Exploring the influence of functional architecture on cortical thickness networks in early psychosis - a longitudinal study

Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.

Solving the inverse problem of post-mortem interval estimation using Bayesian Belief Networks

Bayesian Belief Networks (BBNs) can be applied to solve inverse problems such as the post-mortem interval (PMI) by a simple and logical graphical representation of conditional dependencies between multiple taphonomic variables and the observable decomposition effect. This study is the first cross-comparison retrospective study of human decomposition across three different geographical regions. To assess the effect of the most influential taphonomic variables on the decomposition rate (as measured by the Total Decomposition Score (TDS)), decomposition data was examined from the Forensic Anthropology Research Facility at the University of Tennessee (n = 312), the Allegheny County Office of the Medical Examiner in Pittsburgh, US (n = 250), and the Crime Scene Investigation department at Southwest Forensics in the UK (n = 81). Two different BBNs for PMI estimations were created from the US and the UK training data. Sensitivity analysis was performed to identify the most influential parameters of TDS variance, with weaker variables (e.g., age, sex, clothing) being excluded during model refinement. The accuracy of the BBNs was then compared by additional validation cases: US (n = 28) and UK (n = 10). Both models conferred predictive power of the PMI and accounted for the unique combination of taphonomic variables affecting decomposition. Both models had a mean posterior probability of 86% (US) and 81% (UK) in favor of the experimental hypothesis (that the PMI was on, or less than, the prior last known alive date). Neither the US nor the UK datasets represented any cases below 'moderate' support for the value of PMI evidence. By applying coherent probabilistic reasoning to PMI estimations, one logical solution is provided to model the complexities of human decomposition that can quantify the combined effect of several uncertainties surrounding the PMI estimation. This approach communicates the PMI with an associated degree of confidence and provides predictive power on unknown PMI cases.

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries

Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem.

Dynamic Bayesian Belief Network for long-term monitoring and system barrier failure analysis: Decommissioned wells

There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen. • Proposed adapted method capture better estimates of a well PA event by incorporating dependencies • Method allows for extension of model to long term monitoring of a group of wells linked by common dependencies.

Analysis of the influence of human errors on the occurrence of coastal ship accidents in different wave conditions using Bayesian Belief Networks

The paper describes a study aiming to assess the human error contribution in ship accidents in different weather conditions and the contribution that high significant wave heights have in the occurrence of certain accident typologies. To this aim, a Bayesian Belief Network model is developed, which includes variables related to the maritime accident but also to the different wave conditions. For the quantification of the conditional probability tables the maritime accident database of the Portuguese Maritime Authority is used, which includes 857 validated accidents registered in the period 1997-2006. Similarly, several significant wave height databases are used to populate the node correspondent to this variable. The importance of accurate estimation of the significant wave height is also discussed. To substantiate this discussion a comparison between hard evidence (ε) and a soft one (μ,σ) is performed for the significant wave height node of the BBN model. The application of different combinations of evidence in the model allows the identification of patterns of influence of the human error cause in comparison with others, namely with the sea and weather one. The results show one apparent high-risk acceptance within the crews of the fishing vessels and low risk perception in the recreational vessels. Based on the results, are provided recommendations to decrease the risk associated to specific probable causes.

A probabilistic model estimating oil spill clean-up costs--a case study for the Gulf of Finland

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).

The Supply Chain's Role in Improving Animal Welfare

Simple Summary

The ability of supply chains to deliver high(er) levels and standards of animal welfare is subject to two critical conditions: (a) the innovative and adaptive capacity of actors in the chain to respond to society’s demands; (b) consumers actually buying animal-friendly products. Unless citizens are willing to support suppliers who comply with high(er) standards, their votes for better animal welfare risk exporting poor animal welfare to other countries with less rigorous standards. The logic of market failure in the case of animal welfare points to the superiority of consumer subsidies over producer subsidies to deliver improved animal welfare.

Abstract

Supply chains are already incorporating citizen/consumer demands for improved animal welfare, especially through product differentiation and the associated segmentation of markets. Nonetheless, the ability of the chain to deliver high(er) levels and standards of animal welfare is subject to two critical conditions: (a) the innovative and adaptive capacity of the chain to respond to society’s demands; (b) the extent to which consumers actually purchase animal-friendly products. Despite a substantial literature reporting estimates of willingness to pay (WTP) for animal welfare, there is a belief that in practice people vote for substantially more and better animal welfare as citizens than they are willing to pay for as consumers. This citizen-consumer gap has significant consequences on the supply chain, although there is limited literature on the capacity and willingness of supply chains to deliver what the consumer wants and is willing to pay for. This paper outlines an economic analysis of supply chain delivery of improved standards for farm animal welfare in the EU and illustrates the possible consequences of improving animal welfare standards for the supply chain using a prototype belief network analysis.

Consensus recommendations for advancing breast cancer: risk identification and screening in ethnically diverse younger women

A need exists for a breast cancer risk identification paradigm that utilizes relevant demographic, clinical, and other readily obtainable patient-specific data in order to provide individualized cancer risk assessment, direct screening efforts, and detect breast cancer at an early disease stage in historically underserved populations, such as younger women (under age 40) and minority populations, who represent a disproportionate number of military beneficiaries. Recognizing this unique need for military beneficiaries, a consensus panel was convened by the USA TATRC to review available evidence for individualized breast cancer risk assessment and screening in young (< 40), ethnically diverse women with an overall goal of improving care for military beneficiaries. In the process of review and discussion, it was determined to publish our findings as the panel believes that our recommendations have the potential to reduce health disparities in risk assessment, health promotion, disease prevention, and early cancer detection within and in other underserved populations outside of the military. This paper aims to provide clinicians with an overview of the clinical factors, evidence and recommendations that are being used to advance risk assessment and screening for breast cancer in the military.