Optimization of ultrasound-assisted enzymatic hydrolysis extraction of tea polyphenols from green tea and their antioxidant activities

Production of natural extracts requires suitable processing conditions to facilitate the accumulation and preservation of bioactive ingredients. This study aimed to optimize the conditions for extracting tea polyphenols (TPs) from green tea using ultrasound-assisted compound enzymatic extraction (UACEE) technology with response surface methodology (RSM), based on a three-level, four-variable central composite rotatable design (CCRD). Extracted TPs yields were in the range of 16.48% to 28.77%; the experimental results were fitted to a second-order quadratic polynomial model and showed a good fit to the proposed model (R2 > 0.90). Compared with other ex-traction methods, UACEE exhibited significant advantages in the TPs extraction rate and preservation of catechins composition. The antioxidant activities of these extracts were also analyzed using reducing power and DPPH radical scavenging activity; all extracts showed excellent antioxidant activity in a dose-dependent manner, and UACEE extracts showed the strongest antioxidant activity in vitro

Foliar Fertilizer Application Alters the Effect of Girdling on the Nutrient Contents and Yield of <i>Camellia oleifera</i>

Improving the economic benefits of Camellia oleifera is a major problem for C. oleifera growers, and girdling and foliar fertilizer have significant effects on improving the economic benefits of plants. This study explains the effects of girdling, girdling + foliar fertilizer on nutrient distribution, and the economic benefits of C. oleifera at different times. It also explains the N, P, and K contents of roots, leaves, fruits, and flower buds (sampled in March, May, August, and October 2021) and their economic benefits. The results showed girdling promoted the accumulation of N and K in leaves in March 2021 (before spring shoot emergence) but inhibited the accumulation of P, which led to the accumulation of P in roots and that of N in fruits in August 2021 (fruit expansion period). Foliar fertilizer application after girdling replenished the P content of leaves in March 2021, and P continued to accumulate in large quantities at the subsequent sampling time points. The N and P contents of the root system decreased in March. In October (fruit ripening stage), girdled shrubs showed higher contents of N and K in fruits and flower buds, and consequently lower relative contents of N and K in roots and leaves but higher content of P in leaves. Foliar fertilizer application slowed down the effects of girdling on nutrient accumulation in fruits and flower buds. Spraying foliar fertilizer decreased the N:P ratio in the flower buds and fruits of girdled plants. Thus, foliar fertilizer spray weakened the effects of girdling on the nutrient content and economic benefits of C. oleifera. In conclusion, girdling changed the nutrient accumulation pattern in various organs of C. oleifera at different stages, increased leaf N:K ratio before shoot emergence, reduced root K content at the fruit expansion stage and the N:K ratio of mature fruit, and promoted economic benefits

THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order matrix. Inspired by KFAC, we propose a novel Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation method, called THOR, to make the second-order optimization applicable in the real application models. Specifically, we gradually increase the update interval and use the matrix trace to determine which blocks of Fisher Information Matrix (FIM) need to be updated. Moreover, by resorting the power of hardware, we have designed a Hardware-driven approximation method for computing FIM to achieve better performance. To demonstrate the effectiveness of THOR, we have conducted extensive experiments. The results show that training ResNet-50 on ImageNet with THOR only takes 66.7 minutes to achieve a top-1 accuracy of 75.9 % under an 8 Ascend 910 environment with MindSpore, a new deep learning computing framework. Moreover, with more computational resources, THOR can only takes 2.7 minutes to 75.9 % with 256 Ascend 910