Prevalence and Concordance of oral and Genital Hpv By Sexual orientation among Us Men

The objective of our study was to describe oral and genital human papillomavirus (HPV) infection prevalence and concordance by sexual orientation among US men using a nationally representative sample. We conducted a retrospective cross-sectional analysis of the 2013-2016 National Health and Nutrition Examination Survey. The survey conducts a physical examination and collects oral rinse and genital swab specimens; demographic and health behaviors are self-reported. We used descriptive statistics and multivariate regression models to estimate HPV infection prevalence and the likelihood of HPV infection, respectively. All analyses were adjusted for National Health and Nutrition Examination Survey design and weights, and statistical significance was tested at a 2-sided P value of less than .05. Men who have sex with men had a statistically significantly higher prevalence of oral HPV (high-risk, 9-valent, 4-valent, and HPV 16 and 18), genital HPV (9-valent, 4-valent, and HPV 16 and 18), and concordant oral and genital HPV (high-risk and 9-valent) infections compared with heterosexual men. Improved HPV prevention among men is needed

State Variation in Squamous Cell Carcinoma of the anus incidence and Mortality, and association With Hiv/Aids and Smoking in the United States

PURPOSE: Squamous cell carcinoma of the anus (SCCA) incidence and mortality rates are rising in the United States. Understanding state-level incidence and mortality patterns and associations with smoking and AIDS prevalence (key risk factors) could help unravel disparities and provide etiologic clues. METHODS: Using the US Cancer Statistics and the National Center for Health Statistics data sets, we estimated state-level SCCA incidence and mortality rates. Rate ratios (RRs) were calculated to compare incidence and mortality in 2014-2018 versus 2001-2005. The correlations between SCCA incidence with current smoking (from the Behavioral Risk Factor Surveillance System) and AIDS (from the HIV Surveillance system) prevalence were evaluated using Spearman\u27s rank correlation coefficient. RESULTS: Nationally, SCCA incidence and mortality rates (per 100,000) increased among men (incidence, 2.29-3.36, mortality, 0.46-0.74) and women (incidence, 3.88-6.30, mortality, 0.65-1.02) age ≥ 50 years, but decreased among men age \u3c 50 years and were stable among similar-aged women. In state-level analysis, a marked increase in incidence (≥ 1.5-fold for men and ≥ two-fold for women) and mortality (≥ two-fold) for persons age ≥ 50 years was largely concentrated in the Midwestern and Southeastern states. State-level SCCA incidence rates in recent years (2014-2018) among men were correlated ( CONCLUSION: During 2001-2005 to 2014-2018, SCCA incidence and mortality nearly doubled among men and women age ≥ 50 years living in Midwest and Southeast. State variation in AIDS and smoking patterns may explain variation in SCCA incidence. Improved and targeted prevention is needed to combat the rise in SCCA incidence and mitigate magnifying geographic disparities

Integrated Microgrid Design and Operation Optimization Using Dynamic-Data-Driven Approaches

Our traditional electricity system is experiencing a drastic change as microgrids are fast spreading due to their inherent advantages of energy resilience, prosperity, and sustainability for the communities. The microgrid concept can be defined as the integration of distributed energy sources, energy storage systems and controllable loads into localized energy systems. However, non-linearities in the operation of microgrids, variations in load demand, and uncertainties in power generation from renewable energy sources pose significant challenges to determine the optimal microgrid operation planning. Here, timely planning, analysis, and control of all the components in a microgrid play a crucial role to achieve the resilient, sustainable, and secure smart energy infrastructure. To this end, a dynamic data driven operation control and optimization framework is introduced for addressing significant challenges in operation planning and design of microgrids, the economic and environmental unit commitment and load dispatch problems, and timely monitoring and adaptation of microgrid operations to real-time system fluctuations and contingencies to increase the power network resilience and energy surety. The proposed framework incorporates new and advanced optimization models and algorithms for operation and control including a comprehensive optimization model and a decomposition algorithm for the operation of off-grid AC and DC microgrids, and multi-scale adaptive simulation models. Initially, a dynamic data driven multi-objective optimization framework was developed for the economic and environmental (near) real-time load dispatching considering demand side management decisions for DC microgrids. The framework has been tested and validated via a synthetic microgrid and as the numerical analysis revealed, it is capable of adopting the uncertainties in load demand by minimizing the operation cost and GHG gas emission. In addition to DC microgrids, a comprehensive microgrid operation models and advanced decomposition algorithm to solve the models are proposed for radial and mesh structured AC microgrids. Here, a stochastic network constrained AC microgrid unit commitment problem is first modeled. This model is the first model that presents a mixed-integer linear programming formulation for the network-constrained AC unit commitment problem including the energy storage systems and then proposes an efficient two-stage Benders’ decomposition approach to solve this problem under load and solar power uncertainty. The performance of the algorithm is demonstrated through the numerical results from two case studies on the IEEE-18, and IEEE33 radial test systems. Moreover, a new comprehensive security-constrained operation planning topology reconfiguration optimization model and a computationally efficient decomposition algorithm to solve the developed optimization model for the operation of off-grid AC microgrids are introduced. To the best of our knowledge, this model is the first in the literature that integrates network security constraints, branch flow equations, and transmission switching decisions into a multi-period operation planning problem. The capabilities and performance of the proposed approaches are tested on IEEE-9, IEEE-30, and IEEE-118 test systems. The numerical analysis has shown quite promising results in optimizing the design and operation planning such that the algorithm provides a (near-) optimal solution in a few minutes. Lastly, multi-fidelity simulation models are designed for resilient back-up power smart energy systems. Here, a low-fidelity battery load simulation model is developed for the estimation of the system states and assessment of potential power mismatch in power supply and demand while a high-fidelity component level simulation model is used for the analysis of power losses and inefficiencies in the system. The simulation models were illustrated and validated via data obtained from the City of Coral Gables and the experiments indicated, it is quite capable of determine the battery and PV capacities that are subject to uncertainties in the power supply and demand, and assessing of potential power mismatch in a smart microgrid by computing the amp-hour capacities of batteries in worst case scenarios.</p

UNCERTAINTY AND A NEW MEASURE FOR CLASSIFICATION UNCERTAINTY

Ben-Israel and Iyigun ([1] and [2]) presents a new clustering method which is probabilistic distance clustering (P-D Clustering). In this method, the probability of assignment to cluster for each point is inversely proportional to distances between data point and centers of clusters according to given number of clusters and their centers. In this paper, we study on new uncertainty measure for classification using the assignment probabilities of P-D Clustering. Moreover, the relationship of the new measure with Kullback - Liebner divergence is discussed

A Dynamic Data-driven Approach for Operation Planning of Microgrids

Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the real-time operation plan of an electric microgridwhile considering its conflicting objectives. In particular, the proposed approachis equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scaleidentification procedures; and 3) a multi-objective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scaleof the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstratethe power of the proposed approach in real-time assessment and control of operation in microgrids

MR2: A Two-stage Feature Selection Algorithm in High-throughput Methylation Data for Max-relevance and Min-redundancy

Recent advances reveal that DNA methylation plays an important role in regulating different genome functions where anomalous methylation levels are associated with various cancer types. Feature selection algorithms are geared towards high-throughput analysis of DNA methylation to help identify idiosyncratic DNA methylation profiles associated with cancer types and subtypes. In high dimensional and highly correlated DNA methylation data, feature selection algorithms aim at selecting an efficient and comprehensive feature set to better capture characteristics of phenotypes. In this work, we introduce a two-stage feature selection algorithm (MR2) based on maximum relevance and minimum redundancy criteria. The features that satisfy the relevance conditions are filtered in the first stage, in the second stage, the final subset of loci is selected to reach minimal redundancy by using a k-medoids clustering algorithm that embeds a succinct uncertainty measure score. The performance of the proposed feature selection algorithm is benchmarked against those of the principal component analysis and four other commonly used filtering methods using lung and breast cancer datasets obtained from Gene Expression Omnibus in terms of their classification errors in support vector machine classifiers. Our MR2 algorithm outperforms these filtering based algorithms while at the same time providing more interpretable results

Estimating gene expression from high-dimensional DNA methylation levels in cancer data: A bimodal unsupervised dimension reduction algorithm

•A bi-modal dimension reduction is proposed for unsupervised methylation analysis → 83 characters.•A curve-fitting based filtering is developed to identify informative biomarkers → 82 characters.•Pearson correlation is used to eliminate redundant biomarkers → 65 characters.•The performance of our algorithm is shown using nine real cancer datasets → 76 characters.•Our algorithm outperforms two benchmark algorithms in eight datasets out of nine → 83 characters. Recent molecular and genetic studies have revealed the importance of DNA methylation, a key epigenetic mark, in regulating gene expression and the abnormal profiles of DNA methylation in various diseases including cancer. Here, unsupervised learning methods that are geared towards high-throughput DNA methylation analysis are used to extract useful information from high-dimensional genome wide methylation data in order to provide crucial insights for accurate early diagnosis and treatment of cancer. Herein, these methods are highly dependent on the performance of an earlier step of dimension reduction that aims to find the best subset of attributes to be retained for learning. Widely used algorithms in the literature commonly suffer from resulting in trivial cluster structures and failing to shed light on the relationship between DNA methylation and cancer types due to their myopic and arbitrary search mechanisms. Addressing this issue, we introduce a bimodal unsupervised dimension reduction algorithm (BOUNDER) that identifies the best subset of loci for downstream analysis considering the variability and redundancy across all the samples using bimodal modeling before it feeds into the learning method. BOUNDER models each locus as a bimodal representation using a piecewise linear function with two segments and filters the informative loci based on the fitted line characteristics. To the best of our knowledge, the work presented here is the first study that uses bimodal modeling in unsupervised learning in DNA methylation analysis. BOUNDER is tailored for DNA methylation analysis using a detailed parameter tuning analysis. The performance of BOUNDER is benchmarked against those of widely used conventional algorithms using real lung, breast, kidney, and urological cancer datasets obtained from Gene Expression Omnibus in terms of their accuracies in hierarchical clustering and k-means clustering. Computational experiments reveal that BOUNDER outperforms the PCA and filtering based approach by providing the highest accuracy in 6 out of 9 datasets while providing more interpretable results through a correlation analysis. The BOUNDER algorithm is also shown to be more robust when compared to multiple other conventional dimension reduction algorithms across different datasets

A route-based network simulation framework for airport ground system disruptions

•Our route-based network simulation framework (RuNSim) mimics airport ground system.•Network simulation is used to evaluate the airport’s resilience for the first time.•RuNSim provides quantitative analysis for airport ground system disruptions.•LaGuardia Airport is selected as a case study to show capabilities of RunSim.•Damage on the taxiway network and runway closure cases are assessed in detail. Every year, delayed, cancelled, or diverted flights cost airports significant man-hours and millions of dollars all over the world. The Airlines for America reported that the total direct aircraft operating cost per block minute was $62.55 in 2016, and at this rate, delay minutes cost more than$7 billion in aircraft operations for scheduled US passenger airlines only. Keeping flights on schedule only becomes more difficult with today’s ever increasing demand for air transportation and disruptive events including natural and human-caused hazards. In this study, we analyze the adversarial impact of such disruptive events on an airport ground system using a route-based network simulation framework (RuNSim). Aiming to increase an airport’s ability to take quick, responsive actions against disruptions leading to flight delays and cancellations, RuNSim is comprised of four modules: (1) pre-processing, (2) runway simulation, (3) route-based taxiway simulation, and (4) apron simulation. Our proposed framework incorporates technical as well as regulatory constraints for safety and system uncertainties. The capabilities of the proposed approach are demonstrated through two different case studies based on real data obtained from the LaGuardia Airport ground system. In these case studies, two disruptive events, namely taxiway pavement network damage and runway closure, are investigated in terms of their impact on taxi-in and taxi-out times. Conclusions are drawn on the adverse impacts of these events on the airport ground network along with available actions to minimize such flight delays under these events