Search

The freshwater red alga Batrachospermum gelatinosum has a well‐documented distribution spanning historically glaciated and unglaciated eastern North America. This alga has no known desiccation‐resistant propagule; thus, long‐distance dispersal events are likely rare. We predicted strong genetic structure among drainage basins and admixture among sites within basins. We predicted greater genetic diversity at lower latitude sites because they likely serve as refugia and the origin of northward, post‐Pleistocene range expansion. We used 10 microsatellite loci to investigate genetic diversity from 311 gametophytes from 18 sites in five major drainage basins: South Atlantic Gulf, Mid‐Atlantic, Ohio River, Great Lakes, and Northeast. Our data showed strong genetic partitioning among drainage basins and among sites within basins, yet no isolation by distance was detected. Genetic diversity varied widely among sites and was not strictly related to latitude as predicted. The results from B. gelatinosum provide strong support that each stream site contributes to the unique genetic variation within the species, potentially due to limited dispersal and the prevailing reproductive mode of intragametophytic selfing. Simulations of migration suggested post‐Pleistocene dispersal from the Mid‐Atlantic. Batrachospermum gelatinosum potentially persisted in refugia that were just south of the ice margins rather than in the southernmost part of its range. Research of other taxa with similar ranges could determine whether these results are generally applicable for freshwater red algae. Nevertheless, these results from B. gelatinosum add to the growing literature focused on the patterns and genetic consequences of post‐Pleistocene range expansion by eastern North American biota. [ABSTRACT FROM AUTHOR]

The degradation of products is an integral part of their life-cycle, often following predictable trajectories. However, sudden, unexpected events, termed 'shocks', can substantially alter these degradation paths. Shocks can significantly influence the pace of degradation, leading to accelerated system failure. Moreover, they may initiate changes in degradation patterns, transitioning from linear to non-linear or random trajectories. To address this challenge, we present a novel multi-state reliability model for competing failure processes that account for degradation-shock dependencies by considering the state-varying degradation pattern. The degradation process is divided into s-states, with each state treated according to its pattern based on the time-transform Wiener process. The reliability function is derived based on soft failure caused by continuous degradation involving the s-states, the sudden increase in degradation caused by random shocks, and hard failure due to some shock processes. Additionally, we performed a sensitivity analysis to determine which parameters have the most significant impact on product reliability. Due to the complexity of the likelihood function, we adopted the ABC method to estimate the model parameters. A simulation study and a practical application with micro-electro-mechanical systems (MEMS) degradation results are used to demonstrate the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Approximate Bayesian computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by comparing the observed data with additional process‐generated simulated data sets. For computational benefit, only the values of certain well‐chosen summary statistics are usually compared, instead of the whole data set. Most ABC procedures are computationally expensive, justified only heuristically, and have poor asymptotic properties. In this article, we introduce a new empirical likelihood‐based approach to the ABC paradigm called ABCel. The proposed procedure is computationally tractable and approximates the target log posterior of the parameter as a sum of two functions of the data—namely, the mean of the optimal log‐empirical likelihood weights and the estimated differential entropy of the summary functions. We rigorously justify the procedure via direct and reverse information projections onto appropriate classes of probability densities. Past applications of empirical likelihood in ABC demanded constraints based on analytically tractable estimating functions that involve both the data and the parameter; although by the nature of the ABC problem such functions may not be available in general. In contrast, we use constraints that are functions of the summary statistics only. Equally importantly, we show that our construction directly connects to the reverse information projection and estimate the relevant differential entropy by a k‐NN estimator. We show that ABCel is posterior consistent and has highly favorable asymptotic properties. Its construction justifies the use of simple summary statistics like moments, quantiles, and so forth, which in practice produce accurate approximation of the posterior density. We illustrate the performance of the proposed procedure in a range of applications. [ABSTRACT FROM AUTHOR]

The biased net paradigm was the first general and empirically tractable scheme for parameterizing complex patterns of dependence in networks, expressing deviations from uniform random graph structure in terms of latent "bias events," whose realizations enhance reciprocity, transitivity, or other structural features. Subsequent developments have introduced local specifications of biased nets, which reduce the need for approximations required in early specifications based on tracing processes. Here, we show that while one such specification leads to inconsistencies, a closely related Markovian specification both evades these difficulties and can be extended to incorporate new types of effects. We introduce the notion of inhibitory bias events, with satiation as an example, which are useful for avoiding degeneracies that can arise from closure bias terms. Although our approach does not lead to a computable likelihood, we provide a strategy for approximate Bayesian inference using random forest prevision. We demonstrate our approach on a network of friendship ties among college students, recapitulating a relationship between the sibling bias and tie strength posited in earlier work by Fararo. [ABSTRACT FROM AUTHOR]

The Kullback–Leibler (KL) divergence is frequently used in data science. For discrete distributions on large state spaces, approximations of probability vectors may result in a few small negative entries, rendering the KL divergence undefined. We address this problem by introducing a parameterized family of substitute divergence measures, the shifted KL (sKL) divergence measures. Our approach is generic and does not increase the computational overhead. We show that the sKL divergence shares important theoretical properties with the KL divergence and discuss how its shift parameters should be chosen. If Gaussian noise is added to a probability vector, we prove that the average sKL divergence converges to the KL divergence for small enough noise. We also show that our method solves the problem of negative entries in an application from computational oncology, the optimization of Mutual Hazard Networks for cancer progression using tensor-train approximations. [ABSTRACT FROM AUTHOR]

Arnold and Ghosh (2017a, 2017b) have proposed a broad spectrum of bivariate Kumaraswamy (henceforth, denoted by KW) distributions involving conditional specification, conditional survival specification, and starting from the Arnold and Ng (2011) eight parameter bivariate beta model. In addition, copula based construction of bivariate KW models was considered. Included among the models that they discussed were the Olkin-Trikalinos (denoted by OT-BK) and the Ghosh-BK (denoted by G-BK) models. These two models can accommodate both positive and negative correlation under certain parametric restrictions. However, this comes at the expense of dealing with a density that is mathematically intractable. We focus our attention on estimation in the 4 and 5 parameter OT-BK and the G-BK models, respectively, using a Bayesian approach. A general framework based on approximate Bayesian computation methodology is proposed and studied in this article. In particular, the choice of priors must be such that they satisfy the parameter constraints for these models. We conduct simulation studies for both of these models under a wide selection of priors. For illustrative purposes, a real data set has been re-analyzed. [ABSTRACT FROM AUTHOR]

Population genetic analysis of invasive populations can provide valuable insights into the source of introductions, pathways for expansion, and their demographic histories. Flathead catfish (Pylodictis olivaris) are a prolific invasive species with high fecundity, long‐distance dispersal, and piscivorous feeding habits that can lead to declines in native fish populations. In this study, we analyse the genetics of invasive P. olivaris in the Mid‐Atlantic region to assess their connectivity and attempt to reconstruct the history of introduced populations. Based on an assessment across 13 microsatellite loci, P. olivaris from the Susquehanna River system (N = 537), Schuylkill River (N = 33), and Delaware River (N = 1) have low genetic diversity (global Hobs = 0.504), although we detected no evidence of substantial inbreeding (FIS = −0.083 to 0.022). P. olivaris from these different river systems were genetically distinct, suggesting separate introductions. However, population structure was much weaker within each river system and exhibited a pattern of high connectivity, with some evidence of isolation by distance. P. olivaris from the Susquehanna and Schuylkill rivers showed evidence for recent genetic bottlenecks, and demographic models were consistent with historical records, which suggest that populations were established by recent founder events consisting of a small number of individuals. Our results show the risk posed by small introductions of P. olivaris, which can spread widely once a population is established, and highlight the importance of prevention and sensitive early detection methods to prevent the spread of P. olivaris in the future. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain’s architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components–the so-called agents–discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment. [ABSTRACT FROM AUTHOR]

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype‐based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post‐Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC‐based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease‐risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for estimating root causes of faults in multistage mass production processes that have three properties: (1) only the final inspection data can be acquired, (2) hundreds of products are manufactured in a lot-wise manner, and (3) the acquired dataset does not always follow a Gaussian distribution. The proposed method consists of two components: (i) derive the distribution of part variables from the inspection dataset by fusing the approximate Bayesian computation (ABC) and the process model, and (ii) derive the root cause scores from the normal and abnormal datasets, which quantify how much each part contributes to the abnormal condition. The proposed method can estimate candidates of the fault causes, and numerical experiments are performed to explore the effectiveness and limitations of the method. Furthermore, the application to actual data of an internal camera module yields consistent results with design information given as domain knowledge. [ABSTRACT FROM AUTHOR]

Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall–runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance. HIGHLIGHTS Approximate Bayesian Computation is a likelihood-free approach for inverse parameter estimation.; The method is both computationally simple and versatile to use imprecise information.; Censored and binary data are useful observations for parameter estimation.; Shapley values allow us to estimate the significance of a specific type of observation.; The Monte-Carlo based method is computationally expensive.;

AbstractApproximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models in which simulating synthetic data is very expensive. In this article, we propose an early rejection Markov chain Monte Carlo (ejMCMC) sampler based on Gaussian processes to accelerate inference speed. We early reject samples in the first stage of the kernel using a discrepancy model, in which the discrepancy between the simulated and observed data is modeled by Gaussian process (GP). Hence, synthetic data is generated only if the parameter space is worth exploring. We demonstrate through theory, simulation experiments, and real data analysis that the new algorithm significantly improves inference efficiency compared to existing early-rejection MCMC algorithms. In addition, we employ our proposed method within an ABC sequential Monte Carlo (SMC) sampler. In our numerical experiments, we use examples of ordinary differential equations, stochastic differential equations, and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We develop an R package that is available at https://github.com/caofff/ejMCMC. [ABSTRACT FROM AUTHOR]

Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum (LGM) from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash Fraxinus pennsylvanica using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross‐validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic‐genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially‐explicit model. Approximate Bayesian computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross‐validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data. [ABSTRACT FROM AUTHOR]

We present a novel procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation (ABC). Unlike previous procedures, our proposal is based on the asymptotic properties of ABC. We demonstrate theoretically, and empirically that our procedure can consistently detect the presence of model misspecification. The examples demonstrate that our proposal shows good finite-sample properties, outperforming existing approaches. An empirical application to modeling exchange rate log returns using a g-and-k distribution completes the article. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In response to the striking effects of environmental change, conservation strategies often include the identification of conservation areas that can effectively maintain vulnerable species. Consequently, identifying system‐specific conditions that maintain the demographic and genetic viability of species of conservation concern is essential. Connectivity plays a critical role in the persistence of populations. Islands have been model systems to understand connectivity and metapopulation processes and have emerged as particularly favorable targets for conservation. While islands can be isolated from mainland disturbances, it is unknown what degree of isolation is necessary to avoid unfavorable changes but remain sufficiently connected to maintain population viability. To test this question, we explored connectivity within the Apostle Islands, an archipelago of 22 islands within Lake Superior, by comparing historical and contemporary trends in ice bridge connectivity and by simulating the effect of reduced connectivity within this system. We developed a demographically informed individual‐based model to explicitly test the role of connectivity to influence the persistence and genetic diversity of American marten Martes americana, a forest carnivore at risk across its southern range boundary. We found that genetic diversity was resilient to moderate changes in ice cover, but a complete loss of connectivity resulted in rapid genetic erosion. Despite genetic erosion, populations persisted as long as nominal connectivity occurred between islands. Our work suggests that connectivity will decline, but martens would be resilient to moderate changes and, in the short term, the Apostle Islands can act as a refuge along this species' southern range boundary. Identifying thresholds in connectivity that maintain populations but allow for isolation from disturbance will be necessary to identify suitable areas for species conservation across space and time. [ABSTRACT FROM AUTHOR]

AbstractBayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the assumed model is tractable. We analyze the behavior of the Bayesian synthetic likelihood posterior when the assumed model differs from the actual data generating process. We demonstrate that the Bayesian synthetic likelihood posterior can display a wide range of nonstandard behaviors depending on the level of model misspecification, including multimodality and asymptotic non-Gaussianity. Our results suggest that likelihood tempering, a common approach for robust Bayesian inference, fails for synthetic likelihood whilst recently proposed robust synthetic likelihood approaches can ameliorate this behavior and deliver reliable posterior inference under model misspecification. All results are illustrated using a simple running example. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Selecting a "useful" model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood‐free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis. [ABSTRACT FROM AUTHOR]

African Swine Fever (ASF) disease transmission parameters are crucial for making response and control decisions when faced with an outbreak, yet they are poorly quantified for smallholder and village contexts within Southeast Asia. Whilst disease-specific factors − such as latent and infectious periods − should remain reasonably consistent, host, environmental and management factors are likely to affect the rate of disease spread. These differences are investigated using Approximate Bayesian Computation with Sequential Monte-Carlo methods to provide disease parameter estimates in four naïve pig populations in villages of Lao People’s Democratic Republic. The villages represent smallholder pig farmers of the Northern province of Oudomxay and the Southern province of Savannakhet, and the model utilised field mortality data to validate the transmission parameter estimates over the course of multiple model generations. The basic reproductive number between-pigs was estimated to range from 3.08 to 7.80, whilst the latent and infectious periods were consistent with those published in the literature for similar genotypes in the region (4.72 to 6.19 days and 2.63 to 5.50 days, respectively). These findings demonstrate that smallholder village pigs interact similarly to commercial pigs, however the spread of disease may occur slightly slower than in commercial study groups. Furthermore, the findings demonstrated that despite diversity across the study groups, the disease behaved in a consistent manner. This data can be used in disease control programs or for future modelling of ASF in smallholder contexts. [ABSTRACT FROM AUTHOR]

Species diversity analysis of microbial communities is an important tool for assessing an ecosystem health. The advent of high-throughput genome sequencing techniques has made it possible to process an unprecedented number of RNA sequences. However, many studies report the presence of a significant number of fictitious rare species in datasets generated using these techniques. These species are the product of errors that can occur at any step of the sequence analysis pipeline. The overcount of rare species (especially singletons) affects the estimation of the total number of species, and of the diversity of the community as measured by Shannon's index. To avoid overestimating these quantities, it is crucial to model the source of error. In this work, we present a new model that treats spurious singletons as false-negative record linkage errors, and compare it with another approach where spurious singletons are considered for deletion. We discuss the two inferential approaches both with an application to real data and on theoretical grounds. We demonstrate that, while Shannon's index can differ significantly under the two models, the estimate of the total number of species is equivalent. [ABSTRACT FROM AUTHOR]

Interspecific hybridization has been historically neglected in research and conservation practice, but it is a common phenomenon in nature, and several models have been developed to characterize it genetically. Even though Trichechus inunguis (Amazonian manatee) and T. manatus (West Indian manatee) exhibit large morphological, karyotypic, and molecular differences, a hybrid zone was identified on the northern coast of South America, from the Amazon River estuary toward the Guianas coastline. Two major populations or evolutionarily significant units (ESUs) within T. manatus, namely, the Caribbean and Atlantic, were separated and their differentiation was likely promoted or reinforced by the interspecific hybridization zone. We used nuclear and mtDNA sequences to reconstruct manatee speciation, population diversification through time and space, and secondary contact, which resulted in a hybrid zone. In this hybrid zone, the genetic contribution of each parental species was estimated, and different models for generating the current scenario were tested using statistical phylogeographic tools. All the results suggest a long hybridization history, during which a stable and structured hybrid swarm is generated. The coastline hybrid zone is composed of individuals with a lesser genomic contribution from T. inunguis; this zone works as a genetic sink that restricts gene flow between neighbouring Atlantic (Brazil) and Caribbean (all others) T. manatus populations, which further reinforces the isolation and differentiation of the Brazilian manatees. [ABSTRACT FROM AUTHOR]

Summary This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities that are intractable, costly, and/or noisy. This type of problem can be found in numerous real‐world scenarios, including stochastic optimisation and reinforcement learning, where each evaluation of a density function may incur some computationally‐expensive or even physical (real‐world activity) cost, likely to give different results each time. The surrogate model does not incur this cost, but there are important trade‐offs and considerations involved in the choice and design of such methodologies. We classify the different methodologies into three main classes and describe specific instances of algorithms under a unified notation. A modular scheme that encompasses the considered methods is also presented. A range of application scenarios is discussed, with special attention to the likelihood‐free setting and reinforcement learning. Several numerical comparisons are also provided. [ABSTRACT FROM AUTHOR]

Model updating procedures based on experimental data are commonly used in case of historic buildings to identify numerical models that are subsequently employed to assess their structural behaviour. The reliability of these models is closely related to their ability to account for all the uncertainties that are involved in the knowledge process. In this regard, to handle these uncertainties and quantify their propagation, Bayesian inference is frequently employed being able to deal with the effects of parameter uncertainty, observation errors and model inadequacy. The computation of the posterior distribution through Bayesian inference needs–however–the evaluation of the likelihood function, which requires solving complex multi-dimensional integration problems. To bridge this shortcoming, the paper compares two Bayesian inference approaches to show how different approximations affect the results of simulated inference: a discrete approach for the likelihood computation in the Bayesian Model Updating (BMU) and a Monte Carlo likelihood-free method known as Approximate Bayesian Computation (ABC) are reported. As reference, the typology of historic masonry towers was considered by using their natural frequencies as experimental data for model updating. The two procedures provide very similar results supporting the validity of both methods despite ABC turns out to be a more flexible approach. [ABSTRACT FROM AUTHOR]

The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics. [ABSTRACT FROM AUTHOR]

Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE—Reproductive Isolation Detection using Genomic polymorphisms—a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model‐averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross‐species comparisons. Simulations revealed RIDGE's efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2Ne generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well‐known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions. [ABSTRACT FROM AUTHOR]

Introduction: Quaternary glaciations, in particular the last glacial maximum (LGM), have shaped the contemporary distribution of many species. In Europe, survival of temperate species during range contractions was mainly associated with refugia in Mediterranean peninsulas, allowing south to north recolonization. Additionally, the Ponto-Caspian refugium provided an east to west migration route. Methods: Here, we investigated genetic diversity and structure of the dispersal-restricted Primula vulgaris Huds., a temperate herb of supposed Caucasian origin, using 12 highly polymorphic nuclear microsatellite loci to trace the colonization of this species in three European regions. We studied 38 populations, namely an eastern mountainous region (Caucasus), a central region (Carpathian Mountains and Dinaric Alps) and a western lowland region (North European Plain). The role of an eastern refugium of the species was tested by performing Approximate Bayesian computation analyses to elucidate its demographic and phylogeographic history, detecting bottleneck and expansion events, and estimating effective (ancestral and current) population sizes and time of divergence. Results and discussion: We found the eastern and central regions featuring the highest allelic richness and genetic differentiation being strong between and within regions. Stepwise-like mutation processes (RST > FST), together with isolation-by-distance patterns, contributed to genetic differentiation. Demographic event models showed that P. vulgaris experienced bottlenecks as well as expansions across its range during the Quaternary glacial cycles, with the last expansion about 6,000, 4,000, and 2,000 generations ago for eastern, central, and western populations, respectively. The best supported origin model pointed to the Caucasus population being closest to an ancestral situation, and from where central and western populations diverged subsequently. Divergence time of the Caucasus populations from an ancestral lineage referred to the Upper Pleistocene (Middle Weichselian). Divergence of Carpathian populations, including a Lower Danube valley corridor, was estimated during and across the LGM (Late Weichselian), confirming central populations as potential secondary cryptic refugium. Western populations recently diverged during the Holocene, most probably in the Atlanticum phase (7,000-3,900 years ago). Within regions, genetic structure was also shaped by latitude, longitude, or altitudinal migration, and for western lowlands, by recent bottleneck and genetic drift processes. [ABSTRACT FROM AUTHOR]

Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach. HIGHLIGHTS Approximate Bayesian computation (ABC) was used to calibrate the complex hydrological model.; Multivariate adaptive regression splines (MARS) were used to expedite the ABC process.; The ability of MARS-assisted ABC for calibration of the SWAT model was demonstrated.; The calibration results of ABC and multi-objective evolutionary algorithms were compared.; MARS-assisted ABC has the potential to extend the use of ABC in hydrological modeling.;

In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose in a phase I clinical trial. In general, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect maximum tolerated dose identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation approach to phase I trial design. Not only is the approximate Bayesian computation design free of any dose-toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I trial designs. We apply the approximate Bayesian computation design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.

The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes.

The abnormal aggregation of extracellular amyloid‐β(Aβ)$$ \left(A\beta \right) $$ in senile plaques resulting in calcium Ca+2$$ \left({Ca}^{+2}\right) $$ dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving Aβ$$ A\beta $$ deposition and Ca+2$$ {Ca}^{+2} $$ dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal Aβ$$ A\beta $$ accumulation. Moreover, increasing evidence show a feed‐forward loop between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ levels, that is, Aβ$$ A\beta $$ disrupts neuronal Ca+2$$ {Ca}^{+2} $$ levels, which in turn affects the formation of Aβ$$ A\beta $$. To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modeling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi‐state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2$$ 2 $$‐year visits for AD patients, we employ this method to investigate the interplay between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ levels at various disease development phases. Incorporating the ADNI data in our physics‐based Bayesian model, we discovered that a sufficiently large disruption in either Aβ$$ A\beta $$ metabolism or intracellular Ca+2$$ {Ca}^{+2} $$ homeostasis causes the relative growth rate in both Ca+2$$ {Ca}^{+2} $$ and Aβ$$ A\beta $$, which corresponds to the development of AD. The imbalance of Ca+2$$ {Ca}^{+2} $$ ions causes Aβ$$ A\beta $$ disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of Ca+2$$ {Ca}^{+2} $$ ion transportation and deposition. This suggests that altering the Ca+2$$ {Ca}^{+2} $$ balance or the balance between Aβ$$ A\beta $$ and Ca+2$$ {Ca}^{+2} $$ by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy. [ABSTRACT FROM AUTHOR]

AbstractActive learning provides a framework to adaptively query the most informative experiments towards learning an unknown black-box function. Various approaches of active learning have been proposed in the literature, however, they either focus on exploration or exploitation in the design space. Methods that do consider exploration–exploitation simultaneously employ fixed or ad-hoc measures to control the trade-off that may not be optimal. In this article, we develop a Bayesian hierarchical approach, referred to as BHEEM, to dynamically balance the exploration-exploitation trade-off as more data points are queried. To sample from the posterior distribution of the trade-off parameter, we subsequently formulate an approximate Bayesian computation approach based on the linear dependence of queried data in the feature space. Simulated and real-world examples show the proposed approach achieves at least 21% and 11% average improvement when compared to pure exploration and exploitation strategies, respectively. More importantly, we note that by optimally balancing the trade-off between exploration and exploitation, BHEEM performs better or at least as well as either pure exploration or pure exploitation. [ABSTRACT FROM AUTHOR]

The Australian lungfish is a primitive and endangered representative of the subclass Dipnoi. The distribution of this species is limited to south‐east Queensland, with some populations considered endemic and others possibly descending from translocations in the late nineteenth century shortly after European discovery. Attempts to resolve the historical distribution of this species have met with conflicting results based on descriptive genetic studies. Understanding if all populations are endemic or some are the result of, or influenced by, translocation events, has implications for conservation management. In this work, we analysed the genetic variation at three types of markers (mtDNA genomes, 11 STRs and 5196 nuclear SNPs) using the approximate Bayesian computation (ABC) algorithm to compare several demographic models. We postulated different contributions of Mary River and Burnett River gene pools into the Brisbane River and North Pine River populations, related to documented translocation events. We ran the analysis for each marker type separately, and we also estimated the posterior probabilities of the models combining the markers. Nuclear SNPs have the highest power to correctly identify the true model among the simulated datasets (where the model was known), but different marker types typically provided similar answers. The most supported demographic model able to explain the real dataset implies that an endemic gene pool is still present in the Brisbane and North Pine Rivers and coexists with the gene pools derived from past documented translocation events. These results support the view that ABC modelling can be useful to reconstruct complex historical translocation events with contemporary implications, and will inform ongoing conservation efforts for the endangered and iconic Australian lungfish. [ABSTRACT FROM AUTHOR]

Landlocking of diadromous fish in freshwater systems can have significant genomic consequences. For instance, the loss of the migratory life stage can dramatically reduce gene flow across populations, leading to increased genetic structuring and stronger effects of local adaptation. These genomic consequences have been well‐studied in some mainland systems, but the evolutionary impacts of landlocking in island ecosystems are largely unknown. In this study, we used a genotyping‐by‐sequencing (GBS) approach to examine the evolutionary history of landlocking in common smelt (Retropinna retropinna) on Chatham Island, a small isolated oceanic island 800 kilometres east of mainland New Zealand. We examined the relationship between Chatham Island and mainland smelt and used coalescent analyses to test the number and timing of landlocking events on Chatham Island. Our genomic analysis, based on 21,135 SNPs across 169 individuals, revealed that the Chatham Island smelt was genomically distinct from the mainland New Zealand fish, consistent with a single ancestral colonisation event of Chatham Island in the Pleistocene. Significant genetic structure was also evident within the Chatham Island smelt, with a diadromous Chatham Island smelt group, along with three geographically structured landlocked groups. Coalescent demographic analysis supported three independent landlocking events, with this loss of diadromy significantly pre‐dating human colonisation. Our results illustrate how landlocking of diadromous fish can occur repeatedly across a narrow spatial scale, and highlight a unique system to study the genomic basis of repeated adaptation. [ABSTRACT FROM AUTHOR]

Aim: Biogeographers have used three primary data types to examine shifts in tree ranges in response to past climate change: fossil pollen, genetic data and contemporary occurrences. Although recent efforts have explored formal integration of these types of data, we have limited understanding of how integration affects estimates of range shift rates and their uncertainty. We compared estimates of biotic velocity (i.e. rate of species' range shifts) using each data type independently to estimates obtained using integrated models. Location: Eastern North America. Taxon: Fraxinus pennsylvanica Marshall (green ash). Methods: Using fossil pollen, genomic data and modern occurrence data, we estimated biotic velocities directly from 24 species distribution models (SDMs) and 200 pollen surfaces created with a novel Bayesian spatio‐temporal model. We compared biotic velocity from these analyses to estimates based on coupled demographic‐coalescent simulations and Approximate Bayesian Computation that combined fossil pollen and SDMs with population genomic data collected across the F. pennsylvanica range. Results: Patterns and magnitude of biotic velocity over time varied by the method used to estimate past range dynamics. Estimates based on fossil pollen yielded the highest rates of range movement. Overall, integrating genetic data with other data types in our simulation‐based framework reduced apparent uncertainty in biotic velocity estimates and resulted in greater similarity in estimates between SDM‐ and pollen‐integrated analyses. Main Conclusions: By reducing uncertainty in our assessments of range shifts, integration of data types improves our understanding of the past distribution of species. Based on these results, we propose further steps to reach the integration of these three lines of biogeographical evidence into a unified analytical framework. [ABSTRACT FROM AUTHOR]

ABSTRACT In the most common Bayesian framework for estimating the parameters of a hydrological model (time domain), the specification of the likelihood function can be challenging. In addition, scarcely gauged regions might be hard to model, due to the lack of sufficient timeseries to calibrate the model. To circumvent these problems, the present study seeks to evaluate the applicability of hydrological signatures and Approximate Bayesian Computation methods to estimating the parameters and analyzing the uncertainty of a hydrological model (signature domain). We used the GR2M monthly model, aiming to approximate the signatures estimated from the simulated timeseries to those calculated from the monitoring data. As a result, we found KGEs of over 0.91 and 0.83 for most signatures in the calibration and validation periods, respectively (0.95 and 0.90 in the time domain). The uncertainty intervals varied from signature to signature, with the tendency of being smaller for the signature-domain than for the time-domain.

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future.

The degradation of products is an integral part of their life-cycle, often following predictable trajectories. However, sudden, unexpected events, termed ’shocks’, can substantially alter these degradation paths. Shocks can significantly influence the pace of degradation, leading to accelerated system failure. Moreover, they may initiate changes in degradation patterns, transitioning from linear to non-linear or random trajectories. To address this challenge, we present a novel multi-state reliability model for competing failure processes that account for degradation-shock dependencies by considering the state-varying degradation pattern. The degradation process is divided into s-states, with each state treated according to its pattern based on the time-transform Wiener process. The reliability function is derived based on soft failure caused by continuous degradation involving the s-states, the sudden increase in degradation caused by random shocks, and hard failure due to some shock processes. Additionally, we performed a sensitivity analysis to determine which parameters have the most significant impact on product reliability. Due to the complexity of the likelihood function, we adopted the ABC method to estimate the model parameters. A simulation study and a practical application with micro-electro-mechanical systems (MEMS) degradation results are used to demonstrate the efficiency and effectiveness of the proposed approach.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricular (AV) node plays a vital role in regulating heart rate during AF by filtering electrical impulses from the atria. However, it is often insufficient in regards to maintaining a healthy heart rate, thus the AV node properties are modified using rate-control drugs. Moreover, treatment selection during permanent AF is currently done empirically. Quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. Methods: This study presents a novel methodology for estimating the refractory period (RP) and conduction delay (CD) trends, and their uncertainty in the two pathways of the AV node during 24 h using non-invasive data. This was achieved by utilizing a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates, and short-term variability was quantified by the Kolmogorov-Smirnov distance between adjacent 10-min segments in the 24-h trends. Additionally, the predictive value of the derived parameter trends regarding drug outcome was investigated using several machine learning tools. Results: Holter electrocardiograms from 51 patients with permanent AF during baseline were analyzed, and the predictive power of variations in RP and CD on the resulting heart rate reduction after treatment with four rate control drugs was investigated. Diurnal variability yielded no correlation to treatment outcome, and no prediction of drug outcome was possible using the machine learning tools. However, a correlation between the short-term variability for the RP and CD in the fast pathway and resulting heart rate reduction during treatment with metoprolol (ρ = 0.48, p < 0.005 in RP, ρ = 0.35, p < 0.05 in CD) were found. Discussion: The proposed methodology enables non-invasive estimation of the AV node properties during 24 h, which--indicated by the correlation between the short-term variability and heart rate reduction--may have the potential to assist in treatment selection. [ABSTRACT FROM AUTHOR]

Likelihood-free methods are an essential tool for performing inference for implicit models which can be simulated from, but for which the corresponding likelihood is intractable. However, common likelihood-free methods do not scale well to a large number of model parameters. A promising approach to high-dimensional likelihood-free inference involves estimating low-dimensional marginal posteriors by conditioning only on summary statistics believed to be informative for the low-dimensional component, and then combining the low-dimensional approximations in some way. In this article, we demonstrate that such low-dimensional approximations can be surprisingly poor in practice for seemingly intuitive summary statistic choices. We describe an idealized low-dimensional summary statistic that is, in principle, suitable for marginal estimation. However, a direct approximation of the idealized choice is difficult in practice. We thus suggest an alternative approach to marginal estimation which is easier to implement and automate. Given an initial choice of low-dimensional summary statistic that might only be informative about a marginal posterior location, the new method improves performance by first crudely localizing the posterior approximation using all the summary statistics to ensure global identifiability, followed by a second step that hones in on an accurate low-dimensional approximation using the low-dimensional summary statistic. We show that the posterior this approach targets can be represented as a logarithmic pool of posterior distributions based on the low-dimensional and full summary statistics, respectively. The good performance of our method is illustrated in low to moderate dimensional examples. Computer code to implement the methods for the examples of this article is available at . [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored. [ABSTRACT FROM AUTHOR]

Introduction: Culex quinquefasciatus is a mosquito species of significant public health importance due to its ability to transmit multiple pathogens that can cause mosquito-borne diseases, such as West Nile fever and St. Louis encephalitis. In Harris County, Texas, Cx. quinquefasciatus is a common vector species and is subjected to insecticide-based management by the Harris County Public Health Department. However, insecticide resistance in mosquitoes has increased rapidly worldwide and raises concerns about maintaining the effectiveness of vector control approaches. This concern is highly relevant in Texas, with its humid subtropical climate along the Gulf Coast that provides suitable habitat for Cx. quinquefasciatus and other mosquito species that are known disease vectors. Therefore, there is an urgent and ongoing need to monitor the effectiveness of current vector control programs. Methods: In this study, we evaluated the impact of vector control approaches by estimating the effective population size of Cx. quinquefasciatus in Harris County. We applied Approximate Bayesian Computation to microsatellite data to estimate effective population size. We collected Cx. quinquefasciatus samples from two mosquito control operation areas; 415 and 802, during routine vector monitoring in 2016 and 2017. No county mosquito control operations were applied at area 415 in 2016 and 2017, whereas extensive adulticide spraying operations were in effect at area 802 during the summer of 2016. We collected data for eighteen microsatellite markers for 713 and 723 mosquitoes at eight timepoints from 2016 to 2017 in areas 415 and 802, respectively. We also investigated the impact of Hurricane Harvey’s landfall in the Houston area in August of 2017 on Cx. quinquefasciatus population fluctuation. Results: We found that the bottleneck scenario was the most probable historical scenario describing the impact of the winter season at area 415 and area 802, with the highest posterior probability of 0.9167 and 0.4966, respectively. We also detected an expansion event following Hurricane Harvey at area 802, showing a 3.03-fold increase in 2017. Discussion: Although we did not detect significant effects of vector control interventions, we found considerable influences of the winter season and a major hurricane on the effective population size of Cx. quinquefasciatus. The fluctuations in effective population size in both areas showed a significant seasonal pattern. Additionally, the significant population expansion following Hurricane Harvey in 2017 supports the necessity for post-hurricane vectorcontrol interventions. [ABSTRACT FROM AUTHOR]

Impact of climate change is expected to be especially noticeable at the edges of a species' distribution, where they meet suboptimal habitat conditions. In Mauritania and Iberia, two genetically differentiated populations of harbor porpoises (Phocoena phocoena) form an ecotype adapted to local upwelling conditions and distinct from other ecotypes further north on the NE Atlantic continental shelf and in the Black Sea. By analyzing the evolution of mitochondrial genetic variation in the Iberian population between two temporal cohorts (1990–2002 vs. 2012–2015), we report a substantial decrease in genetic diversity. Phylogenetic analyses including neighboring populations identified two porpoises in southern Iberia carrying a divergent haplotype closely related to those from the Mauritanian population, yet forming a distinct lineage. This suggests that Iberian porpoises may not be as isolated as previously thought, indicating possible dispersion from Mauritania or an unknown population in between, but none from the northern ecotype. Demo‐genetic scenario testing by approximate Bayesian computation showed that the rapid decline in the Iberian mitochondrial diversity was not simply due to the genetic drift of a small population, but models support instead a substantial decline in effective population size, possibly resulting from environmental stochasticity, prey depletion, or acute fishery bycatches. These results illustrate the value of genetics time series to inform demographic trends and emphasize the urgent need for conservation measures to ensure the viability of this small harbor porpoise population in Iberian waters. [ABSTRACT FROM AUTHOR]

Implementing Bayesian inference is often computationally challenging in complex models, especially when calculating the likelihood is difficult. Synthetic likelihood is one approach for carrying out inference when the likelihood is intractable, but it is straightforward to simulate from the model. The method constructs an approximate likelihood by taking a vector summary statistic as being multivariate normal, with the unknown mean and covariance estimated by simulation. Previous research demonstrates that the Bayesian implementation of synthetic likelihood can be more computationally efficient than approximate Bayesian computation, a popular likelihood-free method, in the presence of a high-dimensional summary statistic. Our article makes three contributions. The first shows that if the summary statistics are well-behaved, then the synthetic likelihood posterior is asymptotically normal and yields credible sets with the correct level of coverage. The second contribution compares the computational efficiency of Bayesian synthetic likelihood and approximate Bayesian computation. We show that Bayesian synthetic likelihood is computationally more efficient than approximate Bayesian computation. Based on the asymptotic results, the third contribution proposes using adjusted inference methods when a possibly misspecified form is assumed for the covariance matrix of the synthetic likelihood, such as diagonal or a factor model, to speed up computation. for this article are available online. [ABSTRACT FROM AUTHOR]

This article develops a Bayesian computational platform at the interface between posterior sampling and optimization in models whose marginal likelihoods are difficult to evaluate. Inspired by contrastive learning and Generative Adversarial Networks (GAN), we reframe the likelihood function estimation problem as a classification problem. Pitting a Generator, who simulates fake data, against a Classifier, who tries to distinguish them from the real data, one obtains likelihood (ratio) estimators which can be plugged into the Metropolis–Hastings algorithm. The resulting Markov chains generate, at a steady state, samples from an approximate posterior whose asymptotic properties we characterize. Drawing upon connections with empirical Bayes and Bayesian misspecification, we quantify the convergence rate in terms of the contraction speed of the actual posterior and the convergence rate of the Classifier. Asymptotic normality results are also provided which justify the inferential potential of our approach. We illustrate the usefulness of our approach on examples which have challenged for existing Bayesian likelihood-free approaches. for this article are available online. [ABSTRACT FROM AUTHOR]