Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO2, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO2, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO2, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.

• Klíčová slova
• Air quality, Bayesian maximum entropy (BME), Fuzzy set theory, Multi-criteria decision-making (MCDM), Nonlinear interval number programming (NINP), Transinformation entropy (TE),
• MeSH
• Bayesova věta MeSH
• entropie MeSH
• monitorování životního prostředí metody   MeSH
• oxid dusičitý analýza   MeSH
• ozon * analýza   MeSH
• teoretické modely MeSH
• znečištění ovzduší * analýza   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• oxid dusičitý MeSH
• ozon * MeSH

Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is an empirical biological knowledge estimation from the available experimental data, which complements the missing biological prior knowledge. IntOMICS has the potential to be a powerful resource for exploratory systems biology.

• Klíčová slova
• Bayesian networks, integrative analysis, multi-omics, regulatory network,
• MeSH
• Bayesova věta MeSH
• Markovovy řetězce MeSH
• multiomika * MeSH
• systémová biologie MeSH
• variabilita počtu kopií segmentů DNA * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The globalization in plant material trading has caused the emergence of invasive pests in many ecosystems, such as the alder pathogen Phytophthora ×alni in European riparian forests. Due to the ecological importance of alder to the functioning of rivers and the increasing incidence of P. ×alni-induced alder decline, effective and accessible decision tools are required to help managers and stakeholders control the disease. This study proposes a Bayesian belief network methodology to integrate diverse information on the factors affecting the survival and infection ability of P. ×alni in riparian habitats to help predict and manage disease incidence. The resulting Alder Decline Network (ADnet) management tool integrates information about alder decline from scientific literature, expert knowledge and empirical data. Expert knowledge was gathered through elicitation techniques that included 19 experts from 12 institutions and 8 countries. An original dataset was created covering 1189 European locations, from which P. ×alni occurrence was modeled based on bioclimatic variables. ADnet uncertainty was evaluated through its sensitivity to changes in states and three scenario analyses. The ADnet tool indicated that mild temperatures and high precipitation are key factors favoring pathogen survival. Flood timing, water velocity, and soil type have the strongest influence on disease incidence. ADnet can support ecosystem management decisions and knowledge transfer to address P. ×alni-induced alder decline at local or regional levels across Europe. Management actions such as avoiding the planting of potentially infected trees or removing man-made structures that increase the flooding period in disease-affected sites could decrease the incidence of alder disease in riparian forests and limit its spread. The coverage of the ADnet tool can be expanded by updating data on the pathogen's occurrence, particularly from its distributional limits. Research on the role of genetic variability in alder susceptibility and pathogen virulence may also help improve future ADnet versions.

• Klíčová slova
• Alnus, Decision support tool, Expert knowledge, Integrated pest management (IPM), Phytophthora ×alni, Riparian ecosystems,
• MeSH
• Bayesova věta * MeSH
• ekosystém MeSH
• lesy MeSH
• nemoci rostlin mikrobiologie   statistika a číselné údaje   MeSH
• olše * MeSH
• Phytophthora MeSH
• zachování přírodních zdrojů MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH

BACKGROUND: Ethno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex interaction of the multi-tiered modifiable factors and non-modifiable demographics that influence the incidence and progression of cardiometabolic disease. METHODS: In this study, we learn the structure and parameters of a Bayesian network based on 20 years of data from the US National Health and Nutrition Examination Survey to explore the pathways mediating associations between ethno-racial group and cardiometabolic outcomes. The impact of different factors on cardiometabolic outcomes by ethno-racial group is analyzed using conditional probability queries. RESULTS: Multiple pathways mediate the indirect association from ethno-racial group to cardiometabolic outcomes: (1) ethno-racial group to education and to behavioral factors (diet); (2) education to behavioral factors (smoking, physical activity, and-via income-to alcohol); (3) and behavioral factors to adiposity-based chronic disease (ABCD) and then other cardiometabolic drivers. Improved diet and physical activity are associated with a larger decrease in probability of ABCD stage 4 among non-Hispanic White (NHW) individuals compared to non-Hispanic Black (NHB) and Hispanic (HI) individuals. CONCLUSION: Education, income, and behavioral factors mediate ethno-racial disparities in cardiometabolic outcomes, but traditional behavioral factors (diet and physical activity) are less influential among NHB or HI individuals compared to NHW individuals. This suggests the greater contribution of unmeasured individual- and/or neighborhood-level structural determinants of health that impact cardiometabolic drivers among NHB and HI individuals. Further study is needed to discover the nature of these unmeasured determinants to guide cardiometabolic care in diverse populations.

• Klíčová slova
• cardiometabolic disease, health inequities, obesity, racial inequities, social determinants of health,
• MeSH
• Bayesova věta * MeSH
• běloch MeSH
• černoši nebo Afroameričané MeSH
• chronická nemoc MeSH
• disparity zdravotního stavu * MeSH
• dospělí MeSH
• etnicita MeSH
• Hispánci a Latinoameričané MeSH
• kardiovaskulární nemoci MeSH
• lidé středního věku MeSH
• lidé MeSH
• rasové skupiny MeSH
• senioři MeSH
• socioekonomické faktory MeSH
• výživa - přehledy * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Spojené státy americké MeSH

BACKGROUND: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. CONCLUSIONS: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.

• MeSH
• Bayesova věta MeSH
• hodnocení rizik MeSH
• lidé středního věku MeSH
• lidé MeSH
• lymfatické metastázy MeSH
• nádorové biomarkery metabolismus   MeSH
• nádory endometria patologie   MeSH
• prospektivní studie MeSH
• receptory pro estrogeny metabolismus   MeSH
• receptory progesteronu MeSH
• retrospektivní studie MeSH
• senioři MeSH
• stupeň nádoru MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• nádorové biomarkery MeSH
• receptory pro estrogeny MeSH
• receptory progesteronu MeSH

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

• Klíčová slova
• Ant Colony Optimisation, Bayesian optimisation, Cardiovascular disease, Hyperparameter, Min–max scaler,
• MeSH
• Bayesova věta MeSH
• deep learning * MeSH
• diagnóza počítačová metody   MeSH
• Formicidae MeSH
• kardiovaskulární nemoci * diagnóza   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Despite the excellent fossil record of cephalopods, their early evolution is poorly understood. Different, partly incompatible phylogenetic hypotheses have been proposed in the past, which reflected individual author's opinions on the importance of certain characters but were not based on thorough cladistic analyses. At the same time, methods of phylogenetic inference have undergone substantial improvements. For fossil datasets, which typically only include morphological data, Bayesian inference and in particular the introduction of the fossilized birth-death model have opened new possibilities. Nevertheless, many tree topologies recovered from these new methods reflect large uncertainties, which have led to discussions on how to best summarize the information contained in the posterior set of trees. RESULTS: We present a large, newly compiled morphological character matrix of Cambrian and Ordovician cephalopods to conduct a comprehensive phylogenetic analysis and resolve existing controversies. Our results recover three major monophyletic groups, which correspond to the previously recognized Endoceratoidea, Multiceratoidea, and Orthoceratoidea, though comprising slightly different taxa. In addition, many Cambrian and Early Ordovician representatives of the Ellesmerocerida and Plectronocerida were recovered near the root. The Ellesmerocerida is para- and polyphyletic, with some of its members recovered among the Multiceratoidea and early Endoceratoidea. These relationships are robust against modifications of the dataset. While our trees initially seem to reflect large uncertainties, these are mainly a consequence of the way clade support is measured. We show that clade posterior probabilities and tree similarity metrics often underestimate congruence between trees, especially if wildcard taxa are involved. CONCLUSIONS: Our results provide important insights into the earliest evolution of cephalopods and clarify evolutionary pathways. We provide a classification scheme that is based on a robust phylogenetic analysis. Moreover, we provide some general insights on the application of Bayesian phylogenetic inference on morphological datasets. We support earlier findings that quartet similarity metrics should be preferred over the Robinson-Foulds distance when higher-level phylogenetic relationships are of interest and propose that using a posteriori pruned maximum clade credibility trees help in assessing support for phylogenetic relationships among a set of relevant taxa, because they provide clade support values that better reflect the phylogenetic signal.

• Klíčová slova
• Bayesian phylogenetics, Cephalopoda, Endoceratoidea, Fossilized birth-death process, Multiceratoidea, Nautiloidea, Orthoceratoidea, Phylogeny, Posterior clade probabilities, Tree similarities,
• MeSH
• Bayesova věta MeSH
• fylogeneze MeSH
• hlavonožci * genetika   MeSH
• pravděpodobnost MeSH
• zkameněliny MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Divergence-time estimation based on molecular phylogenies and the fossil record has provided insights into fundamental questions of evolutionary biology. In Bayesian node dating, phylogenies are commonly time calibrated through the specification of calibration densities on nodes representing clades with known fossil occurrences. Unfortunately, the optimal shape of these calibration densities is usually unknown and they are therefore often chosen arbitrarily, which directly impacts the reliability of the resulting age estimates. As possible solutions to this problem, two nonexclusive alternative approaches have recently been developed, the “fossilized birth–death” (FBD) model and “total-evidence dating.” While these approaches have been shown to perform well under certain conditions, they require including all (or a random subset) of the fossils of each clade in the analysis, rather than just relying on the oldest fossils of clades. In addition, both approaches assume that fossil records of different clades in the phylogeny are all the product of the same underlying fossil sampling rate, even though this rate has been shown to differ strongly between higher level taxa. We here develop a flexible new approach to Bayesian age estimation that combines advantages of node dating and the FBD model. In our new approach, calibration densities are defined on the basis of first fossil occurrences and sampling rate estimates that can be specified separately for all clades. We verify our approach with a large number of simulated data sets, and compare its performance to that of the FBD model. We find that our approach produces reliable age estimates that are robust to model violation, on par with the FBD model. By applying our approach to a large data set including sequence data from over 1000 species of teleost fishes as well as 147 carefully selected fossil constraints, we recover a timeline of teleost diversification that is incompatible with previously assumed vicariant divergences of freshwater fishes. Our results instead provide strong evidence for transoceanic dispersal of cichlids and other groups of teleost fishes.

• Klíčová slova
• Bayesian inference *, calibration density *, Cichlidae *, fossil record *, marine dispersal *, phylogeny *, relaxed molecular clock *,
• MeSH
• Bayesova věta MeSH
• biodiverzita MeSH
• biologické modely * MeSH
• čas MeSH
• cichlidy klasifikace   MeSH
• fylogeneze * MeSH
• vznik druhů (genetika) MeSH
• zkameněliny MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Atlantský oceán MeSH

Considerable controversy exists about which hypotheses and variables best explain mammalian brain size variation. We use a new, high-coverage dataset of marsupial brain and body sizes, and the first phylogenetically imputed full datasets of 16 predictor variables, to model the prevalent hypotheses explaining brain size evolution using phylogenetically corrected Bayesian generalized linear mixed-effects modelling. Despite this comprehensive analysis, litter size emerges as the only significant predictor. Marsupials differ from the more frequently studied placentals in displaying a much lower diversity of reproductive traits, which are known to interact extensively with many behavioural and ecological predictors of brain size. Our results therefore suggest that studies of relative brain size evolution in placental mammals may require targeted co-analysis or adjustment of reproductive parameters like litter size, weaning age or gestation length. This supports suggestions that significant associations between behavioural or ecological variables with relative brain size may be due to a confounding influence of the extensive reproductive diversity of placental mammals.

• Klíčová slova
• Bayesian, brain, comparative, imputations, marsupials, phylogenetic,
• MeSH
• Bayesova věta MeSH
• biologická evoluce MeSH
• fylogeneze MeSH
• těhotenství MeSH
• vačnatí * genetika   MeSH
• velikost orgánu MeSH
• zvířata MeSH
• Check Tag
• těhotenství MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The enormous development and expansion of antibiotic-resistant bacterial strains impel the intensive search for new methods for fast and reliable detection of antibiotic susceptibility markers. Here, we combined DNA-targeted surface functionalization, surface-enhanced Raman spectroscopy (SERS) measurements, and subsequent spectra processing by decision system (DS) for detection of a specific oligonucleotide (ODN) sequence identical to a fragment of blaNDM-1 gene, responsible for β-lactam antibiotic resistance. The SERS signal was measured on plasmonic gold grating, functionalized with capture ODN, ensuring the binding of corresponded ODNs. Designed DS consists of a Siamese neural network (SNN) coupled with robust statistics and Bayes decision theory. The proposed approach allows manipulation with complex multicomponent samples and predefine the desired detection level of confidence and errors, automatically determining the number of required spectra and samples. In constant to commonly used classification-type SNN, our method was applied to analyze samples with compositions previously "unknown" to DS. The detection of targeted ODN was performed with ≥99% level of confidence up to 3 × 10-12 M limit on the background of 10-10 M concentration of similar but not targeted ODNs.

• Klíčová slova
• Antibiotics resistance gene, Detection, SERS, Siamese neural network,
• MeSH
• antibakteriální látky farmakologie   MeSH
• Bayesova věta MeSH
• beta-laktamy MeSH
• chemometrika * MeSH
• neuronové sítě * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• antibakteriální látky MeSH
• beta-laktamy MeSH