耐高温酿酒酵母的选育 Ⅰ出发菌株的选择及发酵特性分析

本文报道了经过对我国四株常用的酒精发酵菌株,K氏酵母(Saccharomyces cere-visiae Hansen var.ellipsoideus(Hansen)Dekk)、1038(Saccharomyces cerevi-siae Hansen)、Rasse Ⅱ(Saccharomyces cerevisiae Hsanen)、Rasse Ⅻ(Sac-charomyces cerevisiae Hansen)的发酵效率和耐热性能等生理特性的分析比较,发现虽然1038的耐热性能比其它三个菌株强(42℃时发酵效率为77.16%),但鉴于它发酵效率下降幅度远大于K氏酵母(在40℃发酵效率下降13.53%,而K氏酵母下降9.21%),据此,从既具一定的耐热性,又具较高的产酒率这点出发,拟以K氏酵母菌株作为选育耐高温产酒酵母的出发菌株.     

诱导酵母原生质体摄取蚕豆叶绿体的研究

本实验试图应用原生质体融合技术将异养型酵母细胞改建成光能自养类型。实验结果证明,聚乙二醇(PEG)能够有效地诱导酵母原生质体摄取游离的蚕豆叶绿体;PEG和CaClP_2的浓度,pH值,诱导融合的温度和时间,以及渗透稳定剂的种类都对酵母原生质体摄取叶绿体产生一定的影响。     

关于_σ(C_c(X)σ(紧G_σ-集)的证明

本文指出文献[1]的一个错误,并给出正确的论证。     

通过原生质体融合改建酵母细胞的研究——Ⅳ.原生质体融合技术在酵母育种上的应用

HU-KDF-185菌株是通过原生质体融合并经筛选而获得的酿酒酵母(Saccharomycescerevisiae var. ellipsoideus HUK-1,(1a,his~-)与糖化酵母(Saccharomyces diastaticus, a, arg~-)的种间三倍体融合杂种.作者在实验室条件下对其发酵生物学进行比较研究后,在仙居酒厂对该融合杂种发酵不同淀粉质原料的适应性及其酒精产量作了生产规模的比较试验。结果表明,种间融合杂种HU-KDF-185对番薯渣原料的酒精产量平均为30.77％,这比我国目前广为应用的酒精生产菌株K氏酵母(29.71％)和新市一号(29.54％)分别增产3.56％和4.16％;在大米原料的发酵中,其酒精产量平均为35.38％,比大米酒精生产菌株M-85(32.87％)相对增产酒精7.64％,而番薯丝原料的酒精产量平均为30.20％,比生产菌株新市一号(28.70％)相对增产酒精5.23％.由此可见,种间融合杂种HU-KDF-185在不同类型的淀粉质原料的发酵中,其酒精增产效果显著而稳定,这也证明了我们通过原生质体融合技术所获得的种间融合杂种将是一株较优良的酒精高产菌株.同时也表明原生质体融合技术对酒精酵母的选育,特别是对那些在其生活周期中缺乏或失去有性过程的工业酵母更为有用.     

基于人工神经网络的湍流大涡模拟方法

大涡模拟方法(LES)是研究复杂湍流问题的重要工具,在航空航天、湍流燃烧、气动声学、大气边界层等众多工程领域中具有广泛的应用前景.大涡模拟方法采用粗网格计算大尺度上的湍流结构,并用亚格子(SGS)模型近似表达滤波尺度以下的流动结构对大尺度流场的作用.传统的亚格子模型由于只利用了单点流场信息和简单的函数关系,在先验验证中相对误差较大, 在后验验证中耗散过强. 近几年来,机器学习方法在湍流建模问题中得到了越来越多的应用.本文介绍了基于人工神经网络(ANN)的湍流亚格子模型的最新进展.详细地讨论了人工神经网络混合模型、空间人工神经网络模型和反卷积人工神经网络模型的构造方法.借助于人工神经网络强大的数据插值能力,新的亚格子模型的先验精度和后验精度均有显著提升. 在先验验证中,新模型所预测的亚格子应力的相关系数超过了0.99,在预测精度上远高于传统的大涡模拟模型. 在后验验证中,新模型对各类湍流统计量和瞬态流动结构的预测都优于隐式大涡模拟方法、动态Smagorinsky模型、动态混合模型等传统模型.因此, 人工神经网络方法在发展复杂湍流的先进大涡模拟模型中具有很大的潜力.      

Magnetohydrodynamics computations with lattice gas automata

Lattice gas automata have received considerable interest for the last several years and possibly may become a powerful numerical method for solving various partial differential equations and modeling different physical phenomena, because of their discrete and parallel nature and the capability of handling complicated boundaries. In this paper, we present recent studies on the lattice gas model for magnetohydrodynamics. The FHP-type lattice gas model has been extended to include a bidirectional random walk process, which allows well-defined statistical quantities, such as velocity and magnetic field, to be computed from the microscopic particle representation. The model incorporates a new sequential particle collision method to increase the range of useful Reynolds numbers in the model, an improvement that may also be of use in other lattice gas models. In the context of a Chapman-Enskog expansion, the model approximates the incompressible magnetic hydrodynamic equations in the limit of low Mach number and high. Simulation results presented here demonstrate the validity of the model for several basic problems, including sound wave and Alfvén wave propagation, and diffusive Kolmogoroff-type flows.     

Analysis and prediction of RNA-binding residues using sequence, evolutionary conservation, and predicted secondary structure and solvent accessibility

Identification and prediction of RNA-binding residues (RBRs) provides valuable insights into the mechanisms of protein-RNA interactions. We analyzed the contributions of a wide range of factors including amino acid sequence, evolutionary conservation, secondary structure and solvent accessibility, to the prediction/characterization of RBRs. Five feature sets were designed and feature selection was performed to find and investigate relevant features. We demonstrate that (1) interactions with positively charged amino acids Arg and Lys are preferred by the egatively charged nucleotides; (2) Gly provides flexibility for the RNA binding sites; (3) Glu with negatively charged side chain and several hydrophobic residues such as Leu, Val, Ala and Phe are disfavored in the RNA-binding sites; (4) coil residues, especially in long segments, are more flexible (than other secondary structures) and more likely to interact with RNA; (5) helical residues are more rigid and consequently they are less likely to bind RNA; and (6) residues partially exposed to the solvent are more likely to form RNA-binding sites. We introduce a novel sequence-based predictor of RBRs, RBRpred, which utilizes the selected features. RBRpred is comprehensively tested on three datasets with varied atom distance cutoffs by performing both five-fold cross validation and jackknife tests and achieves Matthew's correlation coefficient (MCC) of 0.51, 0.48 and 0.42, respectively. The quality is comparable to or better than that for state-of-the-art predictors that apply the distancebased cutoff definition. We show that the most important factor for RBRs prediction is evolutionary conservation, followed by the amino acid sequence, predicted secondary structure and predicted solvent accessibility. We also investigate the impact of using native vs. predicted secondary structure and solvent accessibility. The predictions are sufficient for the RBR prediction and the knowledge of the actual solvent accessibility helps in predictions for lower distance cutoffs.     

Molecular simulations of electroosmotic flows in rough nanochannels

A highly efficient molecular dynamics algorithm for micro and nanoscale electrokinetic flows is developed. The long-range Coulomb interactions are calculated using the Particle–Particle Particle–Mesh (P³M) approach. The Poisson equation for the electrostatic potential is solved in physical space using an iterative multi-grid technique. After validation, the method is used to study electroosmotic flow in nanochannels with regular or random roughness on the walls. The results show that roughness reduces the electroosmotic flow rate dramatically even though the roughness is very small compared to the channel width. The effect is much larger than for pressure driven flows because the driving force is localized near the walls where the charge distribution is high. Non-Newtonian behavior is also observed at much lower flow rates. Systematic investigation of the effect of surface charge density and random roughness will help to better understand the mechanism of electrokinetic transport in rough nanochannels and to design and optimize nanofluidic devices.     

Cold-Resistant Nitrogen/Sulfur Dual-Doped Graphene Fiber Supercapacitors with Solar–Thermal Energy Conversion Effect

Although graphene fiber-based supercapacitors are promising for wearable electronic devices, the low energy density of electrodes and poor cold resistance of aqueous electrolytes limit their wide application in cold environments. Herein, porous nitrogen/sulfur dual-doped graphene fibers (NS-GFs) are synthesized by hydrothermal self-assembly followed by thermal annealing, exhibiting an excellent capacitive performance of 401 F cm⁻³ at 400 mA cm⁻³ because of the synergistic effect of heteroatom dual-doping. The assembled symmetric all-solid-state supercapacitor with polyvinyl alcohol/H₂SO₄/graphene oxide gel electrolyte exhibits a high capacitance of 221 F cm⁻³ and a high energy density of 7.7 mWh cm⁻³ at 80 mA cm⁻³. Interestingly, solar–thermal energy conversion of the electrolyte with 0.1 wt % graphene oxide extends the operating temperature range of the supercapacitor to 0 °C. Furthermore, the photocatalysis effect of the dual-doped heteroatoms increases the capacitance of NS-GFs. At an ambient temperature of 0 °C, the capacitance increases from 0 to 182 F cm⁻³ under 1 sun irradiation because of the excellent solar light absorption and efficient solar–thermal energy conversion of graphene oxide, preventing the aqueous electrolyte from freezing. The flexible supercapacitor exhibits a long cycle life, good bending resistance, reliable scalability, and ability to power visual electronics, showing great potential for outdoor electronics in cold environments.