Mayerkumar8745

Angiopoietin-like protein (ANGPTL) 3,4,8 are upcoming cardiovascular biomarkers. Experimental studies showed thyroid hormones altered their levels. We assessed ANGPTL3,4,8 as predictor of cardiovascular functions among naïve-subclinical and naïve-overt hypothyroidism [SCH and OH]; and altered ANGPTL levels with levothyroxine replacement (LT4) and their association with improved cardiovascular risk factors and cardiovascular function.

Prospective follow-up study assessed ANGPTL3,4,8 levels, vascular status (flow mediated dilation% of brachial artery (FMD%), carotid intima media thickness (CIMT), aortic stiffness index (ASI)), left ventricle (LV) parameters (ejection fraction (EF), myocardial performance index (MPI), LV mass), well-known cardiovascular risk factors and HOMA-IR, at two time points among naïve -SCH, naïve-OH and healthy subjects groups; and at six months after achieved euthyroid state with LT4 with calculating their increased or decreased delta changes (∆↑ or ∆↓) in longitudinal arm among LT4H and OH.

A variety of factors differed between rural and urban areas may further influence iodine status and thyroid structure. Hence, this study compared iodine nutrition, the prevalence of thyroid goiter and nodules between rural and urban residents in Guangzhou, a southern coastal city of China.

A total of 1211 rural residents and 1305 urban residents were enrolled in this cross-sectional study. A questionnaire regarding personal characteristics was administered. Urinary iodine concentration (UIC) was examined. Ultrasonography of the thyroid was performed to evaluate thyroid goiter and nodules. Multiple logistic analysis was used to identify the potential associated factors.

The median UIC was significantly lower in rural residents than in urban residents (120.80 μg/L vs. 136.00 μg/L, P<0.001). Although the coverage rate of iodized salt was much higher in rural residents than in urban residents (99.59% vs. 97.29%, P<0.001), the percentages of seafood intake (8.60% vs. 29.29%, P<0.001), iodine-containing drug consumption (0.33% vs. 1.24%, P=0.011), and iodine contrast medium injection (0.58% vs. 1.87%, P=0.004) were lower in rural residents than in urban residents. Both the prevalence of thyroid goiters and nodules was significantly higher in rural residents than in urban residents [goiter 8.06% vs. 1.20%, P<0.001; nodules 61.89% vs. 55.04%, P=0.023]. Living in rural areas was associated with thyroid goiter (OR 5.114, 95% CI 2.893 to 9.040, P<0.001).

There were differences in iodine nutrition and the prevalence of thyroid goiter and nodules in rural and urban residents in Guangzhou. Differentiated and specialized monitoring is recommended in our area.

There were differences in iodine nutrition and the prevalence of thyroid goiter and nodules in rural and urban residents in Guangzhou. Differentiated and specialized monitoring is recommended in our area.

Online media plays an important role in public health emergencies and serves as a communication platform. Infoveillance of online media during the COVID-19 pandemic is an important step toward a better understanding of crisis communication.

The goal of this study is to perform a longitudinal analysis of the COVID-19 related content based on natural language processing methods.

We collected a dataset of news articles published by Croatian online media during the first 13 months of the pandemic. Firstly, we test the correlations between the number of articles and the number of new daily COVID-19 cases. Secondly, we analyze the content by extracting the most frequent terms and apply the Jaccard similarity. Next, we compare the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we apply named entity recognition to extract the most frequent entities and track the dynamics of changes during the observed period.

The results show there is no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there are high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second wave. Finally, the findings indicate that the most influential entities have lower overlaps for the identified persons and higher overlaps for locations and institutions.

Our study shows that online media has a prompt response to the pandemic with a large number of COVID-19 related articles. There is a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well covered.

The consensus tracking problem means that a group of followers tracks the desired trajectory with local communication. In this article, partial components of cluster consensus have been considered. In this scenario, the p components of the followers in different clusters track the leader at different lag times, while p components of each agent in the same cluster reach a consensus, which is called p components of cluster-lag (PCCL) consensus. By using a seminorm ||xi||2,p and a Lyapunov-Krasovskii functional, PCCL consensus for second-order multiagent systems with homogeneous nonlinear systems on cooperative-competitive networks has been considered. For the case that the communication network graph is undirected, a decentralized adaptive controller, which is based on the exchanged neighbors' information from the same cluster, is designed such that all the agents reach PCCL consensus. For the directed graph case, an adaptive protocol based on the intracoupling strength is constructed for each cluster to achieve PCCL consensus. Finally, two simulation examples are illustrated to show the effectiveness of the proposed control protocols.The evidential reasoning (ER) rule has been widely applied in the multiple attribute decision making (MADM), which makes the decision-making process transparent and credible by using a belief structure. To improve the ability of the ER rule in dealing with the interval uncertainty, a new interval ER (IER) rule is proposed in this article. The interval uncertainty is described as the interval grade in the new frame of discernment (FoD) to model the local ignorance. It is proved that the IER rule is a generalization of the ER rule. selleck To study the influence of perturbation on the IER rule, the perturbation is first introduced to the belief structure, and the perturbation analysis (PA) is conducted for the IER rule. An optimization model is established to estimate the perturbation threshold, which can measure the effectiveness of the inference result under perturbation. Two numerical examples and a case study are carried out, respectively, to show the implementation process of the proposed IER rule and validate its effectiveness in different decision-making scenarios.This brief presents the results of a study of the possibilities of reducing the arithmetic complexity of computing basic operations in octonionic neural networks and also proposes new algorithmic solutions for efficiently performing these operations. Here, we primarily mean the operation of multiplying octonions, the operation of computing the dot product of two octonion-valued vectors, and the operation of multiple multiplications of an octonion by several other octonions. In order to reduce the computational complexity of these operations, it is proposed to use the fast Walsh-Hadamard transform, which is well known in digital signal processing. Using this transform reduces the number of multiplications and additions of real numbers required to perform computations. Thus, the use of the proposed algorithms will speed up computations in octonion-valued neural networks.This article is concerned with distributed resilient load frequency control (LFC) for multi-area power interconnection systems against jamming attacks. First, considering uncertainties and high dimension nonlinearity, the model-free adaptive control (MFAC) model is adopted for the power system, in which only input and output (I/O) data are used. Second, jamming attacks are modeled in a stochastic process, and a multistep predictive compensation algorithm is developed to mitigate the impact of jamming attacks. Then, the distributed MFAC protocol with predictive compensation algorithm is designed such that the frequency tracking errors under the predictive compensation algorithm of multi-area power interconnection systems converge consensually into a small neighborhood of origin in the mean square sense. Simulation results show the effectiveness of the approach.We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.Meta reinforcement learning (meta-RL) is a promising technique for fast task adaptation by leveraging prior knowledge from previous tasks. Recently, context-based meta-RL has been proposed to improve data efficiency by applying a principled framework, dividing the learning procedure into task inference and task execution. However, the task information is not adequately leveraged in this approach, thus leading to inefficient exploration. To address this problem, we propose a novel context-based meta-RL framework with an improved exploration mechanism. For the existing exploration and execution problem in context-based meta-RL, we propose a novel objective that employs two exploration terms to encourage better exploration in action and task embedding space, respectively. The first term pushes for improving the diversity of task inference, while the second term, named action information, works as sharing or hiding task information in different exploration stages. We divide the meta-training procedure into task-independent exploration and task-relevant exploration stages according to the utilization of action information. By decoupling task inference and task execution and proposing the respective optimization objectives in the two exploration stages, we can efficiently learn policy and task inference networks. We compare our algorithm with several popular meta-RL methods on MuJoco benchmarks with both dense and sparse reward settings. The empirical results show that our method significantly outperforms baselines on the benchmarks in terms of sample efficiency and task performance.