Displaying publications 121 - 140 of 281 in total

Abstract:
Sort:
  1. 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
  2. A penalized quantitative structure-property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty
    Al-Fakih AM, Algamal ZY, Lee MH, Aziz M
    SAR QSAR Environ Res, 2018 May;29(5):339-353.
    PMID: 29493376 DOI: 10.1080/1062936X.2018.1439531
    A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
    Matched MeSH terms: Bayes Theorem
  3. Modeling of passengers' safety perception for buses on mountainous roads
    Khoo HL, Ahmed M
    Accid Anal Prev, 2018 Apr;113:106-116.
    PMID: 29407657 DOI: 10.1016/j.aap.2018.01.025
    This study had developed a passenger safety perception model specifically for buses taking into consideration the various factors, namely driver characteristics, environmental conditions, and bus characteristics using Bayesian Network. The behaviour of bus driver is observed through the bus motion profile, measured in longitudinal, lateral, and vertical accelerations. The road geometry is recorded using GPS and is computed with the aid of the Google map while the perceived bus safety is rated by the passengers in the bus in real time. A total of 13 variables were derived and used in the model development. The developed Bayesian Network model shows that the type of bus and the experience of the driver on the investigated route could have an influence on passenger's perception of their safety on buses. Road geometry is an indirect influencing factor through the driver's behavior. The findings of this model are useful for the authorities to structure an effective strategy to improve the level of perceived bus safety. A high level of bus safety will definitely boost passenger usage confidence which will subsequently increase ridership.
    Matched MeSH terms: Bayes Theorem
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Fulltext To split or not to split? Multilocus phylogeny and molecular species delimitation of southeast Asian toads (family: Bufonidae)
    Chan KO, Grismer LL
    BMC Evol. Biol., 2019 04 25;19(1):95.
    PMID: 31023232 DOI: 10.1186/s12862-019-1422-3
    BACKGROUND: Recent studies have demonstrated that Bayesian species delimitation based on the multispecies coalescent model can produce inaccurate results by misinterpreting population splits as species divergences. An approach based on the genealogical divergence index (gdi) was shown to be a viable alternative, especially for delimiting allopatric populations where gene flow is low. We implemented these analyses to assess species boundaries in Southeast Asian toads, a group that is understudied and characterized by numerous unresolved species complexes.

    RESULTS: Multilocus phylogenetic analyses showed that deep evolutionary relationships including the genera Sigalegalephrynus, Ghatophryne, Parapelophryne, Leptophryne, Pseudobufo, Rentapia, and Phrynoides remain unresolved. Comparison of genetic divergences revealed that intraspecific divergences among allopatric populations of Pelophyrne signata (Borneo vs. Peninsular Malaysia), Ingerophrynus parvus (Peninsular Malaysia vs. Myanmar), and Leptophryne borbonica (Peninsular Malaysia, Java, Borneo, and Sumatra) are consistent with interspecific divergences of other Southeast Asian bufonid taxa. Conversely, interspecific divergences between Pelophryne guentheri/P. api, Ansonia latiffi/A. leptopus, and I. gollum/I. divergens were low (

    Matched MeSH terms: Bayes Theorem
  11. 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
  12. 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
  13. Phylogenetic relationships of waders (charadriiformes: scolopacidae) in Sarawak inferred from cytochrome oxidase I and recombinant activating gene 1
    Nurul Ashikeen Ab Razak, Mustafa Abdul Rahman, Tuen AA
    Sains Malaysiana, 2016;45:1089-1095.
    Family Scolopacidae includes the sandpipers, shanks, snipes, godwits and curlews. Systematic classifications of shorebirds
    at the higher level have been successfully resolved. Nevertheless, the phylogeny of shorebirds in the familial level is still
    poorly understood. Thus, this phylogenetic study on Scolopacidae was conducted upon the framework provided by the first
    sequence-based species-level phylogeny within the shorebirds to determine the phylogenetic relationships among family
    members of Scolopacidae in West Borneo, Sarawak using combined gene markers, mtDNA Cytochrome Oxidise I (COI)
    and nucDNA Recombinant Activating Gene 1 (RAG1). A total of 1,342 base pair (bp) were inferred from both COI and RAG1
    gene from 45 sequences constituted of 15 species Scolopacidae sampled from Sarawak namely Xenus cinereus, Actitis
    hypoleucos, Tringa totanus, Tringa glareola, Tringa stagnatilis, Heteroscelus brevipes, Calidris alba, Calidris ruficollis,
    Calidris ferruginea, Calidris tenuirostris, Calidris alpina, Gallinago stenura, Gallinago megala, Numenius arquata, and
    Numenius phaeopus. The phylogenetic tree was constructed with Charadrius mongulus derived as an outgroup. The
    Bayesian Inference (BI) tree constructed supported grouping of species into several lineages of Numeniinae, Calidrinae,
    Scolopacinae and Tringinae. The groupings of species into several lineages correlate with morphological features that
    contribute to their adaptation and ability of the species to fit to their ecosystems.
    Matched MeSH terms: Bayes Theorem
  14. Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR
    Alsalem MA, Zaidan AA, Zaidan BB, Albahri OS, Alamoodi AH, Albahri AS, et al.
    J Med Syst, 2019 Jun 01;43(7):212.
    PMID: 31154550 DOI: 10.1007/s10916-019-1338-x
    This paper aims to assist the administration departments of medical organisations in making the right decision on selecting a suitable multiclass classification model for acute leukaemia. In this paper, we proposed a framework that will aid these departments in evaluating, benchmarking and ranking available multiclass classification models for the selection of the best one. Medical organisations have continuously faced evaluation and benchmarking challenges in such endeavour, especially when no single model is superior. Moreover, the improper selection of multiclass classification for acute leukaemia model may be costly for medical organisations. For example, when a patient dies, one such organisation will be legally or financially sued for incidents in which the model fails to fulfil its desired outcome. With regard to evaluation and benchmarking, multiclass classification models are challenging processes due to multiple evaluation and conflicting criteria. This study structured a decision matrix (DM) based on the crossover of 2 groups of multi-evaluation criteria and 22 multiclass classification models. The matrix was then evaluated with datasets comprising 72 samples of acute leukaemia, which include 5327 gens. Subsequently, multi-criteria decision-making (MCDM) techniques are used in the benchmarking and ranking of multiclass classification models. The MCDM used techniques that include the integrated BWM and VIKOR. BWM has been applied for the weight calculations of evaluation criteria, whereas VIKOR has been used to benchmark and rank classification models. VIKOR has also been employed in two decision-making contexts: individual and group decision making and internal and external group aggregation. Results showed the following: (1) the integration of BWM and VIKOR is effective at solving the benchmarking/selection problems of multiclass classification models. (2) The ranks of classification models obtained from internal and external VIKOR group decision making were almost the same, and the best multiclass classification model based on the two was 'Bayes. Naive Byes Updateable' and the worst one was 'Trees.LMT'. (3) Among the scores of groups in the objective validation, significant differences were identified, which indicated that the ranking results of internal and external VIKOR group decision making were valid.
    Matched MeSH terms: Bayes Theorem
  15. Rediscovery of nycticebus coucang insularis robinson, 1917 (primates: lorisidae) at tioman island and its mitochondrial genetic assessment
    Jeffrine J. Rovie-Ryan, Millawati Gani, Norsyamimi Rosli, Han Ming Gan, Gilmoore G. Bolongon, Tan Cheng Cheng, et al.
    Sains Malaysiana, 2018;47:2533-2542.
    Slow lorises (Nycticebus) consist of eight species native to Southeast Asia while three species are recognised in
    Malaysia - N. coucang, N. menagensis and N. kayan. This study reports on the rediscovery of the subspecies N. coucang
    insularis Robinson, 1917 in Tioman Island and the genetic assessment of its mitochondrial DNA variation. Morphological
    measurements conform the specimen as the putative N. coucang but with distinct colour and markings. Two mitochondrial
    DNA segments (cytochrome b and control region) were produced from the subspecies representing their first registered
    sequences in GenBank. Genetically, the subspecies showed 99% of nucleotide similarity to N. coucang species type for
    both the DNA segments and constitute its own unique haplotype. Phylogenetic trees constructed using three methods
    (neighbour joining, maximum likelihood and Bayesian inference) showed two major groups within Nycticebus; the
    basal group was formed by N. pygmaeus while the second group consisted of the remaining Nycticebus species. The
    phylogenetic position of the subspecies, however, remains unresolved due to the observed mixing between N. coucang and
    N. bengalensis. Several reasons could lead to this condition including the lack of well documented voucher specimens and
    the short DNA fragments used. In addition, the possibility of hybridisation event between N. coucang and N. bengalensis
    could not be excluded as a possible explanation since both species occur sympatrically at the Isthmus of Kra region
    until the Thailand-Malaysia border. The rediscovery of this subspecies displays the unique faunal diversity that justifies
    the importance of Tioman Island as a protected area.
    Matched MeSH terms: Bayes Theorem
  16. 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
  17. Using GWAS top hits to inform priors in Bayesian fine-mapping association studies
    Walters K, Cox A, Yaacob H
    Genet Epidemiol, 2019 Sep;43(6):675-689.
    PMID: 31286571 DOI: 10.1002/gepi.22212
    The default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian fine-mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well-studied diseases there are now considerable numbers of genome-wide association study (GWAS) top hits along with estimates of the number of yet-to-be-discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet-to-be-discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants.
    Matched MeSH terms: Bayes Theorem
  18. Fulltext Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System
    Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, et al.
    Sensors (Basel), 2019 Jul 09;19(13).
    PMID: 31324070 DOI: 10.3390/s19133030
    According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.
    Matched MeSH terms: Bayes Theorem
  19. 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
  20. Fulltext Persistent mosquito fogging can be detrimental to non-target invertebrates in an urban tropical forest
    Lee NSM, Clements GR, Ting ASY, Wong ZH, Yek SH
    PeerJ, 2020;8:e10033.
    PMID: 33062440 DOI: 10.7717/peerj.10033
    Background: Human population growth has led to biodiversity declines in tropical cities. While habitat loss and fragmentation have been the main drivers of urban biodiversity loss, man-made interventions to reduce health risks have also emerged as an unintentional threat. For instance, insecticide fogging to control mosquito populations has become the most common method of preventing the expansion of mosquito-borne diseases such as Dengue. However, the effectiveness of fogging in killing mosquitoes has been called into question. One concern is the unintended effect of insecticide fogging on non-target invertebrates that are crucial for the maintenance of urban ecosystems. Here, we investigate the impacts of fogging on: (1) target invertebrate taxon (Diptera, including mosquitoes); (2) non-target invertebrate taxa; and (3) the foraging behavior of an invertebrate pollinator taxon (Lepidoptera) within an urban tropical forest.

    Methods: We carried out fogging with Pyrethroid insecticide (Detral 2.5 EC) at 10 different sites in a forest situated in the state of Selangor, Peninsular Malaysia. Across the sites, we counted the numbers of knocked-down invertebrates and identified them based on morphology to different taxa. We constructed Bayesian hierarchical Poisson regression models to investigate the effects of fogging on: (1) a target invertebrate taxon (Diptera) 3-h post-fogging; (2) selected non-target invertebrate taxa 3-h post-fogging; and (3) an invertebrate pollinator taxon (Lepidoptera) 24-h post-fogging.

    Results: A total of 1,874 invertebrates from 19 invertebrate orders were knocked down by the fogging treatment across the 10 sites. Furthermore, 72.7% of the invertebrates counted 3-h post-fogging was considered dead. Our regression models showed that given the data and prior information, the probability that fogging had a negative effect on invertebrate taxa 3-h post-fogging was 100%, with reductions to 11% of the pre-fogging count of live individuals for the target invertebrate taxon (Diptera), and between 5% and 58% of the pre-fogging count of live individuals for non-target invertebrate taxa. For the invertebrate pollinator, the probability that fogging had a negative effect 24-h post-fogging was also 100%, with reductions to 53% of the pre-fogging count of live individuals.

    Discussion: Our Bayesian models unequivocally demonstrate that fogging has detrimental effects on one pollinator order and non-target invertebrate orders, especially taxa that have comparatively lower levels of chitinisation. While fogging is effective in killing the target order (Diptera), no mosquitos were found dead in our experiment. In order to maintain urban biodiversity, we recommend that health authorities and the private sector move away from persistent insecticide fogging and to explore alternative measures to control adult mosquito populations.

    Matched MeSH terms: Bayes Theorem
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links