Integrating Deep Learning-Based Dose Distribution Prediction with Bayesian Networks for Decision Support in Radiotherapy for Upper Gastrointestinal cancer.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. Conclusion: We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

The causal role of circulating inflammatory markers in osteoporosis: a bidirectional Mendelian randomized study.

Background: Osteoporosis (OP) associated with aging exerts substantial clinical and fiscal strains on societal structures. An increasing number of research studies have suggested a bidirectional relationship between circulating inflammatory markers (CIMs) and OP. However, observational studies are susceptible to perturbations in confounding variables. In contrast, Mendelian randomization (MR) offers a robust methodological framework to circumvent such confounders, facilitating a more accurate assessment of causality. Our study aimed to evaluate the causal relationships between CIMs and OP, identifying new approaches and strategies for the prevention, diagnosis and treatment of OP. Methods: We analyzed publicly available GWAS summary statistics to investigate the causal relationships between CIMs and OP. Causal estimates were calculated via a systematic analytical framework, including bidirectional MR analysis and Bayesian colocalization analysis. Results: Genetically determined levels of CXCL11 (OR = 0.91, 95% CI = 0.85-0.98, P = 0.008, PFDR = 0.119), IL-18 (OR = 0.88, 95% CI = 0.83-0.94, P = 8.66×10-5, PFDR = 0.008), and LIF (OR = 0.86, 95% CI = 0.76-0.96, P = 0.008, PFDR = 0.119) were linked to a reduced risk of OP. Conversely, higher levels of ARTN (OR = 1.11, 95% CI = 1.02-1.20, P = 0.012, PFDR = 0.119) and IFNG (OR = 1.16, 95% CI = 1.03-1.30, P = 0.013, PFDR = 0.119) were associated with an increased risk of OP. Bayesian colocalization analysis revealed no evidence of shared causal variants. Conclusion: Despite finding no overall association between CIMs and OP, five CIMs demonstrated a potentially significant association with OP. These findings could pave the way for future mechanistic studies aimed at discovering new treatments for this disease. Additionally, we are the first to suggest a unidirectional causal relationship between ARTN and OP. This novel insight introduces new avenues for research into diagnostic and therapeutic strategies for OP.

Network meta-analysis of novel targeted therapies for relapsed/refractory chronic lymphocytic leukemia.

Background: The recent development of new antileukemic therapies (anti-CD20 monoclonal antibodies, Bruton tyrosine kinase inhbitors, phosphoinositide 3-kinase inhibitors, and B-cell lymyphoma-2 antagonists) improved the progression-free survival (PFS) compared with selected standard regimens in clinical trials for patients with relapsed/refractory chronic lymphocytic leukemia (CLL). Unfortunately, the relative efficacy of all possible therapeutic options remains unknown because there is no direct evidence for all possible comparisons. Objectives: We aimed to compare the efficacy and safety of novel agents, chemotherapy, and immunotherapy using a Bayesian network meta-analysis (NMA). Design: Systematic literature review with Bayesian NMA. Methods: An extensive systematic literature review of randomized clinical trials for relapsed/refractory CLL was performed. We searched for articles indexed in medical databases (MEDLINE, Embase, The Cochrane Library) and gray literature that could be further implemented into the Bayesian NMA. Results: The systematic search identified 15 randomized trials that formed networks comparing PFS, overall survival (OS), overall response rates, and serious adverse events. Our study showed that all regimens containing novel agents significantly prolonged PFS compared with standard chemoimmunotherapy and immunotherapy. Among targeted drugs, venetoclax (VEN) + rituximab (RTX) had comparable efficacy in terms of PFS to zanubrutinib (ZAN) [hazard ratio (95% credible interval), 1.10 (0.59-2.08)], acalabrutinib (ACA) [0.78 (0.47-1.30)], ibrutinib (IBR) monotherapy [0.72 (0.41-1.27)], and other IBR-based regimens. ZAN was superior to IBR monotherapy [0.65 (0.49-0.86)] but not to ACA [0.71 (0.49-1.02)]. There were no significant differences in OS in any of the above comparisons. Conclusion: All novel therapies have better efficacy than chemoimmunotherapy and immunotherapy regimens. Among novel agents, the relative efficacy of VEN + RTX was similar to all BTKi, while ZAN was superior to IBR and comparable to ACA. Trial registration: PROSPERO CRD42022304330.

Sustainable resilient E-waste management in London: A circular economy perspective.

The circular economy (CE) is reasoned to organize complex systems supporting sustainable resilience by distinguishing between waste materials and economic growth. This is crucial to the electronic waste (e-waste) industry of developed countries, and e-waste operation management has become their top priority because e-waste contains toxic materials and valuable sources of elements. In the UK, although London Metropolitan city boasts an ambitious sustainable resilience target underlying the context of CE, practical implementation has yet to be feasible, with few investigations detailing if and how the existing target implications enable industrial and social-ecological sectors to continue their performance functionalities in the face of undesired disruptions. In this paper, a dynamic Bayesian Network (dynamic BN) approach is developed to address a range of potential risks. The existing London e-waste operation management is considered as an application of study for sustainable resilience development. Through the utilization of dynamic BN, a comprehensive analysis yields a Resilience Index (RI) of 0.5424, coupled with a StdDev of 0.01350. These metrics offer a profound insight into the intricate workings of a sustainable system and its capacity to swiftly rebound from unexpected shocks and disturbances. This newfound understanding equips policymakers with the knowledge needed to navigate the complexities of sustainable e-waste management effectively. The implications drawn from these in-depth analyses furnish policymakers with invaluable information, enabling them to make judicious decisions that advance the cause of sustainable e-waste management. The findings underscore that the absorptive capacity of a sustainable and resilient e-waste operation management system stands as the foremost defense mechanism against unforeseen challenges. Furthermore, it becomes evident that two pivotal factors, namely "diversifying the supply chain" and "enhancing supply chain transparency," play pivotal roles in augmenting the sustainability and resilience of e-waste operation management within the context of London's ambitious sustainability targets. These factors are instrumental in steering the trajectory of e-waste management towards a more sustainable and resilient future, aligning with London's aspirations for a greener and more eco-conscious future.

Bayesian cluster geographically weighted regression for spatial heterogeneous data.

Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be complicated and expensive. One of these algorithms is geographically weighted regression (GWR) which was proposed in the geography literature to allow relationships in a regression model to vary over space. In contrast to traditional linear regression models, which have constant regression coefficients over space, regression coefficients are estimated locally at spatially referenced data points with GWR. The motivation for the adaption of GWR is the idea that a set of constant regression coefficients cannot adequately capture spatially varying relationships between covariates and an outcome variable. GWR has been applied widely in diverse fields, such as ecology, forestry, epidemiology, neurology and astronomy. While frequentist GWR gives us point estimates and confidence intervals, Bayesian GWR enriches our understanding by including prior knowledge and providing probability distributions for parameters and predictions of interest. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian geographically weighted regression (BGWR) with a post-processing step that includes Gaussian mixture model and the Dirichlet process mixture model. Second, this paper examines situations in which a particular covariate holds significant importance in one region but not in another in the Bayesian framework. Lastly, it addresses computational challenges in existing BGWR, leading to enhancements in Markov chain Monte Carlo estimation suitable for large spatial datasets. The efficacy of the proposed method is demonstrated using simulated data and is further validated in a case study examining children's development domains in Queensland, Australia, using data provided by Children's Health Queensland and Australia's Early Development Census.

Comparing the DSM-5 Dimensional Trait and Triarchic Model Conceptions of Psychopathy: An External Validity Analysis.

In the DSM-5 Alternative Model of Personality Disorders (AMPD), psychopathy is marked by the presence of attention seeking, low anxiousness, and lack of social withdrawal, along with traits from the domains of Antagonism and Disinhibition. The triarchic model of psychopathy (TriPM) posits three biobehaviorally based traits underlying it: disinhibition, meanness, and boldness. The current study directly compared relations for measures of the two models with the broad dimensions of externalizing, internalizing, and positive adjustment. Participants (1,678 adults) were surveyed regarding maladaptive personality traits, clinical symptoms, and positive adjustment features. The TriPM model explained more variance than the AMPD in substance use, positive adjustment, and empathy, whereas the AMPD model explained more variance in internalizing symptoms. In addition, AMPD Antagonism and the Psychopathy Specifier diverged from TriPM Meanness and Boldness in their associations with some specific outcomes. Overall, our study provides evidence for complementarity of the two models in characterizing the multifaceted nature of psychopathy.

A Bayesian competing risk analysis of renal cancer patients based on SEER database.

BACKGROUND: Renal cell carcinoma (RCC) remains a global health concern due to its poor survival rate. This study aimed to investigate the influence of medical determinants and socioeconomic status on survival outcomes of RCC patients. We analyzed the survival data of 41,563 RCC patients recorded under the Surveillance, Epidemiology, and End Results (SEER) program from 2012 to 2020. METHODS: We employed a competing risk model, assuming lifetime of RCC patients under various risks follows Chen distribution. This model accounts for uncertainty related to survival time as well as causes of death, including missing cause of death. For model analysis, we utilized Bayesian inference and obtained the estimate of various key parameters such as cumulative incidence function (CIF) and cause-specific hazard. Additionally, we performed Bayesian hypothesis testing to assess the impact of multiple factors on the survival time of RCC patients. RESULTS: Our findings revealed that the survival time of RCC patients is significantly influenced by gender, income, marital status, chemotherapy, tumor size, and laterality. However, we observed no significant effect of race and origin on patient's survival time. The CIF plots indicated a number of important distinctions in incidence of causes of death corresponding to factors income, marital status, race, chemotherapy, and tumor size. CONCLUSIONS: The study highlights the impact of various medical and socioeconomic factors on survival time of RCC patients. Moreover, it also demonstrates the utility of competing risk model for survival analysis of RCC patients under Bayesian paradigm. This model provides a robust and flexible framework to deal with missing data, which can be particularly useful in real-life situations where patients information might be incomplete.

A Bayesian approach to reveal the role of psychological factors on turnover intention among nurses during the COVID-19 pandemic.

BACKGROUND: Turnover intention is considered a significant challenge for healthcare and treatment organizations. The challenging conditions of treating COVID-19 patients and the physical and mental stress imposed on nurses during the pandemic may lead them to leave their jobs. The present study aimed to determine the role of psychological factors (general health, mental workload, work-family conflicts, and resilience) on turnover intention using a Bayesian approach during the COVID-19 pandemic. METHODS: The present cross-sectional study was carried out during the winter of 2021 at three hospitals in Khuzestan Province, Iran. To collect data for this investigation, 300 nurses were chosen based on Cochran's formula and random sampling technique. Seven questionnaires, including General Health, Mental Workload, Work-Family Conflict, Resilience, Job Stress, Fear of COVID-19, and Turnover Intention Questionnaires. Bayesian Networks (BNs) were used to draw probabilistic and graphical models. A sensitivity analysis also was performed to study the effects of the variables. The GeNIe academic software, version 2.3, facilitated the examination of the Bayesian network. RESULTS: The statistically significant associations occurred between the variables of fear of COVID-19 and job stress (0.313), job stress and turnover intention (0.302), and resilience and job stress (0.298), respectively. Job stress had the highest association with the fear of COVID-19 (0.313), and resilience had the greatest association with the work-family conflict (0.296). Also, the association between turnover intention and job stress (0.302) was higher than the association between this variable and resilience (0.219). At the low resilience and high job stress with the probability of 100%, the turnover intention variable increased by 20%, while at high resilience and low job stress with the probability of 100%, turnover intention was found to decrease by 32%. CONCLUSION: In general, the results showed that four psychological factors affect job turnover intention. However, the greatest impact was related to job stress and resilience. These results can be used to manage job turnover intention in medical environments, especially in critical situations such as COVID-19.

Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating.

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game 'Plinko', to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.

Bayesian network analysis of drug treatment strategies for thyroid associated ophthalmopathy.

BACKGROUND: The first line treatment for moderate to severe active thyroid associated ophthalmopathy is glucocorticoid pulse therapy, but for patients with contraindications to hormone therapy or hormone resistance, it is urgent to find a suitable treatment plan. AIMS: To find a reliable alternative to hormone pulse therapy for thyroid associated ophthalmopathy by comparing the efficacy with first-line treatment regimens. METHODS: Search PubMed, Ovid, Web of science, Cochrane library, and Clinical Trials.gov for randomized controlled trials on the treatment of thyroid associated ophthalmopathy published as of July 7, 2024. Quality evaluation and Bayesian network analysis were conducted using RevMan 5.3 software, STATA15.0 software, and ADDIS 1.16.8 software. RESULTS: A total of 666 patients were included in 11 studies and 8 interventions. Network analysis showed that the three interventions of mycophenolate mofetil combined with glucocorticoids, Teprotumumab and 99Tc-MDP were superior to glucocorticoid pulse therapy in improving clinical activity scores and proptosis. The regimen of glucocorticoids combined with statins can improve the quality of life score and diplopia score of patients. Neither methotrexate combined with glucocorticoids nor rituximab alone showed additional advantages when compared with glucocorticoid pulse therapy. CONCLUSION: Mycophenolate mofetil combined with glucocorticoid therapy is very beneficial for moderate to severe active thyroid associated ophthalmopathy. Mycophenolate mofetil may be a good choice when patients have contraindications to hormone use or hormone resistance. Teprotumumab is very promising and may be able to avoid patients undergoing orbital decompression surgery. The durability and safety of its long-term efficacy need to be further observed.

Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores.

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

MI-CSBO: a hybrid system for myocardial infarction classification using deep learning and Bayesian optimization.

Myocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section.

A statistical approach to assess interspecific consumptive competition and functional redundancy in ephemeral resource uses using camera traps.

Camera traps have been widely used in wildlife research, offering significant potential for monitoring species interactions at ephemeral resources. However, raw data obtained from camera traps often face limitations due to observation censoring, where resource consumption by dominant animals may obscure potential resource use by less dominant animals. We extended time-to-detection occupancy modeling to quantify interspecific consumptive competition and redundancy of ecosystem functions through consumption between two species, while accounting for observation censoring. By treating resource use by rival species as censored data, we estimated the proportion of resources potentially used in the absence of rival species and calculated the loss caused by the rival species, which is defined as "Competition Intensity Index." We also defined the Unique Functional Contribution, which represents the net functional loss when a species is removed, calculated by excluding the contribution potentially substituted by the other species. We also considered resource degradation and computed the quantity of resources acquired by each species. This established framework was applied to predation data on bird nests by alien squirrels and other predators (Case 1) as well as scavenging on mammalian carcasses by two carnivores (Case 2). In Case 1, the introduction of squirrels significantly affected the breeding success of birds. Although nests were being preyed upon by native crows also, our model estimated that Unique Functional Contribution by the squirrels was 0.47. This means that, by eradicating the squirrels, the reproductive success of the birds could potentially increase by as much as 47%. In Case 2, the Competition Intensity Index for foxes was 0.17, whereas that for raccoon dogs was 0.46, suggesting an asymmetric effect of resource competition between the two species. The frequency distribution of wet mass available to the two species differed significantly. This approach will enable a more robust construction of resource-consumer interaction networks.

Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data.

An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease (AD) typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as "horizontal" changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent follow-up visits leading to interval-censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structures, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop an R package with a Markov chain Monte Carlo sampler for estimation. We present a simulation study examining the performance of this method relative to an ordinary lasso penalty and apply the proposed method to identify groups of predictive genetic and clinical risk factors for AD in the Religious Orders Study and Memory and Aging Project prospective cohort studies of AD and dementia.

Investigating Directional Invariance in an Item Response Tree Model for Extreme Response Style and Trait-Based Unfolding Responses.

Item response tree (IRTree) approaches have received increasing attention in the response style literature due to their capability to partial out response style latent traits from content-related latent traits by considering separate decisions for agreement and level of agreement. Additionally, it has shown that the functioning of the intensity of agreement decision may depend upon the agreement decision with an item, so that the item parameters and person parameters may differ by direction of agreement; when the parameters across direction are the same, this is called directional invariance. Furthermore, for non-cognitive psychological constructs, it has been argued that the response process may be best described as following an unfolding process. In this study, a family of IRTree models to handle unfolding responses with the agreement decision following the hyperbolic cosine model and the intensity of agreement decision following a graded response model is investigated. This model family also allows for investigation of item- and person-level directional invariance. A simulation study is conducted to evaluate parameter recovery; model parameters are estimated with a fully Bayesian approach using JAGS (Just Another Gibbs Sampler). The proposed modeling scheme is demonstrated with two data examples with multiple model comparisons allowing for varying levels of directional invariance and unfolding versus dominance processes. An approach to visualizing the final model item response functioning is also developed. The article closes with a short discussion about the results.

Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators.

Background: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results: Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Conclusion: Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

Stereotypes as Bayesian prediction of social groups.

A stereotype is a generalization about a class of people which is often used to make probabilistic predictions about individuals within that class. Can stereotypes can be understood as conditional probabilities that distinguish among groups in ways that follow Bayesian posterior prediction? For instance, the stereotype of Germans as industrious can be understood as the conditional probability of someone being industrious given that they are German. Whether such representations follow Bayes' rule was tested in a replication and extension of past work. Across three studies (N = 2,652), we found that people's judgments of different social categories were appropriately Bayesian, in that their direct posterior predictions were aligned with what Bayes' rule suggests they should be. Moreover, across social categories, traits with a high calculated diagnostic ratio generally distinguished stereotypic from non-stereotypic traits. The effects of cognitive ability, political orientation, and motivated stereotyping were also explored.

Novel Reference Equations for Pulmonary Artery Size and Pulsatility Using Echocardiography and their Diagnostic Value in Pulmonary Hypertension.

BACKGROUND: According to the most recent pulmonary hypertension (PH) guidelines, a main pulmonary artery (MPA) diameter>25 mm on transthoracic echocardiography (TTE) supports the diagnosis of PH. However, the size of the pulmonary artery(PA) may vary according to body size, age, and cardiac phases. RESEARCH QUESTIONS: 1)What are the reference limits for PA size on TTE, considering differences in body size, sex, and age? 2)What is the diagnostic value of PA size for classifying pulmonary hypertension? 3)How does the selection of different reference groups (healthy volunteers versus patients referred for right heart catheterization (RHC)) influence the diagnostic odds ratio (DOR)? STUDY DESIGN AND METHODS: The study included a reference cohort of 248 healthy individuals as controls, 693 PH patients proven by RHC, and 156 non-PH patients proven by RHC. In the PH cohort, 300 had group-1 PH, 207 had group-2 PH, and 186 with group-3 PH. MPA and right PA(RPA) diameters and areas were measured in the upper sternal short-axis and the suprasternal notch views. Reference limits (5th-95th percentile) were based on absolute values and height-indexed measures. Quantile regression analysis was used to derive median and 95th quantile reference equations for the PA measures. DORs and probability diagnostic plots for PH were then determined using healthy controls and non-PH cohorts. RESULTS: The 95th percentile for indexed MPA diameter was 15mm/m in diastole and 19mm/m in systole in both sexes. Quantile regression analysis revealed a weak age effect (pseudo R2 of 0.08 to 0.10 for MPA diameters). Among measures, the MPA size in diastole had the highest DOR, 156.2(68.3-357.5), for detection of group-1 PH. Similarly, the DORs were also high for group-2 and 3 PH when compared to controls but significantly lower compared to non-PH cohort. INTERPRETATION: The study presents novel reference limits for MPA based on height indexing and quantile regression.

Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function.

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.