Multiregional Evaluation of the SimPlate Heterotrophic Plate Count Method Compared to the Standard Plate Count Agar Pour Plate Method in Water

A new SimPlate heterotrophic plate count (HPC) method (IDEXX Laboratories, Westbrook, Maine) was compared with the pour plate method at 35°C for 48 h. Six laboratories tested a total of 632 water samples. The SimPlate HPC method was found to be equivalent to the pour plate method by regression analysis (r = 0.95; y = 0.99X + 0.06).     

Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

Comparison of the Pour, Spread, and Drop Plate Methods for Enumeration of Rhizobium spp. in Inoculants Made from Presterilized Peat

Inoculants prepared with presterilized peat were enumerated by the pour, spread, and drop plate techniques. Results indicated that the three plating methods were interchangeable. The drop plate method was preferred because of its economy in materials and labor.     

Effect of Dextrose in Medium for the Preparation of Mycoplasma gallisepticum Plate Antigens

Antigens for Mycoplasma gallisepticum were prepared from organisms propagated in media with and without dextrose supplementation. The antigens made from organisms produced in medium enriched with dextrose were less sensitive than the others in slide agglutination tests.     

The Automatic Spreading of Bacterial Culture over a Solid Agar Plate

Inoculated culture plates are automatically transported from a holder, into which they are placed manually, to a unit in which the culture is spread, by means of an electrically sterilizable loop drawn along a radius, and subsequently to another holder.     

Method for Increasing Atmospheric Carbon Dioxide in Bacteriological Incubators

We have adapted three incubators for culturing mycobacteria in an atmosphere of approximately 5% CO(2) and two incubators for culturing other organisms in atmospheres of approximately 10% CO(2).