Consecutive Training Research Articles

The Efficacy of a Multimodal Recovery Strategy Implemented After a High-Intensity Rugby League Training Session.

Aben, HGJ, Hills, SP, Higgins, D, Cooke, CB, Davis, D, Jones, B, and Russell, M. The efficacy of a multimodal recovery strategy implemented after a high-intensity rugby league training session. J Strength Cond Res 37(8): 1634-1642, 2023-The efficacy of a multimodal recovery strategy implemented within 4 hours of rugby league (RL) training was investigated using repeated-measures, randomized, crossover methods in 10 professional academy RL players (age: 17 ± 1 years). Following standardized training (5,383 m covered, 350-m high-speed running, 28 repeated high-intensity efforts, 24 collisions), players completed a multimodal recovery (REC) strategy (i.e., ∼640 kcal meal + ∼1,285 kcal snacks or drinks, cold-water immersion, sleep hygiene recommendations) or control (i.e., ∼640 kcal meal: CONT) practices. Isometric mid-thigh pulls (IMTP), countermovement jumps (CMJ), and wellness questionnaires were completed before (-3 hours) and after (+24, +48 hours) training. The recovery strategy influenced IMTP peak force ( p = 0.026), but between-trial differences were undetectable. No other between-trial effects (all p > 0.05) were seen for IMTP, CMJ, or wellness variables. Training-induced reductions in CMJ peak power (-4 ± 6% vs baseline: 4,878 ± 642 W) at +24 hours ( p = 0.016) dissipated by +48 hours. Fatigue and lower-body soreness reduced by 16 ± 19% ( p = 0.01) and 32 ± 44% ( p = 0.024) at +48 hours versus +24 hours, respectively. Relative to CONT (i.e., posttraining nutrition), the effects of a single bout of recovery practices appeared limited when implemented after RL-specific training. Therefore, when training included limited collisions, balanced postexercise meals appeared equally effective relative to a multimodal recovery strategy. Transient changes in performance and wellness variables after training may have implications for practitioners. Consecutive training sessions, including a high frequency and intensity of eccentric muscle actions, should be carefully planned, especially near match-play.

Consecutive Bayes factor for the mean vector

In this article, we propose an explicit closed-form fully objective Bayes factor for one-sample hypotheses testing of the mean vector of multivariate normal population. The proposed approach can be regarded as a Bayesian version of the Hotelling’s T 2 test and has various appealing properties in practical applications. It relies on data only through the T 2-statistic and can easily be calculated. The proposed Bayes factor is applicable for the multivariate paired test as well as the univariate case. In this article, we also introduce a simple idea of consecutive minimal training samples which leads to a fully objective Bayes factor. Simulated and real data examples are provided for illustrative purposes.

A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning.

In this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent toolkit. By multiphase learning, we split the traditional single training phase into multiple consecutive training phases, where the performance level of the strong team for each phase increases in an incremental way. On the other hand, by semistatic learning, the strong team in all phases will stop learning when fighting against the weak team, which reduces the possibility that the weak team keeps being defeated and learns nothing at all. Comprehensive experiments prove that, in contrast to the traditional single-phase training method, the multiphase semistatic training method proposed in this paper can significantly increase the training efficiency, shedding lights on how the weak could learn from the strong with less time and computational cost.

DEVELOPMENT OF PROSPECTIVE EFL TEACHERS’ QUESTIONING SKILLS IN CLASSROOM DISCOURSE: INTERDISCIPLINARY APPROACH

The research is devoted to the problem of mastering pre-service EFL teachers’ specific proficiency in using questions as an effective tool in the EL classroom discourse and university discourse. The purpose of the paper is to submit the teaching scheme of training pre-service EFL teachers’ questioning skills, encouraging them to work out their own teaching strategies in the future professional activity. The authors substantiate the possibility of improving MA students and pre-service EFL teachers’, questioning skills via suggested teaching scheme actualising interdisciplinary connections between the practical course (“English Communication in Academic Discourse”) and theoretical-practical course (“Theory and Practice of Speech Communication”) and students’ teaching practice that are majors of the Master’s degree curriculum. The emphasis is laid on the content level – EL classroom discourse acquisition. The experiment was conducted involving 90 MA students, pre-service EFL teachers, of South-Ukrainian National Pedagogical University named after K. D. Ushynsky to clarify the efficiency of the suggested teaching scheme. Data were obtained and analysed via pre-experimental assessment stage, experimental teaching (implementation of the suggested teaching scheme), post-experimental assessment stage employing quantitative and qualitative research methods. Data collection procedures included pedagogical observation, analysis of the students’ results, self-assessment questionnaire, student’s opinion questionnaire, statistical and mathematical processing of obtained data. The findings of the research indicate that the suggested teaching scheme using interdisciplinary approach helps students become more proactive in constructing their knowledge of questions linguodidactic potential, to transform the acquired knowledge to new ways of verbal behaviour, to model successful communication in the EL classroom through appropriate questions, and to modify their teaching styles. The authors come to the conclusion that it is mandatory to include special consecutive training covering initially purely practical courses with a gradual shift to theoretical-practical ones, and finally – teaching practices as a closing step to improve students’ proficiency in using questions effectively in the EL classroom school and university discourse.

Study of the use in autonomy of intraoral scanning systems to optimise dental students' performances in fixed prosthodontics practical learning in second year.

The purpose of this study was to evaluate if the use of intraoral scanner analysis could help students to optimise their performance and their self-assessment abilities in prosthetic preparation without the teacher's assistance in the preclinical course. Ninety-five second-year dental students were randomly divided into three groups: Control group (C), CeRec group (CR) and PrepCheck group (PC). Group C only use silicon keys to control the preparation thickness. The students in Group CR analysed their work with Cerec™ software. The Group PC use a pedagogic software (PrepCheck™) which allowed comparison with the "master" preparation. They used control tools preparations for three consecutive training sessions. Then a final assessment session without control tool to evaluate their performance was organised. All preparations were objectively evaluated thanks to stl. analysis software (Geomagic® Control X). Each preparation was compared with the master preparation by defining tolerance ranges. An objective overall score out of 20 was assigned to each preparation according to their degree of correspondence. In addition, a questionnaire was performed to analyse the students' perceptions regarding the use of intraoral camera. During the assessment session, no significant difference was revealed between the different groups with respect to the students' performance. The comparative analysis between the self-assessment scores and the objective scores using Geomagic® software showed that digital control tools did not help the student to improve their self-assessment abilities without the teacher's assistance. The intraoral cameras and specific pedagogic software in autonomy could not improve students' performance for teeth preparation and self-assessment abilities in second year. These digital tools facilitate objective feedback on preparation defects, but constructive criticism needs human reflection. The teacher's opinions and advice remain essential to develop their students' performance and self-assessment skills. The use of digital tools must be integrated in dentistry course to develop the interpret of the results and optimise feedback.

Variations in Physical and Competitive Performance of Highly Trained Sprinters Across an Annual Training Season.

Loturco, I, Fernandes, V, Bishop, C, Mercer, VP, Siqueira, F, Nakaya, K, Pereira, LA, and Haugen, T. Variations in physical and competitive performance of highly trained sprinters across an annual training season. J Strength Cond Res 37(5): 1104-1110, 2023-We assessed the changes in sprint, jump, and power parameters across the annual training cycle and tested the longitudinal correlations among these variables in top-level sprinters. Thirteen sprinters training with 4 different Olympic sprint coaches were sequentially assessed over 14 months, from January 2019 to March 2020, within 4 consecutive training camps. Performance tests were conducted as follows: standing long jump, squat and countermovement jumps, 10-m and 60-m sprint time, and maximum power output in the half-squat, jump-squat, and hip-thrust exercises. The competitive results of the sprinters throughout the study period were also recorded and analyzed. A repeated measures analysis of variance was used to compare the physical measurements between different testing sessions. A Pearson product-moment correlation was applied to examine the longitudinal relationships between changes in speed-related and power-related parameters. Percentage change was computed and compared with coefficient of variation values to determine whether changes in performance metrics were higher than the test variance, thus providing an indication of whether true changes occurred on an individual basis. Overall, sprinters did not exhibit significant changes in sprint speed, jumping ability, and power output. In addition, variations in competitive times (i.e., 100 m races) followed a similar pattern, within an average range of ±1.36%, for both male and female sprinters. As expected, top-level sprinters presented only small variations in physical and competitive performance over time. Nevertheless, the use of an individual statistical technique (i.e., true changes calculation) revealed that these nonsignificant increases or decreases may represent meaningful changes in their competitive potential.

Investigation on the pattern for train-induced strains of the long-span steel truss railway bridge

Detecting abnormal changes in bridge behavior is essential for bridge management. Influence lines are a generally accepted method to describe the quasi-static bridge behavior. Researchers have proposed that the ratio between the influence lines associated structural strains for a pair of sensors can be used as an indicator of abnormal structural condition. However, these methods have been developed for highway bridges; direct application to railway bridges is challenging, because (i)measured responses corresponding to each axle load are coupled, and (ii)train-induced longitudinal forces and bearing restraint produce additional strains in structural members. This research seeks to resolve these challenges to enable application to long-span railway bridges with spherical bearings. First, the concept of influence lines is illustrated using the elastic beam theory, providing an understanding of the underlying mechanisms of the train-induced responses. Accordingly, two condition indicators base on the axial strain in a pair of structural members are given (i.e., the ratio between integral areas of pseudo-static strains and the ratio between average pseudo-static strain amplitude). In addition, the statistical patterns of the indicators are studied using numerical simulation. Subsequently, the strain patterns are illustrated for field monitoring data from a long-span steel truss railway bridge. Herein, the calculations for the condition indicators from strain measurements are presented, and the pre-processing of the indicators is conducted to consider bridge bearing properties. Results show that the condition indicators are naturally clustered according to tracks and the directions of two consecutive trains; each cluster can be represented using a normal distribution. Note that these distributions do not change with the variation of train loads (i.e., train weight and train speed). The recognized pattern provides a potential approach for the identification of abnormal behavior of railway bridges.

Evidence that heat acclimation training may alter sleep and incidental activity

ABSTRACT This randomized cross-over study tested the hypothesis that heat acclimation training would detrimentally affect sleep variables and alter incidental physical activity compared to a thermoneutral training control condition. Eight recreationally trained males (V̇O2peak 49±4.9 mL.kg-1.min-1) completed two separate interventions separated by at least 31 days: 5 consecutive day training blocks of moderate-intensity cycling (60 min·day-1 at 50% peak power output) in a hot (34.9±0.7 °C and 53±4 % relative humidity) and a temperate (22.2±2.6 °C; 65±8 % relative humidity) environment. Wrist-mounted accelerometers were worn continuously for the length of the training blocks and recorded physical activity, sleep quality and quantity. Data were analysed in a Bayesian framework, with the results presented as the posterior probability that a coefficient was greater or less than zero. Compared to the temperate training environment, heat acclimation impaired sleep efficiency (Pr β<0 = .979) and wake on sleep onset (Pr β>0 = .917). Daily sedentary time was, on average, 35 min longer (Pr β>0 = .973) and light physical activity time 18 min shorter (Pr β>0 = .960) during the heat acclimation period. No differences were observed between conditions in sleep duration, subjective sleep quality, or moderate or vigorous physical activity. These findings may suggest that athletes and coaches need to be cognisant that heat acclimation training may alter sleep quality and increase sedentary behaviour. Highlights Five consecutive days of heat training negatively affected some objective measures of sleep quality and incidental physical activity in recreationally trained athletes. Athletes and coaches need to be aware of the potential unintended consequences of using heat acclimation on sleep behaviours.

Artificial intelligence analysis of ECG signals to predict arrhythmia recurrence after cryoballoon ablation of atrial fibrillation

Abstract Introduction Cryoballoon-based pulmonary vein isolation (CB) is an effective option for rhythm control in atrial fibrillation (AF). There have been multiple attempts to predict arrhythmia recurrence, but with moderate success. Purpose To use artificial intelligence (AI) deep electrocardiogram (ECG) analysis to predict arrythmia recurrence after CB. Methods In a single-center study of 250 consecutive pts (58.2±12.6 years, 30% female) treated with CB for AF (05.2017–04.2019), 60% had paroxysmal AF (PAF), 66.5% had hypertension, and 27.2% had a redo CB. Analyses included left atrial volume (LA vol: indexed for BSA and assed by angio-MSCT in 76% and the rest with echo), left ventricle ejection fraction (LVEF) and hypertrophy (LVH: septal/posterior wall thickness ≥11mm in ♂ and ≥10mm in ♀) along with 30s arrhythmia recurrence at 2-yr follow-up. Baseline 500Hz raw 12-lead digital ECG signals were analyzed by means of convolutional neural network (CNN) architecture that was taught to process the 12-channel ECG signal (XML). The transfer learning method of CNN parameters learned on a very large number of coded consecutive training ECG samples (n=1,000) was adopted to analyze current sample of ECG signals. Results Arrhythmia recurrence at 2-yrs was 46.0% (n=115). There was gradual increase in predictive performance being the lowest if only baseline clinical data were analyzed and the highest combing all baseline clinical data plus anatomical and functional parameters (Table 1: #1–#3). AI baseline ECG analysis alone offered predictive performance similar to that made upon analysis of all baseline clinical data or joint analysis of LA vol, LVH, and LVEF (Table 1: #4). AI results added substantially to predictive performance of models using baseline clinical data alone (#1 vs #5, p&amp;lt;0.001) and joint analysis of baseline clinical plus LA vol, LVH and LVEF (#3 vs #6, p=0.0130) (Fig. 1). Conclusions Prediction of recurrence after CB using raw ECG data with deep AI analysis is feasible. Joint analysis of results of AI of baseline ECG and basic clinical data offers predictive performance similar to that made upon analysis of clinical data including advanced information on LA volume and LVH and function. AI analysis of baseline ECG adds significantly to models aimed at recurrent AF prediction. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): National Institute of Cardiology, Warsaw, Poland

EFFECT ON MYOBLAST DIFFERENTIATION BY EXTREMELY LOW-FREQUENCY PULSED ELECTROMAGNETIC FIELDS

In the myoblasts differentiation process of C2C12 cells, it is imperative to research the expression and synthesis of the myogenic regulatory factors in extremely low-frequency pulsed electromagnetic fields (ELF-PEMFs) exposure. In the present study, using a cell cultivation method of C2C12 cells, the subcultured cells were divided into control and ELF-PEMF treated: A (0[Formula: see text]mT), B (0.5[Formula: see text]mT), C (1.0[Formula: see text]mT) and D (1.5[Formula: see text]mT) groups for five consecutive days training. Furthermore, cells were analyzed using immunochemical staining to detect the cell phenotypic changes, and RT-PCR and Western blot were used to test the expression levels of the myogenic regulatory factor D (MyoD) and the myogenic regulatory factor 5 (Myf5) genes and proteins synthesis in the process of myblasts differentiation. The results indicated that compared with the control group, different intensities of extremely low-frequency pulse electromagnetic fields could induce myoblast nuclear fusion, amplify MyoD and Mfy5 expression, and particularly the 1.5[Formula: see text]mT group at the 5th day exhibited the highest expression of these genes. Our study can pave a way for the development of a non-invasive and effective clinical prevention and treatment strategy in serious disorders like skeletal muscle atrophy.

Data-Driven Event-Triggered Cooperative Control for Multiple Subway Trains With Switching Topologies

In this paper, a data-driven event-triggered cooperative control (DD-ETCC) scheme is proposed for the multiple subway trains to realize speed tracking and dynamic headway adjustment under switching topologies. Firstly, a nonlinear subway train system is transformed into a linearization data model, and the complex nonlinear terms caused by mechanical and aerodynamic resistance and operating environment conditions are estimated by projection algorithm. Then, the DD-ETCC scheme based on the linearization data model and the asynchronous event-triggered condition for multiple subway trains with switching topologies is designed. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the headway distances of consecutive trains are stabilized in a safe range with the proposed scheme. Finally, the effectiveness of the proposed DD-ETCC scheme of multiple trains is illustrated by subway train simulations.

WITHDRAWN: The decrement in swimming performance following an increase in training volume is associated with muscle fibre typology

<h2>Abstract</h2><h3>Objectives</h3> We examined if muscle fibre typology (MFT) was associated with the magnitude of change in performance following alterations in swimming training volume in highly-trained swimmers. <h3>Design</h3> Single group intervention. <h3>Methods</h3> Ten swimmers (<i>n</i> = 2 female) completed four consecutive training phases: i) 2-wk normal training (NT); ii) 1-wk decrease (DEC1; 30 % decrease in volume) iii) 3-wk higher-volume training (HVT; i.e., 25 % increase from NT), and iv) 1-wk decrease (DEC2; 30 % decrease in volume). Swimmers performed a 200-m freestyle time trial (200-m TT), assessments of lower and upper-body power and a 10- and 30-s maximal tethered swimming test in the 2 days directly following each training phase. MFT of the gastrocnemius and soleus was estimated by quantification of muscle carnosine concentration using proton magnetic resonance spectroscopy in the week post-DEC2 and expressed as a carnosine aggregated z-score (CAZ-score). <h3>Results</h3> Some, but not all, performance-related and physical tests were affected by alterations to training volume. The relative increase in the 200-m TT time (i.e., slower time) from DEC1 (2:13.3 ± 00:07.3 m:ss.0) to HVT (2:16.0 ± 00:08.4) was associated with CAZ-score (<i>r</i> = 0.697, <i>p</i> = 0.025). 200-m TT performance was restored following DEC2 but there was no performance supercompensation. <h3>Conclusion</h3> The results of this study suggest that swimmers with a greater estimated proportion of type I fibres were better able to tolerate a short-term period of increased training volume. Nonetheless, a progressive decrease in training volume over 7 d was not sufficient to promote performance supercompensation in highly-trained swimmers.

Health Information Prediction System of Infant Sports Based on Deep Learning Network.

The sensed data from infant sports and training programs are useful in analyzing their health conditions and forecasting any disorders or abnormalities. The sensed information is processed for providing errorless predictions for infant diseases/disorders, coupled with artificial intelligence and sophisticated healthcare technologies. The problem of noncongruent sensed data impacting the forecast occurs due to errors between consecutive training iterations. This problem is addressed using the deep learning (PEST-DL) proposed perceptible error segregation technique. The training process is halted between two consecutive iterations generating errors until a similarity verification based on infant history is performed. The similarity output determines the errors due to mismatching data observations, and therefore, the data augmentation is performed. The first perceptible error is mitigated by training the learning paradigm with all possible infant history data in the learning process. This prevents prediction lag and data omissions due to discrete availability. The learning is trained from the identified error with the precise detected disorder/abnormality data previously detected. Therefore, the first and consecutive training data segregate error instances from the actual training iterations. This improves the prediction accuracy and precision with controlled error and time complexity.

A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images.

Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.

Efficacy and Safety of a Combined Aerobic, Strength and Flexibility Exercise Training Program in Patients with Implantable Cardiac Devices.

Purpose: The “FIDE Project” (Fitness Implantable DEvice) was organized by the Institute of Sports Medicine and Science and the World Society of Arrhythmias with the aim of demonstrating the usefulness of exercise training in improving functional capacity in patients with implantable cardiac devices. Materials and Methods: Thirty sedentary patients were selected for the project (25 males and 5 females), with a mean age of 73 ± 5 years (range 44–94 years). Twenty-five were implanted with a Pacemaker (PM) and five with an Implantable Cardioverter Defibrillator (ICD). Atrial fibrillation/atrial flutter was present in ten (34%) patients, post-ischemic dilated cardiomyopathy in five (17.2%), sick sinus syndrome in six (20,7%), complete atrium-ventricular block in six (20.7%), hypertrophic cardiomyopathy in one (3.4%) and recurrent syncope in one (3.4%). The baseline assessment comprised cardiovascular examination, resting and stress ECG, cardiopulmonary exercise testing (V ˙O2peak), strength assessment of different muscle groups, and a flexibility test. The same measurements were repeated after 15–20 consecutive training sessions, over a 2-month period. The exercise prescription was set to 70–80% of HRR (Heart rate reserve) and to 50–70% of 1RM (1-repetition maximum, muscular force). The training protocol consisted of two training sessions per week performed in our institute, 90 min for each (warm-up, aerobic phase, strength phase and stretching) and one or more at home autonomously. Results: The cardiopulmonary testing after the training period documents a significant improvement in V ˙O2peak (15 ± 4 mL/kg/min vs. 17 ± 4; p = 0.001) and in work load (87 ± 30 watts vs. 108 ± 37; p = 0.001). Additionally, strength capacity significantly increased after the cardiac rehabilitation program, (quadriceps: 21 ± 18 kg vs. 29 ± 16 kg, p = 0.00003). Flexibility tests show a positive trend, but without statistical significance (sit-and-reach test: −19 ± 11 cm vs. −15 ± 11.7 cm; back-scratch test: −19 ± 11.6 cm vs. −15 ± 10 cm; lateral flexibility right −44 ± 1.4 cm vs. −43 ± 9.5 cm; left −43 ± 5 vs. −45 ± 8.7 cm). Conclusion: A brief period of combined aerobic, strength and flexibility exercise training (FIDE project) proved to be effective and safe in improving functional capacity in patients with cardiac implantable devices.

Dynamic Fingerprint Benchmarking Model for WiFi Indoor Localization

WiFi indoor localization is regarded as the most promising reliable technology for indoor localization. Its concept is based on building fingerprint and training a ma-chine learning model on it, then using it for predicting the location based on the measured received signal strength RSS. One aspect of its vulnerability is its time vari-ant change due to the dynamical conditions in the environment and the access points APs. This was the most focus of the researchers in the last decades. However, minori-ty of them have considered the limitations of the existing benchmarking fingerprint in terms of lacking of support to realistic navigations runs when they are used for eval-uating indoor localization models. This article proposes a simple yet novel add-on to existing benchmarking fingerprint that assists in converting it from static fingerprint to consecutive training data with supportability to incremental learning and awareness of various characteristics of indoor navigation runs. The model was evaluated based on two benchmarking fingerprint and the generated incremental data were plotted with two levels of granularity one is corresponding to location and the other to place for referring to high resolution level and low resolution level.

Pulsed-Focused Ultrasound Provides Long-Term Suppression of Epileptiform Bursts in the Kainic Acid-Induced Epilepsy Rat Model

Focused ultrasound (FUS) has potential utility for modulating regional brain excitability and possibly aiding seizure control; however, the duration of any beneficial effect is unknown. This study explores the efficacy and time course of a short series of pulsed FUS in suppressing EEG epileptiform spikes/bursts in a kainic acid (KA) animal model of temporal lobe epilepsy. Forty-four male Sprague–Dawley rats were recorded for 14 weeks with EEG while software calculated EEG numbers of epileptiform spikes and bursts (≥ 3 spikes/s). Four regimens of FUS given in a single session at week 7 were evaluated, with mechanical index (MI) ranging from 0.25 to 0.75, intensity spatial peak temporal average (ISPTA) from 0.1 to 2.8 W per cm2, duty cycle from 1 to 30%, and three consecutive pulse trains for 5 or 10 min each. Controls included sham injections in four and KA without FUS in eleven animals. Histological analysis investigated tissue effects. All animals receiving KA evidenced EEG spikes, averaging 10,378 ± 1651 spikes per 8 h and 1255 ± 199 bursts per 8 h by weeks 6–7. The KA-only group showed a 30% of increase in spikes and bursts by week 14. Compared to the KA-only group, spike counts were reduced by about 25%, burst counts by about 33%, and burst durations by about 50% with FUS. Behavioral seizures were not analyzed, but electrographic seizures longer than 10 s declined up to 70% after some FUS regimens. Repeated-measure ANOVA showed a significant effect of higher intensity and longer sonication duration FUS treatment using 0.75-MI, ISPTA 2.8 W/cm2, 30% duty cycle for 10-min sonications (group effect, F (4, 15) = 6.321, p < 0.01; interaction effect, F (44, 165) = 1.726, p < 0.01), with the hippocampal protective effect lasting to week 14, accompanied by decreased inflammation and gliosis effect. In contrast, spike and burst suppression were achieved using an FUS regimen with 0.25-MI ISPTA 0.5 W/cm2, 30% duty cycle for 10-min sonications. This regimen reduced inflammation and gliosis at weeks 8–14 and protected hippocampal tissue. This study demonstrates that low-intensity pulsed ultrasound can modulate epileptiform activity for up to 7 weeks and, if replicated in the clinical setting, might be a practical treatment for epilepsy.

High fidelity ECMO simulation: a reality check with reality-use of simulation in ECMO teaching program.

Simulation based learning is becoming a crucial part in ECMO education. Simulation can provide a safe but also very realistic learning experience depending on simulation fidelity. In our institution we developed a simulation based ECMO training program that incorporates low- and high-fidelity simulation. Aim of this study was to evaluate effectiveness of low- and high-fidelity simulation teaching strategies in ECMO novices. We conducted four consecutive ECMO training courses that included fifty-one ECMO novices. We describe ECMO training execution and evaluate training effectiveness and perception by structured pre- and post-training questionnaires analysis. Results of our study show extremely high satisfaction rate with simulation training (4.9 ± 0.3, Lickert 5 point scale). High-fidelity simulation was perceived as very realistic and as such represents an important tool in learning immersion and experience. However, participants reported significant decline from their expectations with regard to structured approach to troubleshooting (4.7 ± 0.5 vs 4.3 ± 0.7, p = 0.02) and efficiency improvement (4.7 ± 0.5 vs 4.3 ± 0.6, p = 0.002) after high-fidelity simulation. There was also a significant decline from their expectation on self-confidence improvement (4.7 ± 0.5 vs 4.2 ± 0.7, p = 0.001). Our results therefore show, that complex high-fidelity simulation should probably be used with caution in novice participants, not to discourage them from further learning.

Heat Acclimation with or without Normobaric Hypoxia Exposure Leads to Similar Improvements in Endurance Performance in the Heat.

Background: Combining the key adaptation of plasma volume (PV) expansion with synergistic physiological effects of other acclimation interventions to maximise endurance performance in the heat has potential. The current study investigated the effects of heat acclimation alone (H), combined with normobaric hypoxia exposure (H+NH), on endurance athletic performance. Methods: Well-trained participants completed a heat-stress trial (30 °C, 80% relative humidity (RH), 20.8% fraction of inspired oxygen (FiO2)) of a 75 min steady-state cycling (fixed workload) and a subsequent 15 min cycling time trial for distance before and after intervention. Participants completed 12 consecutive indoor training days with either heat acclimation (H; 60 min·day−1, 30 °C, 80% RH; 20.8% FiO2) or heat acclimation and overnight hypoxic environment (H+NH; ~12 h, 60% RH; 16% FiO2 simulating altitude of ~2500 m). Control (CON) group trained outdoors with average maximum daily temperature of 16.5 °C and 60% RH. Results: Both H and H+NH significantly improved time trial cycling distance by ~5.5% compared to CON, with no difference between environmental exposures. PV increased (+3.8%) and decreased (−4.1%) following H and H+NH, respectively, whereas haemoglobin concentration decreased (−2%) and increased (+3%) in H and H+NH, respectively. Conclusion: Our results show that despite contrasting physiological adaptations to different environmental acclimation protocols, heat acclimation with or without hypoxic exposure demonstrated similar improvements in short-duration exercise performance in a hot environment.

Generation of Istanbul road data set using Google Map API for deep learning-based segmentation

ABSTRACT Deep learning architectures are widely used for road segmentation studies. Data sets reflect the characteristics of the study region in which they are generated. The models that are trained with the data sets are unable to accurately forecast the region outside the area covered by the data sets. That is why local data sets must be generated to meet the requirements of navigation, military activities, urban planning and health management applications where road data are indispensable. In this study, satellite images and corresponding mask images of Istanbul city at different zoom levels were generated via Google Map Platform. Trained models were produced using Deep Residual U-Net. This study consists of three parts. The first part includes training each zoom level, from 14 to 17, separately. The recall statistics of models were found as 95%, 94%, 92% and 86%, and corresponding F1-Score statistics were calculated as 95%, 95%, 92% and 86%, respectively. Next, consecutive training with priory weight and regularization on satellite images were tested to increase the prediction results. The consecutive model and regularization methods resulted in an increase in both recall and F1-Score by 2−3%. In the third part, a new model was generated by combining all images which were produced at different levels. The combined model was found successful for the prediction of roads in all zoom levels. Finally, the performance of these models trained with the Istanbul data set was compared with well-known DeepGlobe and Massachusetts road data sets. Hence, this study illustrated that generating all data sets specifically for the area of interest is crucial.