Standard Probability Research Articles

Combined mammographic breast density and breast arterial calcification is incrementally predictive of coronary artery disease beyond traditional risk factors

Abstract Background Coronary artery disease (CAD) risk is underestimated in women using current risk stratification tools. The mammographic finding of breast arterial calcification (BAC) associates with CAD. Furthermore, breast adipose tissue, measured through breast density (BD) on mammography (low breast density indicates high adiposity) has shown potential to act as a pro-inflammatory, pro-atherogenic fat deposit. Given its uptake in screening programs, mammography may therefore represent a novel risk stratification tool for cardiovascular disease in women. Purpose To evaluate the association between the combined mammographic features of BD and BAC, with CAD. Methods Single-centre, retrospective, cross-sectional study, including 153 women, mean age 62±10, who had both clinically indicated mammography, and coronary computed tomography angiogram (CCTA) for suspected CAD. CAD risk was identified by the CAD Consortium Score, with a 15% threshold for low and high risk. BD was visually assessed and categorised by 4-level BI-RADS grade with grade A-B representing low density, and C-D representing high density (Figure 1). BAC was visually assessed and categorised as present/absent (Figure 1). CAD was categorised as presence/absence of coronary artery plaque on CCTA. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals]. Receiver operator characteristic area under the curve (AUC) was used for model discrimination. Results Low BD (n=103 (67%)) was associated with CAD (OR 3.20 [1.58-6.53], p=0.001, as was BAC presence (n=37 (24%), OR 4.36 [1.58-12], p=0.004). There were 51 (33%) with elevated CAD risk. Participants were categorised into 4 subgroups based on low/high BD and presence/absence of BAC: 29 (19%) had low BD and BAC, 74 (48%) had low BD and no BAC, 8 (5%) had high BD and BAC and 42 (27%) had high BD and no BAC. Significantly higher proportions of CAD were noted with low BD and BAC alone, as well as combined low BD and BAC (Figure 2). Compared with high BD/BAC negative, the presence of low BD and BAC independently associated with CAD (OR 5.27 [1.19-23.3], p=0.03). Significant incremental benefit was seen after adding BD/BAC status to CAD Consortium Score (AUC 0.65 vs. 0.72, p=0.004). Conclusions Combined, and individual mammographic features of low BD and BAC presence are associated with CAD and improve risk prediction beyond standard coronary risk probabilities. Standardised reporting of these features to inform risk identification may be of further benefit and should be tested in prospective screening studies.

The relationship of clinical presentation, d-dimer levels and previous SARS-CoV-2 positivity in outpatients with suspected pulmonary embolism

Abstract Introduction Elevated D-dimer levels can be detected in patients with pulmonary embolism (PE), but also in patients with the disease caused by the novel coronavirus (COVID-19). Aims To investigate relationship between clinical presentation, D-dimer levels and previous SARS-CoV-2 positivity in outpatients with suspected PE. Material and methods This retrospective study included 609 consecutive outpatients (mean age 68±15 years, 50% female) who underwent computed tomography pulmonary angiography for suspected PE from 2021 to 2023. By reviewing electronic databases, clinical and laboratory data were collected, as well data on previously documented SARS-CoV-2 infection. Results PE was confirmed in 130 (21%) patients who had higher D-dimer values than patients without PE (p<0.001). Patients with previous COVID-19 had significantly higher D-dimer values compared to those without prior COVID-19, but only during the first 8 months from infection to pulmonary angiography (Figure, upper panel). Clinical probability of PE, as indicated by Geneva score [odds ratio (OR) 2.36, 95% confidence interval (CI) 1.55 – 3.58)], and age-adjusted D-dimer (OR 4.52, 95%CI 1.58 – 12.90) were independently associated with PE, while previous COVID-19 was not. Main clinical reasons for emergency department presentation are shown in Figure (bottom panel). In 45/609 (7.4%) patients evaluated primarily for elevated D-dimer, the prevalence of COVID-19 in the previous 6 months was higher in those with PE compared to those without PE (100% vs. 49%, p=0.019). Conclusion Within 8 months of infection, patients with a history of COVID-19 had higher D-dimer levels than those without COVID-19. Age-adjusted D-dimer and standard clinical probability scores retained their predictive power in outpatients with suspected PE, regardless of COVID-19 history.

A Method for Predicting the Result of University Employment Education Transformation Based on Multi-Source Information Technology

In the data on the outcomes of higher education employment transformation, there is high correlation among multiple independent variables, leading to multicollinearity. If multicollinearity exists, it can complicate the establishment of predictive analysis models and the interpretation of relationships between variables for higher education employment transformation outcomes. The employment issues of college graduates have become a focal point of social concern. These issues not only reflect the employment status of college graduates but also indirectly indicate the quality of higher education and the level of talent cultivation. Therefore, a prediction method for the effectiveness of higher education employment transformation based on multi-source information technology is proposed. According to the theory of multi-source information technology, an employment data classification framework is constructed to integrate multi-source information. The matching operator function output values are calculated, weights are determined, and the final college student information collection results are output. The standard error between employment unit types and salary ranges is analyzed, clustering training samples are trained, and the distance from each employment information sample to the centroid is calculated to obtain preprocessing results of the transformation outcomes, thus avoiding the impact of multicollinearity on the results of higher education employment transformation. A model structure for analyzing factors influencing higher education employment is established, allowing for an understanding of the characteristics of different types of educational resources and enabling the prediction of the effectiveness of higher education employment transformation. Experimental results show that this method can accurately monitor the dynamic employment situation of university students. After applying this method, the standard error and probability error values are significantly reduced, proving to be significantly effective in predicting the outcomes of higher education employment transformation.

Explicit Formulas of Average Run Length for Triple Moving Average Control Chart to Monitor Changes in Mean Parameter of Normal and Non-normal Process

In this research, a formula is presented for calculating the average run length of a triple moving average (TMA) control chart, which is used to measure control chart performance and determine control chart width. To apply appropriately to data in various processes, a TMA control chart is used to detect small changes in a process by finding the average value of the data over a specified period of time (w). However, determining measure control chart performance and control chart width is difficult because the formula for calculation is not created. The performance of the control charts is measured using the standard normal distribution probability properties and compared with the moving average (MA) control charts and the double moving average (DMA) control charts. The analysis results show that TMA is very effective at detecting small changes, when w is set to have a small value. The study determines that the data within the process are normally and non-normally distributed. Detecting changes on real data by TMA charts has the best detection performance compared to MA and DMA.

Hospital and surgeon surgical valvar volume and survival after multivalve cardiac surgery in Medicare beneficiaries

ObjectiveLong-term outcomes after multivalve cardiac surgery remain underevaluated. MethodsMedicare administrative claims from 2008 to 2019 identified beneficiaries undergoing multivalve surgery. Operative characteristics were doubly adjudicated using International Classification of Diseases and Current Procedural Technology codes. A multivariable flexible parametric model evaluated predictors of survival; regression standardization was performed to predict standardized survival probabilities (SSPs) at varying percentiles of annual valvar volume. ResultsOf 476,092 cardiac surgeries involving the aortic (AVS), mitral (MVS), or tricuspid (TVS) valve, 63,083 (13.3%) were identified as involving multi-valve surgery, including 22,884 MVS/TVS, 30,697 AVS/MVS, 3443 AVS/TVS, and 6059 AVS/MVS/TVS. Surgery occurred at 1157 hospitals by 2922 surgeons. Annual valvar volume (total AVS/MVS/TVS) was tallied for surgeons and hospitals. Median survival varied substantially by type of multivalve surgery: 8.09 (95% CI: 7.90-8.24) years in MVS/TVS, 6.65 (95% CI: 6.49-6.81) years in AVS/MVS, 5.77 (95% CI: 5.37-6.13) years in AVS/TVS, and 6.02 (95% CI: 5.64-6.38) years in AVS/MVS/TVS. SSPs were calculated across combined hospital/surgeon volume percentiles; the median SSP increased with increasing percentile of combined hospital/surgeon volume: 5th percentile, 5.77 (95% CI: 5.58-5.98), 25th percentile, 6.18 (95% CI: 6.07-6.28), 50th percentile, 6.56 (95% CI: 6.44-6.68), 75%th percentile, 6.86 (95% CI: 6.75-6.97), and 95th percentile, 7.58 (95% CI: 7.34-7.83) years, respectively. ConclusionsSurvival varied significantly by type of multivalve surgery, worsened with addition of concomitant interventions and improved substantially with increasing annual hospital and surgeon volume. Hospital volume was associated with an improved early hazard for death that abated beyond 3 months post surgery, while surgeon volume was associated with an improved hazard for death that persisted even beyond the first postoperative year. Consideration should be given to referring multivalve cases to high-volume hospitals and surgeons.

A probabilistic approach to optimal emergency shutdown valve configuration for pressurised CO2 pipelines

This study presents a multi-objective optimisation technique for determining the optimal number of in-line Emergency Shutdown Valves (ESDVs) for pressurised CO2 pipelines, aiming to balance risk associated with pipeline failure against the valve cost based on treating the risk as a probabilistic variable. Using standard Probability Density Functions (PDFs) to model the uncertainties inherent in pipeline characteristics and operating conditions, we perform a Monte Carlo simulation involving the random sampling from these PDFs to derive a probabilistic distribution of risk. This method contrasts with traditional deterministic methods that rely on assuming the worst-case failure scenario or pre-determining an averaged expected risk level — typically calculated by summing the products of failure frequencies and consequence magnitudes for various representative failure scenarios. The proposed approach integrates the risks of all possible failure scenarios directly into the optimisation process without such assumptions or pre-determinations, assessing them collectively based on their individual probabilities of occurrence. Our findings illustrate that the proposed technique allows for more reliable and effective ESDV configurations compared to the traditional methods. This approach not only facilitates safer CO2 transport within the context of Carbon Capture, Utilisation and Storage but also offers a framework for optimising pipeline ESDV configurations in general.

A novel single-loop estimation method for predictive failure probability-based global sensitivity analysis

The existing predictive failure probability (PFP)-based global sensitivity analysis methods may get stuck in practical application due to the large amount of calculation or insufficient precision. Therefore, a novel single-loop estimation method is proposed in this paper to effectively estimate PFP-based global sensitivity measures. Firstly, by introducing standard normal auxiliary variables and equivalent probability transformation, the overall uncertainty of each input variable is decomposed into its inherent uncertainty and distribution parameter uncertainty. Then, Bayes theorem-based formulations are derived for three PFP-based global sensitivity measures, where only the unconditional PFP and the conditional marginal and joint distributions of standard normal auxiliary variables and distribution parameters need to be known. These items can be evaluated simultaneously using only one set of functional calls. Finally, adaptive Kriging model is embedded into the proposed method in place of the time-demanding performance function to further enhance computational efficiency. The effectiveness of the proposed methods is validated through four benchmark reliability problems asnd one engineering problem.

Systematic Implications of Palynomorph Diversity Using Microscopic Trends Among Asteraceous Flora From the Drylands of Baluchistan, Pakistan.

Pollen micromorphological traits with taxonomic implications are first reported from the study area for 50 Asteraceous taxa belonging to nine tribes. Cichorieae (21 taxa), Cardueae (11 taxa), Inuleae (six taxa), and Anthemideae (four taxa) are the leading tribes. The research included Cousinia haeckeliae, Himalaiella afghana, Pterachaenia stewartii (endemic to Afghanistan and Pakistan), and Xylanthemum macropodum (endemic to Baluchistan). Light and scanning electron microscopy were employed for the visualization of pollen photomicrographs. The data was analyzed statistically via SPSS, PAST, and Origin. Significant diagnostic qualitative and quantitative palynological traits were explored for discrimination down to the species level within the tribes. All the investigated taxa possessed radial symmetry, isopolarity, and monad form (characters for distinction at the subdivision level). The aperture types were trizonocolporate, tetrazonocolporate, and tricolporate with number position and character (NPC) formulas N3P4C5, N4P4C5, and N3P4C3. Goniotreme, peritreme, and ptychotreme types of amb were recognized. Echinate, echinate lophate, scabrate, and gemmate sculpturing were present with and without perforated surface patterns. Variations in the shapes in polar and equatorial views and lacuna shapes further assisted the separation of taxa. The observed shape classes were perprolate, prolate spheroidal, prolate, subprolate, oblate spheroidal, suboblate, and oblate. Principal component analysis, correlation, standard probability plots, and ridge line paired features plot for quantitative variables determined the positive correlation between the length and width of colpi in equatorial and polar view with polar axis and equatorial diameter and number of spines between colpi with the number of spines per pollen. The number of spines per pollen was negatively correlated with the width and length of colpi in the polar view. Multiple sample analysis of variance (ANOVA) concluded that a high statistically significant difference exists among the means of analyzed traits. The examined qualitative and quantitative palynological traits revealed noticeable variations, thus providing the source for species discrimination in Asteraceous tribes.

Estimation of the causal effect of sex on neonatal intensive care unit outcomes among very low birth weight infants.

Estimate the causal effect of sex on outcomes in the neonatal intensive care unit (NICU) among very low birth weight (VLBW) infants. Retrospective cohort study using Vermont Oxford Network data to compare NICU outcomes for VLBW males versus females. Odds ratios (OR) for outcomes that differed significantly by sex were computed using standard unweighted analysis and inverse probability weighted (IPW) analysis to correct for selection bias. Using standard analysis, males were significantly more likely to die before discharge and experience six other adverse outcomes. From IPW analysis, male sex caused a 56% increase in the odds of death before discharge (OR = 1.56, 95% confidence interval: 1.18-1.94). Standard unweighted results were significantly biased towards increased risk of adverse outcomes for males (p = 0.005) compared to IPW results for which three outcomes were no longer significantly associated with male sex. Standard statistical methods generally overestimate the casual effect of sex among VLBW infants.

Estimating the Impact of Rurality in Disparities in Cancer Mortality.

Estimation of the independent effect of rurality on cancer mortality requires causal inference methodology and consideration of area-level socioeconomic status and rural designations. Using SEER data, we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 years (N = 3,788,273), examining a 20% random sample (n = 757,655). Standardized competing risk and survival models estimated the association between rural residence, defined by Rural-Urban Continuum Codes, and cancer-specific and all-cause mortality, controlling for age at cancer diagnosis, sex, race/ethnicity, year of diagnosis, and Area Deprivation Index (ADI). We estimated the attributable fraction (AF) of rurality and high ADI (ADI > median) to the probability of mortality. Finally, we examined county measurement issues contributing to mortality rates discordant from hypothesized rates. The 5-year standardized failure probability for cancer mortality for rural patients was 33.9% versus 31.56% for urban. The AF for rural residence was 1.04% at year 1 (0.89% by year 5), the highest among local stage disease (Y1 2.1% to Y5 1.9%). The AF for high ADI was 3.33% in Y1 (2.87% in Y5), while the joint effect of rural residence and high ADI was 4.28% in Y1 (3.71% in Y5). Twenty-two percent of urban counties and 30% of rural were discordant. Among discordant urban counties, 30% were only considered urban because of adjacency to metro area. High ADI was associated with urban discordance and low ADI with rural discordance. Rural residence independently contributes to cancer mortality. The rural impact is the greatest among those with localized disease and in high deprivation areas. Rural-urban county designations may mask high-need urban counties, limiting eligibility to state and federal resources dedicated to rural areas.

Low-Intensity Statin Plus Ezetimibe Versus Moderate-Intensity Statin for Primary Prevention: A Population-Based Retrospective Cohort Study in Asian Population.

While moderate-intensity statin therapy is recommended for primary prevention, statins may not be utilized at a recommended intensity due to dose-dependent adverse events, especially in an Asian population. However, evidence supporting the use of low-intensity statins in primary prevention is limited. We sought to compare clinical outcomes between a low-intensity statin plus ezetimibe and a moderate-intensity statin for primary prevention. This population-based retrospective cohort study used the Korean nationwide claims database (2002-2019). We included adults without atherosclerotic cardiovascular diseases who received moderate-intensity statins or low-intensity statins plus ezetimibe. The primary outcome was a composite of all-cause mortality, myocardial infarction, and ischemic stroke. The safety outcomes were liver and muscle injuries and new-onset diabetes mellitus (DM). We used standardized inverse probability of treatment weighting (sIPTW) and propensity score matching (PSM). In the sIPTW model, 1717 and 36 683 patients used a low-intensity statin plus ezetimibe and a moderate-intensity statin, respectively. In the PSM model, each group included 1687 patients. Compared with moderate-intensity statin use, low-intensity statin plus ezetimibe use showed similar risks of the primary outcome (hazard ratio [HR] = 0.92, 95% CI = 0.81-1.12 in sIPTW and HR = 1.16, 95% CI = 0.87-1.56 in PSM model). Low-intensity statin plus ezetimibe use was associated with decreased risks of liver and muscle injuries (subHR [sHR] = 0.84, 95% CI = 0.74-0.96 and sHR = 0.87, 95% CI = 0.77-0.97 in sIPTW; sHR = 0.84, 95% CI = 0.72, 0.96 and sHR = 0.82, 95% CI = 0.72-0.94 in PSM model, respectively). For new-onset DM and hospitalization of liver and muscle injuries, no difference was observed. Low-intensity statin plus ezetimibe may be an alternative to moderate-intensity statin for primary prevention. Our findings provide evidence on safety and efficacy of statin therapy in Asian population.

Polynomial approaches in improving accuracy of probability distribution estimation using the method of moments

AbstractBACKGROUNDDetermination of a probability density function (PDF) is an area of active research in engineering sciences as it can improve process systems. A previously developed polynomial method‐of‐moments‐based PDF estimation model has been applied in the research to produce accurate approximations to both standard and more complex PDF. A model with a different polynomial basis than a monomial is still to be developed and evaluated. This is the work that is undertaken in this study.RESULTSA set of standard PDF (Normal, Weibull, Log Normal and Bimodal) and more complex distributions (solutions to the Smoluchowski coagulation equation and Population Balance equation) were approximated by the method‐of‐moments using Chebyshev, Hermite and Lagrange polynomial‐based density functions. Results show that Lagrange polynomial‐based models improve the fit compared to monomial based‐modeling in terms of RMSE and Kolmogorov–Smirnov test statistic estimates. The Kolmogorov–Smirnov test‐statistics decreased by 19% and the RMSE values were improved by around 85% compared to the standard monomial basis when using Lagrange polynomial basis.CONCLUSIONThis study indicates that the procedure using Lagrange polynomials with method‐of‐moments is a more reliable reconstruction procedure that calculates the approximate distribution using lesser number of moments, which is desirable. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Underwater image enhancement using multi-task fusion.

Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method

In scenarios involving sudden releases of unidentified gases or concealed pollution emergencies, source control emerges as a critical procedure to safeguard residential air quality. Appropriate inverse source tracking methodology depending on diverse measurement data could be utilized to promptly identify pollutant source parameters. In this study, source term estimation (STE) method, i.e., jointly combining probability adjoint method with the Bayesian inference method, has been proposed. General form of the pollutant inverse transport equation was firstly established. Subsequently, the pollution source information, assumed from single continuous point releases during Fusion Field Trials 2007 under an unsteady wind field, was identified using the Bayesian inference probability adjoint inverse method. Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC) and Differential Evolution Markov Chain Monte Carlo (DE-MCMC) were then compared as sampling methods for Bayesian inference. Results indicated that the DE-MCMC algorithm has superior convergence and could present higher accuracy of pollutant source information than that of MH-MCMC algorithm, particularly for highly nonlinear and multi-modal distribution systems. Furthermore, the integration of Union standard Adjoint Location Probability (UALP) as prior information into the Bayesian inference probability adjoint inverse method effectively narrowed the sampling range, enhancing both the accuracy and robustness of the proposed approach. Finally, the impact of the covariance matrix on the inverse identification accuracy was explored. Overall, this research has provided insights into the future applicability of this Bayesian inference inversion technique for point source identification.

Construction of personalized genomics model and clinical application in precision medicine

Abstract Sequencing technology continues to evolve, and pharmacogenomics is increasingly pivotal in the pursuit of personalized medicine. This study delineates the personalized genomics model into two core modules for framework construction: data preprocessing and prediction. Within the data preprocessing module, a denoising submodule and a genomic feature distribution alignment module are dedicated to processing the genomic features associated with diseases and assimilating them into the spatial feature distribution of the model. The prediction module employs a fully connected neural network alongside a graph convolutional neural network to forecast drug dosages based on the disease's genomic features. We propose clinical applications of personalized genomics models across three distinct pathways. To assess the practical impact of these models, experiments were conducted focusing on their clinical application. After 14 days of medication administration, the International Normalized Ratio (INR) value for the drug model group reached 2.67, surpassing that of the conventional treatment group, with nearly 60% of participants achieving the target range. Furthermore, in evaluating the safety and efficacy of the clinical application of the genomics model, the incidence of serious bleeding events in the drug model group was recorded at 16.67%, which is below the standard allowable probability of 23.81%. Therefore, the personalized genomics model introduced in this study meets the established safety and efficacy standards.

Novel Self-Calibrated Threshold-Free Probabilistic Fibrosis Signature Technique for 3D Late Gadolinium Enhancement MRI.

Myocardial fibrosis, marked by excessive collagen buildup in the heart, is a crucial severity marker of heart muscle injury in several heart diseases, such as myocardial infarction, cardiomyopathies, and atrial fibrillation (AF). It is also vital for evaluating the efficacy of induced scarring (dense fibrosis) post-interventions, such as catheter ablation for AF. Cardiac MRI emerged as the gold standard for evaluating myocardial fibrosis and scarring for diagnosis and intervention planning. However, existing 3D cardiac MRI (CMR) fibrosis analysis methods are unreliable as they rely on variable thresholding and suffer from a lack of standardization and high sensitivity to typical MRI uncertainties. Importantly, these methods quantify severity based on fibrosis volume alone while ignoring the unique MRI characteristics of fibrosis distribution, which could better inform on disease severity. To address these limitations, we propose a novel thresholdfree and self-calibrating probabilistic method named "Fibrosis Signatures" for a comprehensive and reliable fibrosis analysis of 3D MRI cardiac images. Through a novel efficient (linear complexity) probabilistic encoding of 'multibillion' MRI intensity disparities into standardized probability density function, our method derives the patient's unique fibrosis signature profile and index (FSI). Our approach goes beyond mere measuring of fibrosis volume; it encodes both the extent and the unique MRI characteristics of fibrosis distribution beyond mere entropy for a more detailed evaluation of fibrosis burden/severity. Our self-calibrating design effectively adjusts for MRI uncertainties like noise, low spatial resolution, and segmentation errors to ensure robust and reproducible fibrosis evaluation pre- and post-intervention. Validated in numerical phantom and 143 in vivo MRI scans of AF patients and compared to five baseline methods, our method showed strong correlations with traditional volume measures of pre-intervention fibrosis and post-intervention scar and was up to 9- times more reliable and reproducible, highlighting its potential to enhance cardiac MRI's utility.

Abstract C009: Estimating the true contribution of rural residence and area deprivation on cancer mortality

Abstract Introduction True estimation of the rural effect on cancer mortality requires causal inference methodology, consideration of area-level socioeconomic status and examination of area designations (e.g., measurement of rural vs urban). Methods Utilizing Surveillance, Epidemiology and End Results data we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 (n=3,788,273). Cancer types were selected based on prior evidence of rural mortality disparities. A 20% random sample was examined (n=757,655). Rurality was defined using the Rural Urban Continuum Codes (RUCC). County indicators were linked with the Area Deprivation Index (ADI, 0 least deprived to 100 highest deprivation). Standardized competing risk and survival models estimated causal impact of rural residence on cancer-specific mortality. All models controlled for age at diagnosis, sex, race/ethnicity, year of diagnosis, and ADI. We estimated rural residence and ADI’s attributable fraction to the probability of mortality. Models were run overall and by stage. Finally, we identified county measurement issues contributing to county rates discordant from hypothesized mortality rates (i.e., urban counties with mortality>median or rural counties with mortality≤median). Results Approximately 45% were diagnosed ≥65y, 51% were female, 71% non-Hispanic white, and 12% non-Hispanic Black. Cancer diagnoses included those with screening recommendations (70%), obesity related (57%), and tobacco-related cancers (21%). The majority of patients were diagnosed with in situ or localized disease (53%). The 5y standardized failure probability for cancer-specific mortality for rural patients was 33.9% vs. 31.6% among urban. Rural + high ADI mortality was 37.9% vs. 30.7% for urban + low ADI. Rural residence attributable fraction to the probability of cancer mortality was 1.04% at Year 1, declining to 0.9% by Year 5. The rural fraction was highest among local stage disease (Y1 2.1% to Y5 1.9%). Attributable fraction of high ADI was 3.3% in Y1 to 2.9% in Y5, while the joint effect of rural+high ADI ranged from 4.3% in Y1 to 3.7% in Y5. Discordance between hypothesized and actual mortality was greater with urban/rural definitions vs ADI cut-offs (i.e., ADI distinguishes between high and low mortality better). Among 256 urban counties, 21.7% (n=89) had a higher mortality rate than hypothesized (mortality rate > median). Of those, 27 counties had no metropolitan area, but were “attached” to a nearby metro-area according to US Economic Research Service. When these 27 counties were reclassified as rural, the rural fraction was 1.4% at Y1 and 1.2% at Y5. This was a 35% and 26% relative increase, respectively. Among 349 rural counties, 15% had mortality rates higher than expected. Conclusion: Rural residence holds a small independent contribution to cancer mortality; higher among localized disease and in areas with greater deprivation. Urban/rural county designations using standard metrics may mask high need urban counties, limiting eligibility to state or federal resources dedicated to rural areas. Citation Format: Kelly Kenzik, Elizabeth Davis, Jeffrey Franks, Smita Bhatia. Estimating the true contribution of rural residence and area deprivation on cancer mortality [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C009.

Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.

A Wigner Quasiprobability Distribution of Work.

In this article, we introduce a quasiprobability distribution of work that is based on the Wigner function. This proposal rests on the idea that the work conducted on an isolated system can be coherently measured by coupling the system to a quantum measurement apparatus. In this way, a quasiprobability distribution of work can be defined in terms of the Wigner function of the apparatus. This quasidistribution contains the information of the work statistics and also holds a clear operational definition that can be directly measured in a real experiment. Moreover, it is shown that the presence of quantum coherence in the energy eigenbasis is related with the appearance of features related to non-classicality in the Wigner function such as negativity and interference fringes. On the other hand, from this quasiprobability distribution, it is straightforward to obtain the standard two-point measurement probability distribution of work and also the difference in average energy for initial states with coherences.