The effects of aerobic exercise combined with resistance training on inflammatory factors and heart rate variability in middle-aged and elderly women with type 2 diabetes mellitus.

OBJECTIVE: This study investigated the effects of aerobic exercise combined with resistance training on serum inflammatory factors and heart rate variability (HRV) in women with type 2 diabetes mellitus (T2DM). METHODS: A total of 30 patients with diabetic cardiovascular autonomic neuropathy (DCAN) were randomly divided into a control group (n = 15) and an exercise group (n = 15). The control group was treated with routine hypoglycemic drugs, while the exercise group was treated with routine hypoglycemic drugs + resistance training (AE + RT). The levels of fasting plasma glucose (FBG), two-hour plasma glucose (2hPG), serum inflammatory factors C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) were measured before and after the intervention. The HRV was evaluated by 24-h ambulatory electrocardiogram. RESULTS: After the intervention, the levels of FBG, 2hPG, serum inflammatory factors, IL-6 and TNF-α in the exercise group were significantly lower than those in the control group (p < .05) with no significant differences in serum CRP (p > .05). After the intervention, the HRV time domain and frequency domain indexes in the two groups were significantly improved compared with those before the exercise experiment (p < .01) and with no significant difference in (lnlf) (p > .05). The time-domain indexes, i.e., SDNN and RMSSD, as well as the frequency domain index, i.e., (lnhf), were significantly higher in the exercise group than in the control group, whereas lnlf/lnhf were significantly lower than those in the control group (p < .05). CONCLUSIONS: Compared with routine hypoglycemic drug therapy, combining aerobic exercise and resistance training helped to reduce the level of blood glucose and serum inflammatory factors in T2DM patients with DCAN, and improved autonomic nerve function.

Association between cortical thickness and distinct vascular cognitive impairment and dementia in patients with white matter lesions.

NEW FINDINGS: What is the central question of this study? White matter lesions (WMLs) are a brain disease characterized by altered brain structural and functional connectivity, but findings have shown an inconsistent pattern: are there distinct cortical thickness changes in patients with WMLs subtypes? What is the main finding and its importance? Patients with WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed distinct pathophysiology in cortical thickness. These neural correlates of WMLs should be considered in future treatment. ABSTRACT: The effect of cortical thickness on white matter lesions (WMLs) in patients with distinct vascular cognitive impairments is relatively unknown. This study investigated the correlation between cortical thickness and vascular cognitive manifestations. WML patients and healthy controls from Beijing Tiantan Hospital between 2014 and 2018 were included. The patients were further divided into two subgroups, namely WMLs with non-dementia vascular cognitive impairment (WML-VCIND) and WMLs with vascular dementia (WML-VaD) according to the Clinical Dementia Rating (CDR) scale and the Beijing version of the Montreal Cognitive Assessment (MoCA). Changes in cortical thickness were calculated using FreeSurfer. Pearson's correlation analysis was performed to explore the relationship between cognitive manifestations and cortical thickness in WML patients. Forty-five WML patients and 23 healthy controls were recruited. The WML group exhibited significant difference in cortical thickness compared to the control group. Significantly decreased cortical thickness in the middle and superior frontal gyri, middle temporal gyrus, angular gyrus and insula was found in the WML-VaD versus WML-VCIND subgroup. Cortical thickness deficits of the left caudal middle frontal gyrus (r = 0.451, P = 0.002), left rostral middle frontal gyrus (r = 0.514, P < 0.001), left superior frontal gyrus (r = 0.410, P = 0.006), right middle temporal gyrus (r = 0.440, P = 0.003), right pars triangularis (r = 0.462, P = 0.002), right superior frontal gyrus (r = 0.434, P = 0.004) and right insula (r = 0.499, P = 0.001) were positively correlated with the MoCA score in WML patients. The specific pattern of cortical thickness deficits in the WML-VaD subgroup revealed the pathophysiology of WMLs, which should be considered in future treatment of WMLs.

A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model.

The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg-Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.

An Ultra-Sensitive Modular Hybrid EMG-FMG Sensor with Floating Electrodes.

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG-FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain-Machine Interface.

BACKGROUND: For the nonstationarity of neural recordings in intracortical brain-machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. METHODS: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. RESULTS: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. CONCLUSIONS: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

Peripheral Neural Interface.

Peripheral nervous system, widely spread in the whole body, is the important bridge for the transmission of neural signals. Signals from the central nervous system (brain and spinal cord) are transmitted to different parts of the body by the peripheral nerves, while along the way they also feedback all kinds of sensory information. Certain level of information integration and processing also occurs in the system. It has been shown that neural signals could be extracted from the distal end of the stump, indicating that the bridge is still effective after limb damage or amputation, which is the neurophysiological basis for the research and development of peripheral nerve interface for the prosthetic system.

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object.

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.

sEMG-Based Hand-Gesture Classification Using a Generative Flow Model.

Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves 63.86 ± 5.12 % accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent.

Spike propagation in axons under stretch growth conditions in cultured neurons from dorsal root ganglion.

Computational software NEURON was used to simulate the stretch growth neurons in order to investigate the ability of dorsal root ganglion neurons to generate and propagate action potentials after a period of rapid axon stretch growth in vitro, and under what stimulating parameters can evoke action potentials. In the simulation, we found the stretch growth neuron had higher spike amplitude than from the static culture neuron in the soma and all axonal branch. In addition, the conduction velocity was also faster in the stretch growth axon. When the stimulating frequency was less than 15 Hz or the stimulating voltage was lower than 15 mV, no spike was evoked. Increasing stimulating frequency from 15 Hz to 5000 Hz or stimulating voltage from 15 mV to 100 mV had almost no effect on the spike amplitude. Interestingly, the first spike time and absolute refractory period (ARP) in different axonal branches and somas decreased stepwise with incremental increase in the stimulating frequency. It is concluded that the stretch growth neuron had higher amplitude and faster conduction velocity than the static culture neuron. In addition, some stimulating parameters had been analyzed in this study, which provided guidelines for electrophysiological experiments in future.

Neurons in dorsal premotor cortex represent the switching of intended hand path in a delayed reaching task.

Dorsal premotor cortex (PMd) is considered to play a crucial role in motor preparation, yet how the variation of neuronal activity affects the generation of different circumstances dependent movements remains unclear. Here we trained two monkeys to perform a delayed reaching task instructed by two sets of cues, one for indicating the target locations and another for indicating a conditionally presented virtual obstacle in the reaching path, which required the monkey to make a bypassing instead of straight reaching. We recorded the activity of PMd neurons and investigated how they responded to the switching of intended hand path induced by obstacle bypassing. Comparing the neuronal activity between hand bypassing trials and straight reaching trials, we found 30% of the total 687 set-related neurons showed different overall discharging level, and another 24% showed different onset time during the delay period. We also found 16% of the neurons were modulated only by target location and 14% were modulated by both target location and path switching. Our results demonstrate PMd neurons not only represent the planning of reaching to different target locations, as many previous studies have shown, but also represent the switching of intended reaching path induced by hand bypassing, suggesting how PMd neurons coordinate for such circumstances dependent motor planning.

Development of a new miniaturized bioreactor for axon stretch growth.

Peripheral nerve injury requires a physical bridge across the lesion, which is limited by the insufficient supply of donor nerves. Here, we developed a new miniaturized bioreactor system for axon stretch growth. Dorsal root ganglia explants were first placed on two adjoining substrates and formed new synaptic connections. The axon bundles across the border between the top and bottom membranes were then stretched in a stepwise fashion by a microstepper motor system. After several days of stretch, the axon tracts could reach lengths that could develop into living nervous tissue constructs. In order to achieve appropriate neuronal culture to stimulate physiological conditions during axon stretch, we tested a variety of coating methods. Based on these results, the elongator substrates were coated with both poly-D-lysine and rat-tail collagen to maximize the number of axon bundles. Additionally, we found that increasing the axon stretch by 1[Formula: see text][Formula: see text]m at each step resulted in the highest stability. The bridging axons adapted to the stretch by increasing their length from 500[Formula: see text][Formula: see text]m to 5.94[Formula: see text]mm over 7 days of stretch growth. Immunocytochemical analysis confirmed that beta-III-tubulin, a major cytoskeletal constituent and neuronal marker, was present along axons. The findings demonstrate that bioreactor has the potential to generate transplant materials to address neural repair.

Neuronal representation of stand and squat in the primary motor cortex of monkeys.

BACKGROUND: Determining neuronal topographical information in the cerebral cortex is of fundamental importance for developing neuroprosthetics. Significant progress has been achieved in decoding hand voluntary movement with cortical neuronal activity in nonhuman primates. However, there are few successful reports in scientific literature for decoding lower limb voluntary movement with the cortical neuronal firing. We once reported an experimental system, which consists of a specially designed chair, a visually guided stand and squat task training paradigm and an acute neuron recording setup. With this system, we can record high quality cortical neuron activity to investigate the correlation between these neuronal signals and stand/squat movement. METHODS/RESULTS: In this research, we train two monkeys to perform the visually guided stand and squat task, and record neuronal activity in the vast areas targeted to M1 hind-limb region, at a distance of 1 mm. We find that 76.9% of recorded neurons (1230 out of 1598 neurons) showing task-firing modulation, including 294 (18.4%) during the pre-response window; 310 (19.4%) for standing up; 104 (6.5%) for the holding stand phase; and 205 (12.8%) during the sitting down. The distributions of different type neurons have a high degree of overlap. They are mainly ranged from +7.0 to 13 mm in the Posterior-Anterior dimension, and from +0.5 to 4.0 mm in Dosal-lateral dimension, very close to the midline, and just anterior of the central sulcus. CONCLUSIONS/SIGNIFICANCE: The present study examines the neuronal activity related to lower limb voluntary movements in M1 and find topographical information of various neurons tuned to different stages of the stand and squat task. This work may contribute to understanding the fundamental principles of neural control of lower limb movements. Especially, the topographical information suggests us where to implant the chronic microelectrode arrays to harvest the most quantity and highest quality neurons related to lower limb movements, which may accelerate to develop cortically controlled lower limb neuroprosthetics for spinal cord injury subjects.

Biomimetic Peripheral Nerve Stimulation Promotes the Rat Hindlimb Motion Modulation in Stepping: An Experimental Analysis.

Peripheral nerve stimulation is an effective neuromodulation method in patients with lower extremity movement disorders caused by stroke, spinal cord injury, or other diseases. However, most current studies on rehabilitation using sciatic nerve stimulation focus solely on ankle motor regulation through stimulation of common peroneal and tibial nerves. Using the electrical nerve stimulation method, we here achieved muscle control via different sciatic nerve branches to facilitate the regulation of lower limb movements during stepping and standing. A map of relationships between muscles and nerve segments was established to artificially activate specific nerve fibers with the biomimetic stimulation waveform. Then, characteristic curves depicting the relationship between neural electrical stimulation intensity and joint control were established. Finally, by testing the selected stimulation parameters in anesthetized rats, we confirmed that single-cathode extraneural electrical stimulation could activate combined movements to promote lower limb movements. Thus, this method is effective and reliable for use in treatment for improving and rehabilitating lower limb motor dysfunction.

Neural Decoding for Intracortical Brain-Computer Interfaces.

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

Time-Varying Functional Connectivity of Rat Brain during Bipedal Walking on Unexpected Terrain.

The cerebral cortex plays an important role in human and other animal adaptation to unpredictable terrain changes, but little was known about the functional network among the cortical areas during this process. To address the question, we trained 6 rats with blocked vision to walk bipedally on a treadmill with a random uneven area. Whole-brain electroencephalography signals were recorded by 32-channel implanted electrodes. Afterward, we scan the signals from all rats using time windows and quantify the functional connectivity within each window using the phase-lag index. Finally, machine learning algorithms were used to verify the possibility of dynamic network analysis in detecting the locomotion state of rats. We found that the functional connectivity level was higher in the preparation phase compared to the walking phase. In addition, the cortex pays more attention to the control of hind limbs with higher requirements for muscle activity. The level of functional connectivity was lower where the terrain ahead can be predicted. Functional connectivity bursts after the rat accidentally made contact with uneven terrain, while in subsequent movement, it was significantly lower than normal walking. In addition, the classification results show that using the phase-lag index of multiple gait phases as a feature can effectively detect the locomotion states of rat during walking. These results highlight the role of the cortex in the adaptation of animals to unexpected terrain and may help advance motor control studies and the design of neuroprostheses.

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.

Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the "hardware" and "algorithm" components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

Subjective Well-being of Special Education Teachers in China: The Relation of Social Support and Self-Efficacy.

In order to explore the relationship of social support, self-efficacy, and subjective well-being of special education teachers in China, 496 teachers from 67 special education schools were surveyed by questionnaire. We found that (1) the subjective well-being of special education teachers in China was in the medial level. (2) There were significant differences in subjective well-being level among teachers of different genders, teacher position, education background, and teaching age. Male teachers were of higher subjective well-being; subjective well-being of head teachers was lower than those were not head teachers; teachers with the educational background of postgraduate were of higher relaxation and tension than those with junior college educational background; the control scores of emotion and behavior of teachers with teaching age of 3 years and below were significantly lower than those of teachers with teaching age of more than 10 years. (3) Self-efficacy played a partially mediating role in the relationship between social support and subjective well-being of special education teachers. Suggestions to improve the subjective well-being of special education teachers were discussed in the article.

[Variation Characteristics of Ambient Volatile Organic Compounds (VOCs) Volume Fraction During Hangzhou COVID-19 Period].

A continuous observation campaign was carried out with the Syntech Spectras GC955 volatile organics online monitoring system from December 1, 2019 to March 31, 2020 during the COVID-19 period in Hangzhou. Composition characteristics, diurnal variation, and atmospheric chemical reactivity of VOCs were analyzed. The results showed that φ(total VOCs) were the highest before the COVID-19 pandemic in different sites and the lowest during the first response period. The φ(total VOCs) at night was higher than that during the day. The daily variation in Wolongqiao φ(total VOCs) was less than that in Xiasha. The daily variation in φ(total VOCs) during the first level response period was less than that during the other three periods. The diurnal variation in the φ (total VOCs) in Xiasha showed a "V" shape, and that in Wolongqiao showed a typical bimodal structure. The OFP in Xiasha was higher than that in Wolongqiao. The OFP were the highest at the two sites before the COVID-19 pandemic. The OFP was the lowest during the first response period in Xiasha and the lowest during the second response period in Wolongqiao. The OFP of aromatics and olefins was higher, and the OFP of alkynes was the lowest in Xiasha. The OFP of olefin in Wolongqiao was much higher than that of the other three components, followed by alkane and alkyne.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: an event-related electroencephalogram study.

Background.Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.Methods.Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.Main results.Behavioral results suggested that the single motor units (SMUs) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.Conclusion.This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Development of a wide-view visual presentation system for visual retinotopic mapping during functional MRI.

PURPOSE: To develop and validate the functionality of a novel wide-view visual presentation system with a horizontal and vertical eccentricity angle of 60° for retinotopic mapping by functional MRI (fMRI). MATERIALS AND METHODS: The wide-view presentation system consisted of a 52-mm diameter optical fiber, an entrance apparatus and a presentation apparatus. The terminal edge of the optical fiber at the entrance is flat, while the terminal edge on the presentation apparatus is a sphere of 60 mm in diameter. The subjects wore contact lenses with +20, +22, or +25 magnification to focus on the stimulus, and the visual field eccentricity angle could reach 60°. The signal to noise ratio valuation experiment was performed to evaluate the clarity and quality of the MRI picture image. Checkerboard and random dot stimuli were used to prove that this system could be applied to retinotopic mapping by fMRI. RESULTS: The results of the experiment demonstrated that the system is safe in the MRI environment with minimal distortion and can be used for visual retinotopic mapping studies. Wide-field mapping areas (V6, MST) were found in the human visual cortex. Compared with previous studies, the V1 and MT+ surface area approaches but does not fully cover the anatomical area. Nonetheless, the area achieved using the new system is larger than those achievable in previous fMRI studies. CONCLUSION: We developed a versatile, low-cost system for presenting wide-view visual stimuli in the MRI environment. The fMRI retinotopic mapping results proved the viability of this system.