BackgroundObservational studies report inconclusive effects of tea consumption on the risk of Alzheimer’s disease (AD), and the mechanisms are unclear. This study aims to investigate the effects of genetically predicted tea intake (cups of tea consumed per day) on AD, brain volume, and cerebral small vessel disease (CSVD) using the two-sample Mendelian randomization (MR) method.MethodsSummary statistics of tea intake were obtained from UK Biobank (N = 447,485), and AD was from the International Genomics of Alzheimer’s Project (N = 54,162). Genetic instruments were retrieved from UK Biobank using brain imaging-derived phenotypes for brain volume outcomes (N > 33,224) and genome-wide association studies for CSVD (N: 17,663–48,454).ResultsIn the primary MR analysis, tea intake significantly increased the risk of AD using two different methods (ORIVW = 1.48, 95% CI: [1.14, 1.93]; ORWM = 2.00, 95% CI: [1.26, 3.18]) and reached a weak significant level using MR-Egger regression (p < 0.1). The result passed all the sensitivity analyses, including heterogeneity, pleiotropy, and outlier tests. In the secondary MR analysis, per extra cup of tea significantly decreased gray matter (βWM = −1.63, 95% CI: [−2.41, −0.85]) and right hippocampus volume (βWM = −1.78, 95% CI: [−2.76, −0.79]). We found a nonlinear association between tea intake and AD in association analysis, which suggested that over-drinking with more than 13 cups per day might be a risk factor for AD. Association analysis results were consistent with MR results.ConclusionThis study revealed a potential causal association between per extra cup of tea and an increased risk of AD. Genetically predicted tea intake was associated with a decreased brain volume of gray matter and the right hippocampus, which indicates that over-drinking tea might lead to a decline in language and memory functions. Our results shed light on a novel possible mechanism of tea intake to increase the risk of AD by reducing brain volume.
Hepatocellular carcinoma (HCC) has developed into one of the most lethal, aggressive, and malignant cancers worldwide. Although HCC treatment has improved in recent years, the incidence and lethality of HCC continue to increase yearly. Therefore, an in-depth study of the pathogenesis of HCC and the search for more reliable therapeutic targets are crucial to improving the survival quality of HCC patients. Currently, miRNAs have become one of the hotspots in life science research, which are widely present in living organisms and are non-coding RNAs involved in regulating gene expression. MiRNAs exert their biological roles by suppressing the expression of downstream genes and are engaged in various HCC-related processes, including proliferation, apoptosis, invasion, and metastasis. In addition, the expression status of miRNAs is related to the drug resistance mechanism of HCC, which has important implications for the systemic treatment of HCC. This paper reviews the regulatory role of miRNAs in the pathogenesis of HCC and the clinical applications of miRNAs in HCC in recent years.
With the rapid development of China’s urbanization, a large number of people have moved from rural to urban areas. People have proposed higher and more urgent needs for the urban environment. Particularly, the urban street landscape is close to people’s lives, and the upgrading of design methods can improve the quality of life. Besides, the application of artificial intelligence design has become possible as information technology develops. In this paper, a visual simulator is established through algorithm models and applied to street landscape design.
Based on the theory of space syntax, this study quantitatively analyzes the landscape space of Baosteel Zhanjiang Steel Co. Ltd., which is constrained by epidemic preventive measures and steel plant safety production requirements in the post-epidemic age. Space syntax has the benefits of decreasing research expenses, boosting analytical efficiency, assessing space use efficiency, minimizing environmental interference, and addressing epidemic prevention demands.
As cities expand, many old towns face the threat of being renovated or demolished. In recent years, the drawbacks of extensive urban renewal have become increasingly apparent, and the focus of urban development is gradually shifting from efficiency to quality. This study aims to combine urban renewal with emerging technologies to address the dilemma between efficiency and quality in urban renewal. The study found that algorithm models based on graph theory, topology, and shortest path principles neglect the influence of internal states and visual features on pedestrian activity, resulting in lower simulation accuracy. Although incorporating internal states and visual features into the core of the algorithm further improved the simulation accuracy, the model operates in a 3D environment with lower efficiency. To address the problems of insufficient simulation accuracy and low efficiency, this study proposes a dynamic pedestrian activity model based on a multi-agent system and incorporating visual features. The model simulates pedestrian daily activity paths using pheromones and virtual sensors as the core, and it was found that using Visibility Graph Analysis could accurately divide pheromones in the environment, thus obtaining more accurate simulation results. Subsequently, based on the optimized pedestrian model’s agent activity rules and dynamic pheromone theory, a model for automatically generating road paving in urban renewal projects was developed, and the generated results were verified for their rationality through design practice. This technology can effectively promote urban renewal and the preservation of historic neighborhoods, providing technical support for achieving sustainable urban development.
The marine predator algorithm (MPA) is the latest metaheuristic algorithm proposed in 2020, which has an outstanding merit-seeking capability, but still has the disadvantage of slow convergence and is prone to a local optimum. To tackle the above problems, this paper proposed the flexible adaptive MPA. Based on the MPA, a flexible adaptive model is proposed and applied to each of the three stages of population iteration. By introducing nine benchmark test functions and changing their dimensions, the experimental results show that the flexible adaptive MPA has faster convergence speed, more accurate convergence ability, and excellent robustness. Finally, the flexible adaptive MPA is applied to feature selection experiments. The experimental results of 10 commonly used UCI high-dimensional datasets and three wind turbine (WT) fault datasets show that the flexible adaptive MPA can effectively extract the key features of high-dimensional datasets, reduce the data dimensionality, and improve the effectiveness of the machine algorithm for WT fault diagnosis (FD).
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