Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.

Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human-Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.

Shear-stress dependence of dinoflagellate bioluminescence.

Fluid flow stimulates bioluminescence in dinoflagellates. However, many aspects of the cellular mechanotransduction are incompletely known. The objective of our study was to formally test the hypothesis that flow-stimulated dinoflagellate bioluminescence is dependent on shear stress, signifying that organisms are responding to the applied fluid force. The dinoflagellate Lingulodinium polyedrum was exposed to steady shear using simple Couette flow in which fluid viscosity was manipulated to alter shear stress. At a constant shear rate, a higher shear stress due to increased viscosity increased both bioluminescence intensity and decay rate, supporting our hypothesis that bioluminescence is shear-stress dependent. Although the flow response of non-marine attached cells is known to be mediated through shear stress, our results indicate that suspended cells such as dinoflagellates also sense and respond to shear stress. Shear-stress dependence of flow-stimulated bioluminescence in dinoflagellates is consistent with mechanical stimulation due to direct predator handling in the context of predator-prey interactions.