Displaying publications 41 - 60 of 254 in total

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  1. Fulltext A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
    Chai SS, Cheah WL, Goh KL, Chang YHR, Sim KY, Chin KO
    Comput Math Methods Med, 2021;2021:2794888.
    PMID: 34917164 DOI: 10.1155/2021/2794888
    This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.
    Matched MeSH terms: Bayes Theorem
  2. Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
    Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.
    Comput Methods Programs Biomed, 2018 Jul;161:133-143.
    PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018
    Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
    Matched MeSH terms: Bayes Theorem
  3. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Bayes Theorem
  4. Retinal image analysis for disease screening through local tetra patterns
    Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F
    Comput Biol Med, 2018 11 01;102:200-210.
    PMID: 30308336 DOI: 10.1016/j.compbiomed.2018.09.028
    Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR) are the most prevalent diseases responsible for visual impairment in the world. This work investigates discrimination potential in the texture of color fundus images to distinguish between diseased and healthy cases by avoiding the prior lesion segmentation step. It presents a retinal background characterization approach and explores the potential of Local Tetra Patterns (LTrP) for texture classification of AMD, DR and Normal images. Five different experiments distinguishing between DR - normal, AMD - normal, DR - AMD, pathological - normal and AMD - DR - normal cases were conducted and validated using the proposed approach, and promising results were obtained. For all five experiments, different classifiers namely, AdaBoost, c4.5, logistic regression, naive Bayes, neural network, random forest and support vector machine were tested. We experimented with three public datasets, ARIA, STARE and E-Optha. Further, the performance of LTrP is compared with other texture descriptors, such as local phase quantization, local binary pattern and local derivative pattern. In all cases, the proposed method obtained the area under the receiver operating characteristic curve and f-score values higher than 0.78 and 0.746 respectively. It was found that both performance measures achieve over 0.995 for DR and AMD detection using a random forest classifier. The obtained results suggest that the proposed technique can discriminate retinal disease using texture information and has potential to be an important component for an automated screening solution for retinal images.
    Matched MeSH terms: Bayes Theorem
  5. Examining the relationship between local extinction risk and position in range
    Boakes EH, Isaac NJB, Fuller RA, Mace GM, McGowan PJK
    Conserv Biol, 2018 02;32(1):229-239.
    PMID: 28678438 DOI: 10.1111/cobi.12979
    Over half of globally threatened animal species have experienced rapid geographic range loss. Identifying the parts of species' distributions most vulnerable to local extinction would benefit conservation planning. However, previous studies give little consensus on whether ranges decline to the core or edge. We built on previous work by using empirical data to examine the position of recent local extinctions within species' geographic ranges, address range position as a continuum, and explore the influence of environmental factors. We aggregated point-locality data for 125 Galliform species from across the Palearctic and Indo-Malaya into equal-area half-degree grid cells and used a multispecies dynamic Bayesian occupancy model to estimate rates of local extinctions. Our model provides a novel approach to identify loss of populations from within species ranges. We investigated the relationship between extinction rates and distance from range edge by examining whether patterns were consistent across biogeographic realm and different categories of land use. In the Palearctic, local extinctions occurred closer to the range edge than range core in both unconverted and human-dominated landscapes. In Indo-Malaya, no pattern was found for unconverted landscapes, but in human-dominated landscapes extinctions tended to occur closer to the core than the edge. Our results suggest that local and regional factors override general spatial patterns of recent local extinction within species' ranges and highlight the difficulty of predicting the parts of a species' distribution most vulnerable to threat.
    Matched MeSH terms: Bayes Theorem
  6. Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity
    Jatoi MA, Kamel N, Musavi SHA, López JD
    Curr Med Imaging Rev, 2019;15(2):184-193.
    PMID: 31975664 DOI: 10.2174/1573405613666170629112918
    BACKGROUND: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms.

    METHODS: Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared.

    RESULTS: The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB.

    CONCLUSION: In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

    Matched MeSH terms: Bayes Theorem
  7. Fulltext Geographical structure, narrow species ranges, and Cenozoic diversification in a pantropical clade of epiphyllous leafy liverworts
    Bechteler J, Schäfer-Verwimp A, Lee GE, Feldberg K, Pérez-Escobar OA, Pócs T, et al.
    Ecol Evol, 2017 01;7(2):638-653.
    PMID: 28116059 DOI: 10.1002/ece3.2656
    The evolutionary history and classification of epiphyllous cryptogams are still poorly known. Leptolejeunea is a largely epiphyllous pantropical liverwort genus with about 25 species characterized by deeply bilobed underleaves, elliptic to narrowly obovate leaf lobes, the presence of ocelli, and vegetative reproduction by cladia. Sequences of three chloroplast regions (rbcL, trnL-F, psbA) and the nuclear ribosomal ITS region were obtained for 66 accessions of Leptolejeunea and six outgroup species to explore the phylogeny, divergence times, and ancestral areas of this genus. The phylogeny was estimated using maximum-likelihood and Bayesian inference approaches, and divergence times were estimated with a Bayesian relaxed clock method. Leptolejeunea likely originated in Asia or the Neotropics within a time interval from the Early Eocene to the Late Cretaceous (67.9 Ma, 95% highest posterior density [HPD]: 47.9-93.7). Diversification of the crown group initiated in the Eocene or early Oligocene (38.4 Ma, 95% HPD: 27.2-52.6). Most species clades were established in the Miocene. Leptolejeunea epiphylla and L. schiffneri originated in Asia and colonized African islands during the Plio-Pleistocene. Accessions of supposedly pantropical species are placed in different main clades. Several monophyletic morphospecies exhibit considerable sequence variation related to a geographical pattern. The clear geographic structure of the Leptolejeunea crown group points to evolutionary processes including rare long-distance dispersal and subsequent speciation. Leptolejeunea may have benefitted from the large-scale distribution of humid tropical angiosperm forests in the Eocene.
    Matched MeSH terms: Bayes Theorem
  8. Fulltext Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints
    Colwell RK, Gotelli NJ, Ashton LA, Beck J, Brehm G, Fayle TM, et al.
    Ecol Lett, 2016 09;19(9):1009-22.
    PMID: 27358193 DOI: 10.1111/ele.12640
    We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor - a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon-specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.
    Matched MeSH terms: Bayes Theorem
  9. Fulltext Outbreaks of enterovirus D68 in Malaysia: genetic relatedness to the recent US outbreak strains
    Ng KT, Oong XY, Pang YK, Hanafi NS, Kamarulzaman A, Tee KK
    Emerg Microbes Infect, 2015 Aug;4(8):e47.
    PMID: 26421270 DOI: 10.1038/emi.2015.47
    Matched MeSH terms: Bayes Theorem
  10. Fulltext Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
    Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, et al.
    Environ Int, 2023 May;175:107931.
    PMID: 37119651 DOI: 10.1016/j.envint.2023.107931
    This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
    Matched MeSH terms: Bayes Theorem
  11. A comparison of various approaches used in source apportionments for precipitation nitrogen in a mountain region of southwest China
    Cui J, Zhou F, Gao M, Zhang L, Zhang L, Du K, et al.
    Environ Pollut, 2018 Oct;241:810-820.
    PMID: 29909307 DOI: 10.1016/j.envpol.2018.06.028
    Six different approaches are applied in the present study to apportion the sources of precipitation nitrogen making use of precipitation data of dissolved inorganic nitrogen (DIN, including NO3- and NH4+), dissolved organic nitrogen (DON) and δ15N signatures of DIN collected at six sampling sites in the mountain region of Southwest China. These approaches include one quantitative approach running a Bayesian isotope mixing model (SIAR model) and five qualitative approaches based on in-situ survey (ISS), ratio of NH4+/NO3- (RN), principal component analysis (PCA), canonical-correlation analysis (CCA) and stable isotope approach (SIA). Biomass burning, coal combustion and mobile exhausts in the mountain region are identified as major sources for precipitation DIN while biomass burning and volatilization sources such as animal husbandries are major ones for DON. SIAR model results suggest that mobile exhausts, biomass burning and coal combustion contributed 25.1 ± 14.0%, 26.0 ± 14.1% and 27.0 ± 12.6%, respectively, to NO3- on the regional scale. Higher contributions of both biomass burning and coal combustion appeared at rural and urban sites with a significant difference between Houba (rural) and the wetland site (p 
    Matched MeSH terms: Bayes Theorem*
  12. GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
    Tella A, Balogun AL
    Environ Sci Pollut Res Int, 2022 Dec;29(57):86109-86125.
    PMID: 34533750 DOI: 10.1007/s11356-021-16150-0
    Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.
    Matched MeSH terms: Bayes Theorem
  13. Fulltext COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets
    Sadiq M, Hsu CC, Zhang Y, Chien F
    Environ Sci Pollut Res Int, 2021 Dec;28(47):67167-67184.
    PMID: 34245412 DOI: 10.1007/s11356-021-15064-1
    This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations' (ASEAN-7) member countries from March 21, 2020 to April 31, 2020. This paper uses a ST-HAR-type Bayesian posterior model and it highlights the stock market of this ongoing crisis, such as, COVID-19 outbreak in all countries and related industries. The empirical results shown a clear evidence of a transition during COVID-19 crisis regime, also crisis intensity and timing differences. The most negatively impacted industries were health care and consumer services due to the Covid-19 drug-race and international travel restrictions. More so, study results estimated that only a small number of sectors are affected by COVID-19 fear including  health care, consumer services, utilities, and technology, significance at the 1%, 5%, and 10%, that measure current volatility's reliance on weekly and monthly variables. Secondly, it is found that there is almost no chance that the COVID-19 pandemic would positively affect the stock market performance in all the countries, mainly Indonesia and Singapore were the countries most affected. Thirdly, results shown that Thailand's stock market output has dropped by 15%. Results shows that COVID-19 fear causes an eventual reason of public attention towards stock market volatility. The study presented comprehensive way forwards to stabilize movement of ASEAN equity market's volatility index and guided the policy implications to key stakeholders that can better help to mitigate drastic impacts of COVID-19 fear on the performance of equity markets.
    Matched MeSH terms: Bayes Theorem
  14. Fulltext COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings
    Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A
    Eur Radiol, 2021 Jan;31(1):121-130.
    PMID: 32740817 DOI: 10.1007/s00330-020-07087-y
    OBJECTIVES: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients.

    METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.

    RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.

    CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.

    KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

    Matched MeSH terms: Bayes Theorem
  15. Codiversification in an ant-plant mutualism: stem texture and the evolution of host use in Crematogaster (Formicidae: Myrmicinae) inhabitants of Macaranga (Euphorbiaceae)
    Quek SP, Davies SJ, Itino T, Pierce NE
    Evolution, 2004 Mar;58(3):554-70.
    PMID: 15119439
    We investigate the evolution of host association in a cryptic complex of mutualistic Crematogaster (Decacrema) ants that inhabits and defends Macaranga trees in Southeast Asia. Previous phylogenetic studies based on limited samplings of Decacrema present conflicting reconstructions of the evolutionary history of the association, inferring both cospeciation and the predominance of host shifts. We use cytochrome oxidase I (COI) to reconstruct phylogenetic relationships in a comprehensive sampling of the Decacrema inhabitants of Macaranga. Using a published Macaranga phylogeny, we test whether the ants and plants have cospeciated. The COI phylogeny reveals 10 well-supported lineages and an absence of cospeciation. Host shifts, however, have been constrained by stem traits that are themselves correlated with Macaranga phylogeny. Earlier lineages of Decacrema exclusively inhabit waxy stems, a basal state in the Pachystemon clade within Macaranga, whereas younger species of Pachystemon, characterized by nonwaxy stems, are inhabited only by younger lineages of Decacrema. Despite the absence of cospeciation, the correlated succession of stem texture in both phylogenies suggests that Decacrema and Pachystemon have diversified in association, or codiversified. Subsequent to the colonization of the Pachystemon clade, Decacrema expanded onto a second clade within Macaranga, inducing the development of myrmecophytism in the Pruinosae group. Confinement to the aseasonal wet climate zone of western Malesia suggests myrmecophytic Macaranga are no older than the wet forest community in Southeast Asia, estimated to be about 20 million years old (early Miocene). Our calculation of COI divergence rates from several published arthropod studies that relied on tenable calibrations indicates a generally conserved rate of approximately 1.5% per million years. Applying this rate to a rate-smoothed Bayesian chronogram of the ants, the Decacrema from Macaranga are inferred to be at least 12 million years old (mid-Miocene). However, using the extremes of rate variation in COI produces an age as recent as 6 million years. Our inferred timeline based on 1.5% per million years concurs with independent biogeographical events in the region reconstructed from palynological data, thus suggesting that the evolutionary histories of Decacrema and their Pachystemon hosts have been contemporaneous since the mid-Miocene. The evolution of myrmecophytism enabled Macaranga to radiate into enemy-free space, while the ants' diversification has been shaped by stem traits, host specialization, and geographic factors. We discuss the possibility that the ancient and exclusive association between Decacrema and Macaranga was facilitated by an impoverished diversity of myrmecophytes and phytoecious (obligately plant inhabiting) ants in the region.
    Matched MeSH terms: Bayes Theorem
  16. Fulltext Circulation of human respiratory syncytial virus strains among hospitalized children with acute lower respiratory infection in malaysia
    Etemadi MR, Sekawi Z, Othman N, Lye MS, Moghaddam FY
    Evol Bioinform Online, 2013;9:151-61.
    PMID: 23641140 DOI: 10.4137/EBO.S10999
    Human respiratory syncytial virus (RSV) is a major viral pathogen associated with acute lower respiratory tract infections (ALRTIs) among hospitalized children. In this study, the genetic diversity of the RSV strains was investigated among nasopharyngeal aspirates (NPA) taken from children less than 5 years of age hospitalized with ALRTIs in Hospital Serdang, Malaysia. A total of 165 NPA samples were tested for the presence of RSV and other respiratory viruses from June until December 2009. RSV was found positive in 83 (50%) of the samples using reverse transcription polymerase chain reaction (RT-PCR). Further classification of 67 RSV strains showed that subgroups A and B comprised 11/67 (16.4%) and 56/67 (83.6%) of the strains, respectively. The second hypervariable region at the carboxyl-terminal of the G gene was amplified and sequenced in order to do phylogenetic study. The phylogenetic relationships of the samples were determined separately for subgroups A and B using neighbor joining (NJ), maximum parsimony (MP), and Bayesian inference (BI). Phylogenetic analysis of the 32 sequenced samples showed that all 9 RSV-A strains were clustered within NA1 genotype while the remaining 23 strains of the RSV-B subgroup could be grouped into a clade consisted of strains with 60-nucleotide duplication region. They were further classified into newly discovered BA10 and BA9 genotypes. The present finding suggests the emergence of RSV genotypes of NA1 and BA. This is the first documentation of the phylogenetic relationship and genetic diversity of RSV strains among hospitalized children diagnosed with ALRTI in Serdang, Malaysia.
    Matched MeSH terms: Bayes Theorem
  17. DNA-based characterisation and classification of forensically important flesh flies (Diptera: Sarcophagidae) in Malaysia
    Tan SH, Rizman-Idid M, Mohd-Aris E, Kurahashi H, Mohamed Z
    Forensic Sci Int, 2010 Jun 15;199(1-3):43-9.
    PMID: 20392577 DOI: 10.1016/j.forsciint.2010.02.034
    Insect larvae and adult insects found on human corpses provide important clues for the estimation of the postmortem interval (PMI). Among all necrophagous insects, flesh flies (Diptera: Sarcophagidae) are considered as carrion flies of forensic importance. DNA variations of 17 Malaysian, two Indonesian and one Japanese flesh fly species are analysed using the mitochondrial COI and COII. These two DNA regions were useful for identifying most species experimented. However, characterisation of the species was not sufficiently made in the case of Sarcophaga javanica. Seventeen Malaysian species of forensic importance were successfully clustered into distinct clades and grouped into the six species groups: peregrina, albiceps, dux, pattoni, princeps and ruficornis. These groups correspond with generic or subgeneric taxa of the subfamily Sarcophaginae: Boettcherisca, Parasarcophaga, Liosarcophaga, Sarcorohdendorfia-Lioproctia, Harpagophalla-Seniorwhitea and Liopygia. The genetic variations found in COI and COII can be applied not only to identify the species of forensic importance, but also to understand the taxonomic positions, generic or subgeneric status, of the sarcophagine species.
    Matched MeSH terms: Bayes Theorem
  18. Fulltext Global Spread of the B5 Subgenotype EV-A71 and the Phylogeographical Analysis of Chinese Migration Events
    Huang K, Zhang Y, Han Z, Zhou X, Song Y, Wang D, et al.
    Front Cell Infect Microbiol, 2020;10:475.
    PMID: 33102246 DOI: 10.3389/fcimb.2020.00475
    The subgenotype B5 of EV-A71 is a widely circulating subgenotype that frequently spreads across the globe. Several outbreaks have occurred in nations, such as Malaysia, Thailand, Vietnam, and Japan. Appearing first in Taiwan, China, the subgenotype has been frequently reported in mainland of China even though no outbreaks have been reported so far. The current study reconstructed the migration of the B5 subgenotype of EV-A71 in China via phylogeographical analysis. Furthermore, we investigated its population dynamics in order to draw more credible inferences. Following a dataset cleanup of B5 subgenotype of EV-A71, we detected earlier B5 subgenotypes of EV-A71 sequences that had been circulating in Malaysia and Singapore since the year 2000, which was before the 2003 outbreak that occurred in Sarawak. The Bayesian inference indicated that the most recent common ancestor of B5 subgenotype EV-A71 appeared in September, 1994 (1994.75). With respect to the overall prevalence, geographical reconstruction revealed that the B5 subgenotype EV-A71 originated singly from single-source cluster and subsequently developed several active lineages. Based on a large amount of data that was accumulated, we conclude that the appearance of the B5 subgenotype of EV-A71 in mainland of China was mainly due to multiple migrations from different origins.
    Matched MeSH terms: Bayes Theorem
  19. Fulltext Molecular Phylogenesis and Spatiotemporal Spread of SARS-CoV-2 in Southeast Asia
    Zhu M, Shen J, Zeng Q, Tan JW, Kleepbua J, Chew I, et al.
    Front Public Health, 2021 07 30;9:685315.
    PMID: 34395364 DOI: 10.3389/fpubh.2021.685315
    Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented challenge to public health in Southeast Asia, a tropical region with limited resources. This study aimed to investigate the evolutionary dynamics and spatiotemporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the region. Materials and Methods: A total of 1491 complete SARS-CoV-2 genome sequences from 10 Southeast Asian countries were downloaded from the Global Initiative on Sharing Avian Influenza Data (GISAID) database on November 17, 2020. The evolutionary relationships were assessed using maximum likelihood (ML) and time-scaled Bayesian phylogenetic analyses, and the phylogenetic clustering was tested using principal component analysis (PCA). The spatial patterns of SARS-CoV-2 spread within Southeast Asia were inferred using the Bayesian stochastic search variable selection (BSSVS) model. The effective population size (Ne) trajectory was inferred using the Bayesian Skygrid model. Results: Four major clades (including one potentially endemic) were identified based on the maximum clade credibility (MCC) tree. Similar clustering was yielded by PCA; the first three PCs explained 46.9% of the total genomic variations among the samples. The time to the most recent common ancestor (tMRCA) and the evolutionary rate of SARS-CoV-2 circulating in Southeast Asia were estimated to be November 28, 2019 (September 7, 2019 to January 4, 2020) and 1.446 × 10-3 (1.292 × 10-3 to 1.613 × 10-3) substitutions per site per year, respectively. Singapore and Thailand were the two most probable root positions, with posterior probabilities of 0.549 and 0.413, respectively. There were high-support transmission links (Bayes factors exceeding 1,000) in Singapore, Malaysia, and Indonesia; Malaysia involved the highest number (7) of inferred transmission links within the region. A twice-accelerated viral population expansion, followed by a temporary setback, was inferred during the early stages of the pandemic in Southeast Asia. Conclusions: With available genomic data, we illustrate the phylogeography and phylodynamics of SARS-CoV-2 circulating in Southeast Asia. Continuous genomic surveillance and enhanced strategic collaboration should be listed as priorities to curb the pandemic, especially for regional communities dominated by developing countries.
    Matched MeSH terms: Bayes Theorem
  20. The genetic and molecular origin of natural variation for the fragrance trait in an elite Malaysian aromatic rice through quantitative trait loci mapping using SSR and gene-based markers
    Golestan Hashemi FS, Rafii MY, Ismail MR, Mohamed MT, Rahim HA, Latif MA, et al.
    Gene, 2015 Jan 25;555(2):101-7.
    PMID: 25445269 DOI: 10.1016/j.gene.2014.10.048
    MRQ74, a popular aromatic Malaysian landrace, allows for charging considerably higher prices than non-aromatic landraces. Thus, breeding this profitable trait has become a priority for Malaysian rice breeding. Despite many studies on aroma genetics, ambiguities considering its genetic basis remain. It has been observed that identifying quantitative trait loci (QTLs) based on anchor markers, particularly candidate genes controlling a trait of interest, can increase the power of QTL detection. Hence, this study aimed to locate QTLs that influence natural variations in rice scent using microsatellites and candidate gene-based sequence polymorphisms. For this purpose, an F2 mapping population including 189 individual plants was developed by MRQ74 crosses with 'MR84', a non-scented Malaysian accession. Additionally, qualitative and quantitative approaches were applied to obtain a phenotype data framework. Consequently, we identified two QTLs on chromosomes 4 and 8. These QTLs explained from 3.2% to 39.3% of the total fragrance phenotypic variance. In addition, we could resolve linkage group 8 by adding six gene-based primers in the interval harboring the most robust QTL. Hence, we could locate a putative fgr allele in the QTL found on chromosome 8 in the interval RM223-SCU015RM (1.63cM). The identified QTLs represent an important step toward recognition of the rice flavor genetic control mechanism. In addition, this identification will likely accelerate the progress of the use of molecular markers for gene isolation, gene-based cloning, and marker-assisted selection breeding programs aimed at improving rice cultivars.
    Matched MeSH terms: Bayes Theorem
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