RESUMO
In condensed matter physics studies, spectral information plays an important role in understanding the composition of materials. However, it is difficult to obtain a material's spectrum information directly through experiments or simulations. For example, the spectral information deconvoluted by scanning tunneling spectroscopy suffers from the temperature broadening effect, which is a known ill-posed problem and makes the deconvolution results unstable. Existing methods, such as the maximum entropy method, tend to select an appropriate regularization to suppress unstable oscillations. However, the choice of regularization is difficult, and oscillations are not completely eliminated. We believe that the possible improvement direction is to pay different attention to different intervals. Combining stochastic optimization and deep learning, in this paper, we introduce a neural network-based strategy to solve the deconvolution problem. Because the neural network can represent any nonuniform piecewise linear function, our method replaces the target spectrum with a neural network and can find a better approximation solution through an accurate and efficient optimization. Experiments on theoretical datasets using superconductors demonstrate that the superconducting gap is more accurately estimated and oscillates less. Plug in real experimental data, our approach obtains clearer results for material analysis.
RESUMO
Mycoplasma hyopneumoniae (M. hyopneumoniae) vaccine strain 168 is an intrapulmonically injected attenuated live vaccine that is available in the Chinese market. The aim of this study was to develop suitable adjuvants for this live vaccine to provide effective protection after intramuscular inoculation. Several adjuvant components were screened to assess their toxicity for the live vaccine, and various adjuvant formulations were then designed and prepared. Vaccines supplemented with these adjuvants were used to immunize mice intramuscularly to assess the capacity of the adjuvants to induce a specific immune response. The screened formulations were then evaluated in pigs. Seven of the eight adjuvant components did not affect the viability of the live vaccine, and seven different adjuvant formulations were then designed. In mice, the ISCOM-matrix adjuvant and the levamisole-chitosan mixture adjuvant significantly enhanced serum IgG responses against M. hyopneumoniae, while lymphocyte proliferation was enhanced by the ISCOM-matrix adjuvant, the carbomer-astragalus polysaccharide mixture adjuvant and an oil-in-water emulsion adjuvant. These four adjuvants were evaluated in pigs. Enhancement of specific lymphocyte proliferation responses was observed in the groups vaccinated with the ISCOM-matrix adjuvant and the carbomer-astragalus polysaccharide mixture adjuvant. Significant enhancement of serum IgG antibody production was observed before challenge in pigs vaccinated with the carbomer-astragalus polysaccharide mixture adjuvant and the levamisole-chitosan mixture adjuvant, while after challenge, all of the animals that received vaccines containing adjuvants had higher antibody concentrations against M. hyopneumoniae than unvaccinated animals. Animals inoculated with a vaccine containing the ISCOM-matrix adjuvant (median score 3.57) or the carbomer-astragalus polysaccharide mixture adjuvant (median score 5.28) had reduced lesion scores compared to unvaccinated animals (median score 14.81). These studies will help in the development of appropriate adjuvants for intramuscular administration of this live M. hyopneumoniae vaccine.
