RingDecomposerLib: An Open-Source Implementation of Unique Ring Families and Other Cycle Bases.

Many cheminformatics applications like aromaticity detection, SMARTS matching, or the calculation of atomic coordinates require a chemically meaningful perception of the molecular ring topology. The unique ring families (URFs) were recently introduced as a unique, polynomial, and chemically meaningful description of the ring topology. Here we present the first open-source implementation of the URF concept for ring perception. The C library RingDecomposerLib is easy to use, portable, well-documented, and thoroughly tested. Aside from the URFs, other related ring topology descriptions like the relevant cycles (RCs), relevant cycle prototypes (RCPs), and a smallest set of smallest rings (SSSR) can be calculated. We demonstrate the runtime efficiency of the RingDecomposerLib with computing time benchmarks for the complete PubChem Compound Database and thereby show the applicability in large-scale and interactive applications.

Challenges and advanced concepts for the assessment of learning and memory function in mice.

The mechanisms underlying the formation and retrieval of memories are still an active area of research and discussion. Manifold models have been proposed and refined over the years, with most assuming a dichotomy between memory processes involving non-conscious and conscious mechanisms. Despite our incomplete understanding of the underlying mechanisms, tests of memory and learning count among the most performed behavioral experiments. Here, we will discuss available protocols for testing learning and memory using the example of the most prevalent animal species in research, the laboratory mouse. A wide range of protocols has been developed in mice to test, e.g., object recognition, spatial learning, procedural memory, sequential problem solving, operant- and fear conditioning, and social recognition. Those assays are carried out with individual subjects in apparatuses such as arenas and mazes, which allow for a high degree of standardization across laboratories and straightforward data interpretation but are not without caveats and limitations. In animal research, there is growing concern about the translatability of study results and animal welfare, leading to novel approaches beyond established protocols. Here, we present some of the more recent developments and more advanced concepts in learning and memory testing, such as multi-step sequential lockboxes, assays involving groups of animals, as well as home cage-based assays supported by automated tracking solutions; and weight their potential and limitations against those of established paradigms. Shifting the focus of learning tests from the classical experimental chamber to settings which are more natural for rodents comes with a new set of challenges for behavioral researchers, but also offers the opportunity to understand memory formation and retrieval in a more conclusive way than has been attainable with conventional test protocols. We predict and embrace an increase in studies relying on methods involving a higher degree of automatization, more naturalistic- and home cage-based experimental setting as well as more integrated learning tasks in the future. We are confident these trends are suited to alleviate the burden on animal subjects and improve study designs in memory research.

Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due to ethical and legal reasons. For mice, the most commonly used laboratory animals, a valuable tool is the Mouse Grimace Scale (MGS), a coding system for facial expressions of pain in mice. We aim to develop a fully automated system for the surveillance of post-surgical and post-anesthetic effects in mice. Our work introduces a semi-automated pipeline as a first step towards this goal. A new data set of images of black-furred laboratory mice that were moving freely is used and provided. Images were obtained after anesthesia (with isoflurane or ketamine/xylazine combination) and surgery (castration). We deploy two pre-trained state of the art deep convolutional neural network (CNN) architectures (ResNet50 and InceptionV3) and compare to a third CNN architecture without pre-training. Depending on the particular treatment, we achieve an accuracy of up to 99% for the recognition of the absence or presence of post-surgical and/or post-anesthetic effects on the facial expression.