An Intuitive Formulation of the Human Arm Active Endpoint Stiffness.

In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc=VDVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc. In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human-robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.

Integrating a DNA Strand Displacement Reaction with a Whispering Gallery Mode Sensor for Label-Free Mercury (II) Ion Detection.

Mercury is an extremely toxic chemical pollutant of our environment. It has attracted the world's attention due to its high mobility and the ease with which it accumulates in organisms. Sensitive devices and methods specific for detecting mercury ions are, hence, in great need. Here, we have integrated a DNA strand displacement reaction with a whispering gallery mode (WGM) sensor for demonstrating the detection of Hg(2+) ions. Our approach relies on the displacement of a DNA hairpin structure, which forms after the binding of mercury ions to an aptamer DNA sequence. The strand displacement reaction of the DNA aptamer provides highly specific and quantitative means for determining the mercury ion concentration on a label-free WGM sensor platform. Our approach also shows the possibility for manipulating the kinetics of a strand displacement reaction with specific ionic species.

Ultraspecific and highly sensitive nucleic acid detection by integrating a DNA catalytic network with a label-free microcavity.

Nucleic acid detection with label-free biosensors circumvents costly fluorophore functionalization steps associated with conventional assays by utilizing transducers of impressive ultimate detection limits. Despite this technological prowess, molecular recognition at a surface limits the biosensors' sensitivity, specificity, and reusability. It is therefore imperative to integrate novel molecular approaches with existing label-free transducers to overcome those limitations. Here, we demonstrate this concept by integrating a DNA strand displacement circuit with a micron-scale whispering gallery mode (WGM) microsphere biosensor. The integrated biosensor exhibits at least 25-fold improved nucleic acid sensitivity, and sets a new record for label-free microcavity biosensors by detecting 80 pM (32 fmol) of a 22nt oligomer; this improvement results from the catalytic behavior of the circuit. Furthermore, the integrated sensor exhibits extremely high specificity; single nucleotide variants yield 40- to 100-fold lower signal. Finally, the same physical sensor was demonstrated to alternatingly detect 2 different nucleic acid sequences through 5 cycles of detection, showcasing both its reusability and its versatility.

Recent progress of MXene as an energy storage material.

Thanks to its adjustable interlayer distance, large specific surface area, abundant active sites, and diverse surface functional groups, MXene has always been regarded as an excellent candidate for energy storage materials, including supercapacitors and ion batteries. Recent studies have also shown that MXene can serve as an efficient hydrogen storage catalyst. This review aims to summarize the latest research achievements in the field of MXene, especially its performance and application in energy storage. Different synthesis techniques have different effects on the energy storage performance of MXene. In this review, various common synthesis methods and the latest innovations in synthesis methods are discussed. MXene is prone to oxidation, and how to resist oxidation is also an important topic in MXene research. This article introduces the research results on improving the chemical stability of MXene through annealing. In addition, it aims to gain a deeper understanding of the future development and potential of MXene.

The impact of green credit on economic development quality: the mediating effect of enterprise innovation.

Implementing green credit is a crucial step for nations looking to control social capital flows, improve environmental governance, and foster high-quality economic development in the context of the global low-carbon transition. This study analyzes the effects of green credit policy on high-quality economic development (HQED) from the perspective of enterprise innovation using panel data from 30 Chinese provinces. The data is from the period between 2011 and 2020. We use the benchmark regression and mediation effect models to analyze the relationship between green credit and HQED. The research results show that (1) green credit can directly and significantly raise the HQED. (2) Enterprise innovation mediates the relationship between green credit and HQED. Green credit can promote HQED through enterprise technical innovation, human capital innovation, stock market innovation, and incremental market innovation. (3) The most apparent mediating influence in enterprise innovation is played by human capital innovation. Our research provides policy implications for governments, banks, and enterprises to promote green transformation and achieve simultaneous economic and environmental development.

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.

Polarization-dependent excitons in Borophene-Black phosphorus heterostructures.

In this paper, the optical properties of ß1-phase borophene-black phosphorus heterostructures (BBPHs) are theoretically investigated by first principal calculations, including absorption spectra, band structures, IR spectra, and Raman spectra. The calculation results show that constructing BBPHs could form covalent bonds between hetero-layers, making crystal structure more stable, and optical properties of BBPHs are significantly distinctive to that of the borophene and black phosphorus (BP) monomers, and polarization reversal occurs when two monomer materials form a heterostructure. The BBPHs show excellent polarization characteristics in visible or infrared region. Our results is of a certain reference value for the future application of optoelectronic devices based on two-dimensional (2D) heterostructures.

Adaptive Asymptotic Tracking Control for Input-Quantized Nonlinear Systems With Multiple Unknown Control Directions.

This study mainly concentrates on adaptive asymptotic tracking control for input-quantized strict-feedback nonlinear systems subjected to multiple unknown control directions. Novel improved lemmas, which relax the conditions for handling unknown control coefficients in the existing theoretical results, are certificated that can be applied to resolve the tracking problem for nonlinear systems under input quantification and unknown control directions simultaneously. Furthermore, by incorporating positive integral time-varying functions and the disintegration of the hysteresis quantizer into the controller design, the asymptotic tracking control is successfully achieved. Moreover, all signals in the closed-loop system are guaranteed to be bounded. Ultimately, a comparing numerical simulation and a practical simulation of a Nomoto ship model are presented to validate the feasibility of the proposed control algorithm.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

Contrast-enhanced ultrasonography for identifying acute kidney injury in brain-dead donors.

Background: Acute kidney injury (AKI) is frequently found in deceased donors; however, few studies have reported the use of imaging to detect and identify this phenomenon. The purpose of this study was to detect renal microcirculatory perfusion in brain-dead donors using contrast-enhanced ultrasonography (CEUS), investigate the value of CEUS in identifying AKI, and analyze the correlation between CEUS and preimplantation biopsy results and early post-transplant renal function of grafts. Methods: This prospective study recruited 94 kidneys from brain-dead donors (AKI =44, non-AKI =50) from August 2020 to November 2022. The inclusion criteria were age ≥18 years and brain death. The exclusion criteria encompassed donors maintained with extracorporeal membrane oxygenation (ECMO) and the presence of irregular kidney anatomy. The mean age of the donors was 45.1±10.4 [standard deviation (SD)] years, and the majority were male (86.2%). CEUS was performed prior to organ procurement, and time-intensity curves (TICs) were constructed. The time to peak (TTP) and peak intensity (PI) of kidney segmental artery (KA), kidney cortex (KC), and kidney medulla (KM) were calculated using TIC analysis. Results: Arrival time (AT) of KA (P<0.001) and TTP of kidney cortex (TTPKC) (P<0.001) of the non-AKI group were significantly shorter than those of the AKI group. The PI of the KA (P=0.003), KM (P=0.005), and kidney cortex (PIKC; P<0.001) of the non-AKI group were significantly higher than those of the AKI group. Multivariable logistic regression analysis showed that serum creatinine [odds ratio (OR) =1.06; 95% CI: 1.03-1.1; P<0.001], TTPKC (OR =1.38; 95% CI: 1.03-1.84; P=0.03), and PIKC (OR =0.95; 95% CI: 0.91-1; P=0.046) were the independent factors of AKI. The area under the receiver operating characteristic curve (AUC) for identifying AKI for TTPKC and PIKC was 0.73 and 0.71, respectively. TTPKC showed a weak correlation with interstitial fibrosis (r=0.23; P=0.03), PIKC showed a weak correlation with arterial intimal fibrosis ((r=-0.29; P=0.004) and arteriolar hyalinosis (r=-0.27; P=0.008), and PIKC showed the strongest correlation with eGFR on postoperative day 7 (r=-0.46; P=0.046) in the donor kidneys with AKI. Conclusions: CEUS can be used to identify AKI in brain-dead donors. Furthermore, there is a correlation between CEUS-derived parameters and pretransplant biopsy results and early preimplantation renal function of grafts.

Contrast-enhanced ultrasonography-based renal blood perfusion in brain-dead donors predicts early graft function.

PURPOSE: The aim of this study was to quantify renal microcirculatory perfusion in braindead donors using contrast-enhanced ultrasonography (CEUS), and to establish an accurate, noninvasive, and convenient index for predicting delayed graft function (DGF) post-transplantation. METHODS: In total, 90 brain-dead donor kidneys (training group, n=60; validation group, n=30) examined between August 2020 and November 2022 were recruited in this prospective study. CEUS was performed on the kidneys of brain-dead donors 24 hours before organ procurement and time-intensity curves were constructed. The main measures were arrival time, time to peak, and peak intensity of the kidney segmental arteries, cortex, and medulla. Recipients were divided into DGF and non-DGF groups according to early post-transplant graft function. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. RESULTS: The arrival time of the kidney segmental artery and cortex and the time interval between the time to peak of the segmental artery and cortex were identified as independent factors associated with DGF by multivariate stepwise regression analysis. A new index for the joint prediction model of three variables, the contrast-enhanced ultrasonography/Kidney Donor Profile index (CEUS-KDPI), was developed. CEUS-KDPI showed high accuracy for predicting DGF (training group: AUC, 0.91; sensitivity, 90.5%; specificity, 92.3%; validation group: AUC, 0.84; sensitivity, 75.0%; specificity, 92.3%). CONCLUSION: CEUS-KDPI accurately predicted DGF after kidney transplantation. CEUS may be a potential noninvasive tool for bedside examinations before organ procurement and may be used to predict early renal function after kidney transplants kidneys from donors after brain death.

Cordycepin improved neuronal synaptic plasticity through CREB-induced NGF upregulation driven by MG-M2 polarization: a microglia-neuron symphony in AD.

PURPOSE: Microglia-neuron crosstalk is critically involved in synaptic plasticity and degeneration by releasing diverse mediators in Alzheimer's disease (AD). Therefore, determining contributors that modulate the systemic microenvironment is essential. Cordycepin (CCS) is a novel neuroprotective compound obtained from Cordyceps militaris. However, the anti-AD efficacy and potential mechanism of CCS treatment remain unclear. This study aimed to elucidate the microglia-neuron symphony in AD after CCS treatment and to explore the possible mechanisms of its neuroprotective efficacy. METHODS AND RESULTS: CCS treatment improved learning and memory impairment in 9-month-old APP/PS1 mice by behavioral tests. CCS polarized the microglia from M1 to M2, inhibited neuronal apoptosis and promoted synaptic remodeling accompanied by in vivo and in vitro upregulation of NGF. The cAMP-response element-binding protein (CREB) was also activated after MG-M2 polarization. Further, we verified that the sg3 promoter region of NGF (-1018 to -1011) is the key binding site for CREB-induced NGF transcription, which increased NGF expression and secretion. Finally, microglia-derived NGF was confirmed as an important mediator in microglia-neuron symphony to improve the neuronal microenvironment after CCS treatment. CONCLUSIONS: CCS improved the neuronal synaptic plasticity and senescence by promoting MG-M2 activation driven by CREB-induced NGF upregulation and facilitated symphony communication between the microglia and neuron in AD. This study provides a new perspective on the development of a novel strategy for anti-AD therapy and offers new targets for anti-AD drug development.

Catalytic Asymmetric Diels-Alder Reaction of 2'-Hydroxychalcone as a Dienophile with a VANOL-Borate Ester Complex.

Numerous flavonoid Diels-Alder-type natural products have been isolated and received great attention from the synthetic community. Herein, we reported a catalytic strategy for an asymmetric Diels-Alder reaction of 2'-hydroxychalcone with a range of diene substrates using a chiral ligand-boron Lewis acid complex. This method enables the convenient synthesis of a wide range of cyclohexene skeletons in excellent yields with moderate to good enantioselectivities, which is critical to prepare natural product congeners for further biological studies.

Adaptive Fuzzy Event-Triggered Control for High-Order Nonlinear Systems With Prescribed Performance.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

Variable stiffness locomotion with guaranteed stability for quadruped robots traversing uneven terrains.

Quadruped robots are widely applied in real-world environments where they have to face the challenges of walking on unknown rough terrains. This paper presents a control pipeline that generates robust and compliant legged locomotion for torque-controlled quadruped robots on uneven terrains. The Cartesian motion planner is designed to be reactive to unexpected early and late contacts using the estimated contact forces. Moreover, we present a novel scheme of optimal stiffness modulation that aims to coordinate desired compliance and tracking performance. It optimizes joint stiffness and contact forces coordinately in a quadratic programming (QP) formulation, where the constraints of non-slipping contacts and torque limits are imposed as well. In addition, the issue of stability under variable stiffness control is solved by imposing a tank-based passivity constraint explicitly. We finally validate the proposed control pipeline on our quadruped robot CENTAURO in experiments on uneven terrains and, through comparative tests, demonstrate the improvements of the variable stiffness locomotion.

Multi-user motion recognition using sEMG via discriminative canonical correlation analysis and adaptive dimensionality reduction.

The inability of new users to adapt quickly to the surface electromyography (sEMG) interface has greatly hindered the development of sEMG in the field of rehabilitation. This is due mainly to the large differences in sEMG signals produced by muscles when different people perform the same motion. To address this issue, a multi-user sEMG framework is proposed, using discriminative canonical correlation analysis and adaptive dimensionality reduction (ADR). The interface projects the feature sets for training users and new users into a low-dimensional uniform style space, overcoming the problem of individual differences in sEMG. The ADR method removes the redundant information in sEMG features and improves the accuracy of system motion recognition. The presented framework was validated on eight subjects with intact limbs, with an average recognition accuracy of 92.23% in 12 categories of upper-limb movements. In rehabilitation laboratory experiments, the average recognition rate reached 90.52%. The experimental results suggest that the framework offers a good solution to enable new rehabilitation users to adapt quickly to the sEMG interface.

Circ_0078767 Inhibits the Progression of Non-Small-Cell Lung Cancer by Regulating the GPX3 Expression by Adsorbing miR-665.

Non-small-cell lung cancer (NSCLC) is one of the most serious cancers. The circular RNA_0078767 (circ_0078767) expression was decreased in NSCLC tissues. However, the molecular mechanism of circ_0078767 remains unknown. The expression of circ_0078767, microRNA-665 (miR-665), and glutathione peroxidase 3 (GPX3) was detected by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Cell proliferation, migration, and invasion were detected by colony formation assay and transwell assay, respectively. The lactate production and glucose consumption were tested by glycolysis. Western blot examined the protein levels of hexokinase-2 (HK2), matrix metalloproteinase-9 (MMP9), and GPX3 cells. Circinteractome predicted the relationship between miR-665 and circ_0078767 or GPX3 and was verified by dual luciferase reporter assays. The xenotransplantation model was established to study the role of circ_0078767 in vivo. The expression of circ_0078767 and GPX3 was decreased in NSCLC tissues, while the expression of miR-665 was increased. Circ_0078767 can sponge miR-665, and GPX3 is the target of miR-665. In vitro complement experiments showed that knockdown of circ_0078767 significantly promoted malignant behavior of NSCLC, while cotransfection of miR-665 inhibitor partially reduced this change. In addition, the GPX3 overexpression decreased the promoting effects of miR-665 upregulation on proliferation, migration, and invasion of NSCLC cells. Mechanically, circ_0078767 regulates the GPX3 expression in NSCLC cells by spongy miR-665. In addition, in vivo studies have shown that downregulation of circ_0078767 promotes tumor growth. Circ_0078767 silencing promotes proliferation, migration, invasion, and glycolysis of NSCLC cells by regulating the miR-665/GPX3 axis, suggesting that circ_0078767/miR-665/GPX3 axis may be a potential regulatory mechanism for the treatment of NSCLC.

Platelet Activating Factor Receptor Exaggerates Microglia-Mediated Microenvironment by IL10-STAT3 Signaling: A Novel Potential Biomarker and Target for Diagnosis and Treatment of Alzheimer's Disease.

Background: Early diagnosis and effective intervention are the keys to delaying the progression of Alzheimer's Disease (AD). Therefore, we aimed to identify new biomarkers for the early diagnosis of AD through bioinformatic analysis and elucidate the possible underlying mechanisms. Methods and Results: GSE1297, GSE63063, and GSE110226 datasets from the GEO database were used to screen the highly differentially expressed genes. We identified a potential biomarker, Platelet activating factor receptor (PTAFR), significantly upregulated in the brain tissue, peripheral blood, and cerebrospinal fluid of AD patients. Furthermore, PTAFR levels in the plasma and brain tissues of APP/PS1 mice were significantly elevated. Simultaneously, PTAFR could mediate the inflammatory responses to exaggerate the microenvironment, particularly mediated by the microglia through the IL10-STAT3 pathway. In addition, PTAFR was a putative target of anti-AD compounds, including EGCG, donepezil, curcumin, memantine, and Huperzine A. Conclusion: PTAFR was a potential biomarker for early AD diagnosis and treatment which correlated with the microglia-mediated microenvironment. It is an important putative target for the development of a novel strategy for clinical treatment and drug discovery for AD.

Single-input spherical microbubble resonator.

A single-input whispering gallery optical microbubble resonator is presented. Spherical microbubbles with diameters less than 100 µm, micrometer-sized wall thicknesses, and a single opening or input were fabricated by heating the tapered tip of a pressurized glass capillary using a CO(2) laser. Optical whispering gallery modes with Q factors of ∼10(5) were obtained. The bubbles were filled with water and mode shifts of ∼20 GHz were observed. Fano-type resonances were detected when the coupling optical fiber diameter was less than 1 µm, causing the microresonator to switch from being a band-stop filter to a bandpass filter. Larger bubbles with submicrometer wall thickness were also fabricated.

Application of Neural Network Model Based on Multispecies Evolutionary Genetic Algorithm to Planning and Design of Diverse Plant Landscape.

In order to explore the feasibility of applying neural network model to landscape planning, based on the multispecies evolutionary genetic algorithm, a neural network model is proposed in this paper for the system design of diverse plant landscape planning. From the perspective of plant species diversity, this paper discusses landscape planning based on a neural network model. This landscape plan involves more than 180 plant species, mainly shrubs, fungi, and so on. The application of multispecies evolutionary genetic algorithm to landscape planning and design and the application of gene level coding and multispecies parallel evolution strategy to the evolutionary design of neural network have guiding significance for plant landscape planning and design. Compared with the traditional neural network modeling method and genetic algorithm, the proposed method has the advantages of wide network structure search space and simple algorithm calculation and design, independent of specific application background, and has strong application and promotion value. This method makes the model performance evaluation index more comprehensive and accurate and the model solution more reasonable. At the same time, combined with the specific status and corresponding changes of various plants in each season, this paper designs a targeted plan to rationally plan the specific spatial layout of the plant landscape and the combination of different types of plant landscapes, so as to effectively improve the quality of the landscape.