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Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.

Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

Human activities pose a significant threat to the water quality of rivers when pollution exceeds the threshold limit. Urban activities in particular are highlighted as one of the major causes of contamination in surface water bodies in Asian countries. Evaluation of sustainable human population capacities in river watersheds is necessary to maintain better freshwater ecosystems in a country while achieving its development goals as a nation. We evaluated the correlation between the growth rate of the population in a watershed area and water quality parameters of a river ecosystem. The Kelani River in Sri Lanka was selected for the study. The highest correlation coefficients of 0.7, 0.69, 0.69 (p < 0.01) corresponding to biochemical oxygen demand (BOD), dissolved oxygen (DO) and total coliform (TC) were obtained with the population in watersheds of the Kelani river in Sri Lanka. Thus, we propose a quantitative approach to estimating the population capacity of watersheds based on water quality classification standards (WQCS), employing the Bayesian network (BN) classification model. The optimum population ranges were obtained from the probability distribution table of the population node in the BN. The results showed that the population density should be approximately less than 2375 to keep the water quality in the watershed for bathing and drinking purposes and approximately less than 2672 for fish and other aquatic organisms. This research will offer a means that can used to understand the impact of population on water quality in river basins and confer direct influence on natural water bodies.

A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the \"high hazardous\" category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures.

Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class variables. A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process complexities. We also describe algorithms for structural learning, provide real-world applications where they have been used, and compile a collection of related software.

Objectives: We aim to compare the effectiveness of different drug treatments in improving recurrence in patients with chronic subdural hematoma (CSDH). Methods: Eligible randomized controlled trials (RCTs) and prospective trials were searched in PubMed, Cochrane Library, and Embase, from database inception to December 2021. After the available studies following inclusion and exclusion criteria were screened, the main outcome measures were strictly extracted. Taking the random-effects model, dichotomous data were determined and extracted by odds ratio (OR) with 95% credible interval (CrI), and a surface under the cumulative ranking curve (SUCRA) was generated to calculate the ranking probability of comparative effectiveness among each drug intervention. Moreover, we used the node-splitting model to evaluate inconsistency between direct and indirect comparisons of our network meta-analysis (NMA). Funnel plots were used to evaluate publication bias. Results: From the 318 articles found during initial citation screening, 11 RCTs and 3 prospective trials ( n = 3,456 participants) were ultimately included in our study. Our NMA results illustrated that atorvastatin + dexamethasone (ATO+DXM) (OR = 0.06, 95% CrI 0.01, 0.89) was the most effective intervention to improve recurrence in patients with CSDH (SUCRA = 89.40%, 95% CrI 0.29, 1.00). Four drug interventions [ATO+DXM (OR = 0.06, 95% CrI 0.01, 0.89), DXM (OR = 0.18, 95% CrI 0.07, 0.41), tranexamic acid (TXA) (OR = 0.26, 95% CrI 0.07, 0.41), and ATO (OR = 0.41, 95% CrI 0.12, 0.90)] achieved statistical significance in improving recurrence in CSDH patients compared with the placebo (PLB) or standard neurosurgical treatment (SNT) group. Conclusion: Our NMA showed that ATO+DXM, DXM, ATO, and TXA had definite efficacy in improving recurrence in CSDH patients. Among them, ATO+DXM is the best intervention for improving recurrence in patients with CSDH in this particular population. Multicenter rigorous designed prospective randomized trials are still needed to evaluate the role of various drug interventions in improving neurological function or outcome. Systematic Review Registration: ( https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=299491 ), identifier (CRD 42022299491).

Background Over the past decade, traditional demographic, lifestyle, and metabolic factors, along with air pollutants, have increasingly been recognized as key contributors to type 2 diabetes (T2D). However, the comprehensive causal structure among these factors and their individual and interacting interventional effects have seldom been characterized in long-term population studies. Methods Using 11-year follow-up data from 2,102 adults without T2D in the Ansan cohort of the Korean Genome and Epidemiology Study (KoGES), we investigated causal pathways among demographic, lifestyle, metabolic factors, and multiple ambient air pollutants leading to long-term T2D incidence. We employed a Conditional Survival Bayesian Network (CSBN), which integrates survival analysis with Bayesian network modeling to accommodate censored and incomplete data, to visualize the causal structure among risk factors, and to estimate both individual and joint (interaction) interventional effects. Results The CSBN depicted a holistic causal structure showing how multiple risk factors jointly shape T2D development over the 11-year follow-up and helped distinguish putative direct/indirect pathways from associations likely reflecting confounding. Interventional analysis quantified each factor’s causal contribution to the 11-year T2D incidence. Obesity produced the largest individual effect: setting BMI to the obese category approximately doubled 11-year T2D risk compared with normal weight. High alanine aminotransferase (ALT) and older age increased risk by about 40–50%, while family history of T2D, dyslipidemia, overweight, , and gaseous pollutants had intermediate effects. Furthermore, the CSBN uncovered synergistic interactions mainly among metabolic factors. In particular, ALT with family history, dyslipidemia, or obesity displayed strong additive interactions. By contrast, air pollutants were found to influence T2D independently rather than through interactions with other risk factors. Conclusion These findings underscore the importance of integrated public health strategies targeting multiple risk factors to effectively curb T2D incidence. The CSBN’s capability to explicitly model complex causal interactions highlights the necessity for advanced epidemiological analyses to inform targeted preventive measures and efficient resource allocation.

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

Reservoir operations play a pivotal role in modifying drought propagation processes, particularly by influencing the transition from meteorological to hydrological drought. This study investigates the drought propagation characteristics in the middle reaches of the Hanjiang River Basin, China, under both natural and observed (reservoir‐influenced) conditions. The Standardized Precipitation Evapotranspiration Index and Standardized Streamflow Index were utilized to characterize meteorological and hydrological drought, respectively. The Soil and Water Assessment Tool was employed to reconstruct natural streamflow, providing a baseline for comparison. A nonlinear copula function was applied to model the dependence between meteorological and hydrological drought characteristics, and a Copula‐Bayesian network was developed to quantify propagation probabilities. Under the regulation of the Danjiangkou Reservoir, drought propagation characteristics for 1–12‐month timescales have shifted markedly: the average propagation time downstream was prolonged from 0.25–0.70 months to 0.94–2.36 months, while the propagation rate declined from 0.83–0.89 to 0.48–0.65, and the sensitivity decreased from 0.83–0.96 to 0.68–0.79. In the natural scenario, the optimal propagation model was based on the Gumbel copula, whereas the observed scenario was best fitted by the Frank copula. The likelihood of hydrological drought increased with the intensity and duration of meteorological drought. However, compared to natural conditions, reservoir regulation significantly delayed the onset and reduced the probability of hydrological drought occurrence. These findings elucidate the nonlinear dynamics of drought propagation and underscore the regulating effect of large‐scale reservoirs on downstream hydrological responses. Study of meteorological‐hydrological drought propagation under reservoir regulation using a Copula‐Bayesian network in the Hanjiang River Basin.

Endocrine therapy has become the fundamental treatment option for hormone receptor-positive (HR+) and receptor tyrosine-protein kinase erbB-2-negative (HER2−) metastatic breast cancer (mBC). While treatments incorporating cyclin-dependent kinase (CDK)4 and 6 inhibitors are more prevalent than ever, comparisons among those regimens are scarce. The aim of the present study was to identify the most effective maintenance treatment for patients with HR+ and HER2− mBC. To this end, databases including PubMed, Embase, Cochrane Library, Scopus and Google Scholar were searched from inception to August, 2023. The endpoints comprised overall survival (OS) and progression free survival (PFS). For dichotomous variants, hazard ratios (HRs) and odds ratios (ORs) were generated, while standard mean difference (SMD) was used for consecutive variants by Bayesian network meta-analysis to make pairwise comparisons among regimens, to determine the optimal therapy. These processes were conducted using Rstudio 4.2.2 orchestrated with STATA 17.0 MP. A total of 16 randomized controlled trials including 7,174 patients with 11 interventions were analyzed. Compared with aromatase inhibitor (AI), palbociclib plus AI (PalboAI) exhibited a significantly longer PFS up to the 36th month of follow-up [HR=1.7; 95% credible interval, 1.36-2.16], including on the 3rd [OR=2.22; 95% confidence interval (CI), 1.10-4.47], 6th (OR=2.39; 95% CI, 1.21-4.69), 12th (OR=1.94; 95% CI, 1.34-2.79), 18th (OR=2.38; 95% CI, 1.65-3.44), 24th (OR=2.39; 95% CI, 1.67-3.43), 30th (OR=2.10; 95% CI, 1.62-2.74) and 36th (OR=2.66; 95% CI, 1.37-5.18) month of follow-up. Additionally, abemaciclib plus fulvestrant exhibited significant effects compared with AI alone between 12 and 36 months. Ribociclib plus fulvestrant, ribociclib plus AI and dalpiciclib plus AI exerted significant effects compared with AI alone between 12 and 30 months. Considering the effect on OS and PFS together with adverse reactions, safety, medical compliance and route of administration, PalboAI was found to be the optimal treatment for HR+/HER2−mBC. However, additional head-to-head clinical trials are warranted to confirm these findings.