Weighted selective collapsing strategy for detecting rare and common variants in genetic association study

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants.</p> <p>Results</p> <p>We propose a weighted selective collapsing strategy for both candidate gene studies and genome-wide association scans. The strategy considers genetic information from both common and rare variants, selectively collapses all variants in a given region by a forward selection procedure, and uses an adaptive weight to favor more likely causal rare variants. Under this strategy, two tests are proposed. One test denoted by <it>B_wSC</it>is sensitive to the directions of genetic effects, and it separates the deleterious and protective effects into two components. Another denoted by <it>B_wSCd</it>is robust in the directions of genetic effects, and it considers the difference of the two components. In our simulation studies, <it>B_wSC</it>achieves a higher power when the casual variants have the same genetic effect, while <it>B_wSCd</it>is as powerful as several existing tests when a mixed genetic effect exists. Both of the proposed tests work well with and without the existence of genetic effects from common variants.</p> <p>Conclusions</p> <p>Two tests using a weighted selective collapsing strategy provide potentially powerful methods for association studies of sequencing data. The tests have a higher power when both common and rare variants contribute to the heritable variability and the effect of common variants is not strong enough to be detected by traditional methods. Our simulation studies have demonstrated a substantially higher power for both tests in all scenarios regardless whether the common SNPs are associated with the trait or not.</p

Design and analysis of smart home energy management system for energy-efficient and demand response operations

In the movie “Iron Man”, Tony Stark, with his highly connected and smart home system, shows the audience an appealing vision of future work and domestic life. Many audiences desire such a living environment where they can not only interact with their homes but also let the homes manage their operation automatically. As technology progressively steps into such a future, realizing a responsive and autonomous smart home is not just a fantasy. To establish grid-interactive homes that help save costs for users and improve grid reliability, this study introduces an energy management framework for smart home environments. This framework provides optimal operation of multiple appliances, taking into account dynamic responses to external factors such as outside weather conditions, homeowner’s preferences, and particularly, gird conditions like time-varying pricing in demand response programs. As one of the largest energy consumers in the home, the operation of the HVAC system holds great potential for cost savings and energy flexibility—the latter being the ability to adjust its consumption based on grid signals such as time-of-use (TOU) pricing. Achieving cost savings and energy flexibility requires intelligent strategies, one of which is precooling—a control strategy where an air conditioner (AC) cools space when the electricity price is low to avoid expensive operation when the electricity price is high. In previous studies, Model Predictive Control (MPC)-based precooling strategies are typically analyzed through simulations, and field studies in residential buildings are quite limited. In this study, we developed an MPC agent and carried out extensive field tests on nine homes over a period of four months in Oklahoma and Miami. Filed test results show that the MPC agent can reduce energy cost by 28.72%–51.31% on hot summer days and by up to 60.32% on mild summer days, in addition to achieving significant energy flexibility. Moreover, the agent's performance is found to be most impacted by weather conditions, AC performance, user comfort preferences, and floor areas of the homes. In addition, to further comprehend diverse factors that may impact the results of MPC-based precooling, an EnegyPlus virtual testbed and a corresponding control framework for co-simulation are developed. The purpose of developing such a virtual testbed is to create a simulation environment that enables experiments without the limitation and variability of field tests. The virtual testbed is modified by using the Python script to mimic the on/off cycle in the majority of U.S. residential building HVAC systems. By conducting the sensitivity analysis and ablation study, the MPC-based precooling co-simulation results are evaluated. It was observed in our case study that cost savings achieved through MPC-based precooling were primarily influenced by the use of forecast weather. The accuracy of the models and the prediction horizon of the MPC models also plays a substantial but lesser extent role. With the optimal operation framework shifting from the HVAC system to multiple appliances, the proposed energy management framework has a broader scope, encompassing not only the HVAC system but also water heaters, non-thermal appliances, and the power flow between photovoltaics panel (PV), batteries, and the grid. Apart from the cost-savings and energy flexibility that can be achieved, the proposed framework also provides a more realistic simulation scenario by considering the user’s appliance time usage preference, water usage, and thermal comfort preferences. Finally, the framework also embedded multi-objective optimization to support the homeowner’s decision-making between cost saving and thermal comfort. Overall, this study aims to realize the optimal operation of various load-flexible resources under demand response programs in residential buildings. This study investigates the fundamental research for the investigation of methodologies to enhance and understand the interactions between buildings, homeowners, and the grid. Due to the flexibility of the model, this study can be adapted to other residential buildings and even in larger communities

Outward FDI and Innovation Performance of Chinese EMEs: The Role of Institutional Quality and Overseas Industrial Parks of Host Countries

With a sample of 314 Chinese A-share listed emerging market enterprises (EMEs) over the period 2013-2018, and from the perspective of organizational learning theory, FDI theory, institutional theory, and industrial agglomeration theory, this dissertation examines the impact of Chinese EMEs' Outward Foreign Direct Investment (OFDI) on their innovation performance using a two-way fixed effects model (FE), the empirical results show that OFDI has a positive effect on the innovation performance of Chinese EMEs. This dissertation also tests the moderating role of institutional environment (institutional quality) of the host country and the overseas industrial parks in the relationship between Chinese EMEs' OFDI and innovation performance. The results suggest that both the factors of overall institutional quality and overseas industrial parks can strengthen the contribution of OFDI to the innovation performance of Chinese EMEs. In addition, this dissertation also explores whether the establishment of overseas industrial parks can further enhance the positive moderating role of institutional quality development in the host country in this process. Although the results support the existence of this relationship, further research is needed to make a re-verification. In summary, the findings of this dissertation enrich the research related to the role of host country business environment on the effect of Chinese EMEs' outward FDI on their innovation performance during the internationalization process

Multiple Sparse Measurement Gradient Reconstruction Algorithm for DOA Estimation in Compressed Sensing

A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is proposed, in which the DOA estimation problem is cast as the joint sparse reconstruction from multiple measurement vectors (MMV). The proposed method is derived through transforming quadratically constrained linear programming (QCLP) into unconstrained convex optimization which overcomes the drawback that l1-norm is nondifferentiable when sparse sources are reconstructed by minimizing l1-norm. The convergence rate and estimation performance of the proposed method can be significantly improved, since the steepest descent step and Barzilai-Borwein step are alternately used as the search step in the unconstrained convex optimization. The proposed method can obtain satisfactory performance especially in these scenarios with low signal to noise ratio (SNR), small number of snapshots, or coherent sources. Simulation results show the superior performance of the proposed method as compared with existing methods

Vertical deformation monitoring of the suspension bridge tower using GNSS: a case study of the Forth Road Bridge in the UK

The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS

Comparative efficacy of delafloxacin for complicated and acute bacterial skin and skin structure infections:results from a network meta-analysis

Abstract Background Delafloxacin is a novel fluoroquinolone with broad antibacterial activity against pathogens causing acute bacterial skin and skin structure infections (ABSSSI). This network meta-analysis (NMA) was conducted to evaluate the relative efficacy of delafloxacin versus other comparators used for managing patients with ABSSSI. Methods A systematic literature review was conducted to identify randomised controlled trials (RCTs) evaluating adults (≥ 18 years) with ABSSSI, complicated SSSI (cSSSI), complicated skin and soft tissue infections (cSSTI) or severe cellulitis with pathogen of gram-positive, gram-negative, or mixed aetiology. OVID MEDLINE®, Embase, Epub Ahead of Print, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews were searched from inception through 12 April 2019. A feasibility assessment was conducted, followed by an NMA, which was run in a Bayesian framework. The interventions included in the NMA encompassed monotherapy or combination therapies of amoxicillin/clavulanate, ampicillin/sulbactam, ceftaroline, ceftobiprole, dalbavancin, daptomycin, delafloxacin, fusidic acid, iclaprim, linezolid, omadacycline, oxacillin + dicloxacillin, standard therapy, tedizolid, telavancin, tigecycline, vancomycin, vancomycin + aztreonam and vancomycin + linezolid. Results A feasibility assessment was performed and evidence networks were established for composite clinical response (n = 34 studies), early clinical response (n = 16 studies) and microbiological response (n = 14 studies) in the overall study population, composite clinical response (n = 4 studies) in obese subpopulation and for composite clinical response (n = 18 studies) and microbiological response (n = 14 studies) in patients with methicillin-resistant Staphylococcus aureus (MRSA) infection. Delafloxacin performed significantly better than fusidic acid, iclaprim, vancomycin, and ceftobiprole for composite clinical response. Delafloxacin was comparable to dalbavancin, daptomycin, fusidic acid, iclaprim, linezolid, omadacycline, tedizolid, vancomycin, vancomycin + aztreonam and vancomycin + linezolid in the analysis of early clinical response, whereas for microbiological response, delafloxacin was comparable to all interventions. In the obese subpopulation, the results favoured delafloxacin in comparison to vancomycin, whilst the results were comparable with other interventions among the MRSA subpopulation. Conclusions Delafloxacin is a promising new antibiotic for ABSSSI demonstrating greater improvement (composite clinical response) compared to ceftobiprole, fusidic acid, iclaprim, telavancin and vancomycin and comparable effectiveness versus standard of care for all outcomes considered in the study

User-Centered Software Design: User Interface Redesign for Blockly–Electron, Artificial Intelligence Educational Software for Primary and Secondary Schools

According to the 2021 and 2022 Horizon Report, AI is emerging in all areas of education, in various forms of educational aids with various applications, and is carving out a similarly ubiquitous presence across campuses and classrooms. This study explores a user-centered approach used in the design of the AI educational software by taking the redesign of the user interface of AI educational software Blockly–Electron as an example. Moreover, by analyzing the relationship between the four variables of software usability, the abstract usability is further certified so as to provide ideas for future improvements to the usability of AI educational software. User-centered design methods and attribution analysis are the main research methods used in this study. The user-centered approach was structured around four phases. Overall, seventy-three middle school students and five teachers participated in the study. The USE scale will be used to measure the usability of Blockly–Electron. Five design deliverables and an attribution model were created and discovered in the linear relationship between Ease of Learning, Ease of Use, Usefulness and Satisfaction, and Ease of use as a mediator variable, which is significantly different from the results of previous regression analysis for the USE scale. This study provides a structural user-centered design methodology with quantitative research. The deliverables and the attribution model can be used in the AI educational software design. Furthermore, this study found that usefulness and ease of learning significantly affect the ease of use, and ease of use significantly affects satisfaction. Based on this, the usability will be further concretized to facilitate the production of software with greater usability