Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

Integrative Modeling of 3D Genome Organization by Bayesian Molecular Dynamics Simulations with Hi-C Metainference.

Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.

Evaluating the Performance of Different Criteria in Diagnosing AD and Preclinical AD with the Bayesian Latent Class Model.

BACKGROUND: The diagnostic criteria for Alzheimer's disease (AD) should be highly sensitive and specific. Clinicians have varying opinions on the different criteria, including the International Working Group-1 (IWG-1), International Working Group-2 (IWG-2), and AT(N) criteria. Few studies had evaluated the performance of these criteria in diagnosing AD and preclinical AD when the gold standard was absent. METHODS: We estimated and compared the performance of these criteria in diagnosing AD using data from 908 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Additionally, 622 subjects were selected to evaluate and compare the performance of IWG-2 and AT(N) criteria in diagnosing preclinical AD. A novel approach, Bayesian latent class models with fixed effect dependent, was utilized to estimate the diagnostic accuracy of these criteria in detecting different AD statuses simultaneously. RESULTS: The sensitivity of the IWG-1, IWG-2, and AT(N) criteria in diagnosing AD was 0.850, 0.836, and 0.665. The specificity of these criteria was 0.788, 0.746, and 0.747. The IWG-1 criteria had the highest Youden Index in detecting AD. When diagnosing preclinical AD, the sensitivity of the IWG-2 and AT(N) criteria was 0.797 and 0.955. The specificity of these criteria was 0.922 and 0.720. The IWG-2 criteria had the highest Youden Index. CONCLUSION: IWG-1 was more suitable than the IWG-2 and AT(N) criteria in detecting AD. IWG-2 criteria was more suitable than AT(N) criteria in detecting preclinical AD.

Genomic selection for agronomical phenotypes using genome-wide SNPs and SVs in pearl millet.

Pearl millet is an essential crop worldwide, with noteworthy resilience to abiotic stress, yet the advancement of its breeding remains constrained by the underutilization of molecular-assisted breeding techniques. In this study, we collected 1,455,924 single nucleotide polymorphism (SNP) and 124,532 structural variant (SV) markers primarily from a pearl millet inbred germplasm association panel consisting of 242 accessions including 120 observed phenotypes, mostly related to the yield. Our findings revealed that the SV markers had the capacity to capture genetic diversity not discerned by SNP markers. Furthermore, no correlation in heritability was observed between SNP and SV markers associated with the same phenotype. The assessment of the nine genomic prediction models revealed that SV markers performed better than SNP markers. When using the SV markers as the predictor variable, the genomic BLUP model achieved the best performance, while using the SNP markers, Bayesian methods outperformed the others. The integration of these models enabled the identification of eight candidate accessions with high genomic estimated breeding values (GEBV) across nine phenotypes using SNP markers. Four candidate accessions were identified with high GEBV across 22 phenotypes using SV markers. Notably, accession 'P23' emerged as a consistent candidate predicted based on both SNP and SV markers specifically for panicle number. These findings contribute valuable insights into the potential of utilizing both SNP and SV markers for genomic prediction in pearl millet breeding. Moreover, the identification of promising candidate accessions, such as 'P23', underscores the accelerated prospects of molecular breeding initiatives for enhancing pearl millet varieties.

Unique among high passes: Insights into the genetic uniqueness among butterflies of Ladakh Trans-Himalaya through DNA barcoding.

BACKGROUND: The butterfly assemblage of Ladakh Trans-Himalaya demands a thorough analysis of their population genetic structure owing to their typical biogeographic affinity and their adaptability to extreme cold-desert climates. No such effort has been taken till date, and in this backdrop, we created a COI barcode reference library of 60 specimens representing 23 species. METHODS AND RESULTS: Barcodes were generated from freshly collected leg samples using the Sanger sequencing method, followed by phylogenetic clade analyses and divergence calculation. Our data represents 22% of Ladakh's Rhopaloceran fauna with the novel barcode submission for six species, including one Schedule II species, Paralasa mani. Contrary to the 3% threshold rule, the interspecific divergence between two species pairs of typical mountain genus Hyponephele and Karanasa was found to be 2.3% and 2.2%, respectively. The addition of conspecific global barcodes revealed that most species showed little increase in divergence value, while a two-fold increase was noted in a few species. Bayesian clade clustering outcomes largely aligned with current morphological classifications, forming monophyletic clades of conspecific barcodes, with only minor exceptions observed for the taxonomically complicated genus Polyommatus and misidentified records of Aulocera in the database. We also observed variations within the same phylogenetic clades forming nested lineages, which may be attributed to the taxonomic intricacies present at the subspecies level globally, mostly among Eurasian species. CONCLUSIONS: Overall, our effort not only substantiated the effectiveness of DNA Barcoding for the identification and conservation of this climatically vulnerable assemblage but also highlighted the significance of deciphering the unique genetic composition among this geographically isolated population of Ladakh butterflies.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

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

Spatiotemporal distribution and ecological factors of brucellosis among children from 2016 to 2020 in Inner Mongolia, China.

OBJECTIVE: In recent years, the increasing incidence of brucellosis in children has become more serious. However, relatively few studies have been conducted to characterize the spatialtemporal distribution of brucellosis in children. This study aimed to analyze the spatiotemporal distribution characteristics and ecological influencing factors of brucellosis incidence among children in Inner Mongolia. METHODS: This study used data on brucellosis incidence in children aged 0-14 years reported in Inner Mongolia from 2016 to 2020. A Bayesian model was used to analyze the spatial and temporal distribution of brucellosis in children from 2016 to 2020 in Inner Mongolia. Geographical weighted regression model was used to analyze the ecological factors related to the incidence of brucellosis in children. RESULT: Bayesian spatiotemporal analysis indicated that the highest brucellosis risk and increased disease incidence were observed in Hinggan, Inner Mongolia, in children aged 0-14 years. Alxa had the lowest risk but the incidence rate increased rapidly. The incidence of childhood brucellosis was positively associated with the number of sheep at the year-end (ß: 2.5909 ~ 2.5926, P < 0.01), average temperature (ß: 2.8978 ~ 2.9030, P < 0.05), and precipitation level (ß: 3.3261 ~ 3.3268, P < 0.01). CONCLUSION: From 2016 to 2020, the overall incidence of brucellosis in children in Inner Mongolia showed an upward trend, with cases exhibiting spatial aggregation. We should focus on areas where the incidence of brucellosis in children is rising rapidly. The incidence of childhood brucellosis was associated with the number of sheep at the year-end, average temperature and precipitation level. IMPLICATIONS AND CONTRIBUTION: The findings suggest that brucellosis in children is not to be taken lightly. For children should also focus on protection, take corresponding protective measures. While we focus on high-risk areas, we must also monitor areas where the risk of disease is low, but the incidence is rising fast, to prevent outbreaks in low-risk areas from becoming high-risk areas.

Climate change distress and impairment in Germany.

Introduction: Climate change has been widely recognized as one of the most challenging problems facing humanity and it imposes serious mental health threats. It is important, however, to differentiate between the affective experience of distress over climate change and the functional impairments associated with climate change. Such a distinction is crucial because not all negative affective states are pathological, and they might even motivate pro-environmental behavior. Functional impairments, like not being able to work or maintaining social relationships, however, might require immediate treatment. This study assesses climate change distress and climate change impairment within the population of Germany using a population-representative sample. The results identify vulnerable subgroups and thereby can help to facilitate the development of target group specific intervention programs. Furthermore, this study explores whether climate change distress and climate change impairment are associated with general health, physical health, mental health, and diverse health behaviors. Methods: Study participants were drawn from a panel which is representative of the German-speaking population in Germany with Internet access. Participants answered a series of questionnaires regarding their climate change distress, climate change impairment, general health, physical health, mental health, and diverse health behaviors. To evaluate differences between subgroups, Bayesian independent samples t-tests were calculated. To evaluate associations between constructs, Bayesian correlations were calculated. Results: Especially women, younger people, people from West Germany, and people with a high level of formal education seem to experience higher levels of climate change distress. Regarding climate change impairment, the results suggest that especially women, older people, people from West Germany, people with a low level of formal education, people with a low or middle social status, and people with an inadequate/problematic health literacy seem to experience higher levels of climate change impairment. Furthermore, climate change distress and climate change impairment were weakly and differently associated with general health, physical health, mental health, and diverse health behaviors. Discussion: Climate change distress and impairment are not evenly distributed within German society. The results of this study provide a starting point for the development of target group specific intervention programs.

Patient- and Areal-Level Risk Factors Associated With Lung Cancer Mortality in Victoria, Australia: A Bayesian Spatial Survival Analysis.

BACKGROUND: In Australia, lung cancer is the leading cause of cancer-related deaths. In Victoria, the mortality risk is assumed to vary across Local Government Areas (LGAs) due to variations in socioeconomic advantage, remoteness, and healthcare accessibility. Thus, we applied Bayesian spatial survival models to examine the geographic variation in lung cancer survival in Victoria. METHODS: Data on lung cancer cases were extracted from the Victorian Lung Cancer Registry (VLCR). To account for spatial dependence and risk factors of survival in lung cancer patients, we employed a Bayesian spatial survival model. Conditional Autoregressive (CAR) prior was assigned to model the spatial dependence. Deviance Information Criterion (DIC), Watanabe Akaike Information Criterion (WAIC), and Log Pseudo Marginal Likelihood (LPML) were used for model comparison. In the final best-fitted model, the Adjusted Hazard Ratio (AHR) with the 95% Credible Interval (CrI) was reported. The outcome variable was the survival status of lung cancer patients, defined as whether they survived or died during the follow-up period (death was our interest). RESULTS: Our study revealed substantial variations in lung cancer mortality in Victoria. Poor Eastern Cooperative Oncology Group (ECOG) performance status, diagnosed at a regional hospital, Small Cell Lung Cancer (SCLC), advanced age, and advanced clinical stage were associated with a higher risk of mortality, whereas being female, presented at Multidisciplinary Team (MDT) meeting, and diagnosed at a metropolitan private hospital were significantly associated with a lower risk of mortality. CONCLUSION: Identifying geographical disparities in lung cancer survival may help shape healthcare policy to implement more targeted and effective lung cancer care services.

Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

Spatial Nonstationarity in Phenological Responses of Nearctic Birds to Climate Variability.

Climate change is shifting the phenology of migratory animals earlier; yet an understanding of how climate change leads to variable shifts across populations, species and communities remains hampered by limited spatial and taxonomic sampling. In this study, we used a hierarchical Bayesian model to analyse 88,965 site-specific arrival dates from 222 bird species over 21 years to investigate the role of temperature, snowpack, precipitation, the El-Niño/Southern Oscillation and the North Atlantic Oscillation on the spring arrival timing of Nearctic birds. Interannual variation in bird arrival on breeding grounds was most strongly explained by temperature and snowpack, and less strongly by precipitation and climate oscillations. Sensitivity of arrival timing to climatic variation exhibited spatial nonstationarity, being highly variable within and across species. A high degree of heterogeneity in phenological sensitivity suggests diverging responses to ongoing climatic changes at the population, species and community scale, with potentially negative demographic and ecological consequences.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students.

BACKGROUND: A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated. METHODS: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). RESULTS: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. CONCLUSION: These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. TRIAL REGISTRATION: Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Body size and early marine conditions drive changes in Chinook salmon productivity across northern latitude ecosystems.

Disentangling the influences of climate change from other stressors affecting the population dynamics of aquatic species is particularly pressing for northern latitude ecosystems, where climate-driven warming is occurring faster than the global average. Chinook salmon (Oncorhynchus tshawytscha) in the Yukon-Kuskokwim (YK) region occupy the northern extent of their species' range and are experiencing prolonged declines in abundance resulting in fisheries closures and impacts to the well-being of Indigenous people and local communities. These declines have been associated with physical (e.g., temperature, streamflow) and biological (e.g., body size, competition) conditions, but uncertainty remains about the relative influence of these drivers on productivity across populations and how salmon-environment relationships vary across watersheds. To fill these knowledge gaps, we estimated the effects of marine and freshwater environmental indicators, body size, and indices of competition, on the productivity (adult returns-per-spawner) of 26 Chinook salmon populations in the YK region using a Bayesian hierarchical stock-recruitment model. Across most populations, productivity declined with smaller spawner body size and sea surface temperatures that were colder in the winter and warmer in the summer during the first year at sea. Decreased productivity was also associated with above average fall maximum daily streamflow, increased sea ice cover prior to juvenile outmigration, and abundance of marine competitors, but the strength of these effects varied among populations. Maximum daily stream temperature during spawning migration had a nonlinear relationship with productivity, with reduced productivity in years when temperatures exceeded thresholds in main stem rivers. These results demonstrate for the first time that well-documented declines in body size of YK Chinook salmon were associated with declining population productivity, while taking climate into account.

Bayesian network-guided sparse regression with flexible varying effects.

In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genomics or imaging studies. We distinguish between the predictors, which are the features utilized in the outcome prediction model, and the subject-level covariates, which modulate the effects of the predictors on the outcome. We construct a varying coefficients modeling framework where we infer a network among the predictor variables and utilize this network information to encourage the selection of related predictors. We employ variable selection spike-and-slab priors that enable the selection of both network-linked predictor variables and covariates that modify the predictor effects. We demonstrate through simulation studies that our method outperforms existing alternative methods in terms of both feature selection and predictive accuracy. We illustrate VERGE with an application to characterizing the influence of gut microbiome features on obesity, where we identify a set of microbial taxa and their ecological dependence relations. We allow subject-level covariates, including sex and dietary intake variables to modify the coefficients of the microbiome predictors, providing additional insight into the interplay between these factors.

Concurrent perception of competing predictions: A "split-stimulus effect".

Visual illusions are systematic misperceptions that can help us glean the heuristics with which the brain constructs visual experience. In a recently discovered visual illusion (the "frame effect"), it has been shown that flashing a stimulus inside of a moving frame produces a large misperception of that stimulus's position. Across two experiments, we investigated a novel illusion (the "split stimulus effect") where the symmetrical motion of two overlaid frames produces two simultaneous positional misperceptions of a single stimulus. That is, one stimulus is presented but two are perceived. In both experiments, a single red dot was flashed when the moving frames reversed direction, and participants were asked to report how many dots they saw. Naïve participants sometimes reported seeing two dots when only one was presented, indicating spontaneous perception of the illusion. A Bayesian analysis of the population prevalence of this effect was conducted. The dependence of this effect on the frames' speed, the dot's opacity, spatial attention, as the presence/absence of pre-flash motion ("postdiction") was also investigated, and the features of this illusion were compared to similar motion position illusions within a predictive processing framework. In demonstrating this illusory "splitting" effect, this study is the first to show that it is possible to be simultaneously aware of two opposing perceptual predictions about a single object and provides evidence of the hyperpriors that limit and inform the structure of visual experience.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis.

BACKGROUND: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. METHODS: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. RESULTS: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. CONCLUSIONS: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.

In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.

Childhood stunting in Indonesia: assessing the performance of Bayesian spatial conditional autoregressive models.

Stunting continues to be a significant health issue, particularly in developing nations, with Indonesia ranking third in prevalence in Southeast Asia. This research examined the risk of stunting and influencing factors in Indonesia by implementing various Bayesian spatial conditional autoregressive (CAR) models that include covariates. A total of 750 models were run, including five different Bayesian spatial CAR models (Besag-York-Mollie (BYM), CAR Leroux and three forms of localised CAR), with 30 covariate combinations and five different hyperprior combinations for each model. The Poisson distribution was employed to model the counts of stunting cases. After a comprehensive evaluation of all model selection criteria utilized, the Bayesian localised CAR model with three covariates were preferred, either allowing up to 2 clusters with a variance hyperprior of inverse-gamma (1, 0.1) or allowing 3 clusters with a variance hyperprior of inverse-gamma (1, 0.01). Poverty and recent low birth weight (LBW) births are significantly associated with an increased risk of stunting, whereas child diet diversity is inversely related to the risk of stunting. Model results indicated that Sulawesi Barat Province has the highest risk of stunting, with DKI Jakarta Province the lowest. These areas with high stunting require interventions to reduce poverty, LBW births and increase child diet diversity.

Lung cancer and smoking: years lived with disability in Tuscany (Italy). An analysis from the ACAB study.

BACKGROUND: Lung cancer (LC) is among the most common neoplasms, mostly caused by smoking. This study, carried out within the ACAB project, aims to provide local, updated and systematic estimates of years lived with disability (YLD) from LC due to smoking in the Tuscany region, Italy. METHODS: We estimated YLD for the year 2022 for the whole region and at subregional level by local health unit (LHU) using data from the Tuscany Cancer Registry and local surveys. YLD were calculated by applying the severity-specific LC prevalence, estimated with an incidence-based disease model, to the corresponding disability weight. The burden from smoking was computed by: modelling the prevalence of smokers with a Bayesian Dirichlet-Multinomial regression model; estimating the distribution of smokers by pack-years simulating individual smoking histories; collecting relative risks from the literature. RESULTS: In 2022 in Tuscany, LC caused 7.79 (95% uncertainty interval [UI] = 2.26, 17.27) and 25.50 (95% UI = 7.30, 52.68) YLDs per 100,000 females and males, respectively, with slight variations by LHU, and 53% and 66% of the YLDs were caused by smoking. CONCLUSION: The updated estimates of the burden of LC attributable to smoking for the Tuscany region as a whole and for each LHU provide indications to inform strategic prevention plans and set public health priorities. The impact of smoking on YLDs from LC is not negligible and heterogeneous by LHU, thus requiring local interventions.

Modeling seasonal immune dynamics of honey bee (Apis mellifera L.) response to injection of heat-killed Serratia marcescens.

The honey bee, Apis mellifera L., is one of the main pollinators worldwide. In a temperate climate, seasonality affects the life span, behavior, physiology, and immunity of honey bees. In consequence, it impacts their interaction with pathogens and parasites. In this study, we used Bayesian statistics and modeling to examine the immune response dynamics of summer and winter honey bee workers after injection with the heat-killed bacteria Serratia marcescens, an opportunistic honey bee pathogen. We investigated the humoral and cellular immune response at the transcriptional and functional levels using qPCR of selected immune genes, antimicrobial activity assay, and flow cytometric analysis of hemocyte concentration. Our data demonstrate increased antimicrobial activity at transcriptional and functional levels in summer and winter workers after injection, with a stronger immune response in winter bees. On the other hand, an increase in hemocyte concentration was observed only in the summer bee population. Our results indicate that the summer population mounts a cellular response when challenged with heat-killed S. marcescens, while winter honey bees predominantly rely on humoral immune reactions. We created a model describing the honey bee immune response dynamics to bacteria-derived components by applying Bayesian statistics to our data. This model can be employed in further research and facilitate the investigating of the honey bee immune system and its response to pathogens.