Displaying publications 81 - 100 of 254 in total

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  1. Cross-Cultural Invariance of the Mental Toughness Inventory Among Australian, Chinese, and Malaysian Athletes: A Bayesian Estimation Approach
    Gucciardi DF, Zhang CQ, Ponnusamy V, Si G, Stenling A
    J Sport Exerc Psychol, 2016 Apr;38(2):187-202.
    PMID: 27390084 DOI: 10.1123/jsep.2015-0320
    The aims of this study were to assess the cross-cultural invariance of athletes' self-reports of mental toughness and to introduce and illustrate the application of approximate measurement invariance using Bayesian estimation for sport and exercise psychology scholars. Athletes from Australia (n = 353, Mage = 19.13, SD = 3.27, men = 161), China (n = 254, Mage = 17.82, SD = 2.28, men = 138), and Malaysia (n = 341, Mage = 19.13, SD = 3.27, men = 200) provided a cross-sectional snapshot of their mental toughness. The cross-cultural invariance of the mental toughness inventory in terms of (a) the factor structure (configural invariance), (b) factor loadings (metric invariance), and (c) item intercepts (scalar invariance) was tested using an approximate measurement framework with Bayesian estimation. Results indicated that approximate metric and scalar invariance was established. From a methodological standpoint, this study demonstrated the usefulness and flexibility of Bayesian estimation for single-sample and multigroup analyses of measurement instruments. Substantively, the current findings suggest that the measurement of mental toughness requires cultural adjustments to better capture the contextually salient (emic) aspects of this concept.
    Matched MeSH terms: Bayes Theorem*
  2. Fulltext Bayesian inference of the Weibull model based on interval-censored survival data
    Guure CB, Ibrahim NA, Adam MB
    Comput Math Methods Med, 2013;2013:849520.
    PMID: 23476718 DOI: 10.1155/2013/849520
    Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.
    Matched MeSH terms: Bayes Theorem*
  3. Fulltext Joint modelling of longitudinal 3MS scores and the risk of mortality among cognitively impaired individuals
    Guure CB, Ibrahim NA, Adam MB, Said SM
    PLoS One, 2017;12(8):e0182873.
    PMID: 28813458 DOI: 10.1371/journal.pone.0182873
    BACKGROUND: Modified Mini-Mental State Examination (3MS) is an instrument administered by trained personnel to examine levels of participants' cognitive function. However, the association between changes in scores over time and the risk of death (mortality) is not known. The aims of this study are to examine the association between 3MS scores and mortality via cognitive impairment among older women and to determine individuals' risk of changes in scores to better predict their survival and mortality rates.

    METHODS: We propose a Bayesian joint modelling approach to determine mortality due to cognitive impairment via repeated measures of 3MS scores trajectories over a 21-year follow-up period. Data for this study are taken from the Osteoporotic Fracture longitudinal study among women aged 65+ which started in 1986-88.

    RESULTS: The standard relative risk model from the analyses with a baseline 3MS score after adjusting for all the significant covariates demonstrates that, every unit decrease in a 3MS score corresponds to a non-significant 1.059 increase risk of mortality with a 95% CI of (0.981, 1.143), while the extended model results in a significant 0.09% increased risk in mortality. The joint modelling approach found a strong association between the 3MS scores and the risk of mortality, such that, every unit decrease in 3MS scores results in a 1.135 (13%) increased risk of death via cognitive impairment with a 95% CI of (1.056, 1.215).

    CONCLUSION: It has been demonstrated that a decrease in 3MS results has a significant increase risk of mortality due to cognitive impairment via joint modelling, but insignificant when considered under the standard relative risk approach.

    Matched MeSH terms: Bayes Theorem
  4. 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
  5. 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
  6. Fulltext Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait
    Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, et al.
    Sensors (Basel), 2022 May 05;22(9).
    PMID: 35591196 DOI: 10.3390/s22093507
    Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
    Matched MeSH terms: Bayes Theorem
  7. Fulltext Pleistocene divergence across a mountain range and the influence of selection on mitogenome evolution in threatened Australian freshwater cod species
    Harrisson K, Pavlova A, Gan HM, Lee YP, Austin CM, Sunnucks P
    Heredity (Edinb), 2016 Jun;116(6):506-15.
    PMID: 26883183 DOI: 10.1038/hdy.2016.8
    Climatic differences across a taxon's range may be associated with specific bioenergetic demands and may result in genetics-based metabolic adaptation, particularly in aquatic ectothermic organisms that rely on heat exchange with the environment to regulate key physiological processes. Extending down the east coast of Australia, the Great Dividing Range (GDR) has a strong influence on climate and the evolutionary history of freshwater fish species. Despite the GDR acting as a strong contemporary barrier to fish movement, many species, and species with shared ancestries, are found on both sides of the GDR, indicative of historical dispersal events. We sequenced complete mitogenomes from the four extant species of the freshwater cod genus Maccullochella, two of which occur on the semi-arid, inland side of the GDR, and two on the mesic coastal side. We constructed a dated phylogeny and explored the relative influences of purifying and positive selection in the evolution of mitogenome divergence among species. Results supported mid- to late-Pleistocene divergence of Maccullochella across the GDR (220-710 thousand years ago), bringing forward previously reported dates. Against a background of pervasive purifying selection, we detected potentially functionally relevant fixed amino acid differences across the GDR. Although many amino acid differences between inland and coastal species may have become fixed under relaxed purifying selection in coastal environments rather than positive selection, there was evidence of episodic positive selection acting on specific codons in the Mary River coastal lineage, which has consistently experienced the warmest and least extreme climate in the genus.
    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 The impact of high versus standard enteral protein provision on functional recovery following intensive care admission: Protocol for a pre-planned secondary Bayesian analysis of the PRECISe trial
    Heuts S, de Heer P, Gabrio A, Bels JLM, Lee ZY, Stoppe C, et al.
    Clin Nutr ESPEN, 2024 Feb;59:162-170.
    PMID: 38220371 DOI: 10.1016/j.clnesp.2023.10.040
    BACKGROUND: The PRECISe trial is a pragmatic, multicenter randomized controlled trial that evaluates the effect of high versus standard enteral protein provision on functional recovery in adult, mechanically ventilated critically ill patients. The current protocol presents the rationale and analysis plan for an evaluation of the primary and secondary outcomes under the Bayesian framework, with an emphasis on clinically important effect sizes.

    METHODS: This protocol was drafted in agreement with the ROBUST-statement, and is submitted for publication before database lock and primary data analysis. The primary outcome is health-related quality of life as measured by the EQ-5D-5L health utility score and is longitudinally assessed. Secondary outcomes comprise the 6-min walking test and handgrip strength over the entire follow-up period (longitudinal analyses), and 60-day mortality, duration of mechanical ventilation, and EQ-5D-5L health utility scores at 30, 90 and 180 days (cross-sectional). All analyses will primarily be performed under weakly informative priors. When available, informative priors elicited from contemporary literature will also be incorporated under alternative scenarios. In all other cases, objectively formulated skeptical and enthusiastic priors will be defined to assess the robustness of our results. Relevant identified subgroups were: patients with acute kidney injury, severe multi-organ failure and patients with or without sepsis. Results will be presented as absolute risk differences, mean differences, and odds ratios, with accompanying 95% credible intervals. Posterior probabilities will be estimated for clinically important benefit and harm.

    DISCUSSION: The proposed secondary, pre-planned Bayesian analysis of the PRECISe trial will provide additional information on the effects of high protein on functional and clinical outcomes in critically ill patients, such as probabilistic interpretation, probabilities of clinically important effect sizes, and the integration of prior evidence. As such, it will complement the interpretation of the primary outcome as well as several secondary and subgroup analyses.

    Matched MeSH terms: Bayes Theorem
  10. Harnessing global fisheries to tackle micronutrient deficiencies
    Hicks CC, Cohen PJ, Graham NAJ, Nash KL, Allison EH, D'Lima C, et al.
    Nature, 2019 10;574(7776):95-98.
    PMID: 31554969 DOI: 10.1038/s41586-019-1592-6
    Micronutrient deficiencies account for an estimated one million premature deaths annually, and for some nations can reduce gross domestic product1,2 by up to 11%, highlighting the need for food policies that focus on improving nutrition rather than simply increasing the volume of food produced3. People gain nutrients from a varied diet, although fish-which are a rich source of bioavailable micronutrients that are essential to human health4-are often overlooked. A lack of understanding of the nutrient composition of most fish5 and how nutrient yields vary among fisheries has hindered the policy shifts that are needed to effectively harness the potential of fisheries for food and nutrition security6. Here, using the concentration of 7 nutrients in more than 350 species of marine fish, we estimate how environmental and ecological traits predict nutrient content of marine finfish species. We use this predictive model to quantify the global spatial patterns of the concentrations of nutrients in marine fisheries and compare nutrient yields to the prevalence of micronutrient deficiencies in human populations. We find that species from tropical thermal regimes contain higher concentrations of calcium, iron and zinc; smaller species contain higher concentrations of calcium, iron and omega-3 fatty acids; and species from cold thermal regimes or those with a pelagic feeding pathway contain higher concentrations of omega-3 fatty acids. There is no relationship between nutrient concentrations and total fishery yield, highlighting that the nutrient quality of a fishery is determined by the species composition. For a number of countries in which nutrient intakes are inadequate, nutrients available in marine finfish catches exceed the dietary requirements for populations that live within 100 km of the coast, and a fraction of current landings could be particularly impactful for children under 5 years of age. Our analyses suggest that fish-based food strategies have the potential to substantially contribute to global food and nutrition security.
    Matched MeSH terms: Bayes Theorem
  11. Fulltext Polygenic risk scores for prediction of breast cancer risk in Asian populations
    Ho WK, Tai MC, Dennis J, Shu X, Li J, Ho PJ, et al.
    Genet Med, 2022 Mar;24(3):586-600.
    PMID: 34906514 DOI: 10.1016/j.gim.2021.11.008
    PURPOSE: Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups.

    METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases).

    RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk.

    CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry.

    Matched MeSH terms: Bayes Theorem
  12. Fulltext Two Monthly Continuous Dynamic Model Based on Nash Bargaining Theory for Conflict Resolution in Reservoir System
    Homayounfar M, Zomorodian M, Martinez CJ, Lai SH
    PLoS One, 2015;10(12):e0143198.
    PMID: 26641095 DOI: 10.1371/journal.pone.0143198
    So far many optimization models based on Nash Bargaining Theory associated with reservoir operation have been developed. Most of them have aimed to provide practical and efficient solutions for water allocation in order to alleviate conflicts among water users. These models can be discussed from two viewpoints: (i) having a discrete nature; and (ii) working on an annual basis. Although discrete dynamic game models provide appropriate reservoir operator policies, their discretization of variables increases the run time and causes dimensionality problems. In this study, two monthly based non-discrete optimization models based on the Nash Bargaining Solution are developed for a reservoir system. In the first model, based on constrained state formulation, the first and second moments (mean and variance) of the state variable (water level in the reservoir) is calculated. Using moment equations as the constraint, the long-term utility of the reservoir manager and water users are optimized. The second model is a dynamic approach structured based on continuous state Markov decision models. The corresponding solution based on the collocation method is structured for a reservoir system. In this model, the reward function is defined based on the Nash Bargaining Solution. Indeed, it is used to yield equilibrium in every proper sub-game, thereby satisfying the Markov perfect equilibrium. Both approaches are applicable for water allocation in arid and semi-arid regions. A case study was carried out at the Zayandeh-Rud river basin located in central Iran to identify the effectiveness of the presented methods. The results are compared with the results of an annual form of dynamic game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that both models are capable of tackling conflict issues in water allocation in situations of water scarcity properly. Also, comparing the annual dynamic game models, the presented models result in superior results in practice. Furthermore, unlike discrete dynamic game models, the presented models can significantly reduce the runtime thereby avoiding dimensionality problems.
    Matched MeSH terms: Bayes Theorem
  13. 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
  14. Fulltext Bayesian brain in tinnitus: Computational modeling of three perceptual phenomena using a modified Hierarchical Gaussian Filter
    Hu S, Hall DA, Zubler F, Sznitman R, Anschuetz L, Caversaccio M, et al.
    Hear Res, 2021 10;410:108338.
    PMID: 34469780 DOI: 10.1016/j.heares.2021.108338
    Recently, Bayesian brain-based models emerged as a possible composite of existing theories, providing an universal explanation of tinnitus phenomena. Yet, the involvement of multiple synergistic mechanisms complicates the identification of behavioral and physiological evidence. To overcome this, an empirically tested computational model could support the evaluation of theoretical hypotheses by intrinsically encompassing different mechanisms. The aim of this work was to develop a generative computational tinnitus perception model based on the Bayesian brain concept. The behavioral responses of 46 tinnitus subjects who underwent ten consecutive residual inhibition assessments were used for model fitting. Our model was able to replicate the behavioral responses during residual inhibition in our cohort (median linear correlation coefficient of 0.79). Using the same model, we simulated two additional tinnitus phenomena: residual excitation and occurrence of tinnitus in non-tinnitus subjects after sensory deprivation. In the simulations, the trajectories of the model were consistent with previously obtained behavioral and physiological observations. Our work introduces generative computational modeling to the research field of tinnitus. It has the potential to quantitatively link experimental observations to theoretical hypotheses and to support the search for neural signatures of tinnitus by finding correlates between the latent variables of the model and measured physiological data.
    Matched MeSH terms: Bayes Theorem
  15. 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
  16. Feature selection algorithms for Malaysian dengue outbreak detection model
    Husam IS, Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazrura Sahani, Zainudin Mohd Ali
    Sains Malaysiana, 2017;46:255-265.
    Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies.
    Matched MeSH terms: Bayes Theorem
  17. Smoothing a discrete hazard function for the number of patients colonized with Methicillin-resistant Staphylococcus Aureus in an intensive care unit
    Ismail NA, Pettitt AN
    Stat Med, 2004 Apr 30;23(8):1247-58.
    PMID: 15083481
    A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.
    Matched MeSH terms: Bayes Theorem
  18. Fulltext A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features
    Jamaluddin FN, Ibrahim F, Ahmad SA
    J Healthc Eng, 2023;2023:1951165.
    PMID: 36756137 DOI: 10.1155/2023/1951165
    In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔF med and ΔF mean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.
    Matched MeSH terms: Bayes Theorem
  19. Composition, sources, and bioavailability of nitrogen in a longitudinal gradient from freshwater to estuarine waters
    Jani J, Toor GS
    Water Res, 2018 06 15;137:344-354.
    PMID: 29571112 DOI: 10.1016/j.watres.2018.02.042
    Nitrogen (N) transport from land to water is a dominant contributor of N in estuarine waters leading to eutrophication, harmful algal blooms, and hypoxia. Our objectives were to (1) investigate the composition of inorganic and organic N forms, (2) distinguish the sources and biogeochemical mechanisms of nitrate-N (NO3-N) transport using stable isotopes of NO3- and Bayesian mixing model, and (3) determine the dissolved organic N (DON) bioavailability using bioassays in a longitudinal gradient from freshwater to estuarine ecosystem located in the Tampa Bay, Florida, United States. We found that DON was the most dominant N form (mean: 64%, range: 46-83%) followed by particulate organic N (PON, mean: 22%, range: 14-37%), whereas inorganic N forms (NOx-N: 7%, NH4-N: 7%) were 14% of total N in freshwater and estuarine waters. Stable isotope data of NO3- revealed that nitrification was the main contributor (36.4%), followed by soil and organic N sources (25.5%), NO3- fertilizers (22.4%), and NH4+ fertilizers (15.7%). Bioassays showed that 14 to 65% of DON concentrations decreased after 5-days of incubation indicating utilization of DON by microbes in freshwater and estuarine waters. These results suggest that despite low proportion of inorganic N forms, the higher concentrations and bioavailability of DON can be a potential source of N for algae and bacteria leading to water quality degradation in the estuarine waters.
    Matched MeSH terms: Bayes Theorem
  20. Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity
    Jatoi MA, Kamel N, Musavi SHA, López JD
    Curr Med Imaging Rev, 2019;15(2):184-193.
    PMID: 31975664 DOI: 10.2174/1573405613666170629112918
    BACKGROUND: Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms.

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

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

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

    Matched MeSH terms: Bayes Theorem
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