DNA-coated microcrystals

Coprecipitation leads to self-assembly of bioactive DNA on the surface of salt, sugar or amino-acid crystals and provides a rapid inexpensive immobilization method suitable for preparing dry-powder formulations of nucleic acids, useful for storage, imaging and drug delivery

Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)

A context-dependent switch from sensing to feeling in the primate amygdala

The skin transmits affective signals that integrate into our social vocabulary. As the socio-affective aspects of touch are likely processed in the amygdala, we compare neural responses to social grooming and gentle airflow recorded from the amygdala and the primary somatosensory cortex of non-human primates. Neurons in the somatosensory cortex respond to both types of tactile stimuli. In the amygdala, however, neurons do not respond to individual grooming sweeps even though grooming elicits autonomic states indicative of positive affect. Instead, many show changes in baseline firing rates that persist throughout the grooming bout. Such baseline fluctuations are attributed to social context because the presence of the groomer alone can account for the observed changes in baseline activity. It appears, therefore, that during grooming, the amygdala stops responding to external inputs on a short timescale but remains responsive to social context (or the associated affective states) on longer time scales

Mapping the contribution of single muscles to facial movements in the rhesus macaque

The rhesus macaque (Macaca mulatta) is the most utilized primate model in the biomedical and psychological sciences. Expressive behavior is of interest to scientists studying these animals, both as a direct variable (modeling neuropsychiatric disease, where expressivity is a primary deficit), as an indirect measure of health and welfare, and also in order to understand the evolution of communication. Here, intramuscular electrical stimulation of facial muscles was conducted in the rhesus macaque in order to document the relative contribution of each muscle to the range of facial movements and to compare the expressive function of homologous muscles in humans, chimpanzees and macaques. Despite published accounts that monkeys possess less differentiated and less complex facial musculature, the majority of muscles previously identified in humans and chimpanzees were stimulated successfully in the rhesus macaque and caused similar appearance changes. These observations suggest that the facial muscular apparatus of the monkey has extensive homology to the human face. The muscles of the human face, therefore, do not represent a significant evolutionary departure from those of a monkey species. Thus, facial expressions can be compared between humans and rhesus macaques at the level of the facial musculature, facilitating the systematic investigation of comparative facial communication

A note on the probability distribution function of the surface electromyogram signal

AbstractThe probability density function (PDF) of the surface electromyogram (EMG) signals has been modelled with Gaussian and Laplacian distribution functions. However, a general consensus upon the PDF of the EMG signals is yet to be reached, because not only are there several biological factors that can influence this distribution function, but also different analysis techniques can lead to contradicting results. Here, we recorded the EMG signal at different isometric muscle contraction levels and characterised the probability distribution of the surface EMG signal with two statistical measures: bicoherence and kurtosis. Bicoherence analysis did not help to infer the PDF of measured EMG signals. In contrast, with kurtosis analysis we demonstrated that the EMG PDF at isometric, non-fatiguing, low contraction levels is super-Gaussian. Moreover, kurtosis analysis showed that as the contraction force increases the surface EMG PDF tends to a Gaussian distribution