Community-based exercise and nutritional interventions to improve frailty syndrome among older adults: A quasi-experimental study.

This study aimed to explore the efficacy of single and combined effects of exercise and branched-chain amino acid (BCAA) supplements on improving frailty and quality of life in older adults. In total, 120 study participants were allocated into a combined exercise-and-BCAA supplementation group, an exercise-only group, a BCAA supplementation-only group, and a control group. Results showed that Fried's frailty score significantly decreased in the combined exercise-and-BCAA supplementation group (ß= -1.73, p<0.001), exercise-only group (ß= -1.68, p<0.001), and BCAA supplementation-only group (ß= -0.73, p=0.005) compared to the control group. Moreover, the combination of exercise and BCAA supplements and the exercise-only program produced significant improvements in frailty compared to the BCAA supplement-only group and control group (p<0.05). Exercise should be a critical approach for older adults to improve frailty. Healthcare professionals in geriatric care should incorporate exercise programs as frailty management and prevention for older adults.

Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features.

Objectives: A subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs. Methods: From June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs. Results: Forty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85. Conclusions: DL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.

Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

Production of high-purity neofructooligosaccharides by culture of Xanthophyllomyces dendrorhous.

Neofructooligosaccharides (neo-FOS) were produced in submerged cultures of Xanthophyllomyces dendrorhous. Among the various strains of X. dendrorhous that have intracellular (6)G-fructofuranosidase ((6)G-FFase), BCRC 21346 with high enzyme activity (3.60 U/mL) and BCRC 22367 with low enzyme activity (0.59 U/mL) were investigated in this work. Neo-FOS were generated in a 5-L jar fermenter at 20°C, 100rpm and 2vvm with the pH controlled at 6.9±0.1, using 250g/L of sucrose as the substrate. Through the catalytic action of X. dendrorhous(6)G-FFase on sucrose, monosaccharides as well as neo-FOS were produced. A portion of these monosaccharides was consumed by the yeast cells. However, the production of monosaccharides was low in concentration in culture with low (6)G-FFase activity, indicating they might be used up concurrently during the fermentation. Consequently, neo-FOS at a purity of up to 87.4% could be obtained.