The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations (SL01-SL22) along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 (618.29 ng/g dry weight), which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing 'significant' levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.
This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.
Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.
This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.
Today, virgin coconut oil (VCO) is becoming valuable oil and is receiving an attractive topic for researchers because of its several biological activities. In cosmetics industry, VCO is excellent material which functions as a skin moisturizer and softener. Therefore, it is important to develop a quantitative analytical method offering a fast and reliable technique. Fourier transform infrared (FTIR) spectroscopy with sample handling technique of attenuated total reflectance (ATR) can be successfully used to analyze VCO quantitatively in cream cosmetic preparations. A multivariate analysis using calibration of partial least square (PLS) model revealed the good relationship between actual value and FTIR-predicted value of VCO with coefficient of determination (R2) of 0.998.
Selected landmarks from each of 47 maxillary dental casts were used to define a Cartesian-coordinate system from which the positions of selected teeth were determined on standardized digital images. The position of the i-th tooth was defined by a line of length (l(i)) joining the tooth to the origin, and the angle (θ(i)) of this line to the horizontal Cartesian axis. Four teeth, the central incisor, lateral incisor, canine and first molar were selected and their position were collectively used to represent the shape of the dental arch. A pilot study using clustering and principal component analysis strongly suggest the existence of 3 groups of arch shape. In this study, the homogeneity of the 3 groups was further investigated and confirmed by the Dunn and Davies-Bouldein validity indices. This is followed by an investigation of the probability distribution of these 3 groups. The main result of this study suggests 3 groups of multivariate (MV) normal distribution. The MV normal probability distribution of these groups may be used in further studies to investigate the issues of variation of arch shape, which is fundamental to the practice of prosthodontics and orthodontics.
Coloured rice genotypes have greater nutritious value and consumer demand for these varieties is now greater than ever. The documentation of these genotypes is important for the improvement of the rice plant. In this study, 42 coloured rice genotypes were selected for determination of their genetic divergence using 25 simple sequence repeat (SSR) primers and 15 agro-morphological traits. Twenty-one out of the 25 SSR primers showed distinct, reproducible polymorphism. A dendrogram constructed using the SSR primers clustered the 42 coloured rice genotypes into 7 groups. Further, principle component analysis showed 75.28% of total variations were explained by the first-three components. All agro-morphological traits showed significant difference at the (p≤0.05) and (p≤0.01) levels. From the dendrogram constructed using the agro-morphological traits, all the genotypes were clustered into four distinct groups. Pearson's correlation coefficient showed that among the 15 agro-morphological traits, the yield contributing factor had positive correlation with the number of tillers, number of panicles, and panicle length. The heritability of the 15 traits ranged from 17.68 to 99.69%. Yield per plant and harvest index showed the highest value for both heritability and genetic advance. The information on the molecular and agro-morphological traits can be used in rice breeding programmes to improve nutritional value and produce higher yields.
To investigate the breeding habitat preference of black flies, a comprehensive black fly survey was conducted for the first time in Peninsular Malaysia. Preimaginal black flies (pupae and larvae) were collected manually from 180 stream points encompassing northern, southern, central and east coast of the Peninsular Malaysia. A total of 47 black fly species were recorded in this study. The predominant species were Simulium trangense (36.7%) and Simulium angulistylum (33.3%). Relatively common species were Simulium cheongi (29.4%), Simulium tani (25.6%), Simulium nobile (16.2%), Simulium sheilae (14.5%) and Simulium bishopi (10.6%). Principal Component Analysis (PCA) of all stream variables revealed four PCs that accounted for 69.3% of the total intersite variance. Regression analysis revealed that high species richness is associated with larger, deeper, faster and higher discharge streams with larger streambed particles, more riparian vegetation and low pH (F=22.7, d.f.=1, 173; P<0.001). Relationship between species occurrence of seven common species (present in >10% of the sampling sites) was assessed. Forward logistic regression analysis indicated that four species were significantly related to the stream variables. S. nobile and S. tani prefer large, fast flowing streams with higher pH, large streambed particles and riparian trees. S. bishopi was commonly found at high elevation with cooler stream, low conductivity, higher conductivity and more riparian trees. In contrast, S. sheilae was negatively correlated with PC-2, thus, this species commonly found at low elevation, warmer stream with low conductivity and less riparian trees. The results of this study are consistent with previous studies from other geographic regions, which indicated that both physical and chemical stream conditions are the key factors for black fly ecology.
Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method.
In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or "smellprint" emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.
Genetic structure and biodiversity of the medicinal plant Ficus deltoidea have rarely been scrutinized. To fill these lacunae, five varieties, consisting of 30 F. deltoidea accessions were collected across the country and studied on the basis of molecular and morphological data. Molecular analysis of the accessions was performed using nine Inter Simple Sequence Repeat (ISSR) markers, seven of which were detected as polymorphic markers. ISSR-based clustering generated four clusters supporting the geographical distribution of the accessions to some extent. The Jaccard's similarity coefficient implied the existence of low diversity (0.50-0.75) in the studied population. STRUCTURE analysis showed a low differentiation among the sampling sites, while a moderate varietal differentiation was unveiled with two main populations of F. deltoidea. Our observations confirmed the occurrence of gene flow among the accessions; however, the highest degree of this genetic interference was related to the three accessions of FDDJ10, FDTT16 and FDKT25. These three accessions may be the genetic intervarietal fusion points of the plant's population. Principal Components Analysis (PCA) relying on quantitative morphological characteristics resulted in two principal components with Eigenvalue >1 which made up 89.96% of the total variation. The cluster analysis performed by the eight quantitative characteristics led to grouping the accessions into four clusters with a Euclidean distance ranged between 0.06 and 1.10. Similarly, a four-cluster dendrogram was generated using qualitative traits. The qualitative characteristics were found to be more discriminating in the cluster and PCA analyses, while ISSRs were more informative on the evolution and genetic structure of the population.
Upland rice is important for sustainable crop production to meet future food demands. The expansion in area of irrigated rice faces limitations due to water scarcity resulting from climate change. Therefore, this research aimed to identify potential genotypes and suitable traits of upland rice germplasm for breeding programmes. Forty-three genotypes were evaluated in a randomised complete block design with three replications. All genotypes exhibited a wide and significant variation for 22 traits. The highest phenotypic and genotypic coefficient of variation was recorded for the number of filled grains/panicle and yields/plant (g). The highest heritability was found for photosynthetic rate, transpiration rate, stomatal conductance, intercellular CO₂, and number of filled grains/panicle and yields/plant (g). Cluster analysis based on 22 traits grouped the 43 rice genotypes into five clusters. Cluster II was the largest and consisted of 20 genotypes mostly originating from the Philippines. The first four principle components of 22 traits accounted for about 72% of the total variation and indicated a wide variation among the genotypes. The selected best trait of the number of filled grains/panicle and yields/plant (g), which showed high heritability and high genetic advance, could be used as a selection criterion for hybridisation programmes in the future.
Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.
Sg. Papar is one of the rivers in Kota Kinabalu which is mainly used for water supply especially in Papar district. For the past years, many pollution cases concerning Sg. Papar have been reported which originated from various sources including pig farm, agricultural run-off and deforestation. These resulted in a frequent shutdown of the water treatment plants in Papar district leading to water supply disturbance and water supply deficiency in the affected area. The data utilized in this study were obtained from water quality tests performed on river water samples taken from Limbahau water treatment plant recorded from September 2013 to September 2016. Principal Component Analysis (PCA) was used in this study to analyze and correlate the physicochemical parameters with the water treatment plant shutdown. The results revealed that eight parameters (pH, alum, nitrate, TDS, DO, conductivity, colour and chloride) analysed in this study correlate with each other and the parameter that mostly caused the drastic change in the river water and as pollution index is turbidity. This study is critical for understanding the relationship between the water quality paramters and environmental issues.
Zoo animal welfare is a high priority for many institutions worldwide, with modern zoos now ensuring that animals are housed and cared for to the highest standards. However, in countries where this knowledge is not as available or understood, standards may be lower. The aim of this research was to investigate if there were common zoo welfare concerns across developing country zoos. Wild Welfare is a charity working globally to improve welfare for zoo animals and has an independent welfare audit that is carried out before any intervention occurs. The Wild Welfare Audit, consisting of 110 questions, covering nine topics, was completed at 11 zoos in seven developing countries (Brazil, Egypt, Libya, Indonesia, Thailand, Malaysia and Vietnam) following a Likert scale score (1-3). A principal component analysis was also performed to evaluate the audit questions. The results suggest that common areas of concern were animal behaviour, positive animal mental states and human health and safety. These themes were likely due to the lack knowledge and understanding that may be linked to historical and cultural differences. This research has helped to revise the welfare audit as well as inform future intervention strategies for improving developing country zoo animal welfare.
The geographical variation of the gray-bellied squirrel (Callosciurus caniceps) was examined using osteometry of skull in Southeast Asia. From the principal component analysis (PCA), the plots of the northern localities from Nan to Kanchanaburi and those of the southern localities from Narathiwat to Kuala Lumpur in male were completely separated. In female, the plots of the locality from Uttradit to Kanchanaburi and those of the locality from Pattani to Negri Sembilan were completely separated. We called these northern localities and southern localities which are distinguished by the PCA as N group and S group. The size and shape of the skulls of these squirrels indicated the differences between N group and S group from t-test and U-test. These results may be influenced by the two transitions of the phytogeography around the southernmost locality in N group and the northernmost locality in S group in the peninsular Thailand and Malay Peninsula. Localities which are located between N and S groups were called the Middle (M) group. From the PCA among N, S groups and each locality of M group, the plots of localities such as Prachuap Khiri Khan, Chumphon, Krabi, Nakhon Si Thammarat and Trang in both sexes of M group could not be separated from those of N and S groups. We suggest that the sympatric distribution of N and S groups and the hybrid of N and S populations may be seen in these localities of M group.
This study was designed to determine the particle size distribution and develop road dust index combining source and transport factors involving road dust for dust pollution quantification in Rawang. Principal component analysis (PCA) was applied to identify possible sources of potentially toxic elements and spot major pollution areas in Rawang. The health risks (carcinogenic and noncarcinogenic) to adults and children were assessed using the hazard index and total lifetime cancer Risk, respectively. A total of 75 road dust samples were collected and particle sizes (1000, 500, 250, 160, 125 and 63 µm) were determined. Concentrations of potentially toxic elements (Cu, Cd, Co, Cr, Pb, Ni, Zn and As) in particle size of 63 µm were analyzed. The results demonstrated that the highest grain size of 250 µm has contributed almost more than 25% of atmospheric particulate pollution. The highest potentially toxic element concentration was Pb (593.3 mg/kg), whereas the lowest was Co (5.6 mg/kg). Road dust index output indicated that pollution risk fell into moderate levels in eastern and northern areas of Rawang. Similarly, PCA results revealed that potentially toxic elements (Cu, Cd, Pb, Zn, Ni and Cr) were linked with anthropogenic sources (urbanization process, industrial and commercial growth, urban traffic congestion) in northern and southern parts of Rawang. Cobalt and As concentrations were explained mainly from natural sources. Noncarcinogenic risk by hazard index value more than 1.0 was indicated for adults and children. Similarly, carcinogenic risk by total lifetime cancer risk value also showed carcinogenic risks among adults and children.
Arowanas (Osteoglossinae) are charismatic freshwater fishes with six species and two genera (Osteoglossum and Scleropages) distributed in South America, Asia, and Australia. In an attempt to provide a better assessment of the processes shaping their evolution, we employed a set of cytogenetic and genomic approaches, including i) molecular cytogenetic analyses using C- and CMA3/DAPI staining, repetitive DNA mapping, comparative genomic hybridization (CGH), and Zoo-FISH, along with ii) the genotypic analyses of single nucleotide polymorphisms (SNPs) generated by diversity array technology sequencing (DArTseq). We observed diploid chromosome numbers of 2n = 56 and 54 in O. bicirrhosum and O. ferreirai, respectively, and 2n = 50 in S. formosus, while S. jardinii and S. leichardti presented 2n = 48 and 44, respectively. A time-calibrated phylogenetic tree revealed that Osteoglossum and Scleropages divergence occurred approximately 50 million years ago (MYA), at the time of the final separation of Australia and South America (with Antarctica). Asian S. formosus and Australian Scleropages diverged about 35.5 MYA, substantially after the latest terrestrial connection between Australia and Southeast Asia through the Indian plate movement. Our combined data provided a comprehensive perspective of the cytogenomic diversity and evolution of arowana species on a timescale.
Introduction: The Patient Activation Measure (PAM) is one of the most extensively used, widely translated, and tested instruments worldwide in measuring patient activation levels in self-management. This study aimed to determine the validity and reliability of the PAM-13 Malay version among patients with Metabolic Syndrome (MetS) attending a primary care clinic.
Methods: This work is a cross-sectional validation study among patients with MetS attending a university primary care clinic in Selangor. The PAM-13 Malay version underwent a validation process and field testing. Psychometric properties were examined using principal component analysis (PCA) with varimax rotation, scree plot, Monte Carlo simulation, internal consistency, and test-retest reliability analyses.
Results: The content of the PAM-13 Malay version and the original version were conceptually equivalent. The questionnaire was refined after face validation by 10 patients with MetS. The refined version was then field-tested among 130 participants (response rate 89.7%). The Kaiser-Meyer-Olkin test was 0.767, and Bartlett's test of sphericity was ≤0.001, indicating sampling adequacy. Two factors were identified and labeled as (1) Passive and Building Knowledge, and (2) Taking Action and Maintaining Behavior. These labels were chosen as they were conceptually consistent with the items representing the levels of activation in PAM-13. The validated PAM-13 Malay version consisted of 13 items, framed into two domains. The overall Cronbach's α was 0.79, and the intraclass correlation coefficient was 0.45.
Conclusions: The PAM-13 Malay version is valid, reliable, and fairly stable over time. This questionnaire can be used to evaluate the levels of activation among patients with MetS in primary care in Malaysia.
Study site: Universiti Teknologi MARA (UiTM) primary care clinic, Sungai Buloh, Selangor, Malaysia
Abuse of solvent-based adhesives jeopardizes world population, especially the young generation. Adhesive-related exhibits encountered in forensic cases might need to be determined if they could have come from a particular source or to establish link between cases or persons. This study was aimed to discriminate solvent-based adhesives, especially to aid forensic investigation of glue sniffing activities. In this study, thirteen brands with three samples each, totaling at 39 adhesive samples, were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy followed by chemometric methods. Experimental output showed that adhesive samples utilized in this study were less likely to change in their ATR-FTIR profiles over time, at least up to 2 months. No interference from plastic materials was noticed based on ATR-FTIR profile comparison. Physical examination could differentiate the samples into two groups, namely contact adhesives and cement adhesives. A principal component analysis-score linear discriminative analysis (PC-score LDA) model resulted in 100% and 98.6% correct classification in discriminating the two groups of adhesive samples, forming seven discriminative clusters. Test set with adhesive samples applied glass slide and plastic substrates also demonstrated a 100% correct classification into their respective groups. As a conclusion, the method allowed for discrimination of adhesive samples based on the spectral features, displaying relationship among samples. It is hoped that this comparative information is beneficial to trace the possible source of solvent-based adhesives, whenever they are recovered from a crime scene, for forensic investigation.