Applied Sciences

Approximately two-thirds of athletes who are submitted to Anterior Cruciate Ligament Reconstruction (ACLR) never return to their preinjury level of performance, potentially due to muscle strength deficiencies or altered loading patterns during landing or jumping tasks. This study aimed to estimate individual muscle forces during a double-leg drop jump task, and assess sagittal plane between-limb asymmetries in muscle forces and ground reaction forces using a musculoskeletal modelling approach, in athletes who underwent ACLR. Thirty male field-sport athletes (age: 18–35 years; mass: 84.3 ± 12.3 kg; height: 180.2 ± 8.4 cm) post-ACLR (39.8 ± 3.9 weeks) using patellar or quadriceps tendon grafts were tested. Scaled musculoskeletal models were implemented in OpenSim, and muscle forces were estimated using the Computed Muscle Control optimization method. The contralateral limb exhibited greater vertical ground reaction forces across most of the rebound phase (d = 2.01). Compared with the contralateral limb, the ACLR limb showed reduced quadriceps (d = 1.72), soleus (d = 0.95), and gluteus maximus (d = 0.83) forces, indicating deficits in knee extensor, plantarflexor, and hip extensor neuromuscular function. Smaller asymmetries were found for the gluteus medius (d = 0.60) and hamstrings (d = 0.72), while other muscles showed symmetrical activation patterns. These results reveal persistent between-limb asymmetries in muscle recruitment and loading up to nine months post-ACLR, emphasizing the importance of targeted rehabilitation to restore symmetrical neuromuscular control during explosive movements.

The search for therapeutic bioactive peptides has led to the utilization of marine byproducts as collagen sources. This study evaluated the effect of collagen hydrolysates (CH) obtained from the swim bladder (SB) of Totoaba macdonaldi on breast (MCF-7) and colorectal (Caco-2) adenocarcinoma cells and on human dermal fibroblasts (CRL-1474), considering the need for less invasive and less toxic treatment alternatives. Two pretreatment methods for the SB were compared: (1) NaOH and butanol (SBPT), and (2) hexane (SBDF). The pretreated tissues underwent direct enzymatic hydrolysis using bromelain. The resulting hydrolysates were characterized by SDS-PAGE, Raman spectroscopy, and chromatographic profiling. Both pretreatments preserved the structure of type I collagen. Bromelain hydrolysis was efficient, yielding peptides with molecular weights below 20 kDa for CH-SBPT and below 10 kDa for CH-SBDF. CH of Totoaba macdonaldi significantly reduced MCF-7 and Caco-2 cells viability, particularly at 20 mg/mL. In CRL-1474 fibroblasts, CH-SBDF stimulated cell proliferation, while CH-SBPT had neutral effects. Hexane pretreatment is a viable alternative to NaOH, reducing processing steps without compromising yield or bioactivity. CH derived from Totoaba macdonaldi exhibit promising anticancer and regenerative properties, suggesting potential biomedical applications. Further research is needed to isolate specifically active peptides and elucidate their mechanisms of action.

To build robust condition monitoring solutions, it is important to identify signals that capture relevant information. However, how a degradation affects a given part of machinery might not be clear at the beginning. As a result, exploration measurement campaigns collecting large amounts of data are needed for initial evaluation. Vibration signals are typical examples of such data. Although, for explorative measurement campaigns, the battery-powered wireless node brings extra flexibility in terms of positioning the sensor at the desired location and facilitates retrofitting, the limited energy posed by them is the major downside. Sending high-sampled data over wireless channels is costly energy-wise if all samples are to be sent. When multiple sensor nodes transmit real-time measurement data concurrently over a wireless channel, the risk of channel saturation increases significantly. Avoiding this requires identifying an optimal balance between sampling time, transmission duration, and payload size. This can be done by processing and compressing data before transmission, on the sensor node close to the data acquisition and later reconstructing the received samples on the central node. In this paper, we analyze two compression mechanisms to ensure a good compression ratio and still allow good signal reconstruction for later analysis. We study two approaches, one based on the Fast Fourier Transform and one on Singular Value Decomposition, and discuss the pros and cons of each variant.

Epilepsy is one of the most prevalent neurological disorders, affecting over 50 million people worldwide. Accurate detection and characterization of epileptic activity are clinically critical, as seizures are associated with substantial morbidity, mortality, and impaired quality of life. Electroencephalography (EEG) remains the gold standard for epilepsy assessment; however, its manual interpretation is time-consuming, subjective, and prone to inter-rater variability, emphasizing the need for automated analytical approaches. This study proposes an automated ensemble classification framework for outlier detection in EEG signals. Three interpretable baseline models—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and decision tree (DT-CART)—were screened. Ensembles were formed only from base models that had a pre-registered meta-selection rule ($F_1$ on the outlier-class $>0.60$). Under this criterion, DT-CART did not qualify and was excluded from all ensembles; final ensembles combined SVM and k-NN. The framework was evaluated on two publicly available datasets with distinct acquisition conditions. The Bonn EEG dataset comprises 500 artifact-free single-channel recordings from healthy subjects and epilepsy patients under controlled laboratory settings. In contrast, the Guinea-Bissau and Nigeria Epilepsy (GBNE) dataset contains multi-channel EEG recordings from 97 participants acquired in field conditions using low-cost equipment, reflecting real-world diagnostic challenges such as motion artifacts and signal variability. The ensemble framework substantially improved outlier detection performance, with stacking achieving up to a 95.0% $F_1$-score (accuracy 95.0%) on the Bonn dataset and 85.5% $F_1$-score (accuracy 85.5%) on the GBNE dataset. These findings demonstrate that the proposed approach provides a robust, interpretable, and generalizable solution for EEG analysis, with strong potential to enhance reliable, efficient, and scalable epilepsy detection in both laboratory and resource-limited clinical environments.