吡咯伯克霍尔德氏菌B1213产葡聚糖酶条件的优化

以吡咯伯克霍尔德氏菌B1213为研究对象,优化其发酵产葡聚糖酶条件。首先,通过单因素实验对其培养条件（碳源种类、碳源粒度、碳源质量浓度、氮源种类、氮源质量浓度、转速、温度、装液量、接种量、初始pH值、表面活性剂种类、表面活性剂质量浓度和时间）进行初步优化,然后通过Plackett-Burman试验,筛选出4个显著影响B1213产葡聚糖酶的因素,再利用最陡爬坡试验确定其产葡聚糖酶的最大响应区域,最后通过Box-Behnken试验设计确定其最优产葡聚糖酶条件为：40～60目麦麸质量浓度50 g/L,酵母浸粉质量浓度8 g/L,初始pH 6.6,装液量30 mL/250 mL,接种量0.15%,曲拉通100质量浓度25 g/L,温度30℃,转速160 r/min和培养时间83 h。在此培养条件下B1213产葡聚糖酶活力达到1 230 U/mL。研究结果为细菌B1213在白酒酿造中的应用提供理论基础。     

仿刺参冷风干制工艺优化及不同干制方式的比较

以盐渍仿刺参为研究对象,以感官评分和脱水速率加权后的综合评分作为评价指标,采用单因素试验及响应面法对冷风干制(cold-air drying,CAD)工艺条件进行优化,并对CAD、真空冷冻干制(vacuum freeze drying,VFD)、热风干制(hot-air drying,HAD)和真空微波干制(vacuum microwave drying,VMD)工艺处理的仿刺参进行品质比较分析。结果表明:CAD最佳干制工艺条件为真空脱盐时间4.2 h、冷风温度19℃、冷风风速1.70 m/s,仿刺参的综合评分为0.77。在营养保持方面,VFD和CAD比HAD和VMD效果更好;在热收缩率方面,HADVMDCADVFD,其中CAD和VFD差异不大;在复水倍数方面,CADVFDHADVMD;在质构特性上,CAD和VFD质构指标明显优于HAD和VMD。对比结果说明CAD在工业生产中具有更高的实用性。     

Synthesis of Anisotropic Na0.5Bi0.5TiO3 Crystals Using Different Topochemical Microcrystal Conversion Methods

Na_0.5Bi_0.5TiO₃ (NBT) platelets with high aspect ratio were synthesized from Na_0.5Bi_4.5Ti₄O₁₅ (NBIT) precursors via a topochemical microcrystal conversion in molten salt conditions. The effect of the synthesis parameters, such as the molten salt system, synthesis temperature, and the molar ratio of Na₂CO₃ and NBIT, was investigated. The results showed that NaCl–KCl molten salt environment and excess Na₂CO₃ played a positive role in the synthesis, square‐shaped NBT was obtained at 950°C in NaCl–KCl molten salt and a TiO₂‐free environment, and it was a suitable template candidate to achieve NBT‐based textured ceramics using the reactive template grain growth (RTGG) method.     

CBS/ZrO_2体系玻璃陶瓷结构与性能研究

采用SEM、XRD、网络分析仪等分析测试手段研究了ZrO_2含量对CBS/ZrO_2复合陶瓷结构与性能的影响。研究结构表明:随着ZrO_2量的增加,CBS/ZrO_2复合陶瓷的介电常数先增加后降低,介电损耗先降低后增加;ZrO_2量为25%时,CBS/ZrO_2复合陶瓷的介电常数达到最高为7.81,密度最大达到3.144g/cm3;ZrO_2量为15%时,CBS/ZrO_2复合陶瓷介电损耗最低为1.55×10~(-3)。CBS玻璃烧结后主要晶相为CaSiO3,CBS/ZrO_2复合陶瓷主要晶相为CaSiO3和ZrO_2。     

Artificial Intelligence in Aptamer–Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.     

基于混合建模的水泥生料分解过程动态特性研究

为掌握水泥分解炉运行过程的动态特性,采用机理建模与神经网络相结合的方法构建了水泥分解炉一维特性模型,并结合工业数据对该方法的可行性进行验证。结果表明,模型能够准确地计算炉内温度、气体浓度等参数,具有良好的泛化性能。基于所提出的模型,研究了炉内各状态参数的稳态分布特性。此外,对喷煤量、生料下料量、喷氨量以及高温风机转速等操作变量进行阶跃实验,分析上述操作变量改变时分解炉出口温度及出口NO _x 含量的动态响应情况。研究所得相关动态特性规律可以为控制系统的分析、设计和优化提供参考与依据。     

EPS颗粒对超轻水泥基复合保温材料性能的影响

聚苯乙烯泡沫塑料颗粒(EPS颗粒)作为水泥基复合保温材料的超轻骨料,对水泥基复合保温材料力学性能、热工性能影响显著。以水泥为胶凝材料,EPS颗粒、混合材、泡沫剂和改性剂、水等为主要原料,采用物理发泡工艺制备干表观密度不大于120 kg/m³的超轻水泥基复合保温材料(UCIM)。通过设计不同体积掺量的EPS颗粒,分析EPS颗粒掺量对泡沫混凝土基体孔结构、超轻水泥基复合保温材料强度和热工性能的影响规律。结果表明,适宜掺量EPS颗粒可显著提高超轻水泥基复合保温材料抗压强度和抗拉强度,并确保超轻水泥基复合保温材料具有良好的热工性能,即通过EPS颗粒与泡沫混凝土基体的协同作用,协调力学性能和热工性能,制备出高性能超轻水泥基复合保温材料。     

Reaction Engineering Approach (REA) to model the drying kinetics of droplets with different initial sizes—experiments and analyses

Droplets with different initial sizes, which are typical in conventional liquid atomization for spray drying applications, will result in varying drying and crust formation histories. It is essential for any droplet drying model to accurately capture such fundamental phenomena. This study used a newly constructed glass-filament single droplet rig to evaluate the applicability of the Reaction Engineering Approach (REA) in describing such effect. For the three initial sizes (1, 2 and 3 μL) tested, the glass filament gravimetric method clearly distinguished the different drying kinetics and the crust formation phenomenon, delineated by the drying behavior. Analysis from the drying kinetics revealed that the main premise of the REA, which utilizes a material-specific master activation energy curve, is applicable to droplets of different initial sizes at all the three air temperatures tested. This allowed the REA to accurately predict the different temperature and moisture histories given by droplets with different initial sizes. The result supports the REA as a good modeling approach for a wide range of initial droplet conditions. A new master curve approach was proposed to predict the diameter change of droplets with different initial concentrations. Validation with the current and past experimental data revealed that this approach has strong potential to account for the different feed concentrations typically found in spray drying applications.     

基于MMDFE⁃DA的滚动轴承故障诊断方法

针对变工况条件下轴承训练数据集和测试数据集存在分布差异,导致智能诊断模型泛化能力不足,且需针对不同任务分别建模问题,提出一种基于多尺度混合域特征提取(multi?scale mixed domain feature extraction,简称MMDFE)和领域自适应(domain adaptation,简称DA)的滚动...