Platelet Concentrates Preconditioning of Mesenchymal Stem Cells and Combined Therapies: Integrating Regenerative Strategies for Enhanced Clinical Applications.

This article presents a comprehensive review of the factors influencing the efficacy of mesenchymal stem cells (MSCs) transplantation and its association with platelet concentrates (PCs). It focuses on investigating the impact of PCs' composition, the age and health status of platelet donors, application methods, and environmental factors on the outcomes of relevant treatments. In addition, it delves into the strategies and mechanisms for optimizing MSCs transplantation with PCs, encompassing preconditioning and combined therapies. Furthermore, it provides an in-depth exploration of the signaling pathways and proteomic characteristics associated with preconditioning and emphasizes the efficacy and specific effects of combined therapy. The article also introduces the latest advancements in the application of biomaterials for optimizing regenerative medical strategies, stimulating scholarly discourse on this subject. Through this comprehensive review, the primary goal is to facilitate a more profound comprehension of the factors influencing treatment outcomes, as well as the strategies and mechanisms for optimizing MSCs transplantation and the application of biomaterials in regenerative medicine, offering theoretical guidance and practical references for related research and clinical practice.

Mechanisms of transcription factor selectivity.

The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome? Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.

A Dini-Derivative-Aided Zeroing Neural Network for Time-Variant Quadratic Programming Involving Multi-Type Constraints With Robotic Applications.

Time-variant quadratic programming (QP) with multi-type constraints including equality, inequality, and bound constraints is ubiquitous in practice. In the literature, there exist a few zeroing neural networks (ZNNs) that are applicable to time-variant QPs with multi-type constraints. These ZNN solvers involve continuous and differentiable elements for handling inequality and/or bound constraints, and they possess their own drawbacks such as the failure in solving problems, the approximated optimal solutions, and the boring and sometimes difficult process of tuning parameters. Differing from the existing ZNN solvers, this article aims to propose a novel ZNN solver for time-variant QPs with multi-type constraints based on a continuous but not differentiable projection operator that is deemed unsuitable for designing ZNN solvers in the community, due to the lack of the required time derivative information. To achieve the aforementioned aim, the upper right-hand Dini derivative of the projection operator with respect to its input is introduced to serve as a mode switcher, leading to a novel ZNN solver, termed Dini-derivative-aided ZNN (Dini-ZNN). In theory, the convergent optimal solution of the Dini-ZNN solver is rigorously analyzed and proved. Comparative validations are performed, verifying the effectiveness of the Dini-ZNN solver that has merits such as guaranteed capability to solve problems, high solution accuracy, and no extra hyperparameter to be tuned. To illustrate potential applications, the Dini-ZNN solver is successfully applied to kinematic control of a joint-constrained robot with simulation and experimentation conducted.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration-dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

The role of response elements organization in transcription factor selectivity: the IFN-ß enhanceosome example.

What is the mechanism through which transcription factors (TFs) assemble specifically along the enhancer DNA? The IFN-ß enhanceosome provides a good model system: it is small; its components' crystal structures are available; and there are biochemical and cellular data. In the IFN-ß enhanceosome, there are few protein-protein interactions even though consecutive DNA response elements (REs) overlap. Our molecular dynamics (MD) simulations on different motif combinations from the enhanceosome illustrate that cooperativity is achieved via unique organization of the REs: specific binding of one TF can enhance the binding of another TF to a neighboring RE and restrict others, through overlap of REs; the order of the REs can determine which complexes will form; and the alternation of consensus and non-consensus REs can regulate binding specificity by optimizing the interactions among partners. Our observations offer an explanation of how specificity and cooperativity can be attained despite the limited interactions between neighboring TFs on the enhancer DNA. To date, when addressing selective TF binding, attention has largely focused on RE sequences. Yet, the order of the REs on the DNA and the length of the spacers between them can be a key factor in specific combinatorial assembly of the TFs on the enhancer and thus in function. Our results emphasize cooperativity via RE binding sites organization.

Composite adaptive fuzzy control for synchronizing generalized Lorenz systems.

This paper presents a methodology of asymptotically synchronizing two uncertain generalized Lorenz systems via a single continuous composite adaptive fuzzy controller (AFC). To facilitate controller design, the synchronization problem is transformed into the stabilization problem by feedback linearization. To achieve asymptotic tracking performance, a key property of the optimal fuzzy approximation error is exploited by the Mean Value Theorem. The composite AFC, which utilizes both tracking and modeling error feedbacks, is constructed by introducing a series-parallel identification model into an indirect AFC. It is proved that the closed-loop system achieves asymptotic stability under a sufficient gain condition. Furthermore, the proposed approach cannot only synchronize two different chaotic systems but also significantly reduce computational complexity and implemented cost. Simulation studies further demonstrate the effectiveness of the proposed approach.

Singular perturbation-based saturated adaptive control for underactuated Euler-Lagrange systems.

This paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler-Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints; (2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt's Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints. The control effectiveness is validated by simulations on a two-link compliant robot arm.

Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay.

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton-Jacobi-Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms of the convergence of the adaptive critic design and the stability of the closed-loop systems, where parameter convergence can be achieved under a weakened excitation condition. Simulation studies are investigated to verify the efficacy of the presented design scheme.

Lysine120 interactions with p53 response elements can allosterically direct p53 organization.

p53 can serve as a paradigm in studies aiming to figure out how allosteric perturbations in transcription factors (TFs) triggered by small changes in DNA response element (RE) sequences, can spell selectivity in co-factor recruitment. p53-REs are 20-base pair (bp) DNA segments specifying diverse functions. They may be located near the transcription start sites or thousands of bps away in the genome. Their number has been estimated to be in the thousands, and they all share a common motif. A key question is then how does the p53 protein recognize a particular p53-RE sequence among all the similar ones? Here, representative p53-REs regulating diverse functions including cell cycle arrest, DNA repair, and apoptosis were simulated in explicit solvent. Among the major interactions between p53 and its REs involving Lys120, Arg280 and Arg248, the bps interacting with Lys120 vary while the interacting partners of other residues are less so. We observe that each p53-RE quarter site sequence has a unique pattern of interactions with p53 Lys120. The allosteric, DNA sequence-induced conformational and dynamic changes of the altered Lys120 interactions are amplified by the perturbation of other p53-DNA interactions. The combined subtle RE sequence-specific allosteric effects propagate in the p53 and in the DNA. The resulting amplified allosteric effects far away are reflected in changes in the overall p53 organization and in the p53 surface topology and residue fluctuations which play key roles in selective co-factor recruitment. As such, these observations suggest how similar p53-RE sequences can spell the preferred co-factor binding, which is the key to the selective gene transactivation and consequently different functional effects.

Preferred drifting along the DNA major groove and cooperative anchoring of the p53 core domain: mechanisms and scenarios.

While the importance of specific p53-DNA binding is broadly accepted, the recognition process is still not fully understood. Figuring out the initial tetrameric p53-DNA association and the swift and cooperative search for specific binding sites is crucial for understanding the transactivation mechanism and selectivity. To gain insight into the p53-DNA binding process, here we have carried out explicit solvent molecular dynamic (MD) simulations of several p53 core domain-DNA conformations with the p53 and the DNA separated by varying distances. p53 approached the DNA, bound non-specifically, and quickly drifted along the DNA surface to find the major groove, cooperatively anchoring in a way similar to the specific binding observed in the crystal structure. Electrostatics was the major driving force behind the p53 movement. Mechanistically, this is a cooperative process: key residues, particularly Lys120 and Arg280 acted as sensors; upon finding their hydrogen-bonding partners, they lock in, anchoring p53 into the major groove. Concomitantly, the DNA adopted a conformation that facilitated p53 easy access. The initial non-specific core domain-DNA contacts assist in shifting the DNA and the p53 substrates toward conformations "ready" for specific major groove binding, with subsequent optimization of the interactions. This work is an invited contribution for the special issue of the Journal of Molecular Recognition dedicated to Professor Martin Karplus.

Cooperativity dominates the genomic organization of p53-response elements: a mechanistic view.

p53-response elements (p53-REs) are organized as two repeats of a palindromic DNA segment spaced by 0 to 20 base pairs (bp). Several experiments indicate that in the vast majority of the human p53-REs there are no spacers between the two repeats; those with spacers, particularly with sizes beyond two nucleotides, are rare. This raises the question of what it indicates about the factors determining the p53-RE genomic organization. Clearly, given the double helical DNA conformation, the orientation of two p53 core domain dimers with respect to each other will vary depending on the spacer size: a small spacer of 0 to 2 bps will lead to the closest p53 dimer-dimer orientation; a 10-bp spacer will locate the p53 dimers on the same DNA face but necessitate DNA looping; while a 5-bp spacer will position the p53 dimers on opposite DNA faces. Here, via conformational analysis we show that when there are 0-2 bp spacers, p53-DNA binding is cooperative; however, cooperativity is greatly diminished when there are spacers with sizes beyond 2 bp. Cooperative binding is broadly recognized to be crucial for biological processes, including transcriptional regulation. Our results clearly indicate that cooperativity of the p53-DNA association dominates the genomic organization of the p53-REs, raising questions of the structural organization and functional roles of p53-REs with larger spacers. We further propose that a dynamic landscape scenario of p53 and p53-REs can better explain the selectivity of the degenerate p53-REs. Our conclusions bear on the evolutionary preference of the p53-RE organization and as such, are expected to have broad implications to other multimeric transcription factor response element organization.

Composite Learning Adaptive Dynamic Surface Control of Fractional-Order Nonlinear Systems.

Adaptive dynamic surface control (ADSC) is effective for solving the complexity problem in adaptive backstepping control of integer-order nonlinear systems. This article focuses on the ADSC design for parametric uncertain fractional-order nonlinear systems (FONSs). In each backstepping step, the virtual controller is driven to pass through a fractional dynamic surface whose fractional-order derivative can be calculated easily. An ADSC law that ensure tracking error convergence is designed. The proposed ADSC requires a stringent condition called persistent excitation (PE) to achieve parameter convergence. To relax this limitation, a prediction error is defined by using online recorded data and instantaneous data, and a composite learning law is proposed to utilize both the prediction error and the tracking error. Then, a composite learning ADSC (CLADSC) method is developed to guarantee tracking error convergence and accurate parameter estimation under an interval excitation condition that is weaker than the PE one. Finally, an illustrative example is presented to show the performance of our methods.

Adaptive Neural Network Backstepping Control of Fractional-Order Nonlinear Systems With Actuator Faults.

Backstepping control for fractional-order nonlinear systems (FONSs) requires the analytic calculation of fractional derivatives of certain complicated stabilizing functions, which becomes prohibitive as the order of the system increases. This article aims to facilitate the adaptive neural network (NN) backstepping control design for FONSs with actuator faults whose parameters and patterns are fully unknown. A fractional filtering approach, which obviates the requirement of analytic fractional differentiation, is used to generate command signals together with their fractional derivatives. Compensated tracking errors that can eliminate approximation errors of command signals are generated by fractional filters. The proposed adaptive NN command filtered backstepping control (ANNCFBC) approach, together with fractional adaptive laws, guarantees not only the boundedness of all involved variables but also the convergence of both the tracking error and the compensated tracking error to a sufficiently small region. Finally, simulation studies are given to indicate the effectiveness of the proposed control method.

Composite Learning Enhanced Robot Impedance Control.

The desired impedance dynamics can be achieved for a robot if and only if an impedance error converges to zero or a small neighborhood of zero. Although the convergence of impedance errors is important, it is seldom obtained in the existing impedance controllers due to robots modeling uncertainties and external disturbances. This brief proposes two composite learning impedance controllers (CLICs) for robots with parameter uncertainties based on whether a factorization assumption is satisfied or not. In the proposed control designs, the convergence of impedance errors, reflected by the convergence of parameter estimation errors and some auxiliary errors, is achieved by using composite learning laws under a relaxed excitation condition. The theoretical results are proven based on the Lyapunov theory. The effectiveness and advantages of the proposed CLICs are validated by simulations on a parallel robot in three cases.

Sequence analysis of p53 response-elements suggests multiple binding modes of the p53 tetramer to DNA targets.

The p53 tetramer recognizes specifically a 20-bp DNA element. Here, we examined symmetries encoded in p53 response elements (p53REs). We analyzed base inversion correlations within the half-site, as well as in the full-site palindrome. We found that p53REs are not only direct repeats of half-sites; rather, two p53 half-sites couple to form a higher order 20 bp palindrome. The palindrome couplings between the half-sites are stronger for the human than for the mouse genome. The full-site palindrome and half-site palindrome are controlled by insertions between the two half-sites. The most notable feature is that the full-site palindrome with coupling between quarter-sites one and four (H14 coupling) dominates the p53REs without insertions. The most frequently observed insertion in human p53REs of 3 bp enhances the half-site palindrome. The statistical frequencies of the coupling between the half-sites in the human genome correlate with grouped experimental p53 affinities with p53REs. Examination of known p53REs indicates the H14 couplings are stronger for positive regulation than for negatively regulated p53REs, with repressors having the lowest H14 couplings. We propose that the palindromic sequence couplings may encode such potential preferred multiple binding modes of the p53 tetramer to DNA.

Comparison of changes in body composition during puberty development of obese and normal-weight children in China.

OBJECTIVE: To compare the changes in body composition, including fat mass index (FMI) and fat free mass index (FFMI) during puberty development of obese and normal-weight children in China, and to explore the effect of age and gender on body composition. METHODS: A total of 356 children at the age of 7-15 years were enrolled in this study. Body composition of 10 normal-weight and obese children in each age group was measured by dual-energy x-ray absorptiometry (DEXA). FFMI and FMI were calculated according to the following formula: FFMI (kg x m(-2)) = FFM(kg) / height2 (m2) and FMI (kg x m(-2)) = FM (kg) / height2 (m2). RESULTS: The fat mass and fat free mass of obese children were significantly higher than those of normal-weight children (P < 0.05). The FMI and FFMI of obese children increased significantly with age and were higher than those of the same sex, gender, and age normal-weight children (P < 0.05). CONCLUSION: The levels of fat mass, fat free mass, FMI, and FFMI are different in obese and normal-weight children, and gender effects are significant in boys having higher levels of these indicators than in girls. FFMI and FMI can be used as monitoring indexes in weight control of obese children.

Identification and Control of Nonlinear Systems Using Neural Networks: A Singularity-Free Approach.

In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed and a new estimation error is used to update the augmented weights. Based on the identification results, two singularity-free NN indirect adaptive controllers are developed for nonlinear systems with unknown constant control gains or variable control gains, respectively. Because the singularity problem is eradicated, the proposed methods remove limitations on parameter estimates that are used to guarantee the positiveness of the estimated control gain. Consequently, a more accurate estimation result can be achieved and the system state can track the given reference signal more precisely. The effectiveness of the proposed identification and control algorithms are tested and the superiority of the proposed singularity-free approach is demonstrated by simulation results.

Robust Trajectory Tracking Control for Variable Stiffness Actuators With Model Perturbations.

Variable Stiffness Actuators (VSAs) have been introduced to develop new-generation compliant robots. However, the control of VSAs is still challenging because of model perturbations such as parametric uncertainties and external disturbances. This paper proposed a non-linear disturbance observer (NDOB)-based composite control approach to control both stiffness and position of VSAs under model perturbations. Compared with existing non-linear control approaches for VSAs, the distinctive features of the proposed approach include: (1) A novel modeling method is applied to analysis the VSA dynamics under complex perturbations produced by parameter uncertainties, external disturbances, and flexible deflection; (2) A novel composite controller integrated feedback linearization with NDOB is developed to increase tracking accuracy and robustness against uncertainties. Both simulations and experiments have verified the effectiveness of the proposed method on VSAs.

p53-Induced DNA bending: the interplay between p53-DNA and p53-p53 interactions.

Specific p53 binding-induced DNA bending and its underlying responsible forces are crucial for the understanding of selective transcription activation. Diverse p53-response elements exist in the genome; however, it is not known what determines the DNA bending and to what extent. In order to gain knowledge of the forces that govern the DNA bending, molecular dynamics simulations were performed on a series of p53 core domain tetramer-DNA complexes in which each p53 core domain was bound to a DNA quarter site specifically. By varying the sequence of the central 4-base pairs of each half-site, different DNA bending extents were observed. The analysis showed that the dimer-dimer interactions in p53 were similar for the complexes; on the other hand, the specific interactions between the p53 and DNA, including the interactions of Arg280, Lys120, and Arg248 with the DNA, varied more significantly. In particular, the Arg280 interactions were better maintained in the complex with the CATG-containing DNA sequence and were mostly lost in the complex with the CTAG-containing DNA sequence. Structural analysis shows that the base pairings for the CATG sequence were stable throughout the simulation trajectory, whereas those for the CTAG sequence were partially dissociated in part of the trajectory, which affected the stability of the nearby Arg280-Gua base interactions. Thus, DNA bending depends on the balance between the p53 dimer-dimer interactions and p53-DNA interactions, which is in turn related to the DNA sequence and DNA flexibility.

Ligand binding and circular permutation modify residue interaction network in DHFR.

Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network communication in DHFR. We carry out systematic network analysis and molecular dynamics simulations of the native DHFR and 19 of its circularly permuted variants by breaking the chain connections in ten folding element regions and in nine nonfolding element regions as observed by experiment. Our studies suggest that chain cleavage in folding element areas may deactivate DHFR due to large perturbations in the network properties near the active site. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Further, our network analysis reveals that ligand binding has "network-bridging effects" on the DHFR structure. Our results suggest that ligand binding leads to a modification, with most of the interaction networks now passing through the cofactor, shortening the average shortest path. Ligand binding at the active site has profound effects on the network centrality, especially the closeness.