Earticle

Extracting metabolic pathway from microarray gene expression data that dictates a specific biological response is currently one of the important disciplines in system biology research. However due to the complexity of the global metabolic network and the importance to maintain the biological structure, this has become a greater challenge. Previous methods have successfully identified those pathways but without concerning the genetic effect and relationship of the genes, representation of the underlying structure is not precise and cannot be justified to be significant biologically. In this article, probabilistic models that are capable of identifying the significant pathways through metabolic networks related to a specific biological response are implemented. This article utilized combination of two probabilistic models to address the limitations of previous methods with the annotation to pathway database to ensure the pathway is biologically plausible.

Computational prediction of nucleosome positioning relies upon in vitro and in vivo experimental outcome such as sequence positioning and exclusion signatures, structural thermodynamic details, histone-DNA interaction models, etc. On the other hand, CpG island and promoter prediction programs are available which depends upon the algorithm built by the predictive power of trained experimental datasets from sequencing projects. “CpGP dynamics – The dynamics of CpG island and promoter to validate nucleosomal gene expression” is a web-based program which predicts the nucleosome- positioning (NP) and exclusion (NE) signatures in the user provided nucleotide sequence and presents a graphical output. It also utilizes the sequence positions of CpG island and promoter predicted by third-party programs as input to generate graphical sequence output. These two graphical outputs can be merged to discriminate the more accurate sequence positions of CpG island and promoter from a number of likelihood predictions. The program is freely accessible at http://www.cpgpdynamics.webs.com.

Allelopathy is the production of chemicals (allelochemicals) of a plant which can influence the growth and development of another plant that can be either negative by reducing germination or positive by increasing growth. This study was conducted to (1) determine the allelopathic potential of the following grasses (Family Poaceae), Chloris barbata, Eleusine indica and Saccharum spontaneum in Tigbauan, Iloilo on the germination of lettuce (Lactuca sativa) seeds; (2) determine the grass extracts that can inhibit or induce the germination; and (3) determine if there is a significant difference on the allelopathic potential of the grass extracts on the germination of lettuce seeds in each assay used. Filter paper and soil germination assays were the growth assays used. Results of the study revealed that the grasses used had significantly reduced and differ in their germination rate of lettuce seeds at 0.05 level of significance using One-Way Analysis of Variance and post hoc Tukey analyses. Both germination assays used, showed that S. spontaneum has the lowest germination rate. Eleusine indica has the highest germination rate in filter paper assay while Chloris barbata in soil germination assay. The grass extracts used have an allelopathic potential by decreasing the germination rate of lettuce seeds. This study suggested that allelopathic activity is one feature of attributes that enable invasive species to dominate in habitat space of communities they are present. Identifying these attributes and clarifying the relative importance could enhance our ecological understanding of the problematic species and facilitate the management of plant invasions.

Protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. The main objective of this research is to optimize local protein structure with Support Vector Machine (SVM) to predict protein secondary structure. Most of the related study used fixed segment length for secondary structure prediction and this might produce inaccurate results. In this research, dataset is segmented into different segment length of local protein structure. An optimal length of local protein structure is determined and the evaluation is carried out by comparing with the existing methods and initial prediction using native structure. Higher accuracy and true positive rate, low false positive rate are obtained which prove the effectiveness of this prediction method. A statistical method, t-test, is applied to validate the results of the prediction.

Diagnosis of autism is one of the difficult problems facing researchers. In this paper, Electroencephalogram (EEG) based Autism diagnosis using Fisher Linear Discriminat (FLD) Analysis is presented. Multivariate analyses of all the channels (via the concatenated signals) were used. Different preprocessing techniques, different ensemble averages, as well as, different feature extraction techniques are studied. The average correct rates are (90%). Raw data features and FFT features are used. Windsor Filtered Data gave the best mean and the lower standard deviation of both raw and FFT features. Over all, FFT features have a better correct rate of 88.14% and lower standard deviation 0.0404 than raw features.

There are many problems with applying the machine learning technique, which is widely used in the conventional healthcare field, during the mobile u-health service analysis step. First, research on the mobile u-health service is just beginning, and there are very few cases where the existing techniques have been applied in the mobile u-health service environment. Second, since the machine learning technique requires a long learning period, it is not suitable for application in the mobile u-health service environment, which requires real-time disease management. Third, the various machine learning techniques that have been proposed until now do not include a way to assign the weight factors to the disease-related variables, and thus its use as a personalized disease prediction system is somewhat limited. This paper proposes PCADP, which is an ontology-based personalized disease prediction method, to solve such problems and to interpret the bio data analysis of the mobile u-health service system as a process. Moreover, the mobile u-health service ontology framework was modeled as a semantics type in order to meaningfully express the mobile u-health data and service statement based on PCADP. To validate the performance and efficiency of the PCADP technique proposed in this paper, the 5-cross validation method was used to measure the accuracy of the prediction. The validation of PCADP using a virtual disease group verified that the technique proposed in this paper shows much greater accuracy compared to existing methods. Moreover, the PCADP prediction method improved the flexibility and real-time attributes, which are the essential elements of any diagnosis technique in the mobile u-health environment, and showed efficiency in the continuous improvement of the monitoring and system of the diagnosis process.

This paper presents a study of the comparison of different healthcare services to identify the characteristics and of a pervasive and ubiquitous healthcare system which is described as the healthcare provided for home based and moving patients that could enable remote and continuous monitoring and medical services anywhere and anytime. The study has identified the different classification of healthcare services such as the traditional healthcare service, location-based healthcare service, the home and mobile healthcare service and the ubiquitous healthcare services. The important characteristics required in ubiquitous medical service management systems in order to get some clues for ubiquitous healthcare service system architecture design are identified.

A wireless body area network (WBAN) allows the integration of low power, invasive or noninvasive miniaturized sensors around a human body. In this paper, a dynamic duty cycle algorithm is designed to handle energy efficiency, by varying the sleep period according to the amount of traffic that needs to be transmitted. In addition, we propose a priority based carrier sensing method that enhances energy efficiency while still guaranteeing a low latency for the multiple applications used in a body area network. NS-2 simulations have been carried out for the performance evaluation. The simulation results show that the proposed MAC algorithm is archived significant improvement in terms of latency and energy efficiency.

Every aspect of tumor growth required vascular growth. Many natural products inhibited angiogenesis. The makabuhay plant, Tinospora rumphii Boerl was an important species of Menispermaceae family with common utilization in folk medicine in the Philippines. This study aimed to evaluate the angiosuppressive activity of Tinospora rumphii Boerl. stem extract using the chorioallantoic membrane assay in duck’s (Anas platyrhynchos) embryos. There were thirty pieces of one day old fertile duck eggs used in the study. The setups were composed of a positive control (Retinoic acid), negative control (95% ethanol) and makabuhay stem extracts in 25%, 50%, 75% and 100% concentrations. The different concentrations of the extracts were applied on the tenth day of incubation. The antiangiogenic effect of T. rumphii Boerl stem extract was evaluated by taking the average number of branch points using the chorioallantoic membrane. This was determined on the 12th day of incubation. One-way analysis of variance and Scheffe test for significant antiangiogenic activity and pair-wise comparison among the treatments at 0.05 alpha level were determined respectively. Results showed that angiogenesis was induced in the negative control while angiogenesis inhibition was significantly reduced in the treatment of the stem extracts. It was supported by the statistical analysis that there was a significant difference on the antiangiogenic effect of makabuhay stem extract using the CAM assay on the vascularization of duck embryos. A significant difference was indicated between the negative control and among the treatments used. It showed that the greater the dosage, the lesser the branch points observed and counted. Pure makabuhay stem extract had the lowest branch points mean value while the 25% concentration had the highest mean value among the treatments used. Thus, these findings of the study indicated that T. rumphii Boerl. stem extract might had a promising antiangiogenic potential.

This paper is focused on the effect of computer-based clinical reminder system to colorectal cancer screening. The reminder system with five DB modules was developed. The paper investigated a total of 541 patients who had been visited health examination center in two hospitals. In this work, 62.1-96.0% was satisfied with the reminder system. The present research showed that a reminder effort of computerized system in mailing was significantly effective for colorectal cancer screening. In order to maintain the reminder effect, computer-based clinical system should be supported by policy maker and need for monitoring the feedback from stakeholders.

Monitoring systems in intensive care units (ICU) generate a high rate of false alarms 80% [8, 14, 15, 16]. This has the effect of desensitizing the medical staff and extending the response time to these alarms. In this work we present a new method to reduce false alarms in intensive care units that is based on packet wavelet decomposition and alarms classification by SVM.