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The results of this study may establish a foundation for further research investigating the underlying mechanism of alternative splicing events in the progression of gastric cancer.The pandemic COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and it is spreading very rapidly worldwide. To date, the origin and intermediate hosts of SARS-CoV-2 remain unclear. In this study, we conducted comparative analysis among SARS-CoV-2 and non-SARS-CoV-2 coronavirus strains to elucidate their phylogenetic relationships. We found 1, the SARS-CoV-2 strains analyzed could be divided into 3 clades with regional aggregation; 2, the non-SARS-CoV-2 common coronaviruses that infect humans or other organisms to cause respiratory syndrome and epizootic catarrhal gastroenteritis could also be divided into 3 clades; 3, the hosts of the common coronaviruses closest to SARS-CoV-2 were Apodemus chevrieri (a rodent), Delphinapterus leucas (beluga whale), Hypsugo savii (bat) , Camelus bactrianus (camel) and Mustela vison (mink); and 4, the gene sequences of the receptor ACE2 from different hosts could also be divided into 3 clades. The ACE2 gene sequences closest to that of humans in evolution include those from Nannospalax galili (Upper Galilee mountains blind mole rat), Phyllostomus discolor (pale spear-nosed bat), Mus musculus (house mouse), Delphinapterus leucas (beluga whale), and Catharus ustulatus (Swainson's thrush). We conclude that SARS-CoV-2 may have evolved from a distant common ancestor with the common coronaviruses but not a branch of any of them, implying that the prevalent pandemic COVID-19 agent SARS-CoV-2 may have existed in a yet to be identified primary host for a long time.This paper reports on a low-power readout IC (ROIC) for high-fidelity recording of the photoplethysmogram (PPG) signal. The system comprises a highly reconfigurable, continuous-time, second-order, incremental delta-sigma modulator (I-ΔΣM) as a light-to-digital converter (LDC), a 2-channel 10b light-emitting diode (LED) driver, and an integrated digital signal processing (DSP) unit. Selleck AZ191 The LDC operation in intermittent conversion phases coupled with digital assistance by the DSP unit allow signal-aware, on-the-fly cancellation of the dc and ambient light-induced components of the photodiode current for more efficient use of the full-scale input range for recording of the small-amplitude, ac, PPG signal. Fabricated in TSMC 0.18 μm 1P/6M CMOS, the PPG ROIC exhibits a high dynamic range of 108.2 dB and dissipates on average 15.7 μW from 1.5 V in the LDC and 264 μW from 2.5 V in one LED (and its driver), while operating at a pulse repetition frequency of 250 Hz and 3.2% duty cycling. The overall functionality of the ROIC is also demonstrated by high-fidelity recording of the PPG signal from a human subject fingertip in the presence of both natural light and indoor light sources of 60 Hz.EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.This article presents an analytical method that offers both spectral and spatial information to predict local electric fields capable of driving neural activities for neuromuscular activation, and the findings of an experimental investigation on a common strategy utilizing multiple high-frequency (HF) electric fields to create an interference to recruit neural firing at depth. By introducing a cut-off frequency [Formula see text] too high to recruit neural firing in a frequency-based field descriptor, the analytical method offers an effective means to position a focused temporal interference (TI) without mechanically moving the electrodes. The experiment, which was conducted on both forearms of five healthy volunteers, validates the feasibility of the method for selective neuromuscular stimulation, where three nerve/muscles that control human fingers were independently stimulated with two current channels. The numerical and experimental findings demonstrate that the frequency-based method overcomes several limitations associated with surface-based electrical stimulation.In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set. Unlike existing image index methods, our proposed LTI-ST method has the following two unique characteristics. First, it does not need to analyze the test images in the query database to construct the index structure. Instead, it is directly predicted by a network learnt from the training set. This zero-shot capability is critical for flexible, distributed, and scalable implementation and deployment of the image indexing and retrieval services at large scales. Second, unlike the existing distance-based index methods, our index structure is learnt using the LTI-ST deep neural network with binary encoding and decoding on a hierarchical semantic tree.
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