Publications

Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test

Integrins are cell membrane adhesion receptors involved in morphogenesis, immunity, tissue healing, and metastasis. A central, yet unresolved question regarding the function of integrins is how these receptors regulate both their conformation and dynamic nanoscale organization on the membrane to generate adhesion-competent microclusters upon ligand binding. Here we exploit the high spatial (nanometer) accuracy and temporal resolution of single-dye tracking to dissect the relationship between conformational state, lateral mobility, and microclustering of the integrin receptor lymphocyte function-associated antigen 1 (LFA-1) expressed on immune cells. We recently showed that in quiescent monocytes, LFA-1 preorganizes in nanoclusters proximal to nanoscale raft components. We now show that these nanoclusters are primarily mobile on the cell surface with a small (ca. 5%) subset of conformational- active LFA-1 nanoclusters preanchored to the cytoskeleton. Lateral mobility resulted crucial for the formation of microclusters upon ligand binding and for stable adhesion under shear flow. Activation of high-affinity LFA-1 by extracellular Ca 2+ resulted in an eightfold increase on the percentage of immobile nanoclusters and cytoskeleton anchorage. Although having the ability to bind to their ligands, these active nanoclusters failed to support firm adhesion in static and low shear-flow conditions because mobility and clustering capacity were highly compromised. Altogether, our work demonstrates an intricate coupling between conformation and lateral diffusion of LFA-1 and further underscores the crucial role of mobility for the onset of LFA-1 mediated leukocyte adhesion.

JTD Keywords: Cumulative probability distribution, Integrin lymphocyte function-associated antigen 1, Intercellular adhesion molecule, Single molecule detection

This work presents the estimation of a likelihood map for the location of a source of chemical plume using multiple mobile sensors and Bayesian Inference. Previously described methods use a single sensor and just binary detections (concentrations above or below a certain threshold). The main contribution of this work is to extend previous proposals to use concentration information while at the same time being robust against the presence of background signals. The algorithm has two parts. The first part, concerning Adaptive Background Estimation, uses robust statistics measurements to estimate the background level despite the intermittent presence of high concentrations due to plume statistics. The second part of the algorithm estimates likelihood functions for background and for condition plus plume. Then, the algorithm sequentially builds a likelihood probability map for the location of the source. The algorithm allows the use of multiple mobile sensors. The simulation results demonstrate that our algorithm estimates better the source location and is much more robust in the presence of false alarms.

JTD Keywords: Sensors, Inference mechanisms, Probability, Simulation