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Probabilistic classification of the severity classes of unhealthy air pollution events

Masseran N, Safari MAM, Tajuddin RRM

Environ Monit Assess, 2024 May 08;196(6):523.
PMID: 38717514 DOI: 10.1007/s10661-024-12700-4

Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events will exert a disastrous effect on the environment. Therefore, public health and policy-making authorities must be able to determine the characteristics of these events. This study proposes a probabilistic machine learning technique for predicting the classification of extreme and non-extreme events on the basis of data features to address the above issue. The use of the naïve Bayes model in the prediction of air pollution classes is proposed to leverage its simplicity as well as high accuracy and efficiency. A case study was conducted on the air pollution index data of Klang, Malaysia, for the period of January 01, 1997, to August 31, 2020. The trained naïve Bayes model achieves high accuracy, sensitivity, and specificity on the training and test datasets. Therefore, the naïve Bayes model can be easily applied in air pollution analysis while providing a promising solution for the accurate and efficient prediction of extreme or non-extreme air pollution events. The findings of this study provide reliable information to public authorities for monitoring and managing sustainable air quality over time.

Matched MeSH terms: Bayes Theorem*
Fulltext A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog

Hameed SS, Selamat A, Abdul Latiff L, Razak SA, Krejcar O, Fujita H, et al.

Sensors (Basel), 2021 Dec 11;21(24).
PMID: 34960384 DOI: 10.3390/s21248289

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.

Matched MeSH terms: Bayes Theorem
Bayesian source apportionment and eco-probabilistic risk assessment of aliphatic and polycyclic aromatic hydrocarbons in sediments from the offshore area between the Deep Water Tano and West Cape three points blocks, Ghana

Asare EA, Abdul-Wahab D, Asamoah A, Dampare SB, Kaufmann EE, Wahi R, et al.

Mar Pollut Bull, 2025 Feb;211:117487.
PMID: 39721175 DOI: 10.1016/j.marpolbul.2024.117487

This study investigates aliphatic and polycyclic aromatic hydrocarbons in sediments from offshore Ghana, focusing on their distribution, sources, and ecological risk. Samples were collected from 15 sites near Deep Water Tano and West Cape Three Points blocks. GC-FID and GC-MS analyses revealed higher concentrations in West Cape Three Points compared to Deep Water Tano. Bayesian source apportionment indicated microorganisms as the primary contributor to AHs in both areas. For polycyclic aromatic hydrocarbons, pyrogenic sources dominated in Deep Water Tano (63.3 %), while grass/coal/wood combustion was primary in West Cape Three Points (60 %). Probabilistic risk assessment identified benzo[a]pyrene as posing the highest ecological risk. This study demonstrates the utility of Bayesian methods in identifying hydrocarbon sources and highlights the importance of species-specific sensitivities in ecological risk assessments, providing valuable insights for marine environment management.

Matched MeSH terms: Bayes Theorem*
Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis

Gao EY, Tan BKJ, Tan NKW, Ng ACW, Leong ZH, Phua CQ, et al.

Sleep Breath, 2024 Nov 30;29(1):36.
PMID: 39614959 DOI: 10.1007/s11325-024-03173-3

PURPOSE: Conventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aims to clarify the diagnostic accuracy of this innovative approach.
METHODS: Two blinded reviewers searched PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases, then selected and graded the risk of bias of observational studies of adults (≥ 18 years) comparing the diagnostic performance of AI algorithms using craniofacial photographs, versus conventional OSA diagnostic criteria (i.e. apnea-hypopnea index [AHI]). Studies were excluded if they detected apneic events without diagnosing OSA. AI models evaluated with a random split test set or k-fold cross-validation were included in a Bayesian bivariate meta-analysis.
RESULTS: From 5,147 records, 6 studies were included, containing 10 AI models trained/tested on 1,417/983 participants. The risk of bias was low. AI trained on craniofacial photographs achieved a pooled 84.9% sensitivity (95% credible interval [95% CrI]: 77.1-90.7%) and 71.2% specificity (95% CrI: 60.7-81.4%). Bayesian meta-regression identified deep learning (convolutional neural networks) as the most accurate AI algorithm (91.1% sensitivity, 79.2% specificity) comparable to home sleep apnea tests. AHI cutoffs, OSA prevalence, feature engineering, input data, camera type and informativeness of Bayesian prior did not alter diagnostic accuracy. There was no substantial publication bias.
CONCLUSION: AI trained on craniofacial photographs have high diagnostic accuracy and should be considered as a low-cost OSA screening tool. Future work focused on deep learning using smartphone images could improve the feasibility of this approach in primary care.

Matched MeSH terms: Bayes Theorem*
Fulltext Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior

Mohamed NA, Alanzi ARA, Azizan AN, Azizan SA, Samsudin N, Salarzadeh Jenatabadi H

PLoS One, 2024;19(1):e0290376.
PMID: 38261595 DOI: 10.1371/journal.pone.0290376

Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.

Matched MeSH terms: Bayes Theorem
A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama N, Loo CK, Wermter S

Int J Neural Syst, 2019 Jun;29(5):1850052.
PMID: 30764724 DOI: 10.1142/S0129065718500521

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

Matched MeSH terms: Bayes Theorem*
Fulltext Testing students' e-learning via Facebook through Bayesian structural equation modeling

Salarzadeh Jenatabadi H, Moghavvemi S, Wan Mohamed Radzi CWJB, Babashamsi P, Arashi M

PLoS One, 2017;12(9):e0182311.
PMID: 28886019 DOI: 10.1371/journal.pone.0182311

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Matched MeSH terms: Bayes Theorem*
Similarity-based virtual screening with a bayesian inference network

Abdo A, Salim N

ChemMedChem, 2009 Feb;4(2):210-8.
PMID: 19072820 DOI: 10.1002/cmdc.200800290

Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.

Matched MeSH terms: Bayes Theorem*
Fulltext A fact based analysis of decision trees for improving reliability in cloud computing

Asim Shahid M, Alam MM, Mohd Su'ud M

PLoS One, 2024;19(12):e0311089.
PMID: 39625991 DOI: 10.1371/journal.pone.0311089

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. This holds for the Decision Tree (J48) classifier with an accuracy rate of 89.71% for 80/20, 90.28% for 70/30, and 92.82% for 10-fold cross-validation. Additionally, the Hard Disk Drive HDD-Mono classifier has an accuracy rate of 90.35% for 80/20, 92.35% for 70/30, and 90.49% for 10-fold cross-validation. The AdaBoostM1 classifier was found to have the highest accuracy percentage and the least amount of fault prediction for the HDD Multi classifier with an accuracy rate of 93.63% for 80/20, 90.09% for 70/30, and 88.92% for 10-fold cross-validation. Finally, the CPU-Mem Mono classifier has an accuracy rate of 77.87% for 80/20, 77.01% for 70/30, and 77.06% for 10-fold cross-validation. Based on the primary data results, the Naive Bayes Tree (NB Tree) classifier is found to have the highest accuracy rate with less fault prediction of 97.05% for 80/20, 96.09% for 70/30, and 96.78% for 10 folds cross-validation. However, the algorithm complexity is not good, taking 1.01 seconds. On the other hand, the Decision Tree (J48) has the second-highest accuracy rate of 96.78%, 95.95%, and 96.78% for 80/20, 70/30, and 10-fold cross-validation, respectively. J48 also has less fault prediction but with a good algorithm complexity of 0.11 seconds. The difference in accuracy and less fault prediction between NB Tree and J48 is only 0.9%, but the difference in time complexity is 9 seconds. Based on the results, we have decided to make modifications to the Decision Tree (J48) algorithm. This method has been proposed as it offers the highest accuracy and less fault prediction errors, with 97.05% accuracy for the 80/20 split, 96.42% for the 70/30 split, and 97.07% for the 10-fold cross-validation.

Matched MeSH terms: Bayes Theorem*
Higher Versus Lower Protein Delivery in Critically Ill Patients: A Systematic Review and Bayesian Meta-Analysis

Heuts S, Lee ZY, Lew CCH, Bels JLM, Gabrio A, Kawczynski MJ, et al.

Crit Care Med, 2025 Mar 01;53(3):e645-e655.
PMID: 39728669 DOI: 10.1097/CCM.0000000000006562

OBJECTIVES: Recent multicenter trials suggest that higher protein delivery may result in worse outcomes in critically ill patients, but uncertainty remains. An updated Bayesian meta-analysis of recent evidence was conducted to estimate the probabilities of beneficial and harmful treatment effects.
DATA SOURCES: An updated systematic search was performed in three databases until September 4, 2024. The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and the protocol was preregistered in PROSPERO (CRD42024546387).
STUDY SELECTION: Randomized controlled trials that studied adult critically ill patients comparing protein doses delivered enterally and/or parenterally with similar energy delivery between groups were included.
DATA EXTRACTION: Data extraction was performed by two authors independently, using a predefined worksheet. The primary outcome was mortality. Posterior probabilities of any benefit (relative risk [RR] < 1.00) or harm (RR > 1.00) and other important beneficial and harmful effect size thresholds were estimated. Risk of bias assessment was performed using the risk of bias 2.0 tool. All analyses were performed using a Bayesian hierarchical random-effects models, under vague priors.
DATA SYNTHESIS: Twenty-two randomized trials ( n = 4164 patients) were included. The mean protein delivery in the higher and lower protein groups was 1.5 ± 0.6 vs. 0.9 ± 0.4 g/kg/d. The median RR for mortality was 1.01 (95% credible interval, 0.84-1.16). The posterior probability of any mortality benefit from higher protein delivery was 43.6%, while the probability of any harm was 56.4%. The probabilities of a 1% (RR < 0.99) and 5% (RR < 0.95) mortality reduction by higher protein delivery were 38.7% and 22.9%, respectively. Conversely, the probabilities of a 1% (RR > 1.01) and 5% (RR > 1.05) mortality increase were 51.5% and 32.4%, respectively.
CONCLUSIONS: There is a considerable probability of an increased mortality risk with higher protein delivery in critically ill patients, although a clinically beneficial effect cannot be completely eliminated based on the current data.

Matched MeSH terms: Bayes Theorem*
Fulltext Comparison of scoring functions on greedy search Bayesian network learning algorithms

ChongYong, Chua, HongChoon, Ong

Pertanika Journal of Science & Technology, 2017;25(3):719-734.
MyJurnal

Score-based structure learning algorithm is commonly used in learning the Bayesian Network. Other than searching strategy, scoring functions play a vital role in these algorithms. Many studies proposed various types of scoring functions with different characteristics. In this study, we compare the performances of five scoring functions: Bayesian Dirichlet equivalent-likelihood (BDe) score (equivalent sample size, ESS of 4 and 10), Akaike Information Criterion (AIC) score, Bayesian Information Criterion (BIC) score and K2 score. Instead of just comparing networks with different scores, we included different learning algorithms to study the relationship between score functions and greedy search learning algorithms. Structural hamming distance is used to measure the difference between networks obtained and the true network. The results are divided into two sections where the first section studies the differences between data with different number of variables and the second section studies the differences between data with different sample sizes. In general, the BIC score performs well and consistently for most data while the BDe score with an equivalent sample size of 4 performs better for data with bigger sample sizes.

Matched MeSH terms: Bayes Theorem
Fulltext Students' learning style detection using tree augmented naive Bayes

Li LX, Abdul Rahman SS

R Soc Open Sci, 2018 Jul;5(7):172108.
PMID: 30109052 DOI: 10.1098/rsos.172108

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

Matched MeSH terms: Bayes Theorem
On the Choice of the Prior Distribution in the Bayesian Analysis for the Evaluation of Mini-Roundabouts as a Road Safety Measure

Kamarulzaman bin Ibrahim

Sains Malaysiana, 1996;25.

An integral art of the Bayesian approach which is not present in the classical approach is the prior distribution. Different researchers may have different level of prior knowledge regarding the parameter of interest before seeing the data. Sometimes different prior distributions can result in different decisions, as such investigations have to be careful in making the choice of the prior distribution. In this paper, we compare results from the Bayesian analyses based on three possible choices of the prior distributions, which are uniform prior, lognormal prior and an improper prior, in the evaluation of the effectiveness of mini-roundabouts. Data from five before and after studies into the effect of mini-roundabouts when replacing priority junctions are used. The effects of the different prior distributions are distinguishable from the analysis of an anamolous 'desk-drawer' study. The uniform and improper prior pull the estimated treatment effect away from one more than the lognormal prior. The results based on lognormal prior depict a less worst scenario of the ineffectiveness of mini-roundabouts and this may correspond to the deficiency in engineering design at only a few sites. Consequently, it is more appropriate to use the lognormal prior in the analysis of mini-roundabouts as a road safety measure.
Satu ciri yang penting dalam kaedah Bayesian yang tidak ada dalam kaedah klasik ialah taburan prior. Sebelum melihat data, mungkin setiap penyelidik mempunyai tahap pengetahuan prior yang berbeza berkenaan sesuatu parameter yang ingin dikaji. Kadang-kala taburan prior yang berlainan boleh menghasilkan keputusan yang berlainan. Oleh itu, pengkaji perlu berhati-hati dalam memilih taburan prior. Dalam kertas-kerja ini, kami bandingkan keputusan dari analisis Bayesian berdasarkan tiga pilihan taburan prior yang menasabah iaitu prior seragam, prior lognormal dan prior tak wajar untuk menilai keberkesanan bulatan mini. Data dari kajian-kajian sebelum dan selepas terhadap kesan mengantikan persimpangan dengan bulatan mini digunakan. Kesan taburan prior yang berlainan dapat dibezakan berasaskan keputusan analisis terhadap satu kajian 'laci-meja' yang ganjil. Prior seragam dan prior fak wajar felah menyebabkan anggaran nilai kesan rawatan melebihi satu lebih dari prior lognormal. Keputusan berasaskan prior lognormal ini menunjukkan senario yang kurang teruk tentang kurang berkesannya bulatan mini dan mungkin ini boleh dikaitkan dengan rekabentuk kejuruteraan yang tidak baik di beberapa tempat sahaja. Dengan itu, prior lognormal adalah lebih sesuai digunakan untuk menilai bulatan mini sebagai langkah keselamatan jalanraya.

Matched MeSH terms: Bayes Theorem
On an Application of Sequential Bayesian Techniques for Estimating the Fatality Rate of Dengue in Malaysia

Kamarulzaman Bin Ibrahim, Abdul Aziz Jermain

Sains Malaysiana, 1997;26.

Dengue is one of the main factors of mortality of inhabitants in the region of South East Asia. Malaysia is one of the countries which is facing a high incidence of dengue, particularly in the 70's and early 80's. The Ministry of Health has taken various measures in order to reduce the dengue epidemic. These include educating people about dengue and conducting research such as investigation of factors that influence the epidemic of dengue. In this study, a sequential Bayesian approach is applied to data of the proportion of death due to dengue over the period from 1982 to 1992. In the sequential Bayesian approach, the data for the year 1982 becomes the prior information for the 1983 data and so on. The data for the different periods are combined in a chronological manner until the final posterior distribution of the proportion of death due to dengue is obtained. It is found that the overall proportion is 0.59% and its standard deviation is 0.00002%.
Denggi adalah satu daripada faktor utama kematian bagi penduduk di rantau Asia Tenggara. Malaysia pula merupakan satu daripada negara yang sedang mengalami kadar insiden denggi yang tinggi, khususnya dalam tahun 70-an dan pada awal 80-an. Kementerian Kesihatan telah mengambil pelbagai langkah untuk mengurangkan wabak denggi. Ini termasuk memberikan pendidikan tentang denggi dan membuat kajian tentang faktor-faktor yang mempengaruhinya. Dalam kajian ini kaedah Bayesan jujukan digunakan terhadap data perkadaran yang mati akibat denggi dalam tempoh 1982 hingga 1992. Dalam kaedah ini, data tahun 1982 digunakan sebagai maklumat prior untuk data tahun 1983 dan seterusnya. Data dari tahun yang berlainan digabungkan secara kronologi sehingga diperoleh taburan posterior yang terakhir bagi perkadaran yang mati akibat denggi. Didapati bahawa perkadaran keseluruhan ialah 0.59% dan sisihan piawainya 0.00002%.

Matched MeSH terms: Bayes Theorem
Parameter estimation of stochastic differential equation

Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh

Sains Malaysiana, 2012;41:1635-1642.

Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. Spline approach consists of regression spline and smoothing spline. Regression spline with Bayesian approach is considered in the first step of a two-step method in estimating the structural parameters for stochastic differential equation (SDE). The selection of knot and order of spline can be done heuristically based on the scatter plot. To overcome the subjective and tedious process of selecting the optimal knot and order of spline, an algorithm was proposed. A single optimal knot is selected out of all the points with exception of the first and the last data which gives the least value of Generalized Cross Validation (GCV) for each order of spline. The use is illustrated using observed data of opening share prices of Petronas Gas Bhd. The results showed that the Mean Square Errors (MSE) for stochastic model with parameters estimated using optimal knot for 1,000, 5,000 and 10,000 runs of Brownian motions are smaller than the SDE models with estimated parameters using knot selected heuristically. This verified the viability of the two-step method in the estimation of the drift and diffusion parameters of SDE with an improvement of a single knot selection.

Matched MeSH terms: Bayes Theorem
Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia

Annazirin Eli, Mardhiyyah Shaffie, Wan Zawiah W

Sains Malaysiana, 2012;41:1403-1410.

Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.

Matched MeSH terms: Bayes Theorem
Pemetaan kes kemortalan bayi di Semenanjung Malaysia menggunakan kaedah bayes empirik

Nuzlinda Abdul Rahman, Abdul Aziz Jemain, Kamarulzaman Ibrahim, Ahmad Mahir Razali

Sains Malaysiana, 2011;40.

Kajian ini bertujuan untuk memetakan kes kemortalan bayi mengikut daerah di Semenanjung Malaysia bagi tahun 1991 hingga 2000. Penganggaran risiko relatif berdasarkan kaedah Bayes empirik telah digunakan dalam kajian ini. Tiga kaedah penganggaran parameter dihuraikan iaitu kaedah momen, kaedah kebolehjadian maksimum dan kaedah penganggaran gabungan momen dan kebolehjadian maksimum. Keteguhan anggaran parameter yang diperoleh diuji menggunakan kaedah Bootstrap. Hasil kajian mendapati jurang antara kawasan berisiko rendah dengan kawasan berisiko tinggi adalah lebih besar pada awal dekad 2000 berbanding pada awal dekad 1990-an walaupun pada dasarnya kadar mortaliti bayi secara keseluruhannya adalah semakin berkurangan pada peringkat nasional. Kawasan pantai timur Semenanjung Malaysia masih pada takuk yang sama iaitu masih berada dalam kategori berisiko tinggi sepanjang tempoh yang dikaji. Seterusnya, gambaran terdapatnya tompokan risiko juga turut terpapar dalam peta yang dihasilkan. Berdasarkan kaedah Bootstrap, parameter-parameter yang dianggarkan dalam kajian ini adalah teguh.

Matched MeSH terms: Bayes Theorem
Perbandingan filogenetik protein antigen-I yang berpotensi sebagai calon diagnostik dan vaksin terhadap Parasit Cryptocaryon Irritans)

Lokanathan Y, Adura Mohd-Adnan, Sheila Nathan

Sains Malaysiana, 2016;45:1969-1979.

Protein antigen-i parasit ikan C. irritans berpotensi tinggi digunakan sebagai calon dalam pembangunan vaksin komersial terhadap C. irritans. Walau bagaimanapun, kewujudan variasi pada antigen-i serotip C. irritans yang berbeza mempengaruhi tahap perlindungan yang bakal diberikan terhadap varians C. irritans yang berbeza apabila antigen-i digunakan sebagai vaksin. Kajian ini dijalankan untuk membandingkan jujukan pelbagai antigen-i pencilan C. irritans di Malaysia berbanding antigen-i pencilan C. irritans yang pernah dilaporkan. Perbandingan filogenetik dijalankan untuk meramalkan potensi protein tersebut dalam usaha membangunkan calon serodiagnostik dan pemvaksinan terhadap pencilan C. irritans yang berlainan. Penjajaran jujukan berbilang bagi jujukan asid amino antigen-i dilakukan dengan perisian CLUSTALX dan analisis filogenetik antigen-i dilakukan menggunakan kaedah parsimoni maksimum (MP) dan kaedah Bayes. Sembilan transkrip unik (TU) C. irritans yang mempunyai padanan signifikan dengan antigen-i di pangkalan data protein NCBI didapati mempunyai peratus kesamaan antara 41% hingga 71%. Kedua-dua pohon MP dan Bayesian yang dijana menunjukkan varians antigen-i cn56 and cn57 terkelompok bersama dalam satu kumpulan manakala varians antigen-i yang lain terbahagi kepada dua kumpulan berasingan dan pengkelompokan ini disokong oleh kehadiran asid amino yang terpulihara dalam kumpulan masing-masing. Kajian lanjutan boleh dilakukan untuk mengenal pasti varians antigen-i yang sesuai sebagai calon serodiagnosis dan juga dapat memberi perlindungan silang terhadap pelbagai pencilan C. irritans di serata dunia.

Matched MeSH terms: Bayes Theorem
Geographic structure of genetic variation in the Parachute Gecko Ptychozoon lionotum Annandale, 1905 across Indochina and Sundaland with descriptions of three new species

Grismer LL, Wood PLJ, Grismer JL, Quah ESH, Thy N, Phimmachak S, et al.

Zootaxa, 2019 Jul 16;4638(2):zootaxa.4638.2.1.
PMID: 31712473 DOI: 10.11646/zootaxa.4638.2.1

An integrative taxonomic analysis of the Ptychozoon lionotum group across its range in Indochina and Sundaland recovers P. lionotum sensu lato Annandale, 1905 as paraphyletic with respect to P. popaense Grismer, Wood, Thura, Grismer, Brown, Stuart, 2018a and composed of four allopatric, genetically divergent, ND2 mitochondrial lineages. Multivariate and univariate analyses of continuous and discrete morphological and color pattern characters statistically and discretely diagnose each lineage from one another and together, with maximum likelihood and Bayesian inference analyses, provide the foundation for the recognition of each lineage as a new species-hypotheses corroborated with a Generalized Mixed Yule Coalescent species delimitation analysis. Ptychozoon cicakterbang sp. nov. ranges throughout Peninsular Malaysia to Pulau Natuna Besar, Indonesia; P. kabkaebin sp. nov. is endemic to northern and central Laos; and P. tokehos sp. nov. ranges from southern Thailand south of the Isthmus of Kra northward to Chiang Mai, fringing the Chao Phraya Basin and ranging southward through Cambodia to southern Vietnam. Ptychozoon lionotum sensu stricto ranges from northwestern Laos through southern Myanmar to eastern India. The phylogeographic structure within each species varies considerably with P. lionotum s.s. showing no genetic divergence across its 1,100 km range compared to P. cicakterbang sp. nov. showing upwards of 8.2% sequence divergence between syntopic individuals. Significant phylogeographic structure exists within P. tokehos sp. nov. and increased sampling throughout Thailand may require additional taxonomic changes within this species.

Matched MeSH terms: Bayes Theorem
Predicting enteric fever without bacteriological culture results

Ross IN, Abraham T

Trans R Soc Trop Med Hyg, 1987;81(3):374-7.
PMID: 3686631

We used Bayes' theorem to calculate the probability of enteric fever in 260 patients presenting with undiagnosed fever, without recourse to blood or stool culture results. These individuals were divided into 110 patients with enteric fever (63 culture positive, 47 culture negative) and 150 patients with other causes of fever. Comparison of the frequencies of occurrence of 19 clinical and laboratory events, said to be helpful in the diagnosis of enteric fever, in the two groups revealed that only 8 events were significantly more frequent in enteric fever. These were: a positive Widal test at a screening dilution of 1:40; a peak temperature greater than = 39 degrees C; previous treatment for the fever; a white blood cell count less than 9 X 10(6)/litre; a polymorphonuclear leucocyte count less than 3.5 X 10(6)/litre; splenomegaly; fever duration greater than 7 d; and hepatomegaly. When the probability of enteric fever was determined prospectively in 110 patients, using only 6 of these discriminating events, the probability of patients with a positive prediction having enteric fever (diagnostic specificity) was 0.80 (95% confidence interval: 0.68 to 0.91) and the probability of those with a negative prediction not having enteric fever (diagnostic sensitivity) was 0.92 (0.85 to 0.99). Using all 19 events did not alter the diagnostic specificity or diagnostic sensitivity. This study shows that a small number of clinical and laboratory features can objectively discriminate enteric fever from other causes of fever in the majority of patients. Calculating the probability of enteric fever can aid in diagnosis, when culturing for salmonella is either unavailable or is negative.

Matched MeSH terms: Bayes Theorem

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