Targeting senescent cells with NKG2D-CAR T cells.

This study investigates the efficacy of NKG2D chimeric antigen receptor (CAR) engineered T cells in targeting and eliminating stress-induced senescent cells in vitro. Cellular senescence contributes to age-related tissue decline and is characterized by permanent cell cycle arrest and the senescence-associated secretory phenotype (SASP). Immunotherapy, particularly CAR-T cell therapy, emerges as a promising approach to selectively eliminate senescent cells. Our focus is on the NKG2D receptor, which binds to ligands (NKG2DLs) upregulated in senescent cells, offering a target for CAR-T cells. Using mouse embryonic fibroblasts (MEFs) and astrocytes (AST) as senescence models, we demonstrate the elevated expression of NKG2DLs in response to genotoxic and oxidative stress. NKG2D-CAR T cells displayed potent cytotoxicity against these senescent cells, with minimal effects on non-senescent cells, suggesting their potential as targeted senolytics. In conclusion, our research presents the first evidence of NKG2D-CAR T cells' ability to target senescent brain cells, offering a novel approach to manage senescence-associated diseases. The findings pave the way for future investigations into the therapeutic applicability of NKG2D-targeting CAR-T cells in naturally aged organisms and models of aging-associated brain diseases in vivo.

Ageing-associated phenotypes in mice.

Ageing is a continuous process in life featuring progressive damage accumulation that leads to physiological decline, functional deterioration and ultimately death of an organism. Based on the relatively close anatomical and physiological similarity to humans, the mouse has been proven as a valuable model organism in ageing research over the last decades. In this review, we survey methods and tools currently in use to assess ageing phenotypes in mice. We summarize a range of ageing-associated alterations detectable at two major levels of analysis: (1) physiology and pathophysiology and (2) molecular biomarkers. Age-sensitive phenotypes provided in this article may serve to inform future studies targeting various aspects of organismal ageing in mice. In addition, we discuss conceptual and technical challenges faced by previous ageing studies in mice and, where possible, provide recommendations on how to resolve some of these issues.

Aging - What it is and how to measure it.

The current understanding of the biology of aging is largely based on research aimed at identifying factors that influence lifespan. However, lifespan as a sole proxy measure of aging has limitations because it can be influenced by specific pathologies (not generalized physiological deterioration in old age). Hence, there is a great need to discuss and design experimental approaches that are well-suited for studies targeting the biology of aging, rather than the biology of specific pathologies that restrict the lifespan of a given species. For this purpose, we here review various perspectives on aging, discuss agreement and disagreement among researchers on the definition of aging, and show that while slightly different aspects are emphasized, a widely accepted feature, shared across many definitions, is that aging is accompanied by phenotypic changes that occur in a population over the course of an average lifespan. We then discuss experimental approaches that are in line with these considerations, including multidimensional analytical frameworks as well as designs that facilitate the proper assessment of intervention effects on aging rate. The proposed framework can guide discovery approaches to aging mechanisms in all key model organisms (e.g., mouse, fish models, D. melanogaster, C. elegans) as well as in humans.

Targeting the "hallmarks of aging" to slow aging and treat age-related disease: fact or fiction?

Aging is a major risk factor for a number of chronic diseases, including neurodegenerative and cerebrovascular disorders. Aging processes have therefore been discussed as potential targets for the development of novel and broadly effective preventatives or therapeutics for age-related diseases, including those affecting the brain. Mechanisms thought to contribute to aging have been summarized under the term the "hallmarks of aging" and include a loss of proteostasis, mitochondrial dysfunction, altered nutrient sensing, telomere attrition, genomic instability, cellular senescence, stem cell exhaustion, epigenetic alterations and altered intercellular communication. We here examine key claims about the "hallmarks of aging". Our analysis reveals important weaknesses that preclude strong and definitive conclusions concerning a possible role of these processes in shaping organismal aging rate. Significant ambiguity arises from the overreliance on lifespan as a proxy marker for aging, the use of models with unclear relevance for organismal aging, and the use of study designs that do not allow to properly estimate intervention effects on aging rate. We also discuss future research directions that should be taken to clarify if and to what extent putative aging regulators do in fact interact with aging. These include multidimensional analytical frameworks as well as designs that facilitate the proper assessment of intervention effects on aging rate.

Deep phenotyping and lifetime trajectories reveal limited effects of longevity regulators on the aging process in C57BL/6J mice.

Current concepts regarding the biology of aging are primarily based on studies aimed at identifying factors regulating lifespan. However, lifespan as a sole proxy measure for aging can be of limited value because it may be restricted by specific pathologies. Here, we employ large-scale phenotyping to analyze hundreds of markers in aging male C57BL/6J mice. For each phenotype, we establish lifetime profiles to determine when age-dependent change is first detectable relative to the young adult baseline. We examine key lifespan regulators (putative anti-aging interventions; PAAIs) for a possible countering of aging. Importantly, unlike most previous studies, we include in our study design young treated groups of animals, subjected to PAAIs prior to the onset of detectable age-dependent phenotypic change. Many PAAI effects influence phenotypes long before the onset of detectable age-dependent change, but, importantly, do not alter the rate of phenotypic change. Hence, these PAAIs have limited effects on aging.

A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction.

The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids.

Extended Dissipative Sliding-Mode Control for Discrete-Time Piecewise Nonhomogeneous Markov Jump Nonlinear Systems.

This article analyzes the problem of the sliding-mode control (SMC) design for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems (MJNSs) subject to an external disturbance with time-varying transition probabilities (TPs). A discrete-time asynchronous integral sliding surface is constructed, which yields matched-nonlinearity-free sliding-mode dynamics (SMDs). Then, by using the mode-dependent Lyapunov function technique, a sufficient condition is established for ensuring the stochastic stability of SMD with extended dissipation. The solution to designing controller gains is obtained. Moreover, an SMC law and an adaptive law are, respectively, derived for driving the system trajectories to move into a predetermined sliding-mode region with specified precision. Finally, the feasibility and effectiveness of the new design are verified and demonstrated by a simulation example.

Signaling pathways of dietary energy restriction and metabolism on brain physiology and in age-related neurodegenerative diseases.

Several laboratory animal models have shown that dietary energy restriction (ER) can promote longevity and improve various health aspects in old age. However, whether the entire spectrum of ER-induced short- and long-term physiological and metabolic adaptions is translatable to humans remains to be determined. In this review article, we present recent evidence towards the elucidation of the impact of ER on brain physiology and in age-related neurodegenerative diseases. We also discuss modulatory influences of ER on metabolism and overall on human health, limitations of current experimental designs as well as future perspectives for ER trials in humans. Finally, we summarize signaling pathways and processes known to be affected by both aging and ER with a special emphasis on the link between ER and cellular proteostasis.

A randomized study protocol of microendoscopic versus open discectomy in treatment of lumbar disc herniation.

BACKGROUND: Lumbar disk herniation (LDH) is one of the main causes of discogenic low back pain. However, the evidence comparing different approaches for discectomy has lacked definitive conclusions, with conflicting results regarding the benefit of minimally invasive versus open techniques for LDH. We are now conducting a randomized controlled trial to figure out whether or not microendoscopic discectomy yields better clinical outcomes and causes less surgical trauma than open surgery. METHODS: This prospective, randomized, single-blind, controlled, superiority clinical trial was approved by the institutional review board in the People's Hospital of Jianyang City. The conduct of this study followed the Declaration of Helsinki principles and the reporting of this study adhered to the Consolidated Standards of Reporting Trials guidelines for randomized controlled trials. Subjects were randomized into 2 groups as follows: open surgery and microendoscopic group. The outcomes included pain score, functional outcome, satisfaction rate, radiological outcomes, and complications. The statistical analyses in this study were performed using the Statistical Package for the Social Sciences 20.0 software. P < .05 was accepted as statistically significant. RESULTS: The hypothesis was that the open technique would achieve similar clinical outcomes as compared to the microendoscopic technique in LDH. TRIAL REGISTRATION: This study protocol was registered in Research Registry (researchregistry5708).

Eliminating the Permutation Ambiguity of Convolutive Blind Source Separation by Using Coupled Frequency Bins.

Blind source separation (BSS) is a typical unsupervised learning method that extracts latent components from their observations. In the meanwhile, convolutive BSS (CBSS) is particularly challenging as the observations are the mixtures of latent components as well as their delayed versions. CBSS is usually solved in frequency domain since convolutive mixtures in time domain is just instantaneous mixtures in frequency domain, which allows to recover source frequency components independently of each frequency bin by running ordinary BSS, and then concatenate them to form the Fourier transformation of source signals. Because BSS has inherent permutation ambiguity, this category of CBSS methods suffers from a common drawback: it is very difficult to choose the frequency components belonging to a specific source as they are estimated from different frequency bins using BSS. This paper presents a tensor framework that can completely eliminate the permutation ambiguity. By combining each frequency bin with an anchor frequency bin that is chosen arbitrarily in advance, we establish a new virtual BSS model where the corresponding correlation matrices comply with a block tensor decomposition (BTD) model. The essential uniqueness of BTD and the sparse structure of coupled mixing parameters allow the estimation of the mixing matrices free of permutation ambiguity. Extensive simulation results confirmed that the proposed algorithm could achieve higher separation accuracy compared with the state-of-the-art methods.

Adaptive Neural Quantized Control for a Class of MIMO Switched Nonlinear Systems With Asymmetric Actuator Dead-Zone.

This paper concentrates on the adaptive state-feedback quantized control problem for a class of multiple-input-multiple-output (MIMO) switched nonlinear systems with unknown asymmetric actuator dead-zone. In this study, we employ different quantizers for different subsystem inputs. The main challenge of this study is to deal with the coupling between the quantizers and the dead-zone nonlinearities. To solve this problem, a novel approximation model for the coupling between quantizer and dead-zone is proposed. Then, the corresponding robust adaptive law is designed to eliminate this nonlinear term asymptotically. A direct neural control scheme is employed to reduce the number of adaptive laws significantly. The backstepping-based adaptive control scheme is also presented to guarantee the system performance. Finally, two simulation examples are presented to show the effectiveness of our control scheme.

Latent Elastic-Net Transfer Learning.

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

Computational Efficiency-Based Adaptive Tracking Control for Robotic Manipulators with Unknown Input Bouc-Wen Hysteresis.

In order to maintain robotic manipulators at a high level of performance, their controllers should be able to address nonlinearities in the closed-loop system, such as input nonlinearities. Meanwhile, computational efficiency is also required for real-time implementation. In this paper, an unknown input Bouc-Wen hysteresis control problem is investigated for robotic manipulators using adaptive control and a dynamical gain-based approach. The dynamics of hysteresis are modeled as an additional control unit in the closed-loop system and are integrated with the robotic manipulators. Two adaptive parameters are developed for improving the computational efficiency of the proposed control scheme, based on which the outputs of robotic manipulators are driven to track desired trajectories. Lyapunov theory is adopted to prove the effectiveness of the proposed method. Moreover, the tracking error is improved from ultimately bounded to asymptotic tracking compared to most of the existing results. This is of important significance to improve the control quality of robotic manipulators with unknown input Bouc-Wen hysteresis. Numerical examples including fixed-point and trajectory controls are provided to show the validity of our method.

Adaptive Compensation for Nonlinear Time-Varying Multiagent Systems With Actuator Failures and Unknown Control Directions.

This paper investigates a problem of designing an adaptive asymptotic cooperative control scheme for nonlinear time-varying multiagent systems, which can simultaneously tolerate unknown actuator failures and unknown control directions. To address such the problem, we propose a conditional inequality, which allows multiple piecewise Nussbaum functions to acquire the control robustness. Benefiting from this robustness, a part of failure uncertainties and system errors are compensated for, while the remaining parts are handled by adaptive control technique. Moreover, structural properties of the proposed adaptive laws are utilized so that Barbalat's lemma is applicable to make all the followers asymptotically converge to the leader based on the neighborhood information.

Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization.

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In our adaptive control scheme, there is only one parameter required to be estimated online for updating weights of neural networks. Within the framework of Lyapunov theory, it is shown that the proposed algorithm ensures that all the signals in the closed-loop system are ultimately bounded. Moreover, an asymptotic tracking error is obtained by means of introducing Barbalat's lemma to the proposed adaptive law.

Epigenetic alterations in longevity regulators, reduced life span, and exacerbated aging-related pathology in old father offspring mice.

Advanced age is not only a major risk factor for a range of disorders within an aging individual but may also enhance susceptibility for disease in the next generation. In humans, advanced paternal age has been associated with increased risk for a number of diseases. Experiments in rodent models have provided initial evidence that paternal age can influence behavioral traits in offspring animals, but the overall scope and extent of paternal age effects on health and disease across the life span remain underexplored. Here, we report that old father offspring mice showed a reduced life span and an exacerbated development of aging traits compared with young father offspring mice. Genome-wide epigenetic analyses of sperm from aging males and old father offspring tissue identified differentially methylated promoters, enriched for genes involved in the regulation of evolutionarily conserved longevity pathways. Gene expression analyses, biochemical experiments, and functional studies revealed evidence for an overactive mTORC1 signaling pathway in old father offspring mice. Pharmacological mTOR inhibition during the course of normal aging ameliorated many of the aging traits that were exacerbated in old father offspring mice. These findings raise the possibility that inherited alterations in longevity pathways contribute to intergenerational effects of aging in old father offspring mice.

Every-other-day feeding extends lifespan but fails to delay many symptoms of aging in mice.

Dietary restriction regimes extend lifespan in various animal models. Here we show that longevity in male C57BL/6J mice subjected to every-other-day feeding is associated with a delayed onset of neoplastic disease that naturally limits lifespan in these animals. We compare more than 200 phenotypes in over 20 tissues in aged animals fed with a lifelong every-other-day feeding or ad libitum access to food diet to determine whether molecular, cellular, physiological and histopathological aging features develop more slowly in every-other-day feeding mice than in controls. We also analyze the effects of every-other-day feeding on young mice on shorter-term every-other-day feeding or ad libitum to account for possible aging-independent restriction effects. Our large-scale analysis reveals overall only limited evidence for a retardation of the aging rate in every-other-day feeding mice. The data indicate that every-other-day feeding-induced longevity is sufficiently explained by delays in life-limiting neoplastic disorders and is not associated with a more general slowing of the aging process in mice.Dietary restriction can extend the life of various model organisms. Here, Xie et al. show that intermittent periods of fasting achieved through every-other-day feeding protect mice against neoplastic disease but do not broadly delay organismal aging in animals.

Motor Cortex Theta and Gamma Architecture in Young Adult APPswePS1dE9 Alzheimer Mice.

Alzheimer's disease (AD) is a multifactorial disorder leading to progressive memory loss and eventually death. In this study, an APPswePS1dE9 AD mouse model has been analyzed for motor cortex theta, beta and gamma frequency alterations using computerized 3D stereotaxic electrode positioning and implantable video-EEG radiotelemetry to perform long-term M1 recordings from both genders considering age, circadian rhythm and activity status of experimental animals. We previously demonstrated that APPswePS1dE9 mice exibit complex alterations in hippocampal frequency power and another recent investigation reported a global increase of alpha, beta and gamma power in APPswePS1dE9 in females of 16-17 weeks of age. In this cortical study in APPswePS1dE9 mice we did not observe any changes in theta, beta and particularly gamma power in both genders at the age of 14, 15, 18 and 19 weeks. Importantly, no activity dependence of theta, beta and gamma activity could be detected. These findings clearly point to the fact that EEG activity, particularly gamma power exhibits developmental changes and spatial distinctiveness in the APPswePS1dE9 mouse model of Alzheimer's disease.

Rate of Convergence of the FOCUSS Algorithm.

Focal underdetermined system solver (FOCUSS) is a powerful method for basis selection and sparse representation, where it employs the [Formula: see text]-norm with p ∈ (0,2) to measure the sparsity of solutions. In this paper, we give a systematical analysis on the rate of convergence of the FOCUSS algorithm with respect to p ∈ (0,2) . We prove that the FOCUSS algorithm converges superlinearly for and linearly for usually, but may superlinearly in some very special scenarios. In addition, we verify its rates of convergence with respect to p by numerical experiments.