植物物候是否能解释物种共存:以浙江古田山亚热带常绿阔叶林为例

了解物种共存机制一直是生态学领域的研究热点.传统的生态位理论认为,植物通过生态位分化降低对资源的竞争,达到物种共存的目的.然而,对于亚热带地区植物是否可以通过时间生态位的分化(如物候期的分化)以促进物种共存却鲜有报道.本文以位于中国东部地区的古田山亚热带常绿阔叶林为例,利用古田山24 ha样地内102种木本植物2012～2017年间的繁殖物候数据,分析了种间的物候期分化.结果表明,各个物种的开花物候与结实物候都存在时间生态位上的分化.物种间的首花期差异程度大于首果期,开花持续期的差异程度小于结实持续期.不同功能群的物种间物候期存在差异:乔木的首花期显著早于灌木,但首果期晚于灌木;风媒树种的首花期早于虫媒树种,且差异显著;肉果型树种的首果期显著早于干果型树种.该研究结果表明,植物物候存在时间生态位上的分化,以此减小种间对资源(如传粉者和种子传播者)的竞争,从而促进群落物种共存.从物候角度研究时间生态位的分化,有助于丰富关于当代物种共存机制的研究

Research advances in the preventative and therapeutic effects of phycocyanin on oxidative stress-related diseases

Oxidative stress,which has attracted extensive research attention for over forty years,plays a crucial role in the occurrence and deterioration of various diseases.The oxidative stress resulting from harmful stimulation is related to many diseases,e.g.,atherosclerosis,hepatitis,pneumonia,Alzheimer's disease,and cataracts.In this study,oxidative stress alleviation is presented in a new form to realize the prophylaxis and therapy of the above diseases.Phycocyanin,a natural product extracted from algae,is often used in health products and food additives.Research has found that phycocyanin has a beneficial effect in preventive treatments in different ways,such as the elimination of free radicals and reduction of oxidative stress.Here,we review the important role of phycocyanin in the prevention and cure of oxidative stress-related diseases and discuss the prospects for its application,which provide a reference for phycocyanin use in the future

Protective effect of phycocyanin against oxidative damage induced by radiation

[Background] Phycocyanin (PC) is a pigment protein present in cyanobacteria, cryptophyta, etc. [Purpose] This study aims at the protective effect of phycocyanin on oxidative damage induced by irradiation by establishing injury mouse model by X-ray irradiation. [Methods] 72 C57 BL/6 male mice were randomly divided into normal control group, irradiation model group, PC pretreatment group and positive control group. The mice were continuously fed for 7 d. On the 8th day of intragastric administration, except for the normal control group, the rest of the mice were subjected to total body irradiation to a dose of 6 Gy for the preparation of radiation damage model. The plasma and tissues were collected at 1 d, 3 d and 7 d after irradiation, and the activities of antioxidant enzymes in plasma and liver were measured. The content of reactive oxygen species (ROS) in liver tissue was detected by dihydroethidium (DHE) staining. [Results] Phycocyanin increased the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) in the plasma of mice after irradiation, and increased the activity of SOD (p<0.05), decreased malondialdehyde (MDA) content (p<0.05), and reduced liver tissue ROS content (p<0.05). [Conclusion] The results show that phycocyanin can improve the antioxidant capacity of mice, reduce oxidative damage to the body caused by irradiation, and provide a better protective effect on irradiation injury of mice

A thermal load identification method based on physics-guided neural network for honeycomb sandwich structures

The identification of thermal load/thermal shock of aircraft during service is beneficial for collecting information of the service environment and avoiding risks. In the paper, a method based on multivariate information fusion and physics-guided neural network is developed for the inverse problem of thermal load identification of honeycomb sandwich structures. Two thermal feature parameters: temperature gradient and temperature variation rate are used to build the dataset. A 16-layers physics-guided neural network is presented to achieve the predicted results consistent with physical knowledge. In the work, laser irradiation is used as the thermal load, and two laser parameters are to be identified, i.e. spot diameter, power. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The effects of physics-guided loss function and multivariate information fusion are discussed, and it is found that the results based on the proposed method are much better than the results based on the method without physical model. Besides, results based on multivariate information fusion are better than results based on single temperature response. Then, the effects of network models and hyper parameters on the proposed method are also discussed

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies