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.

One-pot synthesis of ultra-small magnetite nanoparticles on the surface of reduced graphene oxide nanosheets as anodes for sodium-ion batteries

Abstract: Nanocomposites with ultra-small magnetite (Fe3O4) nanoparticles (∼3 nm) uniformly anchored on the surfaces of reduced graphene oxide (RGO) nanosheets were successfully synthesized for anodes in sodium-ion batteries by a novel single-step high-temperature coprecipitation approach. The best electrode delivers a reversible Na+ storage capacity of 204 mA h g−1 with excellent capacity retention, i.e., 98% of the second-cycle value was retained after 200 cycles.

Influence of the Biliary System on Biliary Bacteria Revealed by Bacterial Communities of the Human Biliary and Upper Digestive Tracts

Abstract: Biliary bacteria have been implicated in gallstone pathogenesis, though a clear understanding of their composition and source is lacking. Moreover, the effects of the biliary environment, which is known to be generally hostile to most bacteria, on biliary bacteria are unclear. Here, we investigated the bacterial communities of the biliary tract, duodenum, stomach, and oral cavity from six gallstone patients by using 16S rRNA amplicon sequencing. We found that all observed biliary bacteria were detectable in the upper digestive tract. The biliary microbiota had a comparatively higher similarity with the duodenal microbiota, versus those of the other regions, but with a reduced diversity. Although the majority of identified bacteria were greatly diminished in bile samples, three Enterobacteriaceae genera (Escherichia, Klebsiella, and an unclassified genus) and Pyramidobacter were abundant in bile. Predictive functional analysis indicated enhanced abilities of environmental information processing and cell motility of biliary bacteria. Our study provides evidence for the potential source of biliary bacteria, and illustrates the influence of the biliary system on biliary bacterial communities.

Aerosol properties at a midlatitude Northern Hemisphere continental site

Abstract: Measurements of chemical, physical, and optical properties of ambient aerosol particles were obtained at Bondville, Illinois. This research was completed to increase the spatial and temporal resolution of measured aerosol. Results from measurements describe (1) the physical and chemical characteristics of the aerosol and (2) the dependence of light scattering and backscattering on wavelength of light λ, controlled relative humidity RH, and aerosol particle chemical composition. Formulations for the hygroscopic growth factor f(RH), and backscatter ratio b, as functions of λ and RH and estimates of the upscatter fraction β¯, and Angstrom exponent a are also provided. For aerosol sampled at the site from January to December 1995, the mean gravimetric mass concentration for particles with aerodynamic diameter (dpa)≤1 μm had an arithmetic mean and standard deviation of 10.6±6.5 μg/m3, respectively. Ion chromatography (IC) speciated 53% of the total gravimetric mass for particles with dpa≤1 μm. Most of the IC-identified material (88.0±13.5%) consisted of NH4+ and SO42−. Material not identified with IC was primarily elemental and organic carbon. The total aerosol light-scattering coefficient at λ = 550 nm and RH≤40% was 51.6±43.2 Mm−1 for dpa≤10 μm and 42.0±34.9 Mm−1 for dpa≤1 μm. Mean values of f(RH = 82.5%, λ) for total scattering ranged between 1.4 and 1.5 and for back scattering between 1.1 and 1.2. Mean values of b(λ) ranged from 0.11 to 0.18 for RH<40% and from 0.09 to 0.14 for RH = 82.5%. Mean values of β¯ ranged from 0.21 to 0.30 depending on λ, RH, and the particle size distribution. Mean values of a ranged between 1.8 and 2.4 for RH<40% and 1.8 and 2.1 for RH = 82.5%. These measured properties are now available for use in models to reduce uncertainties when quantifying direct aerosol radiative forcing at a continental site influenced by aerosol with anthropogenic origin.

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.