To quantify the effects of mind-body exercise on cognitive function in older adults with cognitive impairment, we systematically searched five databases. Findings were analyzed according to the mean change of global cognition, memory, and executive function. Subgroup analyses were conducted based on the level of cognitive impairment and types of exercise. Thirteen studies were included. Analyses revealed that mind-body exercise was effective in promoting global cognition in individuals with cognitive impairment (standardized mean difference [SMD] = 0.61; 95% confidence interval, 0.21-1.00; p = 0.003), as well as in individuals with mild cognitive impairment (SMD = 0.46; 95% confidence interval, 0.06-0.85; p = 0.02) or dementia; dance was effective in promoting global cognition (SMD = 0.84; 95% confidence interval, 0.23-1.46; p = 0.007) and memory (SMD = 0.27; 95% confidence interval, 0.02-0.52; p = 0.04) in individuals with cognitive impairment, but tai chi was not. Nevertheless, additional well-designed randomized clinical trials are further needed.
A core–double shell heterostructure Co9S8@Co9S8@MoS2-0.5 with multiple interfaces and a tunable electronic structure was constructed as an efficient tri-functional electrocatalyst.
Background: Exercise interventions for mild cognitive impairment (MCI) have been extensively studied. However, there is no bibliometric study on exercise interventions for MCI. This study aimed to identify the collaborative networks, research hotspots, evolution trends, and future directions. Methods: Relevant documents were retrieved from the Web of Science Core Collection database. VOSviewer was used to analyze the co-authorship of the author, countries and institutions, and the keywords co-occurrence. CiteSpace was used to detect burst keywords’ research trends. Results: A total of 569 articles were included and showed an overall increasing trend in annual publications. The most influential subject categories, authors, journals, country, and institutions were “geriatrics gerontology,” “Doi, Takehiko and Shimada, Hiroyuki,” “ Journal of Alzheimer’s Disease ,” USA, and “Veterans Health Administration,” respectively. The research hotspots are “effectiveness,” “neural mechanism” and “correlation” of exercise interventions, and the emerging trend is “intervention quality.” Conclusion: This area is in a rapid development phase, whereby research hotpots are focused and the research trend is clear. The highly productive authors and institutions have made outstanding contributions and the subject categories present an interdisciplinary trend. However, there is weak cooperation between countries and institutions, and a substantial research gap exists between developed and developing countries. Future research may highlight the intervention quality, emphasizing the combination with virtual reality technology.
To achieve high-quality no-till seeding, a wing-shaped stubble-breaking device with excellent stubble-breaking performance was designed for maize stubble. A model of maize stubble was developed based on the Discrete Element Method (DEM) and verified through soil bin tests. The DEM model was used to optimize the design parameters of the device and to investigate the interaction between the blades and the maize stubble during the stubble-breaking process. Field experiments were conducted to evaluate the performance of the device. The results indicated that the DEM model was accurate; when the optimal design parameters of the wing-shaped stubble-breaking device were a 37° slide cutting angle, 31° pitching angle, and 50 mm wing width, the average torque was 41.26 N·m, the soil breakage rate was 85.68%, and the soil backfill rate was 71.65%; the wing-shaped stubble-breaking device could separate the inside and outside of the strip tillage area and cut maize stubbles and soil blocks twice, thus having excellent stubble-breaking performance. This study provided an effective and feasible method for designing stubble-breaking devices and studying the interaction between blades, soil, and roots, which improved soil tillage theory and was beneficial in promoting conservation tillage technology.
Grassland degradation and reduced yields are often linked to the root soil composite of perennial alfalfa roots. This study introduces a novel modeling approach to accurately characterize root biomechanical properties, assist in the design of soil-loosening and root-cutting tools. Our model conceptualizes the root as a composite structure of cortex and stele, applying transversely isotropic properties to the stele and isotropic properties to the cortex. Material parameters were derived from longitudinal tension, longitudinal compression, transverse compression, and shear tests. The constitutive model of stele was Hashin failure criteria, accounting for differences in tensile and compressive strengths. Results reveal that root tensile strength mainly depends on the stele, with its tensile properties exceeding compressive and transverse strengths by 4–10 times. In non-longitudinal tensile stress scenarios, like shear and transverse compression tests, the new model demonstrated superior accuracy over conventional models. Results of shear tests were further validated using non-parametric statistical analysis. This study provides a finite element method (FEM) modeling approach that, by integrating root anatomical features and biomechanical properties, significantly enhances simulation accuracy. This provides a tool for designing low-energy consumption components in grassland degradation restoration and conservation tillage.
High wear rates during the tillage process often result in significant financial losses and wasted farming seasons. In this paper, a bionic design was used to reduce tillage wear. Inspired by wear-resistant animals with ribbed structures, the bionic ribbed sweep (BRS) was designed by combining a ribbed unit with a conventional sweep (CS). BRSs with different parameters (width φ, height h, angle θ, and interval λ) were simulated and optimized using the DEM and RSM methods at a working depth of 60 mm to evaluate the magnitude and trends of three responses: tillage resistance (TR), number of contacts between the sweep and soil particles (CNSP), and Archard wear value (AW). The results showed that a protective layer could be created on the surface of the sweep with a ribbed structure to reduce abrasive wear. Analysis of variance proved that factors φ, θ, and λ had significant effects on AW, CNSP, and TR, while factor h was insignificant. An optimal solution was obtained using the desirability method, including 8.88 mm φ, 1.05 mm h, 3.01 mm λ, and 34.46° θ. Wear tests and simulations showed that wear loss could be effectively reduced at different speeds by the optimized BRS. It was found to be feasible to create a protective layer to reduce partial wear by optimizing the parameters of the ribbed unit.
Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.
To explore how exercise protects against mild cognitive impairment (MCI) from physical, psychological, and social perspectives, we conducted a cross-sectional study in four nursing homes in Changchun, China, selected by convenience sampling. A total of 338 older adults aged 60 years or more with normal cognition or MCI were included. Data including demographic characteristics, exercise habits, frailty status, depression, sleep quality, social support, and cognitive status were collected. Weighted least squares estimation with mean and variance adjusted chi-square and bootstrapping with 2000 resamples were used to conduct the analyses through Mplus 8.3. The results showed that both direct and indirect effects of exercise on MCI were significant. Frailty and depression were two independent mediating factors, and depression could also play a mediating role when combined with sleep quality or frailty. Social support played a partial mediating role between exercise and depression. Formulations of exercise programs for MCI prevention and improvement in nursing home-dwelling older adults should consider the mediating factors.
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