Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field

An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa

Background: Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change. Results: Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model. Conclusions: A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives

Structurally aware discretisation for Bayesian networks

Bayesian networks represent a versatile probabilistic modelling technique widely used to tackle a range of problems in many different domains. However, they are discrete models, and a significant decision when designing a BN is how to split the continuous variables into discrete bins. Default options offered in most BN packages include assigning an equal number of cases to each bin or assigning equal sized bins. However, these methods discretise nodes independently of each other. When learning probabilities from data, this can result in conditional probability tables (CPTs) with missing or uninformed probabilities because data for particular bin combinations (scenarios) is either missing or scarce. This can result in poor model performance

Extreme events, water quality and health: a participatory Bayesian risk assessment tool for managers of reservoirs

Extreme weather events pose major challenges for the delivery of safe drinking water, especially in a country like Australia. As a consequence, a participatory Bayesian Network modelling approach was used to develop a risk assessment tool for estimating, and ranking, water quality-related health risks associated with extreme weather events. The model was developed for a large dam supplying a water treatment plant in New South Wales, Australia. This methodological approach addresses challenges associated with fragmented data (for model parameterisation) and parameter uncertainty by eliciting and integrating quantitative and qualitative data (including expert opinions) into a single framework. Key-stakeholders were engaged in developing and then refining separate conceptual models around the three critical parameters of turbidity, water colour and Cryptosporidium sp. These three conceptual models were then combined into a single conceptual model, which then formed the basis for the Bayesian Network model. The final risk assessment tool was able to quantify the sensitivity of the water treatment plant's efficacy (ability to supply high quality potable water) in response to different extreme event scenarios. Overall, landslip-related events were the most concerning for water quality-related health risks, but an emergent outcome was how the scenarios were ranked quite differently depending on the group, and expertise of the stakeholders’ opinions used to run the model. Such tool can assist stakeholders for an effective long-term water resource management

Examining the potential for energy-positive bulk-water infrastructure to provide long-term urban water security: a systems approach

Urban centres are increasingly requiring more water than existing groundwater and surface water sources can supply. Water authorities must consider energy intensive supply alternatives such as recycling and desalination, leading to a water-energy-climate conundrum. In this study, a systems perspective of the water-energy-climate nexus is applied to South-East Queensland (SEQ), Australia. Under a changing climate, SEQ is predicted to experience reduced reservoir inflows and increased evaporation rates, which will consequently lead to reduced water availability. To exacerbate this issue, anticipated high population growth in SEQ will increase water demand, putting even more stress on the traditional water supply sources. Clearly, there is a strong incentive to pursue solutions that increase water security without contributing to anthropogenic climate change. Using a system dynamics model, the water balance of the bulk water supply system is evaluated over a 100-year life cycle. The outputs of the model are used to investigate potential management and infrastructure options available to SEQ for adapting to increased water scarcity. The historical rainfall patterns of SEQ requires significant contingency to be built into surface water capacity in order to mitigate low rainfall years, and provide adequate water security. In contrast, reverse osmosis (RO) desalination plants do not require this excess capacity because they are rain-independent. However, RO has high energy consumption and associated greenhouse gas emissions when operating and their potential long periods of redundancy due to periods of sufficient surface water supplies remain unresolved issues. The model demonstrates that dual purpose pressure retarded osmosis desalination plants offer a potential solution, by providing water security at a lower cost than surface water reservoir augmentation, while offsetting energy use through renewable energy generation when RO plants would otherwise be sitting idle. Potentially this technology represents a future sustainable solution to overcome water security concerns

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Tex

Why do some patients with stage 1A and 1B endometrial endometrioid carcinoma experience recurrence? A retrospective study in search of prognostic factors

Objectives: Endometrial endometrioid carcinoma (EEC) is the most encountered subtype of endometrial cancer (EC). Our study aimed to investigate the factors affecting recurrence in patients with stage 1A and 1B EEC. Material and methods: Our study included 284 patients diagnosed with the International Federation of Gynecology and Obstetrics stage 1A/1B EEC in our center from 2010 to 2018. The clinicopathological characteristics of the patients were obtained retrospectively from their electronic files. Results: The median age of the patients was 60 years (range 31–89). The median follow-up time of the patients was 63.6 months (range 3.3–185.6). Twenty-two (7.74%) patients relapsed during follow-up. Among the relapsed patients, 59.1% were at stage 1A ECC, and 40.9% were at stage 1B. In our study, the one-, three-, and five-year recurrence-free survival (RFS) rates were 98.9%, 95.4%, and 92.9%, respectively. In the multivariate analysis, grade and tumor size were found to be independent parameters of RFS in all stage 1 EEC patients. Furthermore, the Ki-67 index was found to affect RFS in stage 1A EEC patients, and tumor grade affected RFS in stage 1B EEC patients. In the time-dependent receiver operating characteristic curve analysis, the statistically significant cut-off values were determined for tumor size and Ki-67 index in stage 1 EEC patients. Conclusions: Stage 1-EEC patients in the higher risk group in terms of tumor size, Ki-67, and grade should be closely monitored for recurrence. Defining the prognostic factors for recurrence in stage 1 EEC patients may lead to changes in follow-up algorithms

Association between bone mineral density and type 2 diabetes mellitus: a meta-analysis of observational studies

Type 2 diabetes mellitus (T2DM) influences bone metabolism, but the relation of T2DM with bone mineral density (BMD) remains inconsistent across studies. The objective of this study was to perform a meta-analysis and meta-regression of the literature to estimate the difference in BMD (g/cm2) between diabetic and non-diabetic populations, and to investigate potential underlying mechanisms. A literature search was performed in PubMed and Ovid extracting data from articles prior to May 2010. Eligible studies were those where the association between T2DM and BMD measured by dual energy X-ray absorptiometry was evaluated using a cross-sectional, cohort or case–control design, including both healthy controls and subjects with T2DM. The analysis was done on 15 observational studies (3,437 diabetics and 19,139 controls). Meta-analysis showed that BMD in diabetics was significantly higher, with pooled mean differences of 0.04 (95% CI: 0.02, 0.05) at the femoral neck, 0.06 (95% CI: 0.04, 0.08) at the hip and 0.06 (95% CI: 0.04, 0.07) at the spine. The differences for forearm BMD were not significantly different between diabetics and non-diabetics. Sex-stratified analyses showed similar results in both genders. Substantial heterogeneity was found to originate from differences in study design and possibly diabetes definition. Also, by applying meta-regression we could establish that younger age, male gender, higher body mass index and higher HbA1C were positively associated with higher BMD levels in diabetic individuals. We conclude that individuals with T2DM from both genders have higher BMD levels, but that multiple factors influence BMD in individuals with T2DM

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe