Causal chain event graphs for remedial maintenance.

The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application, we need to define a new class of formal intervention we call remedial to model the causal effects of remedial maintenance. This fixes the root causes of a failure and returns the status of the system to as good as new. We demonstrate that the semantics of the CEG are rich enough to express this novel type of intervention. Furthermore, through the bespoke causal algebras, the CEG provides a transparent framework with which to guide and express the rationale behind predictive inferences about the effects of various types of remedial intervention. A backdoor theorem is adapted to apply to these interventions to help discover when a system is only partially observed.

Creating opportunities to improve detection of older adult abuse: a national interRAI study.

Despite being recognized as a major global health issue, older adult abuse (OAA) remains largely undetected and under-reported. Most OAA assessment tools fail to capture true prevalence. Follow up of patients where abuse exposure is not easily determined is a necessity. The interRAI-HC (International Resident Assessment Instrument-Home Care) currently underestimates the extent of abuse. We investigated how to improve detection of OAA using the interRAI-HC. Analysis of 7 years of interRAI-HC data from an Aotearoa New Zealand cohort was completed. We identified that through altering the criteria for suspicion of OAA, capture rates of at-risk individuals could be nearly doubled from 2.6% to 4.8%. We propose that via adapting the interRAI-HC criteria to include the "unable to determine" whether abuse occurred (UDA) category, identification of OAA sufferers could be substantially improved. Improved identification will facilitate enhanced protection of this vulnerable population.

Pancreas Cancer-Associated Pain Management.

Pain is highly prevalent in patients with pancreas cancer and contributes to the morbidity of the disease. Pain may be due to pancreatic enzyme insufficiency, obstruction, and/or a direct mass effect on nerves in the celiac plexus. Proper supportive care to decrease pain is an important aspect of the overall management of these patients. There are limited data specific to the management of pain caused by pancreatic cancer. Here we review the literature and offer recommendations regarding multiple modalities available to treat pain in these patients. The dissemination and adoption of these best supportive care practices can improve quantity and quality of life for patients with pancreatic cancer. IMPLICATIONS FOR PRACTICE: Pain management is important to improve the quality of life and survival of a patient with cancer. The pathophysiology of pain in pancreas cancer is complex and multifactorial. Despite tumor response to chemotherapy, a sizeable percentage of patients are at risk for ongoing cancer-related pain and its comorbid consequences. Accordingly, the management of pain in patients with pancreas cancer can be challenging and often requires a multifaceted approach.

From evolutionary computation to the evolution of things.

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Causal Algebras on Chain Event Graphs with Informed Missingness for System Failure.

Graph-based causal inference has recently been successfully applied to explore system reliability and to predict failures in order to improve systems. One popular causal analysis following Pearl and Spirtes et al. to study causal relationships embedded in a system is to use a Bayesian network (BN). However, certain causal constructions that are particularly pertinent to the study of reliability are difficult to express fully through a BN. Our recent work demonstrated the flexibility of using a Chain Event Graph (CEG) instead to capture causal reasoning embedded within engineers' reports. We demonstrated that an event tree rather than a BN could provide an alternative framework that could capture most of the causal concepts needed within this domain. In particular, a causal calculus for a specific type of intervention, called a remedial intervention, was devised on this tree-like graph. In this paper, we extend the use of this framework to show that not only remedial maintenance interventions but also interventions associated with routine maintenance can be well-defined using this alternative class of graphical model. We also show that the complexity in making inference about the potential relationships between causes and failures in a missing data situation in the domain of system reliability can be elegantly addressed using this new methodology. Causal modelling using a CEG is illustrated through examples drawn from the study of reliability of an energy distribution network.

Transcriptional Changes in the Ovaries of Perch from Chernobyl.

Fish have been highly exposed to radiation in freshwater systems after the Chernobyl Nuclear Power Plant (NPP) accident in 1986 and in freshwater and marine systems after the more recent Fukushima NPP accident in 2011. In the years after the accident, the radioactivity levels rapidly declined due to radioactive decay and environmental processes, but chronic lower dose exposures persisted. To gain insights into the long-term effects of environmental low dose radiation on fish ovaries development, a high-throughput transcriptomic approach including a de novo assembly was applied to different gonad phenotypes of female perch: developed gonads from reference lakes, developed/irradiated from medium contaminated lake, and both developed/irradiated and undeveloped from more highly contaminated lakes. This is the most comprehensive analysis to date of the gene responses in wildlife reproductive system to radiation. Some gene responses that were modulated in irradiated gonads were found to be involved in biological processes including cell differentiation and proliferation (ggnb2, mod5, rergl), cytoskeleton organization (k1C18, mtpn), gonad development (nell2, tcp4), lipid metabolism (ldah, at11b, nltp), reproduction (cyb5, cyp17A, ovos), DNA damage repair (wdhd1, rad51, hus1), and epigenetic mechanisms (dmap1). Identification of these genes provides a better understanding of the underlying molecular mechanisms underpinning the development of the gonad phenotypes of wild perch and how fish may respond to chronic exposure to radiation in their natural environment, though causal attribution of gene responses remains unclear in the undeveloped gonads.

Impacts of ionising radiation on sperm quality, DNA integrity and post-fertilisation development in marine and freshwater crustaceans.

Crustaceans have been designated as internationally important model organisms in the development of environmental radioprotection measures. Despite the known sensitivity of sperm to ionizing radiation, the impacts of chronic radiation exposure on male fertility in crustaceans have not been studied. For the first time, the present study aimed to assess the impacts of chronic radiation exposure on male fertility, sperm DNA damage and concomitant impacts on breeding in two amphipod crustaceans. Echinogammarus marinus and Gammarus pulex (male fertility only) were exposed to phosphorus-32 at dose rates of 0, 0.1, 1 and 10 mGy/d and sperm parameters, DNA damage and knock-on impacts on breeding were assessed. Sperm quality parameters and DNA damage were assessed using a fluorescent staining method and single cell gel electrophoresis respectively. Concomitant effects of male exposure to radiation on fecundity were determined by pairing phosphorus-32 exposed males to unexposed sexually mature females. In E. marinus, a statistically significant reduction of 9 and 11% in the quality of sperm was recorded at dose rates of 1 and 10 mGy/d respectively, with no significant effects recorded on sperm counts. Conversely in the freshwater G. pulex, no significant impact of radiation on sperm quantity or quality was recorded. For E. marinus, a statistically significant increase in DNA damage was recorded at doses of 10 mGy/d. Reduced fecundity and an increase in the frequency of abnormal embryos was recorded in female E. marinus breeding with males exposed to radiation. These findings suggest sperm quality may be a sensitive indicator of radiation exposure in invertebrates with potential impacts on the unexposed embryo, though unclear dose-response and differences between two closely related species necessitate further study before robust conclusions can be drawn.

Communicating Geographical Risks in Crisis Management: The Need for Research.

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.

The nature and severity of workplace injuries in construction: engendering operational benchmarking.

To remain competitive and manage their safety performance, many construction organisations have engaged in benchmarking themselves against lagging indicators provided by a statutory body. Aggregated metrics that are provided by statutory bodies are not useful for the purpose of operational benchmarking, as 'best practice' is unable to be identified. Access to safety statistics from leading construction organisations' projects is seldom made available for the purposes of benchmarking. In addressing this void and to engender a process of operational benchmarking, a homogeneous dataset is used to examine 26,665 workplace injuries that arose during the delivery of 562 projects over a 10-year period by a leading international Australian construction organisation. The nature and the degree of severity of the injuries that arose are statistically analysed. The findings provide invaluable insights into issues contributing to workplace injuries during construction, which can be used as a basis for operational benchmarking and a platform for engaging in continuous improvement. Practitioner summary: Workplace injuries are a problem in construction. Recognising that safety is a key goal for construction organisations, we analyse the nature of workplace injuries that occurred in 562 projects. Acknowledging the challenges of using lagging indicators, an operational framework for engendering best practice in workplace safety is presented.

Directed functional connectivity using dynamic graphical models.

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

Impact of Environmental Radiation on the Health and Reproductive Status of Fish from Chernobyl.

Aquatic organisms at Chernobyl have now been chronically exposed to environmental radiation for three decades. The biological effects of acute exposure to radiation are relatively well documented, but much less is known about the long-term effects of chronic exposure of organisms in their natural environment. Highly exposed fish in freshwater systems at Chernobyl showed morphological changes in their reproductive system in the years after the accident. However, the relatively limited scope of past studies did not allow robust conclusions to be drawn. Moreover, the level of the radiation dose at which significant effects on wildlife occur is still under debate. In the most comprehensive evaluation of the effects of chronic radiation on wild fish populations to date, the present study measures specific activities of 137Cs, 90Sr, and transuranium elements (238Pu, 239,240Pu, and 241Am), index conditions, distribution and size of oocytes, as well as environmental and biological confounding factors in two fish species perch ( Perca fluviatilis) and roach ( Rutilus rutilus) from seven lakes. In addition, relative species abundance was examined. The results showed that both fish species are, perhaps surprisingly, in good general physiological and reproductive health. Perch, however, appeared to be more sensitive to radiation than roach: in the most contaminated lakes, a delay of the maturation of the gonads and the presence of several undeveloped phenotypes were evident only for perch and not for roach.

Principles of Bayesian Inference Using General Divergence Criteria.

When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern themselves with inference for the parameter value minimising the Kullback-Leibler (KL)-divergence between the model and this process (Walker, 2013). However, it has long been known that minimising the KL-divergence places a large weight on correctly capturing the tails of the sample distribution. As a result, the DM is required to worry about the robustness of their model to tail misspecifications if they want to conduct principled inference. In this paper we alleviate these concerns for the DM. We advance recent methodological developments in general Bayesian updating (Bissiri, Holmes & Walker, 2016) to propose a statistically well principled Bayesian updating of beliefs targeting the minimisation of more general divergence criteria. We improve both the motivation and the statistical foundations of existing Bayesian minimum divergence estimation (Hooker & Vidyashankar, 2014; Ghosh & Basu, 2016), allowing the well principled Bayesian to target predictions from the model that are close to the genuine model in terms of some alternative divergence measure to the KL-divergence. Our principled formulation allows us to consider a broader range of divergences than have previously been considered. In fact, we argue defining the divergence measure forms an important, subjective part of any statistical analysis, and aim to provide some decision theoretic rational for this selection. We illustrate how targeting alternative divergence measures can impact the conclusions of simple inference tasks, and discuss then how our methods might apply to more complicated, high dimensional models.

Late Peripheral Thoracic Aneurysms following Aortic Root Surgery in Patients with Loeys-Dietz Syndrome.

The Loeys-Dietz syndrome is a multisystem connective tissue disorder characterized by aortopathy, arterial tortuosity, peripheral aneurysms, and skeletal features. The peripheral arteriopathy is an important cause of morbidity and potential mortality. This report presents 2 cases: the first demonstrating a 5-cm pseudoaneurysm of the right internal mammary artery and the second demonstrating a 2.3-cm aneurysm of the left internal mammary artery, each following aortic root surgery. These were successfully treated with percutaneous techniques. No complications were seen at follow-up as long as 2 years. Patients with Loeys-Dietzs syndrome require comprehensive long-term vascular follow-up and are likely to require percutaneous vascular interventions.

Targeted DNA Methylation Editing Using an All-in-One System Establishes Paradoxical Activation of EBF3.

Cutaneous melanoma is rapidly on the rise globally, surpassing the growth rate of other cancers, with metastasis being the primary cause of death in melanoma patients. Consequently, understanding the mechanisms behind this metastatic process and exploring innovative treatments is of paramount importance. Recent research has shown promise in unravelling the role of epigenetic factors in melanoma progression to metastasis. While DNA hypermethylation at gene promoters typically suppresses gene expression, we have contributed to establishing the newly understood mechanism of paradoxical activation of genes via DNA methylation, where high methylation coincides with increased gene activity. This mechanism challenges the conventional paradigm that promoter methylation solely silences genes, suggesting that, for specific genes, it might actually activate them. Traditionally, altering DNA methylation in vitro has involved using global demethylating agents, which is insufficient for studying the mechanism and testing the direct consequence of gene methylation changes. To investigate promoter hypermethylation and its association with gene activation, we employed a novel approach utilising a CRISPR-SunTag All-in-one system. Here, we focused on editing the DNA methylation of a specific gene promoter segment (EBF3) in melanoma cells using the All-in-one system. Using bisulfite sequencing and qPCR with RNA-Seq, we successfully demonstrated highly effective methylation and demethylation of the EBF3 promoter, with subsequent gene expression changes, to establish and validate the paradoxical role of DNA methylation. Further, our study provides novel insights into the function of the EBF3 gene, which remains largely unknown. Overall, this study challenges the conventional view of methylation as solely a gene-silencing mechanism and demonstrates a potential function of EBF3 in IFN pathway signalling, potentially uncovering new insights into epigenetic drivers of malignancy and metastasis.

Genetic and epigenetic features of neuroendocrine prostate cancer and their emerging applications.

Prostate cancer is the second most prevalent cancer in men globally. De novo neuroendocrine prostate cancer (NEPC) is uncommon at initial diagnosis, however, (treatment-induced) t-NEPC emerges in up to 25% of prostate adenocarcinoma (PRAD) cases treated with androgen deprivation, carrying a drastically poor prognosis. The transition from PRAD to t-NEPC is underpinned by several key genetic mutations; TP53, RB1, and MYCN are the main genes implicated, bearing similarities to other neuroendocrine tumours. A broad range of epigenetic alterations, such as aberrations in DNA methylation, histone post-translational modifications, and non-coding RNAs, may drive lineage plasticity from PRAD to t-NEPC. The clinical diagnosis of NEPC is hampered by a lack of accessible biomarkers; recent advances in liquid biopsy techniques assessing circulating tumour cells and ctDNA in NEPC suggest that the advent of non-invasive means of monitoring progression to NEPC is on the horizon. Such techniques are vital for NEPC management; diagnosis of t-NEPC is crucial for implementing effective treatment, and precision medicine will be integral to providing the best outcomes for patients.

The risks of radioactive wastewater release.

The wastewater release from the Fukushima Daiichi nuclear plant is expected to have negligible effects on people and the ocean.

Neuroendocrine Neoplasms: Genetics and Epigenetics.

Neuroendocrine neoplasms (NENs) are a group of rare, heterogeneous tumors of neuroendocrine cell origin, affecting a range of different organs. The clinical management of NENs poses significant challenges, as tumors are often diagnosed at an advanced stage where overall survival remains poor with current treatment regimens. In addition, a host of complex and often unique molecular changes underpin the pathobiology of each NEN subtype. Exploitation of the unique genetic and epigenetic signatures driving each NEN subtype provides an opportunity to enhance the diagnosis, treatment, and monitoring of NEN in an emerging era of individualized medicine.