微波辐射对青霉素菌渣破壁效果的影响

引言近年来,我国发酵类制药行业发展迅速,目前抗生素产量已居世界第一位。抗生素菌渣是抗生素发酵工业中的主要固体废弃物,其主要成分包括抗生素菌丝体、剩余培养基成分、发酵中微生物代谢产物及少量的残留抗生素等[1],由于菌渣对人体及环境产生潜在的耐药性风险,依据2008年修订的《国家危险废物名录》,抗生素菌渣被划入危险废物范畴[2]。抗生素菌渣产生量大、含水率高,且含有蛋白质(约占干重的30%~40%)、多糖(10%以上)及麦角固醇(0.5%~1.0%)等营养物质[3],     

一株广谱拮抗细菌的鉴定、发酵条件及其抗菌物质性质

[目的]菌株S46是一株对多种植物病原菌有较好拮抗作用的细菌,为有效防治植物病害,进行菌种的鉴定、发酵培养工艺及抗菌物质性质研究。[方法]通过培养特性、形态特征、生理生化特性和16S rDNA同源性序列分析明确菌株的分类地位;通过发酵单因素实验和各因素正交试验确定菌株的发酵工艺;通过热、酸碱、紫外和日光等处理测定明确活性物质的稳定性。[结果]鉴定菌株S46为唐菖蒲伯克霍尔德氏菌(Burkholderia gladioli),其对多种植物病原菌有拮抗作用,最适发酵培养基配方为大豆粉10g/L,玉米粉15g/L,蔗糖15g/L,磷酸氢二钾1 g/L,酵母粉5g/L;最适培养条件为接种量6%,发酵时间72 h,装瓶量30 mL/250mL,初始pH值7.0,转速180 r/min,温度30℃。该菌株发酵液有较好的热稳定性、紫外光和日光稳定性,但对强酸、强碱和有机试剂都较敏感。[结论]为植物病害的防治提供新的生物防治资源。     

吸水链霉菌A03抗菌活性产物发酵条件的优化

[目的]吸水链霉菌A03是自主分离的高效拮抗菌株,其活性代谢产物对多种植物病原真菌具有强烈抑制作用;为了建立其高产发酵工艺,并为过程放大提供技术参数,利用正交设计和单因素试验对其发酵培养基和培养条件进行优化。[结果]获得的最适发酵培养基配方(g/L):葡萄糖5,可溶性淀粉5,黄豆粉10,磷酸二氢钾0.5,碳酸钙1,七水硫酸镁0.1;最优培养条件:液体种子菌龄40 h,500 mL摇瓶装液量80 mL,接种量为体积分数12%,摇床转速180 r/min,培养温度28℃,发酵周期216 h;发酵液抑菌直径达3.04 cm。[结论]成功建立了该菌株的小试发酵工艺,发酵水平提高了52.0%,发酵周期缩短30.77%。研究结果为其发酵代谢调控和过程放大提供了基本参数。     

人参内生拮抗细菌NJ13的鉴定及发酵条件

[目的]菌株NJ13是1株对人参常见病原菌有较强拮抗作用的内生细菌,为了有效防治人参病害,进行了菌种鉴定和发酵培养工艺的试验。[方法]通过培养特性、形态特征、生理生化特性和16S rDNA同源性序列分析,对菌株NJ13进行鉴定;采用牛津杯法测定抗菌谱;同时通过发酵单因素实验和各因素正交试验确定菌株的发酵工艺。[结果]确定菌株NJ13为甲基营养型芽孢杆菌(Bacillus methylotrophicus),该菌对多种植物病原菌有拮抗作用,适宜的发酵培养基配方:葡萄糖3%,淀粉1.5%,酵母浸粉1.5%,K2HPO40.1%,NaCl 0.5%;最适培养条件:接种量4%,装液量75 mL/250mL,起始pH值8.0,28℃、180 r/min摇瓶培养96 h。[结论]为人参病害的防治提供新的生物防治资源。     

复合材料寿命预测方法研究

通过模拟环境中不同老化因素对复合材料进行加速老化实验研究,采用百分回归分析方法获取环境中单因子和多因子的老化模型参数建立加速老化寿命方程。使用该方程可以对服役中的复合材料进行高置信度、高可靠度^[1]的环境老化寿命和老化剩余强度预测^[2-3]。该方法不仅能比较全面的评估环境中不同老化因素对材料性能的影响,还能充分考虑了主要因素对材料实际使用寿命的影响^[4]。     

离子液体1-乙基-3-甲基咪唑醋酸盐对酿酒酵母AY93161的毒性及其酒精发酵过程的影响

为了考察离子液体在由木质纤维原料制备可发酵糖中的残留对后续酒精发酵过程的影响,对离子液体1-乙基-3-甲基咪唑醋酸盐([Emim]Ac)对酿酒酵母AY93161的毒性及其酒精发酵过程的影响进行研究。通过亚甲基蓝染色,利用OLYMPUS CX41显微镜观察不同[Emim]Ac浓度下对数生长期酵母细胞的形态结构,出芽情况及代谢活性,发现在[Emim]Ac浓度高于5 g·L^-1时,酵母细胞的形态结构会发生变化,在[Emim]Ac浓度高于0.1 g·L^-1时,随着[Emim]Ac浓度的增加,酵母的出芽速率及代谢活性降低。通过平板培养和液体悬浮培养测得[Emim]Ac对酵母的半有效浓度EC50和半抑制浓度IC50分别为4.45、7.70 g·L^-1。通过测定不同[Emim]Ac浓度下酵母酒精发酵的过程数据,发现在[Emim]Ac浓度低于0.1 g·L^-1时,对酵母酒精发酵过程几乎无影响,在[Emim]Ac浓度高于0.1 g·L^-1时,对酵母酒精发酵有抑制作用,[Emim]Ac对酵母酒精发酵的抑制作用主要是由其对菌体生长的抑制所致。     

新型抗生素阀门已投人市场

目前国内生产的阀门 ,不能完全适应抗生素发酵、生物农药发酵、生物肥料发酵等工业卫生级要求 ,应用有一定局限性 ,菌源污染大 ;使用温度低 ;耐腐蚀性差 ,阀门密封易渗漏 ,能源消耗大。为此温州市金鑫药机阀门制造有限公司 ,为克服现有市场上阀门用于抗生素发酵及其它工业中存在的上述缺陷 ,开发成功一种属《中国高新技术产品目录》（２０００年）确定的多种防酸防碱以及特殊环境下运行的泵阀 ,同时也是发酵工业无菌源污染的生产设备。产品已获国家实用新型专利（ＺＬ００２１８７１４ .０） ,公司拥有该产品知识产权。该阀门结构设计技术采…     

利用生物废料的固体发酵生产增值产品

生物废料中含有多种高价值的可再利用物质,例如可溶性糖和纤维等。在固体发酵中,这些废料可以作为支持底物参与反应,生产工业相关代谢产物,从而创造较大的经济价值,同时降低废料对环境的污染。本文综述了通过固体发酵技术,再生利用生物废料生产香料、有机物、聚合物、酶和抗生素等增值产品的研究进展,从而为该项技术的生产应用提供理论依据。     

酯类化合物的消泡性能评价

主要研究了酯类化合物在模拟抗生素发酵体系中的消泡行为,并对它们的消泡性能进行了比较.结果表明,在该体系中是否存在碳碳双键是影响酯类化合物消泡性能的一个重要因素.     

思政教育融入"发酵工程"教学全过程的路径研究

"发酵工程"是我校药品生产技术专业开设的一门专业核心课程,课程内容与人们生产生活息息相关.抗生素、氨基酸、维生素等多种药品都可以通过发酵生产得到,课程中蕴含着丰富的思政元素.本文以《发酵工程》为例,进行思政教育融入"发酵工程"理论教学、实验教学及实训教学全过程的路径研究,为高职院校专业课课程思政的建设提供参考和借鉴.     

INTRODUCTION OF DYNAMICS TO AN APPROACH FOR BATCH PROCESS MONITORING USING INDEPENDENT COMPONENT ANALYSIS

Use of independent component analysis (ICA) in developing statistical monitoring charts for batch processes has been reported previously. This article extends the previous work by introducing time lag shifts to include process dynamics in the ICA model. Comparison of the dynamic ICA-based method with other batch process monitoring approaches based on static ICA, static principal component analysis (PCA), and dynamic PCA is made for an industrial batch polymerization reactor and a simulated fed-batch penicillin fermentation process. For both case studies, it was found that the dynamic ICA approach detected faults earlier than other approaches, with less ambiguity, and was the only approach that detected all the faults.     

INTRODUCTION OF DYNAMICS TO AN APPROACH FOR BATCH PROCESS MONITORING USING INDEPENDENT COMPONENT ANALYSIS

Use of independent component analysis (ICA) in developing statistical monitoring charts for batch processes has been reported previously. This article extends the previous work by introducing time lag shifts to include process dynamics in the ICA model. Comparison of the dynamic ICA-based method with other batch process monitoring approaches based on static ICA, static principal component analysis (PCA), and dynamic PCA is made for an industrial batch polymerization reactor and a simulated fed-batch penicillin fermentation process. For both case studies, it was found that the dynamic ICA approach detected faults earlier than other approaches, with less ambiguity, and was the only approach that detected all the faults.     

Metabolomics of oxidative stress in recent studies of endogenous and exogenously administered intermediate metabolites

Aerobic metabolism occurs in a background of oxygen radicals and reactive oxygen species (ROS) that originate from the incomplete reduction of molecular oxygen in electron transfer reactions. The essential role of aerobic metabolism, the generation and consumption of ATP and other high energy phosphates, sustains a balance of approximately 3000 essential human metabolites that serve not only as nutrients, but also as antioxidants, neurotransmitters, osmolytes, and participants in ligand-based and other cellular signaling. In hypoxia, ischemia, and oxidative stress, where pathological circumstances cause oxygen radicals to form at a rate greater than is possible for their consumption, changes in the composition of metabolite ensembles, or metabolomes, can be associated with physiological changes. Metabolomics and metabonomics are a scientific disciplines that focuse on quantifying dynamic metabolome responses, using multivariate analytical approaches derived from methods within genomics, a discipline that consolidated innovative analysis techniques for situations where the number of biomarkers (metabolites in our case) greatly exceeds the number of subjects. This review focuses on the behavior of cytosolic, mitochondrial, and redox metabolites in ameliorating or exacerbating oxidative stress. After reviewing work regarding a small number of metabolites-pyruvate, ethyl pyruvate, and fructose-1,6-bisphosphate-whose exogenous administration was found to ameliorate oxidative stress, a subsequent section reviews basic multivariate statistical methods common in metabolomics research, and their application in human and preclinical studies emphasizing oxidative stress. Particular attention is paid to new NMR spectroscopy methods in metabolomics and metabonomics. Because complex relationships connect oxidative stress to so many physiological processes, studies from different disciplines were reviewed. All, however, shared the common goal of ultimately developing "omics"-based, diagnostic tests to help influence therapies.     

H‐B‐E (hexanol‐butanol‐ethanol) fermentation for the production of higher alcohols from syngas/waste gas

CO, H₂, and CO₂ are major components of syngas and some industrial CO‐rich waste gases (e.g. waste gases from steel industries), besides some additional minor compounds. It was recently shown that those gases can be bioconverted, by acetogenic/solventogenic bacteria, into ethanol and higher alcohols such as butanol, but also hexanol, through the so‐called HBE fermentation. That process presents some advantages over existing chemical conversion processes. This paper reviews HBE fermentation from C1‐gases after briefly describing the more conventional ABE (acetone‐butanol‐ethanol) fermentation from carbohydrates by Clostridium acetobutylicum, in order to allow for comparison of both processes. Although acetone may appear in carbohydrate fermentation, alcohols are the only major end‐metabolites in the HBE process with Clostridium carboxidivorans. The few acetogenic bacteria known to metabolize C1‐gases and produce butanol or higher alcohols are described. Clostridium carboxidivorans has been used in most cases. Bioconversion of the gaseous substrates takes place in two stages, namely acidogenesis (production of acids) followed by solventogenesis (production of alcohols), characterized by different optimal fermentation conditions. Major parameters affecting each bioconversion stage as well as the overall fermentation process are analyzed. Although it has been claimed that acidification is required in ABE fermentation to initiate the solventogenic stage, strong acidification seems to some extent not to be a prerequisite for solventogenesis in the HBE process. Bioreactors potentially suitable for this type of bioconversion process are described as well. © 2017 Society of Chemical Industry     

基于主元分析和核密度估计的多变量统计过程监控及在工厂聚丙烯催化剂反应器的应用

Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.     

Two‐step principal component analysis for dynamic processes monitoring

In this study, a two‐step principal component analysis (TS‐PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross‐correlation structure and dynamic auto‐correlation structure in process data simultaneously, TS‐PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS‐PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS‐PCA still can achieve better performance than the existing dynamic approaches.     

Role of a Disordered Steroid Metabolome in the Elucidation of Sterol and Steroid Biosynthesis

In 1937 Butler and Marrian found large amounts of the steroid pregnanetriol in urine from a patient with the adrenogenital syndrome, a virilizing condition known to be caused by compromised adrenal secretion even in this pre-cortisol era. This introduced the concept of the study of altered excretion of metabolites as an in vivo tool for understanding sterol and steroid biosynthesis. This approach is still viable and has experienced renewed significance as the field of metabolomics. From the first cyclized sterol lanosterol to the most downstream product estradiol, there are probably greater than 30 steps. Based on a distinctive metabolome clinical disorders have now been attributed to about seven post-squalene cholesterol (C) biosynthetic steps and around 15 en-route to steroid hormones or needed for further metabolism of such hormones. Forty years ago it was widely perceived that the principal steroid biosynthetic defects were known but interest rekindled as novel metabolomes were documented. In his career this investigator has been involved in the study of many steroid disorders, the two most recent being P450 oxidoreductase deficiency and apparent cortisone reductase deficiency. These are of interest as they are due not to mutations in the primary catalytic enzymes of steroidogenesis but in ancillary enzymes needed for co-factor oxido-reduction A third focus of this researcher is Smith-Lemli-Opitz syndrome (SLOS), a cholesterol synthesis disorder caused by 7-dehydrocholesterol reductase mutations. The late George Schroepfer, in whose honor this article has been written, contributed greatly to defining the sterol metabolome of this condition. Defining the cause of clinically severe disorders can lead to improved treatment options. We are now involved in murine gene therapy studies for SLOS which, if successful could in the future offer an alternative therapy for this severe condition.     

步进MPCA及其在间歇过程监控中的应用

针对多向主元分析法(MPCA)在间歇过程监控过程中需要预测过程未来输出的困难,提出了一种新的步进多向主元分析方法。该方法通过建立一系列的PCA模型,避免了对预估过程变量未来输出的需要,通过引入遗忘因子能够自然地处理多阶段间歇过程的情况。对于多阶段链霉素发酵过程的监控表明,相对于普通MPCA,步进MPCA能够更精确地对过程故障行为进行描述。     

电厂过程监测与故障诊断的一种MSPC方法

讨论了大型电站具有随机时变特性过程的状态监测与故障诊断。基于多变量统计过程（MSPC）理论的主元素分析（PCA）技术,分辨故障事件的原因与结果。为适应随机时变特性的过程特点,将典型的PCA与改进的递推和快速滑动窗型PCA算法进行了比较。给出了递推快速滑动窗型算法（MWPCA）,通过与典型的PCA算法在故障诊断应用中的比较,表明了新算法对过程特性变化的自适应能力和计算效率。给出了在现代电站过程中应用FMWPCA算法将诊断与过程特性变量结合的图示方法应用实例。     

A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes

Pearson's correlation measure is only able to model linear dependence between random variables. Hence, conventional principal component analysis (PCA) based on Pearson's correlation measure is not suitable for application to modern industrial processes where process variables are often nonlinearly related. To address this problem, a nonparametric PCA model is proposed based on nonlinear correlation measures, including Spearman's and Kendall tau's rank correlation. These two correlation measures are also less sensitive to outliers comparing to Pearson's correlation, making the proposed PCA a robust feature extraction technique. To reveal meaningful patterns from process data, a generalized iterative deflation method is applied to the robust correlation matrix of the process data to sequentially extract a set of leading sparse pseudoeigenvectors. For online fault diagnosis, the T² and SPE statistics are computed and analyzed with respect to the subspace spanned by the extracted pseudoeigenvectors. The proposed method is applied to two industrial case studies. Its process monitoring performance is demonstrated to be superior to that of the conventional PCA and is comparable to those of Kernel PCA and kernel independent component analysis at a lower computational cost. The proposed PCA is also more robust in sparse feature extraction from contaminated process data. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1494–1513, 2016