Optimization of Pathogen Capture in Flowing Fluids with Magnetic Nanoparticles

Magnetic nanoparticles have been employed to capture pathogens for many biological applications; however, optimal particle sizes have been determined empirically in specific capturing protocols. Here, a theoretical model that simulates capture of bacteria is described and used to calculate bacterial collision frequencies and magnetophoretic properties for a range of particle sizes. The model predicts that particles with a diameter of 460 nm should produce optimal separation of bacteria in buffer flowing at 1 L h⁻¹. Validating the predictive power of the model, Staphylococcus aureus is separated from buffer and blood flowing through magnetic capture devices using six different sizes of magnetic particles. Experimental magnetic separation in buffer conditions confirms that particles with a diameter closest to the predicted optimal particle size provide the most effective capture. Modeling the capturing process in plasma and blood by introducing empirical constants (c_e), which integrate the interfering effects of biological components on the binding kinetics of magnetic beads to bacteria, smaller beads with 50 nm diameters are predicted that exhibit maximum magnetic separation of bacteria from blood and experimentally validated this trend. The predictive power of the model suggests its utility for the future design of magnetic separation for diagnostic and therapeutic applications.     

Using spreadsheets in introductory EE courses

Ways in which spreadsheet programs can be used to introduce practical considerations in introductory electrical engineering courses are discussed. Examples are presented which show that spreadsheets can be used to predict and/or minimize the errors due to loading, the availability of only standard resistor values, and resistor tolerances. The circuits considered in these examples were selected to be as simple as possible. These examples could be used in the first semester of sophomore credits. They illustrate trade-offs, performance criteria, and nonunique solutions, and are intended to illustrate the power of spreadsheet programs. In the first example, a macro is used to implement a simple iterative optimization procedure. In the second example, minimization is done by exhaustive search using the table lookup capabilities of the spreadsheet. In the final example, the spreadsheet's random number generator is used to perform a Monte Carlo analysis     

Dealkylation of various 14-alkylsterols by the nematodeCaenorhabditis elegans

The metabolism of 4 dietary 24-alkylsterols was investigated in the free-living nematodeCaenorhabditis elegans. The major unesterified sterols ofC. elegans in media supplemented with either campesterol, 22-dihydrobrassicasterol or stigmasterol included cholesta-5,7-dienol, cholesterol, cholest-7-enol, and 4α-methylcholest-8(14)-enol. Dietary stigmastanol yielded cholest-7-enol, cholestanol, cholest-8(14)-enol, and 4α-methylcholest-8(14)-enol as major unesterified sterols. Esterified sterols comprised less than 22% of the total sterol. Removal of a C-24 ethyl substituent of sterols was neither hindered by the presence of a Δ²²-bond in the sterol side chain nor was it depedent on unsaturation in ring B of the steroid nucleus.C. elegans reduced a Δ²²-bond during its metabolism of stigmasterol; it did not introduce a Δ²²-bond during stigmastanol metabolism.C. elegans was capable of removing a C-24 methyl substituent regardless of its stereochemical orientation. Metabolic processes involving the steroid ring system of cholesterol (C-7 dehydrogenation, Δ⁵-bond, 4α-methylation, Δ⁸⁽¹⁴⁾-isomerization inC. elegans were not hindered by the presence of a 24-methyl group; various 24-methylsterol metabolites from campesterol were detected, mostly 24-methylcholesta-5,7-dienol. In contrast, no 24-ethylsterol metabolites from the dietary ethylsterols were found. More dietary 24-methylsterol remained unmetabolized than did dietary 24-ethylsterol. A 24α-ethyl group and a 24β-methyl group were dealkylated to a greater extent byC. elegans than was a 24α-methyl group, perhaps reflecting the substrate specificity of the dealkylation enzyme system, or suggesting different enzymes altogether.     

Kinetic Monte-Carlo simulation of network formation

Summary the simulation described in Part I was applied to random step polyaddition of a trifunctional monomer and the results were compared with exact solution for an infinite system. The gel point conversions, the weight-average degree of polymerization before (P _w) and beyond (P _w,sol) the gel point, the sol fraction and the cycle rank were used for comparison. The best way for detection of the gel point conversion is the extrapolation of the gel fraction, w _g, to w _g=0. The largest fluctuations are exhibited by P _w and P _w,sol. To get results closer to the exact ones, one can repeat several experiments with smaller number of units or increase the number of units, the former way being somewhat more economical. Typical orders of magnitude used were 10⁷ monomeric units.     

Depression in college students: Personality and experiential factors.

In this study we examined the nature of college-student depression and its relation to personality variables and to experiences unique to college life. Thirty-five depressed and 39 nondepressed students completed a depression inventory, a series of personality scales, and a survey developed to investigate the impact of potentially distressing components of college life. Results revealed that college-student depression, though mild in intensity, represents a serious problem: Three quarters of depressed students had been depressed for more than 3 months, and half had contemplated suicide. Both personality characteristics and experiential variables were found to be significantly related to depression, together accounting for approximately half the variance. We examined individual personality traits and experiential variables that were found to differ between depressed and nondepressed subjects, and we discuss the implications of the findings for counseling and for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Low strain rate plasticity of continuously cast carbon steels

By a special plastometric method, the straightening operation of a vertically cast strand of two carbon steels was simulated. The temperature dependence of the low strain rate plasticity of both the steels examined is analogous although caused by very different reasons. As to the plain carbon steel, the amount of ferrite and its location is of prime importance, whereas in the case of eutectoid steel various modes of fracture and the influence of temperature on the relative plasticity of type II MnS inclusions are influencial. To avoid transverse cracking, it seems to be suitable to keep the strand at a rather high temperature (above 900 °C). Temperature cycling seems to be fundamental as to the structural changes taking place in the strand (grain size refining, ferrite fraction if any), whereas analogous influences of straining are more or less inexpressive. Of course, the results obtained are of quality only since the practical and laboratory conditions differ significantly. The knowledge obtained could be applied in regulating the intensity of strand cooling with the aim of lowering the waste portion and improving the surface quality of worked products.     

Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.