A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

BACKGROUND: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Duhamel and transanal endorectal pull-throughs for Hirschsprung disease: a Bayesian network meta-analysis.

BACKGROUND: To comprehensively compare the effects of open Duhamel (OD), laparoscopic-assisted Duhamel (LD), transanal endorectal pull-through (TEPT), and laparoscopic-assisted endorectal pull-through (LEPT) in Hirschsprung disease. METHODS: PubMed, Embase, Cochrane Library, Web of Science, CNKI, WanFang, and VIP were comprehensively searched up to August 4, 2022. The outcomes were operation-related indicators and complication-related indicators. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to evaluate the quality of evidence. Network plots, forest plots, league tables and rank probabilities were drawn for all outcomes. For measurement data, weighted mean differences (WMDs) and 95% credibility intervals (CrIs) were reported; for enumeration data, relative risks (RRs) and 95%CrIs were calculated. RESULTS: Sixty-two studies of 4781 patients were included, with 2039 TEPT patients, 1669 LEPT patients, 951 OD patients and 122 LD patients. Intraoperative blood loss in the OD group was more than that in the LEPT group (pooled WMD = 44.00, 95%CrI: 27.33, 60.94). Patients lost more blood during TEPT versus LEPT (pooled WMD = 13.08, 95%CrI: 1.80, 24.30). In terms of intraoperative blood loss, LEPT was most likely to be the optimal procedure (79.76%). Patients undergoing OD had significantly longer gastrointestinal function recovery time, as compared with those undergoing LEPT (pooled WMD = 30.39, 95%CrI: 16.08, 44.94). The TEPT group had significantly longer gastrointestinal function recovery time than the LEPT group (pooled WMD = 11.49, 95%CrI: 0.96, 22.05). LEPT was most likely to be the best operation regarding gastrointestinal function recovery time (98.28%). Longer hospital stay was observed in patients with OD versus LEPT (pooled WMD = 5.24, 95%CrI: 2.98, 7.47). Hospital stay in the TEPT group was significantly longer than that in the LEPT group (pooled WMD = 1.99, 95%CrI: 0.37, 3.58). LEPT had the highest possibility to be the most effective operation with respect to hospital stay. The significantly reduced incidence of complications was found in the LEPT group versus the LD group (pooled RR = 0.24, 95%CrI: 0.12, 0.48). Compared with LEPT, OD was associated with a significantly increased incidence of complications (pooled RR = 5.10, 95%CrI: 3.48, 7.45). Patients undergoing TEPT had a significantly greater incidence of complications than those undergoing LEPT (pooled RR = 1.98, 95%CrI: 1.63, 2.42). For complications, LEPT is most likely to have the best effect (99.99%). Compared with the LEPT group, the OD group had a significantly increased incidence of anastomotic leakage (pooled RR = 5.35, 95%CrI: 1.45, 27.68). LEPT had the highest likelihood to be the best operation regarding anastomotic leakage (63.57%). The incidence of infection in the OD group was significantly higher than that in the LEPT group (pooled RR = 4.52, 95%CrI: 2.45, 8.84). The TEPT group had a significantly increased incidence of infection than the LEPT group (pooled RR = 1.87, 95%CrI: 1.13, 3.18). LEPT is most likely to be the best operation concerning infection (66.32%). Compared with LEPT, OD was associated with a significantly higher incidence of soiling (pooled RR = 1.91, 95%CrI: 1.16, 3.17). Patients with LEPT had the greatest likelihood not to develop soiling (86.16%). In contrast to LD, LEPT was significantly more effective in reducing the incidence of constipation (pooled RR = 0.39, 95%CrI: 0.15, 0.97). LEPT was most likely not to result in constipation (97.81%). LEPT was associated with a significantly lower incidence of Hirschprung-associated enterocolitis (HAEC) than LD (pooled RR = 0.34, 95%CrI: 0.13, 0.85). The OD group had a significantly higher incidence of HAEC than the LEPT group (pooled RR = 2.29, 95%CrI: 1.31, 4.0). The incidence of HAEC was significantly greater in the TEPT group versus the LEPT group (pooled RR = 1.74, 95%CrI: 1.24, 2.45). LEPT was most likely to be the optimal operation in terms of HAEC (98.76%). CONCLUSION: LEPT may be a superior operation to OD, LD and TEPT in improving operation condition and complications, which might serve as a reference for Hirschsprung disease treatment.

Potential therapeutic targets for membranous nephropathy: proteome-wide Mendelian randomization and colocalization analysis.

Background: The currently available medications for treating membranous nephropathy (MN) still have unsatisfactory efficacy in inhibiting disease recurrence, slowing down its progression, and even halting the development of end-stage renal disease. There is still a need to develop novel drugs targeting MN. Methods: We utilized summary statistics of MN from the Kiryluk Lab and obtained plasma protein data from Zheng et al. We performed a Bidirectional Mendelian randomization analysis, HEIDI test, mediation analysis, Bayesian colocalization, phenotype scanning, drug bank analysis, and protein-protein interaction network. Results: The Mendelian randomization analysis uncovered 8 distinct proteins associated with MN after multiple false discovery rate corrections. Proteins related to an increased risk of MN in plasma include ABO [(Histo-Blood Group Abo System Transferase) (WR OR = 1.12, 95%CI:1.05-1.19, FDR=0.09, PPH4 = 0.79)], VWF [(Von Willebrand Factor) (WR OR = 1.41, 95%CI:1.16-1.72, FDR=0.02, PPH4 = 0.81)] and CD209 [(Cd209 Antigen) (WR OR = 1.19, 95%CI:1.07-1.31, FDR=0.09, PPH4 = 0.78)], and proteins that have a protective effect on MN: HRG [(Histidine-Rich Glycoprotein) (WR OR = 0.84, 95%CI:0.76-0.93, FDR=0.02, PPH4 = 0.80)], CD27 [(Cd27 Antigen) (WR OR = 0.78, 95%CI:0.68-0.90, FDR=0.02, PPH4 = 0.80)], LRPPRC [(Leucine-Rich Ppr Motif-Containing Protein, Mitochondrial) (WR OR = 0.79, 95%CI:0.69-0.91, FDR=0.09, PPH4 = 0.80)], TIMP4 [(Metalloproteinase Inhibitor 4) (WR OR = 0.67, 95%CI:0.53-0.84, FDR=0.09, PPH4 = 0.79)] and MAP2K4 [(Dual Specificity Mitogen-Activated Protein Kinase Kinase 4) (WR OR = 0.82, 95%CI:0.72-0.92, FDR=0.09, PPH4 = 0.80)]. ABO, HRG, and TIMP4 successfully passed the HEIDI test. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that all of them share variants with MN. We identified type 1 diabetes, trunk fat, and asthma as having intermediate effects in these pathways. Conclusions: Our comprehensive analysis indicates a causal effect of ABO, CD27, VWF, HRG, CD209, LRPPRC, MAP2K4, and TIMP4 at the genetically determined circulating levels on the risk of MN. These proteins can potentially be a promising therapeutic target for the treatment of MN.

Exploring inflammatory bowel disease therapy targets through druggability genes: a Mendelian randomization study.

Background: Inflammatory bowel disease is an incurable group of recurrent inflammatory diseases of the intestine. Mendelian randomization has been utilized in the development of drugs for disease treatment, including the therapeutic targets for IBD that are identified through drug-targeted MR. Methods: Two-sample MR was employed to explore the cause-and-effect relationship between multiple genes and IBD and its subtypes ulcerative colitis and Crohn's disease, and replication MR was utilized to validate this causality. Summary data-based Mendelian randomization analysis was performed to enhance the robustness of the outcomes, while Bayesian co-localization provided strong evidential support. Finally, the value of potential therapeutic target applications was determined by using the estimation of druggability. Result: With our investigation, we identified target genes associated with the risk of IBD and its subtypes UC and CD. These include the genes GPBAR1, IL1RL1, PRKCB, and PNMT, which are associated with IBD risk, IL1RL1, with a protective effect against CD risk, and GPX1, GPBAR1, and PNMT, which are involved in UC risk. Conclusion: In a word, this study identified several potential therapeutic targets associated with the risk of IBD and its subtypes, offering new insights into the development of therapeutic agents for IBD.

Uncertainty Computation at Finite Distance in Nonlinear Mixed Effects Models-a New Method Based on Metropolis-Hastings Algorithm.

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration.

Distinct T cell signatures are associated with Staphylococcus aureus skin infection in pediatric atopic dermatitis.

Atopic dermatitis (AD) is an inflammatory skin condition with a childhood prevalence of up to 25%. Microbial dysbiosis is characteristic of AD, with Staphylococcus aureus the most frequent pathogen associated with disease flares and increasingly implicated in disease pathogenesis. Therapeutics to mitigate the effects of S. aureus have had limited efficacy and S. aureus-associated temporal disease flares are synonymous with AD. An alternative approach is an anti-S. aureus vaccine, tailored to AD. Experimental vaccines have highlighted the importance of T cells in conferring protective anti-S. aureus responses; however, correlates of T cell immunity against S. aureus in AD have not been identified. We identify a systemic and cutaneous immunological signature associated with S. aureus skin infection (ADS.aureus) in a pediatric AD cohort, using a combined Bayesian multinomial analysis. ADS.aureus was most highly associated with elevated cutaneous chemokines IP10 and TARC, which preferentially direct Th1 and Th2 cells to skin. Systemic CD4+ and CD8+ T cells, except for Th2 cells, were suppressed in ADS.aureus, particularly circulating Th1, memory IL-10+ T cells, and skin-homing memory Th17 cells. Systemic γδ T cell expansion in ADS.aureus was also observed. This study suggests that augmentation of protective T cell subsets is a potential therapeutic strategy in the management of S. aureus in AD.

Probabilistic classification of the severity classes of unhealthy air pollution events.

Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events will exert a disastrous effect on the environment. Therefore, public health and policy-making authorities must be able to determine the characteristics of these events. This study proposes a probabilistic machine learning technique for predicting the classification of extreme and non-extreme events on the basis of data features to address the above issue. The use of the naïve Bayes model in the prediction of air pollution classes is proposed to leverage its simplicity as well as high accuracy and efficiency. A case study was conducted on the air pollution index data of Klang, Malaysia, for the period of January 01, 1997, to August 31, 2020. The trained naïve Bayes model achieves high accuracy, sensitivity, and specificity on the training and test datasets. Therefore, the naïve Bayes model can be easily applied in air pollution analysis while providing a promising solution for the accurate and efficient prediction of extreme or non-extreme air pollution events. The findings of this study provide reliable information to public authorities for monitoring and managing sustainable air quality over time.

Spatial distribution of arboviruses and its association with a social development index and the waste disposal in São Luís, state of Maranhão, Brazil, 2015 to 2019.

OBJECTIVE: To detect spatial and spatiotemporal clusters of urban arboviruses and to investigate whether the social development index (SDI) and irregular waste disposal are related to the coefficient of urban arboviruses detection in São Luís, state of Maranhão, Brazil. METHODS: The confirmed cases of Dengue, Zika and Chikungunya in São Luís, from 2015 to 2019, were georeferenced to the census tract of residence. The Bayesian Conditional Autoregressive regression model was used to identify the association between SDI and irregular waste disposal sites and the coefficient of urban arboviruses detection. RESULTS: The spatial pattern of arboviruses pointed to the predominance of a low-incidence cluster, except 2016. For the years 2015, 2016, 2017, and 2019, an increase of one unit of waste disposal site increased the coefficient of arboviruses detection in 1.25, 1.09, 1.23, and 1.13 cases of arboviruses per 100 thousand inhabitants, respectively. The SDI was not associated with the coefficient of arboviruses detection. CONCLUSION: In São Luís, spatiotemporal risk clusters for the occurrence of arboviruses and a positive association between the coefficient of arbovirus detection and sites of irregular waste disposal were identified.

Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.

OBJECTIVE: Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities. METHOD: Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis. RESULT: The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects. CONCLUSION: The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

Spatial analysis of cholangiocarcinoma in relation to diabetes mellitus and Opisthorchis viverrini infection in Northeast Thailand.

Cholangiocarcinoma (CCA) exhibits a heightened incidence in regions with a high prevalence of Opisthorchis viverrini infection, with previous studies suggesting an association with diabetes mellitus (DM). Our study aimed to investigate the spatial distribution of CCA in relation to O. viverrini infection and DM within high-risk populations in Northeast Thailand. Participants from 20 provinces underwent CCA screening through the Cholangiocarcinoma Screening and Care Program between 2013 and 2019. Health questionnaires collected data on O. viverrini infection and DM, while ultrasonography confirmed CCA diagnoses through histopathology. Multiple zero-inflated Poisson regression, accounting for covariates like age and gender, assessed associations of O. viverrini infection and DM with CCA. Bayesian spatial analysis methods explored spatial relationships. Among 263,588 participants, O. viverrini infection, DM, and CCA prevalence were 32.37%, 8.22%, and 0.36%, respectively. The raw standardized morbidity ratios for CCA was notably elevated in the Northeast's lower and upper regions. Coexistence of O. viverrini infection and DM correlated with CCA, particularly in males and those aged over 60 years, with a distribution along the Chi, Mun, and Songkhram Rivers. Our findings emphasize the association of the spatial distribution of O. viverrini infection and DM with high-risk CCA areas in Northeast Thailand. Thus, prioritizing CCA screening in regions with elevated O. viverrini infection and DM prevalence is recommended.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

BACKGROUND: Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC). METHODS: Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression. RESULTS: During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly. CONCLUSIONS: This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Inferring skin-brain-skin connections from infodemiology data using dynamic Bayesian networks.

The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average R 2 for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

A Bayesian spatio-temporal model of COVID-19 spread in England.

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

BACKGROUND: Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. METHODS: The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. RESULTS: Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. CONCLUSIONS: Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Gut microbiota, inflammatory proteins and COVID-19: a Mendelian randomisation study.

Background: The human gut microbiota has been identified as a potentially important factor influencing the development of COVID-19. It is believed that the disease primarily affects the organism through inflammatory pathways. With the aim of improving early diagnosis and targeted therapy, it is crucial to identify the specific gut microbiota associated with COVID-19 and to gain a deeper understanding of the underlying processes. The present study sought to investigate the potential causal relationship between the gut microbiota and COVID-19, and to determine the extent to which inflammatory proteins act as mediators in this relationship. Methods: Bidirectional mendelian randomization (MR) and Two-step mediated MR analyses were applied to examine causative associations among 196 gut microbiota, 91 inflammatory proteins and COVID-19. The main analytical method used in the MR was the random effects inverse variance weighted (IVW) method. This was complemented by the Bayesian weighted Mendelian randomization (BWMR) method, which was utilized to test the hypothesis of MR. In order for the results to be deemed reliable, statistical significance was required for both methods. Validation was then carried out using an external dataset, and further meta-analyses were conducted to authenticate that the association was reliable. Results: Results of our research indicated that seven gut microbiota were actively associated to the COVID-19 risk. Five inflammatory proteins were associated with COVID-19 risk, of which three were positively and two were negatively identified with COVID-19. Further validation was carried out using sensitivity analyses. Mediated MR results revealed that CCL2 was a possible mediator of causality of family Bifidobacteriaceae and order Bifidobacteriales with COVID-19, mediating at a ratio of 12.73%. Conclusion: Suggesting a genetic causation between specific gut microbiota and COVID-19, our present research emphasizes the underlying mediating role of CCL2, an inflammatory factor, and contributes to a deeper understanding of the mechanism of action underlying COVID-19.

Dividing time-An absolute chronological study of material culture from Early Iron Age urnfields in Denmark.

Chronological frameworks based on artefact typologies are essential for interpreting the archaeological record, but they inadvertently treat transitions between phases as abrupt events and disregard the temporality of transformation processes within and between individual phases. This study presents an absolute chronological investigation of a dynamic material culture from Early Iron Age urnfields in Denmark. The chronological framework of Early Iron Age in Southern Scandinavia is largely unconstrained by absolute dating, primarily due to it coinciding with the so-called 'Hallstatt calibration plateau' (c.750 to 400 cal BC), and it is difficult to correlate it with Central European chronologies due to a lack of imported artefacts. This study applies recent methodological advances in radiocarbon dating and Bayesian chronological modelling, specifically a statistical model for wood-age offsets in cremated bone and presents the first large-scale radiocarbon investigation of regional material culture from Early Iron Age in Southern Jutland, Denmark. Dated material is primarily cremated bone from 111 cremation burials from three urnfields. The study presents absolute date ranges for 16 types of pottery and 15 types of metalwork, which include most of the recognised metalwork types from the period. This provides new insights into gradual change in material culture, when certain artefact types were in production and primary use, how quickly types were taken up and later abandoned, and distinguishing periods of faster and slower change. The study also provides the first absolute chronology for the period, enabling correlation with chronologies from other regions. Urnfields were introduced at the Bronze-Iron Age transformation, which is often assumed to have occurred c.530-500 BC. We demonstrate that this transformation took place in the 7th century BC, however, which revives the discussion of whether the final Bronze Age period VI should be interpreted as a transitional phase to the Iron Age.

Impacts of financial development and green trade on the shadow economy: An insight of eagle countries using Bayesian approaches.

The existence of a shadow economy is recognized as an impediment to sustainable development. By applying the Bayesian approaches, the current article investigates the linkage between financial development, green trade, and the scope of the shadow economy, aiming to contribute to a comprehensive understanding of how these factors address the challenge posed by the shadow economy in Emerging and Growth-Leading Economies (EAGLE) from 2003 to 2016. The results demonstrate that (i) The progress of the financial sector is expected to diminish the scale of the shadow economy. Specifically, the expansion of financial institutions and markets has a strong and negative influence on the shadow economy. (ii) Increased involvement in green trade is likely to result in a decreased shadow economy. Empirical findings provide evidence for effective policymaking in simultaneously promoting sustainable trade practices, strengthening financial systems, and curtailing informal economic activities for inclusive economic development.

Flexible modeling of regulatory networks improves transcription factor activity estimation.

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.