Characterization of different-sized human αA-crystallin homomers and implications to Asp151 isomerization.

Site-specific modifications of aspartate residues spontaneously occur in crystallin, the major protein in the lens. One of the primary modification sites is Asp151 in αA-crystallin. Isomerization and racemization alter the crystallin backbone structure, reducing its stability by inducing abnormal crystallin-crystallin interactions and ultimately leading to the insolubilization of crystallin complexes. These changes are considered significant factors in the formation of senile cataracts. However, the mechanisms driving spontaneous isomerization and racemization have not been experimentally demonstrated. In this study, we generated αA-crystallins with different homo-oligomeric sizes and/or containing an asparagine residue at position 151, which is more prone to isomerization and racemization. We characterized their structure, hydrophobicity, chaperone-like function, and heat stability, and examined their propensity for isomerization and racemization. The results show that the two differently sized αA-crystallin variants possessed similar secondary structures but exhibited different chaperone-like functions depending on their oligomeric sizes. The rate of isomerization and racemization of Asp151, as assessed by the deamidation of Asn151, was also found to depend on the oligomeric sizes of αA-crystallin. The predominant isomerization product via deamidation of Asn151 in the different-sized αA-crystallin variants was L-ß-Asp in vitro, while various modifications occurred around Asp151 in vivo. The disparity between the findings of this in vitro study and in vivo studies suggests that the isomerization of Asp151 in vivo may be more complex than what occurs in vitro.

ADP-Based Fault-Tolerant Control for Multiagent Systems With Semi-Markovian Jump Parameters.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

Nussbaum-Based Adaptive Neural Networks Tracking Control for Nonlinear PDE-ODE Systems Subject to Deception Attacks.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

Characterization of ßB2-crystallin tryptophan mutants reveals two different folding states in solution.

Conserved tryptophan residues are critical for the structure and the stability of ß/γ-crystallin in the lenses of vertebrates. During aging, in which the lenses are continuously exposed to ultraviolet irradiation and other environmental stresses, oxidation of tryptophan residues in ß/γ-crystallin is triggered and impacts the lens proteins to varying degrees. Kynurenine derivatives, formed by oxidation of tryptophan, accumulate, resulting in destabilization and insolubilization of ß/γ-crystallin, which correlates with age-related cataract formation. To understand the contribution of tryptophan modification on the structure and stability of human ßB2-crystallin, five tryptophan residues were mutated to phenylalanine considering its similarity in structure and hydrophilicity to kynurenine. Among all mutants, W59F and W151F altered the stability and homo-oligomerization of ßB2-crystallin-W59F promoted tetramerization whereas W151F blocked oligomerization. Most W59F dimers transformed into tetramer in a month, and the separated dimer and tetramer of W59F demonstrated different structures and hydrophobicity, implying that the biochemical properties of ßB2-crystallin vary over time. By using SAXS, we found that the dimer of ßB2-crystallin in solution resembled the lattice ßB1-crystallin dimer (face-en-face), whereas the tetramer of ßB2-crystallin in solution resembled its lattice tetramer (domain-swapped). Our results suggest that homo-oligomerization of ßB2-crystallin includes potential inter-subunit reactions, such as dissociation, unfolding, and re-formation of the dimers into a tetramer in solution. The W>F mutants are useful in studying different folding states of ßB2-crystallin in lens.

Case report: Ultrasound misdiagnoses atypical parathyroid adenoma as malignant thyroid tumor.

Atypical Parathyroid Adenoma (APA) is a type of tumor that lies somewhere between parathyroid adenoma and parathyroid carcinoma. It often affects adults over the age of 60, and the clinical symptoms are consistent with those of hyperparathyroidism. This condition has a low occurrence, and its ultrasonographic signs are strikingly similar to thyroid malignant tumors, making it easily misdiagnosed. As a result, a case of APA ultrasonography misdiagnosis admitted to our hospital was recorded in order to serve as a reference point for APA diagnosis.

Integral BLF-Based Adaptive Dynamic Event-Triggered Boundary Control for a Flexible Riser System.

For the flexible riser systems modeled with partial differential equations (PDEs), this article explores the boundary control problem in depth for the first time using a dynamic event-triggered mechanism (DETM). Given the intrinsic time-space coupling characteristic inherent in PDE computations, implementing a state-dependent DETM for PDE-based flexible risers presents a significant challenge. To overcome this difficulty, a novel dynamic event-triggered control method is introduced for flexible riser systems, focusing on optimizing available control inputs. In order to save computational costs from the controller to the actuator, a dynamic event-triggered adaptive boundary controller is designed to effectively reduce boundary position vibrations. Additionally, considering external disturbances, an adaptive bounded compensation term is incorporated to counteract the influence of external disturbances on the system. Addressing boundary position constraints, a new integral barrier Lyapunov function (iBLF) tailored specifically for flexible riser systems is introduced, thereby alleviating conservatism in the controller design of flexible risers modeled by PDEs. At last, the validity of the proposed method is demonstrated through a simulation example.

Electrochemical aptasensors based on porous carbon derived from graphene oxide/ZIF-8 composites for the detection of Erwinia cypripedii.

In this research, self-screening aptamer and MOFs-derived nanomaterial have been combined to construct electrochemical aptasensor for environmental detection. By utilizing the large specific surface area of reduced graphene oxide (rGO), ZIF-8 was grown in situ on surface of rGO, and the composites was pyrolyzed to obtain MOFs-derived porous carbon materials (rGO-NCZIF). Thanks to the synergistic effect between rGO and NCZIF, the complex exhibits remarkable characteristics, including a high electron transfer rate and electrocatalytic activity. In addition, the orderly arrangement of imidazole ligands within ZIF-8 facilitated the uniform doping of nitrogen elements into the porous carbon, thereby significantly enhancing its electrochemical performance. After carboxylation, rGO-NCZIF was functionalized with self-screening aptamer for fabricating electrochemical aptasensor, which can be used to detect Erwinia cypripedii, a kind of quarantine plant bacteria, with detection limit of 4.92 × 103 cfu/mL. Due to the simplicity and speed, the aptasensor is suitable for rapid customs inspection and quarantine. Additionally, the universality of this sensing strategy was verified through exosomes detection by changing the aptamer. The results indicated that the rGO-NCZIF-based electrochemical aptasensor had practical value in the environmental and medical fields.

Integrated molybdenum single atom array sensors with multichannels for nitrite detection in foods.

Nitrite (NO2-) is present in a variety of foods, but the excessive intake of NO2- can indirectly lead to carcinogenic, teratogenic, mutagenicity and other risks to the human body. Therefore, the detection of NO2- is crucial for maintaining human health. In this study, an integrated array sensor for NO2- detection is developed based on molybdenum single atom material (IMSMo-SAC) using high-resolution electrohydrodynamic (EHD) printing technology. The sensor comprises three components: a printed electrode array, multichannels designed on polydimethylsiloxane (PDMS) and an electronic signal process device with bluetooth. By utilizing Mo-SAC to facilitate electron transfer during the redox reaction, rapid and efficient detection of NO2- can be achieved. The sensor has a wide linear range of 0.1 µM-107.8 mM, a low detection limit of 33 nM and a high sensitivity of 0.637 mA-1mM-1 cm-2. Furthermore, employing this portable array sensor allows simultaneously measurements of NO2- concentrations in six different foods samples with acceptable recovery rates. This array sensor holds great potential for detecting of small molecules in various fields.

Evolutionary Dynamics-Driven Learning of Optimal Decisions for Nonlinear System: Extend-Policy Iterative Algorithm.

An extend-policy iterative algorithm is proposed for solving the ecological evolving-lung cancer cells growth inhibition optimal drug delivery scheme. With the analysis of the cell proliferation-apoptosis process of lung cancer cells with primitive immune system and external drug interventions, such as chemotherapeutic drugs and immunological agents, a model of ecological containment of lung cancer cells mimicking injection labeling is constructed. The HJB equation for biological tissue damage has also been established by considering the concentration of lung cancer cells in the blood and the amount of drug administered. The final simulation experiment proved the effectiveness of the drug delivery scheme.

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Dynamic Threshold Finite-Time Prescribed Performance Control for Nonlinear Systems With Dead-Zone Output.

This article investigates the tracking control problem for nonlinear systems. An adaptive model is proposed to represent the dead-zone phenomenon and solve its control challenge with a Nussbaum function in conjunction. Drawing inspiration from the existing prescribed performance control schemes, a novel dynamic threshold scheme is developed that fuses a proposed continuous function with a finite-time performance function. A dynamic event-triggered strategy is applied to reduce the redundant transmission. The proposed time-varying threshold control strategy has fewer updates than the traditional fixed threshold and improves the efficiency of resource utilization. A command filter backstepping approach is employed to prevent the complexity explosion faced by the computation. The suggested control strategy ensures that all system signals are bounded. The validity of the simulation results has been verified.

Fault-Tolerant Fuzzy-Resilient Control for Fractional-Order Stochastic Underactuated System With Unmodeled Dynamics and Actuator Saturation.

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1) . First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under such situation, the fault tolerant resilient controller based on underactuated condition is designed. Then, according to the method of the Lyapunov and It∧ o differential formulation to design proper multiple Lyapunov-Krasovskii (L-K) functions, such that, a novel sufficient condition of the robustly asymptotically stability of fuzzy FOSS under underactuated conditions is rigorously proved in terms of linear matrix inequality (LMI). Furthermore, in order to research the mean square stability of the above-mentioned system, so the solution of FOSS is obtained to achieve this purpose. By applying the above method, which is proposed in this work that the controlled system can be obtained with faster response and higher control accuracy. At last, to display the superiority of the above-mentioned scheme is effective, tethered satellite system and numerical results are presented.

Cooperative Control for Stochastic Multiagent Systems With Deferred Dynamic Constraints via a Novel Universal Barrier Function Approach.

This article investigates the cooperative control problem for stochastic multiagent systems (MASs) with dynamic constraints. A new universal barrier function is proposed, which is applicable to many systems with different types of constraint functions, even unconstrained systems. Several mapping functions are constructed to constrain the state variables directly without feasibility conditions, and the tracking control is achieved for stochastic MASs with deferred full-state constraints under the backstepping framework. In order to regulate the tracking error more precisely, the funnel error transformation is improved and the deferred funnel controller is developed by introducing a preassigned finite-time function. Based on the deferred funnel controller, the tracking error can be maintained within the predetermined funnel in the preassigned time. The convergence time can be defined according to the actual requirements, and it is independent of the design controller parameters and initial conditions. Finally, some simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

Adaptive Virotherapy Strategy for Organism With Constrained Input Using Medicine Dosage Regulation Mechanism.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

Unraveling chemical processes during nanoparticle synthesis with liquid phase electron microscopy and correlative techniques.

Liquid phase transmission electron microscopy (LPTEM) has enabled unprecedented direct real time imaging of physicochemical processes during solution phase synthesis of metallic nanoparticles. LPTEM primarily provides images of nanometer scale, and sometimes atomic scale, metal nanoparticle crystallization processes, but provides little chemical information about organic surface ligands, metal-ligand complexes and reaction intermediates, and redox reactions. Likewise, complex electron beam-solvent interactions during LPTEM make it challenging to pinpoint the chemical processes, some involving exotic highly reactive radicals, impacting nanoparticle formation. Pairing LPTEM with correlative solution synthesis, ex situ chemical analysis, and theoretical modeling represents a powerful approach to gain a holistic understanding of the chemical processes involved in nanoparticle synthesis. In this feature article, we review recent work by our lab and others that has focused on elucidating chemical processes during nanoparticle synthesis using LPTEM and correlative chemical characterization and modeling, including mass and optical spectrometry, fluorescence microscopy, solution chemistry, and reaction kinetic modeling. In particular, we show how these approaches enable investigating redox chemistry during LPTEM, polymeric and organic capping ligands, metal deposition mechanisms on plasmonic nanoparticles, metal clusters and complexes, and multimetallic nanoparticle formation. Future avenues of research are discussed, including moving beyond electron beam induced nanoparticle formation by using light and thermal stimuli during LPTEM. We discuss prospects for real time LPTEM imaging and online chemical analysis of reaction intermediates using microfluidic flow reactors.

Evolutionary Dynamics Optimal Research-Oriented Tumor Immunity Architecture.

The article is devoted to evolutionary dynamics optimal control-oriented tumor immune differential game system. First, the mathematical model covering immune cells and tumor cells considering the effects of chemotherapy drugs and immune agents. Second, the bounded optimal control problem covering is transformed into solving Hamilton-Jacobi-Bellman (HJB) equation considering the actual constraints and infinite-horizon performance index based on minimizing the amount of medication administered. Finally, approximate optimal control strategy is acquired through iterative-dual heuristic dynamic programming (I-DHP) algorithm avoiding dimensional disaster effectively and providing optimal treatment scheme for clinical applications.

Visualizing formation of high entropy alloy nanoparticles with liquid phase transmission electron microscopy.

High entropy alloy (HEA) nanoparticles hold promise as active and durable (electro)catalysts. Understanding their formation mechanism will enable rational control over composition and atomic arrangement of multimetallic catalytic surface sites to maximize their activity. While prior reports have attributed HEA nanoparticle formation to nucleation and growth, there is a dearth of detailed mechanistic investigations. Here we utilize liquid phase transmission electron microscopy (LPTEM), systematic synthesis, and mass spectrometry (MS) to demonstrate that HEA nanoparticles form by aggregation of metal cluster intermediates. AuAgCuPtPd HEA nanoparticles are synthesized by aqueous co-reduction of metal salts with sodium borohydride in the presence of thiolated polymer ligands. Varying the metal : ligand ratio during synthesis showed that alloyed HEA nanoparticles formed only above a threshold ligand concentration. Interestingly, stable single metal atoms and sub-nanometer clusters are observed by TEM and MS in the final HEA nanoparticle solution, suggesting nucleation and growth is not the dominant mechanism. Increasing supersaturation ratio increased particle size, which together with observations of stable single metal atoms and clusters, supported an aggregative growth mechanism. Direct real-time observation with LPTEM imaging showed aggregation of HEA nanoparticles during synthesis. Quantitative analyses of the nanoparticle growth kinetics and particle size distribution from LPTEM movies were consistent with a theoretical model for aggregative growth. Taken together, these results are consistent with a reaction mechanism involving rapid reduction of metal ions into sub-nanometer clusters followed by cluster aggregation driven by borohydride ion induced thiol ligand desorption. This work demonstrates the importance of cluster species as potential synthetic handles for rational control over HEA nanoparticle atomic structure.

Direct visualization of nanoparticle morphology in thermally sintered nanoparticle ink traces and the relationship among nanoparticle morphology, incomplete polymer removal, and trace conductivity.

A key challenge encountered by printed electronics is that the conductivity of sintered metal nanoparticle (NP) traces is always several times smaller than the bulk metal conductivity. Identifying the relative roles of the voids and the residual polymers on NP surfaces in sintered NP traces, in determining such reduced conductivity, is essential. In this paper, we employ a combination of electron microscopy imaging and detailed simulations to quantify the relative roles of such voids and residual polymers in the conductivity of sintered traces of a commercial (Novacentrix) silver nanoparticle-based ink. High resolution transmission electron microscopy imaging revealed details of the morphology of the inks before and after being sintered at 150 °C. Prior to sintering, NPs were randomly close packed into aggregates with nanometer thick polymer layers in the interstices. The 2D porosity in the aggregates prior to sintering was near 20%. After heating at 150 °C, NPs sintered together into dense aggregates (nanoaggregates or NAgs) with sizes ranging from 100 to 500 nm and the 2D porosity decreased to near 10%. Within the NAgs, the NPs were mostly connected via sintered metal bridges, while the outer surfaces of the NAgs were coated with a nanometer thick layer of polymer. Motivated by these experimental results, we developed a computational model for calculating the effective conductivity of the ink deposit represented by a prototypical NAg consisting of NPs connected by metallic bonds and having a polymer layer on its outer surface placed in a surrounding medium. The calculations reveal that a NAg that is 35%-40% covered by a nanometer thick polymeric layer has a similar conductivity compared to prior experimental measurements. The findings also demonstrate that the conductivity is less influenced by the polymer layer thickness or the absolute value of the NAg dimensions. Most importantly, we are able to infer that the reduced value of the conductivity of the sintered traces is less dependent on the void fraction and is primarily attributed to the incomplete removal of the polymeric material even after sintering.

Preassigned Time Adaptive Neural Tracking Control for Stochastic Nonlinear Multiagent Systems With Deferred Constraints.

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

Sliding Mode Control Based on Reinforcement Learning for T-S Fuzzy Fractional-Order Multiagent System With Time-Varying Delays.

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL), α ∈ (0,1). First, since there is information communication between an agent and another agent, a new distributed control policy ξi(t) is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of 0 using Lyapunov-Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input ui(t) are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.