紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。     

多元统计分析中一类矩阵迹函数最小化问题的有效算法

研究来源于多元统计分析中的一类矩阵迹函数最小化问题$\min c+ tr(AX)+\sum\limits_{j=1}^{m}tr(B_j X C_jX^{T}),\ \ {\rm s. t.} \ X^TX=I_p,$其中$c$为常数, $A\in R^{p\times n}\ (n\geq p)$, $B_j\in R^{n\times n}, C_j\in R^{p\times p}$为给定系数矩阵. 数值实验表明已有的Majorization算法虽可行, 但收敛速度缓慢且精度不高. 本文从黎曼流形的角度重新研究该问题, 基于Stiefel流形的几何性质, 构造一类黎曼非单调共轭梯度迭代求解算法, 并给出算法收敛性分析.数值实验和数值比较验证所提出的算法对于问题模型是高效可行的.     

Biomolecular Release Stimulated by Electrochemical Signals at a Very Small Potential Applied

DNA release electrochemically stimulated by applying ?10 mV on the modified electrode was studied. The release process was based on the local (interfacial) pH change produced upon H₂O₂ reduction electrocatalyzed by the immobilized microperoxidase‐11. SiO₂ nanoparticles attached to the electrode surface and functionalized with trigonelline and boronic acid species changed their electrical charge from positive to negative upon the interfacial pH change, thus allowing electrostatic adsorption of negatively charged DNA on the positive interface and then its repulsion/release from the negative interface. The loaded/released DNA molecules were labeled with a fluorescent dye to allow easy detection of the released DNA molecules. The important feature of the developed system is the controlled DNA release upon applying very small electrical potential on the modified electrode.     

Dual‐signal amplification strategy for ultrasensitive chemiluminescence detection of PDGF–BB in capillary electrophoresis

Many efforts have been made toward the achievement of high sensitivity in capillary electrophoresis coupled with chemiluminescence detection (CE‐CL). This work describes a novel dual‐signal amplification strategy for highly specific and ultrasensitive CL detection of human platelet‐derived growth factor–BB (PDGF–BB) using both aptamer and horseradish peroxidase (HRP) modified gold nanoparticles (HRP–AuNPs–aptamer) as nanoprobes in CE. Both AuNPs and HRP in the nanoprobes could amplify the CL signals in the luminol–H₂O₂ CL system, owing to the excellent catalytic behavior of AuNPs and HRP in the CL system. Meanwhile, the high affinity of aptamer modified on the AuNPs allows detection with high specificity. As proof‐of‐concept, the proposed method was employed to quantify the concentration of PDGF–BB from 0.50 to 250 fm with a detection limit of 0.21 fm. The applicability of the assay was further demonstrated in the analysis of PDGF–BB in human serum samples with acceptable accuracy and reliability. The result of this study exhibits distinct advantages, such as high sensitivity, good specificity, simplicity, and very small sample consumption. The good performances of the proposed strategy provide a powerful avenue for ultrasensitive detection of rare proteins in biological sample, showing great promise in biochemical analysis. Copyright © 2015 John Wiley & Sons, Ltd.     

Hexametaphosphate‐Capped Silica Mesoporous Nanoparticles Containing CuII Complexes for the Selective and Sensitive Optical Detection of Hydrogen Sulfide in Water

Cu^II‐macrocycle functionalized hexametaphosphate‐capped silica mesoporous nanoparticles have been prepared and used for the selective and sensitive detection of hydrogen sulfide in aqueous environments. The possibility of using different metal complexes combined with different capping anions and choice of different dyes or other sensing molecules as indicators makes this new protocol highly appealing for the preparation of new sensing systems for sulfide detection in different environments.     

Setting local rank constraints by orthogonal projections for image resolution analysis: Application to the determination of a low dose pharmaceutical compound

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used.     

Single‐Atom Gold Catalysis in the Context of Developments in Parahydrogen‐Induced Polarization

A highly isolated monoatomic gold catalyst, with single gold atoms dispersed on multiwalled carbon nanotubes (MWCNTs), has been synthesized, characterized, and tested in heterogeneous hydrogenation of 1,3‐butadiene and 1‐butyne with parahydrogen to maximize the polarization level and the contribution of the pairwise hydrogen addition route. The Au/MWCNTs catalyst was found to be active and efficient in pairwise hydrogen addition and the estimated contributions from the pairwise hydrogen addition route are at least an order of magnitude higher than those for supported metal nanoparticle catalysts. Therefore, the use of the highly isolated monoatomic catalysts is very promising for production of hyperpolarized fluids that can be used for the significant enhancement of NMR signals. A mechanism of 1,3‐butadiene hydrogenation with parahydrogen over the highly isolated monoatomic Au/MWCNTs catalyst is also proposed.