Background:Hypertrophic cardiomyopathy (HCM) is associated primarily with pathogenic mutations in sarcomeric genes. The aim of this study was to identify the prevalence and distribution of ...disease-causing mutations in HCM-associated genes and the genotype-phenotype relationship in Vietnamese patients with HCM.Methods and Results:Genetic testing was performed by next-generation sequencing in 104 unrelated probands for 23 HCM-related genes and in 57 family members for the mutation(s) detected. Clinical manifestations were recorded for genotype-phenotype correlation analysis. Mutation detection rate was 43.4%. Mutations inMYBPC3accounted for 38.6%, followed byTPM1(20.5%),MYH7(18.2%),TNNT2(9.1%),TNNI3(4.5%) andMYL2(2.3%). A mutation inGLAassociated with Fabry disease was found in 1 patient. A mutation inTPM1(c.842T>C, p.Met281Thr) was identified in 8 unrelated probands (18.2%) and 8 family members from 5 probands. Genotype-positive status related toMYH7,TPM1, andTNNT2mutations was associated with severe clinical manifestations.MYH7-positive patients displayed worse prognosis compared withMYBPC3-positive patients. Interestingly,TPM1c.842T>C mutation was associated with high penetrance and severe HCM phenotype.Conclusions:We report for the first time the prevalence of HCM-related gene variants in Vietnamese patients with HCM.MYH7,TPM1, andTNNT2mutations were associated with unfavorable prognosis.
Insulin resistance (IR), a metabolic risk factor, is linked to the pathogenetic mechanism of primary hypertension. Detecting IR in the patients with hypertension will help to predict and stratify the ...added cardiovascular risk, institute appropriate IR management, and manage hypertension optimally. There are many methods for assessing IR, each with distinct advantages and disadvantages. The euglycemic insulin clamp and intravenous glucose tolerance test, gold standards for measuring IR, are used in research but not in clinical practice. Homeostatic model assessment (HOMA‐IR), a method for assessing β‐cell function and IR, is frequently applied presently, particularly in Asia. Besides, the triglyceride–glucose index (TyG) first published by South American authors showed a good correlation with the insulin clamp technique and HOMA‐IR index. This simple, convenient, and low‐cost TyG index is of research interest in many countries in Asia and can be used to screen for IR in the Asian hypertensive community.
Cardiovascular disease (CVD) being the leading cause of the morbidity and mortality in Vietnam, the objective of this study was to estimate the total 10-year CVD risk among adults aged 40-69 years by ...utilizing World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts in Central Vietnam.
In this cross-sectional study, multi-staged sampling was used to select 938 participants from a general population aged from 40 to 69. The CVD risk factors were then collected throughout the interviews with a standardized questionnaire, anthropometric measurements and a blood test. The cardiovascular risk was calculated using the WHO/ISH risk prediction charts.
According to the WHO/ISH charts, the proportion of moderate risk (10-20%) and high risk (>20%) among the surveyed participants were equal (5.1%). When "blood pressure of more than 160/100 mmHg" was applied, the proportion of moderate risk reduced to 2.3% while the high risk increased markedly to 12.8%. Those proportions were higher in men than in women (at 18.3% and 8.5% respectively, p-value <0.001, among the high-risk group), increasing with age. Male gender, smoking, ethnic minorities, hypertension and diabetes were associated with increased CVD risk.
There was a high burden of CVD risk in Central Vietnam as assessed with the WHO/ISH risk prediction charts, especially in men and among the ethnic minorities. The use of WHO/ISH charts provided a feasible and affordable screening tool in estimating the cardiovascular risk in primary care settings.
The implementation of the sliding mode control (SMC) for load frequency control of power networks becomes difficult due to the chattering phenomenon of high-frequency switching. This chattering ...problem in SMC is extremely dangerous for actuators used in power systems. In this paper, a continuous control strategy by combining a second-order mode and integral siding surface is proposed as a possible solution to this problem. The proposed second-order integral sliding mode control (SOISMC) law not only rejects chattering phenomenon in control input, but also guarantees the robustness of the multi-area power network, which has an effect on parametric uncertainties such as the load variations and the matched or mismatched parameter uncertainties. Moreover, the reporting of the simulation indicates that the proposed controller upholds the quality requirement by controlling with operating conditions in the larger range, rejects disturbance, reduces the transient response of frequency, eliminates the overshoot problem, and can better address load uncertainties compared to several previous control methods. The simulation results also show that the proposed SOISMC can be used for practical multi-area power network to lessen high parameter uncertainties and load disturbances.
This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social ...distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.
In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things ...(IoT) devices. In particular, we propose optimal UAVs' path planing approaches for minimizing the completion time and total energy consumption of the UAVs' deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.
Time series classification is one of the most important issues in time series data mining. This problem has attracted more and more attention of researchers in recent years. Among proposed methods in ...literature, 1-Nearest Neighbor (1-NN), its variants and improvements have been widely considered as hard to be beaten on classification of time series. In this paper, we propose a novel non-parametric method to classify time series. The proposed method, namely Weighted Local Dynamic Time Warping Barycenter Averaging k-Nearest Neighbors (WLDBAk-NN), is an improvement of Local Mean-based k-Nearest Neighbors (LMk-NN) algorithm. It improves LMk-NN in that it replaces the local mean vectors by local Dynamic Time Warping Barycenter (DBA) vectors calculated using our method, namely Weighted DBA (WDBA). By experiments, we show that (i) WLDBAk-NN outperforms the Weighted Local Mean-based k-Nearest Neighbors (WLMk-NN) algorithm, and (ii) both WLMk-NN and WLDBAk-NN outperform 1-NN, LMk-NN, k-Nearest Centroid Neighbors (k-NCN), and LMk-NCN in 85 time series datasets of UCR Time Series Classification Archive. The experimental results also show that new local mean vectors used in WLMk-NN and WLDBAk-NN significantly contribute to the improvement of the performance of time series classification.
High Utility Sequential Patterns (HUSP) are a type of patterns that can be found in data collected in many domains such as business, marketing and retail. Two critical topics related to HUSP are: ...HUSP mining (HUSPM) and HUSP Hiding (HUSPH). HUSPM algorithms are designed to discover all sequential patterns that have a utility greater than or equal to a minimum utility threshold in a sequence database. HUSPH algorithms, by contrast, conceal all HUSP so that competitors cannot find them in shared databases. This paper focuses on HUSPH. It proposes an algorithm named HUS-Hiding to efficiently hide all HUSP. An extensive experimental evaluation is conducted on six real-life datasets to evaluate the performance of the proposed algorithm. According to the experimental results, the designed algorithm is more effective than three state-of-the-art algorithms in terms of runtime, memory usage and hiding accuracy.
•A novel structure is designed to facilitate the hiding process.•An algorithm named HUS-Hiding is proposed to hide high utility sequential patterns.•HUS-Hiding is tested on six datasets in terms of runtime, memory usage, missing cost.•HUS-Hiding is more effective than three state-of-the-art algorithms.
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