Metastable Pain-Attention Dynamics during Incremental Exhaustive Exercise

Integrated image and location analysis for wound classification: a deep learning approach

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Multi-modal wound classification using wound image and location by deep neural network

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.

Integrative Proposals of Sports Monitoring: Subjective Outperforms Objective Monitoring

Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes' health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer-environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes' health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.

Pain During a Marathon Run: Prevalence and Correlates in a Cross-Sectional Study of 1,251 Recreational Runners in 251 Marathons

This cross-sectional study aimed to obtain fundamental knowledge about pain during a marathon run. During the first seven months of 2007, announcements on websites of interest to marathon runners were used to recruit participants. A total of 1,251 runners (550 female runners) completed a 41-question online survey about the location and intensity of their primary pain during their last marathon and potentially related variables [perception of effort during the marathon, number of prior marathons run, typical pain intensity during training runs, percentage of training days with run-induced pain, highest intensity pain ever experienced]. Pain location was selected from a list of 27 specified body sites covering the entire body. Kilometer at which pain first occurred indexed pain threshold. Pain intensity at the primary location of pain was measured with a standardized, well-validated 0–10 pain intensity scale. Pearson correlations and multiple regression quantified the associations between average pain intensity and other variables. Sex-related differences in pain were tested using independent t-tests. Effort ratings (6–20) were added as a covariate in an ANCOVA to test if perceived effort accounted for possible sex-related differences in pain. Based on the available research, it was hypothesized that: (i) most runners would report moderate intensity pain, (ii) pain would be associated with both exercise intensity during the marathon and pain during training, and (iii) after adjusting for expected sex-related differences in perceived effort, females would experience pain earlier and rate the pain intensity as higher. All but two runners (99.8%) reported pain during a marathon, and most frequently in the anterior/medial thigh (17.1%), hamstring (10%), and calf (9.3%) locations. Pain threshold occurred at 25.3 ± 9.8 km (15.7 ± 6.1 miles) and the overall pain intensity of the run was 5.26 ± 2.45. No sex-related pain differences were found. Overall pain intensity during a marathon was significantly associated with: pain intensity during training runs (r = 0.39), percentage of training days with run-induced pain (r = 0.23), highest intensity pain ever experienced (r = 0.23), number of prior marathons (r = −0.18), and intensity of effort (r = 0.11) (all P < 0.001). Most runners experience moderate to very strong intensity pain during a marathon; the pain was independent of biological sex, and the pain is weakly associated with marathon race experience, pain during training, race effort, and the highest intensity of pain ever experienced.

Mind-to-mind heteroclinic coordination: Model of sequential episodic memory initiation

Retrieval of episodic memory is a dynamical process in the large scale brain networks. In social groups, the neural patterns, associated with specific events directly experienced by single members, are encoded, recalled, and shared by all participants. Here, we construct and study the dynamical model for the formation and maintaining of episodic memory in small ensembles of interacting minds. We prove that the unconventional dynamical attractor of this process-the nonsmooth heteroclinic torus-is structurally stable within the Lotka-Volterra-like sets of equations. Dynamics on this torus combines the absence of chaos with asymptotic instability of every separate trajectory; its adequate quantitative characteristics are length-related Lyapunov exponents. Variation of the coupling strength between the participants results in different types of sequential switching between metastable states; we interpret them as stages in formation and modification of the episodic memory.