Energy Costs of Chair Sitting and Standing Video Exercises in Chinese Older Adults, Ages 60 to 89 years

International Journal of Exercise Science 16(7): 814-827, 2023. Home-based video exercise interventions improve older adults’ physiological performance and functional capacity. Little is known about the energy costs of video exercises in older adults. The Compendium of Physical Activities (PAs) has few items with PA metabolic equivalents (METs) in older adults. This study measured the energy costs of four chair and two standing exercises (sitting Tai Chi, Yoga, mobility ball, aerobics: standing, slow aerobics, and fast aerobics). Fifteen females and 14 males, 62-87 years (M ± SD, 73 ± 7.7 years), were categorized into three age groups (60-69, 70-79, 80-89). Oxygen uptake (VO2, ml.min-1.kg-1) and heart rate (HR, b.min-1) were measured by indirect calorimetry and heart rate monitor. MET values were calculated as standard- (activity VO2/3.5), rounded- (significant digit rounded to 0, 3, 5, 8), and corrected METs (individual resting metabolism). Results showed chair Yoga, Tai Chi, and mobility ball ranged from 2.0 to 2.8 rounded METs (light intensity). Chair- and standing aerobics ranged from 3.0 to 4.3 rounded METs (moderate intensity). Averaged HR ranged from 91.9 ± 12.7 b.min-1 to 115.4 ± 19.1 b.min-1 for all PAs. Corrected METs were higher than standard METs (P \u3c .05). Standard METs were similar between age groups (P \u3e .05). In conclusion, this study is unique as it measures the energy costs of sitting and standing video exercises that can be performed by older adults at home or in an exercise facility. Knowing the energy costs of PAs for older adults can provide exercises interventions to prevent sedentary lifestyles

Secondary infection with Streptococcus suis serotype 7 increases the virulence of highly pathogenic porcine reproductive and respiratory syndrome virus in pigs

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome virus (PRRSV) and <it>Streptococcus suis </it>are common pathogens in pigs. In samples collected during the porcine high fever syndrome (PHFS) outbreak in many parts of China, PRRSV and <it>S. suis </it>serotype 7 (SS7) have always been isolated together. To determine whether PRRSV-SS7 coinfection was the cause of the PHFS outbreak, we evaluated the pathogenicity of PRRSV and/or SS7 in a pig model of single and mixed infection.</p> <p>Results</p> <p>Respiratory disease, diarrhea, and anorexia were observed in all infected pigs. Signs of central nervous system (CNS) disease were observed in the highly pathogenic PRRSV (HP-PRRSV)-infected pigs (4/12) and the coinfected pigs (8/10); however, the symptoms of the coinfected pigs were clearly more severe than those of the HP-PRRSV-infected pigs. The mortality rate was significantly higher in the coinfected pigs (8/10) than in the HP-PRRSV- (2/12) and SS7-infected pigs (0/10). The deceased pigs of the coinfected group had symptoms typical of PHFS, such as high fever, anorexia, and red coloration of the ears and the body. The isolation rates of HP-PRRSV and SS7 were higher and the lesion severity was greater in the coinfected pigs than in monoinfected pigs.</p> <p>Conclusion</p> <p>HP-PRRSV infection increased susceptibility to SS7 infection, and coinfection of HP-PRRSV with SS7 significantly increased the pathogenicity of SS7 to pigs.</p

The parallax and 3D kinematics of water masers in the massive star-forming region G034.43+0.24

We report a trigonometric parallax measurement of 22 GHz water masers in the massive star-forming region G034.43+0.24 as part of the Bar and Spiral Structure Legacy (BeSSeL) Survey using the Very Long Baseline Array. The parallax is 0.330$\pm$50.018 mas, corresponding to a distance of $3.03^{+0.17}_{-0.16}$ kpc. This locates G034.43+0.24 near the inner edge of the Sagittarius spiral arm and at one end of a linear distribution of massive young stars which cross nearly the full width of the arm. The measured 3-dimensional motion of G034.43+0.24 indicates a near-circular Galactic orbit. The water masers display arc-like distributions, possibly bow shocks, associated with winds from one or more massive young stars

A Knowledge Transfer Framework for General Alloy Materials Properties Prediction

Biomedical metal implants have many applications in clinical treatment. Due to a variety of application requirements, alloy materials with specific properties are being designed continuously. The traditional alloy properties testing experiment is faced with high-cost and time-consuming challenges. Machine learning can accurately predict the properties of materials at a lower cost. However, the predicted performance is limited by the material dataset. We propose a calculation framework of alloy properties based on knowledge transfer. The purpose of the framework is to improve the prediction performance of machine learning models on material datasets. In addition to assembling the experiment dataset, the simulation dataset is also generated manually in the proposed framework. Domain knowledge is extracted from the simulation data and transferred to help train experiment data by the framework. The high accuracy of the simulation data (above 0.9) shows that the framework can effectively extract domain knowledge. With domain knowledge, the prediction performance of experimental data can reach more than 0.8. And it is 10% higher than the traditional machine learning method. The explanatory ability of the model is enhanced with the help of domain knowledge. In addition, five tasks are applied to show the framework is a general method

A New Prenylated Naphthoquinoid from the Aerial Parts of Clinopodium chinense (Benth.) O. Kuntze

A new prenylated naphthoquinoid, named (3R,4aR,10bR)-3,10-dihydroxy-2,2-dimethyl-3,4,4a,10b-tetrahydro-2H-naphtho[1,2-b]-pyran-5H-6-one (1), was isolated from the aerial parts of Clinopodium chinense (Benth.) O. Kuntze, together with six known compounds: apigenin (2), luteolin (3), neoeriocitrin (4), naringenin (5), narirutin (6), and didymin (7). Neoeriocitrin was isolated for the first time from the species C. chinense. Their structures were elucidated by spectroscopic methods, including 1D, 2D (1H-1H-COSY, HSQC, HMBC and NOESY) NMR, HR-ESI-MS. The absolute configuration of 1 was determinated using the CD method. We highlight that the structure of 1 is characterized by a rarely seen prenylated naphthoquinoid framework

Metabolomics for Biomarker Discovery: Moving to the Clinic

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases

GP-GCN: Global features of orthogonal projection and local dependency fused graph convolutional networks for aspect-level sentiment classification

Aspect-level sentiment classification, a significant task of fine-grained sentiment analysis, aims to identify the sentimental information expressed in each aspect of a given sentence The existing methods combine global features and local structures to  obtain good classification results. However, the introduction of global features will bring noise and reduce the classification accuracy. To solve this problem, a new method is proposed, named GP-GCN. In our proposed method, the global feature is further simplified to reduce the noise . The local structures and global features obtained by orthogonal feature projection are introduced into aspect-level sentiment classification. First, the simplified global feature structures of text are built. Through orthogonal projection, GCN not only weakens the dependency of the graph node in updating process but also reduces the dependency between node and corpus. Next, syntactic dependency structure and sentence sequence information are utilised to mine the local dependency structure of sentences. A percentage-based multi-headed attention mechanism is proposed to measure the critical output of GCN, which can better represent sentences for given aspects. Finally, location coding is input to simulate aspect-specific representations between each aspect and its context such that the text becomes more discriminative in sentiment classification. The experimental results show that the proposed method effectively improves the accuracy of text sentiment classification