Modification of MCM-41 by surface silylation with trimethylchlorosilane and adsorption study

Siliceous MCM-41 samples were modified by silylation using trimethylchlorosilane (TMCS). The surface coverage of functional groups was studied systematically in this work. The role of surface silanol groups during modification was evaluated using techniques of FTIR and Si-29 CP/MAS NMR. Adsorption of water and benzene on samples of various hydrophobicities was measured and compared. It was found that the maximum degree of surface attachments of trimethylsilyl (TMS) groups was about 85%, corresponding to the density of TMS groups of 1.9 per nm(2). The degree of silylation is found to linearly increase with increasing pre-outgassing temperature prior to silylation. A few types of silanol groups exist on MCM-41 surfaces, among which both free and geminal ones are responsible for active silylation. Results of water adsorption show that aluminosilicate MCM-41 materials are more or less hydrophilic, giving a type IV isotherm, similar to that of nitrogen adsorption, whereas siliceous MCM-41 are hydrophobic, exhibiting a type V adsorption isotherm. The fully silylated Si-MCM-41 samples are more hydrophobic giving a type III adsorption isotherm. Benzene adsorption on all MCM-41 samples shows type IV isotherms regardless of the surface chemistry. Capillary condensation occurs at a higher relative pressure for the silylated MCM-41 than that for the unsilylated sample, though the pore diameter was found reduced markedly by silylation. This is thought attributed to the diffusion constriction posed by the attached TMS groups. The results show that the surface chemistry plays an important role in water adsorption, whereas benzene adsorption is predominantly determined by the pore geometry of MCM-41.

Robust Mixture Modelling Using the t Distribution

Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster sets of continuous multivariate data. However, for a set of data containing a group or groups of observations with longer than normal tails or atypical observations, the use of normal components may unduly affect the fit of the mixture model. In this paper, we consider a more robust approach by modelling the data by a mixture of t distributions. The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.

Standard errors of fitted component means of normal mixtures

In this paper use consider the problem of providing standard errors of the component means in normal mixture models fitted to univariate or multivariate data by maximum likelihood via the EM algorithm. Two methods of estimation of the standard errors are considered: the standard information-based method and the computationally-intensive bootstrap method. They are compared empirically by their application to three real data sets and by a small-scale Monte Carlo experiment.

Minimum acceptable standards for digital compression of a fetal ultrasound video-clip

If the Internet could be used as a method of transmitting ultrasound images taken in the field quickly and effectively, it would bring tertiary consultation to even extremely remote centres. The aim of the study was to evaluate the maximum degree of compression of fetal ultrasound video-recordings that would not compromise signal quality. A digital fetal ultrasound videorecording of 90 s was produced, resulting in a file size of 512 MByte. The file was compressed to 2, 5 and 10 MByte. The recordings were viewed by a panel of four experienced observers who were blinded to the compression ratio used. Using a simple seven-point scoring system, the observers rated the quality of the clip on 17 items. The maximum compression ratio that was considered clinically acceptable was found to be 1:50-1:100. This produced final file sizes of 5-10 MByte, corresponding to a screen size of 320 x 240 pixels, running at 15 frames/s. This study expands the possibilities for providing tertiary perinatal services to the wider community.

Estimating and evaluating the statistics of gapped local-alignment scores

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate than existing regression-based and lookup table methods. We explore a more sophisticated way of modeling and estimating the score distributions (using a two-component mixture model and expectation maximization), but conclude that this does not improve significantly over simply ignoring scores with small E-values during estimation. Finally, we measure the classification accuracy of p-values estimated in different ways and observe that inaccurate p-values can, somewhat paradoxically, lead to higher classification accuracy. We explain this paradox and argue that statistical accuracy, not classification accuracy, should be the primary criterion in comparisons of similarity search methods that return p-values that adjust for target sequence length.

Locomotion at-1.0 degrees C: burst swimming performance of five species of Antarctic fish

We investigated the burst swimming performance of five species of Antarctic fish at -1.0degreesC. The species studied belonged to the suborder, Notothenioidei, and from the families, Nototheniidae and Bathydraconidae. Swimming performance of the fish was assessed over the initial 300 ms of a startle response using surgically attached miniature accelerometers. Escape responses in all fish consisted of a C-type fast start; consisting of an initial pronounced bending of the body into a C-shape, followed by one or more complete tail-beats and an un-powered glide. We found significant differences in the swimming performance of the five species of fish examined, with average maximum swimming velocities (U-max) ranging from 0.91 to 1.39 m s(-1) and maximum accelerations (A(max)) ranging from 10.6 to 15.6 m s(-2). The cryopelagic species, Pagothenia borchgrevinki, produced the fastest escape response, reaching a U-max and A(max) of 1.39 m s(-1) and 15.6 m s(-2), respectively. We also compared the body shapes of each fish species with their measures of maximum burst performance. The dragonfish, Gymnodraco acuticeps, from the family Bathdraconidae, did not conform to the pattern observed for the other four fish species belonging to the family Nototheniidae. However, we found a negative relationship between buoyancy of the fish species and burst swimming performance. (C) 2002 Elsevier Science Ltd. All rights reserved.

Research on the BET surface area and packing of molecules on the activated carbon

Adsorption of different aromatic compounds (two of them are electrolytes) onto an untreated activated carbon (F100) is investigated. The experimental isotherms are fitted into Langmuir homogenous and heterogeneous Model. Theoretical maximum adsorption capacities that are based on the BET surface area of the adsorbent cannot be close to the real value. The affinity and the heterogeneity of the adsorption system observed to be related to the pK(a) of the solutes. The maximum adsorption capacity (Q(max)) of activated carbon for each solute dependent on the molecular area as well as the type of functional group attached on the aromatic compound and also pH of solution. The arrangement of the molecules on the carbon surface is not face down. Furthermore, it is illustrated that the packing arrangement is most likely edge to face (sorbate-sorbent) with various tilt angles. For characterization of the carbon, the N-2 and CO2 adsorption were used. X-ray Photoelectron Spectroscopy (XPS) measurement was used to surface elemental analysis of activated carbon.

Computational properties of min/max autocorrelation factors

Minimum/maximum autocorrelation factor (MAF) is a suitable algorithm for orthogonalization of a vector random field. Orthogonalization avoids the use of multivariate geostatistics during joint stochastic modeling of geological attributes. This manuscript demonstrates in a practical way that computation of MAF is the same as discriminant analysis of the nested structures. Mathematica software is used to illustrate MAF calculations from a linear model of coregionalization (LMC) model. The limitation of two nested structures in the LMC for MAF is also discussed and linked to the effects of anisotropy and support. The analysis elucidates the matrix properties behind the approach and clarifies relationships that may be useful for model-based approaches. (C) 2003 Elsevier Science Ltd. All rights reserved.

A fuzzy max-flow min-cut theorem

Interval-valued versions of the max-flow min-cut theorem and Karp-Edmonds algorithm are developed and provide robustness estimates for flows in networks in an imprecise or uncertain environment. These results are extended to networks with fuzzy capacities and flows. (C) 2001 Elsevier Science B.V. All rights reserved.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.

Molecular size as the main determinant of solute maximum flux across the skin

One of the most important determinants of dermatological and systemic penetration after topical application is the delivery or flux of solutes into or through the skin. The maximum dose of solute able to be delivered over a given period of time and area of application is defined by its maximum flux (J(max), mol per cm(2) per h) from a given vehicle. In this work, J(max) values from aqueous solution across human skin were acquired or estimated from experimental data and correlated with solute physicochemical properties. Whereas epidermal permeability coefficients (k(p)) are optimally correlated to solute octanol-water partition coefficient (K-ow) and molecular weight (MW) was found to be the dominant determinant of J(max) for this literature data set: log J(max)=-3.90-0.0190MW (n=87, r(2)=0.847, p