Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud.

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Learning Robust Predictive Control: A Spatial-Temporal Game Theoretic Approach.

This article investigates robust predictive control problem for unknown dynamical systems. Since the dynamics unavailability restricts feasibility of model-driven methods, learning robust predictive control (LRPC) framework is developed from the aspect of time consistency. Under feedback-like control causality, the robust predictive control is then reconstructed as spatialbKKtemporal games, and we guarantee stability through time-consistent Nash equilibrium. For gradation clarity, our framework is specified as four-follow contents. First, multistep feedback-like control causality is drawn from time series analysis, and Takens' theorem provides theoretical support from steady-state property. Second, control problem is reconstructed as games, while performance and robustness partition the game into temporal nonzero-sum subgames and spatial zero-sum ones, respectively. Next, multistep reinforcement learning (RL) is designed to solve robust predictive control without system model. Convergence is proven through bounds analysis of oscillatory value functions, and properties of receding horizon are derived from time consistency. Finally, data-driven implementation is given with function approximation, and neural networks are chosen to approximate value functions and feedback-like causality. Weights are estimated with least squares errors. Numerical results verify the effectiveness.

Intense Left-handed Circularly Polarized Luminescence in Chiral Nematic Hydroxypropyl Cellulose Composite Films.

Integrating optically active components into chiral photonic cellulose to fabricate circularly polarized luminescent materials has transformative potential in disease detection, asymmetric reactions, and anticounterfeiting techniques. However, the lack of cellulose-based left-handed circularly polarized light (L-CPL) emissions hampers the progress of these chiral functionalizations. Here, this work proposes an unprecedented strategy: incorporating a chiral nematic organization of hydroxypropyl cellulose with robust aggregation-induced emission luminogens to generate intense L-CPL emission. By utilizing N,N-dimethylformamide as a good solvent for fluorescent components and cellulose matrices, this work produces a right-handed chiral nematic structure film with a uniform appearance in reflective and fluorescent states. Remarkably, this system integrates a high asymmetric factor (0.51) and an impressive emission quantum yield (55.8%) into one fascinating composite. More meaningfully, this approach is versatile, allowing for the incorporation of luminogen derivatives emitting multicolored L-CPL. These chiral fluorescent films possess exceptional mechanical flexibility (toughness up to 0.9 MJ m-3) and structural stability even under harsh environmental exposures, making them promising for the fabrication of various products. Additionally, these films can be cast on the fabrics to reveal multilevel and durable anticounterfeiting capabilities or used as a chiral light source to induce enantioselective photopolymerization, thereby offering significant potential for diverse practical applications.

A novel fluorescent probe for cascade detection of hydrogen sulfide and hypochlorous acid and its application in bioimaging.

Herein we developed a cascade detection mode for the detection of HS- and ClO- by the novel probe NM-Cl bearing a conjugating naphthalene-dicyanoisophorone unit. The probe displayed sensitive and remarkable fluorescent enhancement in response to HS-, but not to other analytes. The mixture of probe and HS- constructed a specific sensing system for ClO- by fluorescent quenching response. The mechanism studies indicated that the successive reacting of HS- substitution Cl atom in probe and then addition of ClO- facilitation a thiofuran ring-forming induced differentiated fluorescence emission. This study provides a novel mechanism for the detection of HS- and ClO-, the imaging of cell and living animal further indicating the good application prospects of the probe in biosensing and bioimaging.

Regulated secretion of mutant p53 negatively affects T lymphocytes in the tumor microenvironment.

Several studies have demonstrated the role of the oncogenic mutant p53 in promoting tumor progression; however, there is limited information on the effects of secreted oncogenic mutant p53 on the tumor microenvironment and tumor immune escape. In this study, we found that secretion of mutant p53, determined by exosome content, is dependent on its N-terminal dileucine motif via its binding to ß-adaptin, and inhibited by the CHK2-mediated-Ser 20 phosphorylation. Moreover, we observed that the mutant p53 caused downregulation and dysfunction of CD4+ T lymphocytes in vivo and downregulated the levels and activities of rate-limiting glycolytic enzymes in vitro. Furthermore, inhibition of mutant p53 secretion by knocking down AP1B1 or mutation of dileucine motif could reverse the quantity and function of CD4+ T lymphocytes and restrain the tumor growth. Our study demonstrates that the tumor-derived exosome-mediated secretion of oncogenic mutant p53 inhibits glycolysis to alter the immune microenvironment via functional suppression of CD4+ T cells, which may be the underlying mechanism for tumor immune escape. Therefore, targeting TDE-mediated p53 secretion may serve as a potential therapeutic target for cancer treatment.

Composite Hydrogel Conduit Incorporated with Platelet-Rich Plasma Improved the Regenerative Microenvironment for Peripheral Nerve Repair.

Peripheral nerve regeneration and functional recovery remain major challenges in clinical practice. Nerve guidance conduits (NGCs) which can regulate the regenerative microenvironment are beneficial for peripheral nerve repair. Platelet-rich plasma (PRP) can secrete multiple growth factors to regulate the regenerative microenvironment. However, current administration methods of PRP are rapidly activated followed by the burst release of growth factors, causing low therapeutic efficiency in vivo. To overcome these disadvantages, a composite nerve conduit was fabricated by incorporating PRP into a gelatin methacrylate (GelMA) and sodium alginate (SA) hydrogel. The GelMA/SA-3/PRP-20 NGCs possess optimal mechanical properties, degradation rate, and superior biological performance. Importantly, GelMA/SA-3/PRP-20 NGCs achieved the sustained release of two major growth factors (VEGF-A, PDGF-BB) from PRP. Moreover, the GelMA/SA-3/PRP-20 NGCs significantly promoted the migration of Schwann cells and the neovascularization of endothelial cells in vitro. While bridging 10 mm rat sciatic nerve defects, the GelMA/SA-3/PRP-20 NGCs promoted axonal regeneration and functional recovery of peripheral nerves. Therefore, the GelMA/SA-3/PRP-20 NGCs could regulate the regenerative microenvironment by sustained release of growth factors from PRP and shed new light on the clinical application of PRP in peripheral nerve repair.

A novel near-infrared fluorescent probe with large Stokes shift for imagining hydrogen sulfide.

Hydrogen sulfide (H2S) plays an important role in regulating varieties of important physiological and pathological processes. Thus the development of fluorescent probe for the detection of H2S is of great significance and has attracted much attention recently. Herein, we reported a novel near-infrared (NIR) emitting fluorescent probe WFP-PC, which contained a positive charged hemicyanine-based WFP-OH as fluorophore and thiobenzoate unit as a specific reaction site. After treated with H2S, the probe exhibited significant fluorescence enhancement and response time within 4 min and detection limit as low as 0.47 µM, accompanied by color changes from purple to blue. The probe was successfully applied to imaging the exogenous/endogenous H2S in cells and mice, suggesting it could be a promising molecular tool for H2S detection in living systems.

Data-Based Predictive Control via Multistep Policy Gradient Reinforcement Learning.

In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.

Antibacterial Soy Protein Isolate Prepared by Quaternization.

Soy protein isolate (SPI) is green, high-yield natural plant protein, which is widely applied in industry (packing material and adhesives) and tissue engineering. It is meaningful to improve the antibacterial property of soy protein isolate to fabricate versatile safe products to meet people's requirements. In this study, quaternized soy protein isolate (QSPI) was synthesized by the reaction between 2,3-epoxypropyltrimethylammonium chloride (EPTMAC) and SPI. The positive charged (17.8 ± 0.23 mV) quaternary ammonium groups endow the QSPI with superior antibacterial properties against multiple bacteria in vitro and in vivo. Notably, QSPI maintains its good biocompatibility and promotes bacterial-infected wound healing in rat models. Furthermore, QSPI possesses superior water solubility in a broad pH range than raw SPI. Altogether, this soy protein isolate derivative with antibacterial property and superior water solubility may extend the application of SPI in industry and tissue engineering.

Injectable shape memory hydroxyethyl cellulose/soy protein isolate based composite sponge with antibacterial property for rapid noncompressible hemorrhage and prevention of wound infection.

Uncontrollable hemorrhage and subsequent wound infection are severe threats to life, especially for the deep noncompressible massive bleeding. However, traditional hemostatic materials are ineffective for extreme bleeding and subsequent wound infection. Here, we prepared an injectable shape memory hydroxyethyl cellulose/soy protein isolate based composite sponge (EHSS) for rapid noncompressible hemorrhage and prevention of wound infection. The nano silver (AgNPs)-loaded shape memory sponge (EHP@Ag) was fabricated by mussel-inspired polydopamine coating EHSS sponge, then reducing and immobilizing AgNPs in situ. The EHP@Ag sponges showed rapid blood-triggered shape recovery speed, which is beneficial for administering noncompressible hemorrhage. The results of the hemostatic experiment in vivo demonstrated that EHP@Ag sponge exhibited a desirable hemostasis effect (hemostasis time: 22.75 ± 3.86 s, blood loss: 285.25 ± 24.93 mg) compared to the commercial gelatin sponge (hemostasis time: 49.25 ± 3.30 s, blood loss: 755.50 ± 24.45 mg). Meanwhile, the EHP@Ag sponge has an efficient antibacterial property. Furthermore, the antibacterial experiment in vivo showed that the EHP@Ag sponges could kill bacteria effectively and reduce the bacteria-induced inflammatory response. In summary, the shape memory sponges can quickly control bleeding and avoid bacterial infection, which shows great potential for clinical application as a multifunctional hemostatic agent.

A hemicyanidin-based NIR fluorescent probe for detection of H2S and imaging study in cells and mice.

The selective detection of hydrogen sulfide in physiological and pathological processes has gained substantial attention in recent years. However, the real-time detection of hydrogen sulfide remains an elusive goal. In this work, a new type of hemicyanidin-based fluorescent "turn-on" probe NTR-HS (Ex = 680 nm, Em = 760 nm) was developed to detected H2S in a very short time (3 min). The fluorescence quantum yield is 0.15 and accompanied with a noticeable color change from violet to blue that can be used to detect H2S in the range 1.04 × 10-7-4 × 10-5 M with a limit of detection of 1.04 x 10-7 M. The NTR-HS probe was also used for imaging of endogenous hydrogen sulfide and mitochondrial localization in HCT116 and HeLa cells. The detection mechanism was studied through fluorescence, UV-Vis, NMR, and mass analysis. Notably, the probe was successfully used to imaging H2S in mice and locating hydrogen sulfide in the large intestine of mice.

Data-Based Optimal Consensus Control for Multiagent Systems With Policy Gradient Reinforcement Learning.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.