A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.
Matched MeSH terms: Principal Component Analysis/methods
This study aimed to investigate the chemical composition and potential sources of PM10 as well as assess the potential health hazards it posed to school children. PM10 samples were taken from classrooms at a school in Kuala Lumpur's city centre (S1) and one in the suburban city of Putrajaya (S2) over a period of eight hours using a low volume sampler (LVS). The composition of the major ions and trace metals in PM10 were then analysed using ion chromatography (IC) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. The results showed that the average PM10 concentration inside the classroom at the city centre school (82µg/m(3)) was higher than that from the suburban school (77µg/m(3)). Principal component analysis-absolute principal component scores (PCA-APCS) revealed that road dust was the major source of indoor PM10 at both school in the city centre (36%) and the suburban location (55%). The total hazard quotient (HQ) calculated, based on the formula suggested by the United States Environmental Protection Agency (USEPA), was found to be slightly higher than the acceptable level of 1, indicating that inhalation exposure to particle-bound non-carcinogenic metals of PM10, particularly Cr exposure by children and adults occupying the school environment, was far from negligible.
The amino acid compositions of bovine, porcine and fish gelatin were determined by amino acid analysis using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as derivatization reagent. Sixteen amino acids were identified with similar spectral chromatograms. Data pre-treatment via centering and transformation of data by normalization were performed to provide data that are more suitable for analysis and easier to be interpreted. Principal component analysis (PCA) transformed the original data matrix into a number of principal components (PCs). Three principal components (PCs) described 96.5% of the total variance, and 2 PCs (91%) explained the highest variances. The PCA model demonstrated the relationships among amino acids in the correlation loadings plot to the group of gelatins in the scores plot. Fish gelatin was correlated to threonine, serine and methionine on the positive side of PC1; bovine gelatin was correlated to the non-polar side chains amino acids that were proline, hydroxyproline, leucine, isoleucine and valine on the negative side of PC1 and porcine gelatin was correlated to the polar side chains amino acids that were aspartate, glutamic acid, lysine and tyrosine on the negative side of PC2. Verification on the database using 12 samples from commercial products gelatin-based had confirmed the grouping patterns and the variables correlations. Therefore, this quantitative method is very useful as a screening method to determine gelatin from various sources.
The job satisfaction of academics is related to a number of variables of complex function such as demographic characters, the work itself, pay, work responsibilities, variety of tasks, promotional opportunities, relationship with co-workers and others. Academics may be simultaneously satisfied with some facets of the job and dissatisfied with others. This paper aims at determining the influential factors that contribute to the enhancement or reduction of academics' job satisfaction among private universities in Bangladesh with special reference to Dhaka, the capital city of Bangladesh. A total of 346 respondents are considered from ten private universities using non-probability sampling. A pre-tested and closed-ended questionnaire using a seven-point Likert scale is used for data collection. In this study, descriptive statistics, Pearson product moment correlation, multiple regression, and factor analysis are exercised as statistical tools. A conceptual model of job satisfaction is developed and applied for academics' job satisfaction. The results reveal that compensation package, supervisory support, job security, training and development opportunities, team cohesion, career growth, working conditions, and organizational culture and policies are positively associated with the academics' job satisfaction. Amongst them, three factors stood out as significant contributors for job satisfaction of academics i.e. compensation package, job security, and working conditions. Therefore, the management of private universities should focus their effort on these areas of human resource management for maintaining academics' job satisfaction and employee retention. The study will be useful for university management in improving overall job satisfaction as it suggests some strategies for employee satisfaction practices.
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
This study aimed to identify the association of dietary patterns with sociodemographic and health-related characteristics among coronary artery disease patients. In this cross-sectional study, the participants were 250 patients coronary artery disease aged ≥ 40 years old. Data collection was done using questionnaires related to sociodemographics, health-related factors, and food-frequency intake information. Three dietary patterns (traditional, western, and healthy) were obtained using principal component analysis. The result showed that dietary patterns were associated with sociodemographic and health-related factors. According to the result, all the factors were taken very seriously when planning a promotional program for healthy lifestyle in prevention of CAD.
Bactrocera dorsalis s.s. (Hendel) and B. papayae Drew & Hancock, are invasive pests belonging to the B. dorsalis complex. Their species status, based on morphology, is sometimes arguable. Consequently, the existence of cryptic species and/or population isolation may decrease the effectiveness of the sterile insect technique (SIT) due to an unknown degree of sexual isolation between released sterile flies and wild counterparts. To evaluate the genetic relationship and current demography in wild populations for guiding the application of area-wide integrated pest management using SIT, seven microsatellite-derived markers from B. dorsalis s.s. and another five from B. papayae were used for surveying intra- and inter-specific variation, population structure, and recent migration among sympatric and allopatric populations of the two morphological forms across Southern Thailand and West Malaysia.
The Hospital Anxiety and Depression Scale (HADS) is a common screening instrument used to determine the levels of anxiety and depression experienced by a patient and has been extensively used in patients with coronary artery disease (CAD). This study aimed to establish the factor structure of HADS in a Malaysian sample of 189 patients with CAD. Factor analysis of HADS using principal component analysis with varimax rotation yielded 3 factors. Confirmatory factor analysis supported the use of HADS in assessing 3 distinct dimensions of psychological distress--namely, anxiety, anhedonia, and psychomotor retardation. The HADS showed good internal consistency and was found to be a valid measure of psychological distress among Malaysian patients with CAD. However, low mean scores on the original 2 factors--that is, anxiety and depression--and also on the 2 depression subscales--anhedonia and psychomotor retardation--suggests that the recommended cutoff score to screen for psychological distress among CAD patients be reevaluated. Further research to determine the generalizability and consistency for the tridimensional structure of the HADS in Malaysia is recommended.
Andrographis paniculata Nees. (AP) is a self-pollinated medicinal herb with a wide range of pharmaceutical properties, facing a low diversity in Malaysia. Cross-pollination of AP accessions leads to considerable rates of heterosis in the agro-morphological characteristics and anticancer phytochemicals of this eminent medicinal herb. However, the poor crossability of the plant at the interpopulation or intraspecific levels is an obstacle from the evolutionary and breeding points of view as an average of 4.56% crossability was recorded for AP in this study. Hence, this research aimed to elicit the impact of parental genetic distances (GDs) on the rate of crossability of AP using seven accessions in 21 possible cross combinations. To this end, a set of 55 randomly amplified polymorphic DNA (RAPD) primers and a total of 13 agro-morphological markers were employed to test the hypothesis. Twenty-two out of the 55 RAPD primers amplified a total of 257 bands of which 107 bands were found to be polymorphic. The principal component analysis (PCA) based on the RAPD markers revealed that the studied AP accessions were distributed to three distinct groups. Furthermore, it was noticed that even a minor increase in GD between two parents can cause a decline in their crossability. Unlike, the morphological-based GDs acted neutrally to crossability. This finding suggests that, despite the low genetic diversity among the Malaysian APs, a population prescreening using RAPD markers would be useful to enhance the rate of fruit set through selecting the genetically adjacent parents.
Analysis of 300 ns (ns) molecular dynamics (MD) simulations of an adenosine A2a receptor (A2a AR) model, conducted in triplicate, in 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and 1-palmitoyl-2-oleoylphosphatidylethanolamine (POPE) bilayers reveals significantly different protein dynamical behavior. Principal component analysis (PCA) shows that the dissimilarities stem from interhelical rather than intrahelical motions. The difference in the hydrophobic thicknesses of these simulated lipid bilayers is potentially a significant reason for the observed difference in results. The distinct lipid headgroups might also lead to different molecular interactions and hence different protein loop motions. Overall, the A2a AR shows higher mobility and flexibility in POPC as compared to POPE.
Lafora disease (LD) is an autosomal recessive, progressive form of myoclonus epilepsy which affects worldwide. LD occurs mainly in countries like southern Europe, northern Africa, South India, and in the Middle East. LD occurs with its onset mainly in teenagers and leads to decline and death within 2 to 10 years. The genes EPM2A and EPM2B are commonly involved in 90 % of LD cases. EPM2A codes for protein laforin which contains an amino terminal carbohydrate binding module (CBM) belonging to the CBM20 family and a carboxy terminal dual specificity phosphatase domain. Mutations in laforin are found to abolish glycogen binding and have been reported in wet lab methods. In order to investigate on structural insights on laforin mutation K81A, we performed molecular dynamics (MD) simulation studies for native and mutant protein. MD simulation results showed loss of stability due to mutation K87A which confirmed the structural reason for conformational changes observed in laforin. The conformational change of mutant laforin was confirmed by analysis using root mean square deviation, root mean square fluctuation, solvent accessibility surface area, radius of gyration, hydrogen bond, and principle component analysis. Our results identified that the flexibility of K87A mutated laforin structure, with replacement of acidic amino acid to aliphatic amino acid in functional CBM domain, have more impact in abolishing glycogen binding that favors LD.
The knowledge of genetic diversity of tree crop is very important for breeding and improvement program for the purpose of improving the yield and quality of its produce. Genetic diversity study and analysis of genetic relationship among 20 Moringa oleifera were carried out with the aid of twelve primers from, random amplified polymorphic DNA marker. The seeds of twenty M. oleifera genotypes from various origins were collected and germinated and raised in nursery before transplanting to the field at University Agricultural Park (TPU). Genetic diversity parameter, such as Shannon's information index and expected heterozygosity, revealed the presence of high genetic divergence with value of 1.80 and 0.13 for Malaysian population and 0.30 and 0.19 for the international population, respectively. Mean of Nei's gene diversity index for the two populations was estimated to be 0.20. In addition, a dendrogram constructed, using UPGMA cluster analysis based on Nei's genetic distance, grouped the twenty M. oleifera into five distinct clusters. The study revealed a great extent of variation which is essential for successful breeding and improvement program. From this study, M. oleifera genotypes of wide genetic origin, such as T-01, T-06, M-01, and M-02, are recommended to be used as parent in future breeding program.
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
The status report on metal pollution in tropical estuaries and coastal waters is important to understand potential environmental health hazards. Detailed baseline measurements were made on physicochemical parameters (pH, temperature, redox potential, electrical conductivity, salinity, dissolved oxygen, total dissolved solid), major ions (Na, Ca, Mg, K, HCO3, Cl, SO4 and NO3) and metals concentrations ((27)Al, (75)As, (138)Ba, (9)Be, (111)Cd, (59)Co, (63)Cu, (52)Cr, (57)Fe, (55)Mn, (60)Ni, (208)Pb, (80)Se, (66)Zn) at estuaries and coastal waters along the Straits of Malacca. Principal component analysis (PCA) was employed to reveal potential pollution sources. Seven principal components were extracted with relation to pollution contribution from minerals-related parameters, natural and anthropogenic sources. The output from this study will generate a profound understanding on the metal pollution status and pollution risk of the estuaries and coastal system.
The aim of this study is to evaluate the psychometric properties of the translated Malay language version of TZO-AZL Preschool Children Quality of Life (TAPQOL) questionnaire in preschool children. Preterm children and term children aged between two and five years were enrolled into the study. The Malay language version of TAPQOL and a set of questions regarding the child's health status were answered by the caregivers. The internal consistency, Spearman's correlation coefficients and principal component analysis (PCA) with Varimax rotation and Mann-Whitney U test for group comparison were employed to evaluate the psychometric properties of this instrument. A total of 258 children (120 preterm children and 138 term children) were recruited to this study with a response rate of 94%. All (sub)domains except one had Cronbach's α coefficients of more than .7. The Spearman's correlation coefficients between 12 subdomains were generally low. PCA supported the structural unidimensionality of the items in the instrument. Preterm children had lower quality of life scores than that of term children. Malay version of TAPQOL has multidimensional construct. It is a reliable and valid instrument for preschool children, with almost similar psychometric properties to the original version.
This cross-sectional study aimed to determine the construct of the phases of the female sexual response cycle (SRC) among women attending an infertility clinic in a Malaysian tertiary center.
This study aimed to assess the relative validity of maternal dietary patterns derived from a semi-quantitative food frequency questionnaire (FFQ). A total of 162 pregnant women aged 19-40-years-old were enrolled from the Universiti Sains Malaysia (USM) Birth Cohort Study in year 2010 and 2011. The FFQ was compared with three 24-h dietary recalls (DRs). Two major dietary patterns were derived from the principle component analysis which are labeled as Healthy and Less-Healthy patterns. The Pearson correlation coefficients between FFQ and DRs for Healthy and Less-Healthy patterns were 0.59 and 0.63, respectively. At least 45% of the participants were correctly classified into the same third from the FFQ and DR for both dietary patterns. The weighted kappa showed moderate agreement for Healthy pattern while good agreement for Less-Healthy pattern between these two dietary assessment methods. Our results indicate reasonable validity of the dietary patterns identified from the FFQ in pregnant women.
Groundwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (PCA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island's hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island's water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.
A field experiment was carried out in order to evaluate genetic diversity of 41 rice genotypes using physiological traits and molecular markers. All the genotypes unveiled variations for crop growth rate (CGR), relative growth rate (RGR), net assimilation rate (NAR), yield per hill (Yhill(-1)), total dry matter (TDM), harvest index (HI), photosynthetic rate (PR), leaf area index (LAI), chlorophyll-a and chlorophyll-b at maximum tillering stage. The CGR values varied from 0.23 to 0.76 gm cm(-2) day(-1). The Yhill(-1) ranged from 15.91 to 92.26 g, while TDM value was in the range of 7.49 to 20.45 g hill(-1). PR was found to vary from 9.40 to 22.34 µmol m(-2) s(-1). PR expressed positive relation with Yhill(-1). Significant positive relation was found between CGR and TDM (r = 0.61**), NAR and CGR (r = 0.62**) and between TDM and NAR (r = 0.31**). High heritability was found in RGR and Yhill(-1). Cluster analysis based on the traits grouped 41 rice genotypes into seven clusters. A total of 310 polymorphic loci were detected across the 20 inter-simple sequence repeats (ISSR) markers. The UPGMA dendrogram grouped 41 rice genotypes into 11 clusters including several sub-clusters. The Mantel test revealed positive correlation between quantitative traits and molecular markers (r = 0.41). On the basis of quantitative traits and molecular marker analyses parental genotypes, IRBB54 with MR84, IRBB60 with MR84, Purbachi with MR263, IRBB65 with BR29, IRBB65 with Pulut Siding and MRQ74 with Purbachi could be hybridized for future breeding program.
The aim of this exploratory study was to describe and compare student nurses (n=142), staff nurses (n=54) and nurse tutors (n=8) perceptions of the clinical learning environment (CLE), and to identify factors that enhanced or inhibited student learning. The setting was a private hospital in Penang, Malaysia. Data were collected using a structured, self-administered questionnaire that consisted of six a priori subscales. Principal component analysis supported a six factor solution and a reduction in the number of items from 44 to 34. Participants' overall perception of the CLE was positive, though there were significant differences in 5 of the 6 subscales between the three groups. For students and their tutors, the most positive component of the CLE was 'supervision by clinical instructors'. Staff nurses reported more favourably on the learner friendliness of the CLE than did students or tutors. Factors that enhanced student learning included students' and staff nurses' attitude towards student learning, variety of clinical opportunities, sufficient equipment, and adequate time to perform procedures. Factors that hindered student learning were: overload of students in the clinical unit, busy wards, and students being treated as workers.