CARDIOVASCULAR CHANGE IN ATHLETES AT DIFFERENT TRAINING STATUS LEVELS

ABSTRACT Introduction: Heart rate and blood pressure are important physiological indicators that reflect cardiovascular function, and they are widely used because they are convenient and practical to measure. Objective: To study the characteristics of cardiovascular changes in athletes under different training conditions. Methods: Thirty-four male students majoring in physical education in universities (group A) and 22 male non-sports majors (group B) with no formal training history were randomly selected. Heart rate before and after exercise and heart rate recovery rate at different stages of the recovery period were compared. Results: As regards heart rate changes in the recovery phase after loading, both groups showed a continuous decline, although the drop in heart rate of group A was slightly lower than that of group B (153.03± 15.88 beats/min, dropped to 110.69± 15.78 beats/minute, 171.00± 14.67 beats/minute dropped to 122. 82± 13.77 beats/min, respectively). However, the heart rate recovery rate of group A (59.40%) was significantly higher than that of group B (49.42%) (P<0.05). Conclusions: Physical exercise plays a significant role in promoting physical fitness and its effect on improving cardiovascular function is especially evident. Level of evidence II; Therapeutic studies - investigation of treatment results.

Tranquillizing Effect of Passiflora incarnata Extract: Outcome on Behavioral and Physiological Indicators in Weaning Pigs with Intact Tails

Tail docking has been used in the pig industry to decrease the occurrence of tail biting behavior. This abnormal behavior has a multifactorial origin since it is a response to simultaneous environmental, nutritional and management changes. Given the calming properties of Passiflora incarnata, we hypothesized that dietary supplementation with the extract in weaned pigs could result in a modification of behavior and physiologic indicators linked to stress. Weaned piglets (n = 120, mean body weight 9.07 ± 2.30 kg) were randomly allocated to one of two dietary treatments: control diet (CON) and CON supplemented with 1 kg/t of P. incarnata (PAS). The trial was 28 days long. The presence of skin lesions was assessed at d-1, d-10, d-19, and d-28, and saliva samples were collected for IgA and cortisol determinations at the same sampling times. Results showed the PAS group was characterized by equal growth performance as the CON group, fewer ear lesions (p < 0.05), less aggressive behavior (p < 0.001), higher enrichment exploration (p < 0.001) and lower cortisol levels (p < 0.01). Time effect was observed for tail lesions (p < 0.001) and behavioral observations (p < 0.001). Additional research is required to determine the effect of P. incarnata extract using a larger number of animals and longer period of supplementation when risks associated with tail biting are uncontrolled.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.

Variation of phenotypic and physiological traits of Robinia pseudoacacia L. from 20 provenances

To select elite Robinia pseudoacacia L. germplasm resources for production, 13 phenotypes and three physiological indicators of 214 seedlings from 20 provenances were systematically evaluated and analyzed. The leaf phenotypic and physiological coefficients of variation among the genotypes ranged from 3.741% to 19.599% and from 8.260% to 42.363%, respectively. The Kentucky provenance had the largest coefficient of variation (18.541%). The average differentiation coefficients between and within provenances were 34.161% and 38.756%, respectively. These close percentages showed that R. pseudoacacia presented high genetic variation among and within provenances, which can be useful for assisted migration and breeding programs. Furthermore, based on the results of correlations, principal component analysis and cluster analysis, breeding improvements targeting R. pseudoacacia’s ornamental value, food value, and stress resistance of were performed. Forty and 30 excellent individuals, accounting for 18.692% and 14.019%, respectively, of the total resources. They were ultimately screened, after comprehensively taking into considering leaf phenotypic traits including compound leaf length, leaflet number and leaflet area and physiological characteristics including proline and soluble protein contents. These selected individuals could provide a base material for improved variety conservation and selection.

Analysis of Tracheobronchial Diverticula Based on Semantic Segmentation of CT Images via the Dual-Channel Attention Network

Tracheobronchial diverticula (TD) is a common cystic lesion that can be easily neglected; hence accurate and rapid identification is critical for later diagnosis. There is a strong need to automate this diagnostic process because traditional manual observations are time-consuming and laborious. However, most studies have only focused on the case report or listed the relationship between the disease and other physiological indicators, but a few have adopted advanced technologies such as deep learning for automated identification and diagnosis. To fill this gap, this study interpreted TD recognition as semantic segmentation and proposed a novel attention-based network for TD semantic segmentation. Since the area of TD lesion is small and similar to surrounding organs, we designed the atrous spatial pyramid pooling (ASPP) and attention mechanisms, which can efficiently complete the segmentation of TD with robust results. The proposed attention model can selectively gather features from different branches according to the amount of information they contain. Besides, to the best of our knowledge, no public research data is available yet. For efficient network training, we constructed a data set containing 218 TD and related ground truth (GT). We evaluated different models based on the proposed data set, among which the highest MIOU can reach 0.92. The experiments show that our model can outperform state-of-the-art methods, indicating that the deep learning method has great potential for TD recognition.

Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons

This research gives an overview of numerous kinds of identification and sensor technology that have been shown to improve the standard of living of older persons in hospital and home settings. Recent advancements in semiconductors and microsystems have enabled the creation of low-cost medical equipment, which are used by various persons as prevention and E-Health Monitoring (EHM) tools. Remote health management, which relies on wearable and non-invasive sensing devices, controllers, and current information and communication technology, provides cost-effective solutions that enable individuals to remain in their familiar homes while being safeguarded. Additionally, when preventative actions are implemented at home, costly medical centers are becoming available for use by intensive care patients. Patients' vital physiological indicators may be monitored in real time by remote devices, which can also watch, analyze, and, most importantly, offer feedback on their health problems. To translate different types of vital indicators into electrical impulses, sensors are employed in computerized healthcare and non-medical devices. Life-sustaining implants, preventative interventions, and long-term E-Health Monitoring (EHM) of handicapped or unwell patients may all benefit from sensors. Whether the individual is in a clinic, hospital, or at home, medical businesses, such as health insurers, want real-time, dependable, and precise diagnostic findings from sensing devices that can be examined virtually.

Alpacas in Poland: health, welfare, and anti-parasitic prophylaxis

Alpacas’ population in Poland has attained 5000 individuals. From 2020 alpacas are recognized as farm animals in Poland. This ruminant is increasingly popular, but still poorly known compared to other farm animals (cattle, sheep, goats). The aim of this review is to present the specificity of alpacas in terms of adequate welfare of these animals. To provide an appropriate welfare level, the knowledge about the species’ biology and typical behaviour is needed. The basis for assessment of the animal's health status is the knowledge of basic physiological indicators, whose divergence from reference values is often the first symptom of many diseases. The health and welfare of virtually all animal species are influenced by infestations by endo- and exoparasites, which can cause many disorders and serious diseases. The growing alpaca population size necessitates investigation of this species, which will help future and current owners to breed these animals and prompt veterinarians to apply appropriate treatment of alpacas bred in Poland.

Response of Interspecific Hybrid Species of Manchurian Ash to MJ and NO Signals and Preliminary Study on the Formation Mechanism of Drought Resistance Advantage

Background: In this study, sodium nitrate (SNP, a donor of nitric oxide) and methyl jasmonate (MJ) were used as exogenous hormones. The experiment was conducted with the offspring (interspecific hybrid) D110 of ash and ash, and their respective parents (non-interspecific hybrid) D113 and 4-3 as experimental materials. The experiment set up three experimental groups of drought stress, exogenous hormone SNP and MJ, and a control group under normal growth (non-drought stress), to study the physiological indicators and gene expression of manchurian ash. Result: The results showed that under drought stress and exogenous application of hormone SNP or MJ, there were significant differences between hybrids and parents in plant growth, photosynthesis, defense enzyme activity, hormone content and gene expression.Conclusions: This experiment provides a new theoretical support for the existing hormone breeding methods of manchurian ash, which can improve the drought resistance of manchurian ash and increase its survival rate in the wild. Increasing the growth rate and breeding efficiency of manchurian ash brings new ideas.