A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.

BACKGROUND: High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. RESULTS: We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. CONCLUSIONS: The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.

Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

The effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment for hepatocellular carcinoma in China: a bayesian network meta-analysis.

PURPOSE: To investigate the effectiveness of problem-based learning (PBL) and case-based learning (CBL) teaching methods in clinical practical teaching in transarterial chemoembolization (TACE) treatment in China. MATERIALS AND METHODS: A comprehensive search of PubMed, the Chinese National Knowledge Infrastructure (CNKI) database, the Weipu database and the Wanfang database up to June 2023 was performed to collect studies that evaluate the effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment in China. Statistical analysis was performed by R software (4.2.1) calling JAGS software (4.3.1) in a Bayesian framework using the Markov chain-Monte Carlo method for direct and indirect comparisons. The R packages "gemtc", "rjags", "openxlsx", and "ggplot2" were used for statistical analysis and data output. RESULTS: Finally, 7 studies (five RCTs and two observational studies) were included in the meta-analysis. The combination of PBL and CBL showed more effectiveness in clinical thinking capacity, clinical practice capacity, knowledge understanding degree, literature reading ability, method satisfaction degree, learning efficiency, learning interest, practical skills examination scores and theoretical knowledge examination scores. CONCLUSIONS: Network meta-analysis revealed that the application of PBL combined with the CBL teaching mode in the teaching of liver cancer intervention therapy significantly improves the teaching effect and significantly improves the theoretical and surgical operations, meeting the requirements of clinical education.

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

Active case-finding of tuberculosis compared with symptom-driven standard of care: a modelling analysis.

BACKGROUND: In settings with large case detection gaps, active case-finding (ACF) may play a critical role in the uberculosis (TB) response. However, ACF is resource intensive, and its effectiveness depends on whether people detected with TB through ACF might otherwise spontaneously resolve or be diagnosed through routine care. We analysed the potential effectiveness of ACF for TB relative to the counterfactual scenario of routine care alone. METHODS: We constructed a Markov simulation model of TB natural history, diagnosis, symptoms, ACF and treatment, using a hypothetical reference setting using data from South East Asian countries. We calibrated the model to empirical data using Bayesian methods, and simulated potential 5-year outcomes with an 'aspirational' ACF intervention (reflecting maximum possible effectiveness) compared with the standard-of-care outcomes. RESULTS: Under the standard of care, 51% (95% credible interval, CrI: 31%, 75%) of people with prevalent TB at baseline were estimated to be diagnosed and linked to care over 5 years. With aspirational ACF, this increased to 88% (95% CrI: 84%, 94%). Most of this difference represented people who were diagnosed and treated through ACF but experienced spontaneous resolution under standard-of-care. Aspirational ACF was projected to reduce the average duration of TB disease by 12 months (95% CrI: 6%, 18%) and TB-associated disability-adjusted life-years by 71% (95% CrI: 67%, 76%). CONCLUSION: These data illustrate the importance of considering outcomes in a counterfactual standard of care scenario, as well as trade-offs between overdiagnosis and averted morbidity through earlier diagnosis-not just for TB, but for any disease in which population-based screening is recommended.

Linking Acrosome Size and Genetic Divergence in an Inter-Oceanic Mussel from the Pacific and Atlantic Coasts: A Case of Incipient Speciation?

In recent years, advances in analyses of the sperm morphology and genetics of Perumytilus purpuratus have allowed to two evolutionary scenarios for this mussel to be suggested: (1) the scenario of cryptic species and (2) the scenario of incipient or in progress speciation. For a better understanding of the evolutionary history of P. purpuratus, we performed extensive sampling along a latitudinal gradient of ca. 7180 km of coastline-from the Southern Pacific Ocean to the Atlantic Ocean-and we delved deeper into the sperm morphology of P. purpuratus, exploring its association with the phylogeny and population genetics to determine whether the variability in sperm traits between the northern and southern regions was a signal of cryptic or incipient species. Overall, our results showed that sperm sizes were strongly correlated with the genetic structure in males of P. purpuratus. We identified at 37° S on the Pacific coast a coincident break of both sperm size and genetic disruption that can be explained by historical events and postglacial recolonization as causal phenomena for the observed divergences. Furthermore, evidence of genetic admixture between lineages was found at 38° S, suggesting the presence of an introgressive hybridization zone and incomplete reproductive isolation in an in fraganti or incipient speciation process.

A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease.

OBJECTIVES: Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase. METHODS: We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect. RESULTS: Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients. CONCLUSION: This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.

Common Methods for Phylogenetic Tree Construction and Their Implementation in R.

A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

Which Drugs are More Effective in Preventing Familial Adenomatous Polyposis Progression based on Network Meta-analysis?

BACKGROUND: Familial adenomatous polyposis (FAP) is an inherited disorder. At present, an increasing number of medications are being employed to treat FAP; however, only a few have been assessed for their efficacy and safety. Therefore, this study aimed to conduct a network meta-analysis to compare the therapeutic outcomes and adverse drug reactions of all FAP-associated medications. METHODS: Six relevant databases were searched to identify pertinent randomized controlled trials (RCTs), and information on the dosage and frequency of various drugs was extracted. Additionally, data on changes in polyp counts and dimensions, as well as treatment-related adverse reactions for different medications were collected. The Bayesian method was employed to directly or indirectly compare the impact of different treatment regimens on changes in polyp numbers and diameters, and the safety of the drugs was investigated. RESULTS: CXB at 16 mg/kg/day significantly reduced polyp numbers. Celecoxib at 8 mg/kg/day and sulindac (150 mg twice daily) plus erlotinib (75 mg/day) were effective for tolerant FAP patients. Additionally, EPAFFA 2 g daily and sulindac (150 mg twice daily) plus erlotinib (75 mg/day) emerged as the most effective for reducing polyp size. CONCLUSION: The most effective treatment for reducing the number of colorectal polyps is celecoxib 16 mg/kg/day. On the other hand, a daily dosage of 2 g EPA-FFA demonstrates the best results in terms of decreasing colorectal polyp diameter.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Introduction: Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. Methods: This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. Results: It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

Comparing the Performance of Three Computational Methods for Estimating the Effective Reproduction Number.

The effective reproduction number (Rt) is one of the most important epidemiological parameters, providing suggestions for monitoring the development trend of diseases and also for adjusting the prevention and control policies. However, a few studies have focused on the performance of some common computational methods for Rt. The purpose of this article is to compare the performance of three computational methods for Rt: the time-dependent (TD) method, the new time-varying (NT) method, and the sequential Bayesian (SB) method. Four evaluation methods-accuracy, correlation coefficient, similarity based on trend, and dynamic time warping distance-were used to compare the effectiveness of three computational methods for Rt under different time lags and time windows. The results showed that the NT method was a better choice for real-time monitoring and analysis of the epidemic in the middle and late stages of the infectious disease. The TD method could reflect the change of the number of cases stably and accurately, and was more suitable for monitoring the change of Rt during the whole process of the epidemic outbreak. When the data were relatively stable, the SB method could also provide a reliable estimate for Rt, while the error would increase when the fluctuation in the number of cases increased. The results would provide suggestions for selecting appropriate Rt estimation methods and making policy adjustments more timely and effectively according to the change of Rt.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview.

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program.

Aim: This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology. Background: Underestimation is a widespread issue in cancer registries within developing countries. Methods: This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value. Results: The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men. Conclusion: The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis / Padrões espaço-temporais da incidência de tuberculose em Ribeirão Preto, Estado de São Paulo, sudeste do Brasil, e suas relações com a vulnerabilidade social: uma análise bayesiana

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

INTRODUÇÃO: O objetivo deste estudo ecológico é avaliar a distribuição espacial e temporal da tuberculose (TB) na área urbana de Ribeirão Preto, São Paulo, entre os anos de 2006 e 2009, e estudar as suas relações com fatores de vulnerabilidade social como renda e educação. MÉTODOS: Foram utilizados dados do TBWeb, um sistema de notificação de dados de TB. As medidas de vulnerabilidade social foram obtidas da Fundação SEADE (Sistema Estadual de Análise de Dados) e informações sobre o número de habitantes, educação e renda dos chefes dos domicílios foram obtidas do Instituto Brasileiro de Geografia e Estatística. A análise estatística utilizou um modelo bayesiano de regressão assumindo que os novos casos de TB observados em cada área assumem uma distribuição de Poisson. RESULTADOS: O modelo bayesiano confirmou a heterogeneidade especial da distribuição da TB em Ribeirão Preto, identificando áreas com elevado risco de TB e os efeitos da vulnerabilidade social sobre a doença. Foi evidenciado que a taxa de TB associa-se com as medidas de renda, educação e vulnerabilidade social. Entretanto, são observadas áreas com baixa vulnerabilidade social e alto nível educacional, mas altas taxas de TB. CONCLUSÕES: O estudo identificou áreas com diferentes riscos de TB, permitindo que o sistema público de saúde lide com as diferentes características de cada região e priorize aquelas que apresentem maior propensão de risco de TB. São evidentes relações complexas entre a incidência de TB e um amplo número de fatores ambientais e intrínsecos, o que mostra a necessidade destes serem estudados em trabalhos futuros.

Mortalidade por Neoplasias no Brasil de 2003 a 2007 / The Mortalit y by Neoplasms in Brazil from 2003 to 2007 / La mortandad por Neoplasias en Brasil entre 2003 Y 2007

A incidência de neoplasias no Brasil e no mundo tem aumentado nos últimos anos, vitimando grande número de pessoas anualmente. O trabalho teve como objetivo a análise epidemiológica descritiva da mortalidade por neoplasias no Brasil, de 2003 a 2007. Os dados foram levantados nos registros do Sistema de Informações de Mortalidade do Ministério da Saúde e a aplicação do método estatístico de Bayes rendeu os mapas das razões de mortalidade padronizadas para 425 microrregiões do país. Os resultados indicam que, entre 2003 e 2007, no Brasil: eram do sexo masculino entre 53 e 54por cento das vítimas de neoplasias registradas; em 2007, a maioria das mulheres falecidas com idades entre 30 e 49 anos foram vitimadas por neoplasias; as proporções de óbitos por neoplasias aumentaram no país e em todos os estados, apresentando as maiores taxas no Rio Grande do Sul, Santa Catarina, Paraná, São Paulo e Distrito Federal; muitas microrregiões com baixas taxas de mortalidadepor câncer apresentaram elevadas proporções de óbitos por causas mal definidas. Concluise que as taxas aferidas no Brasil, para as os óbitos por neoplasias, foram menores que as de outros países.

The occurrence of neoplaasm cases has increased in the last few years in Brazil and worldwide, resulting in many deaths every year. This work aims to make a descriptive epidemiologic analysis of the mortality by cancer in Brazil, from 2003 to 2007. Data was taken from records of the Ministry of Health Information System and the application of Bayes? Statistical approach which portrayed maps concerning the reasons of mortality by neoplasms in 425 micro-regions of the country. The results indicate that, between 2003 and 2007, in Brazil: between 53percent and 54percent of the victims of cancer deaths were male; in 2007, most of the women who died from cancer were between the ages of 30 and 49; the proportion of cancerrelated deaths increased throughout the country, with higher rates found in the states of Rio Grande do Sul, Santa Catarina, Paraná, São Paulo and Distrito Federal; many micro-regions having lower rates for cancer- related deaths presented high values of deaths by uncertain causes. This study concludes that the rates determined in Brazil, for cancer-related deaths were lower than in another countries.

En los últimos años, la incidencia de neoplasias ha presentado aumentos em Brasil y en todo el mundo resultando, a cada año, en muchas muertes. El trabajo objetivo el análisis epidemiológico descriptivo de la mortandad por Neoplasias en Brasil, entre 2003 y 2007. Los datos fueron recolectados en el Sistema de Informaciones de Mortandad del Ministerio de la Salud y, para mapear las razones de la mortandad estandarizada de 425 microregiones del país, se ha aplicado el método estadístico de bayes. Los resultados indican que entre los años de 2003 y 2007, en Brasil: 53por ciento a 54por ciento del total de víctimas registradaspor Neoplasia fueron hombres,; en 2007, la mayor proporción fue de mujeres en edades comprendidas entre 30 y 49 años; las muertes por Neoplasias crecieron en todos los estados del país, presentando los mayores índices las regiones de Rio Grande do Sul, Santa Catarina, Paraná, São Paulo y Distrito Federal; varias microregiones con bajos índices de mortandad por cáncer presentaron altas proporciones de muertes por males no determinados. Se concluye que los índices de mortandad por cáncer en Brasil, comparados con otros países, fueron menores.