Analysis of lipid classes by solid-phase extraction and high-performance size-exclusion chromatography

An improved method to analyze lipid classes of edible oils and fats by solid-phase extraction (SPE) and high-performance size-exclusion chromatography (HPSEC) is presented. A mixture of lipid standards was fractionated by the solid-phase extraction procedure (NH₂ phase) into polar and nonpolar fractions; these were then submitted to analysis by HPSEC. The size-exclusion chromatographic columns were three styrene/divinylbenzene columns with pore sizes of 100 Å and 50 Å. Light-scattering was used for the detection system, and the parameters of the detector were optimized to minimize the difference between the responses of the compounds studied. With this procedure it was possible to separate the following lipid classes: triacylglycerols, diacylglycerols, monoacylglycerols and free fatty acids, sterols, sterol esters, tocopherols and carotenoids. Quantitative analysis was studied for a light-scattering detector with several lipid standards of different molecular weights and unsaturation levels.     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

Optimal approximation of signal priors

In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation is the optimal estimation method. Our results imply that this is not the case. We first obtain an objective function that approximates the error occurred in signal restoration due to an imperfect prior model. Next, we show that in an important special case (small gaussian noise), the error is the same as the score-matching objective function, which was previously proposed as an alternative for likelihood based on purely computational considerations. Our analysis thus shows that score matching combines computational simplicity with statistical optimality in signal restoration, providing a viable alternative to maximum likelihood methods. We also show how the method leads to a new intuitive and geometric interpretation of structure inherent in probability distributions.     

The ortho-to-para conversion in solid hydrogen,catalyzed by hydrogen atoms

The combined techniques of ESR and NMR were used to investigate the process of ortho-para conversion in solid molecular hydrogen, containing small amounts ( 500 ppm) of hydrogen atoms as impurity. Although the impurity atoms catalyze the effective conversion of the neighboring ortho-H₂, the total catalyzed conversion rate at temperatures from 2.2 K to 4.4 K is much less than expected from the rate of the H atoms recombination. A possible explanation is given in terms of the diffusion of H atoms, which is confined to some defects in the crystal.     

A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients

We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.     

Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks

Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which ICA might significantly improve on PCA     

Computer program for automated sleep depth estimation

In this article, we present a new implementation of an amplitude-independent method for continuous-scale sleep depth estimation. Having been implemented as an add-on analysis module under commercially available biosignal recording and analysis software, it can be easily applied in clinical routine. The software gives the user full freedom to change all the analysis parameters inside theoretical limits. Computational sleep depth profiles produced by the presented software compare favourably with visual classifications. Future work will concentrate on systematic optimization of analysis parameters, further evaluation of the method with disturbed sleep and application of the method for automated adaptive sleep analysis.     

Consistency of pseudolikelihood estimation of fully visible Boltzmann machines

A Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple yet statistically consistent.     

School attendance and daily respiratory symptoms in children: influence of moisture damage

We investigated the effect of weekends and school holidays on the daily frequency and severity of respiratory and other symptoms among children attending schools with (index) or without (reference) moisture damage in Spain, the Netherlands, and Finland. Throughout 1 year, parents of 419 children with a respiratory condition attending index (n=15) or reference (n=10) primary schools completed three symptom diaries. We assessed associations between lower respiratory tract, upper respiratory tract or allergy, and other symptom scores and school day, weekend, or summer holiday using mixed regression models stratified by country and moisture damage. We evaluated interactions between moisture damage and type of day. We combined country‐specific estimates (incidence rate ratios [IRRs] and 95% confidence interval [CI]) in meta‐analyses. Symptom scores were lower during weekends and holiday. Lower respiratory tract symptoms were statistically significantly less common during holiday with strongest effect in index schools (IRR=0.7; CI=0.6–0.8). Reporting of other symptoms was more reduced during holiday in index (IRR=0.6; CI=0.4–0.9) than in reference (IRR=0.95; CI=0.8–1.2) schools (interaction P<.01). In conclusion, symptoms were less frequent and/or severe during summer holiday and weekends. This pattern was stronger among children attending moisture‐damaged schools, suggesting potential improvement in moisture damage‐related symptoms during school breaks.