Accelerating Optimizing the Design of Carbon-based Electrocatalyst via Machine Learning

In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closed-loop system. The approach requires at least three parts including high-throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learning-based design of carbon-based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.     

High-throughput glycosylation analysis of intact monoclonal antibodies by mass spectrometry coupled with capillary electrophoresis and liquid chromatography

The analysis of monoclonal antibodies glycosylation is a crucial quality control attribute of biopharmaceutical drugs. High throughput screening approaches for antibody glycoform analysis are required in various stages of process optimization. Here, we present high throughput screening suitable mass spectrometry-based workflows for the analysis of intact antibody glycosylation out of cell supernatants. Capillary electrophoresis and liquid chromatography were coupled with quadrupole time-of-flight mass spectrometry or Orbitrap mass spectrometry. Both separation methods offer fast separation (10–15 min) and the capability to prevent the separated cell supernatant matrix to enter the mass spectrometry by post-separation valving. Both mass spectrometry instruments provide comparable results and both are sufficient to determine the glycosylation pattern of the five major glycoforms of the measured antibodies. However, the Orbitrap yields higher sensitivity of 25 μg/mL (CE-nanoCEasy-Orbitrap mass spectrometry) and 5 μg/mL (liquid chromatography-Orbitrap mass spectrometry). Data processing was optimized for a faster processing and easier detection of low abundant glycoforms based on averaged charge-deconvoluted mass spectra. This approach combines a non-target glycoform analysis while yielding the same glycosylation pattern as the traditional approach based on extracted ion traces. The presented methods enable the high throughput screening of the glycosylation pattern of antibodies down to low μg/mL-range out of cell supernatant without any sample preparation.     

NEWTON'S THEOREM WITH RESPECT TO A LOT OFCENTERS AND THEIR APPLICATIONS

ＮＥＷＴＯＮ＇ＳＴＨＥＯＲＥＭＷＩＴＨＲＥＳＰＥＣＴＴＯＡＬＯＴＯＦＣＥＮＴＥＲＳＡＮＤＴＨＥＩＲＡＰＰＬＩＣＡＴＩＯＮＳ￥(桂祖华)ＧｕｔＺｕｈｕａ（ＤｅｐａｒｔｍｅｎｔｏｆＡｐｐｌｉｅｄＭａｔｈｅｍａｔｉｃｓｏｆＳｈａｎｇｈａｉＪｉａｏｔｏｎｇＵｎ...     

高职院校课堂教学质量影响因素研究——基于Lasso-logistic回归模型

本文以"大学生课堂学习状态问卷"调查为基础,利用Lasso-logistic回归模型对安徽省某高职学院336位学生成绩的影响因素进行分析。结果表明,影响客观评价结果的因素包括性别、父母亲文化程度等个人、家庭因素及学习勤奋刻苦程度等直接相关因素;而影响主观评价结果的因素则主要为学习勤奋刻苦程度、学习目标等直接相关因素,而不包括个人、家庭因素。     

GRW时空中具有常$k$阶平均曲率的类空超曲面的高度估计

本文利用广义的Omori-Yau极大值原则,得到了广义的Robertson-Walker(GRW)时空中具有常高阶平均曲率并且边界包含在一slice中的类空超曲面的高度估计.同时利用这些结果得到了一些拓扑方面的结论.最后对拉普拉斯算子和一些椭圆型的微分算子利用Omori-Yau极大值原则,得到了一些更广泛的非存在性的结果.     

SINGULAR PERTURBATIONS FOR A CLASS OF BOUNDARY VALUE PROBLEMS OF HIGHER ORDER NONLINEAR DIFFERENTIAL EQUATIONS

ＳＩＮＧＵＬＡＲ　ＰＥＲＴＵＲＢＡＴＩＯＮＳ　ＦＯＲ　Ａ　ＣＬＡＳＳ　ＯＦ　ＢＯＵＮＤＡＲＹ　ＶＡＬＵＥ　ＰＲＯＢＬＥＭＳ　ＯＦ　ＨＩＧＨＥＲ　ＯＲＤＥＲ　ＮＯＮＬＩＮＥＡＲ　ＤＩＦＦＥＲＥＮＴＩＡＬ　ＥＱＵＡＴＩＯＮＳＳｈｉＹｕａｎｉｎｇ（史玉明）...     

新型Ce3+掺杂亚磷酸锰开放骨架材料的合成与荧光性质

通过高通量实验方法制备了一系列新型的Ce³⁺离子掺杂亚磷酸锰(NH₄)₄[Mn_4-xCe_x(HPO₃)₆](简称JIS-10∶xCe³⁺) 无机开放骨架材料. 通过粉末X射线衍射(PXRD)谱图、 扫描电子显微镜(SEM)、 微量元素能谱(EDS)、 X射线光电子能谱(XPS)、 傅里叶变换红外(FTIR)光谱和光致发光(PL)光谱等手段对该材料进行了表征, 并研究了Ce³⁺离子掺杂浓度、 反应温度和时间对晶体相变和发光性能的影响. 结果表明, 在波长260 nm的光激发下, Ce³⁺离子在500 nm处有1个绿光发射带而Mn²⁺离子在590 nm处有1个黄光发射带. 调变JIS-10∶xCe³⁺材料中Ce³⁺离子的掺杂浓度发现, 当x=0.06^{时, 即Ce3+离子的掺杂浓度较低时, 样品的发射颜色为黄绿色, 其CIE坐标为(0.38, 0.48); 当Ce3+离子的掺杂浓度增加时, 绿色发光带的增长快于黄色发光带的增长, 从而调整发射颜色; 在x=1.33时观察到最强的发射, 浓度过高发生浓度猝灭.}     

Maximum mass of anisotropic charged strange quark stars in a higher dimensional approach (D ≥ 4)

In this article, a new class of solutions of Einstein-Maxwell field equations of relativistic strange quark stars obtained in dimensions \begin{document}$D\geq4$\end{document}, is shown. We assume that the geometry of space-time is pseudo-spheroid, embedded in Euclidean space of \begin{document}$(D-1)$\end{document} dimensions. The MIT bag model equation of state \begin{document}$(henceforth~EoS)$\end{document} is employed to study the relevant properties of strange quark stars. For the causal and non-negative nature of the square of the radial sound velocity \begin{document}$({v_{r}}^{2})$\end{document}, we observe that some restrictions exist on the reduced radius \begin{document}$(\frac{b}{R})$\end{document}, where R is a parameter related to the curvature of the space-time, and b is the radius of the star. The spheroidal parameter λ used here defines the metric potential of the \begin{document}$g_{rr}$\end{document} component, which is pseudo-spheroidal in nature. We note that the pressure anisotropy and charge have some effects on λ. The maximum mass for a given surface density (\begin{document}$\rho_s$\end{document}) or bag constant \begin{document}$(B)$\end{document} assumes a maximum value in dimension \begin{document}$D=5$\end{document}and decreases for other values of D. The generalized Buchdahl limit for a higher dimensional charged star is also obeyed in this model. We observe that in this model, we can predict the mass of a strange quark star using a suitable value of the electric charge (Q) and bag constant (B). Energy and stability conditions are also satisfied in this model. Stability is also studied considering the dependence of the Lagrangian perturbation of radial pressure (\begin{document}$\Delta p_r$\end{document}) on the frequency of normal modes of oscillations. The tidal Love number and tidal de-formability are also evaluated.     

GAMPMS: Genetic algorithm managed peptide mutant screening

The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor–ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure‐based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) ‐isoform with a library of 64,000 α‐conotoxin (α‐CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α‐CTx MII peptide mutants with the ‐nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α‐CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening. © 2015 Wiley Periodicals, Inc.