Showing papers by "Shandong Normal University published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Regulation of Plant Responses to Salt Stress.

Abstract: Salt stress is a major environmental stress that affects plant growth and development. Plants are sessile and thus have to develop suitable mechanisms to adapt to high-salt environments. Salt stress increases the intracellular osmotic pressure and can cause the accumulation of sodium to toxic levels. Thus, in response to salt stress signals, plants adapt via various mechanisms, including regulating ion homeostasis, activating the osmotic stress pathway, mediating plant hormone signaling, and regulating cytoskeleton dynamics and the cell wall composition. Unraveling the mechanisms underlying these physiological and biochemical responses to salt stress could provide valuable strategies to improve agricultural crop yields. In this review, we summarize recent developments in our understanding of the regulation of plant salt stress.

Protein kinases in plant responses to drought, salt, and cold stress.

Abstract: Protein kinases are major players in various signal transduction pathways. Understanding the molecular mechanisms behind plant responses to biotic and abiotic stresses has become critical for developing and breeding climate-resilient crops. In this review, we summarize recent progress on understanding plant drought, salt, and cold stress responses, with a focus on signal perception and transduction by different protein kinases, especially sucrose nonfermenting1 (SNF1)-related protein kinases (SnRKs), mitogen-activated protein kinase (MAPK) cascades, calcium-dependent protein kinases (CDPKs/CPKs), and receptor-like kinases (RLKs). We also discuss future challenges in these research fields.

Maximum Density Divergence for Domain Adaptation

Abstract: Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence (“match” in ATM) and maximizes the intra-class density (“tight” in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations.

Honeycomb Carbon Nanofibers: A Superhydrophilic O2-Entrapping Electrocatalyst Enables Ultrahigh Mass Activity for the Two-Electron Oxygen Reduction Reaction

Abstract: Electrocatalytic two-electron oxygen reduction has emerged as a promising alternative to the energy- and waste-intensive anthraquinone process for distributed H2 O2 production. This process, however, suffers from strong competition from the four-electron pathway leading to low H2 O2 selectivity. Herein, we report using a superhydrophilic O2 -entrapping electrocatalyst to enable superb two-electron oxygen reduction electrocatalysis. The honeycomb carbon nanofibers (HCNFs) are robust and capable of achieving a high H2 O2 selectivity of 97.3 %, much higher than that of its solid carbon nanofiber counterpart. Impressively, this catalyst achieves an ultrahigh mass activity of up to 220 A g-1 , surpassing all other catalysts for two-electron oxygen reduction reaction. The superhydrophilic porous carbon skeleton with rich oxygenated functional groups facilitates efficient electron transfer and better wetting of the catalyst by the electrolyte, and the interconnected cavities allow for more effective entrapping of the gas bubbles. The catalytic mechanism is further revealed by in situ Raman analysis and density functional theory calculations.

Selective autophagy of intracellular organelles: recent research advances.

Abstract: Macroautophagy (hereafter called autophagy) is a highly conserved physiological process that degrades over-abundant or damaged organelles, large protein aggregates and invading pathogens via the lysosomal system (the vacuole in plants and yeast). Autophagy is generally induced by stress, such as oxygen-, energy- or amino acid-deprivation, irradiation, drugs, etc. In addition to non-selective bulk degradation, autophagy also occurs in a selective manner, recycling specific organelles, such as mitochondria, peroxisomes, ribosomes, endoplasmic reticulum (ER), lysosomes, nuclei, proteasomes and lipid droplets (LDs). This capability makes selective autophagy a major process in maintaining cellular homeostasis. The dysfunction of selective autophagy is implicated in neurodegenerative diseases (NDDs), tumorigenesis, metabolic disorders, heart failure, etc. Considering the importance of selective autophagy in cell biology, we systemically review the recent advances in our understanding of this process and its regulatory mechanisms. We emphasize the 'cargo-ligand-receptor' model in selective autophagy for specific organelles or cellular components in yeast and mammals, with a focus on mitophagy and ER-phagy, which are finely described as types of selective autophagy. Additionally, we highlight unanswered questions in the field, helping readers focus on the research blind spots that need to be broken.

Two-photon small-molecule fluorescence-based agents for sensing, imaging, and therapy within biological systems

Abstract: In this tutorial review, we will explore recent advances for the design, construction and application of two-photon excited fluorescence (TPEF)-based small-molecule probes. The advantages of TPEF-based probes include deep tissue penetration and minimal photo-damage. We discuss the underlying two-photon (TP) fluorophores including hemicyanine and design strategies such as Forster resonance energy transfer (FRET). Moreover, we emphasize applications for the detection or imaging of cations, anions, small neutral molecules, biomacromolecules, cellular microenvironments, subcellular localization and dual-responsive systems. Examples of photodynamic therapy (PDT) using TP irradiation are also illustrated.

Observation of a Near-Threshold Structure in the K+ Recoil-Mass Spectra in e+e- →k+ (Ds- D∗0+ Ds∗- D0)

Abstract: We report a study of the processes of e^{+}e^{-}→K^{+}D_{s}^{-}D^{*0} and K^{+}D_{s}^{*-}D^{0} based on e^{+}e^{-} annihilation samples collected with the BESIII detector operating at BEPCII at five center-of-mass energies ranging from 4.628 to 4.698 GeV with a total integrated luminosity of 3.7 fb^{-1}. An excess of events over the known contributions of the conventional charmed mesons is observed near the D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0} mass thresholds in the K^{+} recoil-mass spectrum for events collected at sqrt[s]=4.681 GeV. The structure matches a mass-dependent-width Breit-Wigner line shape, whose pole mass and width are determined as (3982.5_{-2.6}^{+1.8}±2.1) MeV/c^{2} and (12.8_{-4.4}^{+5.3}±3.0) MeV, respectively. The first uncertainties are statistical and the second are systematic. The significance of the resonance hypothesis is estimated to be 5.3 σ over the contributions only from the conventional charmed mesons. This is the first candidate for a charged hidden-charm tetraquark with strangeness, decaying into D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0}. However, the properties of the excess need further exploration with more statistics.

Interest-aware Message-Passing GCN for Recommendation

Abstract: Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem – when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user’s embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly.

A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

Abstract: In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

A-site perovskite oxides: an emerging functional material for electrocatalysis and photocatalysis

Abstract: Catalysts for electrochemical and photochemical reactions play critical roles in energy storage and conversion as well as degradation of organic pollutants. Due to their unique structure, composition flexibility and high stability, A-site perovskite oxides are promising candidates in electrocatalysis and photocatalysis. In this article, we review the recent progress of A-site perovskite oxides as an emerging functional material in electrocatalysis and photocatalysis. Firstly, we summarize the different factors affecting the structure and composition of A-site perovskite oxides. Then, the performance of A-site perovskite oxides (bimetallic, ternary metal, multimetallic and oxynitride) in electrocatalysis and photocatalysis is systematically discussed. Particularly, the rational optimization strategies (such as doping, introducing defects, and surface modification) of A-site cation perovskite oxides (A = La, Sr, Ba, Ca, Ag, Bi, Na, K) are also described to further enhance the performance. Finally, we conclude the challenges and prospects of A-site perovskite oxides to improve the (electro) photocatalytic activity, conductivity and stability.

Antitumor Agents Based on Metal-Organic Frameworks.

Abstract: Metal-organic frameworks (MOFs) hold great promise for biomedical applications owing to their unique properties. The porous structures make MOFs excellent candidates for the delivery of different drugs; the flexibility in choosing metal ions and organic ligands makes it feasible to prepare MOFs with intrinsic antitumor activities and further devise MOF-drug synergistic systems; many other types of antitumor agents could also be developed using MOFs as the precursors/templates. Thus, the past two decades have witnessed the great development of MOF-based drugs, especially in the antitumor field. This Minireview mainly focuses on the design and applications of MOF-based antitumor agents. Four aspects covering the whole field are introduced: MOFs as carriers, MOFs as antitumor agents, MOF-drug synergistic systems, and MOF-derived antitumor agents. The challenges and opportunities of MOFs for clinical antitumor applications are also discussed.

Engineering a photosensitizer nanoplatform for amplified photodynamic immunotherapy via tumor microenvironment modulation.

Abstract: Photosensitizer-based photodynamic therapy (PDT) can not only kill tumor cells by the generated cytotoxic reactive oxygen species (ROS), but also trigger immunogenic cell death (ICD) and activate an immune response for immunotherapy. However, such photodynamic immunotherapy suffers from major obstacles in the tumor microenvironment. The hypoxic microenvironment greatly weakens PDT, while the immunosuppressive tumor microenvironment caused by aberrant tumor blood vessels and indoleamine 2,3-dioxygenase (IDO) leads to a significant reduction in immunotherapy. To overcome these obstacles, herein, an engineered photosensitizer nanoplatform is designed for amplified photodynamic immunotherapy by integrating chlorin e6 (Ce6, a photosensitizer), axitinib (AXT, a tyrosine kinase inhibitor) and dextro-1-methyl tryptophan (1MT, an IDO inhibitor). In our nanoplatform, AXT improves the tumor microenvironment by normalizing tumor blood vessels, which not only promotes PDT by reducing the level of hypoxia of the tumor microenvironment, but also promotes immunotherapy through facilitating infiltration of immune effector cells into the tumor and reversing the immunosuppressive effect of vascular endothelial growth factor (VEGF). Moreover, 1MT effectively inhibits the activity of IDO, further reducing the immunosuppressive nature of the tumor microenvironment. Therefore, this nanoplatform demonstrates an amplified photodynamic immunotherapy via tumor microenvironment modulation, exhibiting outstanding therapeutic efficacy against tumor growth and metastasis with negligible side toxicity. The current concept of engineering photosensitizer nanoplatforms for overcoming photodynamic immunotherapy obstacles provides a promising strategy against tumors.

The 2020 China report of the Lancet Countdown on health and climate change.

Abstract: Wenjia Cai*, Chi Zhang*, Hoi Ping Suen*, Siqi Ai, Yuqi Bai, Junzhe Bao, Bin Chen, Liangliang Cheng, Xueqin Cui, Hancheng Dai, Qian Di, Wenxuan Dong, Deijing Dou, Weicheng Fan, Xing Fan, Tong Gao, Yang Geng, Dabo Guan, Yafei Guo, Yixin Hu, Junyi Hua, Cunrui Huang, Hong Huang, Jianbin Huang, Tingting Jiang, Kedi Jiao, Gregor Kiesewetter, Zbigniew Klimont, Pete Lampard, Chuanxi Li, Qiwei Li, Ruiqi Li, Tiantian Li, Borong Lin, Hualiang Lin, Huan Liu, Qiyong Liu, Xiaobo Liu, Yufu Liu, Zhao Liu, Zhidong Liu, Zhu Liu, Shuhan Lou, Chenxi Lu, Yong Luo, Wei Ma, Alice McGushin, Yanlin Niu, Chao Ren, Zhehao Ren, Zengliang Ruan, Wolfgang Schöpp, Jing Su, Ying Tu, Jie Wang, Qiong Wang, Yaqi Wang, Yu Wang, Nick Watts, Congxi Xiao, Yang Xie, Hui Xiong, Mingfang Xu, Bing Xu, Lei Xu, Jun Yang, Lianping Yang, Le Yu, Yujuan Yue, Shaohui Zhang, Zhongchen Zhang, Jiyao Zhao, Liang Zhao, Mengzhen Zhao, Zhe Zhao, Jingbo Zhou, Peng Gong

Input-to-state stability of impulsive reaction–diffusion neural networks with infinite distributed delays

Abstract: In this paper, we focus on the global existence–uniqueness and input-to-state stability of the mild solution of impulsive reaction–diffusion neural networks with infinite distributed delays. First, the model of the impulsive reaction–diffusion neural networks with infinite distributed delays is reformulated in terms of an abstract impulsive functional differential equation in Hilbert space and the local existence–uniqueness of the mild solution on impulsive time interval is proven by the Picard sequence and semigroup theory. Then, the diffusion–dependent conditions for the global existence–uniqueness and input-to-state stability are established by the vector Lyapunov function and M-matrix where the infinite distributed delays are handled by a novel vector inequality. It shows that the ISS properties can be retained for the destabilizing impulses if there are no too short intervals between the impulses. Finally, three numerical examples verify the effectiveness of the theoretical results and that the reaction–diffusion benefits the input-to-state stability of the neural-network system.

Ti2O3 Nanoparticles with Ti3+ Sites toward Efficient NH3 Electrosynthesis under Ambient Conditions.

Abstract: Electrocatalytic nitrogen reduction reaction (NRR) enabled by introducing Ti3+ defect sites into TiO2 through a doping strategy has recently attracted widespread attention. However, the amount of Ti3+ ions is limited due to the low concentration of dopants. Herein, we propose Ti2O3 nanoparticles as a pure Ti3+ system that performs efficiently toward NH3 electrosynthesis under ambient conditions. This work has suggested that Ti3+ ions, as the main catalytically active sites, significantly increase the NRR activity. In an acidic electrolyte, Ti2O3 achieves extraordinary performance with a high NH3 yield and a Faradaic efficiency of 26.01 μg h-1 mg-1 cat. and 9.16%, respectively, which are superior to most titanium-based NRR catalysts recently reported. Significantly, it also demonstrates a stable NH3 yield in five consecutive cycles. Theoretical calculations uncovered that the enhanced electrocatalytic activity of Ti2O3 originated from Ti3+ active sites and significantly lowered the overpotential of the potential-determining step.

Constructing a hollow microflower-like ZnS/CuS@C heterojunction as an effective ion-transport booster for an ultrastable and high-rate sodium storage anode

Abstract: Hierarchical heterostructure coupling metal sulfides with carbonaceous functional support are regarded as promising anode candidates for sodium-ion batteries (SIBs) owing to their rich diffusion channels and active sites for Na+-storage, as well as strong charge redistribution features between heterointerfaces. However, achieving superior rate behaviors and ultralong cycling life remains a key challenge. Herein, starting from a well-organized ZnO microflower template, a hollow microflower-like configuration of metal sulfide ZnS/CuS encapsulated in a polydopamine-derived carbon skeleton (denoted as ZnS/CuS@C) is developed. Benefiting from the strongly synergistic coupling effect of heterostructures, this architecture affords swift Na+ immigration and robust structural tolerance, as reflected by an impressive cycling life (reversible capacity of 389.4 mA h g−1 with nearly 100% retention ratio after 700 long-term cycles at 2 A g−1) and competitive rate capability (341.0 mA h g−1 at 5 A g−1 after 1330 cycles and 282.7 mA h g−1 at an ultrahigh rate up to 10 A g−1 even after 1750 cycles). Kinetics analysis and density functional theoretical calculations elucidate that the fabrication of the heterointerface could induce large pseudocapacitive behaviors and trigger ultrafast sodiation kinetics.

Higher-order topological semimetal in acoustic crystals

Abstract: The notion of higher-order topological insulators has endowed materials with topological states beyond the first order. Particularly, a three-dimensional (3D) higher-order topological insulator can host topologically protected 1D hinge states, referred to as the second-order topological insulator, or 0D corner states, referred to as the third-order topological insulator. Similarly, a 3D higher-order topological semimetal can be envisaged if it hosts states on the 1D hinges. Here we report the realization of a second-order topological Weyl semimetal in a 3D-printed acoustic crystal, which possesses Weyl points in 3D momentum space, 2D Fermi arc states on surfaces and 1D gapless states on hinges. Like the arc surface states, the hinge states also connect the projections of the Weyl points. Our experimental results evidence the existence of the higher-order topological semimetal, which may pave the way towards innovative acoustic devices. A second-order topological Weyl semimetal based on a 3D-printed acoustic crystal, exhibiting Weyl points, Fermi arc surface states, and hinge states, has been experimentally demonstrated.