Enthalpy- versus Entropy-Driven Molecular Recognition in the Era of Biologics

Our laboratory has recently identified two nanobodies (small antibodies produced by camelids)—Nb1 and Nb6—that bind efficiently to epithelial growth factor (EGF) and inhibit its ability to activate its receptor (EGFR). Because of the relevance of the EGF/EGFR axis as a target in oncology, these new nanobodies have promising therapeutic potential. This article, however, is focused on another feature of these nanobodies: their distinct thermodynamic signatures. Nb1 binds to EGF through an entropy-driven mechanism whereas Nb6 binds to this factor under enthalpic control. We discuss the advantages and disadvantages of each mechanism in the contexts of traditional medical chemistry (small-molecule drugs) and also of biological drugs. In this latter case, the implications in terms of selectivity are far from being clearly established and further experimental data are required. Their monomeric natures, high stability, and ease of recombinant production make nanobodies ideally suited for thermodynamic studies. Moreover, nanobodies, thanks to their simpler structures in comparison with conventional antibodies, might provide better understanding of the structural basis of the thermodynamic parameters of antigen recognition.     

Selective nonanal molecular recognition with SnO2 nanosheets for lung cancer sensor

SnO₂ nanosheets were developed to detect nonanal gas in the order of ppb which was a marker of lung cancer. The nanosheets showed higher resistance change in nonanal gas than that in carbon monoxide (CO), nitrogen dioxide (NO₂), acetone (CH₃COCH₃), hydrogen (H₂), ethanol (C₂H₆O), ammonia (NH₃), hydrogen sulfide (H₂S), formaldehyde (HCHO), acetaldehyde (CH₃CHO), or butanal (C₄H₈O). Crystal surfaces of the nanosheets would be effective for adsorption of nonanal molecules. Furthermore, it was shown that resistance changed with an increase in carbon number in aldehyde. The nanosheets had molecular selectivity for a series of aldehyde molecules. Molecular recognition of the nanosheets gave us a great advantage to detect nonanal gas which was produced by lung cancer.     

基于人体关节点数据的攻击性行为识别

针对人体攻击性行为识别问题，提出一种基于人体关节点数据的攻击性行为识别方法。首先，利用OpenPose获得单帧图像中的人体关节点数据，并通过最近邻帧特征加权法和分段多项式回归完成由人体自遮挡和环境因素所导致缺失值的补全；然后，对每个人体定义动态"安全距离"阈值，如果两人真实距离小于阈值，则构建行为特征矢量，其中包括帧间人体重心位移、人体关节旋转角角速度和发生交互时的最小攻击距离等；最后，提出改进的LightGBM算法w-LightGBM，并对攻击性行为进行识别。采用公共数据集UT-interaction对所提出的攻击性行为分类识别方法进行测试实验，准确率达到95.45%。实验结果表明，所提方法能够有效识别各种角度的攻击性行为。     

Microstructure,tribological behavior and magnetic properties of Fe−Ni−TiO2 composite coatings synthesized via pulse frequency variation

The Fe−Ni−TiO₂ nanocomposite coatings were electrodeposited by pulse frequency variation. The results showed that the nanocomposite with a very dense coating surface and a nanocrystalline structure was produced at higher frequencies. By increasing the pulse frequency from 10 to 500 Hz, the iron and TiO₂ nanoparticles contentswere increased in expense of nickel content. XRD patterns showed that by increasing the frequency to 500 Hz, an enhancement ofBCC phase was observed and the grain size of deposits was reduced to 35 nm. The microhardness and the surface roughness were increased to 647 HV and 125 nm at 500 Hz due to the grain size reduction and higher incorporation of TiO₂ nanoparticles into the Fe−Ni matrix (5.13 wt.%). Moreover, the friction coefficient and wear rate values were decreased by increasing the pulse frequency;while the saturation magnetization and coercivity values of the composite deposits were increased.     

Emotion recognition at the edge with AI specific low power architectures

Nowadays Deep Learning is applied in almost every research field and helps getting amazing results in a great number of challenging tasks. The main problem is that this kind of learning and consequently Neural Networks that can be defined deep, are resource intensive. They need specialized hardware to perform computation in a reasonable time. Many tasks are mandatory to be as much real-time as possible . It is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them “efficient and usable”. All these optimizations can help us to produce incredibly accurate and fast learning models. The paper reports a study in this direction for the challenging face detection and emotion recognition tasks.     

Technology enhanced learning of quantitative critical thinking

A Quantitative Critical Thinking (QCT) software tool was developed in this study to facilitate students’ learning of quantitative critical thinking via repeated practice by chemical engineering students reading a core module called fluid-solid systems. The software tool generated detailed calculation steps to typical engineering design problems encountered in this module that contained weaknesses, flaws or even errors. Students utilized the software tool to practice identifying these weaknesses, flaws or errors in the design solutions and then present a better or correct design by applying the concepts and knowledge acquired in the module. Since the QCT software tool was built upon an existing design software tool that was able to generate the correct, detailed design calculation steps to design problems, students were able to check their own design calculations against those presented by the software tool during this second learning step, thereby engaging in and learning quantitative critical thinking via a repeated practice approach. The software tool was successful in enhancing the performance of second-year undergraduate students in solving a question that required quantitative critical thinking in the final examination of the module. The average percentage scores achieved by students for the question who reported higher frequencies of usage of the software were generally higher than those who reported lower frequencies of usage or did not utilize the software tool throughout the semester.     

Flow regime recognition in a long pipeline-riser system based on signals at the top of the riser

To identify conveniently multiphase flow regimes in subsea pipeline-risers, we study in this paper experimentally two-phase flows in a 1657 m long pipeline with an S-shaped riser to simulate field experiment, within a wide range of gas and liquid velocities. Three flow regimes, namely severe slugging, transitional flows, and stable flows, are analyzed based on three differential pressure and one pressure signals at the top of the riser; comparatively speaking, the positions of these signals in the experimental system are similar to those of the sea level signals in industrial fields, which are easy and less expensive to obtain. The obtained signals are decomposed into six scales via a multi-scale wavelet analysis, and further four statistical parameters on each scale are extracted, including mean values, standard deviations, ranges, and mean values of absolute. We compared the effects of six SVM classifiers with different kernel functions on the recognition rate of flow regimes, and it is found the recognition rates of SVM classifier with quadratic and cubic kernel functions are the highest. Further, the principal component analysis is employed to reduce the dimension of statistical parameters and it indicates that the recognition rate tends to increase with the rising number of principal components from 1 to 6, and it remains constant if the principal component number is further increased. Moreover, The results suggest that the recognition rate obtained from the pressure difference between the top of the riser and the separator peaks, and then it comes that from the pressure signal at the top of the riser, and that for the pressure difference signal at the top of the riser is the least satisfying one. As for the optimal differential pressure signals between the top of the riser and the separator, the results show that the recognition rate increases rapidly from 70.2% to 90.4% when the sample duration rising from 2.3 s to 18.6 s, and when the sample duration exceeds 74.4 s, the recognition rate exceeds 92.9% and remains unchanged.     

LWD仪器临界电压数据读取分析与解决

锂电池是LWD仪器正常工作的动力来源,是仪器的心脏。仪器出井后,需要在井口读取数据,当锂电池的电量接近耗尽时,会导致数据读取失败的问题。该问题处理不当,会造成长期占用井口,影响钻井时效,严重时还可能会因短路造成昂贵的LWD仪器损坏。锂电池的临界电压问题暴露了该仪器的设计缺陷,在此通过技术方法充分利用INSITE系统本身提供的功能,绕过其设计缺陷将问题解决。     

Enhanced electrical and photoluminescence properties of BiSbO4-doped CaCu3Ti4O12 ceramics by modifying grain boundary response

CaCu₃Ti₄O₁₂-xwt%BiSbO₄ ceramics (CCTO-xwt%BSO, x = 0, 1, 2, 3) were prepared by solid-state reaction method. The microstructure, dielectric properties, varistor properties, photoluminescence properties of CCTO-xwt%BSO ceramics were studied in this work. Results showed that all samples formed CaCu₃Ti₄O₁₂ (CCTO) single phase. Doping BiSbO₄ (BSO) restrained the abnormal grain growth and increased the grain boundary density of ceramics. The introduction of BSO led to the increase of the grain boundary resistance, reducing the dielectric loss and enhancing the temperature stability of dielectric properties. The nonlinear electrical characteristics are enhanced with proper concentration of BSO. And the improved varistor performance with breakdown electric field of ～3.98–34.6 and nonlinear coefficient of ～1.49–2.96 are obtained for CCTO-xwt%BSO samples. In addition, the photoluminescent emission of the samples is enhanced with the addition of appropriate equivalent BSO, showing the potential applications in novel devices with photoluminescent/electrical multifunctional properties.