Reproductive seasonality and maturation of the sergestid shrimp, Acetes japonicus (Decapoda: Sergestidae) in coastal waters of Malacca, Peninsular Malaysia

Sex ratio, length at first maturity and spawning season of the sergestid shrimp (Acetes japonicus) were investigated between April 2006 and March 2007 in coastal waters of Klebang Besar, Malacca, Peninsular Malaysia. Klebang Besar waters are one of the main fishing grounds for A. japonicus in the Peninsular Malaysia. The overall annual sex ratio was found to be 1:1.46 (males: females). The spawning season was March to July with peak in May, however there was also some spawning in October to November. The female attained sexual maturity at a minimum size of 17.5 mm total length. The matured and near to spawn stages (II and III) occurred more than 50% in every month except in August. Therefore, it may be inferred that A. japonicus breeds continuously throughout the year in the coastal waters of Malacca, Peninsular Malaysia.Key words: Spawning, sex ratio, maturity, Acetes japonicus

Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data.

Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

An iterative pilot-data-aided estimator for SFBC relay-assisted OFDM-based systems

In this article, we propose and assess an iterative pilot-data-aided channel estimation scheme for space frequency block coding relay-assisted OFDM-based systems. The relay node (RN) employs the equalise-and-forward protocol, and both the base station (BS) and the RN are equipped with antenna arrays, whereas the user terminal (UT) is a single-antenna device. The channel estimation method uses the information carried by pilots and data to improve the estimate of the equivalent channels for the path BS-RN-UT. The mean minimum square error criterion is used in the design of the estimator for both the pilot-based and data-aided iterations. In different scenarios, with only one data iteration, the results show that the proposed scheme requires only half of the pilot density to achieve the same performance of non-data-aided schemes

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

The development of a body comparison measure: the CoSS

Purpose This study reports on the development and validation of a brief and widely applicable measure of body comparison (the Comparison of Self-Scale—CoSS), which is a maintaining feature of eating disorders. Methods A sample of 412 adults completed the CoSS, an existing measure of aspects of body comparison, and eating pathology and associated states. Test–retest reliability was examined over 2 weeks. Results Exploratory factor analysis showed that 22 CoSS items loaded onto two factors, resulting in two scales—Appearance Comparison and Social Comparison—with strong internal consistency and test–retest reliability. Conclusions In clinical terms, the CoSS was superior to the existing measure of body comparison in accounting for depression and anxiety. Given that it is a relatively brief measure, the CoSS could be useful in the routine assessment of body comparison, and in formulating and treating individuals with body image concerns. However, the measure awaits full clinical validation

Efficient and accurate greedy search methods for mining functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.</p> <p>Methods</p> <p>In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.</p> <p>Results</p> <p>The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.</p> <p>Conclusions</p> <p>Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.</p

Current status of endoplasmic reticulum stress in type ii diabetes

The endoplasmic reticulum (ER) plays a multifunctional role in lipid biosynthesis, calcium storage, protein folding, and processing. Thus, maintaining ER homeostasis is essential for cellular functions. Several pathophysiological conditions and pharmacological agents are known to disrupt ER homeostasis, thereby, causing ER stress. The cells react to ER stress by initiating an adaptive signaling process called the unfolded protein response (UPR). However, the ER initiates death signaling pathways when ER stress persists. ER stress is linked to several diseases, such as cancer, obesity, and diabetes. Thus, its regulation can provide possible therapeutic targets for these. Current evidence suggests that chronic hyperglycemia and hyperlipidemia linked to type II diabetes disrupt ER homeostasis, thereby, resulting in irreversible UPR activation and cell death. Despite progress in understanding the pathophysiology of the UPR and ER stress, to date, the mechanisms of ER stress in relation to type II diabetes remain unclear. This review provides up-to-date information regarding the UPR, ER stress mechanisms, insulin dysfunction, oxidative stress, and the therapeutic potential of targeting specific ER stress pathways