Lactobacillus reuteri-fortified camel milk infant formula: Effects of encapsulation,in vitro digestion,and storage conditions on probiotic cell viability and physicochemical characteristics of infant formula

Lactobacillus reuteri fortified camel milk infant formula (CMIF) was produced. The effect of encapsulation in different matrices (sodium alginate and galacto-oligosaccharides) via spray drying, simulated infant gastrointestinal digestion (SIGID), and storage conditions (temperature and humidity) on the viability of L. reuteri in CMIF and the physicochemical properties of CMIF were evaluated. Compared with free cells, probiotic cell viability was significantly enhanced against SIGID conditions upon encapsulation. However, L. reuteri viability in CMIF decreased after 60 d of storage, predominantly at higher storage humidity and temperature levels. At the end of the storage period, significant changes in the color values were observed in all CMIF, with a reduction in their greenness, an increase in yellowness, and a wide variation in their whiteness. Moreover, pH values and caking behavior of all CMIF stored at higher temperature (40°C) and humidity [water activity (a_w) = 0.52] levels were found to be significantly higher than the samples stored under other conditions. Over 30 d of storage at lower humidity conditions (a_w = 0.11 and 0.33) and room temperature (25°C), no significant increase in CMIF lipid oxidation rates was noted. Fourier-transform infrared spectroscopy analysis showed that, compared with the other storage conditions, CMIF experienced fewer changes in functional groups when stored at a_w = 0.11. Microscopic images showed typical morphological characteristics of milk powder, with round to spherical-shaped particles. Overall, camel milk fortified with encapsulated L. reuteri can be suggested as a promising alternative in infant formula industries, potentially able to maintain its physicochemical characteristics as well as viability of probiotic cells when stored at low humidity levels (a_w = 0.11) and temperature (25°C), over 60 d of storage.     

Lessons from a Minimal Genome: What Are the Essential Organizing Principles of a Cell Built from Scratch?

One of the primary challenges facing synthetic biology is reconstituting a living system from its component parts. A particularly difficult landmark is reconstituting a self-organizing system that can undergo autonomous chromosome compaction, segregation, and cell division. Here, we discuss how the syn3.0 minimal genome can inform us of the core self-organizing principles of a living cell and how these self-organizing processes can be built from the bottom up. The review underscores the importance of fundamental biology in rebuilding life from its molecular constituents.     

培养原料种类的减少对人工窖泥发酵剂菌群结构的影响

为实现人工窖泥发酵剂（APMSC）的品质控制，从前期开发的发酵剂配方中分别取出老熟窖泥、曲药、酒糟和黄水初步形成4个简化配方，采用扩增子测序和色谱技术比较了各配方发酵剂的微生物群落结构及香味物质含量差异。结果显示，各配方发酵剂样品中共检测到70个微生物操作分类单元（OTU），其中71.43%为各组样品共有，未检测到独有OTU。各配方样品的优势微生物均存在于其余分组中，仅物种相对丰度有所差异。根据酿酒有益微生物的丰度高低，选择了一种简化配方（无老熟窖泥作原料）发酵剂养护窖池，使酿造白酒的主体香味物质含量得到显著提高。结果表明，即使缺失了某一种培养原料，人工窖泥发酵剂的功能微生物物种也可能由其他原料补充，而原料种类的减少是实现发酵剂品质控制的主要途径。     

基于计算机控制的新型鸡精组分建模研究

以鲜鸡肉粉(富钙型)、味精、食盐、白砂糖、淀粉为主要原料,根据谷氨酸钠、呈味核苷酸二钠、总氮、水分含量的要求,利用计算机数学建模法计算出鸡精中各原料的组分。选出8组较优的方案制成鸡精,上述4种物质的含量与模型非常接近,其中第1组方案的营养价值和感官评价均最好,配方为鲜鸡肉粉14%、味精22%、食盐36%、白砂糖7%、淀粉5%。实验结果表明基于计算机控制的新型鸡精组分建模准确,具有指导意义。     

基于汉风色典的不同色差公式的色差均匀性

为给纺织行业在纺织品贸易和质量控制等方面优选色差公式提供参考,研究了3个重要的和1个最新色差公式的色差均匀性,借助国际上色样多、视觉等距性好的汉风色典,利用X-Rite测色仪测试近2万个色样的色度参数,然后分别从向度、彩度、浓度3个方向选取3万多对相邻色样,采用MatLab编程计算4种色差并统计分析其均值、不匀率和性能因子。结果表明:不同方向不同评价指标所得结果不尽相同;在色差均匀性方面,CAM16-UCS最好,CIEDE2000_(2∶1∶1)略优于CMC_(2∶1),且二者明显优于CIELAB;在色差大小方面由小到大依次为CIEDE2000_(2∶1∶1)、CMC_(2∶1)、CAM16-UCS、CIELAB。     

再生混凝土抗压强度试验研究

设计3种不同再生骨料取代率的再生混凝土立方体试件，分别在7 d、14 d、28 d、100 d龄期进行抗压强度试验，结果表明：长龄期再生混凝土立方体抗压强度有所增大，但高取代率比低取代率的抗压强度降低，再生混凝土后期强度增长速率高于普通混凝土。对试验室实测的再生混凝土立方体抗压强度与试验龄期进行拟合，拟合结果相关系数均大于0.97，吻合结果较好。     

Correlation between B value deviation and sintering temperature of perovskite solid solution materials

The effects of different sintering temperatures on the system of Ba_0.5Sr_0.5TiO₃–La_0.8Ca_0.2MnO₃ (BST-LCM) material were investigated by means of scanning electron microscopy, X-ray, resistance-temperature measurements, and X-ray photoelectron spectroscopy. X-ray diffraction and X-ray photoelectron spectroscopy confirm that BST-LCM is completely miscible and BST is the main phase, which under the premise of following the theory of small polaron conduction, titanium ions interact with manganese ions, which affects the electrical properties of the system of BST-LCM material. For the electrical properties of related, the LCM content is inversely proportional to the activation energy. Through the linear fitting of the experimental data, the empirical formula between the sintering temperature B value and the content of solid solution is obtained, and the approximate B value within a certain range can be preliminarily calculated.     

Preparation of Docosahexaenoic Acid-Rich Diacylglycerol-Rich Oil by Lipase-Catalyzed Glycerolysis of Microbial Oil from Schizochytrium sp. in a Solvent-Free System

Docosahexaenoic acid (DHA)-rich diacylglycerol (DAG)-rich oil was prepared by lipase-catalyzed glycerolysis of microbial oil from Schizochytrium sp. in a solvent-free system. The reaction parameters including lipase type, substrate molar ratio, temperature, lipase concentration, and reaction time were screened. The selected conditions were determined as follows: Novozym® 435 (Novozymes A/S, Bagsvaerd, Denmark) as biocatalyst at 8 wt%, substrate ratio (DHA-rich microbial TAG/glycerol) of 1:1 mol/mol, temperature of 50 °C, and reaction time of 12 hours. Under these conditions, the triacylglycerol (TAG), DAG, and monoacylglycerol (MAG) contents in the product were 36.4%, 48.2%, and 15.4%, respectively. The lipase was reused successively for 18 cycles without significant loss of activity under the conditions given above. Fatty acid composition analysis of the final product showed that the contents of DHA in TAG, DAG, and MAG were 53.9%, 44.9%, and 34.8%, respectively. DHA-rich DAG has the potential to be used as an ingredient in infant formula to increase the bioavailability of DHA.     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.