Quantitative mapping of aqueous microfluidic temperature with sub-degree resolution using fluorescence lifetime imaging microscopy

The use of a water-soluble, thermo-responsive polymer as a highly sensitive fluorescence-lifetime probe of microfluidic temperature is demonstrated. The fluorescence lifetime of poly(N-isopropylacrylamide) labelled with a benzofurazan fluorophore is shown to have a steep dependence on temperature around the polymer phase transition and the photophysical origin of this response is established. The use of this unusual fluorescent probe in conjunction with fluorescence lifetime imaging microscopy (FLIM) enables the spatial variation of temperature in a microfluidic device to be mapped, on the micron scale, with a resolution of less than 0.1 degrees C. This represents an increase in temperature resolution of an order of magnitude over that achieved previously by FLIM of temperature-sensitive dyes

A critical review of some recent developments in quantitative research on gender and achievement in the United Kingdom

Over recent years the findings of a number of quantitative research studies have been published in the UK on gender and achievement. Much of this work has emanated from Stephen Gorard and his colleagues and has not only been highly critical of existing approaches to handling quantitative data but has also suggested a number of alternative and, what they claim to be, more valid ways of measuring differential patterns of achievement and underachievement between groups. This article shows how much of this work has been based upon rather under-developed measures of achievement and underachievement that tend, in turn, to generate a number of misleading findings that have questionable implications for practice. It will be argued that this body of work provides a useful case study in the problems of quantitative research that fails to engage adequately with the substantive theoretical and empirical literature and considers some of the implications of this for future research in this area.

Retinal VEGF mRNA measured by SYBR Green I fluorescence: A versatile approach to quantitative PCR

PURPOSE: To determine whether continuous monitoring of SYBR Green I fluorescence provides a reliable and flexible method of quantitative RT-PCR. Our aims were (i) to test whether SYBR Green I analysis could quantify a wide range of known VEGF template concentrations, (ii) to apply this method in an experimental model, and (iii) to determine whether 20 existing primer pairs could be used to quantify their cognate mRNAs.

Efficient Evaluation of Multidimensional Time-Varying Density Forecasts

We propose two simple evaluation methods for time varying density forecasts of continuous higher dimensional random variables. Both methods are based on the probability integral transformation for unidimensional forecasts. The first method tests multinormal densities and relies on the rotation of the coordinate system. The advantage of the second method is not only its applicability to any continuous distribution but also the evaluation of the forecast accuracy in specific regions of its domain as defined by the user’s interest. We show that the latter property is particularly useful for evaluating a multidimensional generalization of the Value at Risk. In simulations and in an empirical study, we examine the performance of both tests.

Dynamic Density Forecasts for Multivariate Asset Returns

We propose a simple and flexible framework for forecasting the joint density of asset returns. The multinormal distribution is augmented with a polynomial in (time-varying) non-central co-moments of assets. We estimate the coefficients of the polynomial via the Method of Moments for a carefully selected set of co-moments. In an extensive empirical study, we compare the proposed model with a range of other models widely used in the literature. Employing a recently proposed as well as standard techniques to evaluate multivariate forecasts, we conclude that the augmented joint density provides highly accurate forecasts of the “negative tail” of the joint distribution.

Quantitative genetics of preference and performance on chickpeas in the noctuid moth, Helicoverpa armigera

If a novel, resistant host-plant genotype arises in the environment, insect populations utilising that host must be able to overcome that resistance in order that they can maintain their ability to feed on that host. The ability to evolve resistance to host-plant defences depends upon additive genetic variation in larval performance and adult host-choice preference. To investigate the potential of a generalist herbivore to respond to a novel resistant host, we estimated the heritability of larval performance in the noctuid moth, Helicoverpa armigera, on a resistant and a susceptible variety of the chickpea, Cicer arietinum, at two different life stages. Heritability estimates were higher for neonates than for third-instar larvae, suggesting that their ability to establish on plants could be key to the evolution of resistance in this species; however, further information regarding the nature of selection in the field would be required to confirm this prediction. There was no genetic correlation between larval performance and oviposition preference, indicating that female moths do not choose the most suitable plant for their offspring. We also found significant genotype by environment interactions for neonates (but not third-instar larvae), suggesting that the larval response to different plant genotypes is stage-specific in this species.

Estimating trophic link density from quantitative but incomplete diet data

The trophic link density and the stability of food webs are thought to be related, but the nature of this relation is controversial. This article introduces a method for estimating the link density from diet tables which do not cover the complete food web and do not resolve all diet items to species level. A simple formula for the error of this estimate is derived. Link density is determined as a function of a threshold diet fraction below which diet items are ignored (

Neural network forecasts of input-output technology

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.

Quantitative analysis of proton imaging measurements of laser-induced plasmas

A method for obtaining quantitative information about electric field and charge distributions from proton imaging measurements of laser-induced plasmas is presented. A parameterised charge distribution is used as target plasma. The deflection of a proton beam by the electric field of such a plasma is simulated numerically as well as the resulting proton density, which will be obtained on a screen behind the plasma according to the proton imaging technique. The parameters of the specific charge distributions are delivered by a combination of linear regression and nonlinear fitting of the calculated proton density distribution to the measured optical density of a radiochromic film screen changed by proton exposure. It is shown that superpositions of spherical Gaussian charge distributions as target plasma are sufficient to simulate various structures in proton imaging measurements, which makes this method very flexible.