Partial inhibition of class III PI3K VPS-34 ameliorates motor aging and prolongs health span.

Global increase of life expectancy is rarely accompanied by increased health span, calling for a greater understanding of age-associated behavioral decline. Motor independence is strongly associated with the quality of life of elderly people, yet the regulators for motor aging have not been systematically explored. Here, we designed a fast and efficient genome-wide screening assay in Caenorhabditis elegans and identified 34 consistent genes as potential regulators of motor aging. Among the top hits, we found VPS-34, the class III phosphatidylinositol 3-kinase that phosphorylates phosphatidylinositol (PI) to phosphatidylinositol 3-phosphate (PI(3)P), regulates motor function in aged but not young worms. It primarily functions in aged motor neurons by inhibiting PI(3)P-PI-PI(4)P conversion to reduce neurotransmission at the neuromuscular junction (NMJ). Genetic and pharmacological inhibition of VPS-34 improve neurotransmission and muscle integrity, ameliorating motor aging in both worms and mice. Thus, our genome-wide screening revealed an evolutionarily conserved, actionable target to delay motor aging and prolong health span.

Tunable all-optical microwave logic gates based on nonreciprocal topologically protected edge modes.

All-optical logic gates have been studied intensively owing to their potential to enable broadband, low-loss and high-speed communications. However, poor tunability has remained a key challenge in this field. In this work, we propose a Y-shaped structure composed of Yttrium Iron Garnet (YIG) layers that can serve as tunable all-optical logic gates, including, but not limited to, OR, AND and NOT gates, by applying external magnetic fields to magnetize the YIG layers. Our findings reveal that these logic gates are founded on protected one-way edge modes, where by tuning the wavenumber k of the operating mode to a sufficiently small (or even zero) value, the gates can become nearly immune to nonlocal effects. This not only enhances their reliability but also allows for maintaining extremely high precision in their operations. Furthermore, the operating band itself of the logic gates is also shown to be tunable. We introduce a straightforward and practical method for controlling and switching these gates between "work", "skip", and "stop" modes. These findings have potentially significant implications for the design of high-performance and robust all-optical microwave communication systems.

Realization of tunable index-near-zero modes in nonreciprocal magneto-optical heterostructures.

Epsilon-near-zero (ENZ) metamaterial with the relative permittivity approaching zero has been a hot research topic for decades. The wave in the ENZ region has infinite phase velocity (v=1/ε µ), but it cannot efficiently travel into the other devices or air due to the impedance mismatch or near-zero group velocity. In this paper, we demonstrate that the tunable index-near-zero (INZ) modes with vanishing wavenumbers (k = 0) and nonzero group velocities (vg ≠ ~0) can be achieved in nonreciprocal magneto-optical systems. The INZ modes have been experimentally demonstrated in the photonic crystals at Dirac point frequencies, and that impedance-matching effect has been observed as well [Nat. Commun.8, 14871 (2017)10.1038/ncomms14871]. Our theoretical analysis reveals that the INZ modes exhibit tunability when changing the parameters of the one-way (nonreciprocal) waveguides. Moreover, owing to the zero-phase-shift characteristic and decreasing vg of the INZ modes, several perfect optical buffers are proposed in the microwave and terahertz regimes. The theoretical results are further verified by the numerical simulations using the finite element method. Our findings may open new avenues for research in the areas of ultra-strong or -fast nonlinearity, perfect cloaking, high-resolution holographic imaging, and wireless communications.

Realization of broadband truly rainbow trapping in gradient-index metamaterials.

Unidirectionally propagating wave (UPW) such as surface magnetoplasmon (SMP) has been a research hotspot in the last decades. In the study of the UPW, metals are usually treated as perfect electric conductors (PECs). However, it was reported that the transverse resonance condition induced by the PEC wall(s) may significantly narrow up the complete one-way propagation (COWP) band. In this paper, ultra-broadband one-way waveguides are built by utilizing the epsilon-negative (ENG) metamaterial (MM) and/or the perfect magnetic conductor (PMC) boundary. In both cases, the total bandwidth of the COWP bands are efficiently enlarged by more than three times than the one in the original metal-dielectric-semiconductor-metal structure. Moreover, the one-way waveguides consisting of gradient-index metamaterial are proposed to achieve broadband truly rainbow trapping (TRT). In the full-wave simulations, clear broadband TRT without back reflection is observed in terahertz regime. Besides, giant electric field enhancement is achieved in a PMC-based one-way structure, and the amplitude of the electric field is enormously enhanced by five orders of magnitude. Our findings are beneficial for researches on broadband terahertz communication, energy harvesting and strong-field devices.

Trend in the incidence of hepatitis A in mainland China from 2004 to 2017: a joinpoint regression analysis.

BACKGROUND: China has experienced a continuous decreasing trend in the incidence of hepatitis A in recent years. Temporal trend analyses are helpful in exploring the reasons for the changing trend. Thus, this study aims to analyse the incidence trend of viral hepatitis A by region and age group in mainland China from 2004 to 2017 to evaluate the effectiveness of prevention and control measures. METHODS: Data on hepatitis A and population information were collected and analysed with a joinpoint regression model. Annual percentage changes (APCs) and average annual percentage changes (AAPCs) were estimated for the whole country and for each region and age group. RESULTS: From 2004 to 2017, the seasonality and periodicity of hepatitis A case numbers were obvious before 2008 but gradually diminished from 2008 to 2011 and disappeared from 2012-2017. The national incidence of hepatitis A (AAPC = - 12.1%) and the incidence rates for regions and age groups showed decreasing trends, with differences in the joinpoints and segments. Regarding regions, the hepatitis A incidence in the western region was always the highest among all regions, while a nonsignificant rebound was observed in the northeastern region from 2011 to 2017 (APC = 14.2%). Regarding age groups, the hepatitis A incidence showed the fastest decrease among children (AAPC = - 15.3%) and the slowest decrease among elderly individuals (AAPC = - 6.6%). Among all segments, the hepatitis A incidence among children had the largest APC value in 2007-2017, at - 20.4%. CONCLUSION: The national annual incidence of hepatitis A continually declined from 2004 to 2017 and the gaps in hepatitis A incidence rates across different regions and age groups were greatly narrowed. Comprehensive hepatitis A prevention and control strategies, including the use of routine vaccination during childhood in mainland China, especially the implementation of the national Expanded Program on Immunization (EPI) in 2008, resulted in substantial progress from 2004 to 2017. However, gaps remain. Regular monitoring and analysis of hepatitis A epidemic data and prompt adjustment of hepatitis A prevention and control strategies focusing on children, elderly individuals and those living in certain regions are recommended.

miR-210 regulates the inflammation of otitis media with effusion by inhibiting the expression of hypoxia-inducible factor (HIF)-1a.

Otitis media with effusion (OME) is the major cause of hearing impairment in children. miR-210 plays a critical role in inflammatory diseases, however, its role in OME is unknown. In this study, the miR-210 level in serum and middle ear effusion of is significantly down-regulated in serum, middle ear effusion from OME patients (100 cases) compared with healthy volunteers (50 cases). The expression of miR-210 is closely related to inflammatory factors and bone conduction disorder in patients with OME. In the in vitro study，the miR-210 level is significantly reduced in culture supernatant of lipopolysaccharide (LPS) treated human middle ear epithelial cells (HMEECs). miR-210 overexpression inhibited the LPS-induced in inflammatory cytokines production, cell viability reduction and cell apoptosis. Bioinformatics and dual-luciferase reporter assay showed that HIF-1a was a target gene of miR-210. The biological effects of miR-210 on cell viability, cell apoptosis and inflammation cytokines in LPS-induced HMEECs were reversed by HIF-1a overexpression. Furthermore, phosphorylation of NF-κB p65 was significantly decreased by miR-210 mediated HIF-1a in LPS-induced HMEECs. This study suggested that miR-210 may play a role in OME. Further studies are warranted to assess miR-210 as a potential target for the diagnosis and treatment of OME.

A Jump-Gain Integral Recurrent Neural Network for Solving Noise-Disturbed Time-Variant Nonlinear Inequality Problems.

Nonlinear inequalities are widely used in science and engineering areas, attracting the attention of many researchers. In this article, a novel jump-gain integral recurrent (JGIR) neural network is proposed to solve noise-disturbed time-variant nonlinear inequality problems. To do so, an integral error function is first designed. Then, a neural dynamic method is adopted and the corresponding dynamic differential equation is obtained. Third, a jump gain is exploited and applied to the dynamic differential equation. Fourth, the derivatives of errors are substituted into the jump-gain dynamic differential equation, and the corresponding JGIR neural network is set up. Global convergence and robustness theorems are proposed and proved theoretically. Computer simulations verify that the proposed JGIR neural network can solve noise-disturbed time-variant nonlinear inequality problems effectively. Compared with some advanced methods, such as modified zeroing neural network (ZNN), noise-tolerant ZNN, and varying-parameter convergent-differential neural network, the proposed JGIR method has smaller computational errors, faster convergence speed, and no overshoot when disturbance exists. In addition, physical experiments on manipulator control have verified the effectiveness and superiority of the proposed JGIR neural network.

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis.

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance.

A Novel Swarm-Exploring Neurodynamic Network for Obtaining Global Optimal Solutions to Nonconvex Nonlinear Programming Problems.

A swarm-exploring neurodynamic network (SENN) based on a two-timescale model is proposed in this study for solving nonconvex nonlinear programming problems. First, by using a convergent-differential neural network (CDNN) as a local quadratic programming (QP) solver and combining it with a two-timescale model design method, a two-timescale convergent-differential (TTCD) model is exploited, and its stability is analyzed and described in detail. Second, swarm exploration neurodynamics are incorporated into the TTCD model to obtain an SENN with global search capabilities. Finally, the feasibility of the proposed SENN is demonstrated via simulation, and the superiority of the SENN is exhibited through a comparison with existing collaborative neurodynamics methods. The advantage of the SENN is that it only needs a single recurrent neural network (RNN) interact, while the compared collaborative neurodynamic approach (CNA) involves multiple RNN runs.

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms.

Predicting coronary heart disease in Chinese diabetics using machine learning.

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

Electric and magnetic polarization singularities of first-order Laguerre-Gaussian beams diffracted at a half-plane screen.

Based on the vector Fresnel diffraction integrals, analytical expressions for the electric and magnetic components of first-order Laguerre-Gaussian beams diffracted at a half-plane screen are derived and used to study the electric and magnetic polarization singularities in the diffraction field for both two- and three-dimensional (2D and 3D) cases. It is shown that there exist 2D and 3D electric and magnetic polarization singularities in the diffraction field, which do not coincide each other in general. By suitably varying the waist width ratio, off-axis displacement parameter, amplitude ratio, or propagation distance, the motion, pair-creation, and annihilation of circular polarization singularities, and the motion of linear polarization singularities take place in 2D and 3D electric and magnetic fields. The V point, at which two circular polarization singularities with the same topological charge but opposite handedness collide, appears in the 2D electric field under certain conditions in the diffraction field and free-space propagation. A comparison with the free-space propagation is also made.

A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming.

Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.

Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram.

Background: Electrocardiogram (ECG) provides a straightforward and non-invasive approach for various applications, such as disease classification, biometric identification, emotion recognition, and so on. In recent years, artificial intelligence (AI) shows excellent performance and plays an increasingly important role in electrocardiogram research as well. Objective: This study mainly adopts the literature on the applications of artificial intelligence in electrocardiogram research to focus on the development process through bibliometric and visual knowledge graph methods. Methods: The 2,229 publications collected from the Web of Science Core Collection (WoSCC) database until 2021 are employed as the research objects, and a comprehensive metrology and visualization analysis based on CiteSpace (version 6.1. R3) and VOSviewer (version 1.6.18) platform, which were conducted to explore the co-authorship, co-occurrence and co-citation of countries/regions, institutions, authors, journals, categories, references and keywords regarding artificial intelligence applied in electrocardiogram. Results: In the recent 4 years, both the annual publications and citations of artificial intelligence in electrocardiogram sharply increased. China published the most articles while Singapore had the highest ACP (average citations per article). The most productive institution and authors were Ngee Ann Polytech from Singapore and Acharya U. Rajendra from the University of Technology Sydney. The journal Computers in Biology and Medicine published the most influential publications, and the subject with the most published articles are distributed in Engineering Electrical Electronic. The evolution of research hotspots was analyzed by co-citation references' cluster knowledge visualization domain map. In addition, deep learning, attention mechanism, data augmentation, and so on were the focuses of recent research through the co-occurrence of keywords.

A gender specific risk assessment of coronary heart disease based on physical examination data.

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .

A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis.

Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments.

A New Finite-Time Circadian Rhythms Learning Network for Solving Nonlinear and Nonconvex Optimization Problems With Periodic Noises.

Nonlinear and nonconvex optimization problems are vital and fundamental problems in science and engineering fields. In this article, a novel finite-time circadian rhythms learning network (called FT-CRLN) is proposed for solving nonlinear and nonconvex optimization problems with periodic noises. Different from the traditional recurrent neural networks, the proposed FT-CRLN can suppress the periodic noise notably and achieve excellent convergence performance in solving nonlinear and nonconvex problems. The theoretical analysis and rigorous mathematical proof verify the superior convergence, high accuracy, and strong robustness of the proposed FT-CRLN. The simulation results demonstrate the effectiveness and robustness of the proposed FT-CRLN in solving nonlinear and nonconvex problems compared with other state-of-art neural networks.

A bagging dynamic deep learning network for diagnosing COVID-19.

COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.

Composite polarization singularities in superimposed Laguerre-Gaussian beams beyond the paraxial approximation.

The closed-form expression for the free-space propagation of superimposed Laguerre-Gaussian beams beyond the paraxial approximation is derived, and the composite polarization singularities formed by the transverse and longitudinal electric-field components are studied in detail. It is shown that there exist composite C-points and L-lines in vector nonparaxial fields. By suitably varying a control parameter, such as the off-axis distance, relative phase, or amplitude ratio, the motion, creation, and annihilation of composite C-points may appear, and in the process the sum of topological charge remains unchanged. The shift, deformation, combination, and disappearance of composite L-lines may take place. The topological relationship holds true. The results are compared with the previous work.

Far-field properties in two off-axis superimposed Laguerre-Gaussian beams beyond the paraxial approximation.

The analytical far-field expressions for the TE and TM terms and energy flux distributions in two off-axis superimposed nonparaxial Laguerre-Gaussian beams with azimuthal and radial indices l(1) = -l(2) = +1, p(1) = p(2) = 0 are derived and used to study the far-field properties including phase singularities and energy flux distributions of the resulting beam, where our main attention focuses on the dependence of phase singularities on the controlling parameters such as the off-axis distance, relative phase, amplitude ratio, and waist widths of superimposed beams, and the symmetry property of edge dislocations and energy flux distributions. The results are interpreted and compared with previous work.