Improving gelling properties of myofibrillar proteins incorporating with cellulose micro/nanofibres

The effects of cellulose microfibres (CMFs, Average size: 100 ± 5 μm) and cellulose nanofibres (CNFs, Average size: 60 ± 3 nm) on the properties of myofibrillar protein (MP) gels from duck breast meat were studied. The results demonstrated that CMFs and CNFs were mostly connected to MP by non-covalent bonds, the diffusion and cross-linking of MP molecules was promoted, and a denser and more complete gel network was formed. With the increases of CMFs and CNFs concentration (0–10%), the hardness was increased by 13.15% and 19.78% for CMFs10% and CNFs10% gels, respectively, and the elasticity was increased by 40% and 80%, respectively. At the same concentration (0–10%), the increase in gel hardness, viscoelasticity and immobilised water content was greater in the CNFs-MP group than in the CMFs-MP group. The CNFs-MP group had a tighter gel network, and CNFs had a better potential to improve the gelation performance of MP.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.     

基于多因素耦合分析的冲击地压卸压技术研究

为有效预防ZF2803工作面的冲击地压,本文采用多因素耦合分析法研究了初次来压、见方、煤柱、断层、邻近空区支承压力、煤层厚度变化及巷道底煤等7个因素的单独影响区域及叠加效果,确定了7个危险区:中等冲击危险区4个、强冲击危险区3个,给出了相应的煤层及底板大钻孔预卸压措施,工业性应用取得了良好的防治效果。     

Burkholderia thailandensis E264/pBMTL3-tdpR发酵生产抗癌药物ThailandepsinA的研究

在伯克氏菌Bth264野生株产新型抗癌药物Thailandepsin A以及调节基因tdp R正向调控Thailandepsin A生物合成的基础上,利用基因工程菌Bth264/p BMTL3-tdp R发酵生产Thailandepsin A,以提高产量。以0.5%乳糖为诱导剂,确定最佳诱导条件:发酵15 h添加乳糖,诱导时间6 h;通过单因素实验,确定葡萄糖和胰蛋白胨作为碳氮源、装液量65/250 m L以及接种量1%;同时结合优化发酵培养基进行发酵,Thailandepsin A产量达到252.14 mg·L-1,比优化前的产量提高56%;另外在发酵过程中,添加大孔树脂HP-20原位吸附产物,Thailandepsin A产量可达283.75 mg·L-1,比不加树脂提高13.8%;最后,基于RT-PCR和比较Ct值法,基因工程菌和野生菌相比,Thailandepsin A生物合成基因tdp B、tdp C1的转录水平分别提高11.4倍和6.0倍,对应的产量增加4.6倍,从而在很大程度上说明调节基因tdp R的过表达促进生物合成基因转录水平的提高以及产量的增加。     

Combating Biofilm Associated Infection In Vivo: Integration of Quorum Sensing Inhibition and Photodynamic Treatment based on Multidrug Delivered Hollow Carbon Nitride Sphere

Recently, quorum sensing (QS) inhibitors (QSIs) have been combined with antibiotics to enhance antibiofilm efficacy in vitro and in vivo. However, targeting QS signals alone is not enough to prevent bacterial infections. Drug resistance and recurrence of biofilms makes it difficult to eradicate. Herein, photodynamic therapy (PDT) is selected to unite QSIs and antibiotics. A synergistically antibiofilm system, which combines QSIs, antibiotics, and PDT based on hollow carbon nitride spheres (HCNSs) is envisaged. First, HCNS provides the multidrug delivering ability, enabling QSIs and antibiotics to be released in sequence. Subsequently, multistage releases sensitize bacteria effectively, potentiating the chemotherapeutic effects of the antibiotics. Finally, the integration of QSIs and PDT not only minimizes the possibility of drug resistance, but also overcomes the problem of limited mass and extension of PDT. Even after 48 h of incubation, the bacterial biofilm is obviously inhibited. And its biofilm disperse efficiency exceeds 48% (compared with QSI‐potentiated chemotherapy group) and 40% (compared with PDT group). Besides, the inhibition of the QS system influences phenotypes related to virulence factor production and surface hydrophobicity, which weaken biofilm invasion and formation. Eventually, this system is applied to disperse bacterial biofilm in vivo. Overall, PDT and QS modulation are devoted to eradicate drug resistance and recurrence of the biofilm.     

High-rate capability of carbon-coated micron-sized hexagonal TT-Nb2O5 composites for lithium-ion battery

With excellent specific capacity, superior cycle stability, safety and strong practical, Nb₂O₅ has been considered as one of the prospective anode materials for lithium-ion batteries (LIBs). However, current study suggests that Nb₂O₅ electrode materials for LIBs still face the vital issues of low electrical conductivity and poor rate performance. Therefore, carbon-coated TT-Nb₂O₅ materials are designed and synthesized through solid state method in this work, which present high specific capacity (228 mA h g^?1 at 0.2C), satisfactory rate properties (107 mA h g^?1 at 20 C). The outstanding electrochemical property can not only give the credit to the pseudocapacitance effect of TT-Nb₂O₅, but also attribute to introduction of carbon. The homogeneous carbon-coated materials enhance the electrical conductivity, increase the electron transmission speed and alleviate particle crushing. This research not only offers a new method for preparing excellent electrode materials, but also provides a kind of excellent anode material with prospective application for LIBs.     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.