Blackburn N, Hagstrom A, Wikner J, Cuadros-Hansson R, Bjornsen PK
Marine Biological Laboratory, DK-3000 Helsingor, Denmark.
Appl Environ Microbiol. 1998 Sep;64(9):3246-55. doi: 10.1128/AEM.64.9.3246-3255.1998.
Annual bacterial plankton dynamics at several depths and locations in the Baltic Sea were studied by image analysis. Individual bacteria were classified by using an artificial neural network which also effectively identified nonbacterial objects. Cell counts and frequencies of dividing cells were determined, and the data obtained agreed well with visual observations and previously published values. Cell volumes were measured accurately by comparison with bead standards. The survey included 690 images from a total of 138 samples. Each image contained approximately 200 bacteria. The images were analyzed automatically at a rate of 100 images per h. Bacterial abundance exhibited coherent patterns with time and depth, and there were distinct subsurface peaks in the summer months. Four distinct morphological classes were resolved by the image analyzer, and the dynamics of each could be visualized. The bacterial growth rates estimated from frequencies of dividing cells were different from the bacterial growth rates estimated by the thymidine incorporation method. With minor modifications, the image analysis technique described here can be used to analyze other planktonic classes.
通过图像分析研究了波罗的海几个深度和地点的年度浮游细菌动态。利用人工神经网络对单个细菌进行分类，该网络还能有效识别非细菌物体。确定了细胞计数和分裂细胞的频率，所得数据与肉眼观察结果和先前发表的值吻合良好。通过与珠子标准进行比较，准确测量了细胞体积。该调查包括来自总共138个样本的690张图像。每张图像包含约200个细菌。图像以每小时100张的速度自动分析。细菌丰度随时间和深度呈现出连贯的模式，夏季月份在次表层有明显的峰值。图像分析仪分辨出四个不同的形态类别，每个类别的动态都可以可视化。根据分裂细胞频率估算的细菌生长速率与通过胸苷掺入法估算的细菌生长速率不同。经过微小修改，此处描述的图像分析技术可用于分析其他浮游生物类别。
