PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

Investigation of morphological changes in the tamsui river estuary using an integrated coastal and estuarine processes model

© 2020 by the authors. This study is to investigate morphological changes in the Tamsui River Estuary in Taiwan driven by multiple physical processes, such as river flows, tides, waves, and storm surges, and then to study the impacts of sediment flushing operated at the Shihmen reservoir upstream on the river estuary. An integrated coastal and estuarine processes model (CCHE2D-Coast) (Center for Computational Hydroscience and Engineering Two-Dimensional-Coast) was validated by simulating these physical processes in the estuary driven by three historical typhoons in 2008. The site-specifically validated model was then applied to simulate morphological changes in the estuary in response to reservoir sediment flush scenarios from the upstream. For the impact assessment of sediment flushing, a synthetic hydrological event was designed by including a historical typhoon and a typical monsoon. It was found that during the typhoon, the sediments will be mostly deposited in the estuarine river reach of Tamsui and the Wazihwei sandy beach. During the monsoon period, most of the sediments tend to be deposited in the second fishing port of Tamsui, the northern breakwater, and the estuary, while the Wazihwei sandy beach in the river mouth would be scoured by backflow. Simulations of the complex flow fields and morphological changes will facilitate the best practice of sediment management in the coastal and estuarine regions

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD

Antioxidant activity and growth inhibition of human colon cancer cells by crude and purified fucoidan preparations extracted from Sargassum cristaefolium

AbstractFucose-containing sulfated polysaccharides, also termed “fucoidans”, which are known to possess antioxidant, anticoagulant, anticancer, antiviral, and immunomodulating properties, are normally isolated from brown algae via various extraction techniques. In the present study, two methods (SC1 and SC2) for isolation of fucoidan from Sargassum cristaefolium were compared, with regard to the extraction yields, antioxidant activity, and inhibition of growth of human colon cancer cells exhibited by the respective extracts. SC1 and SC2 differ in the number of extraction steps and concentration of ethanol used, as well as the obtained sulfated polysaccharide extracts, namely, crude fucoidan preparation (CFP) and purified fucoidan preparation (PFP), respectively. Thin layer chromatography, Fourier transform infrared analysis, and measurements of fucose and sulfate contents revealed that the extracts were fucoidan. There was a higher extraction yield for CFP, which contained less fucose and sulfate but more uronic acid, and had weaker antioxidant activity and inhibition of growth in human colon cancer cells. In contrast, there was a lower extraction yield for PFP, which contained more fucose and sulfate but less uronic acid, and had stronger antioxidant activity and inhibition of growth in human colon cancer cells. Thus, since the difference in bioactive activities between CFP and PFP was not remarkable, the high extraction yield of SC1 might be favored as a method in industrial usage for extracting fucoidan

Detection of SARS-associated Coronavirus in Throat Wash and Saliva in Early Diagnosis

Early detection of SARS-CoV in throat wash and saliva suggests that these specimens are ideal for SARS diagnosis

Hepatocyte Growth Factor Increases Osteopontin Expression in Human Osteoblasts through PI3K, Akt, c-Src, and AP-1 Signaling Pathway

BACKGROUND: Hepatocyte growth factor (HGF) has been demonstrated to stimulate osteoblast proliferation and participated bone remodeling. Osteopontin (OPN) is a secreted phosphoglycoprotein that belongs to the SIBLING family and is present during bone mineralization. However, the effects of HGF on OPN expression in human osteoblasts are large unknown. METHODOLOGY/PRINCIPAL FINDINGS: Here we found that HGF induced OPN expression in human osteoblasts dose-dependently. HGF-mediated OPN production was attenuated by c-Met inhibitor and siRNA. Pretreatment of osteoblasts with PI3K inhibitor (Ly294002), Akt inhibitor, c-Src inhibitor (PP2), or AP-1 inhibitor (curcumin) blocked the potentiating action of HGF. Stimulation of osteoblasts with HGF enhanced PI3K, Akt, and c-Src activation. In addition, incubation of cells with HGF also increased c-Jun phosphorylation, AP-1-luciferase activity, and c-Jun binding to the AP-1 element on the OPN promoter. HGF-mediated AP-1-luciferase activity and c-Jun binding to the AP-1 element was reduced by c-Met inhibitor, Ly294002, Akt inhibitor, and PP2. CONCLUSIONS/SIGNIFICANCE: Our results suggest that the interaction between HGF and c-Met increases OPN expression in human osteoblasts via the PI3K, Akt, c-Src, c-Jun, and AP-1 signaling pathway