Effects of Cycling Intensity on Acute Signaling Adaptations to 8-weeks Concurrent Training in Trained Cyclists

© 2022 Jones, Eddens, Kupusarevic, Simoes, Furber, Van Someren and Howatson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/This study examined whether the intensity of endurance stimuli modifies the adaptation in strength and endurance following concurrent training and whether the acute molecular response to concurrent exercise is affected by training status. Using a parallel group design, trained cyclists were randomized to either resistance exercise followed by moderate intensity continuous training (RES + MICT, n = 6), or resistance exercise followed by work matched high intensity interval training (RES + HIIT, n = 7), across an 8 weeks training programme. A single RES + MICT or RES + HIIT exercise stimulus was completed 1 week before and within 5 days of completing the training programme, to assess phosphorylation of protein kinases of the mTOR and AMPK signaling pathways. There were no main effects of time or group on the phosphorylation of protein kinases in response to concurrent exercise stimulus pre- and post-training intervention (p > 0.05). Main effects of time were observed for all maximal strength exercises; back-squat, split-squat, and calf-raise (p 0.05). Whilst preliminary data due to limited sample size the intensity of endurance activity had no effect on performance outcomes, following concurrent training. Further, the acute molecular response to a concurrent exercise stimulus was comparable before and after the training intervention, suggesting that training status had no effect on the molecular responses assessed.Peer reviewedFinal Published versio

Aerobic exercise intensity does not affect the anabolic signaling following resistance exercise in endurance athletes

Abstract: This study examined whether intensity of endurance stimulus within a concurrent training paradigm influenced the phosphorylation of signaling proteins associated with the mTOR and AMPK networks. Eight male cyclists completed (1) resistance exercise (RES), 6 × 8 squats at 80% 1-RM; (2) resistance exercise and moderate intensity cycling of 40 min at 65% V̇O2peak, (RES + MIC); (3) resistance exercise and high intensity interval cycling of 40 min with 6 alternating 3 min intervals of 85 and 45% V̇O2peak (RES + HIIC), in a cross-over design. Muscle biopsies were collected at rest and 3 h post-RES. There was a main effect of condition for mTORS2448 (p = 0.043), with a greater response in the RES + MIC relative to RES condition (p = 0.033). There was a main effect of condition for AMPKα2T172 (p = 0.041), with a greater response in RES + MIC, relative to both RES + HIIC (p = 0.026) and RES (p = 0.046). There were no other condition effects for the remaining protein kinases assessed (p > 0.05). These data do not support a molecular interference effect in cyclists under controlled conditions. There was no intensity-dependent regulation of AMPK, nor differential activation of anabolism with the manipulation of endurance exercise intensity.Peer reviewe

Implementing Rules with Aritificial Neurons

Rule based systems are an important class of computer languages. The brain, and more recently neuromorphic systems, is based on neurons. This paper describes a mechanism that converts a rule based system, specified by a user, to spiking neurons. The system can then be run in simulated neurons, producing the same output. The conversion is done making use of binary cell assemblies, and finite state automata. The binary cell assemblies, eventually implemented in neurons, implement the states. The rules are converted to a dictionary of facts, and simple finite state automata. This is then cached out to neurons. The neurons can be simulated on standard simulators, like NEST, or on neuromorphic hardware. Parallelism is a benefit of neural system, and rule based systems can take advantage of this parallelism. It is hoped that this work will support further exploration of parallel neural and rule based systems, and su

Private trade and monopoly structures : the East India Companies and the commodity trade to Europe in the eighteenth century

Our research is about the trade in material goods from Asia to Europe over this period, and its impact on Europe’s consumer and industrial cultures. It entails a comparative study of Europe’s East India Companies and the private trade from Asia over the period. The commodities trade was heavily dependent on private trade. The historiography to date has left a blind spot in this area, concentrating instead on corruption and malfeasance. Taking a global history approach we investigate the trade in specific consumer goods in many qualities and varieties that linked merchant communities and stimulated information flows. We set out how private trade functioned alongside and in connection with the various European East India companies; we investigate how this changed over time, how it drew on the Company infrastructure, and how it took the risks and developed new and niche markets for specific Asian commodities that the Companies could not sustain

From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation

Background: The KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cost-utility analyses, the KIDSCREEN-10 is not currently suitable for application in this context. This paper provides an algorithm for mapping the KIDSCREEN-10 index onto the CHU9D utility scores. Methods: A sample of 590 Australian adolescents (aged 11–17) completed both the KIDSCREEN-10 and the CHU9D. Several econometric models were estimated, including ordinary least squares estimator, censored least absolute deviations estimator, robust MM-estimator and generalised linear model, using a range of explanatory variables with KIDSCREEN-10 items scores as key predictors. The predictive performance of each model was judged using mean absolute error (MAE) and root mean squared error (RMSE). Results: The MM-estimator with stepwise-selected KIDSCREEN-10 items scores as explanatory variables had the best predictive accuracy using MAE, whilst the equivalent ordinary least squares model had the best predictive accuracy using RMSE. Conclusions: The preferred mapping algorithm (i.e. the MM-estimate with stepwise selected KIDSCREEN-10 item scores as the predictors) can be used to predict CHU9D utility from KIDSCREEN-10 index with a high degree of accuracy. The algorithm may be usefully applied within cost-utility analyses to generate cost per quality adjusted life year estimates where KIDSCREEN-10 data only are available

Democratic population decisions result in robust policy-gradient learning: A parametric study with GPU simulations

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. © 2011 Richmond et al

Single high-dose erythropoietin administration immediately after reperfusion in patients with ST-segment elevation myocardial infarction: results of the Erythropoietin in Myocardial Infarction Trial

Background Preclinical studies and pilot clinical trials have shown that high-dose erythropoietin (EPO) reduces infarct size in acute myocardial infarction. We investigated whether a single high-dose of EPO administered immediately after reperfusion in patients with ST-segment elevation myocardial infarction (STEMI) would limit infarct size. Methods A total of 110 patients undergoing successful primary coronary intervention for a first STEMI was randomized to receive standard care either alone (n = 57) or combined with intravenous administration of 1,000 U/kg of epoetin β immediately after reperfusion (n = 53). The primary end point was infarct size assessed by gadolinium-enhanced cardiac magnetic resonance after 3 months. Secondary end points included left ventricular (LV) volume and function at 5-day and 3-month follow-up, incidence of microvascular obstruction (MVO), and safety. Results Erythropoietin significantly decreased the incidence of MVO (43.4% vs 65.3% in the control group, P = .03) and reduced LV volume, mass, and function impairment at 5-day follow-up (all P < .05). After 3 months, median infarct size (interquartile range) was 17.5 g (7.6-26.1 g) in the EPO group and 16.0 g (9.4-28.2 g) in the control group (P = .64); LV mass, volume, and function were not significantly different between the 2 groups. The same number of major adverse cardiac events occurred in both groups. Conclusions Single high-dose EPO administered immediately after successful reperfusion in patients with STEMI did not reduce infarct size at 3-month follow-up. However, this regimen decreased the incidence of MVO and was associated with transient favorable effects on LV volume and function

Brain Complexity: Analysis, Models and Limits of Understanding

Abstract. Manifold initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms. This paper reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis. Projects investigating the nature of computations by cortical columns are discussed which exemplify the application of this standard method. New findings are mentioned indicating that the concept of the basic uniformity of the cortex is untenable. The claim is discussed that non-decomposability is not an intrinsic property of complex, integrated systems but is only in our eyes, due to insufficient mathematical techniques. Using Rosen’s modeling relation, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e., cognitive and other complex systems are decomposable, must be abandoned.

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

Impact of renal impairment on atrial fibrillation: ESC-EHRA EORP-AF Long-Term General Registry

Background: Atrial fibrillation (AF) and renal impairment share a bidirectional relationship with important pathophysiological interactions. We evaluated the impact of renal impairment in a contemporary cohort of patients with AF. Methods: We utilised the ESC-EHRA EORP-AF Long-Term General Registry. Outcomes were analysed according to renal function by CKD-EPI equation. The primary endpoint was a composite of thromboembolism, major bleeding, acute coronary syndrome and all-cause death. Secondary endpoints were each of these separately including ischaemic stroke, haemorrhagic event, intracranial haemorrhage, cardiovascular death and hospital admission. Results: A total of 9306 patients were included. The distribution of patients with no, mild, moderate and severe renal impairment at baseline were 16.9%, 49.3%, 30% and 3.8%, respectively. AF patients with impaired renal function were older, more likely to be females, had worse cardiac imaging parameters and multiple comorbidities. Among patients with an indication for anticoagulation, prescription of these agents was reduced in those with severe renal impairment, p <.001. Over 24 months, impaired renal function was associated with significantly greater incidence of the primary composite outcome and all secondary outcomes. Multivariable Cox regression analysis demonstrated an inverse relationship between eGFR and the primary outcome (HR 1.07 [95% CI, 1.01–1.14] per 10 ml/min/1.73 m2 decrease), that was most notable in patients with eGFR <30 ml/min/1.73 m2 (HR 2.21 [95% CI, 1.23–3.99] compared to eGFR ≥90 ml/min/1.73 m2). Conclusion: A significant proportion of patients with AF suffer from concomitant renal impairment which impacts their overall management. Furthermore, renal impairment is an independent predictor of major adverse events including thromboembolism, major bleeding, acute coronary syndrome and all-cause death in patients with AF