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We describe a biophysical mechanism for animal magnetoreception, orientation and navigation in the geomagnetic field (GMF), based on the ion forced oscillation (IFO) mechanism in animal cell membrane voltage-gated ion channels (VGICs) (IFO-VGIC mechanism). We review previously suggested hypotheses. We describe the structure and function of VGICs and argue that they are the most sensitive electromagnetic sensors in all animals. We consider the magnetic force exerted by the GMF on a mobile ion within a VGIC of an animal with periodic velocity variation. We apply this force in the IFO equation resulting in solution connecting the GMF intensity with the velocity variation rate. We show that animals with periodic velocity variations, receive oscillating forces on their mobile ions within VGICs, which are forced to oscillate exerting forces on the voltage sensors of the channels, similar or greater to the forces from membrane voltage changes that normally induce gating. Thus, the GMF in combination with the varying animal velocity can gate VGICs and alter cell homeostasis in a degree depending, for a given velocity and velocity variation rate, on GMF intensity (unique in each latitude) and the angle between velocity and GMF axis, which determine animal position and orientation., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

As a new means of brain neuroregulation and research, transcranial magneto-acoustic stimulation (TMAS) uses the coupling effect of ultrasound and a static magnetic field to regulate neural activity in the corresponding brain areas. Calcium ions can promote the secretion of neurotransmitters and play a key role in the transmission of neural signals in brain cognition. In this study, to explore the effects of TMAS on cognitive function and neural signaling in the CA1 region of the hippocampus, TMAS was applied to male 2-month-old C57 mice with a magnetic field strength of 0.3 T and ultrasound intensity of 2.6 W/cm 2 . First, the efficiency of neural signaling in the CA1 region of the mouse hippocampus was detected by fiber photometry. Second, the effects of TMAS on cognitive function in mice were investigated through multiple behavioral experiments, including spatial learning and memory ability, anxiety and desire for novelty. The experimental results showed that TMAS could improve cognitive function in mice, and the efficiency of neural signaling in the CA1 area of the hippocampus was significantly increased during stimulation and maintained for one week after stimulation. In addition, the neural signaling efficiency in the CA1 area of the hippocampus increased in the open field (OF) experiment and recovered after one week, the neural signaling efficiency in the new object exploration (NOE) experiment was significantly enhanced, and the intensity slowed after one week. In conclusion, TMAS enhances cognitive performance and promotes neural signaling in the CA1 region of the mouse hippocampus., (Copyright © 2024. Published by Elsevier Inc.)

The sensor that simultaneously perceives bending strain and magnetic field has the potential to detect the finger bending state and hand position of the human and robot. Based on unique magneto-mechanical coupling effect of magnetostrictive materials, the proposed a bi-perceptive flexible sensor, consisting of the Co-Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input-output characteristics of the simulation model. An asynchronous multi-task deep learning method is proposed, which takes the output of the position task as the partial input of the bending state task to analyze the output information of the sensor quickly and accurately. The sensing system, integrating with the proposed model, can better predict the bending state and approach distance of human or robot hand., (© 2024. The Author(s).)

Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s) 2024.)

Background: Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method., Methods: Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation., Results: This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis., Conclusion: This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms., (© 2024. The Author(s).)

Plant metabolites from natural product extracts offer unique advantages against carcinogenesis in the development of drugs. The target-based virtual screening from food-derived compounds represents a promising approach for tumor therapy. In this study, we performed virtual screening to target the presumed inhibitor-binding pocket and identified a highly potent Kv10.1 inhibitor, liensinine (Lien), which can inhibit the channel in a dose-dependent way with an IC 50 of 0.24 ± 0.07 μM. Combining molecular dynamics simulations with mutagenesis experiments, our data show that Lien interacts with Kv10.1 by binding with Y539, T543, D551, E553, and H601 in the C-linker domain of Kv10.1. In addition, the interaction of sequence alignment and 3D structural modeling revealed differences between the C-linker domain of the Kv10.1 channel and the Kv11.1 channel. Furthermore, antitumor experiments revealed that Lien suppresses the proliferation and migration of HCC both in vitro and in vivo. In summary, the food-derived compound, Lien, may serve as a lead compound for antihepatoma therapeutic drugs targeting Kv10.1.

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.

The magnetic diameter is a crucial factor affecting the magnetic properties of magnetic fluids. The magnetic diameter distribution can be estimated based on the magnetic properties. However, the magnetic dipole interaction of magnetic nanoparticles (MNPs) and the variation of the magnetic diameter with temperature have received relatively little attention in previous research. Hence, this research proposes the AP-MMF1-L method to inverse the magnetic diameter which considers the magnetic dipole interaction and derives the magnetic diameter at different temperatures. Firstly, the AP-MMF1-L uses the least square method between the first-order modified mean-field Langevin function (MMF1-L) and the measured magnetization curve as the objective function. Meanwhile, the hybrid Artificial bee colony-particle swarm (AP) optimization algorithm is introduced to inverse the optimal magnetic diameter distribution. Secondly, the hydrodynamic diameter distribution experimental values are compared with the theoretical values, demonstrating the AP-MMF1-L method obtains accurate inversion results of the magnetic diameter distribution when compared to other models. Finally, the arithmetic mean of the magnetic diameter at different temperatures is investigated, revealing a decreasing trend as the temperature rises, approximately following a linear distribution. The AP-MMF1-L provides a novel and effective tool for accurately determining the magnetic diameter of the MNPs across various temperatures., (© 2024 IOP Publishing Ltd.)

Ether-à-go-go (EAG) potassium channels play a crucial role in the regulation of neuronal excitability and cancer progression, rendering them potential drug targets for cancer therapy. However, the scarcity of information regarding the selection sites on hEAG1 has posed a challenge in the discovery of new hEAG1 inhibitors. In this study, we introduced a novel natural product, corydaline, which selectively inhibits the hEAG1 channel without sensitivity to other KCNH channels. The IC 50 of corydaline for the hEAG1 channel was 11.3 ± 0.6 μM, whereas the IC 50 for hEAG2 and hERG1 were 73.6 ± 9.9 μM and 111.4 ± 8.5 μM, respectively. Molecular dynamics simulations together with site-directed mutagenesis, have unveiled that the site corydaline forms interactions with Lys 217 , Phe 273 , Pro 276 , Trp 295 and Arg 366 , situated within the intracellular transmembrane segments S1-S4 of the voltage-sensor domain, be considered a novel drug pocket for hEAG1. Additionally, the intergaration of sequence alignment and 3D structural modeling revealed differences between the voltage sensor domain of hEAG1 channel and other EAG channels, suggesting the feasibility of a VSD modulation approach that could potentially lead to the selective inhibition of hEAG1 channels. Furthermore, antitumor experiments demonstrated that corydaline can inhibit the proliferation and migration of hepatic carcinoma cells by targeting hEAG1. The identification of this novel druggable pocket offers the possibility for drug screening against diseases linked to abnormal hEAG1 channels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Transformers, reactors and other electrical equipment often work under harmonics and DC-bias working conditions. It is necessary to quickly and accurately simulate the hysteresis characteristics of soft magnetic materials under various excitation conditions in order to achieve accurate calculations of core loss and the optimal design of electrical equipment. Based on Preisach hysteresis model, a parameter identification method for asymmetric hysteresis loop simulation is designed and applied to the simulation of hysteresis characteristics under bias conditions of oriented silicon steel sheets. In this paper, the limiting hysteresis loops of oriented silicon steel sheets are obtained through experiments under different working conditions. The first-order reversal curves(FORCs) with asymmetric characteristics is generated numerically, and then the Everett function is established under different DC bias conditions. The hysteresis characteristics of the oriented silicon steel sheets under harmonics and DC bias are simulated by improving FORCs identification method of the Preisach model. By comparing the results of simulation and experiment, the effectiveness of the proposed method is verified, so as to provide an important reference for material production and application.

In order to broaden the application of nanocrystalline soft magnetic materials in electrical engineering under extreme conditions, nanocrystalline alloys must also have good characteristics under high-frequency and nonsinusoidal excitation. In this paper, the magnetic properties of Fe-based nanocrystalline alloys excited by high repetition frequency pulses were measured. Excitation frequency and duty cycles are two important factors in the study of magnetic properties under pulse excitation. With the amplitude of the pulse remaining constant, different local hysteresis curves were obtained by changing the frequency and duty cycle. The experimental results proved that the higher the frequency is and the smaller the duty cycle is, the narrower the local hysteresis loop is. Finally, the finite element method (FEM) was used to model the magnetic core coupling with an impulse circuit based on the measured magnetic properties. Compared with the experimental results, the simulation results showed that the field-circuit coupling analysis model can effectively reflect the influence law of the frequency and duty cycle on magnetic properties.

Objective: Liquid metal (LM) nowadays is considered a new biomedical material for medical treatment. The most common application of LM in medical therapy is taking LM as a carrier for oncology therapeutics. However, the feasibility and direct effect of LM in tumor treatment are still unknown, and how to delineate the negative resection margin (NRM) of the tumor is also a crucial problem in surgery. We aimed to inject LM into interstitial channels of extremities of mice to overlay the surface of the primary tumor to investigate the effect of LM on inhibiting tumor growth and highlight the NRM of the tumor., Methods: In this study, all 50 BALB/c-nude female mice were used to construct the transplanted HepG2-type hepatocellular carcinoma model. One week after the establishment of the model, the mice were divided into three groups, named LM group, PBS group and Control group by injecting different liquid materials into the forelimb interstitial channel of the mice. T2WI image on MRI and Magneto-acoustic tomography (MAT) were used to show the distribution of LM and PBS in vivo. The group comparisons of tumor growth and blood tests were evaluated by one-way ANOVA and post-hoc analysis. And the biocompatibility of LM to BALB/c nude mice was evaluated by histopathological analysis of LM group and control group., Results: The volume change ratio of tumor was significantly lower in LM group than in PBS and Control group after 10 days of grouping. Compared with PBS and Control group, the main indexes of blood tests in LM group were significantly lower and close to normal level. In addition, the distribution of LM in vivo could be clearly observed under T2WI anatomic images and the crossprofile of the tumor in MAT. LM also has a obvious contrast in MRI T2WI and enhanced the amplitude of imaging signal in MAT., Conclusion: LM may inhibit the growth of transplanted hepatoma tumor through tumor encapsulation. In vivo, tumor imaging and LM distribution imaging were achieved by MRI T2WI, which verified that LM injected with interstitial injection made the NRM of tumor more prominent and had the potential of being MRI contrast agent. At the same time, LM could also be a new conductive medium to improve the imaging quality of MAT. Moreover, LM performed mild biocompatibility., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DD declared a shared affiliation with the authors CL, AY, GL, WZ, JL, GM, HX to the handling editor at the time of review., (Copyright © 2022 Lu, Yang, Xia, Liu, Zhao, Zhang, Li, Liu, Ma and Xia.)

Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF 6 gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF 6 HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs.

Transcutaneous auricular vagus nerve stimulation (taVNS) can improve autonomic nerve function and is currently undergoing extensive clinical research; however, its efficacy heterogeneity has caused great controversy. Heart rate variability (HRV), a biomarker reflecting autonomic function, exhibits a time-varying pattern with circadian rhythms, which may be the main reason for the inconsistent stimulation effects. To test this conjecture, we performed isochronous acute stimulation experiments at intervals of 12 h. The results showed that HRV indicators representing vagal nerve activity significantly increased when stimulation was performed in the morning, and the enhancement of high frequency continued into the recovery period. However, the evening stimulation did not yield similar results. In addition, we found that improvements in the measures of autonomic balance were more pronounced in the presence of lower vagal activity. By increasing the stimulation duration, we also found that the effect of taVNS on HRV was not regulated by duration; in other words, HRV changes only had the best effect at the beginning of stimulation. These studies allowed us to determine the optimal stimulation phase and duration and potentially screen the optimal candidates for taVNS., Competing Interests: The authors have declared that no competing interests exist.

The homodimerization of CD44 plays a key role in an intercellular-to-extracellular signal transduction and tumor progression. Acylated modification and specific membrane environments have been reported to mediate translocation and oligomerization of CD44; however, the underlying molecular mechanism remains elusive. In this study, extensive molecular dynamics simulations are performed to characterize the dimerization of palmitoylated CD44 variants in different bilayer environments. CD44 forms homodimer depending on the cysteines on the juxta-membrane domains, and the dimerization efficiency and packing configurations are defected by their palmitoylated modifications. In the phase-segregated (raft included) membrane, homodimerization of the palmitoylated CD44 is hardly observed, whereas PIP2 addition compensates to realize dimerization. However, the structure of CD44 homodimer formed in the phase-segregated bilayer turns susceptive and PIP2 addition allows for an extensive conformation of the cytoplasmic domain, a proposal prerequisite to access the cytoskeleton linker proteins. The results unravel a delicate competitive relationship between PIP2, lipid raft, and palmitoylation in mediating protein homodimerization, which helps to clarify the dynamic dimer conformations and involved cellular signaling of the CD44 likewise proteins., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fan Yang et al.)

The fluctuation of heart rate is regulated by autonomic nervous system. In human sleep, the autonomic nervous system plays a leading role. Therefore, we can use heart-rate variability (HRV) to stage the sleep process. Based on two independent public datasets, we construct three end-to-end automatic sleep staging models: fully connected neural networks (FCN), convolutional neural networks (CNN) and long short-term memory networks (LSTM). Only the HRV sequence was used to classify and identify the four sleep stages of the subject's sleep process: wake(W), light sleep (LS), slow-wave sleep (SWS) and rapid eye movement (REM), and the confusion matrix was calculated. The three models were compared by performance index (precision, accuracy, F1, Kappa statistic) and Friedman test. Among these models, the CNN has the best classification effect. The precision of W, REM, LS and SWS were 88.31%, 98.07%, 81.16% and 99.36%, respectively. It's the average accuracy, average F1 value and Kappa statistic were 91.72%, 0.8850 and 0.8844 ± 0.0095, respectively. The experimental results show that the convolutional neural network can achieve good sleep staging effect based on the signal of HRV solely, which is suitable for sleep detection in the home., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer's disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Geng, Wang, Fu, Zhang, Yang and An.)

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

Superconducting power lead equipment for epoxy insulation, such as high-temperature superconducting DC power or liquefied natural gas energy pipelines, as well as high-temperature superconducting cables, has long been used in extreme environments, from liquid nitrogen temperatures to normal temperatures. It is easy to induce surface discharge and flashover under the action of strong electric field, which accelerates the insulation failure of current leads. In this paper, two-dimensional nano-material GO was used to control the electrical properties of epoxy resins. The DC surface discharge and flashover characteristics of the prepared epoxy resin-GO composite insulation materials were tested at room temperature with liquid nitrogen. The surface discharge mechanism of the epoxy resin-GO composite insulation materials was analyzed. The experimental results show that the insulation properties of epoxy composites doped with GO changed. Among them, the surface flashover voltage of 0.05 wt% material is the best, which can inhibit the discharge phenomenon and improve its insulation properties in extreme environments, from room temperature to liquid nitrogen temperature. It is found that the development process of surface discharge of composite insulating materials under liquid nitrogen is quite different from that under room temperature. Before critical flashover, the repetition rate and amplitude of surface discharge remain at a low level until critical flashover. Furthermore, the voltage of the first flashover is significantly higher than that of the subsequent flashover under the action of the desorption gas on the surface of the composite insulating material and the gasification layer produced by the discharge. Given that the surface flashover voltage of 0.05 wt% epoxy composite is the best, the research and analysis of 0.05 wt% composite is emphasized. In the future design of superconducting power lead insulation, the modification method of adding GO to epoxy resin can be considered in order to improve its insulation performance.

Lung cancer has the highest mortality among cancers worldwide due to its high incidence and lack of the effective cures. We have previously demonstrated that the membrane ion channel TMEM16A is a potential drug target for the treatment of lung adenocarcinoma and have identified a pocket of inhibitor binding that provides the basis for screening promising new inhibitors. However, conventional drug discovery strategies are lengthy and costly, and the unpredictable side effects lead to a high failure rate in drug development. Therefore, finding new therapeutic directions for already marketed drugs may be a feasible strategy to obtain safe and effective therapeutic drugs. Here, we screened a library of over 1400 Food and Drug Administration-approved drugs through virtual screening and activity testing. We identified a drug candidate, Zafirlukast (ZAF), clinically approved for the treatment of asthma, that could inhibit the TMEM16A channel in a concentration-dependent manner. Molecular dynamics simulations and site-directed mutagenesis experiments showed that ZAF can bind to S387/N533/R535 in the nonselective inhibitor binding pocket, thereby blocking the channel pore. Furthermore, we demonstrate ZAF can target TMEM16A channel to inhibit the proliferation and migration of lung adenocarcinoma LA795 cells. In vivo experiments showed that ZAF can significantly inhibit lung adenocarcinoma tumor growth in mice. Taken together, we identified ZAF as a novel TMEM16A channel inhibitor with excellent anticancer activity, and as such, it represents a promising candidate for future preclinical and clinical studies., Competing Interests: Conflict of interest The authors declare no conflict of interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Transcutaneous auricular vagus nerve stimulation (taVNS) has shown positive effects on a variety of diseases. Considering that decreased heart rate variability (HRV) is closely associated with morbidity and mortality for a variety of diseases, it is important to investigate the effect of taVNS on HRV. In Study 1, we conducted a two-stage cross-over trial to compare the effects of taVNS and sham taVNS (staVNS) on HRV. In Study 2, we systematically tested the effects of different taVNS parameters on high frequency (HF) component of HRV. The results showed that taVNS significantly increased measurements of root mean square of the difference between successive RR intervals (RMSSD), percentage of number of pairs of adjacent RR intervals differing greater than 50ms (pRR50), standard deviation of all RR intervals (SDRR), HF. Significantly, enhancement of HF and pRR50 persisted into recovery period. In addition, higher baseline LF/HF ratio was associated with greater LF/HF ratio decrease. Findings also showed that there was no significant difference in measurements of HF between different taVNS parameters. These studies suggest that taVNS could increase HRV, it may help taVNS in the treatment of low HRV related diseases. However, taVNS may not have parameter-specific effects on HRV., Competing Interests: The authors have declared that no competing interests exist.

TMEM16A, the calcium-activated chloride channel, is broadly expressed and plays pivotal roles in diverse physiological processes. To understand the structural and functional relationships of TMEM16A, it is necessary to fully clarify the structural basis of the gating of the TMEM16A channel. Herein, we performed the protein electrostatic analysis and molecular dynamics simulation on the TMEM16A in the presence and absence of Ca 2+ . Data showed that the separation of TM4 and TM6 causes pore expansion, and Q646 may be a key residue for the formation of π-helix in the middle segment of TM6. Moreover, E705 was found to form a group of H-bond interactions with D554/K588/K645 below the hydrophobic gate to stabilize the closed conformation of the pore in the Ca 2+ -free state. Interestingly, in the Ca 2+ bound state, the E705 side chain swings 100 o to serve as Ca 2+ -binding coordination and released K645. K645 is closer to the hydrophobic gate in the calcium-bound state, which facilitates the provision of electrostatic forces for chloride ions as the ions pass through the hydrophobic gate. Our findings provide the structural-based insights to understanding the mechanisms of gating of TMEM16A., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Epileptogenesis is the gradual dynamic process that progressively led to epilepsy, going through the latent stage to the chronic stage. During epileptogenesis, how the abnormal discharges make theta rhythm loss in the deep brain remains not clear. In this paper, a loss of theta rhythm was estimated based on time-frequency power using the longitudinal electroencephalography (EEG), recorded by deep brain electrodes (e.g., the intracortical microelectrodes such as stereo-EEG electrodes) with monitored epileptic spikes in a rat from the first region in the hippocampal circuit. Deep-brain EEG was collected from the period between adjacent sporadic interictal spikes (lasting 3.56 s-35.38 s) to the recovery period without spikes by videos while the rats were performing exploration. We found that loss of theta rhythm became more serious during the period between adjacent interictal spikes than during the recovery period without spike, and during epileptogenesis, more loss was observed at the acute stage than the chronic stage. We concluded that the emergence of the interictal spike was the direct cause of loss of theta rhythm, and the inhibitory effect of the interictal spike on ongoing theta rhythm was persistent as well as time dependent during epileptogenesis. With the help of the intracortical microelectrodes, this study provides a temporary proof of interictal spikes to produce ongoing theta rhythm loss, suggesting that the interictal spikes could correlate with the epileptogenesis process, display a time-dependent feature, and might be a potential biomarker to evaluate the deficits in theta-related memory in the brain.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the elderly. The aim of this study was to explore the effects of AD on cardiac function and autonomic nervous function, and the feasibility of electrocardiogram (ECG) in monitoring the development of AD. APP/PS1 double transgenic mice were used in the Morris water maze (MWM) experiment to evaluate the changes of cognitive ability of AD mice, then the non-invasive ECG acquisition system was used and the changes of ECG intervals and heart rate variability (HRV) were analyzed. AD mice already had cognitive dysfunction at the age of 5 months, reaching the level of mild dementia, and the degree of dementia increased with the course of disease. There were no significant changes in ECG intervals in the AD group at each month. The mean square of successive RR interval differences, percentage of intervals >6 ms different from preceding interval, and normalized high frequency power component in the AD group were decreased and low-to-high frequency power ratio and normalized low frequency power component were increased. Combined with the results of the MWM, it was shown that the regulation mechanism of sympathetic and parasympathetic nerves in mice was already imbalanced in early stage AD, which was manifested as the increase of excessive activity of sympathetic nerves and the inhibition of parasympathetic activities. Therefore, ECG-based analysis of HRV may become a means of daily monitoring of AD and provide an auxiliary basis for clinical diagnosis.

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

The current lead insulation of high-temperature superconductivity equipment is under the combined action of large temperature gradient field and strong electric field. Compared with a uniform temperature field, its electric field distortion is more serious, and it is easy to induce surface discharge to generate high-energy particles, destroy the insulation surface structure and accelerate insulation degradation. In this paper, the degradation reaction process of bisphenol F epoxy resin under the impact of high-energy particles, such as O 3 - , HO - , H 3 O + and NO + , is calculated based on ReaxFF simulation. According to the different types of high-energy particles under different voltage polarities, the micro-degradation mechanism, pyrolysis degree and pyrolysis products of epoxy resin are analyzed. The results show that in addition to the chemical reaction of high-energy particles with epoxy resin, their kinetic energy will also destroy the molecular structure of the material, causing the cross-linked epoxy resin to pyrolyze, and the impact of positive particles has a more obvious impact on the pyrolysis of epoxy resin.

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus ( p  < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this study., (Copyright © 2021 Xiaoxuan Fu et al.)