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关键词： 生物技术   食品科学   应用  

摘要： 食品安全和质量对社会发展和人们生活都具有非常大的影响,促进食品科学的改进和应用也能推动食品质量的进一步提升。食品科学所涉及的工程项目比较复杂,因此需要结合一些现代化技术来解决食品科学中的一些不合理问题。而生物技术能强化食品科学工作成效、保障食品行业的稳定发展。为此,本文将对生物技术在食品科学中的应用进行探究分析,主要从食品安全性检测、食品成分检测等角度出发,希望能为读者提供有价值的信息。

关键词： 高温大曲   宏基因组学   微生物群落   碳水化合物活性酶   异质性  

摘要： 高温大曲为酱香型白酒生产所需的糖化发酵剂和生香剂,对基酒的质量及风格起着至关重要的作用。高温大曲发酵过程中曲堆不同位置曲坯的温湿度、水分存在较大差异,导致微生物群落结构及美拉德褐变反应程度差异明显,进而形成黑、黄、白三种颜色且风格不同的高温大曲。本文应用宏基因组学探究了不同高温大曲微生物群落结构及功能差异,结果表明,黑曲中的微生物以罗萨氏菌属(Rasamsonia)、曲霉属(Aspergillus)、丝衣霉属(Byssochlamys)等真菌为主,具有较强的糖苷水解酶(Glycoside Hydrolases,GHs)、辅助氧化还原酶(Auxiliary Activities,AAs)生成能力;与黑曲相似,黄曲中的微生物以罗萨氏菌属、篮状菌属(Talaromyces)、曲霉属等真菌为主,黄曲中可能积累较高含量的糖苷水解酶、糖基转移酶(Glycosyl Transferases,GTs);白曲中的微生物以克罗彭施泰特氏菌属(Kroppenstedtia)、链孢子菌属(Desmospora)、枝芽孢菌属(Virgibacillus)、芽孢杆菌属(Bacillus)等细菌为主,可以合成较多的糖基转移酶、碳水化合物酯酶(Carbohydrate Esterases,CEs)和多糖裂合酶(Polysaccharide Lyases,PLs)。本文研究了不同高温大曲微生物群落结构及功能差异,为深入研究高温大曲发酵工艺奠定了技术基础,同时为制定合理的高温大曲生产应用方案提供了参考。

关键词： 酸盐分离   镍电积阳极液   膜分离   树脂吸附   离子交换  

摘要： 目前酸盐分离技术主要有树脂吸附法、离子交换法和膜分离技术三大类,但将其应用于镍湿法冶炼中镍电积阳极液的报道较少。总结了三大类酸盐分离技术的原理和应用,并分别采用树脂吸附法、扩散渗析法、特种纳滤膜和电渗析结合的方法对镍电积阳极液进行酸盐分离试验。结果表明,三种方法对镍电积阳极液均有一定的分离效果,其中树脂吸附法易饱和,需反洗再生,造成体积膨胀;扩散渗析法对硫酸的回收率在80%以上,镍的截留率在90%以上,但回收的硫酸浓度较低,同样存在体积膨胀问题;特种纳滤膜和电渗析结合法对镍离子的截留率可达90%以上,氢离子去除率可达70%以上,同时浓缩后的氢离子浓度可提高400%以上,镍离子浓度可提高500%以上,淡化液中各种离子浓度小于50 mg/L,可用作纯水,并且该方法不会造成系统体积膨胀,具有工业化应用价值。

关键词： 氯离子   水压试验用水   滴定-富集联用法   硝酸汞容量法   离子交换   快速检测  

摘要： 为了实现对电站锅炉、压力容器水压试验用水等水样中氯离子浓度从痕量到微量的快速、准确检测,本研究提出了滴定-富集联用法,以比浊法做为分析方法的判断标准,浊度大于比浊标液可直接采用滴定法;浊度小于比浊标液需先使用富集法,再对富集液进行滴定,对于痕量氯离子水样可实现半小时快速分析。通过分析方法检验,样品的加标回收率在98.67%~110.00%之间,平行测定的相对标准偏差在1.05%~5.91%之间,均在相关规定范围内。说明本检测方法测定结果准确,可实现水中Cl-浓度的现场快速检测分析。

关键词： 锈菌目  

ISBN: (纸本)9787521921625

摘要： 本书分为四章, 主要内容包括: 概论、锈菌重寄生菌分离鉴定与抑菌活性、重寄生菌全基因组测定与分析、锈菌重寄生菌天然产物。具体内容包括: 植物锈病与重寄生菌概述 ; 常见重寄生菌及应用 ; 重寄生菌的分离与回接 ; 重寄生拟盘多毛孢菌的鉴定与抑菌活性 ; 重寄生枝孢菌的鉴定和抑菌活性等。

关键词： 生物工程  

ISBN: (纸本)9787030753304

摘要： 本书共七章，内容包括：技术恐惧及其成因、生物技术恐惧的生成背景、生物技术恐惧的社会扩散、生物技术恐惧的社会影响、生物技术恐惧的理性分析、生物技术恐惧的社会调适等。

摘要： The Roystonea palm genus of the Caribbean and surrounding areas is a charismatic group of palms of iconic stature which are now propagated as ornamentals throughout the tropics of the world. In this thesis, the first chapter discusses the background and history of Roystonea specifically in South Florida, and the second chapter uses phylogenomics to explore the relationships among species and to reconstruct the historical biogeography of the genus. Multiple individuals from 8 of 9 extant species of Roystonea were collected either in the wild, or from the living collections of Fairchild Tropical Botanic Garden and Montgomery Botanical Center (both located in South Florida). Over 700 genes were sequenced using a targeted bait set. The resulting phylogeny shows multiple instances of two individuals of the same species not sister to each other within the genus, suggesting that there is either hybridization and gene flow between the species, or that most of the currently described species are not monophyletic. The implication is that there is strong gene flow between populations of Roystonea in the Caribbean and further studies are required to more accurately determine species boundaries.

关键词： 生物工程  

ISBN: (纸本)9787518440818

摘要： 本书着眼于淀粉深加工相关生物技术及重要加工制品，结合最新研究成果和产业动态，围绕淀粉科学前沿理论、先进淀粉生物技术、淀粉质产品开发与应用等方面的研究进展、工业化应用及发展趋势进行总结和探讨。

摘要： Genome-wide association studies (GWAS) and predictive genomics have become increasingly important in genetics research over the past decade. GWAS involves the analysis of the entire genome of a large group of individuals to identify genetic variants associated with a particular trait or disease. Predictive genomics combines information from multiple genetic variants to predict the polygenic risk score (PRS) of an individual for developing a disease. Machine learning is a branch of artificial intelligence that has revolutionized various fields of study, including computer vision, natural language processing, and robotics. Machine learning focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. Deep learning is a subset of machine learning that uses deep neural networks to recognize patterns and relationships. In this dissertation, we first compared various machine learning and statistical models for estimating breast cancer PRS. A deep neural network (DNN) was found to be the most effective, outperforming other techniques such as BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the receiver operating characteristic curves area under the curves (ROC AUCs) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. While BLUP, BayesA, and LDpred generated PRS that followed a normal distribution in the case population, the PRS generated by DNN followed a bimodal distribution. This allowed DNN to achieve a recall of 18.8% at 90% precision in the test cohort, which extrapolates to 65.4% recall at 20% precision in a general population. Interpretation of the DNN model identified significant variants that were previously overlooked by GWAS, highlighting their importance in predicting breast cancer risk. We then developed a linearizing neural network architecture (LINA) that provided first-order and second-order interpretations on both the insta