10,000 publications from this institution
Background Reverse cholesterol transport from peripheral tissues is considered the principal atheroprotective mechanism of high‐density lipoprotein, but quantifying reverse cholesterol transport in humans in vivo remains a challenge. We describe here a method for measuring flux of cholesterol though 3 primary components of the reverse cholesterol transport pathway in vivo in humans: tissue free cholesterol ( FC ) efflux, esterification of FC in plasma, and fecal sterol excretion of plasma‐derived FC . Methods and Results A constant infusion of [2,3‐ 13 C 2 ]‐cholesterol was administered to healthy volunteers. Three‐compartment SAAM II (Simulation, Analysis, and Modeling software; SAAM Institute, University of Washington, WA) fits were applied to plasma FC , red blood cell FC , and plasma cholesterol ester 13 C–enrichment profiles. Fecal sterol excretion of plasma‐derived FC was quantified from fractional recovery of intravenous [2,3‐ 13 C 2 ]‐cholesterol in feces over 7 days. We examined the key assumptions of the method and evaluated the optimal clinical protocol and approach to data analysis and modeling. A total of 17 subjects from 2 study sites (n=12 from first site, age 21 to 75 years, 2 women; n=5 from second site, age 18 to 70 years, 2 women) were studied. Tissue FC efflux was 3.79±0.88 mg/kg per hour (mean ± standard deviation), or ≈8 g/d. Red blood cell–derived flux into plasma FC was 3.38±1.10 mg/kg per hour. Esterification of plasma FC was ≈28% of tissue FC efflux (1.10±0.38 mg/kg per hour). Recoveries were 7% and 12% of administered [2,3‐ 13 C 2 ]‐cholesterol in fecal bile acids and neutral sterols, respectively. Conclusions Three components of systemic reverse cholesterol transport can be quantified, allowing dissection of this important function of high‐density lipoprotein in vivo. Effects of lipoproteins, genetic mutations, lifestyle changes, and drugs on these components can be assessed in humans.
In this short essay, I ask whether our current practice of highly selective conferences is helping us achieve SIGCOMM's research goals. 1 This requires first articulating what those goals are, and then evaluating our practices in relation to those goals. To no one's surprise, this essay contends that there is a significant mismatch between what I believe SIGCOMM's goals should be and what our current practices achieve. I then propose a radical restructuring of our conferences that would provide better alignment and, as an additional benefit, a stronger sense of community. However, I wrote this essay not to promote the specifics of a particular proposal, but to encourage our community to (i) engage in a thorough reexamination of how we organize SIGCOMM-sponsored conferences and (ii) seriously entertain the possibility of radical changes in our practices.
This paper compares generative AI models with template-based methods for discovering new materials, evaluating their ability to generate stable structures and target desired properties.
<strong class="journal-contentHeaderColor">Abstract.</strong> The maximum rate of Rubisco carboxylation (<span class="inline-formula"><i>V</i><sub>cmax</sub></span>) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecological research. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to <span class="inline-formula"><i>V</i><sub>cmax</sub></span>, based on plants' optimal distribution of nitrogen between light harvesting and carboxylation pathways. We also derive <span class="inline-formula"><i>V</i><sub>cmax</sub></span> from satellite (GOME-2) observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis using a data assimilation technique. These two independent global <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products agree well (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.79</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">15.46</mn><mspace width="0.125em" linebreak="nobreak"/><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="129pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="1cb08b9869c7d3facff26f397ef438e1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00001.svg" width="129pt" height="15pt" src="essd-14-4077-2022-ie00001.png"/></svg:svg></span></span>molâm<span class="inline-formula"><sup>â2</sup></span>âs<span class="inline-formula"><sup>â1</sup></span>, <span class="inline-formula"><i>P</i><i><</i>0.001</span>) and compare well with 3672 ground-based measurements (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.69</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">13.8</mn><mspace linebreak="nobreak" width="0.125em"/><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="123pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="e4b1cd6460782f3283fc52e81199344e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00002.svg" width="123pt" height="15pt" src="essd-14-4077-2022-ie00002.png"/></svg:svg></span></span>molâm<span class="inline-formula"><sup>â2</sup></span>âs<span class="inline-formula"><sup>â1</sup></span> and <span class="inline-formula"><i>P</i><i><</i>0.001</span> for SIF; <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M13" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.55</mn><mo>,</mo><mi mathvariant="normal">RMSE</mi><mo>=</mo><mn mathvariant="normal">18.28</mn><mrow class="unit"><mi mathvariant="normal">µ</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="128pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="1e7590bac7c2176af37541ae765f8518"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-14-4077-2022-ie00003.svg" width="128pt" height="15pt" src="essd-14-4077-2022-ie00003.png"/></svg:svg></span></span>molâm<span class="inline-formula"><sup>â2</sup></span>âs<span class="inline-formula"><sup>â1</sup></span> and <span class="inline-formula"><i>P</i><i><</i>0.001</span> for LCC). The LCC-derived <span class="inline-formula"><i>V</i><sub>cmax</sub></span> product is also used to constrain the retrieval of <span class="inline-formula"><i>V</i><sub>cmax</sub></span> from TROPical Ozone Mission (TROPOMI) SIF data to produce an optimized <span class="inline-formula"><i>V</i><sub>cmax</sub></span> product using both SIF and LCC information. The global distributions of these products are compatible with <span class="inline-formula"><i>V</i><sub>cmax</sub></span> computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH, and leaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products are primed to play a major role in global ecosystem research. The three remote sensing <span class="inline-formula"><i>V</i><sub>cmax</sub></span> products based on SIF, LCC, and SIF<span class="inline-formula">+</span>LCC are available at <a href="https://doi.org/10.5281/zenodo.6466968">https://doi.org/10.5281/zenodo.6466968</a> (Chen et al., 2022), and the code for implementing the ecological optimality theory is available at <span class="uri">https://github.com/SmithEcophysLab/optimal_vcmax_R</span> and <a href="https://doi.org/10.5281/zenodo.5899564">https://doi.org/10.5281/zenodo.5899564</a> (last access: 31 August 2022) (Smith et al., 2022).
We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. The proposed method uses the fact that each distinct object has a unique epipolar constraint associated with its motion. This is in contrast to using local optical flow information for segmentation. Thus motion discontinuities based on self-occlusion are distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The segmentation problem is formulated as a scene partitioning problem and a statistic-based algorithm which uses only nearest neighbor interactions and a finite number of iterations is developed. A Kalman filter based approach is used for tracking motion parameters with time. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>