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In this note, simultaneous-arrival-to-origin (SATO) convergence is defined --- all the elements of the system state arriving at the origin at the same time. Accordingly, a relevant sufficient condition is proposed for SATO convergence. Based on this formulation of SATO convergence, the classical sign function and the norm-normalized sign function are revisited. We investigate their differences with applications in sliding-mode control design, paying special attention to their convergence performance. It is found that both functions (expectedly when properly invoked) contribute to system stability, while the norm-normalized sign function additionally enables the system to achieve SATO convergence. This finding shows the distinctive merit of the norm-normalized sign function in achieving more than finite-time stability for a sliding-mode control system. Extensions to the scenario with a networked system are studied, where with the utilization of the norm-normalized sign function, the networked system (now with the SATO convergence property) drives all the agents to reach consensus simultaneously. Additionally, for double integrator systems and Euler-Lagrange systems, singularity-fr

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), where at the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also a norm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

Light sheet microscopy combined with a microchip is an emerging tool in biomedical research that notably improves efficiency. However, microchip-enhanced light-sheet microscopy is limited by noticeable aberrations induced by the complex refractive indices in the chip. Herein, we report a droplet microchip that is specifically engineered to be capable of large-scale culture of 3D spheroids (over 600 samples per chip) and has a polymer index matched to water (difference

Long-term and high-speed 3D imaging of live cells beyond diffraction limit remains a big challenge for current super-resolution microscopy implementations, owing to their severe phototoxicity and limited volumetric imaging rate. While emerging light-field microscopy (LFM) has mitigated this issue through rapid and mild 3D imaging of dynamic biological processes with single 2D snapshots, it suffers from a suboptimal spatial resolution close to diffraction limit, which greatly compromises its applications for live-cell imaging. Here, we propose a super-resolution light-field reconstruction strategy based on an optics-aware view-channel-depth (VCD 2.0) deep-learning strategy. Through a stepwise inversion of the physical degradation process during light-filed imaging, VCD 2.0 strongly pushes the light-field microscopy beyond diffraction limit, and achieves instant 3D reconstructions from raw 2D light-field snapshots with an ∼14-fold resolution improvement. After being combined with a simple-and-robust light-field add-on design, VCD 2.0 further enables long-term 3D super-resolution imaging (over 30000 volumes) of various intracellular dynamics (FOV of ∼220×220×10 μm3) even on an ordinary 2D fluorescence microscope. Using this novel and readily-accessible approach, we successfully capture the fast morphology changes of diverse organelles, such as the extension and constriction in lysosome, at a near isotropic resolution of ∼120 nm, and a high volume rate up to 50 Hz.

Background: The chronic hyperglycemia of diabetes mellitus (DM) leads to the dysfunction of microglia, which is associated with the occurrence of diabetic cerebrovascular injuries (DCIs) and various complications, the main reasons of death in patients with DM. Although long term insulin therapy can effectively control blood glucose levels, it is not sufficient to rapidly restore the function of microglia. Therefore, the search for new alternative methods of treating diabetes-related dysfunction of microglia is urgently needed to improve the survival and quality of life of patients with DM. Methods: We evaluated the therapeutic effects of transcranial photobiomodulation (tPBM) at near infrared-Ⅱ wavelengths (1267 nm) with high tissue penetration depth on microglial function in diabetic mice. Afterwards, we investigated the mechanisms of tPBM in terms of changes in blood-brain barrier permeability and meningeal lymphatic vessels function. Results: The treatment of tPBM significantly improved insulin therapy via modulation of microglial morphology and reactivity to cerebrovascular injury. We also demonstrated that tPBM stimulated the brain drainage system through activation of the meningeal lymphatics contributing the removal of inflammatory factor, such as the interferon gamma (IFN-γ) from the brain parenchyma. These therapeutic effects of tPBM contribute the repair of microglial reactivity to the cerebrovascular injury via an increase in the expression of microglial chemokine receptor P2RY12. Conclusions: Our results demonstrate that tPBM can be an efficient innovative method for the treatment of microglial dysfunction caused by diabetes to prevent diabetic brain disorders and has the potential to be quickly introduced in clinical practice since tPBM is widely used for the therapy of brain diseases.

This paper proposes a new design of an event-based dissipative asynchronous controller for the interval type2 (IT2) fuzzy Markov jump systems (MJSs) subject to sensor saturation and actuator nonlinearity. By resorting to a generalized performance index, the $H_{\infty}$, passive, and dissipative fuzzy control problems are solved in a unified framework. The event-based scheme is developed for the IT2 fuzzy MJSs subject to sensor saturation and actuator nonlinearity, and the energy consumption of communication can be reduced. Moreover, the system and controller modes are asynchronous, and a hidden Markov model (HMM) is employed to observe the modes of the original system. The Membership-Function-Dependent (MFD) approach is applied to analyze the stability of the closed-loop system. Finally, two examples are given to demonstrate the effectiveness of the proposed algorithms.

Aldehyde oxidases (AOXs) are a group of metabolic enzymes that play critical roles in the degradation of xenobiotics and chemicals. However, the physiological function of this enzyme in insects remains poorly understood. In this study, three TcAOX genes (TcAOX1, TcAOX2, TcAOX3) were identified and characterized from Tribolium castaneum genome. Spatiotemporal expression profiling showed that TcAOX1 expression was most highly expressed at the early pupal stage and was predominantly expressed in the antennae of adults, indicating that TcAOX1 was involved in the degradation of chemical signals; TcAOX2 expression was most highly expressed at the late pupal stage and was mainly expressed in the fat body, epidermis of larvae and adults, respectively; and TcAOX3 expression was in all stages and was primarily expressed in the head of adults. Moreover, the transcripts of TcAOX2 and TcAOX3 were significantly induced after exposure to plant oil, and RNA interference (RNAi) targeting of each of them enhanced the susceptibility of beetles to this plant toxicant, suggesting that these two genes are associated with plant toxicant detoxification. Intriguingly, knockdown of the TcAOX1 led to reductions in female egg-laying but unchanged the hatchability and the development of genital organs, suggesting that this gene may mediate fecundity by effecting the inactivation of chemical signals in T. castaneum. Overall, these results shed new light on the function of AOX genes in insects, and could facilitate the development of research on pest control management.

Severe cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with elevated blood glucose levels and metabolic complications. However, the molecular mechanisms for how SARS-CoV-2 infection alters glycometabolic control are incompletely understood. Here, we connect the circulating protein GP73 with enhanced hepatic gluconeogenesis during SARS-CoV-2 infection. We first demonstrate that GP73 secretion is induced in multiple tissues upon fasting and that GP73 stimulates hepatic gluconeogenesis through the cAMP/PKA signaling pathway. We further show that GP73 secretion is increased in cultured cells infected with SARS-CoV-2, after overexpression of SARS-CoV-2 nucleocapsid and spike proteins and in lungs and livers of mice infected with a mouse-adapted SARS-CoV-2 strain. GP73 blockade with an antibody inhibits excessive glucogenesis stimulated by SARS-CoV-2 in vitro and lowers elevated fasting blood glucose levels in infected mice. In patients with COVID-19, plasma GP73 levels are elevated and positively correlate with blood glucose levels. Our data suggest that GP73 is a glucogenic hormone that likely contributes to SARS-CoV-2-induced abnormalities in systemic glucose metabolism.

In this article, we investigate parameter estimation, kink points testing and statistical inference for a longitudinal multikink expectile regression model. The estimators for the kink locations and regression coefficients are obtained by using a bootstrap restarting iterative algorithm to avoid local minima. A backward selection procedure based on a modified BIC is applied to estimate the number of kink points. We theoretically demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators. In particular, the estimators of kink locations are shown to achieve root-n consistency. A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile, and its limiting distributions are derived under both the null and the local alternative hypotheses. The traditional Wald-type and cluster bootstrap confidence intervals for kink locations are also constructed. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors. Two applications to the Nation Growth, Lung and Health Study and Capital Bike sharing dataset in Washington D.C. are also presented. The R codes for simulation studies and the real data are available at https://github.com/wangleink/MKER.

This study was to analyze the correlation between abnormal thyroid function detection and thyroid autoimmune disease, so as to provide theoretical guidance for clinical diagnosis of thyroid autoimmune disease.The Chinese databases were searched with a combination of words "thyroid", "thyroid autoimmune disease", "thyroid function testing", "meta-analysis". The entire database was searched in English language databases with the search terms "thyroid", "thyroid autoimmune disease", "thyroid function test ", "meta-analysis". Review manager software was applied for meta-analysis.A total of 8 articles were finally included. 4 articles reported the T3 level, showing P=0.95, I2=0%, odds ratio (OR) =2.39, 95% confidence interval (CI): 0.79-7.25; 3 articles reported the T4 levels, showing P=0.81, I2=0%, OR =2.16, 95% CI: 0.43-10.71; and 4 articles reported the thyroid stimulating hormone (TSH) level, showing P=0.48, I2=0%, OR =3.20, 95% CI: 1.45-7.07.After a systematic review of the literature, a significant difference was found in T3 and TSH levels between patients with autoimmune thyroid disease and those with a healthy thyroid, but the difference in T4 level was not significant.

In the paper, data-driven finite-horizon robust formation-containment control scheme is developed based on integral reinforcement learning and zero-sum game for perturbed multi-agent networks with completely unknown nonlinear dynamics. At first, distributed finite-time sliding mode estimators are designed to obtain the desired states of leaders and followers, respectively. Then finite-horizon robust leader formation control and follower containment control are achieved based on proposed model-free integral reinforcement learning algorithms implemented by critic-actor-disturbance structure, in the framework of multi-player zero-sum game where the non-quadratic performance index for each agent considers the influence of saturated inputs and disturbances of local neighbors thoroughly. Furthermore, it is proved that the whole network has bounded L 2 gain robust stability and Nash equilibrium of zero-sum game exists. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Mammalian reovirus (reovirus) is a multilayered, turreted member of Reoviridae characterized by transcription of dsRNA genome within the innermost capsid shell. Here, we present high-resolution in situ structures of reovirus transcriptase complex in an intact double-layered virion, and in the uncoated single-layered core particles in the unloaded, reloaded, pre-elongation, and elongation states, respectively, obtained by cryo-electron microscopy and sub-particle reconstructions. At the template entry of RNA-dependent RNA polymerase (RdRp), the RNA-loading region gets flexible after uncoating resulting in the unloading of terminal genomic RNA and inactivity of transcription. However, upon adding transcriptional substrates, the RNA-loading region is recovered leading the RNAs loaded again. The priming loop in RdRp was found to play a critical role in regulating transcription, which hinders the elongation of transcript in virion and triggers the rearrangement of RdRp C-terminal domain (CTD) during elongation, resulting in splitting of template-transcript hybrid and opening of transcript exit. With the integration of these structures, a transcriptional model of reovirus with five states is proposed. Our structures illuminate the RdRp activation and regulation of the multilayered turreted reovirus.

A lightweight composite with outstanding damping effects and impact resistance is significant for the design of functional structures in advanced equipment, such as aircraft and space vehicles. In this paper, the mechanical properties of solution-blended graphene nanoplatelets (GNPs)/polyether-ether-ketone (PEEK) nanocomposites were characterized by both experimental and numerical methods. It can be found that the layer number and packing configuration of graphene layers are critical to the efficiency of energy dissipation in the composite, while a pack of six- layer graphene and the perpendicular arrangement to the shockwave direction provide the most outstanding energy dissipation ability. The reflection of shockwave caused by graphene reinforcements in the nanocomposite was found to be the dominating reason for the enhanced energy dissipation effect. Physical mechanisms of energy dissipation are investigated by a molecular modeling method to provide insights into the cross-scale design of graphene-reinforced nanocomposites as structural materials.

This paper designs an event-triggered $${H_\infty }$$ filter for interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy nonlinear networked systems with parameter uncertainties and delays. First, an IT2 T–S fuzzy model is used to describe the nonlinear plant and the filter subject to parameter uncertainties, which can be captured with the lower and upper membership functions. Then, an event-triggered scheme and a quantizer are proposed to reduce the unnecessary waste of the network resources. Second, the asynchronous premise variables problem is considered in the design of the fuzzy filter, which means that the premise variable of the designed filter needs not to be identical with the plant. Third, by constructing a Lyapunov–Krasovskii function with quadruple integral items, and employing the Wirtinger inequality and some slack matrices, a stability condition with less conservative is obtained. Finally, a numerical example demonstrates the effectiveness of the proposed method.

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.

Color-tunable upconversion luminescence has wide prospects for anti-counterfeiting and disease diagnosis/treatment. To date, achieving high-quality tunable red and blue emissions using a single excitation wavelength remains a formidable challenge, due to the large energy difference between the red and blue photons. In this Letter, based on Tm

Modern optical imaging techniques provide powerful tools for observing cortical structure and functions at high resolutions. Various skull windows have been established for different applications of cortical imaging, and each has its advantages and limitations. Most critical of the limitations, none of the current skull windows is suitable for observing the responses to some acute craniocerebral injuries on a large scale and at high resolution. Here, we developed a “Through-Intact-Skull (TIS) window” that enables the observation of an immune response on a bilateral cortical scale and at single-cell resolution after traumatic brain injury without affecting the pathological environment of the brain. The TIS window also has the advantages of craniotomy-freeness, centimeter-field of view, synaptic resolution, large imaging depth, long-term observation capability, and suitability for awake mice. Therefore, the TIS window is a promising new approach for intravital cortical microscopy in basic research in neuroscience.

The kth ( [Image: see text] ) power expectile regression (ER) can balance robustness and effectiveness between the ordinary quantile regression and ER simultaneously. Motivated by a longitudinal ACTG 193A data with nonignorable dropouts, we propose a two-stage estimation procedure and statistical inference methods based on the kth power ER and empirical likelihood to accommodate both the within-subject correlations and nonignorable dropouts. Firstly, we construct the bias-corrected generalized estimating equations by combining the kth power ER and inverse probability weighting approaches. Subsequently, the generalized method of moments is utilized to estimate the parameters in the nonignorable dropout propensity based on sufficient instrumental estimating equations. Secondly, in order to incorporate the within-subject correlations under an informative working correlation structure, we borrow the idea of quadratic inference function to obtain the improved empirical likelihood procedures. The asymptotic properties of the corresponding estimators and their confidence regions are derived. The finite-sample performance of the proposed estimators is studied through simulation and an application to the ACTG 193A data is also presented.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

Cryo-electron tomography (cryo-ET) is widely used to explore the 3D density of biomacromolecules. However, the heavy noise and missing wedge effect prevent directly visualizing and analyzing the 3D reconstructions. Here, we introduced REST, a deep learning strategy-based method to establish the relationship between low-quality and high-quality density and transfer this knowledge to restore signals in cryo-ET. Experimental results on purified ribosome and recombinant nucleosome datasets showed that REST had outstanding performance in denoising and compensating the missing wedge. The application in dynamic nucleosome structures suggests that REST has the capability to reveal individual macromolecules which present different conformations without subtomogram averaging. Moreover, REST could greatly improve the reliability of particle picking. These advantages enable REST to be a powerful tool for the straightforward interpretation of target macromolecules by visual inspection of the density and of a broad range of other applications in cryo-ET, such as segmentation, particle picking, and subtomogram averaging.

Visualization of intact vasculatures is crucial to understanding the pathogeneses of different neurological and vascular diseases. Although various fluorescent vessel labeling methods have been used in combination with tissue clearing for three-dimensional (3D) visualization of different vascular networks, little has been done to quantify the labeling effect of each vessel labeling routine, as well as their applicability alongside various clearing protocols, making it difficult to select an optimal combination for finely constructing different vasculatures. Therefore, it is necessary to systematically assess the overall performance of these common vessel labeling methods combined with different tissue-clearing protocols.A comprehensive evaluation of the labeling quality of various vessel labeling routines in different organs, as well as their applicability alongside various clearing protocols, were performed to find the optimal combinations for 3D reconstruction of vascular networks with high quality.Four commonly-used vessel labeling techniques and six typical tissue optical clearing approaches were selected as candidates for the systematic evaluation.The vessel labeling efficiency, vessel labeling patterns, and compatibility of each vessel labeling method with different tissue-clearing protocols were quantitatively evaluated and compared. Based on the comprehensive evaluation results, the optimal combinations were selected for 3D reconstructions of vascular networks in several organs, including mouse brain, liver, and kidney.This study provides valuable insight on selecting the proper pipelines for 3D visualization of vascular networks, which may facilitate understanding of the underlying mechanisms of various neurovascular diseases.

Non-alcoholic fatty liver disease (NAFLD) is not considered only a liver disease but also associated with an increased risk of extra-hepatic diseases including bone metabolism disorders. In our study, we aim to explore the changes of several bone turnover markers (BTMs) under different fat deposition and stiffness levels of the liver.We analyzed the physical examination data of 3353 subjects from February 2018 to June 2021 in this study. The steatosis and stiffness of liver were quantitatively detected using the fat attenuation parameter (FAP) and liver stiffness measurements (LSM) of transient elastography (TE). Serum 25-hydroxyvitamin D3 (25(OH)D3), osteocalcin, carboxy-terminal collagen crosslinks (CTX), amino terminal elongation peptide of total type 1 procollagen (P1NP) were tested. Clinical and other biochemical data were also collected.With the increasing of FAP, the levels of 25(OH)D3 and osteocalcin decreased, the difference was statistically significant. No correlation was found between LSM and all the four BTMs. Logistic regression analysis revealed that FAP ≥ 244 dB/m was negatively correlated with 25(OH)D3 (in both males and females) and osteocalcin (only in males). No correlation was found between FAP ≥ 244 dB/m and P1NP or CTX.The degree of liver adipose deposition was found to be negatively associated with the serum levels of 25(OH)D3 (in both males and females) and osteocalcin (only in males) in southwest China.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

Because of lacking of the systematic investigation of correlations between the physical examination indicators (PEIs), currently most of them are independently used for disease warning. This results in very limited diagnostic values of general physical examination. Here, we first systematically analyzed the correlations between 221 PEIs in healthy and in 34 unhealthy states in 803,614 peoples in China. Specifically, the study population included 711,928 healthy participants, 51,341 patients with hypertension, 12,878 patients with diabetes, and 34,997 with other unhealthy status. We revealed rich relevant between PEIs in healthy physical status (7,662 significant correlations, 31.5% of all). However, in disease conditions, the PEI correlations changed. We further focused on the difference of these PEIs between healthy and 35 unhealthy physical status, 1,239 significant PEI difference were discovered suggesting as candidate disease markers. Finally, we established machine learning algorithms to predict the health status by using 15%-16% PEIs by feature extraction, which reached 66%-99% precision predictions depending on the physical state. This new encyclopedia of PEI correlation provides rich information to chronic disease diagnosis. Our developed machine learning algorithms will have fundamental impact in practice of general physical examination.

This brief considers the event-based finite-time consensus problems for second-order multi-agent systems (MASs) with input delay. By backstepping design method, the controllers are developed to ensure true velocity track virtual velocity within a fixed time. Once the true velocity tracks on the virtual velocity, all agents can reach the agreement within a finite time. Moreover, an improved event-based control scheme is proposed by discontinuous communication. Besides, no Zeno behavior occurs. The efficiency of the proposed algorithms is illustrated by a detailed numerical example.