Using zero-inflated and hurdle regression models to analyze schistosomiasis data of school children in the southern areas of Ghana.

BACKGROUND: Schistosomiasis is a neglected disease prevalent in tropical and sub-tropical areas of the world, especially in Africa. Detecting the presence of the disease is based on the detection of the parasites in the stool or urine of children and adults. In such studies, typically, data collected on schistosomiasis infection includes information on many negative individuals leading to a high zero inflation. Thus, in practice, counts data with excessive zeros are common. However, the purpose of this analysis is to apply statistical models to the count data and evaluate their performance and results. METHODS: This is a secondary analysis of previously collected data. As part of a modelling process, a comparison of the Poisson regression, negative binomial regression and their associated zero inflated and hurdle models were used to determine which offered the best fit to the count data. RESULTS: Overall, 94.1% of the study participants did not have any schistosomiasis eggs out of 1345 people tested, resulting in a high zero inflation. The performance of the negative binomial regression models (hurdle negative binomial (HNB), zero inflated negative binomial (ZINB) and the standard negative binomial) were better than the Poisson-based regression models (Poisson, zero inflated Poisson, hurdle Poisson). The best models were the ZINB and HNB and their performances were indistinguishable according to information-based criteria test values. CONCLUSION: The zero-inflated negative binomial and hurdle negative binomial models were found to be the most satisfactory fit for modelling the over-dispersed zero inflated count data and are recommended for use in future statistical modelling analyses.

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding.

Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the learned latent space, limiting its ability for estimating the neural reactivation. Here, we extend the P-GPLVM to enable the latent variable inference of new data constrained by previously learned smoothness and mapping information. We also describe a principled approach for the constrained latent variable inference for temporally compressed patterns of activity, such as those found in population burst events during hippocampal sharp-wave ripples, as well as metrics for assessing the validity of neural pattern reactivation and inferring the encoded experience. Applying these approaches to hippocampal ensemble recordings during active maze exploration, we replicate the result that P-GPLVM learns a latent space encoding the animal's position. We further demonstrate that this latent space can differentiate one maze context from another. By inferring the latent variables of new neural data during running, certain neural patterns are observed to reactivate, in accordance with the similarity of experiences encoded by its nearby neural trajectories in the training data manifold. Finally, reactivation of neural patterns can be estimated for neural activity during population burst events as well, allowing the identification for replay events of versatile behaviors and more general experiences. Thus, our extension of the P-GPLVM framework for unsupervised analysis of neural activity can be used to answer critical questions related to scientific discovery.

[Estimation of cancer incidence in Brazil and its regions in 2018: methodological aspects]. / Estimativa de incidência de câncer no Brasil e regiões em 2018: aspectos metodológicos.

The aim of this study was to develop a methodology for estimating cancer incidence in Brazil and its regions. Using data from population-based cancer registries (RCBP, acronym in Portuguese) and the Brazilian Mortality Information System (SIM, acronym in Portuguese), annual incidence/mortality (I/M) ratios were calculated by type of cancer, age group and sex in each RCBP. Poisson longitudinal multilevel models were applied to estimate the I/M ratios by region in 2018. The estimate of new cancer cases in 2018 was calculated by applying the estimated I/M ratios to the number of SIM-corrected deaths that occurred that year. North and Northeast concentrated the lowest I/M ratios. Pancreatic, lung, liver and esophageal cancers had the lowest I/M ratios, whereas the highest were estimated for thyroid, testicular, prostate and female breast cancers. For 2018, 506,462 new cancer cases were estimated in Brazil. Female breast and prostate were the two main types of cancer in all regions. In the North and Northeast, cervical and stomach cancers stood out. Differences in the I/M ratios between regions were observed and may be related to socioeconomic development and access to health services.

O objetivo deste estudo foi desenvolver metodologia para estimar a incidência de câncer no Brasil e regiões. A partir de dados dos registros de câncer de base populacional (RCBP) e do Sistema de Informações sobre Mortalidade (SIM) foram calculadas razões de incidência e mortalidade (I/M) anuais, tipo de câncer, faixa etária e sexo em cada RCBP. Para estimar as razões I/M por região em 2018, foram aplicados modelos multiníveis longitudinais de Poisson. A estimativa de casos novos de câncer, em 2018, foi calculada aplicando-se as razões I/M estimadas ao número de óbitos corrigidos do SIM ocorridos naquele ano. Norte e Nordeste concentraram as menores razões I/M. Os cânceres de pâncreas, pulmão, fígado e esôfago tiveram as menores razões I/M, enquanto as maiores razões I/M foram estimadas para câncer de tireoide, testículo, próstata e mama feminina. Para 2018, foram estimados 506.462 casos novos de câncer no Brasil. Mama feminina e próstata foram os dois principais tipos de câncer em todas as regiões. No Norte e no Nordeste, destacaram-se os cânceres do colo do útero e de estômago. Diferenças nas razões I/M entre as regiões foram observadas e podem estar relacionadas ao desenvolvimento socioeconômico e ao acesso a serviços de saúde.

El objetivo de este estudio fue desarrollar una metodología para estimar la incidencia de cáncer en Brasil y sus regiones. A partir de datos de los registros de cáncer de base poblacional (RCBP) y el Sistema de Informaciones de Mortalidad (SIM), se calcularon las tasas anuales de incidencia y mortalidad (I/M), tipo de cáncer, grupo de edad y sexo en cada RCBP. Para estimar las tasas de I/M por región en 2018, se aplicaron modelos multinivel longitudinales de Poisson. Los nuevos casos de cáncer en 2018 se estimaron mediante la aplicación de las tasas I/M que se esperan para el número de muertes corregidas de SIM que habían ocurrido ese año. Las regiones Norte y Nordeste concentraron las más bajas tasas de I/M. Los cánceres de páncreas, pulmón, hígado y esófago tuvieron las más bajas tasas de I/M, mientras que las más altas tasas de I/M se estimaron para los cánceres de tiroides, testículos, próstata y mama femenina. Para 2018, se estimaron 506.462 nuevos casos de cáncer en Brasil. La mama femenina y la próstata representaron técnicas de estimación y configuraron ser los tipos principales de cáncer en todas las regiones. En el Norte y el Nordeste se destacaron los cánceres de cuello uterino y estómago. Se observaron diferencias en las tasas de I/M entre regiones, las cuales pueden estar relacionadas con el desarrollo socioeconómico y el acceso a los servicios de salud.

Comparative analysis of the yields of dicentrics and chromosomal translocations.

PURPOSE: Chromosomal dicentrics and translocations are commonly employed as biomarkers to estimate radiation doses. The main goal of this article is to perform a comparative analysis of yields of both types of aberrations. The objective is to determine if there are relevant distinctions between both yields, allowing for a comprehensive assessment of their respective suitability and accuracy in the estimation of radiation doses. MATERIALS AND METHODS: The analysis involved data from a partial-radiation simulation study with the calibration data obtained through two scoring methods: conventional and PAINT modified. Subsequently, a Bayesian bivariate zero-inflated Poisson model was employed to compare the posterior marginal density of the mean of dicentrics and translocations and assess the differences between them. RESULTS: When employing the conventional method of scoring, the findings indicate that there is no notable disparity between the yield of observed translocations and dicentrics. However, when utilizing the PAINT modified method, a notable discrepancy is observed for higher doses, indicating a relevant difference in the mean number of the two types of aberrations. CONCLUSIONS: The choice of scoring method significantly influences the analysis of radiation-induced aberrations, especially when distinguishing between complex and simple chromosomal formations. Further research and analysis are necessary to gain a deeper understanding of the factors and mechanisms impacting the formation of dicentrics and translocations.

Cycling safely: Examining the factors associated with bicycle accidents in Seoul, South Korea.

This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

Association between underlying dentin shadows (ICDAS 4) and OHRQoL among adolescents from southern Brazil.

This study aimed to assess the association between underlying dentin shadows (UDS) and oral health-related quality of life (OHRQoL) among 15-19-year-old adolescents from southern Brazil. This population-based cross-sectional study included a representative sample of 1,197 15-19-year-old adolescents attending 31 public and private schools from Santa Maria, Brazil. The Oral Health Impact Profile-14 (OHIP-14) was used to evaluate the OHRQoL, and clinical examinations were performed by two calibrated examiners (intra/interexaminer kappa values for caries examination ≥ 0.80) to diagnose UDS (ICDAS code 4 caries lesions). Sociodemographic information and clinical characteristics (overall caries experience, traumatic dental injury, malocclusion, and gingivitis) were also collected as adjusting variables. Multilevel Poisson regression models were used to assess the association between UDS and OHRQoL. Rate ratios (RR) and 95% confidence intervals (CI) were estimated. The UDS prevalence was 8.8% (n = 106 adolescents). In the adjusted models, adolescents with UDS had poorer OHRQoL than those without UDS, and the strength of the association was dependent on the number of lesions per individual. Individuals with 1-2 UDS had a mean OHIP-14 score 8% higher (RR = 1.08; 95%CI: 1.01-1.17) than adolescents without UDS, while those with 3-4 UDS had a mean score 35% higher (RR = 1.35; 95%CI: 1.12-1.63). This negative association was related to physical disability, psychological disability, social disability, and handicap domains. This study showed that UDS was associated negatively with OHRQoL among 15-19-year-old adolescents from southern Brazil. The negative effect of UDS on OHRQoL emphasizes the importance of addressing issues regarding OHRQoL even in the posterior teeth of adolescents.

Socioeconomic status in life course is associated with dental appearance dissatisfaction.

The present study aimed to investigate the prevalence of dissatisfaction with dental appearance among 24-year-old Brazilian adults and the associated factors in life course. A subsample (n = 720) of the 1982 Pelotas Birth Cohort in southern Brazil was investigated at the ages of 15 and 24 years using clinical (caries and periodontal) examinations and interviews. The outcome was dissatisfaction with dental appearance at the age of 24 years. Covariate variables included socioeconomic factors, oral health, and dissatisfaction with general appearance collected during different periods of life. Poisson regression models with robust variance were applied. The prevalence of dissatisfaction with dental appearance was 43.5% (95%CI: 39.8-47.1). Individuals with downward income mobility (PR = 1.22, 95%CI: 1.07-1.79) and those always poor (PR = 1.21, 95%CI: 1.00-1.57) presented a higher prevalence of dissatisfaction with their dental appearance even after oral health variables and dissatisfaction with general appearance were controlled for. Moderate/severe malocclusion at 15 years (PR = 1.34, 95%CI: 1.13-1.59), highest experience of untreated dental caries at 24 years (PR = 1.82, 95%CI: 1.46-2.27), and dental pain experience at 24 years (PR = 1.29, 95%CI: 1.22-1.75) were associated with the outcome. Also, the prevalence of dissatisfaction with dental appearance was 20% higher (PR = 1.20, 95%CI: 1.01-1.43) among those dissatisfied with their general appearance. Our findings demonstrated a high prevalence of dissatisfaction with dental appearance among young adults. Lifetime economic disadvantage and dental problems (malocclusion at 15 years, untreated dental caries at 24 years, and dental pain at 24 years) were associated with dissatisfaction with dental appearance among young adults.

[Negative Poisson's Ratio Material Structure Based on 3D Printing and Its Application in Medical Field].

First of all, the overall framework of 3D printing is briefly introduced, including the basic principles of the additive manufacturing process, the classification and summary of the seven processes. Secondly, the common negative Poisson's ratio structure is introduced. Compared with the conventional structure, the negative Poisson's ratio structure has stronger energy absorption capacity, better fracture resistance and better indentation resistance, which are its advantages in printing manufacturing. Finally, 3D printing, the application of negative Poisson's ratio structure and the combination of the two are introduced from the different perspective of medical field, for example, the application of cardiovascular stent, biomedical material structure preparation, and lumbar disc implants. This paper suggests that the structural design of negative Poisson's ratio in 3D printing guides the development of new application directions in the medical field. Negative Poisson's ratio materials have a wide range of applications, not only in the medical field but also in mechanical equipment, automotive manufacturing, aerospace, and other high-tech industries.

Exact Distribution of the Quantal Content in Synaptic Transmission.

During electrochemical signal transmission through synapses, triggered by an action potential (AP), a stochastic number of synaptic vesicles (SVs), called the "quantal content," release neurotransmitters in the synaptic cleft. It is widely accepted that the quantal content probability distribution is a binomial based on the number of ready-release SVs in the presynaptic terminal. But the latter number itself fluctuates due to its stochastic replenishment, hence the actual distribution of quantal content is unknown. We show that exact distribution of quantal content can be derived for general stochastic AP inputs in the steady state. For fixed interval AP train, we prove that the distribution is a binomial, and corroborate our predictions by comparison with electrophysiological recordings from MNTB-LSO synapses of juvenile mice. For a Poisson train, we show that the distribution is nonbinomial. Moreover, we find exact moments of the quantal content in the Poisson and other general cases, which may be used to obtain the model parameters from experiments.

A comparison of the MPN and pour plate methods for estimating shellfish contamination by Escherichia coli.

AIMS: Shellfish production areas are classified for suitability for human consumption using counts of Escherichia coli in shellfish samples. Two alternative laboratory methods are approved in the European Union and UK for measuring E. coli in shellfish samples; the most probable number (MPN) and pour plate methods. These methods have inherently different statistical uncertainty and may give different counts for the same sample. Using two approaches: simulated data and spiking experiments, we investigate the theoretical properties of the two methods to determine their reliability for shellfish waters classification. METHODS AND RESULTS: Assuming a Poisson distribution of E. coli in shellfish samples, we simulate concentrations in 10 000 samples using the MPN and pour plate methods. We show that for higher concentrations of E. coli the pour plate method becomes increasingly more reliable than the MPN method. The MPN method has higher probabilities than pour plate of generating results exceeding shellfish classification thresholds, while conversely having higher probabilities of failing to detect counts that exceed regulatory thresholds. The theoretical analysis also demonstrates that the MPN method can produce genuine extreme outliers, even when E. coli are randomly distributed within the sampled material. A laboratory spiking experiment showed results consistent with the theoretical analysis, suggesting the Poisson assumption used in the theoretical analysis is reasonable. CONCLUSION: The large differences in statistical properties between the pour plate and MPN methods should be taken into consideration in classifying shellfish beds, with the pour plate method being more reliable over the crucial range of E. coli concentrations used to determine class boundaries.

The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials.

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries.

Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Number of tetanus toxoid injections before birth and associated factors among pregnant women in low and middle income countries: Negative binomial poisson regression.

BACKGROUND: In low- and middle-income countries where vaccination rates are low, tetanus is still an important threat to public health. Although maternal and neonatal tetanus remains a major global health concern, its magnitude and determinates are not well studied. Therefore, this study aimed to assess the number of tetanus toxoid injections and associated factors among pregnant women in low- and middle-income countries. METHODS: Data from the most recent Demographic and Health Surveys, which covered 60 low- and middle-income countries from 2010 to 2022, was used for secondary data analysis. The study included a total of 118,704 pregnant women. A statistical software package, STATA 14, was used to analyze the data. A negative binomial regression of a cross-sectional study was carried out. Factors associated with the number of tetanus vaccinations were declared significant at a p-value of < 0.05. The incidence rate ratio and confidence interval were used to interpret the results. A model with the smallest Akaike Information Criterion and Bayesian Information Criterion values and the highest log likelihood was considered the best-fit model for this study. RESULTS: In low- and middle-income countries, 26.0% of pregnant women took at least two doses of the tetanus toxoid vaccine. Factors such as maternal education, primary (IRR = 1.22, 95% CI: 1.17, 1.26), secondary (IRR = 1.19, 95% CI: 1.15, 1.23), higher (IRR = 1.16, 95% CI: 1.12, 1.20), employment (IRR = 1.11, 95% CI: 1.09, 1.13), 1-3 ANC visits (IRR = 2.49, 95% CI: 2.41, 2.57), ≥4 visits (IRR = 2.94, 95% CI: 2.84, 3.03), wealth index (IRR = 1.06; 95% CI: 11.04, 1.08), ≥birth order (IRR = 1.04, 95% CI: 1.02, 1.27), distance to health facility (IRR = 1.02, 95% CI: 1.00, 1.03), and health insurance coverage (IRR = 1.08; 95% CI: 1.06, 1.10) had a significant association with the number of tetanus vaccinations among pregnant women. CONCLUSIONS AND RECOMMENDATIONS: This study concludes that the number of tetanus toxoid vaccinations among pregnant women in low- and middle-income countries is low. In the negative binomial model, the frequency of tetanus vaccinations has a significant association with maternal employment, educational status, wealth index, antenatal care visits, birth order, distance from a health facility, and health insurance. Therefore, the ministries of health in low and middle-income countries should give attention to those women who had no antenatal care visits and women from poor wealth quantiles while designing policies and strategies.

Analysis of hospital dental care for patients with special needs in Brazil.

This analytical cross-sectional study aimed to analyze the access of patients with special needs (PSN) in Brazilian municipalities to hospital dental care of the Unified Health System (Sistema Único de Saúde - SUS), based on data from the Hospital Information System of the Unified Health System (Sistema de Informações Hospitalares do SUS- SIH/SUS - SIH), from 2010 to 2018. The Kolmogorov-Smirnov normality test was performed; the Poisson regression was used to verify factors associated with the variable total number of hospitalization authorizations with the main procedure of dental treatment for PSN ("Total de Autorizações de Internação Hospitalar" - AIH), the Spearman correlation test with a significance level of 5% was used to characterize the relationships between the Municipal Human Development Index per municipality - (Índice de Desenvolvimento Humano Municipal - HDI) and the Oral Health Coverage in the Family Health Strategy by municipality (Cobertura de saúde bucal na estratégia saúde da família por município - SBSF Coverage), and the relationship of the AIH with SBSF Coverage. A total of 127,691 procedures were performed, of which 71,517 (56%) were clinical procedures, such as restorations, endodontic treatments, supra and subgingival scaling, among others. Municipalities in the Midwest (PR=5.117) and Southeast (RP = 4.443) regions had more precedures than the others. A weak correlation was found between AIH and SBSF Coverage (r = -0.2, p < 0.001) and HDI and SBSF Coverage (r = -0.074, p < 0.001). Population size, region, health coverage, oral hygiene, and number of dentists in hospitals affected the availability of dental procedures in PSN.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences.

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

A resampling-based approach to share reference panels.

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation.

Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework.

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.