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.

NIR-II Fluorescence imaging for cerebrovascular diseases

Abstract: As the major pipeline for transporting oxygen and nutrients to living tissues, well‐ordered and functional vasculature is vital to maintain the function of organisms. The cerebrovascular disorders induced by acute or chronic diseases, including traumatic brain injury (TBI), stroke, and brain tumors, could cause vascular cognitive impairment and even mortality. Due to the complication of these cerebrovascular diseases, early diagnosis and monitoring their pathological processes in real time in animal models could enlighten us with insights into early prevention and effective treatment. Over the last decade, the NIR‐II fluorescence imaging technology has been well developed in both nanofluorophores and imaging systems. For cerebrovascular disorders, the collaborative use of wide‐field imaging setup with NIR‐II nanoprobes enable arteriovenous staging, and calculation of blood flow velocity to distinguish ischemic area in TBI and stroke. The changes in molecular and cellular levels of TBI provide the guidance on the design of targeted and activatable NIR‐II nanoprobes for evaluating and monitoring the microenvironment variations. For the brain tumors, both targeted strategy and focus ultrasound sonication are efficient approaches for overcoming the blood‐brain‐barrier and brain‐tumor‐barrier for delivery of nanoprobes. Therefore, NIR‐II fluorescence imaging‐guided surgical navigation of brain tumors and resected lesions biopsy intraoperatively ensure the accuracy of surgery based on the precise definition of tumor margins. This review summarizes the recent advances in NIR‐II fluorescence imaging technology, which is mainly inclusive of well‐developed NIR‐II nanofluorophores and their applications in cerebrovascular diseases with address of its challenges and great potential in these aspects.

CuI-Si heterojunction solar cells with carbon nanotube films as flexible top-contact electrodes

Abstract: We report the fabrication of CuI-Si heterojunction solar cells with carbon nanotubes (CNTs) as a transparent electrode. A flexible CNT network was transferred onto the top of a polycrystalline CuI layer, making a conformal coating with good contact with the underlying CuI. The solar cells showed power conversion efficiencies in the range of 6% to 10.5%, while the efficiency degradation was less than 10% after the device was stored in air for 8 days. Compared with conventional rigid electrodes such as indium tin oxide (ITO) glass, the flexibility of the CNT films ensures better contact with the active layers and removes the need for press-contact electrodes. Degraded cells can recover their original performance by acid doping of the CNT electrode. Our results suggest that CNT films are suitable electrical contacts for rough materials and structures with an uneven surface. Open image in new window

Tetraphenylcyclopentadiene-Based Hyperbranched Polymers: Convenient Syntheses from One Pot ``A4 + B2'' Polymerization and High External Quantum Yields up to 9.74% in OLED Devices

Abstract: Through the A4 + B2 type Suzuki–Miyaura coupling reaction, hyperbranched polymers CP1, CP2, and CP3 were easily prepared, which exhibited high luminous efficiency in solution, neat film, and solid ...

Insights into the mechanism of the reaction between tetrachloro-p-benzoquinone and hydrogen peroxide and their implications in the catalytic role of water molecules in producing the hydroxyl radial.

Abstract: Detailed mechanisms for the formation of hydroxyl or alkoxyl radicals in the reactions between tetrachloro-p-benzoquinone (TCBQ) and organic hydroperoxides are crucial for better understanding the potential carcinogenicity of polyhalogenated quinones. Herein, the mechanism of the reaction between TCBQ and H2O2 has been systematically investigated at the B3LYP/6-311++G** level of theory in the presence of different numbers of water molecules. We report that the whole reaction can easily take place with the assistance of explicit water molecules. Namely, an initial intermediate is formed first. After that, a nucleophilic attack of H2O2 onto TCBQ occurs, which results in the formation of a second intermediate that contains an OOH group. Subsequently, this second intermediate decomposes homolytically through cleavage of the O-O bond to produce a hydroxyl radical. Energy analyses suggest that the nucleophilic attack is the rate-determining step in the whole reaction. The participation of explicit water molecules promotes the reaction significantly, which can be used to explain the experimental phenomena. In addition, the effects of F, Br, and CH3 substituents on this reaction have also been studied.

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.