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1.
基质辅助激光解吸电离质谱成像(matrix-assisted laser desorption ionization mass spectrometry imaging,MALDI-MSI)技术是一种新型的分析技术,具有无需提取、分离、纯化待测样品,操作简便、灵敏度高等优点,广泛应用于生物、医学等领域。虽然其在食品领域还未建立完善的测定方法,但由于该技术可对食品中的化合物进行空间成像分析,故其在食品领域中也将具有广阔的应用前景。本文在介绍MALDI-MSI技术成像原理的基础上,综述了该技术的样品制备和在食品成分分析中的应用,并对其应用前景进行了展望,以期为MALDI-MSI技术在食品研究中的应用和推广提供借鉴。 相似文献
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Wenhao Qiu Guangyao Lian Peng Zhou Kaoli Huang 《Quality and Reliability Engineering International》2020,36(5):1592-1603
An extended failure mode effect and criticality analysis (FMECA)-based sample allocation method for testability verification is presented in this study to deal with the poor representativeness of test sample sets and the randomness of the testability evaluation results caused by unreasonable selection of failure samples. First, the fault propagation intensity is introduced as part of the extended information of FMECA, and the sample allocation impact factors of component units and failure modes are determined under this framework. Then, the failure mode similarity and impact factor support are defined, and the game decision method for weighing the relationship between similarity and support is proposed to obtain the weight of failure mode impact factor. Finally, a two-step allocation framework of test samples is formulated to realize the sample allocation of component units and failure modes. This method is applied to the testability verification test of a launch control system. Results show that this method can obtain more representative test samples compared with the traditional sample allocation method while effectively reducing randomness of single testability evaluation result. 相似文献
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Irena Dapic Lucia Baljeu‐Neuman Naomi Uwugiaren Jesper Kers David R. Goodlett Garry L. Corthals 《Mass spectrometry reviews》2019,38(4-5):403-441
Tissues and biofluids are important sources of information used for the detection of diseases and decisions on patient therapies. There are several accepted methods for preservation of tissues, among which the most popular are fresh‐frozen and formalin‐fixed paraffin embedded methods. Depending on the preservation method and the amount of sample available, various specific protocols are available for tissue processing for subsequent proteomic analysis. Protocols are tailored to answer various biological questions, and as such vary in lysis and digestion conditions, as well as duration. The existence of diverse tissue‐sample protocols has led to confusion in how to choose the best protocol for a given tissue and made it difficult to compare results across sample types. Here, we summarize procedures used for tissue processing for subsequent bottom‐up proteomic analysis. Furthermore, we compare protocols for their variations in the composition of lysis buffers, digestion procedures, and purification steps. For example, reports have shown that lysis buffer composition plays an important role in the profile of extracted proteins: the most common are tris(hydroxymethyl)aminomethane, radioimmunoprecipitation assay, and ammonium bicarbonate buffers. Although, trypsin is the most commonly used enzyme for proteolysis, in some protocols it is supplemented with Lys‐C and/or chymotrypsin, which will often lead to an increase in proteome coverage. Data show that the selection of the lysis procedure might need to be tissue‐specific to produce distinct protocols for individual tissue types. Finally, selection of the procedures is also influenced by the amount of sample available, which range from biopsies or the size of a few dozen of mm2 obtained with laser capture microdissection to much larger amounts that weight several milligrams. 相似文献
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针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题:由于目标快速移动引起边界效应,使得相关滤波器在学习与更新过程中可能会引入错误,最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上,提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法,选择DCNN深度特征有效地表征待跟踪目标的初始状态,通过在线分类过程中样本相似性比对与半监督学习,辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程,提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率,针对VOT2016标准测试视频的实验表明:当目标面临快速运动时,对比CN、SAMF、STC算法,所提DA-CFT跟踪算法将跟踪成功率分别由60.4%~73.4%、67.2%~82.9%、80.9%~88.1%提升至85.6%~91.0%。 相似文献
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Bernette Maria Oosterlaken Heiner Friedrich Gijsbertus de With 《Microscopy research and technique》2022,85(1):412-417
Transmission electron microscopy (TEM) is an important analysis technique to visualize (bio)macromolecules and their assemblies, including collagen fibers. Many protocols for TEM sample preparation of collagen involve one or more washing steps to remove excess salts from the dispersion that could hamper analysis when dried on a TEM grid. Such protocols are not standardized and washing times as well as washing solvents vary from procedure to procedure, with each research group typically having their own protocol. Here, we investigate the influence of washing with water, ethanol, but also methanol and 2-propanol, for both mineralized and unmineralized collagen samples via a protocol based on centrifugation. Washing with water maintains the hydrated collagen structure and the characteristic banding pattern can be clearly observed. Conversely, washing with ethanol results in dehydration of the fibrils, often leading to aggregation of the fibers and a less obvious banding pattern, already within 1 min of ethanol exposure. As we show, this process is fully reversible. Similar observations were made for methanol and propanol. Based on these results, a standardized washing protocol for collagenous samples is proposed. 相似文献
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为使我国地表水中石油类检测过程更具可操作性和推广性,使各实验室石油类检测结果更具可对比性和量值溯源性,本文从地表水中石油类样品采集、样品检测及标准物质选取等三个方面对当前国内外相关标准中规定内容进行了汇总梳理,得出目前采用的样品采集方法均难以保证样品代表性、各种检测方法关注点不同且各有利弊、标准物质的选取对最终测定结果有较大影响的结论。为解决上述难题,建议开展以下3方面工作:(1)研发一种符合地表水中石油类赋存状况的水样采集器;(2)将地表水环境质量标准中"石油类"指标分为"低沸点挥发性石油类"和"高沸点半挥发性石油类"指标分别检测;(3)分流域或河流开展石油类标准物质研制工作。 相似文献
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Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods. 相似文献
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Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms. 相似文献