Effects of Stroboscopic Vision on Depth Jump Motor Control: A Biomechanical Analysis

Researchers commonly use the \u27free-fall\u27 paradigm to investigate motor control during landing impacts, particularly in drop landings and depth jumps (DJ). While recent studies have focused on the impact of vision on landing motor control, previous research fully removed continuous visual input, limiting ecological validity. The aim of this investigation was to evaluate the effects of stroboscopic vision on depth jump (DJ) motor control. Ground reaction forces (GRF) and lower-extremity surface electromyography (EMG) were collected for 20 young adults (11 male; 9 female) performing six depth jumps (0.51 m drop height) in each of two visual conditions (full vision vs. 3 Hz stroboscopic vision). Muscle activation magnitude was estimated from EMG signals using root-mean-square amplitudes (RMS) over specific time intervals (150 ms pre-impact; 30–60 ms, 60–85 ms, and 85–120 ms post-impact). The main effects of and interactions between vision and trial number were assessed using two-way within-subjects repeated measures analyses of variance. Peak GRF was 6.4% greater, on average, for DJs performed with stroboscopic vision compared to full vision (p = 0.042). Tibialis anterior RMS EMG during the 60–85 ms post-impact time interval was 14.1% lower for DJs performed with stroboscopic vision (p = 0.020). Vastus lateralis RMS EMG during the 85–120 ms post-impact time interval was 11.8% lower for DJs performed with stroboscopic vision (p = 0.017). Stroboscopic vision altered DJ landing mechanics and lower-extremity muscle activation. The observed increase in peak GRF and reduction in RMS EMG of the tibialis anterior and vastus lateralis post-landing may signify a higher magnitude of lower-extremity musculotendinous stiffness developed pre-landing. The results indicate measurable sensorimotor disruption for DJs performed with stroboscopic vision, warranting further research and supporting the potential use of stroboscopic vision as a sensorimotor training aid in exercise and rehabilitation. Stroboscopic vision could induce beneficial adaptations in multisensory integration, applicable to restoring sensorimotor function after injury and preventing injuries in populations experiencing landing impacts at night (e.g., military personnel)

Non-adrenergic vasoconstriction and vasodilatation of guinea-pig aorta by β-phenylethylamine and amphetamine – role of nitric oxide determined with L-NAME and NO scavengers

Sympathomimetic and trace amines, including β-phenylethylamine (PEA) and amphetamine, increase blood pressure and constrict isolated blood vessels. By convention this is regarded as a sympathomimetic response, however, recent studies suggest trace amine-associated receptor (TAAR) involvement. There is also uncertainty whether these amines also release nitric oxide (NO) causing opposing vasodilatation. These questions were addressed in guineapig isolated aorta, a species not previously examined. Guinea-pig aortic rings were set up to measure contractile tension. Cumulative concentration-response curves were constructed for the reference α-adrenoceptor agonist, phenylephrine, PEA or d-amphetamine before and in the presence of vehicles, the α1-adrenoceptor antagonist, prazosin (1 μM), the nitric oxide synthase inhibitor, Nω-nitro-L-arginine (L-NAME), or NO scavengers, curcumin and astaxanthin. Prazosin inhibited phenylephrine contractions with low affinity consistent with α1L-adrenoceptors. However, PEA and amphetamine were not antagonised, indicating nonadrenergic responses probably via TAARs. L-NAME potentiated contractions to PEA both in the absence and presence of prazosin, indicating that PEA releases NO to cause underlying opposing vasodilatation, independent of α1-adrenoceptors. L-NAME also potentiated amphetamine and phenylephrine. PEA was potentiated by the NO scavenger astaxanthin but less effectively. Curcumin, an active component of turmeric, however, inhibited PEA. Trace amines therefore constrict blood vessels non-adrenergically with an underlying NO-mediated non-adrenergic vasodilatation. This has implications in the pressor actions of these amines when NO is compromised

Land Surface Emission Modeling to Support Physical Precipitation Retrievals

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization. Working Group

Nogo-B Receptor Stabilizes Niemann-Pick Type C2 Protein and Regulates Intracellular Cholesterol Trafficking

SummaryThe Nogo-B receptor (NgBR) is a recently identified receptor for the N terminus of reticulon 4B/Nogo-B. Other than its role in binding Nogo-B, little is known about the biology of NgBR. To elucidate a basic cellular role for NgBR, we performed a yeast two-hybrid screen for interacting proteins, using the C-terminal domain as bait, and identified Niemann-Pick type C2 protein (NPC2) as an NgBR-interacting protein. NPC2 protein levels are increased in the presence of NgBR, and NgBR enhances NPC2 protein stability. NgBR localizes primarily to the endoplasmic reticulum (ER) and regulates the stability of nascent NPC2. RNAi-mediated disruption of NgBR or genetic deficiency in NgBR lead to a decrease in NPC2 levels, increased intracellular cholesterol accumulation, and a loss of sterol sensing, all hallmarks of an NPC2 mutation. These data identify NgBR as an NPC2-interacting protein and provide evidence of a role for NgBR in intracellular cholesterol trafficking

What Brown saw and you can too

A discussion is given of Robert Brown's original observations of particles ejected by pollen of the plant \textit{Clarkia pulchella} undergoing what is now called Brownian motion. We consider the nature of those particles, and how he misinterpreted the Airy disc of the smallest particles to be universal organic building blocks. Relevant qualitative and quantitative investigations with a modern microscope and with a "homemade" single lens microscope similar to Brown's, are presented.Comment: 14.1 pages, 11 figures, to be published in the American Journal of Physics. This differs from the previous version only in the web site referred to in reference 3. Today, this Brownian motion web site was launched, and http://physerver.hamilton.edu/Research/Brownian/index.html, is now correc

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue

The impact of azithromycin therapy on the airway microbiota in asthma

There is interest in the use of macrolide antibiotics in asthma. Macrolides have been shown to improve airway hyperresponsiveness (AHR) and measures of airway inflammation.The degree of AHR may relate to the microbiota present in the airways, with a recent study reporting that patients with asthma with a significant improvement in AHR following treatment with clarithromycin had a higher bacterial diversity prior to treatment. To our knowledge, the impact on the asthmatic airway microbiota of an antibiotic has not been reported and we therefore set out to establish if macrolide therapy was associated with a change in airway microbiota in asthma

Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions