The effect of acute exercise on undercarboxylated osteocalcin in obese men

Summary : The purpose of this study was to examine if the reduction in glucose post-exercise is mediated by undercarboxylated osteocalcin (unOC). Obese men were randomly assigned to do aerobic or power exercises. The change in unOC levels was correlated with the change in glucose levels post-exercise. The reduction in glucose post-acute exercise may be partly related to increased unOC.

Introduction : Osteocalcin (OC) in its undercarboxylated (unOC) form may contribute to the regulation of glucose homeostasis. As exercise reduces serum glucose and improves insulin sensitivity in obese individuals and individuals with type 2 diabetes (T2DM), we hypothesised that this benefit was partly mediated by unOC.

Methods : Twenty-eight middle-aged (52.4 ± 1.2 years, mean ± SEM), obese (BMI = 32.1 ± 0.9 kg m⁻²) men were randomly assigned to do either 45 min of aerobic (cycling at 75% of VO2peak) or power (leg press at 75% of one repetition maximum plus jumping sequence) exercises. Blood samples were taken at baseline and up to 2 h post-exercise.

Results : At baseline, unOC was negatively correlated with glucose levels (r = −0.53, p = 0.003) and glycosylated haemoglobin (HbA1c) (r = −0.37, p = 0.035). Both aerobic and power exercises reduced serum glucose (from 7.4 ± 1.2 to 5.1 ± 0.5 mmol L⁻¹, p = 0.01 and 8.5 ± 1.2 to 6.0 ± 0.6 mmol L⁻¹, p = 0.01, respectively). Aerobic exercise significantly increased OC, unOC and high-molecular-weight adiponectin, while power exercise had a limited effect on OC and unOC. Overall, those with higher baseline glucose and HbA1c had greater reductions in glucose levels after exercise (r = −0.46, p = 0.013 and r = −0.43, p = 0.019, respectively). In a sub-group of obese people with T2DM, the percentage change in unOC levels was correlated with the percentage change in glucose levels post-exercise (r = −0.51, p = 0.038).

Conclusions : This study reports that the reduction in serum glucose post-acute exercise (especially aerobic exercise) may be partly related to increased unOC.r exercises. The change in unOC levels was correlated with the change in glucose levels post-exercise. The reduction in glucose post-acute exercise may be partly related to increased unOC.

How does organizational culture affect IS effectiveness : a culture-information system fit framework

Previous research has examined the impact of organizational culture(OC) on the implementation of many information systems. However, there is a lack of overall picture on how OC affects the effectiveness of different information systems differently. Based on the Competing Value Framework, this paper proposes a comprehensive framework to explain how the fit between organizational culture and types of IS results in different types of IS effectiveness. This framework can be used by managers to create a proper organizational culture that is compatible with the use of specific information systems.

An overview of liability and compensation for personal injury in China under the general principles of civil law

This article reviews the personal injury tort system in the People's Republic of China (PRC). The Chinese torts law has a number of unique features. To begin with, it is quite new — the legal framework of torts law was established only in 1986. The unique features of the Chinese torts law also stem from its long and difficult evolution over nearly 40 years. Equally important has been the remarkable blend of influences that have shaped its current law — a mixture of socialist objectives, capitalist pragmatism, and feudal doctrines combined with jurisprudential models taken from a range of western civil codes and, more recently, the common law.

Part one of the article briefly analyses the most important features of the existing Chinese legal system. Part two provides a background to the enactment of the General Principles of Civil Law (GPCL), which incorporates Chinese torts law. The review looks at the development and drafting of the GPCL legislation, and the influences that guided the formulation of legal principles. Part three of the article provides an overview of the torts law provisions in the GPCL. Part four examines the law of personal injury established by the GPCL. Part five uses some case studies to illustrate the principles highlighted in the previous two parts and part six contains a brief conclusion and some pointers to the directions that Chinese torts law may take in the future.

QDFA : query-dependent feature aggregation for medical image retrieval

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

Content based image retrieval using unclean positive examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.

Organic compounds present in the natural Amazonian aerosol : characterization by gas chromatography–mass spectrometry

[1] As part of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA)-Cooperative LBA Airborne Regional Experiment (CLAIRE) 2001 campaign in July 2001, separate day and nighttime aerosol samples were collected at a ground-based site in Amazonia, Brazil, in order to examine the composition and temporal variability of the natural “background” aerosol. We used a high-volume sampler to separate the aerosol into fine (aerodynamic diameter, AD < 2.5 μm) and coarse (AD > 2.5 μm) size fractions and quantified a range of organic compounds in methanolic extracts of the samples by a gas chromatographic-mass spectrometric technique. The carbon fraction of the compounds could account for an average of 7% of the organic carbon (OC) in both the fine and coarse aerosol fractions. We observed the highest concentrations of sugars, sugar alcohols, and fatty acids in the coarse aerosol samples, which suggests that these compounds are associated with primary biological aerosol particles (PBAP) observed in the forest atmosphere. Of these, trehalose, mannitol, arabitol, and the fatty acids were found to be more prevalent at night, coinciding with a nocturnal increase in PBAP in the 2–10 μm size range (predominantly yeasts and other small fungal spores). In contrast, glucose, fructose, and sucrose showed persistently higher daytime concentrations, coinciding with a daytime increase in large fungal spores, fern spores, pollen grains, and, to a lesser extent, plant fragments (generally >20 μm in diameter), probably driven by lowered relative humidity and enhanced wind speeds/convective activity during the day. For the fine aerosol samples a series of dicarboxylic and hydroxyacids were detected with persistently higher daytime concentrations, suggesting that photochemical production of a secondary organic aerosol from biogenic volatile organic compounds may have made a significant contribution to the fine aerosol. Anhydrosugars (levoglucosan, mannosan, galactosan), which are specific tracers for biomass burning, were detected only at low levels in the fine aerosol samples. On the basis of the levoglucosan-to-OC emission ratio measured for biomass burning aerosol, we estimate that an average of ∼16% of the OC in the fine aerosol was due to biomass burning during CLAIRE 2001, indicating that the major fraction was associated with biogenic particles.

Real-time detection of parked vehicles from multiple image streams

We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Polar transformation system for offline handwritten character recognition

Offline handwritten recognition is an important automated process in pattern recognition and computer vision field. This paper presents an approach of polar coordinate-based handwritten recognition system involving Support Vector Machines (SVM) classification methodology to achieve high recognition performance. We provide comparison and evaluation for zoning feature extraction methods applied in Polar system. The recognition results we proposed were trained and tested by using SVM with a set of 650 handwritten character images. All the input images are segmented (isolated) handwritten characters. Compared with Cartesian based handwritten recognition system, the recognition rate is more stable and improved up to 86.63%.

Automated classification of human daily activities in ambulatory environment

This paper presents a human daily activity classification approach based on the sensory data collected from a single tri-axial accelerometer worn on waist belt. The classification algorithm was realized to distinguish 6 different activities including standing, jumping, sitting-down, walking, running and falling through three major steps: wavelet transformation, Principle Component Analysis (PCA)-based dimensionality reduction and followed by implementing a radial basis function (RBF) kernel Support Vector Machine (SVM) classifier. Two trials were conducted to evaluate different aspects of the classification scheme. In the first trial, the classifier was trained and evaluated by using a dataset of 420 samples collected from seven subjects by using a k-fold cross-validation method. The parameters σ and c of the RBF kernel were optimized through automatic searching in terms of yielding the highest recognition accuracy and robustness. In the second trial, the generation capability of the classifier was also validated by using the dataset collected from six new subjects. The average classification rates of 95% and 93% are obtained in trials 1 and 2, respectively. The results in trial 2 show the system is also good at classifying activity signals of new subjects. It can be concluded that the collective effects of the usage of single accelerometer sensing, the setting of the accelerometer placement and efficient classifier would make this wearable sensing system more realistic and more comfortable to be implemented for long-term human activity monitoring and classification in ambulatory environment, therefore, more acceptable by users.

Sample subset optimization for classifying imbalanced biological data

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model training for more accurate classification. In this study, we propose a sample subset optimization technique for classifying biological data with moderate and extremely high imbalanced class distributions. By using this optimization technique with an ensemble of SVMs, we build multiple roughly balanced SVM base classifiers, each trained on an optimized sample subset. The experimental results demonstrate that the ensemble of SVMs created by our sample subset optimization technique can achieve higher area under the ROC curve (AUC) value than popular sampling approaches such as random over-/under-sampling; SMOTE sampling, and those in widely used ensemble approaches such as bagging and boosting.

Detection of cross-channel anomalies from multiple data channels

We identify and formulate a novel problem: crosschannel anomaly detection from multiple data channels. Cross channel anomalies are common amongst the individual channel anomalies, and are often portent of significant events. Using spectral approaches, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single channel anomalies. Our mathematical analysis shows that our method is likely to reduce the false alarm rate. We demonstrate our method in two applications: document understanding with multiple text corpora, and detection of repeated anomalies in video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large scale data stream analysis.

A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

Robust modular artmap for multi-class shape recognition

This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAO-SVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.

A generic classifier-ensemble approach for biomedical named entity recognition

In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to put into a classifier-ensemble is a crucial issue of multiple classifier-ensemble design. With this observation in mind, we proposed a generic genetic classifier-ensemble method for the classifier selection in biomedical NER. Various diversity measures and majority voting are considered, and disjoint feature subsets are selected to construct individual classifiers. A basic type of individual classifier – Support Vector Machine (SVM) classifier is adopted as SVM-classifier committee. A multi-objective Genetic algorithm (GA) is employed as the classifier selector to facilitate the ensemble classifier to improve the overall sample classification accuracy. The proposed approach is tested on the benchmark dataset – GENIA version 3.02 corpus, and compared with both individual best SVM classifier and SVM-classifier ensemble algorithm as well as other machine learning methods such as CRF, HMM and MEMM. The results show that the proposed approach outperforms other classification algorithms and can be a useful method for the biomedical NER problem.

Generative models for automatic recognition of human daily activities from a single triaxial accelerometer

In this work, we compare two generative models including Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) with Support Vector Machine (SVM) classifier for the recognition of six human daily activity (i.e., standing, walking, running, jumping, falling, sitting-down) from a single waist-worn tri-axial accelerometer signals through 4-fold cross-validation and testing on a total of thirteen subjects, achieving an average recognition accuracy of 96.43% and 98.21% in the first experiment and 95.51% and 98.72% in the second, respectively. The results demonstrate that both HMM and GMM are not only able to learn but also capable of generalization while the former outperformed the latter in the recognition of daily activities from a single waist worn tri-axial accelerometer. In addition, these two generative models enable the assessment of human activities based on acceleration signals with varying lengths.