Imbalanced matrix metalloproteinase-9 and tissue inhibitor of metalloproteinase-1 activities in patients with thromboangiitis obliterans

The pathogenic mechanisms of thromboangiitis obliterans (TAO) are not entirely known and the imbalance of matrix metalloproteinases (MMPs) plays a role in vascular diseases. We evaluated the MMP-2 and MMP-9 circulating levels and their endogenous tissue inhibitors of metalloproteinases (TIMP-1 and TIMP-2) in TAO patients with clinical manifestations. The study included 20 TAO patients (n = 10 female, n = 10 male) aged 38-59 years under clinical follow-up. The patients were classified into two groups: (1) TAO former smokers (n = 11) and (2) TAO active smokers (n = 9); the control group included normal volunteer non-smokers (n = 10) and active smokers without peripheral artery disease (n = 10). Patient plasma samples were used to analyze MMP-2 and MMP-9 levels using zymography, and TIMP-1 and TIMP-2 concentrations were determined by enzyme-linked immunosorbent assays. The analysis of MMP-2/TIMP-2 and MMP-9/TIMP-1 ratios (which were used as indices of net MMP-2 and MMP-9 activity, respectively) showed significantly higher MMP-9/TIMP-1 ratios in TAO patients (p < 0.05). We found no significant differences in MMP-2/TIMP-2 ratios (p > 0.05). We found higher MMP-9 levels and decreased levels of TIMP-1 in the TAO groups (active smokers and former smokers), especially in active smokers compared with the other groups (all p < 0.05). MMP-2 and TIMP-2 were not significantly different in patients with TAO as compared to the control group (p > 0.05). In conclusion, our results showed increased MMP-9 and reduced TIMP-1 activity in TAO patients, especially in active smokers compared with non-TAO patients. These data suggest that smoke compounds could activate MMP-9 production or inhibit TIMP-1 activity.

Directional imbalance of freight rates : evidence from Japanese inter-prefectural data

By analyzing a comprehensive dataset on transport transactions in Japan, we describe a directional imbalance in freight rates by transport mode and examine its potential sources, such as economies of density and directionally imbalanced transport flow. There are certain numbers of observed links which show asymmetric transport costs. Instrumental variable analysis is used to show that economies of density account for deviation from symmetric freight rates between prefectures. Our results show that a 10% increase in outbound transport flow relative to inbound transport flow leads to a 2.1% decrease in outbound freight rate relative to inbound freight rate.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

High body mass index is not a barrier to physical activity: Analysis of international rugby players' anthropometric data

Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

A Petrov-Galerkin method for a singularly perturbed ordinary differential equation with non-smooth data

In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.