Shasha Zhang

© 2016 American Chemical Society.Layered transition metal chalcogenides with large spin orbit coupling have recently sparked much interest due to their potential applications for electronic, optoelectronic, spintronics, and valleytronics. However, most current understanding of the electronic structure near band valleys in momentum space is based on either theoretical investigations or optical measurements, leaving the detailed band structure elusive. For example, the exact position of the conduction band valley of bulk MoS2 remains controversial. Here, using angle-resolved photoemission spectroscopy with submicron spatial resolution, we systematically imaged the conduction/valence band structure evolution across representative chalcogenides MoS2, WS2, and WSe2, as well as the thickness dependent electronic structure from bulk to the monolayer limit. These results establish a solid basis to understand the underlying valley physics of these materials, and also provide a link between chalcogenide electronic band structure and their physical properties for potential valleytronics applications.

Cholesteryl ester transfer protein mediates cholesteryl ester transfer from the atheroprotective high density lipoprotein cholesterol to the atherogenic low density lipoprotein cholesterol. In the past decade, this property has driven the development of CETP inhibitors, which have been evaluated in large scale clinical trials for treating cardiovascular diseases. Despite the pharmacological interest, little is known about the fundamental mechanism of CETP in CE transfer. Recent electron microscopy experiments have suggested a tunnel mechanism, and molecular dynamics simulations have shown that the flexible N-terminal distal end of CETP penetrates into the HDL surface and takes up a CE molecule through an open pore. However, it is not known whether a CE molecule can completely transfer through an entire CETP molecule. Here, we used all-atom molecular dynamics simulations to evaluate this possibility. The results showed that a hydrophobic tunnel inside CETP is sufficient to allow a CE molecule to completely transfer through the entire CETP within a predicted transfer time and at a rate comparable with those obtained through physiological measurements. Analyses of the detailed interactions revealed several residues that might be critical for CETP function, which may provide important clues for the effective development of CETP inhibitors and treatment of cardiovascular diseases.

Commonly used methods for determining protein structure, including X-ray crystallography and single-particle reconstruction, often provide a single and unique three-dimensional structure. However, in these methods, the protein dynamics and flexibility/fluctuation remain mostly unknown. Here, we utilized advances in electron tomography to study the antibody flexibility and fluctuation through structural determination of individual antibody particles rather than averaging multiple antibody particles together. Through individual-particle electron tomography 3D reconstruction from negatively-stained ET images, we obtained 120 ab-initio 3D density maps at an intermediate resolution from 120 individual IgG1 antibody particles. Using these maps as a constraint, we derived 120 conformations of the antibody via structural flexible docking of the crystal structure to these maps by targeted molecular dynamics simulations. Statistical analysis of the various conformations disclosed the antibody 3D conformational flexibility through the distribution of its domain distances and orientations. This blueprint approach, if extended to other flexible proteins, may serve as a useful methodology towards understanding protein dynamics and functions.

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children. The results indicate that the proposed approach can achieve an accuracy of 95% in ASD detection, and strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance.