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PubMed2026

Proteome-level evidence that tebuconazole, both alone and in interaction with thiacloprid, affects epigenetic events in bumblebee heads.

Tebuconazole, a widely used ergosterol biosynthesis-inhibiting fungicide, can affect nontargets, especially when combined with insecticides. We employed label-free quantitative proteomics to investigate the effects of long-term exposure to sublethal concentrations (100 μg/L) of tebuconazole, either by itself or alongside the neonicotinoid thiacloprid (100 μg/L), on the heads of Bombus terrestris workers. A Bayesian factor power analysis revealed that the experiment produced conclusive proteomic results. Tebuconazole treatment revealed eleven differentially abundant proteins, which increased elevenfold with thiacloprid. The proteins that changed in the same direction in both treatments suggest the occurrence of epigenetic events because they are involved in histone trimethylation (H3K4me3), pre-mRNA processing, and folate (vitamin B9) metabolism. Following co-exposure, the abundance of histone H2A.V and its associated proteins was affected. Two important detoxification-related proteins, CYP6BE1 and CYP6AQ1 (honey bee homologs), were identified, as well as proteins that suggest hormonal and neurotoxic effects. Overall, this study suggests that tebuconazole affects key epigenetic processes in bumblebee heads at the proteome level, though this was not confirmed at the biological level or through orthogonal methods. The tested chemicals were previously found to affect trimethylations, but not H3K4me3. We suggest analyzing the different trimethylations, their interplay, and associated hallmarks, such as folate levels. SIGNIFICANCE: The effects of pesticides and their combinations on organisms can be unexpected until they are examined using modern, complex methods. High-throughput proteomics can provide data on important biochemical processes affected by pesticides, offering a different perspective to that at the expression level. Despite their low acute toxicity, a group of fungicides that inhibit (ergo)sterol biosynthesis (EBI or SBI) are considered dangerous to pollinators, including bumblebees. This is due to the increasing toxicity of insecticides through the inhibition of cytochrome P450 detoxification enzymes. We found that tebuconazole had a similar effect on epigenetic events when used alone or in combination with the insecticide thiacloprid. Key proteins suggest that H3K4 histone trimethylation (H3K4me3) was impacted. To our knowledge, this expands the existing evidence suggesting that tebuconazole/triazole fungicides affect histone trimethylation H3K27me3. Since literature shows that thiacloprid affects H3K9me3, it is possible that thiacloprid and tebuconazole interact in these epigenetic events that affect each other. Overall, our results suggest that tebuconazole affects proteins involved in histone trimethylation, pre-mRNA processing, and folate metabolism. These are all hallmarks of epigenetic processes and were further extended by the co-exposure of tebuconazole and thiacloprid to more differently abundant proteins. Additionally, the results provide data on cytochrome P450s of the CYP6 family, which act as detoxifying proteins, as well as proteins that indicate hormonal and neurotoxic effects in bumblebee heads. Finally, the results of the Bayesian power analysis confirmed the meaningfulness of the proteomic data analyzed in this study. If the new findings obtained at the proteome level are verified by different methods, the full extent of the side effects of tebuconazole can be revealed.

Animals↗

PubMed2026

PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.

MOTIVATION: Genome-wide association study (GWAS) meta-analysis tools are essential for integrating summary statistics across multiple cohorts, thereby increasing statistical power and validating genetic associations. Widely cited tools, such as METAL, PLINK, and GWAMA, have facilitated numerous significant discoveries in the field of GWAS. Nevertheless, these tools offer a limited set of meta-analysis methods and typically require users to have prior experience with command-line tools to be executed. RESULTS: We present here PYRAMA, an open-source tool which is designed for meta-analysis of genome wide association studies. This work introduces an easy-to-use software package that includes several meta-analysis methods that are absent in similar software packages. PYRAMA is faster compared to other tools, supports robust methods for analysis and meta-analysis, fixed-effects, random-effects and Bayesian meta-analysis and it is currently the only tool that supports meta-analysis with imputation of summary statistics. It is available both as a standalone tool and as a freely available web server. AVAILABILITY AND IMPLEMENTATION: https://github.com/pbagos/PYRAMA, https://doi.org/10.5281/zenodo.17830449.

Genome-Wide Association Study↗

PubMed2006

A Bayesian hierarchical approach to multirater correlated ROC analysis.

In a common ROC study design, several readers are asked to rate diagnostics of the same cases processed under different modalities. We describe a Bayesian hierarchical model that facilitates the analysis of this study design by explicitly modelling the three sources of variation inherent to it. In so doing, we achieve substantial reductions in the posterior uncertainty associated with estimates of the differences in areas under the estimated ROC curves and corresponding reductions in the mean squared error (MSE) of these estimates. Based on simulation studies, both the widths of coverage intervals and MSE of estimates of differences in the area under the curves appear to be reduced by a factor that often exceeds five. Thus, our methodology has important implications for increasing the power of analyses based on ROC data collected from an available study population.

Bayes Theorem↗

PubMed1996

Problems in pharmacokinetic analysis of alcohol disposition: a trial of the Bayesian least-squares method.

The technical problems of the pharmacokinetic analysis of alcohol disposition were studied using the Michaelis-Menten elimination kinetic model. This model was defined by two forms of equations: differential and integrated, with the latter being derived by integration of the differential equation. We compared the parameter values, estimated by one-line curve-fitting, using these two equation forms. We concluded that, for the kinetic analysis of alcohol disposition, curve-fitting with the differential equation was superior to that with the integrated equation. We also studied the methodological problems involved in one-line fitting. The ordinary least-squares (OLS) method was compared with the Bayesian least-squares (BLS) method. Correlation between the Vmax and beta (ethanol elimination rate) values, and between the Vmax and K(m) values was seen when the parameter values were estimated by the OLS method. These results suggested that one-line fitting by the OLS method was not adequate for Michaelis-Menten-type elimination kinetic analysis. BLS analysis resulted in no correlation between the estimated parameter values that did not change with the level of the dose. The BLS method seemed to be more useful than the OLS method for the estimation of individual pharmacokinetic parameter values.

Animals↗

PubMed1984

Recurrence risk estimation of acute intermittent porphyria based on analysis of porphobilinogen deaminase activity: a Bayesian approach.

Red cell porphobilinogen deaminase is known to be an indicator of the carrier state for acute intermittent porphyria (AIP). This enzyme was assayed in three groups of individuals at least 15 years old: 105 affected individuals or obligate carriers, 234 unaffected first-degree relatives of patients, and 217 unrelated control persons. Analysis of the distribution of the control enzyme activities suggested presence of three commingled distributions. Also, the overlap between carrier-group and control-group values must be taken into account for genetic counseling of relatives whose enzyme activity lies within the overlap. A Bayesian approach is proposed to derive risks for these individuals, using the observed carrier and control distributions. The method is illustrated by deriving risks for a family from our sample.

Adolescent↗

PubMed2005

A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data.

MOTIVATION: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. RESULTS: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.

Algorithms↗

PubMed2004

Bayesian spatial and ecological models for small-area accident and injury analysis.

In this article, recently developed Bayesian spatial and ecological regression models are applied to analyse small-area variation in accident and injury. This study serves to demonstrate how Bayesian modelling techniques can be implemented to assess potential risk factors measured at group (e.g. area) level. Presented here is a unified modelling framework that enables thorough investigations into associations between injury rates and regional characteristics, residual variation and spatial autocorrelation. Using hospital separation data for 83 local health areas in British Columbia (BC), Canada, in 1990-1999, we explore and examine ecological/contextual determinants of motor vehicle accident injury (MVAI) among male children and youth aged 0-24 and for those of six age groups (<1, 1-4, 5-9, 10-14, 15-19 and 20-24). Eighteen local health area characteristics are studied. They include a broad spectrum of socio-economic indicators, residential environment indicators (roads and parks), medical services availability and utilisation, population health, proportion of recent immigrants, crime rates, rates of speeding charge and rates of seatbelt violation. Our study indicates a large regional variation in MVAI in males aged 0-24 in British Columbia, Canada, in 1990-1999, and that adjusting for appropriate risk factors eliminates nearly all the variation observed. Socio-economic influence on MVAI was profoundly apparent in young males of all ages with the injury being more common in communities of lower socio-economic status. High adult male crime rates were significantly associated with high injury rates of boys aged 1-14. Seatbelt violations and excess speeding charges were found to be positively associated with the injury rates of young men aged 20-24. This and similar ecological studies shed light on reasons for regional variations in accident occurrence as well as in the resulting injuries and hospital utilisation. Thereby they are potentially useful in identifying priority areas for injury/accident prevention and in informing regional health planning and policy development.

Accidents↗

PubMed2026

Sugar kelp (Saccharina latissima) population genetics map onto geographic distance and oceanographic features across coastal Maine.

Sugar kelp (Saccharina latissima; order Laminariales) plays a vital role in kelp forest ecosystems, as well as an expanding kelp aquaculture industry, in the Gulf of Maine, United States. However, ocean warming is eroding the resilience of Maine's kelp forests and may be compromising their local genetic diversity, with impacts on population structure and gene flow. Here, we used genome-wide single nucleotide polymorphism (SNP) data to assess the genetic diversity, structure, and connectivity of S. latissima populations at 11 outer coastal sites spanning the historical range of kelp forests in Maine. Our analyses identified moderate genetic diversity and limited inbreeding within sites (average heterozygosity: 0.27). Further, they revealed that three clusters comprising four genetically distinct populations exist across the study region. Population structure was strongly associated with geographic distance and oceanographic features, as supported by principal coordinate analysis, FST calculations, Bayesian clustering, and spore dispersal modeling. Lastly, our outlier analysis identified genes potentially under selection. Thus, our findings highlight distinct, genetically unique kelp populations along Maine's coast and emphasize the need for regional management strategies that support both ecosystem resilience and sustainable aquaculture under climate change.

Gulf of Maine↗

PubMed2004

Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution.

We describe a model of neutral DNA evolution that allows substitution rates at a site to depend on the two flanking nucleotides ("context"), the branch of the phylogenetic tree, and position within the sequence and implement it by using a flexible and computationally efficient Bayesian Markov chain Monte Carlo approach. We then apply this approach to characterize phylogenetic variation in context-dependent substitution patterns in a 1.7-megabase genomic region in 19 mammalian species. In contrast to other substitution types, CpG transition substitutions have accumulated in a relatively clock-like fashion. More broadly, our results support the notion that context-dependent DNA replication errors, cytosine deamination, and biased gene conversion are major sources of naturally occurring mutations whose relative contributions have varied in mammalian evolution as a result of changes in generation times, effective population sizes, and recombination rates.

Animals↗

PubMed2005

Risk analysis of a patient monitoring system using Bayesian Network modeling.

In a modern technological environment where information systems are characterized by complexity, situations of non-effective operation should be anticipated. Often system failures are a result of insufficient planning or equipment malfunction, indicating that it is essential to develop techniques for predicting and addressing a system failure. Particularly for safety-critical applications such as the healthcare information systems, which are dealing with human health, risk analysis should be considered a necessity. This paper presents a new method for performing a risk analysis study of health information systems. Specifically, the CCTA Risk Analysis and Management Methodology (CRAMM) has been utilized for identifying and valuating the assets, threats, and vulnerabilities of the information system, followed by a graphical modeling of their interrelationships using Bayesian Networks. The proposed method exploits the results of the CRAMM-based risk analysis for developing a Bayesian Network model, which presents concisely all the interactions of the undesirable events for the system. Based on "what-if" studies of system operation, the Bayesian Network model identifies and prioritizes the most critical events. The proposed risk analysis framework has been applied to a vital signs monitoring information system for homecare telemedicine, namely the VITAL-Home System, developed and maintained for a private medical center (Medical Diagnosis and Treatment S.A.).

Bayes Theorem↗

PubMed2001

[Empirical bayesian model applied to the spatial analysis of leprosy occurrence].

OBJECTIVE: To analyze the spatial distribution of leprosy, identify areas of potential case underreporting or high transmission risk, and to assess the ecological association of leprosy distribution with multibacillary cases. METHODS: This study was carried out in 94 neighborhoods of Recife, Brazil. Data was obtained from the Ministry of Health's Disease Reporting System. An ecological approach with the empirical Bayesian method was applied for local rate flattening, using data from a neighborhood matrix. RESULTS: The mean annual occurrence was 17.3% of new cases in individuals under the age of 15 (28.3% corresponded to multibacillary forms), revealing an intense disease transmission. The spatial distribution of leprosy indicated three areas where there was a concentration of high detection rates and low-income neighborhoods. CONCLUSIONS: The Bayesian method allowed to reassess epidemiological indicators based on data from neighboring spatial units. This enabled to identify areas that should be prioritized in municipal control programs, either because of underreporting of cases or the higher number of occurrences related to multibacillary forms in individuals under 15.

Adolescent↗

PubMed2004

Conflicting phylogenies of balsaminoid families and the polytomy in Ericales: combining data in a Bayesian framework.

The balsaminoid Ericales, namely Balsaminaceae, Marcgraviaceae, Tetrameristaceae, and Pellicieraceae have been confidently placed at the base of Ericales, but the relations among these families have been resolved differently in recent analyses. Sister to this basal group is a large polytomy comprising all other families of Ericales, which is associated with short internodes. Because there are more than 13 kb of sequences for a large sampling of representatives, a thorough examination of the available data with novel methods seemed in place. Because of its computational speed, Bayesian phylogenetics allows for the use of parameter-rich models that can accommodate differences in the evolutionary process between partitions in a simultaneous analysis. In addition, there are recently proposed Bayesian strategies of assessing incongruence between partitions. We have applied these methods to the current problems in Ericales phylogeny, taking into account reported pitfalls in Bayesian analysis such as model selection uncertainty. Based on our results we infer several, previously unresolved relationships in the order Ericales. In balsaminoid families, we find that the closest relatives of Balsaminaceae are Marcgraviaceae. In the Ericales polytomy, we find strong support for Pentaphylacaceae sensu APG II as the sister group of Maesaceae. In addition, Symplocaceae receive a position as sister to Theaceae and these families form a monophyletic group together with Styracaceae-Diapensiaceae. At the base of this clade are Actinidiaceae and Clethraceae. The positions of Ebenaceae and Lecythidaceae remain uncertain.

Balsaminaceae↗

PubMed2005

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-a case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake.

Bayesian regularized BP neural network(BRBPNN) technique was applied in the chlorophyll-a prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS 11.0 software, the BRBPNN model was established between chlorophyll-a and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.00078426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll-a declined in the order of alga amount > secchi disc depth (SD) > electrical conductivity (EC). Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-a concentration, total phosphorus (TP) and total nitrogen (TN) were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data(chlorophyll-a prediction) and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.

Bayes Theorem↗

PubMed2005

Analysis and visualization of tree space.

We explored the use of multidimensional scaling (MDS) of tree-to-tree pairwise distances to visualize the relationships among sets of phylogenetic trees. We found the technique to be useful for exploring "tree islands" (sets of topologically related trees among larger sets of near-optimal trees), for comparing sets of trees obtained from bootstrapping and Bayesian sampling, for comparing trees obtained from the analysis of several different genes, and for comparing multiple Bayesian analyses. The technique was also useful as a teaching aid for illustrating the progress of a Bayesian analysis and as an exploratory tool for examining large sets of phylogenetic trees. We also identified some limitations to the method, including distortions of the multidimensional tree space into two dimensions through the MDS technique, and the definition of the MDS-defined space based on a limited sample of trees. Nonetheless, the technique is a useful approach for the analysis of large sets of phylogenetic trees.

Bayes Theorem↗

PubMed2005

Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

PURPOSE: To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. METHODS: In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 +/- 2.65 years and 8.11 +/- 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual's series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria. RESULTS: Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT. CONCLUSIONS: The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.

Adult↗

PubMed2005

An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks.

Thyroid function diagnosis is an important classification problem, and we made reanalysis of the human thyroid data, which had been analyzed by the multivariate analysis, by the two notable neural networks. One is the self-organizing map approach which clusters the patients and displays visually a characteristic of the distribution according to laboratory tests. We found that self-organizing map (SOM) consists of three well separated clusters corresponding to hyperthyroid, hypothyroid and normal, and more detailed information for patients is obtained from the position in the map. Besides, the missing value SOM which we had introduced to investigate QSAR problem turned out to be also useful in treating such classification problem. We estimated the classification rates of thyroid disease using Bayesian regularized neural network (BRNN) and found that its prediction accuracy is better than multivariate analysis. Automatic relevance determination (ARD) method of BRNN was surely verified to be effective by the direct calculation of classification rates using BRNN without ARD for all possible combinations of laboratory tests.

Bayes Theorem↗

PubMed2001

Analysis of multicomponent mass spectra applying Bayesian probability theory.

In this paper we develop a method for the decomposition of mass spectra of gas mixtures, together with the relevant calibration measurements. The method is based on Bayesian probability theory. Given a set of spectra, the algorithm returns the relative concentrations and the associated margin of confidence for each component of the mixture. In addition to the concentrations, such a data set enables the derivation of improved values of the cracking coefficients of all contributing species, even for those components for which the set does not contain a calibration measurement. This latter feature also allows one to analyze mixtures that contain radicals in addition to stable molecules. As an example, we analyze and discuss the mass spectra obtained from the pyrolysis of azomethane, which contain the radical CH3 apart from nitrogen and C1- and C2-hydrocarbons.

Azo Compounds↗

PubMed2004

The evolutionary history of seahorses (Syngnathidae: Hippocampus): molecular data suggest a West Pacific origin and two invasions of the Atlantic Ocean.

Sequence data derived from four markers (the nuclear RP1 and Aldolase and the mitochondrial 16S rRNA and cytochrome b genes) were used to determine the phylogenetic relationships among 32 species belonging to the genus Hippocampus. There were marked differences in the rate of evolution among these gene fragments, with Aldolase evolving the slowest and the mtDNA cytochrome b gene the fastest. The RP1 gene recovered the highest number of nodes supported by >70% bootstrap values from parsimony analysis and >95% posterior probabilities from Bayesian inference. The combined analysis based on 2317 nucleotides resulted in the most robust phylogeny. A distinct phylogenetic split was identified between the pygmy seahorse, Hippocampus bargibanti, and a clade including all other species. Three species from the western Pacific Ocean included in our study, namely H. bargibanti, H. breviceps, and H. abdominalis occupy basal positions in the phylogeny. This and the high species richness in the region suggests that the genus evolved somewhere in the West Pacific. There is also fairly strong molecular support for the remaining species being subdivided into three main evolutionary lineages: two West Pacific clades and a clade of species present in both the Indo-Pacific and the Atlantic Ocean. The phylogeny obtained herein suggests at least two independent colonization events of the Atlantic Ocean, once before the closure of the Tethyan seaway, and once afterwards.

Animals↗