Applications and prospects of cryo-EM in drug discovery.

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry.

[The correlation based mid-infrared temperature and emissivity separation algorithm].

Temperature and emissivity separation is the key problem in infrared remote sensing. Based on the analysis of the relationship between the atmospheric downward radiance and surface emissivity containing atmosphere residue without the effects of sun irradiation, the present paper puts forward a temperature and emissivity separation algorithm for the ground-based mid-infrared hyperspectral data. The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue as a criterion to optimize the surface temperature, and the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue depends on the bias between the estimated surface temperature and true surface temperature.

[Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from hyperspectral FTIR data].

The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm(-1) in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to produce the test data, and the result of network test shows the MLP is robust.