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Significant attempts have been made to promote neuronal extension and migration in nerve development and regeneration. Although mechanical stretch induces persistent elongation of the axon, the underlying molecular mechanisms are not yet clear. Some axonal guidance cues secreted in the growth cone that affect the axonal growth could attract or repel axons in neurite connection. As semaphorin 3A (Sema3A) is an important repulsion guidance molecule, inhibition of Sema3A has been postulated to promote neuronal development. In this study, the effects of mechanical stretch on dorsal root ganglion neuronal growth and the underlying mechanisms were investigated by assessing the extension direction, neurite length, cell body size, mitochondrial membrane potential, and the expression of Sema3A and its receptors. Our results showed that cell viability significantly increased at tensile strains of 2.5, 5, and 10% for 4 h, with the most prominent effect at 5% tensile strain. Moreover, neurons migrated closer to the stretching direction at 5% tensile strain (0-12 h), while the neurons of the control group moved in a disorderly manner. Furthermore, Sema3A-Neuropilin-1/Plexin-A1 signaling pathway was found to be suppressed after mechanical stretch at 5% tensile strain for 4 h by immunofluorescence staining, immunoprecipitation, and western blot assay. Finally, a Sema3A-SiRNA (SiRNA = small interfering RNA) treatment led to remarkable guidance growth in the stretch-grown neurons. Importantly, there was significant decrease of repulsive cue Sema3A expression and remarkable increase of attractive molecule Netrin-1 expression after mechanical stretching treatment, which jointly promoted neurite outgrowth. This study provides a promising new approach for the development of mechanical stretching therapy or guidance factor-related drugs in injured neuronal regeneration.

The survival and prognosis of hepatocellular carcinoma (HCC) are poor, mainly due to metastasis. Therefore, insights into the molecular mechanisms underlying HCC invasion and metastasis are urgently needed to develop a more effective antimetastatic therapy. Here, we report that KIAA1217, a functionally unknown macromolecular protein, plays a crucial role in HCC metastasis. KIAA1217 expression was frequently upregulated in HCC cell lines and tissues, and high KIAA1217 expression was closely associated with shorter survival of patients with HCC. Overexpression and knockdown experiments revealed that KIAA1217 significantly promoted cell migration and invasion by inducing epithelial-mesenchymal transition (EMT) in vitro. Consistently, HCC cells overexpressing KIAA1217 exhibited markedly enhanced lung metastasis in vivo. Mechanistically, KIAA1217 enhanced EMT and accordingly promoted HCC metastasis by interacting with and activating JAK1/2 and STAT3. Interestingly, KIAA1217-activated p-STAT3 was retained in the cytoplasm instead of translocating into the nucleus, where p-STAT3 subsequently activated the Notch and Wnt/β-catenin pathways to facilitate EMT induction and HCC metastasis. Collectively, KIAA1217 may function as an adaptor protein or scaffold protein in the cytoplasm and coordinate multiple pathways to promote EMT-induced HCC metastasis, indicating its potential as a therapeutic target for curbing HCC metastasis.

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed event-triggering mechanism (ETM) is designed such that each agent broadcasts its states only when an event occurs. It is shown that with the proposed ETM, the leader-following consensus is achieved with aperiodic intermittent communication and Zeno behavior is excluded by contradiction. Moreover, the consensus tracking errors converge to small sets around the origin. Finally, an example is provided to illustrate the effectiveness of the obtained theoretical results.

This article addresses the finite-time adaptive fuzzy control problem for a class of nonstrict-feedback uncertain nonlinear systems via an event-triggered strategy. A novel design scheme, consisting of finite-time adaptive fuzzy controller and event-triggering mechanism (ETM), is proposed to decrease the number of data transmission and the number of control actuation updates. With the proposed event-triggered adaptive fuzzy control scheme, all the solutions of the resulting closed-loop system are guaranteed to be semi-globally bounded within finite time. Moreover, the feasibility of the proposed ETM is verified by excluding Zeno behavior. In contrast to existing results on similar problems, the restrictions on nonlinearities are relaxed and the more general uncertain nonlinear systems are considered. Finally, an example is provided to illustrate our theoretical results.

Graphene, as a highly conducting material, incorporated into silk fibroin (SF) substrates is promising to fabricate an electroactive flexible scaffold toward neural tissue engineering. It is well known that aligned morphology could promote cell adhesion and directional growth. The purpose of this study was to develop aligned conductive scaffolds made of graphene and SF (G/SF) by electrospinning technique for neural tissue engineering applications. The physicochemical characterization of scaffolds revealed that the mechanical and electrochemical property of aligned G/SF scaffolds continually raised with the increasing contents of graphene (A0% G/SF, A1% G/SF, A2% G/SF, and A3% G/SF), but the mechanical property descended when the graphene concentration reached to 4% (the A4% G/SF group). The results of the cell experiment in vitro indicated that all the aligned G/SF scaffolds were no neurotoxic to primary cultured spinal cord neurons. In addition, the neurite elongation in all aligned groups was significantly enhanced by the upregulation of Netrin-1 expression compared to them in the control group. Thus, A3% G/SF scaffolds not only possessed the optimal property based on the mechanical and electrochemical performances but also displayed the beneficial capability to neurite outgrowth, which might perform a suitable candidate to successfully scaffold electrically active tissues during neural regeneration or engineering.

Precise measurement of two-neutrino double beta decay (DBD) half-life is an important step for the searches of Majorana neutrinos with neutrinoless double beta decay. We report the measurement of DBD half-life of 136 Xe using the PandaX-4T dual-phase Time Projection Chamber (TPC) with 3.7-tonne natural xenon and the first 94.9-day physics data release. The background model in the fiducial volume is well constrained in situ by events in the outer active region. With a 136 Xe exposure of 15.5 kg-year, we establish the half-life as 2.27 ± 0.03 s t a t . ± 0.10 s y s t . × 1 0 21 years. This is the first DBD half-life measurement with natural xenon and demonstrates the physics capability of a large-scale liquid xenon TPC in the field of rare event searches.

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne$\cdot$year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of $3.8\times10^{-47} $cm$^2$ at a dark matter mass of 30 GeV/$c^2$., Comment: proof draft as PRL

This paper is concerned with the resilient speed control of an autonomous surface vehicle (ASV) in the presence of actuator anomalies. A data-driven model-free resilient speed control method is presented based on available input and output data only with pulse-width-modulation inputs. Specifically, a data-driven neural predictor is designed to learn the unknown system dynamics of the speed control system of the ASV. Then, a resilient speed control law is designed based on the learned dynamics obtained from the neural network predictor, where a cost function is designed for selecting the optimal duty cycle for the motor. The stability of the data-driven neural predictor is analyzed by using input-state stability (ISS) theory. The advantage of the developed data-driven model-free resilient control method is that the optimal speed control performance can be achieved in the presence of actuator anomalies without any modeling process. Simulation results show the learning ability of the data-driven neural predictor and the effectiveness of the proposed data-driven model-free resilient speed control method for the ASV subject to actuator anomalies.

Self-interacting Dark Matter (SIDM) is a leading candidate proposed to solve discrepancies between predictions of the prevailing cold dark matter theory and observations of galaxies. Many SIDM models predict the existence of a light force carrier that mediate strong dark matter self-interactions. If the mediator couples to the standard model particles, it could produce characteristic signals in dark matter direct detection experiments. We report searches for SIDM models with a light mediator using the full dataset of the PandaX-II experiment, based on a total exposure of 132 tonne-days. No significant excess over background is found, and our likelihood analysis leads to a strong upper limit on the dark matter-nucleon coupling strength. We further combine the PandaX-II constraints and those from observations of the light element abundances in the early universe, and show that direct detection and cosmological probes can provide complementary constraints on dark matter models with a light mediator., Comment: 5 pages, 5 figures

In this paper, we aim to solve the full state constrained control problem for nonstrict-feedback nonlinear systems subject to completely unknown nonlinearities, unknown control gains and external disturbances. The full state constraints are assumed to be asymmetric and time-varying. Fuzzy logic systems (FLSs) are utilized to approximate the completely unknown nonlinearities. A shifting function is first introduced to remove the feasibility condition on the virtual controllers while using the traditional barrier Lyapunov functions (BLFs). Then, a dynamic surface control (DSC) based adaptive fuzzy controller is developed to prevent the state constraints from being violated. The main feature of this paper lies in that a unified structure for both symmetric and asymmetric full state constraints is constructed without feasibility condition, and the Lyapunov stability of the closed-loop system is proven. Simulation results are given to demonstrate the effectiveness of the proposed control structure.

Photosensitization is a promising way to make UiO-66 visible light responsive. In this work, zirconium (Zr) precursors and synthetic methods (solvent-assisted or nonsolvent-assisted) were found to ...

This paper considers a robust adaptive fuzzy control problem for a class of uncertain nonlinear systems via an event-triggered control strategy. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. A novel robust adaptive control scheme together with a novel event-triggering mechanism (ETM) is proposed to reduce communication burden. It should be noted that both the control signal and the adaptive parameters are updated only at the triggering time instants in the proposed scheme, which further saves the system energy and resources. It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin. Moreover, with the proposed ETM, Zeno behavior can be strictly excluded. Finally, a one-link manipulator system is used to demonstrate the effectiveness of the proposed control scheme.

We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of $\rm 1.6 \times 10^{-41} cm^2$ and $\rm 9.0 \times 10^{-42} cm^2$ ($90\%$ c.l.) for neutron-only SD and tensor coupling, respectively, for a mass $M_\mathrm{WIMP} \sim {\rm 40~GeV}/c^2$. The SD limits are the best currently available for $M_\mathrm{WIMP} > {\rm 40~GeV}/c^2$. We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale., 6 pages, 4 figures

An extremely sensitive enzyme sensor for detection of 17β-estradiol based on electropolymerized L -lysine molecules on a glassy carbon electrode (GCE) modified with critic acid@graphene (CA-GR) and cross-linked with laccase enzyme has been developed in this work. As the laccase immobilization, glutaraldehyde was chosen as cross-linker through the groups reactions. The novel enzyme sensor could recognize and determinate 17β-estradiol effectively. The morphology of the enzyme modified electrode was characterized by transmission electron microscopy (TEM) and electron microscopy (SEM). The amino interaction between cross-linker and enzyme was characterized by Fourier transform infrared spectroscopy (FTIR). Under the optimal experimental conditions, good linear relationships were achieved in the range of 4 × 10–13 − 5.7 × 10–11 M and a limit of detection as low as 1.3 × 10–13 M. Moreover, the enzyme sensor exhibited good reproducibility, stability and high selectivity to 17β-estradiol. Excellent performance was showed in the human urine samples analysis, thus confirming great prospect for further application in clinic diagnosis and biological research.

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$., 6 pages, 5 figures, 2 tables

In this study, the cooperative output tracking problem is addressed for strict-feedback uncertain nonlinear multiagent systems. A novel distributed adaptive fuzzy control strategy is developed based on the event-triggered transmission scheme. With the proposed approach, only sampled output information needs to be exchanged among the communication network, which not only reduces the communication burden, but also makes the controller implementation more flexible. In addition, Zeno behavior is avoided and the cooperative output tracking error converges to an adjustable set around the origin by choosing different design parameters. The effectiveness of the main result is verified by a simulation example.

Control of autonomous surface vehicles is a challenging task due to their nonlinearities, imposed disturbances, strong couplings, under-actuation, and various constraints. Prevailing methods are basing on an explicit mathematical model. The paper presents a learning-based motion controller for an autonomous surface vehicle without any mathematical models. A model-based deep reinforcement learning approach is propose for achieving the trajectory following task. At first, a deep neural network is trained for approximating the dynamical model of the autonomous surface vehicle by using recorded input and output data only. Then, a model predictive controller based on the learned neural network together with a reward function is presented for the autonomous surface vehicle to follow arbitrary trajectories. It is shown that after learning with random data collected from the autonomous surface vehicles, the proposed learning-based controller is able to follow trajectories with excellent sample efficiency. Simulation results are given to illustrate the proposed model-based deep reinforcement learning method for trajectory following of an autonomous surface vehicle.

We report a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or $^{220}$Rn source, and with $^{241}$Am-Be neutron source, within an energy range from $1-25$ keV (ER) and $4-80$ keV (NR), under the two drift fields of 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can well describe the calibration data. The systematic uncertainties of the fitted models are obtained via statistical comparison against the data., Comment: 12 pages, 7 figures

INTRODUCTION: Sirtuin1 (SIRT1), one of NAD(+)-dependent protein deacetylases, is proved to be neuroprotective in aging diseases, but its effect on neuronal apoptosis has not been clarified. To investigate the role of SIRT1 in inhibiting neuronal apoptosis, SIRT1 was interfered or overexpressed in cortical neurons. METHODS: We exerted overloading laminar shear stress with 10 dyn/cm(2) for 4, 8, and 12 h on neurons to cause cortical neuronal apoptosis, and the apoptosis percentage was tested by TUNEL assay. The adenovirus plasmids containing SIRT1 RNA interference or SIRT1 wild type gene were transfected into neurons before shear stress loading. SIRT1 mRNA and protein level were tested by Real-time PCR, immunofluorescence and western blots assay. RESULTS: SIRT1 was primarily expressed in nucleus of cortical neurons, and its mRNA level was significantly increased after 4 h stimulation. SIRT1 RNAi cortical neurons had higher TUNEL positive cells, while SIRT1 overexpression significantly decreased the percentage of died cells induced by shear stress compared to control group. CONCLUSIONS: SIRT1 plays a neuroprotective role in shear stress induced apoptosis and could be as potential pharmacological targets against neuronal degeneration in future.