18O同位素标记定量肽段串联体蛋白质结合同位素稀释-多反应监测质谱的蛋白质绝对定量新方法

建立了定量肽段串联体蛋白质(concatamers of Q peptides, QconCATs)结合¹⁸O同位素标记-多反应监测质谱的蛋白质绝对定量新方法。首先对QconCAT重组蛋白质进行了纯度表征,十二烷基硫酸钠-聚丙烯酰胺凝胶电泳(SDS-PAGE)表征结果表明重组蛋白质的纯度在99%以上,相对分子质量约为63.4 kDa。对QconCAT重组蛋白质酶切后的肽段混合物进行质谱分析,并经pFind和pLabel软件处理,验证了目标肽段。还考察了QconCAT重组蛋白质的酶切效率和¹⁸O标记效率,并对QconCAT蛋白质结合¹⁸O标记-同位素稀释-多反应监测质谱方法进行了评价。实验结果表明,采用该方法对腾冲嗜热厌氧菌(Thermoanaerobacter tengcongensis, TTE)中选定蛋白质的肽段进行绝对含量测定时,相对标准偏差小于20%,准确度较高,说明该方法可用于复杂生物样本中蛋白质的绝对定量。更重要的是所建方法不仅解决了细胞培养氨基酸稳定同位素标记(SILAC)技术的重标试剂价格昂贵的问题,也为定量蛋白质组学提供了一种新的方法。     

应用多接收器电感耦合等离子体质谱法测定海洋碳酸盐中稳定铅同位素组成

研究了多接收器电感耦合等离子体质谱仪(MC-ICP MS)测定铅同位素比值时,影响测试结果准确度和精密度的主要因素及其优化过程。在优化条件下,10 h内连续30次测定4 ng/mL NIST SRM 981同位素标准溶液铅同位素比值,获得208Pb/206Pb、207Pb/206Pb和206Pb/204Pb相对标准偏差(RSDs)分别为0.005%、0.004%和0.054%。长期监测208Pb/206Pb、207Pb/206Pb和206Pb/204Pb,标准偏差(2SDs)分别为0.000 06、0.000 05和0.006 7。采用NEPTUNE MC-ICP MS法测定了低铅海洋碳酸盐样品中稳定铅的同位素比值,并对南海橙黄滨珊瑚(Porites lutea)和库氏砗磲(Tridacna gigas)进行了分析,得到全流程空白为8～10 pg,重复样误差优于0.1%。经0.50 mol/L HNO3洗脱之后,得到海南珊瑚样品中208Pb/206Pb、207Pb/206Pb的比值分别为2.086 2±0.001 5、0.849 90±0.001 47(n=16);海南砗磲样品中208Pb/206Pb、207Pb/206Pb的比值分别为2.116 9±0.004 2、0.864 81±0.001 62(n=9)。进一步考察了南海海洋碳酸盐中204Pb的同位素比值。分析结果表明,南海海洋碳酸盐中稳定铅同位素比值与中国气溶胶、珠江三角洲大气沉降、黄土及南海海底玄武岩等具有很好的相关性。方法适用于复杂基体高钙低铅的海洋碳酸盐样品中铅同位素比值的分析。     

Development of a manual method for the determination of mineral oil in foods and paperboard

So far the majority of the measurements of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) were obtained from on-line high performance liquid chromatography–gas chromatography–flame ionization detection (on-line HPLC–GC–FID). Since this technique is not available in many laboratories, an alternative method with more easily available tools has been developed. Preseparation on a small conventional liquid chromatographic column was optimized to achieve robust separation between the MOSH and the MOAH, but also to keep out the wax esters from the MOAH fraction. This was achieved by mixing a small portion of silica gel with silver nitrate into highly activated silica gel and by adding toluene into the eluent for the MOAH. Toluene was also added to the MOSH fraction to facilitate reconcentration and to serve as a keeper preventing loss of volatiles during solvent evaporation. A 50 μl volume was injected on-column into GC–FID to achieve a detection limit for MOSH and MOAH below 1 mg/kg in most foods.     

Multi-Class Cost-Constrained Random Coding for Correlated Sources over the Multiple-Access Channel

This paper studies a generalized version of multi-class cost-constrained random-coding ensemble with multiple auxiliary costs for the transmission of N correlated sources over an N-user multiple-access channel. For each user, the set of messages is partitioned into classes and codebooks are generated according to a distribution depending on the class index of the source message and under the constraint that the codewords satisfy a set of cost functions. Proper choices of the cost functions recover different coding schemes including message-dependent and message-independent versions of independent and identically distributed, independent conditionally distributed, constant-composition and conditional constant composition ensembles. The transmissibility region of the scheme is related to the Cover-El Gamal-Salehi region. A related family of correlated-source Gallager source exponent functions is also studied. The achievable exponents are compared for correlated and independent sources, both numerically and analytically.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

Reducing Cognitive Effort in Scoring Negotiation Space Using the Fuzzy Clustering Model

Negotiation scoring systems are fundamental tools used in negotiation support to facilitate parties searching for negotiation agreement and analyzing its efficiency and fairness. Such a scoring system is obtained in prenegotiation by implementing selected multiple criteria decision-aiding methods to elicit the negotiator’s preferences precisely and ensure that the support is reliable. However, the methods classically used in the preference elicitation require much cognitive effort from the negotiators, and hence, do not prevent them from using heuristics and making simple errors that result in inaccurate scoring systems. This paper aims to develop an alternative tool that allows scoring the negotiation offers by implementing a sorting approach and the reference set of limiting profiles defined individually by the negotiators in the form of complete packages. These limiting profiles are evaluated holistically and verbally by the negotiator. Then the fuzzy decision model is built that uses the notion of increasing the preference granularity by introducing a series of limiting sub-profiles for corresponding sub-categories of offers. This process is performed automatically by the support algorithm and does not require any additional preferential information from the negotiator. A new method of generating reference fuzzy scores to allow a detailed assignment of any negotiation offer from feasible negotiation space to clusters and sub-clusters is proposed. Finally, the efficient frontier and Nash’s fair division are used to identify the recommended packages for negotiation in the bargaining phase. This new approach allows negotiators to obtain economically efficient, fair, balanced, and reciprocated agreements while minimizing information needs and effort.     

Multiple scattering and modeling of laser in fog

When a laser is transmitted in fog, and the water droplets will scatter and absorb the laser, which affects the intensity of the laser transmission and the accuracy of radar detection. Therefore, it is of great significance to study the laser transmission in the fog. At present, the main method of calculating the scattering and attenuation characteristics of fog is based on the radiation transmission theory, which is realized by a large number of numerical calculations or physical simulation methods, which takes time and cannot meet the requirements for obtaining the fast and accurate results. Therefore, in this paper established are a new laser forward attenuation model and backward attenuation model in low visibility fog. It is found that in low visibility environments, the results calculated by the Monte Carlo method are more accurate than those from most of the existing forward attenuation models. For the cases of 0.86-μm, 1.06-μm, 1.315-μm, 10.6-μm typical lasers incident on different fogs with different visibilities, a backscatter model is established, the error between the fitting result and the calculation result is analyzed, the backward attenuation fitting parameters of the new model are tested, and a more accurate fitting result is obtained.     

Exciton emission dynamics in single InAs/GaAs quantum dots due to the existence of plasmon-field-induced metastable states in the wetting layer

A very long lifetime exciton emission with non-single exponential decay characteristics has been reported for single InA-s/GaAs quantum dot(QD) samples,in which there exists a long-lived metastable state in the wetting layer(WL)through radiative field coupling between the exciton emissions in the WL and the dipole field of metal islands.In this article we have proposed a new three-level model to simulate the exciton emission decay curve.In this model,assuming that the excitons in a metastable state will diffuse and be trapped by QDs,and then emit fluorescence in QDs,a stretchedlike exponential decay formula is derived as I(t)=At~(β-1)e~(-(rt)β),which can describe well the long lifetime decay curve with an analytical expression of average lifetime     

Ultrafast dynamics control on ablation of Cu using shaped femtosecond pulse trains

The ablation processes of Cu film are investigated using temporal shaped femtosecond pulse trains. The depth is modulated by changing the number and interval of the sub-pulses. The underlying ultrafast dynamic processes are discussed based on plasma shielding and electron multiple heating mechanisms. When the sub-pulse interval is less than 0.4 ps electron multiple heating is the dominant mechanism, while the plasma shielding dominates the subsequent ablation processes when the sub-pulse interval is larger than 0.4 ps. The curve of depth obtained by three pulse trains shows more significant oscillation as the function of sub-pulse interval under the low-fluence. We propose that the oscillation of depth is due to the coherent phonon oscillation excited by the pulse train. The study provides a basis for giving insight into the ultrafast dynamics for improving micromachining and nano-fabrications using shaped femtosecond pulse trains.