Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Towards An End-to-End Framework for Flow-Guided Video Inpainting

Abstract: Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E2 FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be Jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI.

Air-stable means more: designing air-defendable lithium metals for safe and stable batteries

Abstract: Lithium (Li) metal has been considered as the ultimate anode choice for the next-generation high energy density rechargeable batteries. However, besides the critical issues in electrochemistry such as interfacial stability and dendrite growth, stringent operation conditions and safety concerns induced by the poor air/moisture stability of Li metal largely hinder its transformation from laboratory to industry. Because of the high reactivity, Li metal suffers from severe corrosion or even potential safety hazards when exposed to humid air. Therefore, recent progress in enhancing the stability of Li metal in ambient air is of great significance for real-world mass production and practical application of lithium metal batteries (LMBs). This review focuses on the development of air-stable and high-performance Li metals to facilitate cost-effective fabrication of safety-enhanced LMBs. Surface modification and architecture design of Li anodes are identified as two most important directions to achieve the goal. The strategies that simultaneously enhance air/water-resistance and electrochemical properties are summarized, and the perspectives and future directions targeting the commercialization of air-stable LMBs are discussed.

Sowing Silver Seeds within Patterned Ditches for Dendrite-Free Lithium Metal Batteries.

Abstract: The interfacial instability of lithium (Li) metal is one of the critical challenges, which hinders the application of rechargeable Li metal batteries (LMBs). Designing facile and effective surface/interface is extremely important for practical LMBs manufacturing. Here, a highly stable Li anode with silver nanowires sowed in the patterned ditches via a simple calendaring process is developed. The remarkably increased electroactive surface area and the superior lithiophilic Ag seeds enable Li stripping/plating mainly inside the ditches. Benefitting from such unique structural design, the ditches-patterned and Ag-modified composite Li anode (D-Ag@Li) achieves excellent cyclability under 2 mA cm-2 / 4 mAh cm-2 over 360 h cycling with low nucleation overpotential of 16 mV. Pairing with the D-Ag@Li anode, the full cells with LiNi0.8 Mn0.1 Co0.1 O2 and LiFePO4 (LFP) cathodes achieve long cycle life with 94.2% retention after 2000 cycles and 74.2% after 4000 cycles, respectively. Moreover, ultrasonic transmission mapping shows no gas generation for the LFP pouch full cell pouch cell based on D-Ag@Li over prolonged cycling, demonstrating the feasibility and effectiveness of the authors' strategy for LMBs.

QM/MM studies of structural and energetic properties of the far-red fluorescent protein HcRed

Abstract: The far-red fluorescent protein HcRed was investigated using molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) calculations. Three models of HcRed (anionic chromophore) were considered, differing in the protonation states of nearby Glu residues (A: Glu214 and Glu146 both protonated; B: Glu214 protonated and Glu146 deprotonated; C: Glu214 and Glu146 both deprotonated). SCC-DFTB/MM MD simulations of model B yield good agreement with the available crystallographic data at ambient pH. Bond lengths in the QM region are well reproduced, with a root mean square (rms) deviation between experimental and average MD data of 0.079 A; the chromophore is almost co-planar, which is consistent with experimental observation; and the five hydrogen bonds involving the chromophore are conserved. QM/MM geometry optimizations were performed on representative snapshot structures from the MD simulations for each model. They confirm the structural features observed in the MD simulations. According to the DFT(B3LYP)/MM results, the cis-conformation of the chromophore is more stable than the trans-form by 9.1–12.9 kcal mol−1 in model B, and by 12.4–19.9 kcal mol−1 in model C, consistent with the experimental preference for the cis-isomer. However, in model A when both Glu214 and Glu146 are protonated, the stability is inverted with the trans-form being favored. The different protonation states of the titratable active-site residues Glu214 and Glu146 thus critically influence the manner in which the relative stability and degree of planarity of the cis- and trans-conformers vary with pH. Coupled with the known correlation of chromophore conformation with fluorescence efficiency, this work provides a detailed structural basis for the observed phenomenon that red fluorescent proteins such as HcRed, mKate and Rtms5 show bright fluorescence at high pH.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.