Zeolite Beta Formation from Clear Sols: Silicate Speciation, Particle Formation and Crystallization Monitored by Complementary Analysis Methods.

The formation of silicate nanoaggregates (NAs) at the very early stages of precursor sols and zeolite beta crystallization from silicate nanoparticles (NPs) are investigated in detail using a combination of different analysis methods, including liquid-state 29 Si, 27 Al, 14 N, and 1 H NMR spectroscopy, mass spectrometry (MS), small-angle X-ray scattering (SAXS), X-ray diffraction (XRD), and transmission electron microscopy at cryogenic temperatures (cryo-TEM). Prior to hydrothermal treatment, silicate NAs are observed if the Si/OH ratio in the reaction mixture is greater than 1. Condensation of oligomers within the NAs then generates NPs. Aluminum doped into the synthesis mixtures is located exclusively in the NPs, and is found exclusively in a state that is fourfold connected to silicate, favoring their condensation and aggregation. These results are in agreement with general trends observed for other systems. Silicate NAs are essential intermediates for zeolite formation and are generated by the aggregation of hydrated oligomers, aluminate, and templating cations. Subsequent further intra-nanoaggregate silicate condensation results in the formation of NPs. 1 H and 14 N liquid NMR as well as diffusion ordered spectroscopy (DOSY) experiments provide evidence for weakly restricted rotational and translational mobility of the organic template within NAs as a consequence of specific silicate-template interactions. NAs thus appear as key species in clear sols, and their presence in the precursor sol favors silicate condensation and further crystallization, promoted either by increasing the Si/OH ratio or by heating.

Comparison of physiological and clinical markers for chronic sprint-interval training exercise performed either in the fasted or fed states among healthy adults.

Sprint-interval training (SIT) and intermittent fasting are effective independent methods in achieving clinical health outcomes. However, the impact of both modalities when performed concurrently is unclear. The aim of this study was to compare the effects of 6 weeks of SIT performed in the fasted versus fed state on physiological and clinical health markers in healthy adults. Methods. Thirty recreationally-active participants were equally randomised into either the fasted (FAS; 4 males, 11 females) or the fed (FED; 6 males, 9 females) group. For all exercise sessions, FAS participants had to fast ≥10 h prior to exercising while FED participants had to consume food within 3 h to exercise. All participants underwent three sessions of SIT per week for 6 weeks. Each session consists of repeated bouts of 30-s Wingate Anaerobic cycle exercise. Pre- and post-training peak oxygen uptake (VO2peak), isokinetic leg strength, insulin sensitivity, blood pressure and serum lipid levels were assessed. Results. There were no differences in baseline physiological and clinical measures between both groups (all p > 0.05). VO2peak improved by 6.0 ± 8.8% in the FAS group and 5.3 ± 10.6% in the FED group (both p < 0.05), however the difference in improvement between groups was not statistically significant (p > 0.05). A similar pattern of results was seen for knee flexion maximum voluntary contraction at 300°·s-1. SIT training in either fasted or fed state had no impact on insulin sensitivity (both p > 0.05). There was significant reduction in diastolic blood pressure (8.2 ± 4.2%) and mean arterial pressure (7.0 ± 3.2%) in the FAS group (both p < 0.05) but not FED group (both p > 0.05). Conclusion. VO2peak and leg strength improved with SIT regardless of whether participants trained in the fasted or fed state. Chronic SIT in the fasted state may potentially reduce blood pressure to a greater extent than the same chronic SIT in the fed state.

Effects of a novel exercise training protocol of Wingate-based sprint bouts dispersed over a day on selected cardiometabolic health markers in sedentary females: a pilot study.

PURPOSE: Sprint interval training (SIT) provides a strong stimulus for improving cardiovascular fitness, which is among the key markers for premature mortality. Recent literature demonstrated that SIT protocols with as few as two stacked 20 s Wingate Anaerobic Test (WAnT) cycle sprints provide sufficient training stimulus for a robust increase in maximal aerobic power. However, this effect is lost when only one bout is performed. This suggests training adaptation is still dependent on the volume of SIT. Therefore, the purpose of this study was to determine the effects of three dispersed 30 s WAnT bouts, done over a day but interspersed with 4 hours of recovery time, on selected cardiometabolic health markers. METHODS: Eighteen sedentary women, age 36±8 years, were recruited and underwent 8 weeks of supervised training using the WAnT protocol, 3 days a week. Criterion measure of cardiovascular fitness (ie, V̇O2peak), skinfolds and blood lipids such as triglyceride, low density lipoprotein (LDL) and high density lipoprotein (HDL) were measured before and after training intervention. RESULTS: V̇O2peak improved by a mean of 14.0% after training (21.7±5.7 vs 24.7±5.7 mL/kg/min, p<0.01). No significant change was observed for body fat and lipid profile. CONCLUSION: Performing three dispersed WAnT bouts with a 4-hour recovery period between bouts throughout a day, 3 days per week for 8 weeks provides sufficient training stimulus for a robust increase in V̇O2peak, which is comparable with other previous SIT protocols with very short recovery intervals. However, no other changes in the other cardiometabolic health markers were detected.

Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.

Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.