 to 0. 96, P = 0. 025). Using a immunohistochemical assay on the NPC tissues, the SL genotype carriers were found to have lower TERT expression than the LL genotype carriers (P = 0. 035). Conclusions Our study indicates that the TERT MNS16A polymorphism may contribute to the risk of NPC onset in Chinese population.

We present a novel application for searching for vehicles in surveillance videos based on semantic attributes. At the interface, the user specifies a set of vehicle characteristics (such as color, direction of travel, speed, length, height, etc.) and the system automatically retrieves video events that match the provided description. A key differentiating aspect of our system is the ability to handle challenging urban conditions such as high volumes of activity and environmental factors. This is achieved through a novel multi-view vehicle detection approach which relies on what we call motionlet classifiers, i.e. classifiers that are learned with vehicle samples clustered in the motion configuration space. We employ massively parallel feature selection to learn compact and accurate motionlet detectors. Moreover, in order to deal with different vehicle types (buses, trucks, SUVs, cars), we learn the motionlet detectors in a shape-free appearance space, where all training samples are resized to the same aspect ratio, and then during test time the aspect ratio of the sliding window is changed to allow the detection of different vehicle types. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as "Show me all blue trucks larger than 7ft length traveling at high speed northbound last Saturday, from 2pm to 5pm".

Background Baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5, also called as survivin) is a member of the inhibitor of apoptosis protein (IAP) family, which plays an important role in the occurrence and progression of cancer. Recently, a polymorphism in the promoter of BIRC5, -31C/G (rs9904341), was shown to influence BIRC5 expression. Methods We examined whether the -31C/G was related to the risk of developing nasopharyngeal carcinoma (NPC) in a case-control population from Guangxi province in southern China, which consists of 855 patients with NPC and 1036 controls. This polymorphism was genotyped by TaqMan assay. The genetic associations with the occurrence and progression of NPC were estimated by logistic regression. Results We observed a statistically significant increased occurrence of NPC associated with the CC genotype (odds ratio [OR], 1.40; 95% confidence interval [CI], 1.13–1.73; P = 0.0020) compared with the genotypes containing G allele (CG + GG genotype). However, no significant association was observed for the -31C/G with the severity of NPC (as measured by tumor-node-metastasis staging system). Conclusion Our findings suggest that the functional polymorphism -31C/G in the promoter of BIRC5 gene may play a role in mediating the susceptibility to NPC among Chinese.

As an important component of social order, the students’ credit has become an important issue. The credit of college students is one of the most important credits, also, an important part of social trust. For the risk of loans for students, the paper proposed a novel loan risk prevention mechanism named “Six Components into One”, which not only needs the public administrators to standardize funding legislation, establish and improve the personal credit system, but more importantly, to strengthen the moral education such that it improves students credit level.

According to Holland’s Statistics for occupational interest, we compiled the Preliminary Questionnaire of the Undergraduates’ Occupational Interests. Then questionnaire of the Undergraduates’ Occupational Interests was formed by the confirmatory factor analysis and correct of the questionnaire. Finally, it has been used to conduct a poll among some students of Liao Cheng University, and the result has been analyzed by SPSS statistics software. The result shows that the situation of career interest development from high to low include: art type, no differentiation, enterprise type, research-based type, conventional type, reality type and social type. More importantly, it is good to improve the guidance for the orientation of undergraduates’ occupational interest.

A relationship between pairwise comparison classical numerical ranking and a partial order based pairwise comparison nonnumerical ranking is discussed. A consistent non-numerical ranking that is equivalent to a given consistent numerical ranking is constructed and the correctness of this construction is proven. A new concept of consistency for the pairwise comparison classical numerical ranking is given.

Nanocomposites of polystyrene loaded with various amounts of anatase (ranging between 0 % wt. and 20 % wt.) have been synthesized and investigated by thermal analysis. The research was focused on the simulation of Thermogravimetric Analysis data, aiming to a refined understanding of the interactions between of polystyrene and TiO2 nanoparticles. The dependence of the first derivative of residual mass on temperature has been used to determine more accurately the temperature at which the mass loss is maxim. Several functions have been used to simulate the dependence of the first derivative of mass loss on the temperature of the sample. The highest correlation coefficient was obtained for the asymmetric Gaussian combination, which connects two halves of a Gaussian line with different linewidth. An increase of the thermal stability of the polymeric matrix upon loading with TiO2 is reported.

The title reaction represents an atom-economical and environmentally friendly transformation with high functional-group tolerance.

Learning in the presence of data imbalances presents a great challenge to machine learning. Imbalanced data sets represent a significant problem because the corresponding classifier has a tendency to ignore samples which have smaller representation in the training sets. In this paper, we propose an ensemble-based learning algorithm as a new ensemble classifier model called as SVM-C5.0 Ensemble Classifier Model, SCECM. SCECM adopts a differentiated sampling rate algorithm (DSRA) based on an improved Adaboost algorithm and further employs unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes.

It has been observed that classification in imbalanced data sets have drawn more attention to researchers in knowledge discovery and data mining fields. In such problems, almost all the samples are labeled as one class, while far fewer samples are labeled as the other class, which are usually more important. But traditional classifiers that try to pursue whole accurate performance over a full range of samples are not suitable to deal with classification in imbalanced data sets, since they tend to biases towards majority class while pay less attention to the rare one. In the present work, we perform a review of the most important research lines on this topic and point out several directions for further investigation.

The report aims to investigate the relationship between the expression of cyclin D1 and Cyclooxgenase-2 (COX-2), thus to explore the molecular mechanisms of the antitumor efficacy of Celecoxib, a COX-2 inhibitor. Human ovarian SKOV-3 carcinoma cell xenograft-bearing mice were treated with Celecoxib by infusing gaster (i.g.) twice/day for 21 days. The mRNA levels of COX-2 and cyclin D1 were determined by RT-PCR. The expression of cyclin D1 at the protein level was detected by immunohistochemistry, while COX-2 protein expression was determined by Western blot. A high-dose of Celecoxib (100 mg/kg) significantly inhibited tumor growth (P < 0.05), and the expression of cyclin D1 was reduced by 61%. Celecoxib decreased the proliferation cell index by 40% (P < 0.001) and increased apoptotic index by 52% (P < 0.05) in high-dose Celecoxib treated group. Our results suggest that the antitumor efficacy of Celecoxib against ovarian cancer in mice may in part be mediated through suppression of cyclin D1, which may contribute to its ability to suppress proliferation.

This paper presents the design and implementation of meteorological data display system based on SQL Server meteorological data warehouse. The purpose is to apply data warehouse to the field of meteorological research. The system uses SQL Server Reporting Services platform to generate meteorological data report and shows the reports based on two-dimensional maps. Moreover, the system is published to the internet in Web form to provide support for meteorological research work.

Protein Secondary structure prediction is essential for the tertiary structure modeling, and it is the one of the major challenge of bioinformatics. In this article, we proposed a gradually enhanced, multi-layered prediction systematic model, Compound Pyramid Model (CPM). This model is composed of four independent coordination's layers by intelligent interfaces, synthesizes several methods, such as SVM, KDD* process model and so on. The model penetrates the whole domain knowledge, and the effective attributes are chosen by Causal Cellular Automata, and the high pure structure database is constructed for training. On the RS126 data set, state overall per-residue accuracy, Q3, reached 83.99%. On the CB513 data set, Q3 reached 85.58%. Meanwhile, on the CASP8's sequences, the results are found to be superior to those produced by other methods, such as SAM, PSI-Blast, Prospect, JUFO, and so on. The result shows that our method has strong generalization ability.

According to the existing theories of soil mechanics and fluid mechanics, as well as the features about the water and soil curves of unsaturated expansive soil. It is obtained the equations about gaseous water transfer and liquid water transfer of unsaturated expansive soil. It is be seen from coefficients in the equation that water migration bringing water content changes relating to moisture gradient, moisture level, temperature, matric suction, as well as soil density and the ratio of the volume. In different soil the water curve parameter is different, then the coefficients of differential equations is also different. As a example calculation using this equation on water content changes caused by gaseous wate migration, gaseous and water mixed migration, we see that the computed results are consistent with the experimental results very well. It is verified that the equation is rational.

The computer networks experiment is practised in applied universities. Ii is necessary that the course of design thought should be established. Network software should be installed and experiment environment of network devices should be built perfectly. The teacher should design the comprehensive experiment contents and use bilingual teaching. The purpose is to cultivate network technology engineers with high quality, and then they will serve the construction of district's economy better.

On the basis of the gas state equation, fluid mechanics, soil mechanics theory and characteristics of unsaturated expansive soil. The water vapour flow per unit area method of calculating is geted. It revealed that the water vapour flow is co-decision by temperature, soil and water situation. and it is directly proportional to the suction gradient. and it has non-linear relationship between soil porosity, water saturation, the absolute temperature and suction. According to the calculating of test data to illustrate the feasibility of the method of calculation, in order to further study lay the foundation for migration of water vapour.

From a management point of mechanism innovation, this paper opens up the new research ideas for management of poor college students funded. In order to further support poor students in university management, we should improve our management systems supporting poor students in colleges and universities, and continuously improve the existing subsidy policy management mechanism. Also, we should continue to intensify efforts to improve funded regulatory body for the poor students.

Methods for predicting protein secondary structure provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a method of improved SVM for predicting protein secondary structure. Using evolutionary information contained in amino acid's physicochemical properties, position-specific scoring matrix generated by psi-blast as input to improved SVM, secondary structure can be predicted at significantly increased accuracy. Based on KDTICM theory, we have constructed a compound pyramid model, which is composed of four layers of the intelligent interface and integrated in several ways, such as improved SVM, mixed-modal BP, KDD* method and so on. On the RS126 data set, state overall per-residue accuracy, Q3 reached 83.06%, while SOV99 accuracy increased to 80.6%.On the CB513 data set, Q3 reached 80.49%, SOV99 accuracy increased to 79.84%.This article briefly introduces this model and highlights the improved SVM method.

In this paper, we propose a method of homology analysis for predicting protein secondary structure. The BP network model is based on pair-wise sequence alignment, and the accuracy of the prediction is improved. In the modeling stage, we use penalty function in the pair-wise sequence alignment and the result of alignment is put into the training set of the BP network. Considering the information of neighboring residues of the protein sequence, we use the BP network in modeling stage. At last, we apply this homology analysis method to the homology analysis module located at integrative layer of compound pyramid model proposed by us and get better results in our experiment comparing with other methods.

Accurate secondary-structure prediction is a key element in the prediction of tertiary structure, in all but the simplest homology modeling cases. After the study on this subject for 30 years and more, there have been some breakthroughs. Based on KDTICM theory, we have proposed a model, which is composed of four layers of the intelligent interface and integrated in several ways, such as SVM, KDD*, homology analysis and so on. Experiment shows that this model obtains Q3 accuracy of 83.06%, 80.49% on RS126 and CB513, and for the proteins which contain more alpha/beta structure, the Q3 accuracy obtained is 93.12%. This article briefly introduces this model and highlights the improved SVM method.

Visual processing of people, including detection, tracking, recognition, and behavior interpretation, is a key component of intelligent video surveillance systems. Computer vision algorithms with the capability of “looking at people” at multiple scales can be applied in different surveillance scenarios, such as farfield people detection for wide-area perimeter protection, mid-field people detection for retail/banking applications or parking lot monitoring, and near-field people/face detection for facility security and access. In this chapter, we address the people detection problem in different scales as well as human tracking and motion analysis for real video surveillance applications including people search, retail loss prevention, people counting, and display effectiveness.

Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of video analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of video analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.

Virtual boundary (tripwire) crossing detection is an essential component in almost all modern digital visual surveillance systems. In this paper, we address the problem of achieving reliable tripwire crossing results in crowded scenarios and propose a new technique for the replacement of conventional tracking based tripwire techniques. We introduce the concept of "ground patches" which are a set of sub-regions sampled around the defined tripwire. Each ground patch is declared active when it is occupied by one or more foreground objects. Temporal and appearance features are extracted from each of the patches, and they are correlated across the virtual boundary to determine if a crossing occurs. The proposed method has been applied in situations with different traffic loads, and promising results are obtained.

Video analytics have recently emerged as a promising technique of retail fraud detection for loss prevention. Efficient video analytic algorithms are highly desired for a practical fraud detection system. In this paper, we present a real-time algorithm for recognizing a cashier's actions at the Point of Sale (POS), which can be further used to analyze cashier behaviors for identifying fraudulent incidents. The algorithm uses a set of simple but effective features derived from a global representation of motion energy called Polar Motion Map (PMM). These features capture the motion patterns exhibited in a cashier's actions as a focused beam of motion energy, characterizing the actions as the extension and retraction movement of the cashier's arm with respect to a prespecified region. Our algorithm demonstrates comparable accuracy against one of the state-of-the-art event recognition techniques [1] while running significantly faster.

We present a novel framework for recognizing repetitive sequential events performed by human actors with strong temporal dependencies and potential parallel overlap. Our solution incorporates sub-event (or primitive) detectors and a spatiotemporal model for sequential event changes. We develop an effective and efficient method to integrate primitives into a set of sequential events where strong temporal constraints are imposed on the ordering of the primitives. In particular, the combination process is approached as an optimization problem. A specialized Viterbi algorithm is designed to learn and infer the target sequential events and handle the event overlap simultaneously. To demonstrate the effectiveness of the proposed framework, we report detailed quantitative analysis on a large set of cashier checkout activities in a retail store.

In virtually every business context there is a need to establish some form of monitoring system to ensure that employees comply with business processes and policies. Compliance failures range from organized theft to gaps in procedure that can be easily remedied through retraining. It is clearly important for businesses to capture and record these deviations to minimize loss prevention and maximize workplace safety and efficiency. In this workshop, we discuss the growing problem of compliance failure and how our system addresses this problem in a retail context to detect checkout-related fraud through the integration of visual and non-visual data.

At first, this paper reviews the development history of the protein secondary structure prediction. Some concerned secondary structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49% and RS126 with 82.79% respectively. In the end, this paper points out several possible trends in the protein secondary structure prediction.

Background: Lost or downexpression of the gene deleted in liver cancer 1 (DLC1) has been implicated in the development of hepatocellular carcinoma (HCC). We examined the relationship between DLC1 polymorphisms and HCC risk among Chinese. Methods: Three DLC1 polymorphisms, Ex11 + 255T > G (rs3739298), Ex11-620G > A (rs532841) and IVS19 + 108C > T (rs621554), were genotyped in 434 patients with HCC and 480 controls by PCR-RFLP. The associations with the susceptibility to HCC were evaluated while controlling for confounding factors. Results: We observed significantly increased susceptibility to HCC for the C/C genotype compared with T/T of IVS19 + 108C > T in the HBV carriers (OR = 2.95, 95% CI, 1.65–5.26, P G, Ex11-620G > A and IVS19 + 108C > T), the haplotype T-G-C was also significantly associated with an increased susceptibility to HCC among HBV carriers (OR = 2.16, 95% CI, 1.08–4.35, P = 0.009). The stratified analysis indicated no modification of the confounding factors on the increased susceptibility to HCC related to the DLC1 polymorphism/haplotype. Conclusions: Our findings suggest that DLC1 genetic polymorphism or haplotype play a role in mediating the susceptibility to HBV-related HCC.

A significant portion of retail shrink is attributed to employees and occurs around the point of sale (POS). In this paper, we target a major type of retail fraud in surveillance videos, known as sweethearting (or fake scan), where a cashier intentionally fails to enter one or more items into the transaction in an attempt to get free merchandise for the customer. We first develop a motion-based algorithm to identify video segments as candidates for primitive events at the POS. We then apply spatio-temporal features to recognize true primitive events from the candidates and prune those falsely alarmed. In particular, we learn location-aware event models by Multiple-Instance Learning to address the location-sensitive issues that appear in our problem. Finally, we validate the entire transaction by combining primitive events according to temporal ordering constraints. We demonstrate the effectiveness of our approach on data captured from a real grocery store.

The moving least-square technique is used to construct shape function in the Element Free Galerkin Method at present, but sometimes the algebra equations system obtained from the moving least-square approximation is ill-conditioned and the shape function needs large quantity of inverse operation. In this paper, the weighted orthogonal functions are used as basis ones, the application in the calculation of plate bending shows that the improved moving least-square approximation is effective and efficient.

Given the rapid development of digital video technologies, large-scale multi-camera networks are now more prevalent than ever. There is an increasing demand for automated multi-camera/sensor event-modeling technologies that can efficiently and effectively extract events and activities occurring in the surveillance network. In this chapter, we present a composite event detection system for multicamera networks. The proposed framework is capable of handling relationships between primitive events generated from (1) a single camera view, (2) multiple camera views, and (3) nonvideo sensors with spatial and temporal variations. Composite events are represented in the form of full binary trees, where the leaf nodes represent primitive events, the root node represents the target composite event, and the middle nodes represent rule definitions. The multi-layer design of composite events provides great extensibility and flexibility in different applications. A standardized XML-style event language is designed to describe the composite events such that inter-agent communication and event detection module construction can be conveniently achieved. In our system, a set of graphical interfaces is also developed for users to easily define both primitive and high-level composite events. The proposed system is designed in distributed form, where the system components can be deployed on separate processors, communicating with each other over a network. The capabilities and effectiveness of our system have been demonstrated in several real-life applications, including retail loss prevention, indoor tailgating detection, and false positive reduction.

To detect the expression of p16 protein and Ki-67 antigen in gastric mucosa-associated lymphoid tissue lymphoma (MALTL) and normal gastric mucosa and to investigate the clinical significance of their expression in the occurrence and development of gastric MALTL.47 samples of gastric MALTL diagnosed pathologically in the department of pathology of the PLA General Hospital from March 1993 to June 2005 were collected. By using immunohistochemical methods, the expression of p16 protein and Ki-67 antigen was detected in 20 samples of normal gastric tissue and the 47 samples of gastric MALTL.The positive rate of p16 protein was 21.3% (10/47) and 90.0% in gastric MALTL and normal gastric tissue respectively. The positive rate of p16 protein in gastric MALTL was lower than that in normal gastric tissue (P0.05). The expression of p16 was related to the degree of lymph node metastasis. The positive rate of Ki-67 labelling index (LI) in gastric MALTL was higher than that in normal gastric tissue. A negative correlation was found between the expression of p16 protein and Ki-67 LI (P0.05).Detection p16 and Ki-67 may help to predict the possibility of lymph node metastasis and prognosis in gastric MALTL.

Estrogens have been proposed to act as tumor promoters and induce hepatocarcinogenesis. Recently, we observed a significant association between the risk for hepatocellular carcinoma and the polymorphisms of the estrogen receptor (ESR) α (ESR1) gene, supporting the hypothesis of involvement for the estrogen-ESR axis in the estrogen-induced hepatocarcinogenesis. In this study, based on another hypothesis in which estrogen metabolites can directly cause DNA damage and affect tumor initiation, we examined whether the polymorphisms of the estrogen-metabolizing enzymes (EME), which are involved in biogenesis (CYP17, CYP19), bioavailability (CYP1A1, CYP1B1), and degradation (catechol-O-methyltransferase) of the estrogens, have any bearing on the risk for hepatocellular carcinoma. Seven functional polymorphisms in five EMEs (CYP17 MspAI site, CYP19 Trp39Arg, Ile462Val and MspI site in CYP1A1, CYP1B1 Val432Leu, and Ala72Ser and Val158Met in catechol-O-methyltransferase) were genotyped in 434 patients with hepatocellular carcinoma and 480 controls by PCR-RFLP analysis. The associations between the polymorphisms and hepatocellular carcinoma risk were evaluated while controlling for confounding factors. No significant association with the risk for hepatocellular carcinoma was observed with the seven polymorphisms in hepatitis B virus carriers and non–hepatitis B virus carriers after correction for multiple comparisons. After stratification by common confounding factors of hepatocellular carcinoma, the EME polymorphism remained no significant association with the hepatocellular carcinoma risk. Furthermore, no signs of gene-gene interactions were observed for each combination of the seven polymorphisms. Our findings suggest that the polymorphisms of EMEs may not contribute significantly to the risk for hepatocellular carcinoma. (Cancer Epidemiol Biomarkers Prev 2008;17(12):3621–7)

Previous studies have suggested that the functional polymorphisms in the promoters of matrix metalloproteinases (MMP) genes were associated with the risk of cancers, but no study has ever explored these polymorphisms as risk factors for hepatocellular carcinoma. Recently, we firstly examined whether seven functional polymorphisms in the promoters of MMP-1, MMP-2, MMP-3, MMP-9, MMP-12, and MMP-13 have any bearing on the risk of hepatocellular carcinoma, but we found none. In this study, we focused on an additional six MMP polymorphisms, including four functional polymorphisms in the promoters of MMP-7 (A-181G and C-153T) and MMP-8 (C-799T and A-381G), and two nonsynonymous polymorphisms in MMP-10 (A180G) and MMP-21 (C572T). With the polymorphism validation, we found that only MMP-7 A-181G, MMP-8 C-799T, and MMP-21 C572T were polymorphic. These three polymorphisms were then genotyped in 434 patients with hepatocellular carcinoma and 480 controls by PRC-RFLP analysis. The associations between the polymorphisms and hepatocellular carcinoma risk were evaluated while controlling for confounding factors. No significant association with the risk of hepatocellular carcinoma was observed with the three polymorphisms in the overall sample, hepatitis B virus carriers, and non–hepatitis B virus carriers after correction for multiple comparisons. Furthermore, when the analyses were stratified by age, sex, status of smoking and drinking, pack-years of smoking, and family history of hepatocellular carcinoma, there was also no significant association between these polymorphisms and hepatocellular carcinoma risk. Our findings suggest that the polymorphisms MMP-7 A-181G, MMP-8 C-799T, and MMP-21 C572T may not play a major role in mediating susceptibility to hepatocellular carcinoma. (Cancer Epidemiol Biomarkers Prev 2008;17(9):2514–8)

Many facial image analysis methods rely on learning-based techniques such as Adaboost or SVMs to project classifiers based on the selection of local image filters (e.g., Haar and Gabor filters) from large sets of training data. In general, the learning process consists of selecting discriminative image filters from a large feature pool that contains filters uniformly sampled from the parameter space. In this paper, we argue that we are able to improve these methods by incorporating a local feature adaptation technique prior to learning, which generates a more compact and meaningful pool of image filters, consequently reducing both learning and detection/recognition computational costs, while at the same time improving accuracies. In the first stage of our approach, local feature adaptation is carried out by a nonlinear optimization method that determines image filter parameters (such as position, orientation and scale) in order to match the geometrical structure of each training sample. In the second stage, Adaboost feature selection technique is applied to the adapted feature pool to obtain the final set of discriminative local image filters. We demonstrate the effectiveness and efficiency of the proposed framework in the face detection domain. In the experiments, we have applied our method using a pool of wavelet features, including Haar and Gabor filters. The results showed that with local feature adaptation, significant improvements in terms of detection accuracy and computational cost reduction are achieved over learning based on the same features sampled uniformly from the parameter space.

Moving object classification in far-field video is a key component of smart surveillance systems. In this paper, we propose a reliable system for person-vehicle classification which works well in challenging real-word conditions, including the presence of shadows, low resolution imagery, perspective distortions, arbitrary camera viewpoints, and groups of people. Our system runsin real-time (30 Hz) on conventional machines and has low memory consumption. We achieved accurate results by relying on powerful discriminative features, including a novel measure of object deformation based on differences of histograms of oriented gradients. We also provide an interactive user interface, enabling users to specify regions of interest for each class and correct for perspective distortions by specifying different sizes indifferent positions of the camera view. Finally, we use anautomatic adaptation process to continuously update the parameters of the system so that its performance increases for a particular environment. Experimental results demonstrate the effectiveness of our system in standard dataset and a variety of video clips captured with our surveillance cameras.

We describe a set of tools for retail analytics based on a combination of video understanding and transaction-log. Tools are provided for loss prevention (returns fraud and cashier fraud), store operations (customer counting) and merchandising (display effectiveness). Results are presented on returns fraud and customer counting.

Surveillance video is used in two key modes, watching for known threats in real-time and searching for events of interest after the fact. Typically, real-time alerting is a localized function, e.g. airport security center receives and reacts to a "perimeter breach alert", while investigations often tend to encompass a large number of geographically distributed cameras like the London bombing, or Washington sniper incidents. Enabling effective search of surveillance video for investigation & preemption, involves indexing the video along multiple dimensions. This paper presents a framework for surveillance search which includes, video parsing, indexing and query mechanisms. It explores video parsing techniques which automatically extract index data from video, indexing which stores data in relational tables, retrieval which uses SQL queries to retrieve events of interest and the software architecture that integrates these technologies.