The design of new materials with special performances is still a great challenge, especially for the deep-ultraviolet nonlinear optical materials in which it is difficult to balance large bandgaps and strong second harmonic generation responses due to their inverse relationship. Cation variation not only influences the whole structure frameworks but also directly participates in the formation of electronic structures, both of which could lead to the uncontrollability of the properties of the designed materials. Here, a novel approach, aiming at purposeful and foreseeable material designs, is proposed to characterize the role of cations. By the verification of several series of borates, the influences of cation variation on property changes are explored systematically. Accordingly, a feasible strategy of designing deep-ultraviolet nonlinear optical materials by substituting barium for lead has been concluded, which could obviously blue-shift the ultraviolet cutoff edge and maintain the relatively strong second harmonic generation response (more than 2 times of KH₂PO₄), achieving the property optimization, and especially works efficiently in fluorooxoborates. The property optimization design strategy and the cation characterization method are not only helpful in exploring nonlinear optical materials but also enlightening in material design and selection.

Efficient in vivo delivery of anti-inflammatory proteins to modulate the microenvironment of an injured spinal cord and promote neuroprotection and functional recovery is a great challenge. Nucleoside-modified messenger RNA (mRNA) has become a promising new modality that can be utilized for the safe and efficient delivery of therapeutic proteins. Here, we used lipid nanoparticle (LNP)-encapsulated human interleukin-10 (hIL-10)-encoding nucleoside-modified mRNA to induce neuroprotection and functional recovery following rat spinal cord contusion injury. Intralesional administration of hIL-10 mRNA-LNP to rats led to a remarkable reduction of the microglia/macrophage reaction in the injured spinal segment and induced significant functional recovery compared to controls. Furthermore, hIL-10 mRNA treatment induced increased expression in tissue inhibitor of matrix metalloproteinase 1 and ciliary neurotrophic factor levels in the affected spinal segment indicating a time-delayed secondary effect of IL-10 5 d after injection. Our results suggest that treatment with nucleoside-modified mRNAs encoding neuroprotective factors is an effective strategy for spinal cord injury repair.

Over the past 60 years, the semiconductor industry has been the core driver for the development of information technology, contributing to the birth of integrated circuits, Internet, artificial intelligence, and Internet of Things. Semiconductor technology has been evolving in structure and material with co-optimization of performance–power–area–cost until the state-of-the-art sub-5-nm node. Two-dimensional (2D) semiconductors are recognized by the industry and academia as a hopeful solution to break through the quantum confinement for the future technology nodes. In the recent 10 years, the key issues on 2D semiconductors regarding material, processing, and integration have been overcome in sequence, making 2D semiconductors already on the verge of application. In this paper, the evolution of transistors is reviewed by outlining the potential of 2D semiconductors as a technological option beyond the scaled metal oxide semiconductor field-effect transistors. We mainly focus on the optimization strategies of mobility (μ), equivalent oxide thickness (EOT), and contact resistance (R_C), which enables high ON current (I_on) with reduced driving voltage (V_dd). Finally, we prospect the semiconductor technology roadmap by summarizing the technological development of 2D semiconductors over the past decade.

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.

Locomotion in mollusc Aplysia is implemented by a pedal rolling wave, a type of axial locomotion. Well-studied examples of axial locomotion (pedal waves in Drosophila larvae and body waves in leech, lamprey, and fish) are generated in a segmented nervous system via activation of multiple coupled central pattern generators (CPGs). Pedal waves in molluscs, however, are generated by a single pedal ganglion, and it is unknown whether there are single or multiple CPGs that generate rhythmic activity and phase shifts between different body parts. During locomotion in intact Aplysia, bursting activity in the parapedal commissural nerve (PPCN) was found to occur during tail contraction. A cluster of 20 to 30 P1 root neurons (P1Ns) on the ventral surface of the pedal ganglion, active during the pedal wave, were identified. Computational cluster analysis revealed that there are 2 phases to the motor program: phase I (centered around 168°) and phase II (centered around 357°). PPCN activity occurs during phase II. The majority of P1Ns are motoneurons. Coactive P1Ns tend to be electrically coupled. Two classes of pedal interneurons (PIs) were characterized. Class 1 (PI1 and PI2) is active during phase I. Their axons make a loop within the pedal ganglion and contribute to locomotor pattern generation. They are electrically coupled to P1Ns that fire during phase I. Class 2 (PI3) is active during phase II and innervates the contralateral pedal ganglion. PI3 may contribute to bilateral coordination. Overall, our findings support the idea that Aplysia pedal waves are generated by a single CPG.

Microglia are resident macrophage cells in the central nervous system that search for pathogens or abnormal neural activities and migrate to resolve the issues. The effective search and targeted motion of macrophages mean dearly to maintaining a healthy brain, yet little is known about their migration dynamics. In this work, we study microglial motion with and without the presence of external mechanostimuli. We discover that the cells are promptly attracted by the applied forces (i.e., mechanotaxis), which is a tactic behavior as yet unconfirmed in microglia. Meanwhile, in both the explorative and the targeted migration, microglia display dynamics that is strikingly analogous to bacterial run-and-tumble motion. A closer examination reveals that microglial run-and-tumble is more sophisticated, e.g., they display a short-term memory when tumbling and rely on active steering during runs to achieve mechanotaxis, probably via the responses of mechanosensitive ion channels. These differences reflect the sharp contrast between microglia and bacteria cells (eukaryotes vs. prokaryotes) and their environments (compact tissue vs. fluid). Further analyses suggest that the reported migration dynamics has an optimal search efficiency and is shared among a subset of immune cells (human monocyte and macrophage). This work reveals a fruitful analogy between the locomotion of 2 remote systems and provides a framework for studying immune cells exploring complex environments.

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.

The transition metal trichalcogenides (TMTCs) are quasi-one-dimensional (1D) MX3-type van der Waals layered semiconductors, where M is a transition metal element of groups IV and V, and X indicates chalcogen element. Due to the unique quasi-1D crystalline structures, they possess several novel electrical properties such as variable bandgaps, charge density waves, and superconductivity, and highly anisotropic optical, thermoelectric, and magnetic properties. The study of TMTCs plays an essential role in the 1D quantum materials field, enabling new opportunities in the material research dimension. Currently, tremendous progress in both materials and solid-state devices has been made, demonstrating promising applications in the realization of nanoelectronic devices. This review provides a comprehensive overview to survey the state of the art in materials, devices, and applications based on TMTCs. Firstly, the symbolic structure, current primary synthesis methods, and physical properties of TMTCs have been discussed. Secondly, examples of TMTC applications in various fields are presented, such as photodetectors, energy storage devices, catalysts, and sensors. Finally, we give an overview of the opportunities and future perspectives for the research of TMTCs, as well as the challenges in both basic research and practical applications.

Heterogeneous catalysts, especially metal oxides, play a curial role in improving energy conversion efficiency and production of valuable chemicals. However, the surface structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. This paper describes a strategy of the multiscale simulation to investigate the SnO_x reduction process and to build a structure–performance relation of SnO_x for CO₂ electroreduction. Employing high-dimensional neural network potential accelerated molecular dynamics and stochastic surface walking global optimization, coupled with density functional theory calculations, we propose that SnO₂ reduction is accompanied by surface reconstruction and charge density redistribution of active sites. A regulatory factor, the net charge, is identified to predict the adsorption capability for key intermediates on active sites. Systematic electronic analyses reveal the origin of the interaction between the adsorbates and the active sites. These findings uncover the quantitative correlation between electronic structure properties and the catalytic performance of SnO_x so that Sn sites with moderate charge could achieve the optimally catalytic performance of the CO₂ electroreduction to formate.

Osteoarthritis (OA) is always characterized as excessive reactive oxygen species (ROS) inside articular cavity. Mimicking natural metalloenzymes with metal ions as the active centers, stable metal organic framework (MOF) formed by natural polyphenols and metal ions shows great potential in alleviating inflammatory diseases. Herein, a series of novel copper-morin-based MOF (CuMHs) with different molar ratios of Cu²⁺ and MH were employed to serve as ROS scavengers for OA therapy. As a result, CuMHs exhibited enhanced dispersion in aqueous solution, improved biocompatibility, and efficient ROS-scavenging ability compared to MH. On the basis of H₂O₂-stimulated chondrocytes, intracellular ROS levels were efficiently declined and cell death was prevented after treated by Cu₆MH (Cu²⁺ and MH molar ratio of 6:1). Meanwhile, Cu₆MH also exhibited efficient antioxidant and anti-inflammation function by down-regulating the expression of IL6, MMP13, and MMP3, and up-regulating cartilage specific gene expression as well. Importantly, Cu₆MH could repair mitochondrial function by increasing mitochondrial membrane potential, reducing the accumulation of calcium ions, as well as promoting ATP content production. In OA joint model, intra-articular (IA) injected Cu₆MH suppressed the progression of OA. It endowed that Cu₆MH might be promising nanoenzymes for the prevention and treatment of various inflammatory diseases.