Showing papers by "Shanghai 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.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

Abstract: The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Achieving Effective Remote Working During the COVID-19 Pandemic: A Work Design Perspective.

Abstract: Existing knowledge on remote working can be questioned in an extraordinary pandemic context. We conducted a mixed-methods investigation to explore the challenges experienced by remote workers at this time, as well as what virtual work characteristics and individual differences affect these challenges. In Study 1, from semi-structured interviews with Chinese employees working from home in the early days of the pandemic, we identified four key remote work challenges (work-home interference, ineffective communication, procrastination, and loneliness), as well as four virtual work characteristics that affected the experience of these challenges (social support, job autonomy, monitoring, and workload) and one key individual difference factor (workers' self-discipline). In Study 2, using survey data from 522 employees working at home during the pandemic, we found that virtual work characteristics linked to worker's performance and well-being via the experienced challenges. Specifically, social support was positively correlated with lower levels of all remote working challenges; job autonomy negatively related to loneliness; workload and monitoring both linked to higher work-home interference; and workload additionally linked to lower procrastination. Self-discipline was a significant moderator of several of these relationships. We discuss the implications of our research for the pandemic and beyond.

Nanoengineering of 2D MXene-Based Materials for Energy Storage Applications

Abstract: 2D MXene-based nanomaterials have attracted tremendous attention because of their unique physical/chemical properties and wide range of applications in energy storage, catalysis, electronics, optoelectronics, and photonics. However, MXenes and their derivatives have many inherent limitations in terms of energy storage applications. In order to further improve their performance for practical application, the nanoengineering of these 2D materials is extensively investigated. In this Review, the latest research and progress on 2D MXene-based nanostructures is introduced and discussed, focusing on their preparation methods, properties, and applications for energy storage such as lithium-ion batteries, sodium-ion batteries, lithium-sulfur batteries, and supercapacitors. Finally, the critical challenges and perspectives required to be addressed for the future development of these 2D MXene-based materials for energy storage applications are presented.

Digital twin-based sustainable intelligent manufacturing: a review

Abstract: As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

High temperature proton exchange membrane fuel cells: progress in advanced materials and key technologies

Abstract: High temperature proton exchange membrane fuel cells (HT-PEMFCs) are one type of promising energy device with the advantages of fast reaction kinetics (high energy efficiency), high tolerance to fuel/air impurities, simple plate design, and better heat and water management. They have been expected to be the next generation of PEMFCs specifically for application in hydrogen-fueled automobile vehicles and combined heat and power (CHP) systems. However, their high-cost and low durability interposed by the insufficient performance of key materials such as electrocatalysts and membranes at high temperature operation are still the challenges hindering the technology's practical applications. To develop high performance HT-PEMFCs, worldwide researchers have been focusing on exploring new materials and the related technologies by developing novel synthesis methods and innovative assembly techniques, understanding degradation mechanisms, and creating mitigation strategies with special emphasis on catalysts for oxygen reduction reaction, proton exchange membranes and bipolar plates. In this paper, the state-of-the-art development of HT-PEMFC key materials, components and device assembly along with degradation mechanisms, mitigation strategies, and HT-PEMFC based CHP systems is comprehensively reviewed. In order to facilitate further research and development of HT-PEMFCs toward practical applications, the existing challenges are also discussed and several future research directions are proposed in this paper.

Hierarchical crack buffering triples ductility in eutectic herringbone high-entropy alloys

Abstract: In human-made malleable materials, microdamage such as cracking usually limits material lifetime. Some biological composites, such as bone, have hierarchical microstructures that tolerate cracks but cannot withstand high elongation. We demonstrate a directionally solidified eutectic high-entropy alloy (EHEA) that successfully reconciles crack tolerance and high elongation. The solidified alloy has a hierarchically organized herringbone structure that enables bionic-inspired hierarchical crack buffering. This effect guides stable, persistent crystallographic nucleation and growth of multiple microcracks in abundant poor-deformability microstructures. Hierarchical buffering by adjacent dynamic strain-hardened features helps the cracks to avoid catastrophic growth and percolation. Our self-buffering herringbone material yields an ultrahigh uniform tensile elongation (~50%), three times that of conventional nonbuffering EHEAs, without sacrificing strength.

Smoothing the energy transfer pathway in quasi-2D perovskite films using methanesulfonate leads to highly efficient light-emitting devices.

Abstract: Quasi-two-dimensional (quasi-2D) Ruddlesden–Popper (RP) perovskites such as BA2Csn–1PbnBr3n+1 (BA = butylammonium, n > 1) are promising emitters, but their electroluminescence performance is limited by a severe non-radiative recombination during the energy transfer process. Here, we make use of methanesulfonate (MeS) that can interact with the spacer BA cations via strong hydrogen bonding interaction to reconstruct the quasi-2D perovskite structure, which increases the energy acceptor-to-donor ratio and enhances the energy transfer in perovskite films, thus improving the light emission efficiency. MeS additives also lower the defect density in RP perovskites, which is due to the elimination of uncoordinated Pb2+ by the electron-rich Lewis base MeS and the weakened adsorbate blocking effect. As a result, green light-emitting diodes fabricated using these quasi-2D RP perovskite films reach current efficiency of 63 cd A−1 and 20.5% external quantum efficiency, which are the best reported performance for devices based on quasi-2D perovskites so far. Owing to large exciton binding energy, quasi-2D perovskite is promising for light-emitting application, yet inhomogeneous phases distribution limits the potential. Here, the authors improve the performance by using MeS additive to regulate the phase distribution and to reduce defect density in the films.

V2VR: Reliable Hybrid-Network-Oriented V2V Data Transmission and Routing Considering RSUs and Connectivity Probability

Abstract: Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

Identifying carbon as the source of visible single-photon emission from hexagonal boron nitride

Abstract: Single-photon emitters (SPEs) in hexagonal boron nitride (hBN) have garnered increasing attention over the last few years due to their superior optical properties. However, despite the vast range of experimental results and theoretical calculations, the defect structure responsible for the observed emission has remained elusive. Here, by controlling the incorporation of impurities into hBN via various bottom-up synthesis methods and directly through ion implantation, we provide direct evidence that the visible SPEs are carbon related. Room-temperature optically detected magnetic resonance is demonstrated on ensembles of these defects. We perform ion-implantation experiments and confirm that only carbon implantation creates SPEs in the visible spectral range. Computational analysis of the simplest 12 carbon-containing defect species suggest the negatively charged $${\rm{V}}_{\rm{B}}{\rm{C}}_{\rm{N}}^ -$$ defect as a viable candidate and predict that out-of-plane deformations make the defect environmentally sensitive. Our results resolve a long-standing debate about the origin of single emitters at the visible range in hBN and will be key to the deterministic engineering of these defects for quantum photonic devices. Comparison of hexagonal boron nitride samples grown with different techniques and with varying carbon-doping content provides evidence that the defects emitting single photons in the visible range are carbon related.

Non-Fragile $H_{∞ }$ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation

Abstract: This work explores the $H_{∞ }$ synchronization issue for singularly perturbed coupled neural networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a novel double-layer switching regulation containing Markov chain and persistent dwell-time switching regulation (PDTSR) is used. The first layer of switching regulation is the Markov chain to characterize the switching stochastic properties of the systems suffering from random component failures and sudden environmental disturbances. Meanwhile, PDTSR, as the second-layer switching regulation, is used to depict the variations in the transition probability of the aforementioned Markov chain. For systems under double-layer switching regulation, the purpose of the addressed issue is to design a mode-dependent synchronization controller for the network with the desired controller gains calculated by solving convex optimization problems. As such, new sufficient conditions are established to ensure that the synchronization error systems are mean-square exponentially stable with a specified level of the $H_{∞ }$ performance. Eventually, the solvability and validity of the proposed control scheme are illustrated through a numerical simulation.

Green finance, fintech and environmental protection: Evidence from China

Abstract: This paper is one of the first to offer a comprehensive analysis of the impact of green finance related policies in China, utilizing text analysis and panel data from 290 cities between 2011 and 2018. Employing the Semi-parametric Difference-in-Differences (SDID) we show that overall China's green finance related policies have led to a significant reduction in industrial gas emissions in the review period. Additionally, we found that Fintech development contributes to the depletion of sulphur dioxide emissions and has a positive impact on environmental protection investment initiatives. China is poised to be a global leader in green finance policy implementation and regulators need to accelerate the formulation of green finance products and enhance the capacity of financial institutions to offer green credit. While minimizing the systemic risk fintech poses, policy makers should encourage fintechs to actively participate in environmental protection initiatives that promote green consumption.

Hierarchical Alternate Interaction Network for RGB-D Salient Object Detection

Abstract: Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to improve the detection accuracy, while pay insufficient attention to the quality of depth information. In practice, a depth map is often with uneven quality and sometimes suffers from distractors, due to various factors in the acquisition procedure. In this article, to mitigate distractors in depth maps and highlight salient objects in RGB images, we propose a Hierarchical Alternate Interactions Network (HAINet) for RGB-D SOD. Specifically, HAINet consists of three key stages: feature encoding, cross-modal alternate interaction, and saliency reasoning. The main innovation in HAINet is the Hierarchical Alternate Interaction Module (HAIM), which plays a key role in the second stage for cross-modal feature interaction. HAIM first uses RGB features to filter distractors in depth features, and then the purified depth features are exploited to enhance RGB features in turn. The alternate RGB-depth-RGB interaction proceeds in a hierarchical manner, which progressively integrates local and global contexts within a single feature scale. In addition, we adopt a hybrid loss function to facilitate the training of HAINet. Extensive experiments on seven datasets demonstrate that our HAINet not only achieves competitive performance as compared with 19 relevant state-of-the-art methods, but also reaches a real-time processing speed of 43 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/HAINet .

The impact of low-carbon city pilot policy on the total factor productivity of listed enterprises in China

Abstract: In recent years, the low-carbon city pilot policy has been important work in China It aims to control the city's greenhouse gas emissions, find a "win-win" path of low-carbon and economy and drive the innovation and development of cities through low-carbon goals In this context, we consider the low-carbon city pilot policy as the starting point Based on the data of A-share listed companies in Shanghai and Shenzhen from 2005 to 2015 in 285 prefecture-level cities, the OP and LP methods are used to calculate total factor productivity (TFP) In this paper, using PSM-DID and other methods, we empirically test whether and how the low-carbon city pilot policy affects enterprise TFP The results indicate that the construction of low-carbon cities significantly promotes an increase in the TFP of local enterprises In addition, improving technological innovation and optimizing the efficiency of resource allocation are two important transmission mechanisms The above conclusions are robust to a series of tests Therefore, the implementation of a low-carbon city pilot policy can help achieve the "win-win" goal of emission reduction and high-quality enterprise development This study provides strong support for further expanding the scope of low-carbon city pilot policy projects and the scientific implementation of climate change policies, and it provides beneficial policy enlightenment for the scientific implementation of urban emission reduction control to win the battle against climate change

2D vanadium carbide MXenzyme to alleviate ROS-mediated inflammatory and neurodegenerative diseases.

Abstract: Reactive oxygen species (ROS) are generated and consumed in living organism for normal metabolism. Paradoxically, the overproduction and/or mismanagement of ROS have been involved in pathogenesis and progression of various human diseases. Here, we reported a two-dimensional (2D) vanadium carbide (V2C) MXene nanoenzyme (MXenzyme) that can mimic up to six naturally-occurring enzymes, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), glutathione peroxidase (GPx), thiol peroxidase (TPx) and haloperoxidase (HPO). Based on these enzyme-mimicking properties, the constructed 2D V2C MXenzyme not only possesses high biocompatibility but also exhibits robust in vitro cytoprotection against oxidative stress. Importantly, 2D V2C MXenzyme rebuilds the redox homeostasis without perturbing the endogenous antioxidant status and relieves ROS-induced damage with benign in vivo therapeutic effects, as demonstrated in both inflammation and neurodegeneration animal models. These findings open an avenue to enable the use of MXenzyme as a remedial nanoplatform to treat ROS-mediated inflammatory and neurodegenerative diseases.

Turning on Zn 4s Electrons in a N2‐Zn‐B2 Configuration to Stimulate Remarkable ORR Performance

Abstract: A zinc-based single-atom catalyst has been recently explored with distinguished stability, of which the fully occupied Zn2+ 3d10 electronic configuration is Fenton-reaction-inactive, but the catalytic activity is thus inferior. Herein, we report an approach to manipulate the s-band by constructing a B,N co-coordinated Zn-B/N-C catalyst. We confirm both experimentally and theoretically that the unique N2 -Zn-B2 configuration is crucial, in which Zn+ (3d10 4s1 ) can hold enough delocalized electrons to generate suitable binding strength for key reaction intermediates and promote the charge transfer between catalytic surface and ORR reactants. This exclusive effect is not found in the other transition-metal counterparts such as M-B/N-C (M=Mn, Fe, Co, Ni and Cu). Consequently, the as-obtained catalyst demonstrates impressive ORR activity, along with remarkable long-term stability in both alkaline and acid media. This work presents a new concept in the further design of electrocatalyst.

Non-noble Metal Electrocatalysts for the Hydrogen Evolution Reaction in Water Electrolysis

Abstract: Water electrolysis is a sustainable approach for hydrogen production by using electricity from clean energy sources. However, both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) associated with water electrolysis are kinetically sluggish, leading to low efficiency in corresponding electrolysis devices. In addition, current electrocatalysts that can catalyze both HER and OER to practical rates require noble metals such as platinum that are low in abundance and high in price, severely limiting commercialization. As a result, the development of high-performance and cost-effective non-noble metal electrocatalysts to replace noble ones has intensified. Based on this, this review will comprehensively present recent research in the design, synthesis, characterization and performance validation/optimization of non-noble metal HER electrocatalysts and analyze corresponding catalytic mechanisms. Moreover, several important types of non-noble metal electrocatalysts including zero-dimensional, one-dimensional, two-dimensional and three-dimensional materials are presented with an emphasis on morphology/structure, synergetic interaction between metal and support, catalytic property and HER activity/stability. Furthermore, existing technical challenges are summarized and corresponding research directions are proposed toward practical application. Water electrolysis is a sustainable approach for hydrogen production by using electricity from clean energy sources. However, both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are kinetically sluggish, causing low efficiency of the electrolysis devices. The currently used noble metals, such as Pt-based electrocatalysts for catalyzing both HER and OER to practical rates, have low abundances and high price, limiting their commercialization. In this regard, developing high-performance and cost-effective non-noble metal electrocatalysts to replace noble ones has become a hot research topic.

Point-of-care testing detection methods for COVID-19.

Abstract: COVID-19 is an acute respiratory disease caused by SARS-CoV-2, which has high transmissibility. People infected with SARS-CoV-2 can develop symptoms including cough, fever, pneumonia and other complications, which in severe cases could lead to death. In addition, a proportion of people infected with SARS-CoV-2 may be asymptomatic. At present, the primary diagnostic method for COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR), which tests patient samples including nasopharyngeal swabs, sputum and other lower respiratory tract secretions. Other detection methods, e.g., isothermal nucleic acid amplification, CRISPR, immunochromatography, enzyme-linked immunosorbent assay (ELISA) and electrochemical sensors are also in use. As the current testing methods are mostly performed at central hospitals and third-party testing centres, the testing systems used mostly employ large, high-throughput, automated equipment. Given the current situation of the epidemic, point-of-care testing (POCT) is advantageous in terms of its ease of use, greater approachability on the user's end, more timely detection, and comparable accuracy and sensitivity, which could reduce the testing load on central hospitals. POCT is thus conducive to daily epidemic control and achieving early detection and treatment. This paper summarises the latest research advances in POCT-based SARS-CoV-2 detection methods, compares three categories of commercially available products, i.e., nucleic acid tests, immunoassays and novel sensors, and proposes the expectations for the development of POCT-based SARS-CoV-2 detection including greater accessibility, higher sensitivity and lower costs.