赤红球菌组氨酸激酶的融合表达和功能分析

对赤红球菌的组氨酸激酶基因进行密码子优化，将优化后的组氨酸激酶基因（rhks）构建重组表达质粒pGEX-4T-2-rhks。将此质粒导入到大肠杆菌BL21（DE3）中进行异源表达。在25 ℃和1 mmol/L异丙基-β-D-硫代吡喃半乳糖苷诱导条件下，组氨酸激酶融合蛋白（GST-RHK）获得成功表达，并具有催化活性。经谷胱甘肽琼脂糖亲和层析纯化，获得电泳纯的GST-RHK，其中纯化倍数为3.1，得率为19.5%。该蛋白大小约为72.75 kDa，Km、Vmax和Kcat值分别为20.92 μmol/L、0.17 μmol/（L·min）和1.4 min－1。野生型赤红球菌、组氨酸激酶基因增强株sdrhkE和组氨酸激酶基因敲减株sdrhkD在分别含有苯酚、甲苯、氯苯、异辛烷4 种有机溶剂的培养基中培养，菌株sdrhkD的生长情况都优于野生型赤红球菌，菌株sdrhkE的生长情况都低于野生型赤红球菌。本研究为进一步揭示赤红球菌SD3中组氨酸激酶涉及的信号转导途径与赤红球菌有机溶剂耐受性的关联机制提供一定参考依据。     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

microRNA在亨廷顿病中的研究进展

亨廷顿病(Huntington's disease, HD)是神经退行性疾病(neurodegenerative disorders)中一种单基因遗传病,由亨廷顿基因(the huntingtin gene, HTT)第一个外显子中的CAG三核苷酸序列重复扩增引起,尚无法治愈。HTT编码的蛋白产物被称为亨廷顿蛋白(Huntingtin protein, Htt)。突变亨廷顿蛋白(mutated huntingtin protein, mHtt)容易形成聚集体,具有毒性,导致一系列细胞学异常和神经元功能障碍。microRNA(miRNA)在基因转录后水平调控中起重要作用,其表达的改变与HD病理过程有关,成为治疗HD的潜在生物标志物。近年来,对一些特定miRNA在HD中调控机制及靶基因预测方面的研究可为HD提供潜在的治疗方法。本文就miRNA在HD中的相关研究进展进行综述。     

一类Feistel密码的线性分析

该文提出一种新的求取分组密码线性偏差上界的方法，特别适用于密钥线性作用的Feistel密码．该分析方法的思路是，首先对密码体制线性偏差进行严格的数学描述，分别给出密码线性偏差与轮函数F及S盒的线性偏差的数学关系；然后通过求取线性方程组最小重量解，确定密码线性偏差的上界．