Displaying publications 61 - 80 of 281 in total

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  1. Probabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment
    Iftikhar S, Ishtiaq R, Zahra N, Ruba F, Lam SM, Abbas A, et al.
    Chemosphere, 2025 Feb;370:144031.
    PMID: 39732408 DOI: 10.1016/j.chemosphere.2024.144031
    Phosphate (PO4(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO4(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R2 (0.95) on new adsorbents. SHapely Additive exPlanations analysis showed that adsorption experimental conditions had the most significant impact (43%), followed by synthesis conditions (29%) and adsorbent characteristics (28%). Optimized conditions included an initial PO4(III) concentration of 125 mg/L, carbon content of 11.5%, oxygen content of 23%, a contact time of 1440 min, a heating rate of 5 °C/min, 200 rpm, and a surface area of 410 m2/g, using Ra-LDO adsorbent synthesized from rape cabbage feedstock. This study developed and presented a practical online framework for predicting PO4(III) removal onto biochar using a web-based graphical user interface.
    Matched MeSH terms: Bayes Theorem
  2. Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization
    Ahmadianfar I, Shirvani-Hosseini S, Samadi-Koucheksaraee A, Yaseen ZM
    Environ Sci Pollut Res Int, 2022 Jul;29(35):53456-53481.
    PMID: 35287188 DOI: 10.1007/s11356-022-19300-0
    Undeniably, there is a link between water resources and people's lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na+) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters.
    Matched MeSH terms: Bayes Theorem
  3. Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method
    Wang W, Ji C, Li C, Wu W, Gisen JIA
    Environ Sci Pollut Res Int, 2025 May;32(22):12978-12991.
    PMID: 37269522 DOI: 10.1007/s11356-023-27988-x
    Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.
    Matched MeSH terms: Bayes Theorem
  4. Examining the relationship between local extinction risk and position in range
    Boakes EH, Isaac NJB, Fuller RA, Mace GM, McGowan PJK
    Conserv Biol, 2018 02;32(1):229-239.
    PMID: 28678438 DOI: 10.1111/cobi.12979
    Over half of globally threatened animal species have experienced rapid geographic range loss. Identifying the parts of species' distributions most vulnerable to local extinction would benefit conservation planning. However, previous studies give little consensus on whether ranges decline to the core or edge. We built on previous work by using empirical data to examine the position of recent local extinctions within species' geographic ranges, address range position as a continuum, and explore the influence of environmental factors. We aggregated point-locality data for 125 Galliform species from across the Palearctic and Indo-Malaya into equal-area half-degree grid cells and used a multispecies dynamic Bayesian occupancy model to estimate rates of local extinctions. Our model provides a novel approach to identify loss of populations from within species ranges. We investigated the relationship between extinction rates and distance from range edge by examining whether patterns were consistent across biogeographic realm and different categories of land use. In the Palearctic, local extinctions occurred closer to the range edge than range core in both unconverted and human-dominated landscapes. In Indo-Malaya, no pattern was found for unconverted landscapes, but in human-dominated landscapes extinctions tended to occur closer to the core than the edge. Our results suggest that local and regional factors override general spatial patterns of recent local extinction within species' ranges and highlight the difficulty of predicting the parts of a species' distribution most vulnerable to threat.
    Matched MeSH terms: Bayes Theorem
  5. Kernel Bayesian ART and ARTMAP
    Masuyama N, Loo CK, Dawood F
    Neural Netw, 2018 Feb;98:76-86.
    PMID: 29202265 DOI: 10.1016/j.neunet.2017.11.003
    Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively.
    Matched MeSH terms: Bayes Theorem
  6. Fulltext Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors
    Pius Owoh N, Mahinderjit Singh M, Zaaba ZF
    Sensors (Basel), 2018 Jul 03;18(7).
    PMID: 29970823 DOI: 10.3390/s18072134
    Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.
    Matched MeSH terms: Bayes Theorem
  7. Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia
    Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS
    Geospat Health, 2018 05 07;13(1):613.
    PMID: 29772882 DOI: 10.4081/gh.2018.613
    This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
    Matched MeSH terms: Bayes Theorem
  8. Spatial analysis of infant mortality in peninsular malaysia over three decades using mixture models
    Nuzlinda Abdul Rahman, Abdul Aziz Jemain
    Sains Malaysiana, 2013;42:1003-1010.
    Infant mortality is one of the central public issues in most of the developing countries. In Malaysia, the infant mortality rates have improved at the national level over the last few decades. However, the issue concerned is whether the improvement is uniformly distributed throughout the country. The aim of this study was to investigate the geographical distribution of infant mortality in Peninsular Malaysia from the year 1970 to 2000 using a technique known as disease mapping. It is assumed that the random variable of infant mortality cases comes from Poisson distribution. Mixture models were used to find the number of optimum components/groups for infant mortality data for every district in Peninsular Malaysia. Every component is assumed to have the same distribution, but different parameters. The number of optimum components were obtained by maximum likelihood approach via the EM algorithm. Bayes theorem was used to determine the probability of belonging to each district in every components of the mixture distribution. Each district was assigned to the component that had the highest posterior probability of belonging. The results obtained were visually presented in maps. The analysis showed that in the early year of 1970, the spatial heterogeneity effect was more prominent; however, towards the end of 1990, this pattern tended to disappear. The reduction in the spatial heterogeneity effect in infant mortality data indicated that the provisions of health services throughout the Peninsular Malaysia have improved over the period of the study, particularly towards the year 2000.
    Matched MeSH terms: Bayes Theorem
  9. A Comparison Between Bayesian and Maximum Likelihood Estimations in Estimating Finite Mixture Model for Financial Data
    Seuk-Yen Phoong, Mohd Tahir Ismail
    Sains Malaysiana, 2015;44:1033-1039.
    Over the years, maximum likelihood estimation and Bayesian method became popular statistical tools in which applied to fit finite mixture model. These trends begin with the advent of computer technology during the last decades. Moreover, the asymptotic properties for both statistical methods also act as one of the main reasons that boost the popularity of the methods. The difference between these two approaches is that the parameters for maximum likelihood estimation are fixed, but unknown meanwhile the parameters for Bayesian method act as random variables with known prior distributions. In the present paper, both the maximum likelihood estimation and Bayesian method are applied to investigate the relationship between exchange rate and the rubber price for Malaysia, Thailand, Philippines and Indonesia. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of time series data, Akaike Information Criterion and Bayesian Information Criterion are adopted in this paper. The result depicts that the Bayesian method performs better than maximum likelihood estimation on financial data.
    Matched MeSH terms: Bayes Theorem
  10. 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
  11. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods
    Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.
    Sci Total Environ, 2020 Jan 20;701:134979.
    PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979
    Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
    Matched MeSH terms: Bayes Theorem
  12. Fulltext Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence
    Campero-Jurado I, Márquez-Sánchez S, Quintanar-Gómez J, Rodríguez S, Corchado JM
    Sensors (Basel), 2020 Nov 01;20(21).
    PMID: 33139608 DOI: 10.3390/s20216241
    Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers' environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.
    Matched MeSH terms: Bayes Theorem
  13. Fulltext Forecasting shear stress parameters in rectangular channels using new soft computing methods
    Sheikh Khozani Z, Sheikhi S, Mohtar WHMW, Mosavi A
    PLoS One, 2020;15(4):e0229731.
    PMID: 32271780 DOI: 10.1371/journal.pone.0229731
    Shear stress comprises basic information for predicting the average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw, non-dimension wall shear stress ([Formula: see text]) and non-dimension bed shear stress ([Formula: see text]) in smooth rectangular channels were predicted by a three methods, the Bayesian Regularized Neural Network (BRNN), the Radial Basis Function (RBF), and the Modified Structure-Radial Basis Function (MS-RBF). For this aim, eight data series of research experimental results in smooth rectangular channels were used. The results of the new method of MS-RBF were compared with those of a simple RBF and BRNN methods and the best model was selected for modeling each predicted parameters. The MS-RBF model with RMSE of 3.073, 0.0366 and 0.0354 for %SFw, [Formula: see text] and [Formula: see text] respectively, demonstrated better performance than those of the RBF and BRNN models. The results of MS-RBF model were compared with three other proposed equations by researchers for trapezoidal channels and rectangular ducts. The results showed that the MS-RBF model performance in estimating %SFw, [Formula: see text] and [Formula: see text] is superior than those of presented equations by researchers.
    Matched MeSH terms: Bayes Theorem
  14. Fulltext Effective Connectivity of a Default Mode Network In Human Brain: In Search of a Dominant Node Using Spectral Dynamic Causal Modeling
    Mohd Nawi, N. S. A., Rahmad, A. A., Abdul Hamid, K., Rahman, S., Osman, S. S., Surat, S., et al.
    Physics and Technology in Medicine, 2020;1(1):1-14.
    MyJurnal
    The connectivity patterns among the DMN nodes when the brain is resting are still in great debate. Among the unknowns is whether a dominant node exists in the network and if any, how does it influences the other nodes. Resting state functional magnetic resonance imaging (rsfMRI) was utilized in data acquisition on 25 healthy male and female participants. The DMN nodes selected were posterior cingulate cortex (PCC), bilateral inferior parietal cortex (IPL) and medial prefrontal cortex (mPFC). Fully connected causal models were constructed comprising four DMN nodes. The time invariant covariance of the random fluctuations between nodes was then estimated to obtain the effective connectivity (EC) between the DMN nodes. The EC values among the DMN nodes were averaged over the participants using Bayesian Parameter Averaging (BPA). All the DMN nodes have self-inhibitory dynamics. All connections between nodes were significant (P > 0.9) with a condition for any of the two nodes, one node inhibited the others. The PCC which exhibited the highest signal intensity was in fact inhibited by others. Inter-hemispheric RIPC to LIPC connections acted the same way, with excitatory LIPC to RIPC and inhibitory RIPC to LIPC connections. The results also showed a stronger mPFC to RIPC connection in the right hemisphere (as compared to mPFC to LIPC connection in the left hemisphere) and a weaker PCC to RIPC connection in the right hemisphere (as compared to PCC to LIPC connection in the left hemisphere). PCC can be regarded as a dominant node among the four nodes, being connected to all other nodes in different ways. All the four nodes were significantly activated and connected to each other even though the brain was in a state of resting.
    Matched MeSH terms: Bayes Theorem
  15. Fulltext New Insights on the Evolution of the Sweet Taste Receptor of Primates Adapted to Harsh Environments
    Tamrin NAM, Zainudin R, Esa Y, Alias H, Isa MNM, Croft L, et al.
    Animals (Basel), 2020 Dec 10;10(12).
    PMID: 33321745 DOI: 10.3390/ani10122359
    Taste perception is an essential function that provides valuable dietary and sensory information, which is crucial for the survival of animals. Studies into the evolution of the sweet taste receptor gene (TAS1R2) are scarce, especially for Bornean endemic primates such as Nasalis larvatus (proboscis monkey), Pongo pygmaeus (Bornean orangutan), and Hylobates muelleri (Muller's Bornean gibbon). Primates are the perfect taxa to study as they are diverse dietary feeders, comprising specialist folivores, frugivores, gummivores, herbivores, and omnivores. We constructed phylogenetic trees of the TAS1R2 gene for 20 species of anthropoid primates using four different methods (neighbor-joining, maximum parsimony, maximum-likelihood, and Bayesian) and also established the time divergence of the phylogeny. The phylogeny successfully separated the primates into their taxonomic groups as well as by their dietary preferences. Of note, the reviewed time of divergence estimation for the primate speciation pattern in this study was more recent than the previously published estimates. It is believed that this difference may be due to environmental changes, such as food scarcity and climate change, during the late Miocene epoch, which forced primates to change their dietary preferences. These findings provide a starting point for further investigation.
    Matched MeSH terms: Bayes Theorem
  16. The utility of the Laplace effect size prior distribution in Bayesian fine-mapping studies
    Walters K, Cox A, Yaacob H
    Genet Epidemiol, 2021 Jun;45(4):386-401.
    PMID: 33410201 DOI: 10.1002/gepi.22375
    The Gaussian distribution is usually the default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian population-based fine-mapping association studies, but a recent study showed that the heavier-tailed Laplace prior distribution provided a better fit to breast cancer top hits identified in genome-wide association studies. We investigate the utility of the Laplace prior as an effect size prior in univariate fine-mapping studies. We consider ranking SNPs using Bayes factors and other summaries of the effect size posterior distribution, the effect of prior choice on credible set size based on the posterior probability of causality, and on the noteworthiness of SNPs in univariate analyses. Across a wide range of fine-mapping scenarios the Laplace prior generally leads to larger 90% credible sets than the Gaussian prior. These larger credible sets for the Laplace prior are due to relatively high prior mass around zero which can yield many noncausal SNPs with relatively large Bayes factors. If using conventional credible sets, the Gaussian prior generally yields a better trade off between including the causal SNP with high probability and keeping the set size reasonable. Interestingly when using the less well utilised measure of noteworthiness, the Laplace prior performs well, leading to causal SNPs being declared noteworthy with high probability, whilst generally declaring fewer than 5% of noncausal SNPs as being noteworthy. In contrast, the Gaussian prior leads to the causal SNP being declared noteworthy with very low probability.
    Matched MeSH terms: Bayes Theorem
  17. Fulltext Geographical structure, narrow species ranges, and Cenozoic diversification in a pantropical clade of epiphyllous leafy liverworts
    Bechteler J, Schäfer-Verwimp A, Lee GE, Feldberg K, Pérez-Escobar OA, Pócs T, et al.
    Ecol Evol, 2017 01;7(2):638-653.
    PMID: 28116059 DOI: 10.1002/ece3.2656
    The evolutionary history and classification of epiphyllous cryptogams are still poorly known. Leptolejeunea is a largely epiphyllous pantropical liverwort genus with about 25 species characterized by deeply bilobed underleaves, elliptic to narrowly obovate leaf lobes, the presence of ocelli, and vegetative reproduction by cladia. Sequences of three chloroplast regions (rbcL, trnL-F, psbA) and the nuclear ribosomal ITS region were obtained for 66 accessions of Leptolejeunea and six outgroup species to explore the phylogeny, divergence times, and ancestral areas of this genus. The phylogeny was estimated using maximum-likelihood and Bayesian inference approaches, and divergence times were estimated with a Bayesian relaxed clock method. Leptolejeunea likely originated in Asia or the Neotropics within a time interval from the Early Eocene to the Late Cretaceous (67.9 Ma, 95% highest posterior density [HPD]: 47.9-93.7). Diversification of the crown group initiated in the Eocene or early Oligocene (38.4 Ma, 95% HPD: 27.2-52.6). Most species clades were established in the Miocene. Leptolejeunea epiphylla and L. schiffneri originated in Asia and colonized African islands during the Plio-Pleistocene. Accessions of supposedly pantropical species are placed in different main clades. Several monophyletic morphospecies exhibit considerable sequence variation related to a geographical pattern. The clear geographic structure of the Leptolejeunea crown group points to evolutionary processes including rare long-distance dispersal and subsequent speciation. Leptolejeunea may have benefitted from the large-scale distribution of humid tropical angiosperm forests in the Eocene.
    Matched MeSH terms: Bayes Theorem
  18. Fulltext Bayesian network classification of gastrointestinal bleeding
    Nazziwa Aisha, Mohd Bakri Adam, Shamarina Shohaimi, Aida Mustapha
    Pertanika Journal of Science & Technology, 2014;22(2):567-575.
    MyJurnal
    The source of gastrointestinal bleeding (GIB) remains uncertain in patients presenting without hematemesis. This paper aims at studying the accuracy, specificity and sensitivity of the Naive Bayesian Classifier (NBC) in identifying the source of GIB in the absence of hematemesis. Data of 325 patients admitted via the emergency department (ED) for GIB without hematemesis and who underwent confirmatory testing were analysed. Six attributes related to demography and their presenting signs were chosen. NBC was used to calculate the conditional probability of an individual being assigned to Upper Gastrointestinal bleeding (UGIB) or Lower Gastrointestinal bleeding (LGIB). High classification accuracy (87.3 %), specificity (0.85) and sensitivity (0.88) were achieved. NBC is a useful tool to support the identification of the source of gastrointestinal bleeding in patients without hematemesis.
    Matched MeSH terms: Bayes Theorem
  19. Fulltext Early sedation with dexmedetomidine in ventilated critically ill patients and heterogeneity of treatment effect in the SPICE III randomised controlled trial
    Shehabi Y, Serpa Neto A, Howe BD, Bellomo R, Arabi YM, Bailey M, et al.
    Intensive Care Med, 2021 Apr;47(4):455-466.
    PMID: 33686482 DOI: 10.1007/s00134-021-06356-8
    PURPOSE: To quantify potential heterogeneity of treatment effect (HTE), of early sedation with dexmedetomidine (DEX) compared with usual care, and identify patients who have a high probability of lower or higher 90-day mortality according to age, and other identified clusters.

    METHODS: Bayesian analysis of 3904 critically ill adult patients expected to receive invasive ventilation > 24 h and enrolled in a multinational randomized controlled trial comparing early DEX with usual care sedation.

    RESULTS: HTE was assessed according to age and clusters (based on 12 baseline characteristics) using a Bayesian hierarchical models. DEX was associated with lower 90-day mortality compared to usual care in patients > 65 years (odds ratio [OR], 0.83 [95% credible interval [CrI] 0.68-1.00], with 97.7% probability of reduced mortality across broad categories of illness severity. Conversely, the probability of increased mortality in patients ≤ 65 years was 98.5% (OR 1.26 [95% CrI 1.02-1.56]. Two clusters were identified: cluster 1 (976 patients) mostly operative, and cluster 2 (2346 patients), predominantly non-operative. There was a greater probability of benefit with DEX in cluster 1 (OR 0.86 [95% CrI 0.65-1.14]) across broad categories of age, with 86.4% probability that DEX is more beneficial in cluster 1 than cluster 2.

    CONCLUSION: In critically ill mechanically ventilated patients, early sedation with dexmedetomidine exhibited a high probability of reduced 90-day mortality in older patients regardless of operative or non-operative cluster status. Conversely, a high probability of increased 90-day mortality was observed in younger patients of non-operative status. Further studies are needed to confirm these findings.

    Matched MeSH terms: Bayes Theorem
  20. Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF
    Verma N, Dhiman RK, Singh V, Duseja A, Taneja S, Choudhury A, et al.
    Hepatol Int, 2021 Jun;15(3):753-765.
    PMID: 34173167 DOI: 10.1007/s12072-021-10175-w
    BACKGROUND: Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.

    METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.

    RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p  12 had the lowest 30-day survival (5.7%).

    CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

    Matched MeSH terms: Bayes Theorem
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