Refrigerated storage stimulates isoflavone and γ-aminobutyric acid accumulation in germinated soybeans.

This study aims to investigate the quality changes of germinated soybeans during refrigerated storage (4 °C), with an emphasis on the stimulatory effect of refrigeration on their special functional compounds. After germinating for two days, germinated soybeans were stored at 4 °C for seven days, while the germinated soybeans stored at 25 °C served as control group. The results showed that refrigerated storage significantly affected the physiological changes in germinated soybeans. The weight loss rate, browning rate, malondialdehyde (MDA) content and H2O2 content all decreased dramatically during refrigerated storage compared to the control group. The total phenolic and total flavonoid contents of germinated soybeans under refrigeration exhibited a trend of increasing and then decreasing over time. Additionally, during refrigerated storage, the total isoflavone content reached a peak of 8.72 g/kg on the fifth day, in which the content of daidzein and glycitin increased by 45% and 49% respectively, when compared with the control group. Moreover, the content of γ-aminobutyric acid (GABA) peaked on the first day, and kept a high level during storage. In which, the refrigerated group was 2.35-, 2.88-, 1.67-fold respectively after storage for three to seven days. These results indicated that refrigeration stimulated the biosynthesis of isoflavones and GABA in germinated soybeans during storage. More importantly, there was a sequential difference in the timing of the stimulation of the two functional components under refrigeration.

Tensor-based dictionary learning for dynamic tomographic reconstruction.

In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction.