The structural comparison of proteins is a vital step in structural biology that is used to predict and analyse a new unknown protein function. Although a number of different techniques have been explored, the study to develop new alternative methods is still an active research area. The present paper introduces a text modelling-based technique for the structural comparison of proteins. The method models the secondary and tertiary structure of proteins in two linear sequences and then applies them to the comparison of two structures. The technique used for pairwise comparison of the sequences has been adopted from computational linguistics and its well-known techniques for analysing and quantifying textual sequences. To this end, an n-gram modelling technique is used to capture regularities between sequences, and then, the cross-entropy concept is employed to measure their similarities. Several experiments are conducted to evaluate the performance of the method and compare it with other commonly used programs. The assessments for information retrieval evaluation demonstrate that the technique has a high running speed, which is similar to other linear encoding methods, such as 3D-BLAST, SARST, and TS-AMIR, whereas its accuracy is comparable to CE and TM-align, which are high accuracy comparison tools. Accordingly, the results demonstrate that the algorithm has high efficiency compared with other state-of-the-art methods.
Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
The fracture risk assessment tool (FRAX(®)) has been developed for the identification of individuals with high risk of fracture in whom treatment to prevent fractures would be appropriate. FRAX models are not yet available for all countries or ethnicities, but surrogate models can be used within regions with similar fracture risk. The International Society for Clinical Densitometry (ISCD) and International Osteoporosis Foundation (IOF) are nonprofit multidisciplinary international professional organizations. Their visions are to advance the awareness, education, prevention, and treatment of osteoporosis. In November 2010, the IOF/ISCD FRAX initiative was held in Bucharest, bringing together international experts to review and create evidence-based official positions guiding clinicians for the practical use of FRAX. A consensus meeting of the Asia-Pacific (AP) Panel of the ISCD recently reviewed the most current Official Positions of the Joint Official Positions of ISCD and IOF on FRAX in view of the different population characteristics and health standards in the AP regions. The reviewed position statements included not only the key spectrum of positions but also unique concerns in AP regions.
The study was conducted based on two objectives as framework. The first objective is to determine the point of microwave signal reflection while penetrating into the simulation models and, the second objective is to analyze the reflection pattern when the signal penetrate into the layers with different relative permittivity, εr. Thus, several microwave models were developed to make a close proximity of the in vivo human brain. The study proposed two different layers on two different characteristics models. The radii on the second layer and the corresponding antenna positions are the factors for both models. The radii for model 1 is 60 mm with an antenna position of 10 mm away, in contrast, model 2 is 10 mm larger in size with a closely adapted antenna without any gap. The layers of the models were developed with different combination of materials such as Oil, Sandy Soil, Brain, Glycerin and Water. Results show the combination of Glycerin + Brain and Brain + Sandy Soil are the best proximity of the in vivo human brain grey and white matter. The results could benefit subsequent studies for further enhancement and development of the models.
The high variations of background luminance, low contrast and excessively enhanced contrast of hand bone radiograph often impede the bone age assessment rating system in evaluating the degree of epiphyseal plates and ossification centers development. The Global Histogram equalization (GHE) has been the most frequently adopted image contrast enhancement technique but the performance is not satisfying. A brightness and detail preserving histogram equalization method with good contrast enhancement effect has been a goal of much recent research in histogram equalization. Nevertheless, producing a well-balanced histogram equalized radiograph in terms of its brightness preservation, detail preservation and contrast enhancement is deemed to be a daunting task.
This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.
In this study, the methanolysis process of sunflower oil was investigated to get high methyl esters (biodiesel) content using sodium methoxide. To reach to the best process conditions, central composite design (CCD) through response surface methodology (RSM) was employed. The optimal conditions predicted were the reaction time of 60 min, an excess stoichiometric amount of alcohol to oil ratio of 25%w/w and the catalyst content of 0.5%w/w, which lead to the highest methyl ester content (100%w/w). The methyl ester content of the mixture from gas chromatography analysis (GC) was compared to that of optimum point. Results, confirmed that there was no significant difference between the fatty acid methyl ester content of sunflower oil produced under the optimized condition and the experimental value (P ≥ 0.05). Furthermore, some fuel specifications of the resultant biodiesel were tested according to American standards for testing of materials (ASTM) methods. The outcome showed that the methyl ester mixture produced from the optimized condition met nearly most of the important biodiesel specifications recommended in ASTM D 6751 requirements. Thus, the sunflower oil methyl esters resulted from this study could be a suitable alternative for petrol diesels.
Malaria is one of the serious global health problem, causing widespread sufferings and deaths in various parts of the world. With the large number of cases diagnosed over the year, early detection and accurate diagnosis which facilitates prompt treatment is an essential requirement to control malaria. For centuries now, manual microscopic examination of blood slide remains the gold standard for malaria diagnosis. However, low contrast of the malaria and variable smears quality are some factors that may influence the accuracy of interpretation by microbiologists. In order to reduce this problem, this paper aims to investigate the performance of the proposed contrast enhancement techniques namely, modified global and modified linear contrast stretching as well as the conventional global and linear contrast stretching that have been applied on malaria images of P. vivax species. The results show that the proposed modified global and modified linear contrast stretching techniques have successfully increased the contrast of the parasites and the infected red blood cells compared to the conventional global and linear contrast stretching. Hence, the resultant images would become useful to microbiologists for identification of various stages and species of malaria.
Numerical models, able to simulate the response of the human cardiovascular system (CVS) in the presence of an implantable rotary blood pump (IRBP), have been widely used as a predictive tool to investigate the interaction between the CVS and the IRBP under various operating conditions. The present study investigates the effect of alterations in the model parameter values, that is, cardiac contractility, systemic vascular resistance, and total blood volume on the efficiency of rotary pump assistance, using an optimized dynamic heart-pump interaction model previously developed in our laboratory based on animal experimental measurements obtained from five canines. The effect of mean pump speed and the circulatory perturbations on left and right ventricular pressure volume loops, mean aortic pressure, mean cardiac output, pump assistance ratio, and pump flow pulsatility from both the greyhound experiments and model simulations are demonstrated. Furthermore, the applicability of some of the previously proposed control parameters, that is, pulsatility index (PI), gradient of PI with respect to pump speed, pump differential pressure, and aortic pressure are discussed based on our observations from experimental and simulation results. It was found that previously proposed control strategies were not able to perform well under highly varying circulatory conditions. Among these, control algorithms which rely on the left ventricular filling pressure appear to be the most robust as they emulate the Frank-Starling mechanism of the heart.
Echocardiogram is an ultrasound image of the heart that demonstrates the size, motion and composition of cardiac structures and is also used to diagnose various abnormalities of the heart including abnormal chamber size, shape and congenital heart disease. Echocardiography provides important morphological and functional details of the heart. Most of the presented automatic cardiac disease recognition systems that use echocardiograms based on defective anatomical region detection. In this paper we present a simple technique for cardiac geometry detection via echocardiogram images which conquer these borders and exploits cues from cardiac structure. To demonstrate the effectiveness of this technique, we present results for cardiac geometry detection through difference intensity of echocardiogram images. We have developed a simple program code for the prediction of cardiac geometry using difference intensity of echocardiogram images. With this code, users can generate node or point for detection of cardiac geometry as ventricle and atrium in size, shape and location.
Experimental findings indicate that in-situ chondrocytes die readily following impact loading, but remain essentially unaffected at low (non-impact) strain rates. This study was aimed at identifying possible causes for cell death in impact loading by quantifying chondrocyte mechanics when cartilage was subjected to a 5% nominal tissue strain at different strain rates. Multi-scale modelling techniques were used to simulate cartilage tissue and the corresponding chondrocytes residing in the tissue. Chondrocytes were modelled by accounting for the cell membrane, pericellular matrix and pericellular capsule. The results suggest that cell deformations, cell fluid pressures and fluid flow velocity through cells are highest at the highest (impact) strain rate, but they do not reach damaging levels. Tangential strain rates of the cell membrane were highest at the highest strain rate and were observed primarily in superficial tissue cells. Since cell death following impact loading occurs primarily in superficial zone cells, we speculate that cell death in impact loading is caused by the high tangential strain rates in the membrane of superficial zone cells causing membrane rupture and loss of cell content and integrity.
Vitiligo is a cutaneous pigmentary disorder characterized by depigmented macules and patches that result from loss of epidermal melanocytes. Physician evaluates the efficacy of treatment by comparing the extent of vitiligo lesions before and after treatment based on the overall visual impression of the treatment response. This method is called the physician's global assessment (PGA) which is subjective. In this article, we present an innovative digital image processing method to determine vitiligo lesion area in an objective manner.
The aim of this study was to optimize the different process parameters including pressure, temperature, and polymer concentration, to produce fine small spherical particles with a narrow particle size distribution using a supercritical antisolvent method for drug encapsulation. The interaction between different process parameters was also investigated.
Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
Fabrication of a test strip for detection of benzoic acid was successfully implemented by immobilizing tyrosinase, phenol and 3-methyl-2-benzothiazolinone hydrazone (MBTH) onto filter paper using polystyrene as polymeric support. The sensing scheme was based on the decreasing intensity of the maroon colour of the test strip when introduced into benzoic acid solution. The test strip was characterized using optical fiber reflectance and has maximum reflectance at 375 nm. It has shown a highly reproducible measurement of benzoic acid with a calculated RSD of 0.47% (n = 10). The detection was optimized at pH 7. A linear response of the biosensor was obtained in 100 to 700 ppm of benzoic acid with a detection limit (LOD) of 73.6 ppm. At 1:1 ratio of benzoic acid to interfering substances, the main interfering substance is boric acid. The kinetic analyses show that, the inhibition of benzoic is competitive inhibitor and the inhibition constant (K(i)) is 52.9 ppm. The activity of immobilized tyrosinase, phenol, and MBTH in the test strip was fairly sustained during 20 days when stored at 3 °C. The developed test strip was used for detection of benzoic acid in food samples and was observed to have comparable results to the HPLC method, hence the developed test strip can be used as an alternative to HPLC in detecting benzoic acid in food products.
Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.
In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.
A force-sensing platform (FSP), sensitive to changes of the postural control system was designed. The platform measured effects of postural perturbations in static and dynamic conditions. This paper describes the implementation of an FSP using force-sensing resistors as sensing elements. Real-time qualitative assessment utilized a rainbow color scale to identify areas with high force concentration. Postprocessing of the logged data provided end-users with quantitative measures of postural control. The objective of this research was to establish the feasibility of using an FSP to test and gauge human postural control. Tests were conducted in eye open and eye close states. Readings obtained were tested for repeatability using a one-way analysis of variance test. The platform gauged postural sway by measuring the area of distribution for the weighted center of applied pressure at the foot. A fuzzy clustering algorithm was applied to identify regions of the foot with repetitive pressure concentration. Potential application of the platform in a clinical setting includes monitoring rehabilitation progress of stability dysfunction. The platform functions as a qualitative tool for initial, on-the-spot assessment, and quantitative measure for postacquisition assessment on balance abilities.
Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.