Maximal accelerations for twelve weeks elicit improvement in a single out of a collection of cycling performance indicators in trained cyclists

INTRODUCTION: Cycling is a time-consuming sport. Cyclists, as many other athletes, therefore, focus on training effectively. The hypothesis was tested that twelve weeks of supplementary maximal acceleration training caused more favourable changes in cycling performance indicators as compared to changes measured in comparable control cyclists. METHODS: Trained cyclists (n = 24) participated. A control group and a group performing maximal acceleration training, as a supplement to their usual training, were formed. The maximal acceleration training consisted of series of ten repetitions of outdoor brief maximal accelerations, which were initiated from low speed and performed in a large gear ratio. The cyclists in the control group performed their usual training. Performance indicators, in form of peak power output in a 7-s maximal isokinetic sprint test, maximal aerobic power output in a graded test, and submaximal power output at a predetermined blood lactate concentration of 2.5 mmol L(−1) in a graded test were measured before and after the intervention. RESULTS: Peak power output in the sprint test was increased (4.1% from before to after the intervention) to a larger extent (p = 0.045) in the cyclists who had performed the maximal acceleration training than in the control cyclists (−2.8%). Changes in maximal aerobic power output and in submaximal power output at a blood lactate concentration of 2.5 mmol L(−1) were not significantly different between the groups (p > 0.351). DISCUSSION: The results indicated that the applied supplementary maximal acceleration training caused modest favourable changes of performance indicators, as compared to the changes measured in a group of comparable control cyclists

Combining real-time fluorescence spectroscopy and flow cytometry to reveal new insights in DOC and cell characterization of drinking water

Sudden changes in drinking water quality can cause harmful consequences for end users. Thus, real-time monitoring of drinking water quality can allow early warning and provide crucial gains for securing safe water distribution. This study investigated the advantages of simultaneous real-time measuring of flow cytometry and fluorescence spectroscopy. A contamination event was investigated in a laboratory-scale analysis by spiking drinking water samples with organic nutrients. Flow cytometric data were analyzed by creating fingerprints based on differentiation into high and low nucleic acid cells (HNA/LNA). The detailed characterization of these data showed that an increase in HNA cells indicated an increase in the bacterial growth potential even before actual TCC increases. The fluorescence data was decomposed via the PARAFAC method to reveal seven fluorescent components. Three aromatic protein-like components were associated with the microbiological condition of the drinking water cells; namely, Components 4 (λEx = 279 nm, λEm = 351 nm), 6 (λEx = 279 nm, λEm = 332 nm), and 7 (λEx = 276 nm, λEm = 302 nm). Component 6 was identified as a possible organic variable for appropriate monitoring of TCC, whereas Components 4 and 7 were identified as organic compounds representing nutrients for organisms present in drinking water. Overall, combining both methods for real-time monitoring can be a powerful tool to guarantee drinking water quality

Learning optimal environments using projected stochastic gradient ascent

In this work, we propose a new methodology for jointly sizing a dynamical system and designing its control law. First, the problem is formalized by considering parametrized reinforcement learning environments and parametrized policies. The objective of the optimization problem is to jointly find a control policy and an environment over the joint hypothesis space of parameters such that the sum of rewards gathered by the policy in this environment is maximal. The optimization problem is then addressed by generalizing the direct policy search algorithms to an algorithm we call Direct Environment Search with (projected stochastic) Gradient Ascent (DESGA). We illustrate the performance of DESGA on two benchmarks. First, we consider a parametrized space of Mass-Spring-Damper (MSD) environments and control policies. Then, we use our algorithm for optimizing the size of the components and the operation of a small-scale autonomous energy system, i.e. a solar off-grid microgrid, composed of photovoltaic panels, batteries, etc. On both benchmarks, we compare the results of the execution of DESGA with a theoretical upper-bound on the expected return. Furthermore, the performance of DESGA is compared to an alternative algorithm. The latter performs a grid discretization of the environment's hypothesis space and applies the REINFORCE algorithm to identify pairs of environments and policies resulting in a high expected return. The choice of this algorithm is also discussed and motivated. On both benchmarks, we show that DESGA and the alternative algorithm result in a set of parameters for which the expected return is nearly equal to its theoretical upper-bound. Nevertheless, the execution of DESGA is much less computationally costly

Verapamil impairs secretion of stimulated atrial natriuretic factor in humans

AbstractThe adaptation of the secretory rate of atrial natriuretic factor to repeated adequate stimuli and the influence of the calcium antagonist verapamil on the release of atrial natriuretic factor were investigated in 16 patients. In eight patients (Group 1) right atrial pressure was abruptly increased by rapid right ventricular pacing for 4 min (stimulation I). After a 15 min interval, the identical stimulation was repeated (stimulation II). Eight patients (Group 2) underwent the same protocol but received 5 mg of verapamil intravenously after stimulation I.Pacing increased right atrial pressure in both groups identically by 70%. In Group 1, release of atrial natriuretic factor caused by the second stimulation (median 290 pg/ml over basal) was significantly (2.5-fold) larger than atrial natriuretic factor release induced by the first stimulation (median 116 pg/ml over basal). In the verapamil-treated patients (Group 2), the effect of right atrial pressure increase on release of atrial natriuretic factor was abolished after stimulation II. In both groups, changes in plasma concentrations of cyclic guanosine monophosphate corresponded to changes in atrial natriuretic factor concentrations.Thus, the myoendocrine cells are apparently capable of a fast upward regulation of their response to repeated secretory stimuli. Verapamil appears to block the stimulatory effect of a sudden increase in right atrial pressure upon release of atrial natriuretic factor

Identifizierung von Kandidatengenvarianten der Eutergesundheit beim Milchrind

In der Arbeit wurde ein bioinformatischer Ansatz zur Priorisierung von Kandidatengenen eines Merkmales entwickelt und praktisch angewendet. Durch hypothesenbasierte Auswertung von Referenzliteratur und Integration spezifischer Transkriptomdaten gelang innerhalb eines QTLs für Eutergesundheit beim Deutschen Holsteinrind die Priorisierung von vier Kandidatengenen. DNA-Sequenzpolymorphismen (SNPs) dieser Gene wurden identifiziert. Experimentelle Analysen erbrachten Indizien für die Beteiligung von SNPs im Gen CD79A an der Ausprägung differentieller Resistenz gegen Euterinfektionen beim Milchrind

Comparative computational analysis of pluripotency in human and mouse stem cells

Pluripotent cells can be subdivided into two distinct states, the naïve and the primed state, the latter being further advanced on the path of differentiation. There are substantial differences in the regulation of pluripotency between human and mouse, and in humans only stem cells that resemble the primed state in mouse are readily available. Reprogramming of human stem cells into a more naïve-like state is an important research focus. Here, we developed a pipeline to reanalyze transcriptomics data sets that describe both states, naïve and primed pluripotency, in human and mouse. The pipeline consists of identifying regulated start-ups/shut-downs in terms of molecular interactions, followed by functional annotation of the genes involved and aggregation of results across conditions, yielding sets of mechanisms that are consistently regulated in transitions towards similar states of pluripotency. Our results suggest that one published protocol for naïve human cells gave rise to human cells that indeed share putative mechanisms with the prototypical naïve mouse pluripotent cells, such as DNA damage response and histone acetylation. However, cellular response and differentiation-related mechanisms are similar between the naïve human state and the primed mouse state, so the naïve human state did not fully reflect the naïve mouse state

Collective flow in heavy ion reactions and the properties of excited nuclear matter

Quantum Molecular Dynamics (QMD) calculations of central collisions between heavy nuclei are used to study fragment production and the creation of collective flow. It is shown that the final phase space distributions are compatible with the expectations from a thermally equilibrated source, which in addition exhibits a collective transverse expansion. However, the microscopic analyses of the transient states in the intermediate reaction stages show that the event shapes are more complex and that equilibrium is reached only in very special cases but not in event samples which cover a wide range of impact parameters as it is the case in experiments. The basic features of a new molecular dynamics model (UQMD) for heavy ion collisions from the Fermi energy regime up to the highest presently available energies are outlined