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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.

Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.

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).

Comprehensive assessment of debris flow hazards is a challenging issue due to the complexity and uncertainty of its factors. For this reason, the practical forecasting of debris flows requires developing a reliable and realistic forecasting model. In this paper, a Bayesian Networks (BNs) model is proposed for identification of debris flows events in the northern basins of Iran. BNs model illustrates the uncertainty of the results as probability percentage of debris flow occurrence in different categories (non-occurrence, occurrence with low-intensity and occurrence with high-intensity). In this research, average basin elevation, average basin slope, watershed area, the current rainfall, antecedent rainfalls of 3-day ago and discharge of 1-day ago were used as the predictor variables. Moreover, K-means clustering method was applied in modeling by the BNs model. To identify the effective predictors in debris flow occurrence, sensitivity analysis was performed. For this purpose, scenarios which employ various predictor variables were tested. The scenario which uses all predictor variables has a forecasting accuracy of 91%. This scenario was selected as the best scenario. However, a scenario which employs only effective predictors also proposed for practical uses. The results of the various forecasting scenarios showed that average basin elevation, watershed area, current rainfall and discharge of 1-day ago are the effective predictors in the forecasting debris flows. The BNs model may be proposed for future tests in the other debris flow prone regions.

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.

Background The current standard of care for advanced human epidermal growth factor receptor 2 (HER2)‐positive breast cancer is pertuzumab plus trastuzumab and docetaxel as first‐line therapy. However, with the development of newer treatment regimens, there is a lack of evidence regarding which is the optimal treatment strategy. The aim of this network meta‐analysis was to evaluate the efficacy and safety of first‐line regimens for advanced HER2‐positive breast cancer by indirect comparisons. Methods A systematic review and Bayesian network meta‐analysis were conducted. The PubMed, EMBASE, and Cochrane Library databases were searched for relevant articles published through to December 2023. The hazard ratio (HR) and 95% credible interval (CrI) were used to compare progression‐free survival (PFS) between treatments, and the odds ratio and 95% CrI were used to compare the objective response rate (ORR) and safety. Results Twenty randomized clinical trials that included 15 regimens and 7094 patients were analyzed. Compared with the traditional trastuzumab and docetaxel regimen, PFS was longer on the pyrotinib and trastuzumab plus docetaxel regimen (HR: 0.41, 95% CrI: 0.22–0.75) and the pertuzumab and trastuzumab plus docetaxel regimen (HR: 0.65, 95% CrI: 0.43–0.98). Consistent with the results for PFS, the ORR was better on the pyrotinib and trastuzumab plus docetaxel regimen and the pertuzumab and trastuzumab plus docetaxel regimen than on the traditional trastuzumab and docetaxel regimen. The surface under the cumulative ranking curve indicated that the pyrotinib and trastuzumab plus docetaxel regimen was most likely to rank first in achieving the best PFS and ORR. Comparable results were found for grade ≥3 AE rates of ≥10%. Conclusions Our results suggest that the pyrotinib and trastuzumab plus docetaxel regimen is most likely to be the optimal first‐line therapy for patients with HER2‐positive breast cancer. We conducted a network meta‐analysis to compare all treatment regimens from randomized controlled trials for advanced HER2‐positive breast cancer in first‐line setting.