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Neuropsychiatric symptoms occur in the majority of patients with Parkinson's disease (PD). It is one of the most common causes of disability. This paper reviews the mind-body exercise methods affecting anxiety symptoms in PD patients at home and abroad, including Tai Chi, Baduanjin, Wuqinxi, and mindfulness yoga, in order to provide inspiration for constructing the intervention program for non-exercise symptoms in PD patients, reduce disability rate and improve quality of life. (神经精神症状(NPS)在帕金森病(PD)患者的病程中常见, 是导致患者残疾的原因之一。本研究就国内外影响PD患者焦虑症状的身心运动方法进行综述, 包括太极拳、八段锦、五禽戏、正念瑜伽, 以期为构建PD患者非运动症状干预方案提供启发, 降低致残率, 提高生存质量。)

AbstractMicrobes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules. [ABSTRACT FROM AUTHOR]

In applications of item response theory (IRT) models, it is known that empirical violations of the local independence (LI) assumption can significantly bias parameter estimates. To address this issue, we propose a threshold-autoregressive item response theory (TAR-IRT) model that additionally accounts for order dependence among the item responses of each examinee. The TAR-IRT approach also defines a new family of IRT models for polytomous item responses under both unidimensional and multidimensional frameworks, with order-dependent effects between item responses and relevant dimensions. The feasibility of the proposed model was demonstrated by an empirical study using a polytomous response data. A simulation study for polytomous item responses with order effects of different magnitude in an education context shows that the TAR modeling framework could provide more accurate ability estimation than the partial credit model when order effect exists.

As the number of online degree programs continues to grow, one of the greatest challenges is developing a sense of community among learners who do not convene at the same time and place. This study examined the sense of community among medical professionals in an online graduate program for healthcare professionals. We took the sample from a fully online program delivered jointly by a state university and a local children's hospital in the Midwest. We administered Rovai's Classroom Community Survey with 11 additional demographic questions. We also utilized online interviews to further explore students' understanding of sense of community. A bi-factor model was fitted to the online sense of community survey data. Using multivariate analysis of variance (MANOVA) and univariate analysis of variance (ANOVA) we identified potential group differences. The qualitative data were analyzed thematically in a recursive and iterative process. Study results suggested that a dominant factor existed: sense of community with two sub-domain factors including sense of learning and sense of connectedness. No significant differences in sense of community with regard to gender, native language, or area of medical practice were detected. However, results showed a difference in sense of community between the three courses examined. This study is the first to examine the sense of community among online medical professionals. Since our findings are in contrast to those of previous studies, this opens the door to additional studies around the possible differences between the community characteristics and needs of medical professionals as online students.

In view of problems of serious thermal damage, local high temperature and uneven temperature distribution of long-distance coal face, on the basis of analysis of heat source on the coal face of a coal mine, a layout method of cooling quantity shunting was proposed. That is, under the condition of constant cooling quantity, the air cooler is installed in intake airflow roadway, and one ventilator is matched at the same time. The ventilator is connected with the air cooler through air duct, and part of the cooling quantity is directly transferred to the coal face by the air duct to play the role of cooling. CFD software is used to establish three-dimensional models, and the two layout methods of conventional cooling layout and cooling quantity shunting are numerically simulated respectively. The simulation results show that the distribution of the temperature field is more uniform, and the low temperature area is wider after using the layout method of cooling capacity shunting, which can effectively improve the cooling effect of coal face.