We investigated the movement strategies of young, healthy participants (7 men/7 women) during the movement of a fragile object using nonlinear analysis. The kinematic variables of position, velocity, and acceleration were quantified using largest Lyapunov exponent (LyE) and approximate entropy (ApEn) analysis to identify the structure of their movement variability and movement predictability, respectively. Subjects performed a total of 15 discrete trials of an upper extremity movement task without crushing the object at each fragility condition, using each hand (left/right). We tested four fragility conditions hypothesizing that an increase in fragility would result in higher movement predictability and decreased temporal variability. Comparisons between the structure of movement variability and movement predictability were based on fragility condition, handedness, and kinematic measures. In this specific population, object fragility and participant handedness did not significantly impact the structure of movement variability (LyE) in the primary direction of movement (Z direction), although some effects were observed in the anterior/posterior directions. ApEn values were minimized across conditions, showing increased movement predictability, and is suggested for the analysis of discrete kinematic movements. In healthy populations, the results of this study suggest minimal effects on task performance and movement predictability as a result of object fragility.
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http://dx.doi.org/10.1123/jab.2014-0056 | DOI Listing |
Cureus
December 2024
Cultural Technology and Communication, Intelligent Systems Lab, University of the Aegean, Mytilene, GRC.
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition marked by movement hyperactivity, often persisting into adulthood. Understanding the movement patterns associated with ADHD is crucial for improving diagnostic precision and tailoring interventions. This study leverages the HYPERAKTIV dataset, which includes high-resolution temporal data on motor activity from people diagnosed with ADHD.
View Article and Find Full Text PDFInterv Neuroradiol
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Department on Stroke Medicine and Vascular Neurology, North Bristol NHS Trust, Bristol, UK.
Background: Early identification and quantification of core infarct is of importance in stroke management for treatment selection, prognostication, and complication prediction. Non-contrast computed tomography (CT) (NCCT) remains the primary tool, but it suffers from limited sensitivity and inter-rater variability; CT perfusion is inconsistently available and commonly blighted by movement artefact. We assessed the performance of a standardised form of CT angiographic source imaging (CTASI) obtained through addition of a delayed phase at 40 seconds post-contrast injection (DP40) following fast-acquisition CT angiography.
View Article and Find Full Text PDFCell Commun Signal
January 2025
School of Pharmacy, Naval Medical University, Shanghai, 200433, China.
Background: Cancer-associated fibroblasts (CAFs) are key components of the pancreatic adenocarcinoma (PAAD) tumor microenvironment (TME), where they promote tumor progression and metastasis through immunosuppressive functions. Although significant progress has been made in understanding the crosstalk between cancer cells and CAFs, many underlying mechanisms remain unclear. Recent studies have highlighted the importance of calcium signaling in enhancing interactions between tumor cells and the surrounding stroma, with the S100 family of proteins serving as important regulators.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Technology & Innovation Hub, Shirley Ryan AbilityLab, Chicago, IL, USA.
Early screening and evaluation of infant motor development are crucial for detecting motor deficits and enabling timely interventions. Traditional clinical assessments are often subjective, without fully capturing infants' "real-world" behavior. This has sparked interest in portable, low-cost technologies to objectively and precisely measure infant motion at home, with a goal of enhancing ecological validity.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Information Engineering, Electronics and Telecommunications, University of Rome La Sapienza, Piazzale Aldo Moro 5, Rome, 00185, ITALY.
Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity in vivo remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. Approach: To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity.
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