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首页> 外文会议>Sensing technologies for global health, military medicine, disaster response, and environmental monitoring; and Biometric technology for human identification VIII >Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system
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Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system

机译：数字微生物学：使用无标记激光散射传感系统对未知细菌病原体进行检测和分类

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The majority of tools for pathogen sensing and recognition are based on physiological or genetic properties of microorganisms. However, there is enormous interest in devising label-free and reagentless biosensors that would operate utilizing the biophysical signatures of samples without the need for labeling and reporting biochemistry. Optical biosensors are closest to realizing this goal and vibrational spectroscopies are examples of well-established optical label-free biosensing techniques. A recently introduced forward-scatter phenotyping (FSP) also belongs to the broad class of optical sensors. However, in contrast to spectroscopies, the remarkable specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and/or serovar. Both FSP technology and spectroscopies rely on statistical machine learning to perform recognition and classification. However, the commonly used methods utilize either simplistic unsupervised learning or traditional supervised techniques that assume completeness of training libraries. This restrictive assumption is known to be false for real-life conditions, resulting in unsatisfactory levels of accuracy, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates preliminary studies on the use of FSP system to classify selected serotypes of non-O157 Shiga toxin-producing E. coli in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated and distributed detection of unknown bacterial classes.
机译：用于病原体感测和识别的大多数工具都是基于微生物的生理或遗传特性。然而，设计免标记和无试剂的生物传感器引起了极大的兴趣，这种传感器将利用样品的生物物理特征进行操作而无需标记和报告生物化学。光学生物传感器最接近实现这一目标，振动光谱学是成熟的无光学标签生物传感技术的例子。最近推出的前向散射表型（FSP）也属于光学传感器的大类。然而，与光谱学相反，FSP的显着特异性来自细菌材料在穿过菌落的相干光波前编码的形态信息。该系统收集弹性散射的光模式，在恒定的环境下，这种模式对于每种细菌和/或血清型都是唯一的。 FSP技术和光谱学都依靠统计机器学习来执行识别和分类。但是，常用的方法要么采用简单的无监督学习，要么采用假定训练库完整的传统有监督技术。众所周知，这种限制性假设对于现实生活条件是错误的，从而导致准确性不令人满意，并因此限制了生物检测和分类任务的总体性能。提出的工作展示了使用FSP系统对非O157志贺毒素生产性大肠杆菌的选定血清型在非穷举性框架中进行分类的初步研究，也就是说，没有完全了解可能遇到的所有类别。我们的研究使用贝叶斯方法通过非穷举训练数据集进行学习，以实现未知细菌类别的自动和分布式检测。

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