均匀设计法优化黄芩愈伤组织培养基

目的:优化黄芩愈伤组织培养条件。方法:采用均匀设计法,以黄芩甙含量为主要考察指标,对黄芩愈伤组织生长的MS培养基成分进行多因素多水平考察。结果:最佳培养基为MS附加0.25mg/L NAA、0.5mg/L 6-BA、17g/L葡萄糖和35g/L蔗糖。最佳培养基黄芩甙含量为27.44mg/gDW。结论:用均匀设计法优化愈伤组织培养基省时、方便,且最佳培养基黄芩甙含量与预测值相近,且明显高于均匀设计实验方案中的黄芩甙含量。经SAS软件分析得出的优化方程拟合度很好。     

醉马草毒性成分的提取研究

目的:为研究醉马草有毒成分的种类、特性及提取方法,达到最终纯化出有毒成分的目的。方法:结合系统预试验、全紫外波段扫描、多种方法对比提取效率、毒理实验等各种实验方法系统的对醉马草进行了研究。结果:醉马草含有生物碱、黄酮等多种成分,其中毒性成分主要是亲水部位的生物碱,中毒小白鼠表现为心跳呼吸加速、流泪、四肢无力嗜睡等症状,在醉马草整个生长期中,苗期所含总生物碱量最大,总生物碱在紫外波长为196nm、274nm和340nm有吸收峰,三种提取方法中乙醇煎煮法比较实用,正交实验法确定最佳提取工艺为80℃、50%的乙醇30倍体积下温育3h。     

响应面方法优化菊粉酶液体发酵培养基的研究

目的：为提高菊粉酶的产量。方法：运用Plackett-Burman设计法和响应面分析法,对Kluyveromyces S120液体发酵生产菊粉酶的培养基进行了优化。首先通过Plackett-Burman方法对8个相关影响因素的效应进行评价,并筛选出了有显著正效应的菊芋粉、玉米浆、(NH_4)H_2PO_4等三个因素,其他五个因素没有显著影响。然后根据Box-Behnken的中心组合设计实验和响应面分析方法确定了上述三个主要影响因素的最佳浓度。结果：在优化培养基下,菊粉酶产量为102．82u/mL,是优化前的2．1倍。     

Systems and synthetic biology approaches in understanding biological oscillators

Background: Self-sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single-cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators. Results: Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest, de novo oscillators in both live cells and cell-free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations. Conclusion: With the recent development of biological and computational tools, both approaches have made significant achievements.     

Rational design and synthesis of affinity matrices based on proteases immobilized onto cellulose membranes

Discovery of new protease inhibitors may result in potential therapeutic agents or useful biotechnological tools. Obtainment of these molecules from natural sources requires simple, economic, and highly efficient purification protocols. The aim of this work was the obtainment of affinity matrices by the covalent immobilization of dipeptidyl peptidase IV (DPP-IV) and papain onto cellulose membranes, previously activated with formyl (FCM) or glyoxyl groups (GCM). GCM showed the highest activation grade (10.2?µmol aldehyde/cm²). We implemented our strategy for the rational design of immobilized derivatives (RDID) to optimize the immobilization. pH 9.0 was the optimum for the immobilization through the terminal α-NH₂, configuration predicted as catalytically competent. However, our data suggest that protein immobilization may occur via clusters of few reactive groups. DPP-IV?GCM showed the highest maximal immobilized protein load (2.1?µg/cm²), immobilization percentage (91%), and probability of multipoint covalent attachment. The four enzyme-support systems were able to bind at least 80% of the reversible competitive inhibitors bacitracin/cystatin, compared with the available active sites in the immobilized derivatives. Our results show the potentialities of the synthesized matrices for affinity purification of protease inhibitors and confirm the robustness of the RDID strategy to optimize protein immobilization processes with further practical applications.     

Comparison between the univariate and multivariate analysis on the partial characterization of the endoglucanase produced in the solid state fermentation by Aspergillus oryzae ATCC 10124

Endoglucanase production by Aspergillus oryzae ATCC 10124 cultivated in rice husks or peanut shells was optimized by experimental design as a function of humidity, time, and temperature. The optimum temperature for the endoglucanase activity was estimated by a univariate analysis (one factor at the time) as 50°C (rice husks) and 60°C (peanut shells), however, by a multivariate analysis (synergism of factors), it was determined a different temperature (56°C) for endoglucanase from peanut shells. For the optimum pH, values determined by univariate and multivariate analysis were 5 and 5.2 (rice husk) and 5 and 7.6 (peanut shells). In addition, the best half-lives were observed at 50°C as 22.8?hr (rice husks) and 7.3?hr (peanut shells), also, 80% of residual activities was obtained between 30 and 50°C for both substrates, and the pH stability was improved at 5–7 (rice hulls) and 6–9 (peanut shells). Both endoglucanases obtained presented different characteristics as a result of the versatility of fungi in different substrates.     

Miklós Bodanszky Award Lecture: Advances in the selective targeting of protein phosphatase‐1 and phosphatase‐2A with peptides

Protein phosphatase‐1 and phosphatase‐2A are two ubiquitously expressed enzymes known to catalyze the majority of dephosphorylation reactions on serine and threonine inside cells. They play roles in most cellular processes and are tightly regulated by regulatory subunits in holoenzymes. Their misregulation and malfunction contribute to disease development and progression, such as in cancer, diabetes, viral infections, and neurological as well as heart diseases. Therefore, targeting these phosphatases for therapeutic use would be highly desirable; however, their complex regulation and high conservation of the active site have been major hurdles for selectively targeting them in the past. In the last decade, new approaches have been developed to overcome these hurdles and have strongly revived the field. I will focus here on peptide‐based approaches, which contributed to showing that these phosphatases can be targeted selectively and aided in rethinking the design of selective phosphatase modulators. Finally, I will give a perspective on www.depod.org , the human dephosphorylation database, and how it can aid phosphatase modulator design. © 2017 The Authors. Journal of Peptide Science published by European Peptide Society and John Wiley & Sons Ltd.     

Uncertainty Quantification in Life Cycle Assessments: Interindividual Variability and Sensitivity Analysis in LCA of Air‐Conditioning Systems

The life cycle environmental profile of energy‐consuming products, such as air conditioning, is dominated by the products’ use phase. Different user behavior patterns can therefore yield large differences in the results of a cradle‐to‐grave assessment. Although this variation and uncertainty is increasingly recognized, it remains often poorly characterized in life cycle assessment (LCA) studies. Today, pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐phase parameters, in turn facilitating quantification and reduction of this uncertainty in LCA. This study examined the case of energy use in building cooling systems, focusing on global warming potential (GWP) as the impact category. In Singapore, building cooling systems or air conditioning consumes up to 37% of national electricity demand. Lack of consideration of variation in use‐phase interaction leads to the oversized designs, wasted energy, and therefore reducible GWP. Using a high‐resolution data set derived from sensor observations, energy use and behavior patterns of single‐office occupants were characterized by probabilistic distributions. The interindividual variability and use‐phase variables were propagated in a stochastic model for the life cycle of air‐conditioning systems and simulated by way of Monte Carlo analysis. Analysis of the generated uncertainties identified plausible reductions in global warming impact through modifying user interaction. Designers concerned about the environmental profile of their products or systems need better representation of the underlying variability in use‐phase data to evaluate the impact. This study suggests that data can be reliably provided and incorporated into the life cycle by proliferation of pervasive sensing, which can only continue to benefit future LCA.     

Development of an integrated CAD–FEA system for patient-specific design of spinal cages

Spinal cages are used to create a suitable mechanical environment for interbody fusion in cases of degenerative spinal instability. Due to individual variations in bone structures and pathological conditions, patient-specific cages can provide optimal biomechanical conditions for fusion, strengthening patient recovery. Finite element analysis (FEA) is a valuable tool in the biomechanical evaluation of patient-specific cage designs, but the time- and labor-intensive process of modeling limits its clinical application. In an effort to facilitate the design and analysis of patient-specific spinal cages, an integrated CAD–FEA system (CASCaDeS, comprehensive analytical spinal cage design system) was developed. This system produces a biomechanical-based patient-specific design of spinal cages and is capable of rapid implementation of finite element modeling. By comparison with commercial software, this system was validated and proven to be both accurate and efficient. CASCaDeS can be used to design patient-specific cages with a superior biomechanical performance to commercial spinal cages.     

Computational resources and tools for antimicrobial peptides

Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure–activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.