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Genetic Polymorphism of Aedes albopictus Population Inferred From ND5 Gene Variabilities In Subang Jaya, Malaysia

Adilah-Amrannudin N, Hamsidi M, Ismail NA, Ismail R, Dom NC, Ahmad AH, et al.

J Am Mosq Control Assoc, 2016 Dec;32(4):265-272.
PMID: 28206858 DOI: 10.2987/16-6579.1

This study was performed to establish the genetic variability of Aedes albopictus within Subang Jaya, Selangor, Malaysia, by using the nicotinamide adenine dinucleotide dehydrogenase 5 subunit (ND5) mitochondrial DNA (mtDNA) marker. A total of 90 samples were collected from 9 localities within an area of the Subang Jaya Municipality. Genetic variability was determined through the amplification and sequencing of a fragment of the ND5 gene. Eight distinct mtDNA haplotypes were identified. The evolutionary relationship of the local haplotypes alongside 28 reference strains was used to construct a phylogram, the analysis of which revealed low genetic differentiation in terms of both nucleotide and haplotype diversity. Bayesian method was used to infer the phylogenetic tree, revealing a unique relationship between local isolates. The study corroborates the reliability of ND5 to identify distinct lineages for polymorphism-based studies and supplements the existing body of knowledge regarding its genetic diversity. This in turn could potentially aid existing vector control strategies to help mitigate the risk and spread of the dengue virus.

Matched MeSH terms: Bayes Theorem
Fulltext Detection of mumps virus of genotype g in Bangladeshi children suffering from encephalitis

Daisuke, Mori, Wahida Khanam, Kamruddin Ahmed

Borneo Journal of Medical Sciences, 2021;15(1):73-79.
MyJurnal

Although mumps virus (MuVi) is an important agent of encephalitis, however, mumps vaccine has not yet been included in the national immunization programme of Bangladesh. Furthermore, the genotype distribution of this virus in Bangladesh is unknown. Cerebrospinal fluid samples collected from 97 children with encephalitis from April 2009 to March 2010 were subjected to polymerase chain reaction (PCR) test to determine the causative agents. MuVi was detected in two samples, these samples were further subjected to conventional PCR using specific primers, then amplicons were sequenced, and genotype was determined as genotype G. Phylogenetic analysis showed that these strains were clustered with strains from Nepal, India, the UK, Thailand, and the USA. By Bayesian inference, we also determined that the ancestor of Bangladeshi and Indian MuVi were same and segregated only about two decades back. These results will help future surveillance and the detection of invading MuVi strains from other countries.

Matched MeSH terms: Bayes Theorem
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.

Sci Total Environ, 2019 May 01;663:1-15.
PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Matched MeSH terms: Bayes Theorem
Fulltext Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction

Vijayasarveswari V, Andrew AM, Jusoh M, Sabapathy T, Raof RAA, Yasin MNM, et al.

PLoS One, 2020;15(8):e0229367.
PMID: 32790672 DOI: 10.1371/journal.pone.0229367

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

Matched MeSH terms: Bayes Theorem
Phylogenetic comparison between Type IX Secretion System (T9SS) protein components suggests evidence of horizontal gene transfer

Emrizal R, Nor Muhammad NA

PeerJ, 2020;8:e9019.
PMID: 32617187 DOI: 10.7717/peerj.9019

Porphyromonas gingivalis is one of the major bacteria that causes periodontitis. Chronic periodontitis is a severe form of periodontal disease that ultimately leads to tooth loss. Virulence factors that contribute to periodontitis are secreted by Type IX Secretion System (T9SS). There are aspects of T9SS protein components that have yet to be characterised. Thus, the aim of this study is to investigate the phylogenetic relationship between members of 20 T9SS component protein families. The Bayesian Inference (BI) trees for 19 T9SS protein components exhibit monophyletic clades for all major classes under Bacteroidetes with strong support for the monophyletic clades or its subclades that is consistent with phylogeny exhibited by the constructed BI tree of 16S rRNA. The BI tree of PorR is different from the 19 BI trees of T9SS protein components as it does not exhibit monophyletic clades for all major classes under Bacteroidetes. There is strong support for the phylogeny exhibited by the BI tree of PorR which deviates from the phylogeny based on 16S rRNA. Hence, it is possible that the porR gene is subjected to horizontal transfer as it is known that virulence factor genes could be horizontally transferred. Seven genes (porR included) that are involved in the biosynthesis of A-LPS are found to be flanked by insertion sequences (IS5 family transposons). Therefore, the intervening DNA segment that contains the porR gene might be transposed and subjected to conjugative transfer. Thus, the seven genes can be co-transferred via horizontal gene transfer. The BI tree of UgdA does not exhibit monophyletic clades for all major classes under Bacteroidetes which is similar to the BI tree of PorR (both are a part of the seven genes). Both BI trees also exhibit similar topology as the four identified clusters with strong support and have similar relative positions to each other in both BI trees. This reinforces the possibility that porR and the other six genes might be horizontally transferred. Other than the BI tree of PorR, the 19 other BI trees of T9SS protein components also exhibit evidence of horizontal gene transfer. However, their genes might undergo horizontal gene transfer less frequently compared to porR because the intervening DNA segment that contains porR is easily exchanged between bacteria under Bacteroidetes due to the presence of insertion sequences (IS5 family transposons) that flank it. In conclusion, this study can provide a better understanding about the phylogeny of T9SS protein components.

Matched MeSH terms: Bayes Theorem
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
Gene flow creates a mirage of cryptic species in a Southeast Asian spotted stream frog complex

Chan KO, Hutter CR, Wood PL, Grismer LL, Das I, Brown RM

Mol Ecol, 2020 10;29(20):3970-3987.
PMID: 32808335 DOI: 10.1111/mec.15603

Most new cryptic species are described using conventional tree- and distance-based species delimitation methods (SDMs), which rely on phylogenetic arrangements and measures of genetic divergence. However, although numerous factors such as population structure and gene flow are known to confound phylogenetic inference and species delimitation, the influence of these processes is not frequently evaluated. Using large numbers of exons, introns, and ultraconserved elements obtained using the FrogCap sequence-capture protocol, we compared conventional SDMs with more robust genomic analyses that assess population structure and gene flow to characterize species boundaries in a Southeast Asian frog complex (Pulchrana picturata). Our results showed that gene flow and introgression can produce phylogenetic patterns and levels of divergence that resemble distinct species (up to 10% divergence in mitochondrial DNA). Hybrid populations were inferred as independent (singleton) clades that were highly divergent from adjacent populations (7%-10%) and unusually similar (<3%) to allopatric populations. Such anomalous patterns are not uncommon in Southeast Asian amphibians, which brings into question whether the high levels of cryptic diversity observed in other amphibian groups reflect distinct cryptic species-or, instead, highly admixed and structured metapopulation lineages. Our results also provide an alternative explanation to the conundrum of divergent (sometimes nonsister) sympatric lineages-a pattern that has been celebrated as indicative of true cryptic speciation. Based on these findings, we recommend that species delimitation of continuously distributed "cryptic" groups should not rely solely on conventional SDMs, but should necessarily examine population structure and gene flow to avoid taxonomic inflation.

Matched MeSH terms: Bayes Theorem
Fulltext Population Genetic Structure of Ornate Threadfin Bream (Nemipterus hexodon) in Thailand

Supmee V, Songrak A, Suppapan J, Sangthong P

Trop Life Sci Res, 2021 Mar;32(1):63-82.
PMID: 33936551 DOI: 10.21315/tlsr2021.32.1.4

Ornate threadfin bream (Nemipterus hexodon) is an economically important fishery species in Southeast Asia. In Thailand, N. hexodon decreased dramatically due to overexploitation for commercial purposes. To construct an effective sustainable management plan, genetic information is necessary. Thus, in our study, the population genetic structure and demographic history of N. hexodon were investigated using 419 bp of the mitochondrial DNA sequence in cytochrome oxidase subunit I gene (mtDNA COI). A total of 142 samples was collected from nine localities in the Gulf of Thailand (Chonburi, Samut Songkhram, Surat Thani, Nakhon Si Thammarat, Songkhla), and the Andaman Sea (Satun, Trang, Krabi, Phang Nga). Fourteen polymorphic sites defined 18 haplotypes, revealing a high haplotype diversity and low nucleotide diversity among nine localities. The analysis of molecular variance (AMOVA) analysis, pairwise F
ST
, and minimum spanning network result revealed that the genetic structure of N. hexodon was separated into two populations: the Gulf of Thailand and the Andaman Sea population. The genetic structure of N. hexodon can be explained by a disruption of gene flow from the geographic barrier and the Pleistocene isolation of the marine basin hypothesis. Neutrality tests, Bayesian skyline analysis, mismatch distribution, and the estimated values of population growth suggested that N. hexodon had experienced a population expansion. The genetic information would certainly help us gain insight into the population genetic structure of N. hexodon living on the coast of Thailand.

Matched MeSH terms: Bayes Theorem
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
Fulltext A Meta-Analysis on the Performance of Cystatin C- versus Creatinine-based eGFR Equations in Predicting Vancomycin Clearance

Mohd Tahir NA, Mohd Saffian S, Islahudin FH, Abdul Gafor AH, Makmor-Bakry M

J Korean Med Sci, 2020 Sep 21;35(37):e306.
PMID: 32959542 DOI: 10.3346/jkms.2020.35.e306

BACKGROUND: The objective of this study was to compare the performance of cystatin C- and creatinine-based estimated glomerular filtration rate (eGFR) equations in predicting the clearance of vancomycin.
METHODS: MEDLINE and Embase databases were searched from inception up to September 2019 to identify all studies that compared the predictive performance of cystatin C- and/or creatinine-based eGFR in predicting the clearance of vancomycin. The prediction errors (PEs) (the value of eGFR equations minus vancomycin clearance) were quantified for each equation and were pooled using a random-effects model. The root mean squared errors were also quantified to provide a metric for imprecision.
RESULTS: This meta-analysis included evaluations of seven different cystatin C- and creatinine-based eGFR equations in total from 26 studies and 1,234 patients. The mean PE (MPE) for cystatin C-based eGFR was 4.378 mL min-1 (95% confidence interval [CI], -29.425, 38.181), while the creatinine-based eGFR provided an MPE of 27.617 mL min-1 (95% CI, 8.675, 46.560) in predicting clearance of vancomycin. This indicates the presence of unbiased results in vancomycin clearance prediction by the cystatin C-based eGFR equations. Meanwhile, creatinine-based eGFR equations demonstrated a statistically significant positive bias in vancomycin clearance prediction.
CONCLUSION: Cystatin C-based eGFR equations are better than creatinine-based eGFR equations in predicting the clearance of vancomycin. This suggests that utilising cystatin C-based eGFR equations could result in better accuracy and precision to predict vancomycin pharmacokinetic parameters.

Matched MeSH terms: Bayes Theorem
Fulltext Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data

Farzana Kabir Ahmad, Siti Sakira Kamaruddin

Scientific Research Journal, 2015;12(1):1-10.
MyJurnal

The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.

Matched MeSH terms: Bayes Theorem
Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

Hosseinpour M, Sahebi S, Zamzuri ZH, Yahaya AS, Ismail N

Accid Anal Prev, 2018 Sep;118:277-288.
PMID: 29861069 DOI: 10.1016/j.aap.2018.05.003

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables.

Matched MeSH terms: Bayes Theorem
Fulltext Genomic Selection in Commercial Perennial Crops: Applicability and Improvement in Oil Palm (Elaeis guineensis Jacq.)

Kwong QB, Ong AL, Teh CK, Chew FT, Tammi M, Mayes S, et al.

Sci Rep, 2017 06 06;7(1):2872.
PMID: 28588233 DOI: 10.1038/s41598-017-02602-6

Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shell-to-fruit ratio (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %), oil per bunch (O/B, %) and oil per palm (O/P, kg/palm/year). Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection.

Matched MeSH terms: Bayes Theorem
Comprehensive multi-locus phylogeny of Old World tree frogs (Anura: Rhacophoridae) reveals taxonomic uncertainties and potential cases of over- and underestimation of species diversity

Chan KO, Grismer LL, Brown RM

Mol Phylogenet Evol, 2018 10;127:1010-1019.
PMID: 30030179 DOI: 10.1016/j.ympev.2018.07.005

The family Rhacophoridae is one of the most diverse amphibian families in Asia, for which taxonomic understanding is rapidly-expanding, with new species being described steadily, and at increasingly finer genetic resolution. Distance-based methods frequently have been used to justify or at least to bolster the recognition of new species, particularly in complexes of "cryptic" species where obvious morphological differentiation does not accompany speciation. However, there is no universally-accepted threshold to distinguish intra- from interspecific genetic divergence. Moreover, indiscriminant use of divergence thresholds to delimit species can result in over- or underestimation of species diversity. To explore the range of variation in application of divergence scales, and to provide a family-wide assessment of species-level diversity in Old-World treefrogs (family Rhacophoridae), we assembled the most comprehensive multi-locus phylogeny to date, including all 18 genera and approximately 247 described species (∼60% coverage). We then used the Automatic Barcode Gap Discovery (ABGD) method to obtain different species-delimitation schemes over a range of prior intraspecific divergence limits to assess the consistency of divergence thresholds used to demarcate current species boundaries. The species-rich phylogeny was able to identify a number of taxonomic errors, namely the incorrect generic placement of Chiromantis inexpectatus, which we now move to the genus Feihyla, and the specific identity of Rhacophorus bipunctatus from Peninsular Malaysia, which we tentatively reassign to R. rhodopus. The ABGD analysis demonstrated overlap between intra- and interspecific divergence limits: genetic thresholds used in some studies to synonymize taxa have frequently been used in other studies to justify the recognition of new species. This analysis also highlighted numerous groups that could potentially be split or lumped, which we earmark for future examination. Our large-scale and en bloc approach to species-level phylogenetic systematics contributes to the resolution of taxonomic uncertainties, reveals possible new species, and identifies numerous groups that require critical examination. Overall, we demonstrate that the taxonomy and evolutionary history of Old-World tree frogs are far from resolved, stable or adequately characterized at the level of genus, species, and/or population.

Matched MeSH terms: Bayes Theorem
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
Fulltext Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy

Wah, Yap Bee, Nurain Ibrahim, Hamzah Abdul Hamid, Shuzlina Abdul-Rahman, Fong, Simon

Pertanika Journal of Science & Technology, 2018;26(1):329-340.
MyJurnal

Feature selection has been widely applied in many areas such as classification of spam emails, cancer cells, fraudulent claims, credit risk, text categorisation and DNA microarray analysis. Classification involves building predictive models to predict the target variable based on several input variables (features). This study compares filter and wrapper feature selection methods to maximise the classifier accuracy. The logistic regression was used as a classifier while the performance of the feature selection methods was based on the classification accuracy, Akaike information criteria (AIC), Bayesian information criteria (BIC), Area Under Receiver operator curve (AUC), as well as sensitivity and specificity of the classifier. The simulation study involves generating data for continuous features and one binary dependent variable for different sample sizes. The filter methods used are correlation based feature selection and information gain, while the wrapper methods are sequential forward and sequential backward elimination. The simulation was carried out using R, an open-source programming language. Simulation results showed that the wrapper method (sequential forward selection and sequential backward elimination) methods were better than the filter method in selecting the correct features.

Matched MeSH terms: Bayes Theorem
Nitrate sources and processes in the surface water of a tropical reservoir by stable isotopes and mixing model

Fadhullah W, Yaccob NS, Syakir MI, Muhammad SA, Yue FJ, Li SL

Sci Total Environ, 2020 Jan 15;700:134517.
PMID: 31629263 DOI: 10.1016/j.scitotenv.2019.134517

Nitrate is one of the primary nutrients associated with sedimentation and fuels eutrophication in reservoir systems. In this study, water samples from Bukit Merah Reservoir (BMR) were analysed using a combination of water chemistry, water stable isotopes (δ2H-H2O and δ18O-H2O) and nitrate stable isotopes (δ15N-NO3- and δ18O-NO3-). The objective was to evaluate nitrate sources and processes in BMR, the oldest man-made reservoir in Malaysia. The δ15N-NO3- values in the river and reservoir water samples were in the range +0.4 to +14.9‰ while the values of δ18O-NO3- were between -0.01 and +39.4‰, respectively. The dual plots of δ15N-NO3- and δ18O-NO3- reflected mixing sources from atmospheric deposition (AD) input, ammonium in fertilizer/rain, soil nitrogen, and manure and sewage (MS) as the sources of nitrate in the surface water of BMR. Nitrate stable isotopes suggested that BMR undergoes processes such as nitrification and mixing. Denitrification and assimilation were not prevalent in the system. The Bayesian mixing model highlighted the dominance of MS sources in the system while AD contributed more proportion in the reservoir during both seasons than in the river. The use of δ13C, δ15N, and C:N ratios enabled the identification of terrestrial sources of the organic matter in the sediment, enhancing the understanding of sedimentation associated with nutrients previously reported in BMR. Overall, the nitrate sources and processes should be considered in decision-making in the management of the reservoir for irrigation, Arowana fish culture and domestic water supply.

Matched MeSH terms: Bayes Theorem
Fulltext Quality dimensions features for identifying high-quality user replies in text forum threads using classification methods

Osman A, Salim N, Saeed F

PLoS One, 2019;14(5):e0215516.
PMID: 31091242 DOI: 10.1371/journal.pone.0215516

The Text Forum Threads (TFThs) contain a large amount of Initial-Posts Replies pairs (IPR pairs) which are related to information exchange and discussion amongst the forum users with similar interests. Generally, some user replies in the discussion thread are off-topic and irrelevant. Hence, the content is of different qualities. It is important to identify the quality of the IPR pairs in a discussion thread in order to extract relevant information and helpful replies because a higher frequency of irrelevant replies in the thread could take the discussion in a different direction and the genuine users would lose interest in this discussion thread. In this study, the authors have presented an approach for identifying the high-quality user replies to the Initial-Post and use some quality dimensions features for their extraction. Moreover, crowdsourcing platforms were used for judging the quality of the replies and classified them into high-quality, low-quality or non-quality replies to the Initial-Posts. Then, the high-quality IPR pairs were extracted and identified based on their quality, and they were ranked using three classifiers i.e., Support Vector Machine, Naïve Bayes, and the Decision Trees according to their quality dimensions of relevancy, author activeness, timeliness, ease-of-understanding, politeness, and amount-of-data. In conclusion, the experimental results for the TFThs showed that the proposed approach could improve the extraction of the quality replies and identify the quality features that can be used for the Text Forum Thread Summarization.

Matched MeSH terms: Bayes Theorem
Phylogenetic relationships within the genus holothuria inferred from 16S mitochondiral rRNA gene sequences

Kamarul Rahim Kamarudin, ‘Aisyah Mohamed Rehan, Ridzwan Hashim, Usup G, Maryam Mohamed Rehan

Sains Malaysiana, 2016;45:1079-1087.

This study aimed to resolve the taxonomic status of a morphologically undetermined sea cucumber species of order Apodida
from Malaysia (GenBank accession no.: FJ223867) using partial 16S mitochondrial rRNA gene sequences and subsequently
to determine the validity of morphological taxonomy of Holothuria species into its current subgenera. The undetermined
species clustered with all taxa of Holothuria in previous study. Phylogenetic analyses using maximum parsimony and
Bayesian methods suggest that the undetermined species was genetically closer to Holothuria (Lessonothuria) pardalis and
Holothuria (Acanthotrapeza) coluber; and its position in both phylogenetic trees further suggests its status as a Holothuria
taxon. Subgenera of Holothuria, Merthensiothuria and Metriatyla are monophyletic with strong bootstrap supports and
posterior probabilities of clades, thus strengthening their morphological taxonomies. Nonetheless, the non-monophyly of
subgenera of Halodeima, Microthele and Platyperona suggests a requirement for their taxonomic revisions using integrative
taxonomy. The status of Holothuria (Halodeima) edulis subgroups in the maximum parsimony and Bayesian trees is
indistinct and further taxonomic revisions are necessary. In terms of sister relationship, both phylogenetic trees suggest
that subgenus Holothuria is a sister taxon of subgenus Roweothuria while the other sister relationships were unclear due
to the undetermined species, paraphyly and polyphyly of a number of subgenera. Further studies with more specimens of
genus Holothuria from broader geographical locations and various mtDNA genes along with morphological approaches
may facilitate to provide better insights into the molecular phylogeny of subgenera of Holothuria.

Matched MeSH terms: Bayes Theorem
Fulltext Identifying risk factors for female cardiovascular disease patients in Malaysia: a bayesian approach

Nurliyana Juhan, Yong Zulina Zubairi, Zarina Mohd Khalid, Ahmad Syadi Mahmood Zuhdi

MATEMATIKA, 2018;34(101):15-23.
MyJurnal

Cardiovascular disease (CVD) includes coronary heart disease, cerebrovascular disease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All females face the threat of CVD. But becoming aware of symptoms and signs is a great challenge since most adults at increased risk of cardiovascular disease (CVD) have no symptoms or obvious signs especially in females. The symptoms may be identified by the assessment of their risk factors. The Bayesian approach is a specific way in dealing with this kind of problem by formalizing a priori beliefs and of combining them with the available observations. This study aimed to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian logistic regression and obtain a feasible model to describe the data. A total of 874 STEMI female patients in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the univariate and multivariate analysis. Model performance was assessed through the model calibration and discrimination. The final multivariate model of STEMI female patients consisted of six significant variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group. Females aged 65 years and above have higher incidence of CVD and mortality is high among female patients with Killip class IV. Also, renal disease was a strong predictor of CVD mortality. Besides, performance measures for the model was considered good. Bayesian logistic regression model provided a better understanding on the associated risk factors of CVD for female patients which may help tailor prevention or treatment plans more effectively.

Matched MeSH terms: Bayes Theorem

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