Codon use bias is an important characteristic in the process of genetic information transmission of species. With the publication of the genome of C. panzhihuaensis, it is of great significance to investigate the codon use bias for understanding the genetic evolution of this species and for molecular breeding. In this study, Perl language and CodonW 1.4.2 software was used to systematically analyze the coding gene sequences of the C. panzhihuaensis genome, obtain the characteristics of codon use, and identify the sources of variation affecting codon use. The results showed that GC3s content, GCall content, and ENC value were 46.75%, 47.67%, and 52.8 respectively. As the first seed plant, the GC3 content of Cycas is similar to that of most gymnosperms and most dicotyledons, but considerably different from that of monocotyledons.RSCU value was greater than 1, among which 21 codons ended in U or A. Enc-plot analysis, PR2-plot neutral plot analysis, and correlation coefficient analysis of Axis1 with GC3s and CAI revealed that the codon bias was mainly affected by natural selection, but also by mutation pressure and other factors. Thirty-one optimal codons of Cycas genes were screened. This research can provide a reference for the phylogeny and genome codon evolution of Cycas.

Our previous study found that intravesical perfusion metformin has excellent inhibitory effects against bladder cancer (BC). However, this administration route allows the drug to be diluted and excreted in urine. Therefore, increasing the adhesion of metformin to the bladder mucosal layer may prolong the retention time and increase the pharmacological activity. It is well known that chitosan (Cs) has a strong adhesion to the bladder mucosal layer. Thus, this study established a novel for-mulation of metformin to enhance its antitumor activity by extending its retention time. In this research, we prepared Cs freeze-dried powder and investigated the effect of metformin-loaded chitosan hydrogels (MLCH) in vitro and in vivo. The results showed that MLCH had a strong in-hibitory effect against proliferation and colony formation in vitro. The reduction in BC weight and expression of tumour biomarkers in orthotopic mice showed the robust antitumor activity of MLCH via intravesical administration in vivo. The non-toxic profile of MLCH was observed as well, using histological examinations. Mechanistically, MLCH showed stronger functional activation of the AMPK/mTOR signalling pathway compared with metformin alone. These findings highlighted the potential of this novel formulation as an efficient candidate for managing BC via intravesical ad-ministration.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.