Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS).

INTRODUCTION: The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. METHODS: Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), ß-amyloid (Aß) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aß42/Aß40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aß PET, and tau PET, respectively. Plasma biomarkers, Aß PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aß PET positivity were also determined in different diagnostic groups. RESULTS: The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aß PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. DISCUSSION: The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aß PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aß PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.

Kinematics, dynamics and control of stiffness-tunable soft robots.

Modeling and control methods for stiffness-tunable soft robots (STSRs) have received less attention compared to standard soft robots. A major challenge in controlling STSRs is their infinite degrees of freedom, similar to standard soft robots. In this paper, demonstrate a novel STSR by combing a soft-rigid hybrid spine-mimicking actuator with a stiffness-tunable module. Additionally, we introduce a new kinematic and dynamic modeling methodology for the proposed STSR. Based on the STSR characteristics, we model it as a series of PRP segments, each composed of two prismatic joints(P) and one revolute joint(R). This method is simpler, more generalizable, and more computationally efficient than existing approaches. We also design a multi-input multi-output (MIMO) controller that directly adjusts the pressure of the STSR's three pneumatic chambers to precisely control its posture. Both the novel modeling methodology and MIMO control system are implemented and validated on the proposed STSR prototype.

A rehabilitation robot control framework with adaptation of training tasks and robotic assistance.

Robot-assisted rehabilitation has exhibited great potential to enhance the motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients' potential movement capabilities. Moreover, challenging more difficult tasks with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both the training task and robotic assistance according to the subjects' performance, which can be estimated from the users' electromyography signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion while responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients' active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptation training task with high efficiency.

Initial levels of ß-amyloid and tau deposition have distinct effects on longitudinal tau accumulation in Alzheimer's disease.

BACKGROUND: To better assist with the design of future clinical trials for Alzheimer's disease (AD) and aid in our understanding of the disease's symptomatology, it is essential to clarify what roles ß-amyloid (Aß) plaques and tau tangles play in longitudinal tau accumulation inside and outside the medial temporal lobe (MTL) as well as how age, sex, apolipoprotein E (APOE) ε4 (APOE-ε4), and Klotho-VS heterozygosity (KL-VShet) modulate these relationships. METHODS: We divided the 325 Aß PET-positive (A+) participants into two groups, A+/T- (N = 143) and A+/T+ (N = 182), based on the threshold (1.25) of the temporal meta-ROI 18F-flortaucipir (FTP) standardized uptake value ratio (SUVR). We then compared the baseline and slopes of A+/T- and A+/T+ individuals' Aß plaques and temporal meta-ROI tau tangles with those of A-/T- cognitively unimpaired individuals (N = 162) without neurodegeneration. In addition, we looked into how baseline Aß and tau may predict longitudinal tau increases and how age, sex, APOE-ε4, and KL-VShet affect these associations. RESULTS: In entorhinal, amygdala, and parahippocampal (early tau-deposited regions of temporal meta-ROI), we found that baseline Aß and tau deposition were positively linked to more rapid tau increases in A+/T- participants. However, in A+/T+ individuals, the longitudinal tau accumulation in fusiform, inferior temporal, and middle temporal cortices (late tau-deposited regions of temporal meta-ROI) was primarily predicted by the level of tau tangles. Furthermore, compared to older participants (age ≥ 65), younger individuals (age < 65) exhibited faster Aß-dependent but slower tau-related tau accumulations. Additionally, compared to the KL-VShet- group, KL-VShet+ individuals showed a significantly lower rate of tau accumulation associated with baseline entorhinal tau in fusiform and inferior temporal regions. CONCLUSION: These findings offer novel perspectives to the design of AD clinical trials and aid in understanding the tau accumulation inside and outside MTL in AD. In particular, decreasing Aß plaques might be adequate for A+/T- persons but may not be sufficient for A+/T+ individuals in preventing tau propagation and subsequent downstream pathological changes associated with tau.

Design of Under-Actuated Soft Adhesion Actuators for Climbing Robots.

Since climbing robots mainly rely on adhesion actuators to achieve adhesion, robust adhesion actuators have always been the challenge of climbing robot design. A novel under-actuated soft adhesion actuator (USAA) proposed in this paper for climbing robots can generate adhesion through robot's load applied to the actuator. The actuator is composed of a soft film/substrate structure with an annular groove on the substrate and a cavity on the soft film. To fabricate the actuator, we first study the influence of the geometric parameters of the USAA on the maximum adhesion of the actuator by analysis and experiments, and then combine these parameters and the boundary conditions of the static meniscus in the mold to design the mold. Moreover, we fabricate a climbing robot equipped with USAAs and evaluate its performance on horizontal and inclined surfaces with a wide range of characteristics. The USAA can generate strong and controllable adhesion to various smooth and semi-smooth surfaces. Furthermore, the fabricated robot performs well on various surfaces under a certain load (at least 500 g) and speed (369 mm/min) through experiments. It's adaptability to a variety of surfaces enables a wide range of applications and pushes the boundaries of soft adhesion actuators.

A Channel Estimation Method for Ultrasonic Through-Metal Communication.

A piecewise suboptimal threshold (PSOT) is proposed to select the most significant samples (MSSs) of the channel impulse response (CIR) for channel estimation of ultrasonic through-metal (UTM) communication. We analyze the correlation of echoes in the UTM channel, i.e., the periodicity and the exponential decay of echoes. The summation of mean-square error (MSE) is partitioned using the periodicity of the echoes so that each partitioned MSE contains only one echo. The PSOT is derived by setting the first derivative of the partitioned MSE to zero. Channel tap energies, namely, the received energies of ultrasonic waves, are required to calculate the PSOT, which can be estimated using the exponential decay of echoes. After selecting MSSs by PSOT, we propose a further selection approach to get rid of possible noise samples in MSS using the correlation of echoes. The simulation results show that the proposed method is robust. Specifically, the performance of the proposed method does not depend on ultrasonic velocity, is unaffected by the estimated number of the echoes at a low signal-to-noise ratio (SNR), and can be applied to UTM channels with different metal thicknesses. The simulation also shows that the proposed method has a lower MSE than other methods, especially at low SNR.

Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition.

Recognizing facial expression has attracted much more attention due to its broad range of applications in human-computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn-Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.

An Analytical Framework for the IEEE 802.15.4 MAC Layer Protocol under Periodic Traffic.

As the reference communication standard of wireless sensor networks (WSNs), the IEEE 802.15.4 standard has been adopted in various WSN-based applications. In many of these applications, one of the most common traffic pattern types is a periodic traffic patterns, however, the majority of existing analytical models target either saturated or unsaturated network traffic patterns. Furthermore, few of them can be directly extended to the periodic traffic scenario, since periodic traffic brings unstable load status to sensor nodes. To better characterize the WSNs with periodic traffic, we propose an accurate and scalable analytical framework for the IEEE 802.15.4 MAC protocol. By formulating the relationship between clear channel assessment (CCA) and its successful probability from the perspective of channel state and node state, single node's behavior and whole network's performance under different network scales and traffic loads can be derived. Extensive simulations are conducted to validate the proposed framework in terms of both local statistics and overall statistics, and the results show that the model can represent the actual behavior and the real performance of both single node and whole network. Besides, as the simplified version of double CCAs mode (DS mode), single CCA mode (SS mode), is also analyzed with simple modifications on the proposed analytical framework. Combining the analytical framework with simulation results, the applicable network scenarios of two modes are also demonstrated respectively. Finally, an approximate distribution of one data packet's backoff duration is proposed. With this approximate distribution, a conservative estimation of data packet's average transmission latency in networks with given configurations can be easily carried out.

A Multi-Mode Rehabilitation Robot With Magnetorheological Actuators Based on Human Motion Intention Estimation.

Lower extremity paralysis has become common in recent years, and robots have been developed to help patients recover from it. This paper presents such a robotic system that allows for two working modes, the robot-active mode and human-active mode. The robot is designed to be equipped with magnetorheological (MR) actuators that have the advantages of high torque, fast response, flexible controllability, low power consumption and safety guarantee. The design and characteristics of the MR actuator are introduced. In the robot-active mode, the MR actuator works as a clutch to transfer the torque to the robotic joint safely. In the human-active mode, the MR actuator functions as a brake to provide resistance to help strengthen muscles. The working mode is determined by the human motion intention, which is detected via the skin surface electromyography (EMG) signals. The human-robot interaction torques are estimated using the EMG-driven impedance model. The biomechanical analysis based on AnyBody Modeling System (AMS) is used to help optimization. Then, an adaptive control method is proposed to realize the assist-as-needed (AAN) training strategy, where the robot can switch between these two modes. Experiments are conducted to validate the proposed design.