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Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding. [ABSTRACT FROM AUTHOR]

Fission yeast ‘cut’ mutants show defects in temporal coordination of nuclear division with cytokinesis, resulting in aberrant mitosis and lethality. Among other causes, the ‘cut’ phenotype can be triggered by genetic or chemical perturbation of lipid metabolism, supposedly resulting in shortage of membrane phospholipids and insufficient nuclear envelope expansion during anaphase. Interestingly, penetrance of the ‘cut’ phenotype in mutants of the transcription factor cbf11 and acetyl-coenzyme A carboxylase cut6, both related to lipid metabolism, is highly dependent on growth media, although the specific nutrient(s) affecting ‘cut’ occurrence is not known. In this study, we set out to identify the growth media component(s) responsible for ‘cut’ phenotype suppression in Δcbf11 and cut6–621 cells. We show that mitotic defects occur rapidly in Δcbf11 cells upon shift from the minimal EMM medium (‘cut’ suppressing) to the complex YES medium (‘cut’ promoting). By growing cells in YES medium supplemented with individual EMM components, we identified ammonium chloride, an efficiently utilized nitrogen source, as a specific and potent suppressor of the ‘cut’ phenotype in both Δcbf11 and cut6–621. Furthermore, we found that ammonium chloride boosts lipid droplet formation in wild-type cells. Our findings suggest a possible involvement of nutrient-responsive signaling in ‘cut’ suppression. [ABSTRACT FROM AUTHOR]

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are used as models in lipid metabolic research or for industrial lipid production. Lipid droplets are highly dynamic storage bodies and their cellular content represents a convenient readout of the lipid metabolic state. Fluorescence microscopy is a method of choice for quantitative analysis of cellular lipid droplets, as it relies on widely available equipment and allows analysis of individual lipid droplets. Furthermore, microscopic image analysis can be automated, greatly increasing overall analysis throughput. Here, we describe an experimental and analytical workflow for automated detection and quantitative description of individual lipid droplets in three different model yeast species: the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus, and the budding yeast Saccharomyces cerevisiae. Lipid droplets are visualized with BODIPY 493/503, and cell-impermeable fluorescent dextran is added to the culture media to help identify cell boundaries. Cells are subjected to 3D epifluorescence microscopy in green and blue channels and the resulting z-stack images are processed automatically by a MATLAB pipeline. The procedure outputs rich quantitative data on cellular lipid droplet content and individual lipid droplet characteristics in a tabular format suitable for downstream analyses in major spreadsheet or statistical packages. We provide example analyses of lipid droplet content under various conditions that affect cellular lipid metabolism.

Background: Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space., Methods: The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data., Results: The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications., Conclusions: Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.