Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an AP@0.5 IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

The nematode Caenorhabditis elegans (C. elegans) is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and restrictions of the comprehensive use of C. elegans for toxicological trials. With the general applicability of the detection of C. elegans from microscope images via machine learning, as well as of smartphone-based microscopes, this article investigates the suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish. Thereby, the article introduces a smartphone-based microscope (including optics, lighting, and housing) for monitoring C. elegans and the corresponding classification via a trained Histogram of Oriented Gradients (HOG) feature-based Support Vector Machine for the automatic detection of C. elegans. Evaluation showed classification sensitivity of 0.90 and specificity of 0.85, and thereby confirms the general practicability of the chosen approach.

The ability to generalize well to different environments is of importance for audio de-noising systems in real-world scenarios. Especially single-channel signals require efficient noise filtering without impacting speech intelligibility negatively. Our previous work has shown that a probabilistic latent space model combined with a U-Network architecture increases performance and generalization ability to some extent. Here, we further evaluate magnitude-only, as well as complex-valued U-Network models, on two different datasets, and in a train-test mismatch scenario. Adaptability of models is evaluated by introducing a curve-based score similar to area-under-the-curve metrics. The proposed probabilistic latent space models outperform their ablated variants in most conditions, as well as well-known comparison methods, while increases in network size are negligible. Improvements of up to 0.97 dB SI-SDR in matched, and 2.72 dB SI-SDR in mismatched conditions are observed, with highest total SI-SDR scores of 20.21 dB and 18.71 dB, respectively. The proposed stability-score aligns well with observed performance behaviour, further validating the probabilistic latent space model.