A Transition State Theory for Calculating Hopping Times and Diffusion in Highly Confined Fluids

Monte Carlo simulation is used to study the dynamical crossover from single file diffusion to normal diffusion in fluids confined to narrow channels. We show that the long time diffusion coefficients for a series of systems involving hard and soft interaction potentials can be described in terms of a hopping time that measures the time it takes for a particle to escape the cage formed by its neighbors in the pore. Free energy barriers for the particle hopping process are calculated and used to show that transition state theory effectively describes the hopping time for all the systems studied, over a range of pore diameters. Our work suggests that the combination of hopping times and transition state theory offers a useful and general framework to describe the dynamics of these highly confined fluids.Comment: 6 figure

Free energy calculations of gramicidin dimer dissociation

Molecular dynamics simulations, combined with umbrella sampling, is used to study how gramicidin A (gA) dimers dissociate in the lipid bilayer. The potential of mean force and intermolecular potential energy are computed as functions of the distance between center of masses of the two gA monomers in two directions of separation: parallel to the bilayer surface and parallel to the membrane normal. Results from this study show that the dissociation of gA dimers occurs via lateral displacement of gA monomers followed by tilting of dimers with respect to the lipid bilayer normal. It is found that the dissociation energy of gA dimers in the dimyristoylphosphatidylcholine bilayer is 14 kcal mol-1 (∼22 kT), which is approximately equal to the energy of breaking six intermolecular hydrogen bonds that stabilize the gA channel dimer

An anomaly detection method for identifying locations with abnormal behavior of temperature in school buildings

Abstract Time series data collected using wireless sensors, such as temperature and humidity, can provide insight into a building’s heating, ventilation, and air conditioning (HVAC) system. Anomalies of these sensor measurements can be used to identify locations of a building that are poorly designed or maintained. Resolving the anomalies present in these locations can improve the thermal comfort of occupants, as well as improve air quality and energy efficiency levels in that space. In this study, we developed a scoring method to identify sensors that shows collective anomalies due to environmental issues. This leads to identifying problematic locations within commercial and institutional buildings. The Dynamic Time Warping (DTW) based anomaly detection method was applied to identify collective anomalies. Then, a score for each sensor was obtained by taking the weighted sum of the number of anomalies, vertical distance to an anomaly point, and dynamic time-warping distance. The weights were optimized using a well-defined simulation study and applying the grid search algorithm. Finally, using a synthetic data set and the results of a case study we could evaluate the performance of our developed scoring method. In conclusion, this newly developed scoring method successfully detects collective anomalies even with data collected over one week, compared to the machine learning models which need more data to train themselves

Time Varying Apparent Volume of Distribution and Drug Half-Lives Following Intravenous Bolus Injections.

We present a model that generalizes the apparent volume of distribution and half-life as functions of time following intravenous bolus injection. This generalized model defines a time varying apparent volume of drug distribution. The half-lives of drug remaining in the body vary in time and become longer as time elapses, eventually converging to the terminal half-life. Two example fit models were substituted into the general model: biexponential models from the least relative concentration error, and gamma variate models using adaptive regularization for least relative error of clearance. Using adult population parameters from 41 studies of the renal glomerular filtration marker 169Yb-DTPA, simulations of extracellular fluid volumes of 5, 10, 15 and 20 litres and plasma clearances of 40 and 100 ml/min were obtained. Of these models, the adaptively obtained gamma variate models had longer times to 95% of terminal volume and longer half-lives

A Gamma-Distribution Convolution Model of 99Mtc-Mibi Thyroid Time-Activity Curves

Background The convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to 99mTc-MIBI data. The GDC model is a convolution of two gamma distribution functions that simultaneously models the distribution and washout kinetics of the radiotracer., The GDC model was fitted to thyroid region of interest (ROI) TAC data from 1 min per frame 99mTc-MIBI image series for 90 min; GDC models were generated for three patients having left and right thyroid lobe and total thyroid ROIs, and were contrasted with washout-only models, i.e., less complete models. GDC model accuracy was tested using 10 Monte Carlo simulations for each clinical ROI. Results The nine clinical GDC models, obtained from least counting error of counting, exhibited corrected (for 6 parameters) fit errors ranging from 0.998% to 1.82%. The range of all thyroid mean residence times (MRTs) was 212 to 699 min, which from noise injected simulations of each case had an average coefficient of variation of 0.7% and a not statistically significant accuracy error of 0.5% (p = 0.5, 2-sample paired t test). The slowest MRT value (699 min) was from a single thyroid lobe with a tissue diagnosed parathyroid adenoma also seen on scanning as retained marker. The two total thyroid ROIs without substantial pathology had MRT values of 278 and 350 min overlapping a published 99mTc-MIBI thyroid MRT value. One combined value and four unrelated washout-only models were tested and exhibited R-squared values for MRT with the GDC, i.e., a more complete concentration model, ranging from 0.0183 to 0.9395. Conclusions The GDC models had a small enough TAC noise-image misregistration (0.8%) that they have a plausible use as simulations of thyroid activity for querying performance of other models such as washout models, for altered ROI size, noise, administered dose, and image framing rates. Indeed, of the four washout-only models tested, no single model approached the apparent accuracy of the GDC model using only 90 min of data. Ninety minutes is a long gamma-camera acquisition time for a patient, but a short a time for most kinetic models. Consequently, the results should be regarded as preliminary.PubMedWoSScopu

Concentration versus time curve for E2 and GV models for four <i>V</i>_E values at <i>CL</i> of 100 ml/min (left panel) and 40 ml/min (right panel).

<p>Concentration versus time curve for E2 and GV models for four <i>V</i>_E values at <i>CL</i> of 100 ml/min (left panel) and 40 ml/min (right panel).</p

Schematic diagram showing E2 compartmental and GV variable volume models of drug distribution.

<p>The E2 model could also be drawn as a variable volume model in which case a scale factor <i>α</i>_exp = <i>V</i>_E/<i>V</i>_d(∞) < 1 would define the physical volume at time <i>t</i> to be <i>α</i>_exp<i>V</i>_d(<i>t</i>). Similarly, for the variable volume adaptively obtained GV model, one can define <i>α</i> = <i>V</i>_E/<i>V</i>_d(∞) < 1, and an expanding physical volume <i>αV</i>_<i>d</i>(<i>t</i>). Note, both <i>α</i>_exp and <i>α</i> are constants at all times for their respective models. The term <i>V</i>_<i>SS</i> can be confusing because 1) <i>V</i>_<i>SS</i> implies that <i>V</i>_E is always a steady state volume, which is not the case as the GV model <i>αV</i>_<i>d</i>(<i>t</i>) <i>< V</i>_E is concentration depleted at late time, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e034" target="_blank">Eq (30)</a>. 2) <i>V</i>_<i>SS</i> implies that <i>V</i>_E only exists at <i>t</i> = ∞, whereas <i>V</i>_E is defined all of the time, i.e., on <i>t</i> = [0,∞) by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e010" target="_blank">8</a> & <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158798#pone.0158798.e007" target="_blank">7</a>). Finally, 3) <i>V</i>_<i>SS</i> implies an expected physical volume of distribution for sums of exponential term bolus models, and the apparent volume of distribution for a constant infusion experiment, whereas <i>V</i>_E applies to more models as the expected volume of physical distribution of a drug for both the bolus and constant infusion experiments.</p

Elymus dahuricus Turcz.

原著和名: ハマムギ科名: イネ科 = Gramineae採集地: 北海道 枝幸郡 枝幸町 北見神威岬 (北海道 北見 枝幸町 神威岬)採集日: 1989/8/26採集者: 萩庭丈壽整理番号: JH038150国立科学博物館整理番号: TNS-VS-98815

Wilcoxon tests with median parameter comparisons.

<p>Wilcoxon tests with median parameter comparisons.</p