Implementing pathogen reduction technology while discontinuing blood donor deferral criteria for sexual risk behaviors: A simulation study.

BACKGROUND: Combining pathogen reduction technology (PRT) with blood screening may alleviate concerns over the risk of transfusion-transmitted infections (TTI) and support changes in blood donor selection to potentially increase blood availability. This study aimed to estimate the residual risk of human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) transfusion-transmission in Canada after implementing PRT, while eliminating deferrals for sexual risk behaviors. STUDY DESIGN AND METHODS: A probabilistic approach that combined Bayesian networks with Monte Carlo simulations was used to estimate the risk of transfusing HIV-, HBV-, or HCV-contaminated blood components. Different scenarios were considered to compare the current residual risk after PRT implementation, with and without donor deferral criteria for sexual risk behaviors. Donor profiles and blood component outcomes were simulated based on a literature review including the prevalence and incidence of HIV, HBV, and HCV in the Canadian blood donor population; the use of current blood screening assays; and HIV, HBV, and HCV blood donor viral loads. RESULTS: In the universal PRT scenario (i.e., with PRT/without deferral criteria), the estimated risks of HIV, HBV, and HCV transmission were significantly lower than those in the currently observed scenario (i.e., without PRT/with deferral criteria). CONCLUSIONS: This risk model suggests that PRT for platelets and plasma (and eventually for RBCs when available) significantly reduces the residual risks of HIV, HBV and HCV transfusion-transmission and could enable the removal of blood donor deferral criteria for sexual risk behaviors.

Digital phenotyping of depression during pregnancy using self-report data.

BACKGROUND: Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy. METHODS: An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input. RESULTS: Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression. LIMITATIONS: Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy. CONCLUSIONS: Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.

Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China.

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.

Economic burden of cardiorespiratory hospitalizations associated with respiratory syncytial virus among United States adults in 2017-2019.

Morbidity and mortality caused by respiratory syncytial virus (RSV) in older adults and those with underlying health conditions can be potentially alleviated through vaccination. To assist vaccine policy decision-makers and payers, we estimated the annual economic burden of RSV-associated cardiorespiratory hospitalizations among insured US adults aged ≥18 y in the Merative MarketScan claims database from September through August of 2017-2018 and 2018-2019. Negative binomial regression models were used to estimate the number of RSV-associated cardiorespiratory hospitalizations using MarketScan-identified cardiorespiratory diagnosis codes in the presence or absence of RSV circulation per weekly laboratory test positivity percentages from the Centers for Disease Control and Prevention. This number was multiplied by mean cardiorespiratory hospitalization costs to estimate total costs for RSV-associated cardiorespiratory hospitalizations. Number and cost for International Classification of Diseases (ICD)-coded RSV hospitalizations were quantified from MarketScan. In 2017-2018 and 2018-2019, respectively, 18,515,878 and 16,462,120 adults with commercial or Medicare supplemental benefits were assessed. In 2017-2018, 301,248 cardiorespiratory hospitalizations were observed; 0.32% had RSV-specific ICD codes, costing $44,916,324, and 5.52% were RSV-associated cardiorespiratory hospitalizations, costing $734,078,602 (95% CI: $460,826,580-$1,103,358,799). In 2018-2019, 215,525 cardiorespiratory hospitalizations were observed; 0.34% had RSV-specific ICD codes, costing $33,053,105, and 3.14% were RSV-associated cardiorespiratory hospitalizations, costing $287,549,472 (95% CI: $173,377,778-$421,884,259). RSV contributes to substantial economic burden of cardiorespiratory hospitalizations among US adults. Modeling excess risk using viral positivity data provides a comprehensive estimation of RSV hospitalization burden and associated costs, compared with relying on ICD diagnosis codes alone.

Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment.

Establishing marine species distributions is essential for guiding management and can be estimated by identifying potential favorable habitat at a population level and incorporating individual-level information (e.g., movement constraints) to inform realized space use. In this research, we applied a combined modeling approach to tracking data of adult female and juvenile South American sea lions (Otaria flavescens; n = 9) from July to November 2011 to make habitat predictions for populations in northern Chile. We incorporated topographic and oceanographic predictors with sea lion locations and environmentally based pseudo-absences in a generalized linear model for estimating population-level distribution. For the individual approach, we used a generalized linear mixed-effects model with a negative exponential kernel variable to quantify distance-dependent movement from the colony. Spatial predictions from both approaches were combined in a bivariate color map to identify areas of agreement. We then used a GIS-based risk model to characterize bycatch risk in industrial and artisanal purse-seine fisheries based on fishing set data from scientific observers and artisanal fleet logs (2010-2015), the bivariate sea lion distribution map, and criteria ratings of interaction characteristics. Our results indicate population-level associations with productive, shallow, low slope waters, near to river-mouths, and with high eddy activity. Individual distribution was restricted to shallow slopes and cool waters. Variation between approaches may reflect intrinsic factors restricting use of otherwise favorable habitat; however, sample size was limited, and additional data are needed to establish the full range of individual-level distributions. Our bycatch risk outputs identified highest risk from industrial fisheries operating nearshore (within 5 NM) and risk was lower, overall, for the artisanal fleet. This research demonstrates the potential for integrating potential and realized distribution models within a spatial risk assessment and fills a gap in knowledge on this species' distribution, providing a basis for targeting bycatch mitigation outreach and interventions.

Seasonal drives on potentially toxic elements dynamics in a tropical estuary impacted by mine tailings.

This study investigates the impact of seasonality on estuarine soil geochemistry, focusing on redox-sensitive elements, particularly Fe, in a tropical estuary affected by Fe-rich mine tailings. We analyzed soil samples for variations in particle size, pH, redox potential (Eh), and the content of Fe, Mn, Cr, Cu, Ni, and Pb. Additionally, sequential extraction was employed to understand the fate of these elements. Results revealed dynamic changes in the soil geochemical environment, transitioning between near-neutral and suboxic/anoxic conditions in the wet season and slightly acidic to suboxic/oxic conditions in the dry season. During the wet season, fine particle deposition (83%) rich in Fe (50 g kg-1), primarily comprising crystalline Fe oxides, occurred significantly. Conversely, short-range ordered Fe oxides dominated during the dry season. Over consecutive wet/dry seasons, substantial losses of Fe (-55%), Mn (-41%), and other potentially toxic elements (Cr: -44%, Cu: -31%, Ni: -25%, Pb: -9%) were observed. Despite lower pseudo-total PTE contents, exchangeable PTEs associated with carbonate content increased over time (Cu: +188%, Ni: +557%, Pb: +99%). Modeling indicated climatic variables and short-range oxides substantially influenced PTE bioavailability, emphasizing the ephemeral Fe oxide control during the wet season and heightened ecological and health risks during the dry seasons.

Predictive modeling of frailty status in elderly abdominal surgery patients by preoperative quadriceps ultrasound testing.

OBJECTIVE: To develop a predictive model based on preoperative quadriceps ultrasound measurements to determine frailty status in elderly patients undergoing abdominal surgery. METHODS: The clinical data of 148 elderly patients who underwent abdominal surgery from July 2018 to June 2022 were retrospectively analyzed. The patients were assessed for frailty using the Fried Frailty Phenotype Assessment Scale after operation and divided into a no-frailty group (n=89) and a frailty group (n=59). The differences in the patient's clinical data, perioperative indexes, and imaging indexes were compared. The risk factors affecting the frailty status of elderly patients undergoing abdominal surgery were analyzed by logistic regression. The efficacy of the prediction model was evaluated by receiver operating characteristic (ROC) curve, with model validity confirmed through calibration curves and decision curve analysis (DCA). RESULTS: The proportion of patients with age ≥80 and BMI ≥23 kg/m2 in the frailty group was significantly higher than that in the no-frailty group (both P<0.01). The operation duration and postoperative hospital stay in the frail group were significantly longer the non-frail group, and the complication rate within postoperative 7 days was significantly higher than that in the non-frail group (all P<0.05). The cross-sectional area of rectus femoris muscle, vastus medialis muscle thickness, vastus intermedius muscle thickness, rectus femoris muscle thickness, and lateral femoris muscle thickness were significantly less in the frail group than those of the no-frail group (all P<0.001). Multifactorial logistic regression analysis showed that BMI, surgical duration, vastus medialis muscle thickness, vastus intermedius muscle thickness, rectus femoris muscle thickness, and lateral femoral muscle thickness were independent risk factors affecting frailty status in elderly patients undergoing abdominal surgery (all P<0.05). The predictive model demonstrated high accuracy with an AUC of 0.926. CONCLUSION: BMI and thickness of all quadriceps muscle components were significant factors affecting the frailty status of elderly patients undergoing abdominal surgery. In addition, the developed model, with excellent accuracy, offers a potential tool for preoperative risk assessment in this patient population.

The efficacy of the National Surgical Quality Improvement Program surgical risk calculator in head and neck surgery: A meta-analysis.

BACKGROUND: The National Surgical Quality Improvement Program surgical risk calculator (SRC) estimates the risk for postoperative complications. This meta-analysis assesses the efficacy of the SRC in the field of head and neck surgery. METHODS: A systematic review identified studies comparing the SRC's predictions to observed outcomes following head and neck surgeries. Predictive accuracy was assessed using receiver operating characteristic curves (AUCs) and Brier scoring. RESULTS: Nine studies totaling 1774 patients were included. The SRC underpredicted the risk of all outcomes (including any complication [observed (ob) = 35.9%, predicted (pr) = 21.8%] and serious complication [ob = 28.7%, pr = 17.0%]) except mortality (ob = 0.37%, pr = 1.55%). The observed length of stay was more than twice the predicted length (p < 0.02). Discrimination was acceptable for postoperative pneumonia (AUC = 0.778) and urinary tract infection (AUC = 0.782) only. Predictive accuracy was low for all outcomes (Brier scores ≥0.01) and comparable for patients with and without free-flap reconstructions. CONCLUSION: The SRC is an ineffective instrument for predicting outcomes in head and neck surgery.

Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service assessments.

BACKGROUND: Early identification of children and families who may benefit from support is crucial for implementing strategies that can prevent the onset of child maltreatment. Predictive risk modeling (PRM) may offer valuable and efficient enhancements to existing risk assessment techniques. OBJECTIVE: To evaluate the PRM's effectiveness against the existing assessment tool in identifying children and families needing home visiting services. PARTICIPANTS AND SETTING: Children born in hospitals affiliated with the Bridges Maternal Child Health Network in Orange County, California, from 2011 to 2016 (N = 132,216). METHODS: We developed a PRM tool by integrating a machine learning algorithm with a linked dataset of birth records and child protection system (CPS) records. To align with the existing assessment tool (baseline model), we limited the predicting features to the information used by the existing tool. The need for home visiting services was measured by substantiated maltreatment allegation reported during the first three years of the child's life. RESULTS: Of the children born in Bridges Network hospitals between 2011 and 2016, 2.7 % experienced substantiated maltreatment allegations by the age of three. Within the top 30 % of children with high-risk scores, the PRM tool outperformed the baseline model, accurately identifying 75.3 %-84.1 % of all children who would experience maltreatment substantiation, surpassing the baseline model's performance of 46.2 %. CONCLUSIONS: Our study underscores the potential of PRM in enhancing the risk assessment tool used by a prevention program in a child welfare center in California. The findings provide valuable insights to practitioners interested in utilizing data for PRM development, highlighting the potential of machine learning algorithms to generate accurate predictions and inform targeted preventive services.

Translating Cadaveric Injury Risk to Dummy Injury Risk at Iso-energy.

Injury risk assessment based on cadaver data is essential for informing safety standards. The common 'matched-pair' method matches energy-based inputs to translate human response to anthropometric test devices (ATDs). However, this method can result in less conservative human injury risk curves due to intrinsic differences between human and ATDs. Generally, dummies are stiffer than cadavers, so force and displacement cannot be matched simultaneously. Differences in fracture tolerance further influence the dummy risk curve to be less conservative under matched-pair. For example, translating a human lumbar injury risk curve to a dummy of equivalent stiffness using matched-pair resulted in a dummy injury risk over 80% greater than the cadaver at 50% fracture risk. This inevitable increase arises because the dummy continues loading without fracture to attenuate energy beyond the 'matched' cadaver input selected. Human injury response should be translated using an iso-energy approach, as strain energy is well associated with failure in biological tissues. Until cadaver failure, dummy force is related to cadaver force at iso-energy. Beyond cadaver failure, dummy force is related to cadaver force through failure energy. This method does not require perfect cadaver/dummy biofidelity and ensures that energy beyond cadaver failure does not influence the injury risk function.

Social vulnerability is associated with higher risk-adjusted rates of postoperative complications in a broad surgical population.

OBJECTIVE: The purpose of this study was to determine if an association between Social Vulnerability Index (SVI) and risk-adjusted complications exists in a broad spectrum of surgical patients. SUMMARY BACKGROUND DATA: Growing evidence supports the impact of social circumstances on surgical outcomes. SVI is a neighborhood-based measure accounting for sociodemographic factors putting communities at risk. METHODS: This was a multi-hospital, retrospective cohort study including a sample of patients within one healthcare system (2012-2017). Patient addresses were geocoded to determine census tract of residence and estimate SVI. Patients were grouped into low SVI (score<75) and high SVI (score≥75) cohorts. Perioperative variables and postoperative outcomes were tracked and compared using local ACS-NSQIP data. Multivariable logistic regression was performed to generate risk-adjusted odds ratios of postoperative complications in the high SVI cohort. RESULTS: Overall, 31,224 patients from five hospitals were included. Patients with high SVI were more likely to be racial minorities, have 12/18 medical comorbidities, have high ASA class, be functionally dependent, be treated at academic hospitals, and undergo emergency operations (all p â€‹< â€‹0.05). Patients with high SVI had significantly higher rates of 30-day mortality, overall morbidity, respiratory, cardiac and infectious complications, urinary tract infections, postoperative bleeding, non-home discharge, and unplanned readmissions (all p â€‹< â€‹0.05). After risk-adjustment, only the associations between high SVI and mortality and unplanned readmission became non-significant. CONCLUSIONS: High SVI was associated with multiple adverse outcomes even after risk adjustment for preoperative clinical factors. Targeted preventative interventions to mitigate risk of these specific complications should be considered in this high-risk population.

Dissecting climate change risk and financial market instability: Implications for ecological risk management.

This research investigates the impact of climate challenges on financial markets by introducing an innovative approach to measure climate risk, specifically the aggregate climate change concern (ACCC) index. The study aims to assess and quantify the potential influence of climate change and risk-related factors on the performance and dynamics of financial markets. In this paper, concern is defined as the attention paid to the risk of climate change and the associated negative consequences. The findings demonstrate that the aggregate index exhibits robust predictability of market risk premiums, both within the sample and out-of-sample. By comparison, the index contains additional information beyond 14 economic predictors and 12 risk/uncertainty indexes in forecasting stock market return. In addition, the index proves valuable for mean-variance investors in asset allocation, leading to significant economic gains. The study identifies the index's ability to capture the reversal of temporary price crashes caused by overreactions to climate change risk. Furthermore, it exhibits stronger return forecasting capability for green stocks, non-state-owned enterprise (non-SOE) stocks, and stocks in regions with low air pollution. Particularly during periods of low air pollution and relaxed regulation, the index displays an enhanced ability to forecast returns. The study's findings provide valuable insights for policymakers and financial institutions as they address 21st-century environmental challenges. Moreover, these findings can inform the design of adaptive measures and interventions aimed at mitigating ecological risks and promoting sustainable economic growth.

Impact of More Detailed Measures of Disease Severity on Racial Disparities in Cardiac Surgery Mortality among Native Hawaiians and Pacific Islanders.

Studies that examine racial disparities in health outcomes often include analyses that account or adjust for baseline differences in co-morbid conditions. Often, these conditions are defined as dichotomous (Yes/No) variables, and few analyses include clinical and/or laboratory data that could allow for more nuanced estimates of disease severity. However, disease severity - not just prevalence - can differ substantially by race and is an underappreciated mechanism for health disparities. Thus, relying on dichotomous disease indicators may not fully describe health disparities. This study explores the effect of substituting continuous clinical and/or laboratory data for dichotomous disease indicators on racial disparities, using data from the Queen's Medical Center's (QMC) cardiac surgery database (a subset of the national Society of Thoracic Surgeon's cardiothoracic surgery database) as an example case. Two logistic regression models predicting in-hospital mortality were constructed: (I) a baseline model including race and dichotomous (Yes/No) indicators of disease (diabetes, heart failure, liver disease, kidney disease), and (II) a more detailed model with continuous laboratory values in place of the dichotomous indicators (eg, including Hemoglobin A1c level rather than just diabetes yes/no). When only dichotomous disease indicators were used in the model, Native Hawaiian and other Pacific Islander (NHPI) race was significantly associated with in-hospital mortality (OR: 1.57[1.29,2.47], P=.04). Yet when the more specific laboratory values were included, NHPI race was no longer associated with in-hospital mortality (OR: 1.67[0.92,2.28], P=.28). Thus, researchers should be thoughtful in their choice of independent variables and understand the potential impact of how clinical measures are operationalized in their research.

Computational risk model for predicting 2-year malignancy of pulmonary nodules using demographic and radiographic characteristics.

OBJECTIVES: To determine whether discriminatory performance of a computational risk model in classifying pulmonary lesion malignancy using demographic, radiographic, and clinical characteristics is superior to the opinion of experienced providers. We hypothesized that computational risk models would outperform providers. METHODS: Outcome of malignancy was obtained from selected patients enrolled in the NAVIGATE trial (NCT02410837). Five predictive risk models were developed using an 80:20 train-test split: univariable logistic regression model based solely on provider opinion, multivariable logistic regression model, random forest classifier, extreme gradient boosting model, and artificial neural network. Area under the receiver operating characteristic curve achieved during testing of the predictive models was compared to that of prebiopsy provider opinion baseline using the DeLong test with 10,000 bootstrapped iterations. RESULTS: The cohort included 984 patients, 735 (74.7%) of which were diagnosed with malignancy. Factors associated with malignancy from multivariable logistic regression included age, history of cancer, largest lesion size, lung zone, and positron-emission tomography positivity. Testing area under the receiver operating characteristic curve were 0.830 for provider opinion baseline, 0.770 for provider opinion univariable logistic regression, 0.659 for multivariable logistic regression model, 0.743 for random forest classifier, 0.740 for extreme gradient boosting, and 0.679 for artificial neural network. Provider opinion baseline was determined to be the best predictive classification system. CONCLUSIONS: Computational models predicting malignancy of pulmonary lesions using clinical, demographic, and radiographic characteristics are inferior to provider opinion. This study questions the ability of these models to provide additional insight into patient care. Expert clinician evaluation of pulmonary lesion malignancy is paramount.

Using electronic health record data to predict future self-harm or suicidal ideation in young people treated by child and youth mental health services.

INTRODUCTION: Identifying young people who are at risk of self-harm or suicidal ideation (SHoSI) is a priority for mental health clinicians. We explore the utility of routinely collected data in developing a tool to aid early identification of those at risk. METHOD: We used electronic health records of 4610 young people aged 5-19 years who were treated by Child and Youth Mental Health Services (CYMHS) in greater Brisbane, Australia. Two Lasso models were trained to predict the risk of future SHoSI in young people currently rated SHoSI; and those who were not. RESULTS: For currently non-SHoSI children, an Area Under the Receiver Operating Characteristics (AUC) of 0.78 was achieved. Those with the highest risk were 4.97 (CI 4.35-5.66) times more likely to be categorized as SHoSI in the future. For current SHoSI children, the AUC was 0.62. CONCLUSION: A prediction model with fair overall predictive power for currently non-SHoSI children was generated. Predicting persistence for SHoSI was more difficult. The electronic health records alone were not sufficient to discriminate at acceptable levels and may require adding unstructured data such as clinical notes. To optimally predict SHoSI models need to be tested and validated separately for those young people with varying degrees of risk.

An ensemble modeling approach to predict spatial risk patches of the Persian leopard-livestock conflicts in Lorestan Province, Iran.

This study was conducted in the Lorestan Province in the west of Iran with two objectives of identifying major environmental variables in spatial risk modeling and identifying spatial risk patches of livestock predation by the Persian leopard. An ensemble approach of three models of maximum entropy (MaxEnt), generalized boosting model (GBM), and random forest (RF) were applied for spatial risk modeling. Our results revealed that livestock density, distance to villages, forest density, and human population density were the most important variables in spatial risk modeling of livestock predation by the leopard. The center of the study area had the highest probability of livestock predation by the leopard. Ten spatial risk patches of livestock predation by the leopard were identified in the study area. In order to mitigate the revenge killing of the leopards, the findings of this study highlight the imperative of implementing strategies by the Department of Environment (DoE) to effectively accompany the herds entering the wildlife habitats with shepherds and a manageable number of guarding dogs. Accordingly, the identified risk patches in this study deserve considerable attention, especially three primary patches found in the center and southeast of Lorestan Province.

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Mapping the Environmental Risk of Beech Leaf Disease in the Northeastern United States.

The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in 2012 in northeast Ohio, U.S.A., BLD had been documented in 10 northeastern states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across northern Ohio, western Pennsylvania, western New York, and northern West Virginia, U.S.A. In the absence of symptoms, an area cannot necessarily be deemed free of BLD (i.e., absence of BLD cannot be certain) due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM), and maximum entropy (Maxent) to predict the spatial pattern of BLD risk based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.