Hyperkalemia: An adaptive response in chronic renal insufficiency

Hyperkalemia: An adaptive response in chronic renal insufficiency.BackgroundHyperkalemia is a common feature of chronic renal insufficiency, usually ascribed to impaired K+ homeostasis. However, various experimental observations suggest that the increase in extracellular [K+] actually functions in a homeostatic fashion, directly stimulating renal K+ excretion through an effect that is independent of, and additive to, aldosterone.MethodsWe have reviewed relevant studies in experimental animals and in human subjects that have examined the regulation of K+ excretion and its relation to plasma [K+].ResultsStudies indicate that (1) extracellular [K+] in patients with renal insufficiency correlates directly with intracellular K+ content, and (2) hyperkalemia directly promotes K+ secretion in the principal cells of the collecting duct by increasing apical and basolateral membrane conductances. The effect of hyperkalemia differs from that of aldosterone in that K+ conductances are increased as the primary event. The changes in principal cell function and structure induced by hyperkalemia are indistinguishable from the effects seen in adaptation to a high K+ diet.ConclusionsWe propose that hyperkalemia plays a pivotal role in K+ homeostasis in renal insufficiency by stimulating K+ excretion. In patients with chronic renal insufficiency, a new steady state develops in which extracellular [K+] rises to the level needed to stimulate K+ excretion so that it again matches intake. When this new steady state is achieved, plasma [K+] remains stable unless dietary intake increases, glomerular filtration rate falls, or drugs are given that disrupt the new balance

NeMO-Net The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%

Initial KAATSU Cuff Tightness: Effect of Limb Anthropometrics on Blood Flow Restriction

abstractINTRODUCTION KAATSU training involves low load (20%1RM) resistance exercise combined with partial blood flow restriction (BFR). BFR is achieved by positioning a specially designed pneumatic cuff around the proximal aspect of the limb, cinching it to an initial cuff tightness (ICT), then inflating the cuff to a higher restrictive training pressure. ICTs can potentially impact the degree of BFR (%BFR) caused at the higher training pressures, yet many studies use the same ICTs for all subjects (1). Identifying that discrepancies in %BFR exist between subjects with different limb anthropometrics is an important step in moving toward standardization of BFR dose for KAATSU training prescription. The purpose of this study was to identify variation in %BFR between subjects experiencing the same ICT and what limb anthropometrics (circumference, muscle, and fat composition) may be determinants. METHODS Forty-two volunteers (26 men, 16 women) provided informed consent. Caliper skin folds, Gulick tape circumferences, and peripheral quantitative computed tomography (pQCT) scans were performed on the randomly assigned ipsilateral arm and leg at the level of the KAATSU cuff application. %BFR was measured via pulse-wave Doppler ultrasound at baseline (no cuff) and at an ICT of 30 mmHg. Variable relationships were assessed using Pearson correlations and stepwise linear regression. RESULTS The average %BFR (avg±st. dev.) for the arm and leg was 16.01±11.42% and 16.75±9.27% with a range of 46.66% and 36.41%, respectively. The dependent variable for regression analysis was %BFR. In the arm, pQCT-determined muscle (R2=0.614) and fat composition (R2=0.587) were significant (p<0.05) determinants of %BFR. Circumference was also a determinant (R2=0.163). There were no significant correlations between %BFR and the anthropometrics for the leg. pQCT fat composition and sum of skin folds correlated significantly (r=0.915, p<0.05). pQCT circumference and Gulick circumference measures correlated significantly (r=0.991, p<0.05). DISCUSSION Conflicting BFR training results have been reported in the literature. A potential cause could be universal ICT usage causing some individuals to receive an inadequate training stimulus. Individuals using a 30 mmHg ICT will experience different %BFR when limb anthropometrics vary. Thus a method of assigning ICTs specific to individuals’ anthropometric characteristics is needed to ensure equally potent stimuli. Skin fold measures and circumference measures were highly correlated with pQCT data. As a result, skin fold and Gulick circumference measures can be used to predict arm composition at the level of the cuff and may inform prescription of appropriate ICTs that result in more consistent initial %BFR across individuals

KAATSU Cuff Tightness and Limb Anthropometry: Effect on Blood Flow Restriction

abstractKAATSU resistance training involves low loads (20%1RM) and partial blood flow restriction (BFR). When applying a BFR cuff, the initial cuff tightness (ICT) is important. ICTs can potentially impact the degree of BFR (%BFR) caused by the subsequent inflation to the target training pressures. It’s known that limb anthropometrics can affect the amount of BFR that is produced at specific pressures. Understanding the interaction between limb anthropometrics and ICT is an important first step in standardizing BFR dose between individuals for KAATSU training prescription. Purpose: To determine what limb anthropometrics (circumference, muscle or fat composition) have the greatest effect on %BFR with various ICTs. Methods: Forty-two volunteers (26 men, 16 women) provided informed consent. Caliper skin folds (anterior and posterior), Gulick tape circumferences, and peripheral quantitative computed tomography (pQCT) scans were performed on the randomly assigned ipsilateral arm and leg at the level of the KAATSU cuff. %BFR was measured via pulse-wave Doppler ultrasound at baseline (no cuff) and at 5 ICT pressures (20, 30, 40, 50 and 60mmHg). Variable relationships were assessed using Pearson correlations and stepwise linear regression. Results: The dependent variable for regression analysis was %BFR at each ICT. pQCT-determined muscle (R2= .147, .614, .445, .360, & .232, respectively) and fat composition (R2= .138, .587, .429, .338, & .220, respectively) were significant (p<.05) determinants of BFR at all ICT pressures in the arm. At 30mmHg, circumference was also a determinant (R2=.163). There were no significant correlations between %BFR and any of the ICT pressures for the leg. pQCT fat composition and sum of skin folds correlated significantly (r=.915, p<.05). pQCT circumference and Gulick circumference measures correlated significantly (r=.991, p<.05). Conclusion: Arm anthropometrics impact the %BFR created by 5 ICTs in the arm. Skin fold measures and circumference measures were highly correlated with pQCT data. As a result, skin fold and Gulick circumference measures can be used to predict arm composition at the level of the cuff and may inform prescription of appropriate ICTs that result in more consistent initial %BFR across individuals

Radio frequency component and method of making same

An electrical component and a method of constructing it are disclosed. The component includes a hollow tubular structure. The structure includes a series of axially spaced apart rings and at least one outer perimeter housing member. The housing member interconnects the rings for defining an internal configuration of the hollow tubular structure for electrical purposes. The rings and the housing member each include inter-engageable elements for helping secure mechanically the rings and housing member together to facilitate final assembly of the electrical component

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program

Thinking like a man? The cultures of science

Culture includes science and science includes culture, but conflicts between the two traditions persist, often seen as clashes between interpretation and knowledge. One way of highlighting this false polarity has been to explore the gendered symbolism of science. Feminism has contributed to science studies and the critical interrogation of knowledge, aware that practical knowledge and scientific understanding have never been synonymous. Persisting notions of an underlying unity to scientific endeavour have often impeded rather than fostered the useful application of knowledge. This has been particularly evident in the recent rise of molecular biology, with its delusory dream of the total conquest of disease. It is equally prominent in evolutionary psychology, with its renewed attempts to depict the fundamental basis of sex differences. Wars over science have continued to intensify over the last decade, even as our knowledge of the political, economic and ideological significance of science funding and research has become ever more apparent