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.

Biolabeling Hematopoietic System Cells Using Near-Infrared Fluorescent Gold Nanoclusters

Abstract: Highly bright fluorescent gold nanoclusters (Au NCs) have been prepared by one-step reduction of aqueous precursor solution in the presence of multidentate thioether-terminated poly(methacrylic acid) (PTMP-PMAA). The fluorescence quantum yield of the resultant Au NCs is 4.8% higher than that of the similarly sized Au NCs prepared by the etching method (1.8–4.0%). These Au NCs show excellent photostability and have been successfully applied to label the hematopoietic cells first. The results show that Au NCs were endocytosed by the cancer cells significantly more than the normal cells, in comparison with control experiments labeled with fluorescent quantum dots (CdTe). The cytotoxicity experiments demonstrate the excellent biocompatibility of Au NCs, proven by a relatively lower cytotoxicity than CdTe. These robust near-infrared Au NCs show great potential in biolabeling, tracking, and imaging of other cells and diseases, especially in the diagnosis and treatment of chronic myeloid leukemia.

Lighting Up NIR-II Fluorescence in Vivo: An Activable Probe for Noninvasive Hydroxyl Radical Imaging.

Abstract: The detection of hydroxyl radical (·OH) in vivo faces challenges as ·OH has short lifetime and low concentration in the body. Fluorescence imaging within the second near-infrared window (NIR-II, 1000-1700 nm) is a promising approach to in vivo organ and tissue imaging, but ·OH fluorescent probes emitting at this region have not been reported up to now because of the difficulty of probe design. Herein, we report the strategy to fabricate the first NIR-II probe for ·OH by directly breaking/recovering the conjugated system and rigid planar structure of an organic fluorophore, which could regulate the fluorescence intensity regardless of emission wavelength. This activable probe, Hydro-1080, emitted in 1000-1400 nm after responding to ·OH. Hydro-1080 exhibited excellent sensitivity (LOD = 0.5 nM) and selectivity to ·OH. It was able to track subtle variation of [·OH] in liver induced by external stimuli and offered clear images with high contrast. This work also indicates that this simple and straightforward strategy can be extended to develop NIR-II fluorescent probes efficiently.

Probing Perovskite Inhomogeneity beyond the Surface: TOF-SIMS Analysis of Halide Perovskite Photovoltaic Devices.

Abstract: Understanding the origins and evolution of inhomogeneity in halide perovskite solar cells appears to be a key to advancing the technology. Time-of-flight secondary-ion mass spectrometry (TOF-SIMS) is one of the few techniques that can obtain chemical information from all components of halide organic–inorganic perovskite photovoltaics in one-dimension (standard depth profiling), two-dimensions (high-resolution 100 nm imaging), as well as three-dimensions (tomography combining high-resolution imaging with depth profiling). TOF-SIMS has been used to analyze perovskite photovoltaics made by a variety of methods, and the breadth of insight that can be gained from this technique is illustrated here including: cation uniformity (depth and lateral), changes in chemistry upon alternate processing, changes in chemistry upon degradation (including at interfaces), and lateral distribution of passivating additives. Using TOF-SIMS on multiple perovskite compositions, we show that the information regarding halide perovs...

Light-Enhanced O2-Evolving Nanoparticles Boost Photodynamic Therapy To Elicit Antitumor Immunity.

Abstract: Breast cancer remains to show high mortality and poor prognosis in women despite of significant progress in recent diagnosis and treatment. Herein, we report the rational design of a highly efficient ultrasmall nanotheranostic agent with excellent photodynamic therapy (PDT) performance to against breast cancer and its metastasis by eliciting antitumor immunity. The ultrasmall nanoagent (3.1 ± 0.4 nm) was fabricated from polyethylene glycol modified Cu2- xSe nanoparticles, β-cyclodextrin, and chlorin e6 under ambient conditions. The resultant nanoplatform (CS-CD-Ce6 NPs) can be passively accumulated into the tumor to exhibit dramatic antitumor efficacy through the excellent PDT effect under near-infrared irradiation. The excellent PDT performance of this nanoplatform is owing to its role as a Fenton-like Haber-Weiss catalyst for the efficient degradation of H2O2 within the tumor to release hydroxyl radicals (·OH) and very toxic singlet oxygen (1O2) under irradiation. The generated vast amounts of reactive oxygen species not only killed primary tumor cells but also elicited immunogenic cell death (ICD) to release damage-associated molecular patterns (DAMPs) and induced proinflammatory M1-macrophages polarization. Thereby, antitumor immune responses against the metastasis of breast cancer were robustly evoked. Our work demonstrates that ultrasmall Cu2- xSe nanoparticle-based nanoplatform offers a promising way to prevent cancer metastasis via immunogenic effects through its excellent PDT performance.

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.