Microclimates Might Limit Indirect Spillover of the Bat Borne Zoonotic Hendra Virus.

Infectious diseases are transmitted when susceptible hosts are exposed to pathogen particles that can replicate within them. Among factors that limit transmission, the environment is particularly important for indirectly transmitted parasites. To try and assess a pathogens' ability to be transmitted through the environment and mitigate risk, we need to quantify its decay where transmission occurs in space such as the microclimate harbouring the pathogen. Hendra virus, a Henipavirus from Australian Pteropid bats, spills-over to horses and humans, causing high mortality. While a vaccine is available, its limited uptake has reduced opportunities for adequate risk management to humans, hence the need to develop synergistic preventive measures, like disrupting its transmission pathways. Transmission likely occurs shortly after virus excretion in paddocks; however, no survival estimates to date have used real environmental conditions. Here, we recorded microclimate conditions and fitted models that predict temperatures and potential evaporation, which we used to simulate virus survival with a temperature-survival model and modification based on evaporation. Predicted survival was lower than previously estimated and likely to be even lower according to potential evaporation. Our results indicate that transmission should occur shortly after the virus is excreted, in a relatively direct way. When potential evaporation is low, and survival is more similar to temperature dependent estimates, transmission might be indirect because the virus can wait several hours until contact is made. We recommend restricting horses' access to trees during night time and reducing grass under trees to reduce virus survival.

Hendra virus survival does not explain spillover patterns and implicates relatively direct transmission routes from flying foxes to horses.

Hendra virus (HeV) is lethal to humans and horses, and little is known about its epidemiology. Biosecurity restrictions impede advances, particularly on understanding pathways of transmission. Quantifying the environmental survival of HeV can be used for making decisions and to infer transmission pathways. We estimated HeV survival with a Weibull distribution and calculated parameters from data generated in laboratory experiments. HeV survival rates based on air temperatures 24 h after excretion ranged from 2 to 10 % in summer and from 12 to 33 % in winter. Simulated survival across the distribution of the black flying fox (Pteropus alecto), a key reservoir host, did not predict spillover events. Based on our analyses we concluded that the most likely pathways of transmission did not require long periods of virus survival and were likely to involve relatively direct contact with flying fox excreta shortly after excretion.

Not All Frailty Assessments Are Created Equal: Comparability of Electronic Health Data-Based Frailty Assessments in Assessing Older People in Residential Care.

Objectives: To evaluate the comparability of frailty assessment tools - the electronic frailty index (eFI), retrospective electronic frailty index (reFI), and clinical frailty scale (CFS) - in older residents of care facilities. Methods: Data from 813 individuals aged 65 or older, with frailty and co-morbidities, collected between 2022 and 2023, were analysed using various statistical methods. Results: The results showed significant differences in frailty classification among the tools: 78.3% were identified as moderately to severely frail by eFI, 59.6% by reFI, and 92.1% by CFS. Statistical tests confirmed significant differences (p < .05) in their assessments, indicating variability in measurement methods. Discussion: This study advances the understanding of frailty assessment within aged-care settings, highlighting the differences in the efficacy of these assessment tools. It underscores the challenges in frailty assessments and emphasizes the need for continuous refinement of assessment methods to address the diverse facets of frailty in aged care.

Knowledge Gaps in the Biology, Ecology, and Management of the Pacific Crown-of-Thorns Sea Star Acanthaster sp. on Australia's Great Barrier Reef.

AbstractCrown-of-thorns sea stars (Acanthaster sp.) are among the most studied coral reef organisms, owing to their propensity to undergo major population irruptions, which contribute to significant coral loss and reef degradation throughout the Indo-Pacific. However, there are still important knowledge gaps pertaining to the biology, ecology, and management of Acanthaster sp. Renewed efforts to advance understanding and management of Pacific crown-of-thorns sea stars (Acanthaster sp.) on Australia's Great Barrier Reef require explicit consideration of relevant and tractable knowledge gaps. Drawing on established horizon scanning methodologies, this study identified contemporary knowledge gaps by asking active and/or established crown-of-thorns sea star researchers to pose critical research questions that they believe should be addressed to improve the understanding and management of crown-of-thorns sea stars on the Great Barrier Reef. A total of 38 participants proposed 246 independent research questions, organized into 7 themes: feeding ecology, demography, distribution and abundance, predation, settlement, management, and environmental change. Questions were further assigned to 48 specific topics nested within the 7 themes. During this process, redundant questions were removed, which reduced the total number of distinct research questions to 172. Research questions posed were mostly related to themes of demography (46 questions) and management (48 questions). The dominant topics, meanwhile, were the incidence of population irruptions (16 questions), feeding ecology of larval sea stars (15 questions), effects of elevated water temperature on crown-of-thorns sea stars (13 questions), and predation on juveniles (12 questions). While the breadth of questions suggests that there is considerable research needed to improve understanding and management of crown-of-thorns sea stars on the Great Barrier Reef, the predominance of certain themes and topics suggests a major focus for new research while also providing a roadmap to guide future research efforts.

Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods on twin data.

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods-latent class analysis, grade of membership and grade of membership "fuzzy" clustering (Fanny)-to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Bayesian latent trait modeling of migraine symptom data.

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk.

Disease risk mapping is important for predicting and mitigating impacts of bat-borne viruses, including Hendra virus (Paramyxoviridae:Henipavirus), that can spillover to domestic animals and thence to humans. We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. conspicillatus. We included additional climatic variables that might affect spillover risk through other biological processes (such as bat or horse behaviour, plant phenology and bat foraging habitat). Models were fit with a Poisson point process model and a log-Gaussian Cox process. In response to climate change, risk expanded southwards due to an expansion of P. alecto suitable habitat, which increased the number of horses at risk by 175-260% (110,000-165,000). In the northern limits of the current distribution, spillover risk was highly uncertain because of model extrapolation to novel climatic conditions. The extent of areas at risk of spillover from P. conspicillatus was predicted shrink. Due to a likely expansion of P. alecto into these areas, it could replace P. conspicillatus as the main HeV reservoir. We recommend: (1) HeV monitoring in bats, (2) enhancing HeV prevention in horses in areas predicted to be at risk, (3) investigate and develop mitigation strategies for areas that could experience reservoir host replacements.

Hendra Virus Spillover is a Bimodal System Driven by Climatic Factors.

Understanding environmental factors driving spatiotemporal patterns of disease can improve risk mitigation strategies. Hendra virus (HeV), discovered in Australia in 1994, spills over from bats (Pteropus sp.) to horses and thence to humans. Below latitude - 22°, almost all spillover events to horses occur during winter, and above this latitude spillover is aseasonal. We generated a statistical model of environmental drivers of HeV spillover per month. The model reproduced the spatiotemporal pattern of spillover risk between 1994 and 2015. The model was generated with an ensemble of methods for presence-absence data (boosted regression trees, random forests and logistic regression). Presences were the locations of horse cases, and absences per spatial unit (2.7 × 2.7 km pixels without spillover) were sampled with the horse census of Queensland and New South Wales. The most influential factors indicate that spillover is associated with both cold-dry and wet conditions. Bimodal responses to several variables suggest spillover involves two systems: one above and one below a latitudinal area close to - 22°. Northern spillovers are associated with cold-dry and wet conditions, and southern with cold-dry conditions. Biologically, these patterns could be driven by immune or behavioural changes in response to food shortage in bats and horse husbandry. Future research should look for differences in these traits between seasons in the two latitudinal regions. Based on the predicted risk patterns by latitude, we recommend enhanced preventive management for horses from March to November below latitude 22° south.

Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora.

Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef declines globally. However, little is known about how seasonal scale variations in environmental factors influence disease dynamics at the level of individual coral colonies. In this study, we applied a multi-state Markov model (MSM) to investigate the dynamics of black band disease (BBD) developing from apparently healthy corals and/or a precursor-stage, termed 'cyanobacterial patches' (CP), in relation to seasonal variation in light and seawater temperature at two reef sites around Pelorus Island in the central sector of the Great Barrier Reef. The model predicted that the proportion of colonies transitioning from BBD to Healthy states within three months was approximately 57%, but 5.6% of BBD cases resulted in whole colony mortality. According to our modelling, healthy coral colonies were more susceptible to BBD during summer months when light levels were at their maxima and seawater temperatures were either rising or at their maxima. In contrast, CP mostly occurred during spring, when both light and seawater temperatures were rising. This suggests that environmental drivers for healthy coral colonies transitioning into a CP state are different from those driving transitions into BBD. Our model predicts that (1) the transition from healthy to CP state is best explained by increasing light, (2) the transition between Healthy to BBD occurs more frequently from early to late summer, (3) 20% of CP infected corals developed BBD, although light and temperature appeared to have limited impact on this state transition, and (4) the number of transitions from Healthy to BBD differed significantly between the two study sites, potentially reflecting differences in localised wave action regimes.

Adverse Effects Cause Varenicline Discontinuation: A Meta-Analysis.

PURPOSE: Varenicline, the newest agent marketed for smoking cessation is regarded as effective in providing prolonged smoking abstinence. However, its adverse effect profile may cause discontinuation, potentially reducing smoking abstinence rates, thus requiring an examination of the frequency and impact of adverse effects on discontinuation. METHODS: We sought only Randomised Controlled Trials (RCTs) evaluating the effectiveness and safety of varenicline on humans, with a follow-up period of at least three months and an average Fagerstrom Test for Nicotine Dependence (FTND) score at least 5 (moderate dependence) for both the active and placebo groups. PubMed, Medscape, JCU One Search, ClinicalTrials.gov (U.S.), and the Cochrane Collaboration from January 2006 to January 2015 were searched. Fixed and random effects models were run to determine relationships between adverse effects and premature discontinuation from varenicline. RESULTS: 12 RCTs were included, involving 5 459 patients, with those receiving varenicline found to be nearly twice as likely (Odds ratio (OR) = 1.82 [1.47; 2.26]) to experience adverse effects compared to those patients on a placebo. The active group experienced nearly a 1.5 times higher (OR = 1.47 [1.19; 1.81]) rate of discontinuation. Nausea, insomnia, and headache are the most commonly reported adverse effects, with ORs of 4.40 [3.80; 5.11], 1.75 [1.48; 2.08], and 1.20 [1.02; 1.41] respectively. CONCLUSION: Adverse effects experienced during varenicline treatment appear to be associated with higher discontinuation, which are linked to lowered smoking cessation rates, suggesting a need for strategies to minimise the impacts of adverse effects, to better ensure adherence.

Climatic suitability influences species specific abundance patterns of Australian flying foxes and risk of Hendra virus spillover.

Hendra virus is a paramyxovirus of Australian flying fox bats. It was first detected in August 1994, after the death of 20 horses and one human. Since then it has occurred regularly within a portion of the geographical distribution of all Australian flying fox (fruit bat) species. There is, however, little understanding about which species are most likely responsible for spillover, or why spillover does not occur in other areas occupied by reservoir and spillover hosts. Using ecological niche models of the four flying fox species we were able to identify which species are most likely linked to spillover events using the concept of distance to the niche centroid of each species. With this novel approach we found that 20 out of 27 events occur disproportionately closer to the niche centroid of two species (P. alecto and P. conspicillatus). With linear regressions we found a negative relationship between distance to the niche centroid and abundance of these two species. Thus, we suggest that the bioclimatic niche of these two species is likely driving the spatial pattern of spillover of Hendra virus into horses and ultimately humans.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.