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Body temperature plays a critical role in rehabilitation, and numerous studies proved that the regulation of body temperature contributes to the sensorimotor recovery of patients with brain diseases such as stroke. The hypothalamus plays a key role in thermoregulation. Ultrasound deep brain stimulation (UDBS) can noninvasively modulate deep brain nuclei and have potential applications in the treatment of Parkinson’s disease, Alzheimer’s disease, and depression, among others. The purpose of this study was to investigate whether ultrasound stimulation of the hypothalamus could regulate body temperature in free-moving mice. Results showed that thermoregulation was related to ultrasonic parameters (pulse repetition frequency (PRF), duty cycle, total time, and acoustic pressure). UDBS of the preoptic area of the anterior hypothalamus at 500 Hz PRF could significantly reduce body temperature ($\text{T}\,\,= -0.25\,\,\pm \,\,0.073\,\,^{\circ }\text{C}$ at t = 5 min, $\text{T}\,\,= -0.51\,\,\pm \,\,0.19\,\,^{\circ }\text{C}$ at t = 10 min, $\text{T}\,\,= -0.84\,\,\pm \,\,0.27\,\,^{\circ }\text{C}$ at t = 15 min). Meanwhile, UDBS of the dorsomedial hypothalamus at 10 Hz PRF triggered a significant increase in body temperature ($\text{T}\,\,=0.5\,\,\pm \,\,0.077\,\,^{\circ }\text{C}$ at t = 5 min, $\text{T}\,\,=1.16\,\,\pm \,\,0.23\,\,^{\circ }\text{C}$ at t = 10 min). These results suggest that UDBS, as a noninvasive neuromodulation tool, may play a key role in the future clinical treatment of malignant hyperthermia and hypothermia. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a crucial task in intelligent transportation systems, which can be applied in autonomous vehicles and traffic scene video surveillance systems. The past few years have witnessed much progress on the research of pedestrian detection methods, especially through the successful use of the deep learning based techniques. However, occlusion and large scale variation remain the challenging issues for pedestrian detection. In this work, we propose a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to tackle these difficulties. Specifically, we present a part-aware Region-of-Interest (RoI) pooling module to mine body parts with different responses, and select the part with the strongest response via voting. As such, a partially visible pedestrian instance can receive a high detection confidence score, making it less likely to become a missing detection. This module operates in parallel with an instance RoI pooling module to combine local parts and global context information. To handle vast scale variation, we construct a fully convolutional network in which multi-scale feature maps are generated efficiently, and small-scale and large-scale pedestrians are detected separately. By integrating these structures, the proposed detector achieves the state-of-the-art performance on the Caltech, KITTI, INRIA and ETH pedestrian detection datasets. [ABSTRACT FROM AUTHOR]

The hypothalamus plays an important role in the control of aging. Transcranial ultrasound stimulation (TUS) has been reported as a noninvasive method of neuromodulation. However, the effect of TUS of the hypothalamus on aging remains unclear. Therefore, the aim of this study is to verify whether TUS of the hypothalamus could affect the behaviors of aging mice and the expression level of apoptosis factors and inflammatory cytokines. TUS was delivered to the hypothalamus of mice (${n} = {44}$) for 14 days (15 min/day) at a fundamental frequency of 1 MHz, pulse repetition frequency of 1 kHz (US1) or 10 Hz (US2), duty cycle of 10%, and acoustic pressure of 0.13 MPa. The effect of TUS on aging was evaluated by the behavioral tests or Western blotting in different stages. The behavioral results showed that mice in the US2 group improved their movement and learning. In addition, there was a significant improvement in the grip strength after TUS in the second behavioral tests (Sham: 0.0351 ± 0.0020 N/g; US1: 0.0340 ± 0.0023 N/g; US2: 0.0425 ± 0.0029 N/g, ${p} = {0.034}$). Furthermore, the level of inflammation (TNF- $\alpha $ : Sham: 0.69 ± 0.084; US1: 0.39 ± 0.054; US2: 0.49 ± 0.1, ${p} = {0.021}$) and apoptosis (Bax: Sham: 0.47 ± 0.049; US1: 0.42 ± 0.054; US2: 0.18 ± 0.055, ${p} = {0.001}$) was significantly reduced after TUS in this stage. We did not see a long-lasting effect of TUS in the third behavioral tests. In addition, we found that TUS is safe according to hematoxylin and eosin (HE) staining. In conclusion, TUS could effectively modulate the hypothalamus, which may provide a new method for controlling aging. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms. [ABSTRACT FROM AUTHOR]

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods. [ABSTRACT FROM AUTHOR]