Cucumis sativus is an important vegetable crop in the world. Agrobacterium mediated transgenic technology is an important means to study plant gene function and variety improvement. In order to further accelerate the transgenic research and breeding process of cucumber, aiming at the Agrobacterium mediated genetic transformation method of cucumber, this paper expounds the research progress and existing problems of Agrobacterium mediated transgenic cucumber from the aspects of influencing factors of cucumber regeneration ability, genetic transformation conditions and various added substances in the process, and prospects the future of improving the efficiency of cucumber genetic transformation and the application of safety screening markers, in order to provide reference for cucumber stress resistance breeding and fruit quality improvement.

We investigate the impact on intertemporal distribution caused by a change of policy from tax to deficit financing of public investment, using a simple theoretical framework which combines the one-period McGuire-Olson economy with the conventional long-run Solow economy. This theoretical framework provides a simple way to highlight some significant interdependencies between private and public investments as well as the negative impact of taxation on aggregate productivity, and to trace some possible transmission mechanisms between deficit financing policies and the long-run path of consumption per head. The main tentative (theoretical) result is that although under fairly acceptable assumptions the likely impact of a deficit financing policy is to benefit the present at the expense of the future, under equally acceptable assumptions concerning the possibility of an excessive macro private saving–investment propensity, and/or of a significant productivity loss due to the excess burden of taxation, the adverse intertemporal distributional impact of deficit financing might become negligible, or even disappear altogether.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.