Bayesian Model-based Approach Research Articles

A clustering approach to integrative analyses of multiomic cancer data

Rapid technological advances have allowed for molecular profiling across multiple omics domains for clinical decision-making in many diseases, especially cancer. However, as tumor development and progression are biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and druggable biological entities, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative frameworks for high-dimensional multiple-domain cancer data. These Bayesian mixture model-based approaches coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalog of genomic aberrations associated with cancer taxonomy. The flexible and scalable Bayesian nonparametric strategy performs simultaneous bidirectional clustering of the tumor samples and genomic probes to achieve dimension reduction. We describe an efficient variable selection procedure that can identify relevant genomic aberrations and potentially reveal underlying drivers of disease. Although the work is motivated by lung cancer datasets, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.

Unravelling factors influencing demand for modern contraception and evaluating coverage progress since 2015 in Ethiopia, Kenya, and Nigeria: insights from multilevel and geostatistical modelling

IntroductionThe United Nations established the Sustainable Development Goals (SDGs) in 2015 to enhance global development. In this study, we examine an SDG indicator: the percentage of women aged 15–49 whose family planning needs are met by modern contraception (mDFPS). We evaluate both the factors influencing its coverage and its progress since 2015.MethodsWe used nationally representative surveys data (Demographic and Health Surveys (DHS) and Performance Monitoring for Action (PMA)) from Ethiopia, Kenya, and Nigeria. We assessed predictors of mDFPS. We also computed mDFPS coverage across countries and subnational areas, assessing coverage changes from the SDGs onset to the most recent period, using a Bayesian model-based geostatistical approach. We assessed whether the subnational areas exceeded the minimum recommended WHO mDFPS coverage of 75%.ResultsVaried individual and community-level determinants emerged, highlighting the countries’ uniqueness. Factors such as being part of a female-headed household, and low household wealth, lowered the odds of mDFPS, while rural-residence had low odds only in Ethiopia and Nigeria. The results indicate mDFPS stagnation in most administrative areas across the three countries. Geographic disparities persisted over time, favouring affluent regions. The predicted posterior proportion of mDFPS and exceedance probability (EP) for WHO target for Ethiopia was 39.85% (95% CI: [4.51, 83.01], EP = 0.08) in 2016 and 46.28% (95% CI: [7.15, 85.99], EP = 0.13) in 2019. In Kenya, the adjusted predicted proportion for 2014 was 30.19% (95% CI: [2.59, 80.24], EP = 0.06) and 44.16% (95%CI: [9.35, 80.24], EP = 0.13) in 2022. In Nigeria, the predicted posterior proportion of mDFPS was 17.91% (95% CI: [1.24, 61.29], EP = 0.00) in 2013, and it was 23.08% (95% CI: [1.80, 56.24], EP = 0.00) in 2018. None of the sub-national areas in Ethiopia and Nigeria exceeded the WHO target. While 9 out of 47 counties in Kenya in 2022 exceeded the WHO mDFPS target.ConclusionThe study unveils demographic, geographic, and socioeconomic mDFPS disparities, signalling progress and stagnation across administrative areas. The findings offer policymakers and governments insights into targeting interventions for enhanced mDFPS coverage. Context-specific strategies can address local needs, aiding SDG attainment.

Olaparib, AZD1390, ceralasertib, saruparib and consolidation durvalumab (CONCORDE) phase Ib platform study of novel DNA damage response inhibitor (DDRi) agents in combination with radiotherapy in non-small cell lung cancer (NSCLC).

TPS8119 Background: Lung cancer is the leading cause of cancer mortality globally, with cases projected to exceed 62,000 by 2035. Outcomes remain poor despite advances in radiotherapy (RT) technologies. Combining novel mechanism-based agents with RT could improve the therapeutic index. DNA inhibition of cellular response to radiation-induced DNA damage can overcome intrinsic radio resistance and poses a promising strategy. Methods: CONCORDE is an open label, randomised, multi-arm, phase Ib, clinical trial opened to accrual in 11 UK hospitals designed to assess multiple DDRi in combination with radical thoracic RT (60Gy in 30 fractions over 6 weeks). Patients are randomised 3:1 to RT+/-DDRi. The study utilises an adaptive Bayesian model-based approach to dose escalation aiming to identify the recommended phase II dose for each treatment combination. Two arms will deliver up to 12 months consolidation durvalumab+/-DDRi following RT. Patients are eligible based on key criteria: not suitable for concurrent chemotherapy RT, inoperable, stage IIB/III NSCLC, performance status (KPS≥70). The dose limiting toxicity (DLT) period for treatment-related toxicities is 13.5 months post start of RT treatment, with most toxicities expected within the first 4.5 months (short DLT period). At least one patient must complete the short DLT period before dose escalation. A RT-alone calibration arm aids toxicity attribution. This trial is in progress: CONCORDE-A (olaparib (PARP inhibitor)), CONCORDE-B (AZD1390 (ATM inhibitor)), CONCORDE-C (ceralasertib (ATR inhibitor) followed by consolidation durvalumab+/-ceralasertib) and CONCORDE-E (AZD5305/saruparib (PARP1select inhibitor) followed by consolidation durvalumab) are open to accrual as of 24/11/2023. 57 of 74 registered participants have been randomised across 4 study arms. Of those 57 randomised: 55 started treatment: 20 received RT-alone, 12 received olaparib+RT, 14 received AZD1390+RT, 6 received ceralasertib+RT, 3 received AZD5305+RT and 2 withdrew. CONCORDE continues to recruit to the target sample size of 160 (40/arm) and welcomes further UK centres. A parallel multimodality translational program to identify biomarkers of treatment response, toxicity and the impact on the immune system are in development. Biomarkers of interest include plasma toxicity markers, immune cell profiling, radiomics and ctDNA. Clinical trial information: 10142971.

A model-based hierarchical Bayesian approach to Sholl analysis.

Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.

Adaptive augmented cubature Kalman filter/smoother for ECG denoising.

Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability. Furthermore, we propose a method to significantly reduce the computational complexity required for CKF/CKS implementation in ECG processing. The denoising performance of the proposed filter was compared to those of various nonlinear Kalman-based frameworks involving the Extended Kalman filter/smoother (EKF/EKS), the unscented Kalman filter/smoother (UKF/UKS), and the ensemble Kalman filter (EnKF) that was recently proposed for ECG enhancement. In this study, we conducted a comprehensive evaluation and comparison of the performance of various nonlinear Kalman-based frameworks for ECG signal processing, which have been proposed in recent years. Our assessment was carried out on multiple normal ECG segments extracted from different entries in the MIT-BIH Normal Sinus Rhythm Database (NSRDB). This database provides a diverse set of ECG recordings, allowing us to examine the filters' denoising capabilities across various scenarios. By comparing the performance of these filters on the same dataset, we aimed to provide a thorough analysis and identification of the most effective approach for ECG denoising. Two kinds of noises were introduced to such segments: 1-stationary white Gaussian noise and 2-non-stationary real muscle artifact noise. For evaluation, four comparable measures namely the SNR improvement, PRD, correlation coefficient and MSEWPRD were employed. The findings demonstrated that the suggested algorithm outperforms the EKF/EKS, EnKF/EnKS, UKF/UKS methods in both stationary and nonstationary environments regarding SNR improvement, PRD, correlation coefficient and MSEWPRD metrics.

Knowledge-based modeling of simulation behavior for Bayesian optimization

Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.

Neck Kinematics in Patients With Chronic Mechanical Neck Pain: An Observational Study to Explore the Integration of Multiple Influencing Factors Through a Bayesian Model-Based Approach.

The aim of this observational, cross-sectional study is to evaluate potential differences in kinematics, specifically range of motion (ROM) and velocity, during planar cervical movements between patients with non-traumatic chronic neck pain and disability and asymptomatic controls, while accounting for potential influencing variables of age, sex and fear of movement. The influence of pain intensity, neck disability, age, sex or fear of motion on kinematics was analyzed through robust multivariate Bayesian regression models fitted using the brms library in R. Forty-three patients with neck pain (aged 36.70 ± 13.75 years; 10 men and 33 women) and 42 asymptomatic participants (aged 32.74 ± 13.24 years; 25 men and 17 women) completed the study protocol. The presence of neck pain/disability was associated with lower ROM and peak velocity during all planar movements when considering the influence of age, sex or fear of motion, with standardized regression coefficients that had a small effect size (ranged from 0.11 to 0.28) and estimated differences of less than 2.21° in ROM and 25.61°/s in peak velocity. Although patients with chronic mechanical neck pain showed reduced ROM and peak velocity, the small effect sizes and the low estimated differences between groups question the relevance and clinical usefulness of kinematic analysis of planar movements in samples of patients similar to those included in our study. It is probable that there are differences between the groups, but it is insufficient to rely solely on kinematic variables for patient discrimination. This limitation likely arises from the substantial variability in patient kinematics.

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.

Probabilistic Bayesian Approach for Delamination Localization in GFRP Composites Using Nonlinear Guided Waves

Abstract Nondestructive evaluation (NDE) techniques that use nonlinear wave–damage interactions have gained significant attention recently due to their improved sensitivity in detecting incipient damage. This study presents the use of finite element (FE) simulation with the experimental investigation to quantify the effects of guided waves’ propagation through multiple delaminations in unidirectional glass fiber-reinforced polymer (GFRP) composites. Further, it utilizes the outcomes of nonlinear interactions between guided waves and delaminations to locate the latter. This is achieved through probabilistic Bayesian updating with a structural reliability approach. Guided waves interacting with delaminations induce nonlinear acoustic signatures that can be quantified by the nonlinearity index (NLI). The study found that the NLI changes with the interrogation frequency, as confirmed by numerical and experimental observations. By using the numerical outcomes obtained from the nonlinear responses, a Bayesian model-based approach with subset simulation is proposed and subsequently used to locate multiple delaminations. The results indicate that both the log-likelihood and log-evidence are key factors in determining the localization phenomenon. The proposed method successfully localizes multiple delaminations and evaluates their number, interlaminar position, width, and type.

Model-based entropy estimation for data with covariates and dependence structures

Entropy is widely used in ecological and environmental studies, where data often present complex interactions. Difficulties arise in linking entropy to available covariates or data dependence structures, thus, all existing entropy estimators assume independence. To overcome this limit, we take a Bayesian model-based approach which focuses on estimating the probabilities that compose the index, accounting for any data dependence and correlation. An estimate of entropy can be constructed from the model fitted values, returning an observation-specific measure of entropy rather than an overall index. This way, the latent heterogeneity of the system can be represented by a curve in time or a surface in space, according to the characteristics of the survey study at hand. An empirical study illustrates the flexibility and interpretability of our results over temporally and spatially correlated data. An application is presented about the biodiversity of spatially structured rainforest tree data.

Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach.

Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system's service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm's performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.

Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.

Genetic diversity and structure of natural populations of Arbutus unedo across altitude, bioclimate and geographical origin as revealed by ISSR markers

In order to assess the genetic diversity and structure of Arbutus unedo species, 66 individuals’ trees from 11 natural populations representing its main geographical area in Morocco and belonging to different bioclimates and altitude were analyzed using 14 ISSR primers. A total of 142 bands were obtained, of which 134 were polymorphic with a polymorphism percentage of 94.06% and a polymorphism information content (PIC) of =0.47. The use of 134 markers revealed a high level of genetic variation within and between populations. The multi-locus values of Hs and Ht were 0.24 and 0.34, respectively. The overall AMOVA analysis showed that 79.30% of the total genetic variability was within populations and only 20.70% was between populations. A large genetic differentiation between populations was detected (FST=0.206) which could be attributed to restricted gene flow (Nm=0.97). The results of the hierarchical AMOVA, revealed very little genetic differentiation between altitudinal, bioclimatic and geographic groups (FCT=3.20%, FCT= 2.30% and FCT=2.10% respectively). Furthermore, geographic distances were revealed not correlated with genetic distances between populations (r=0.05, Mantel t-test=0.38, P=0.64) suggesting that isolation by distance has not played an important role in shaping the genetic structure of this species. The NJ dendrogram and Bayesian model-based clustering approach identified four groups of A. unedo independently of their bioclimate, geographic origin and altitude. The data obtained in this study could play a crucial role in establish efficient strategies for genetic resources conservation and to work out the scheme of breeding programs of A. unedo

Population genetic analyses unveiled genetic stratification and differential natural selection signatures across the G-gene of viral hemorrhagic septicemia virus.

Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates' genetic diversity and evolutionary dynamics based on G-gene sequences. Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC. Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples.

BetaBayes—A Bayesian Approach for Comparing Ecological Communities

Ecological communities change because of both natural and human factors. Distinguishing between the two is critical to ecology and conservation science. One of the most common approaches for modelling species composition changes is calculating beta diversity indices and then relating index changes to environmental changes. The main difficulty with these analyses is that beta diversity indices are paired comparisons, which means indices calculated with the same community are not independent. Mantel tests and generalised dissimilarity modelling (GDM) are two of the most commonly used statistical procedures for analysing such data, employing randomisation tests to consider the data’s dependence. Here, we introduce a Bayesian model-based approach called BetaBayes that explicitly incorporates the data dependence. This approach is based on the Bradley–Terry model, which is a widely used approach for modelling paired comparisons that involves building a standard regression model containing two varying intercepts, one for each community involved in the beta diversity index, that capture their respective contributions. We used BetaBayes to analyse a famous dataset collected in Panama that contains information on multiple 1 ha plots from the rain forests of Panama. We calculated the Bray–Curtis index between all pairs of plots, analysed the relationship between the index and two covariates (geographic distance and elevation), and compared the results of BetaBayes with those from the Mantel test and GDM. BetaBayes has two distinctive features. The first is its flexibility, which allows the user to quickly change it to fit the data structure; namely, by adding varying effects, incorporating spatial autocorrelation, and modelling complex nonlinear relationships. The second is that it provides a clear path for performing model validation and model improvement. BetaBayes avoids hypothesis testing, instead focusing on recreating the data generating process and quantifying all the model configurations that are consistent with the observed data.

Transfer function model-based Bayesian estimation of dissipation and sound speed in impedance tube measurements

The air dissipation, mainly due to the boundary effect at interior walls of impedance tubes, is noteworthy for normal incidence measurements of acoustic materials using the transfer function method. This work proposes a transfer function model-based Bayesian approach to estimate the dissipation factors. Other parameters, such as the sound speed and the microphone positions, are also critical, therefore, estimated as well, along with the dissipation. The measurement of the empty impedance tube with a rigid termination applies the two-microphone transfer function method. A hypothetical air layer assumed in front of the rigid backing enables the numerical comparison between the air layer’s theoretical and experimental transfer function values. Experimental results incorporating the estimated dissipation coefficient validate the accuracy of the proposed approach. Furthermore, the estimated parameters are incorporated into further evaluations of material properties using the transfer function method, which effectively reduces the residual absorption error of the impedance tube measurement.

Using Bayesian time series models to estimate bycatch of an endangered albatross

Developing unbiased estimates of incidental bycatch poses a challenge for species where fishing-induced mortality is a rare occurrence. Expanding rare mortality events using ratio estimators or bycatch of proxy species can result in highly variable estimates based on untested and often untestable assumptions. We estimated short-tailed albatross bycatch in a U.S. West Coast groundfish fishery using Bayesian time series modeling. The best model used a constant bycatch rate and inferred annual expected bycatch and variability using a Poisson distribution, given specified levels of observed effort. Fleet-wide bycatch estimates varied annually and peaked at 1.35 birds in 2011 (the year of the only observed mortality). The probability of exceeding the limit of five estimated takes in a 2-year period was very low throughout the time series, and estimated takes in the unobserved portion of the fleet are more likely with lower observer coverage and higher fishing effort. The Bayesian model-based approach avoids assumptions inherent in ratio estimators and proxy methods; it incorporates uncertainty, reduces volatility, and enables comparisons of bycatch estimates to management thresholds. This analytical approach offers natural resource managers a framework for estimating bycatch in data-limited contexts, which can result in better guidance for management actions and mitigation strategies.

EP05.01-007 CONCORDE - A Phase Ib Platform Study of Novel Agents in COmbinatioN with COnventional RaDiothErapy in Non-small Cell Lung Cancer (NSCLC)

The CONCORDE study is sponsored by the University of Leeds and funded by Cancer Research UK and AstraZeneca. It is an innovative, hypothesis-driven open-label, randomised, phase Ib, multi-institution, platform study for patients with NSCLC receiving radical radiotherapy (RT). It aims to assess five DNA damage response inhibitors (DDRi) with participants randomised to receive one agent in combination with RT or RT alone. Two of the arms will also deliver Durvalumab and DDRi consolidation. CONCORDE adopts a Bayesian adaptive model-based approach to dose escalation.

Small Domain Estimation of Census Coverage – A Case Study in Bayesian Analysis of Complex Survey Data

Abstract Many countries conduct a full census survey to report official population statistics. As no census survey ever achieves 100% response rate, a post-enumeration survey (PES) is usually conducted and analysed to assess census coverage and produce official population estimates by geographic area and demographic attributes. Considering the usually small size of PES, direct estimation at the desired level of disaggregation is not feasible. Design-based estimation with sampling weight adjustment is a commonly used method but is difficult to implement when survey nonresponse patterns cannot be fully documented and population benchmarks are not available. We overcome these limitations with a fully model-based Bayesian approach applied to the New Zealand PES. Although theory for the Bayesian treatment of complex surveys has been described, published applications of individual level Bayesian models for complex survey data remain scarce. We provide such an application through a case study of the 2018 census and PES surveys. We implement a multilevel model that accounts for the complex design of PES. We then illustrate how mixed posterior predictive checking and cross-validation can assist with model building and model selection. Finally, we discuss potential methodological improvements to the model and potential solutions to mitigate dependence between the two surveys.

Population structure and genomic footprints of selection in five major Iranian horse breeds.

The genetic structure and characteristics of Iranian native breeds are yet to be comprehensibly investigated and studied. Therefore, we employed genomic information of 364 Iranian native horses representing the Asil (n=109), Caspian (n=40), Dareshuri (n=44), Kurdish (n=95), and Turkoman (n=76) breeds to reveal the genetic structure and characteristics. For these and 19 other horse breeds, principal component analysis, Bayesian model-based, Neighbor-Net, and bootstrap-based TreeMix approaches were applied to investigate and compare their genetic structure. Additionally, three haplotype-based methods including haplotype homozygosity pooled, integrated haplotype score, and number of segregating sites by length were applied to trace genomic footprints of selection of Asil, Caspian, Dareshuri, Kurdish, and Turkoman groups. Then, the Mahalanobis distance based on the negative-log10 rank-based P-values was estimated based on the haplotype homozygosity pooled, integrated haplotype score, and number of segregating sites by length values. Asil, Caspian, Dareshuri, Kurdish, and Turkoman can be categorized into five different genetic clusters. Based on the top 1% of Mahalanobis distance based on the negative-log10 rank-based P-values of SNPs, we identified 24 SNPs formerly reported to be associated with different traits and >100 genes undergoing selection pressures in Asil, Caspian, Dareshuri, Kurdish, and Turkoman. The detected QTL undergoing selection pressures were associated with withers height, equine metabolic syndrome, overall body size, insect bite hypersensitivity, guttural pouch tympany, white markings, Rhodococcus equi infection, jumping test score, alternate gaits, and body weight traits. Our findings will aid to have a better perspective of the genetic characteristics and population structure of Asil, Caspian, Dareshuri, Kurdish, and Turkoman horses as Iranian native horse breeds.