Lateral phase separations in binary mixtures of phospholipids having different charges and different crystalline structures

Synthetic dipalmitoyl phosphatidylserine exhibits a sharp chain-melting transition temperature at 51 degrees C as judged by partitioning of the spin label 2,2,6,6-tetramethylpiperidine-1-oxyl. Phase diagrams representing lateral phase separations in binary mixtures of dipalmitoyl phosphatidylserine with dipalmitoyl phosphatidylcholine as well as with dimyristoyl phosphatidylcholine are derived from paramagnetic resonance determinations of 2,2,6,6,-tetramethylpiperidine-1-oxyl partitioning, freeze-fracture electron microscopic studies and theoretical arguments that limit the general form of acceptable phase diagrams. The reported phase diagrams are the first to describe binary mixtures in which one lipid is charged and the second lipid uncharged. These phase diagrams also are the first to include the problem of solid phases with different crystalline conformations as it relates to the occurrence of a pretransition in phosphatidylcholines and its absence in phosphatidylserines. In addition to the phase diagrams reported here for these two binary mixtures, a brief theoretical discussion is given of other possible phase diagrams that may be appropriate to other lipid mixtures with particular consideration given to the problem of crystalline phases of different structures and the possible occurrence of second-order phase transitions in these mixtures.     

Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.     

Food choice causes interrupted feeding in the generalist grasshopper Schistocerca americana: further evidence for inefficient decision-making

Individual grasshoppers were observed foraging on either single food plant species or binary mixtures of them. Individuals ate slightly less on the mixtures, but grew faster on them. In the mixtures, individuals fed regularly from both plant species, typically eating both within a single meal. Feeding bouts were longer on the preferred plant species in the mixture. The pattern of foraging behavior was markedly different in mixtures and single food treatments. In mixtures, although the total length of time engaged in ingestion over the day was slightly less than in single food treatments, there were relatively more short feeding bouts and more gaps between feeding bouts within meals, so that the time spent in activities associated with the food was markedly longer on mixtures. This is discussed in terms of efficiency and the problem of choice that faces individual generalists. The efficiency factor and its impact on vigilance is added to the other costs of being a polyphagous herbivore.     

Pollen allergies in czechoslovakia

The article discusses the possible utilisation of the palynological and geobotanical observations in immunotherapy of children with pollinosis in Czechoslovakia. Three approaches to this problem exist: 1. Use of the individual pollen mixtures (prepared by the Institute of Sera and Vaccines (USOL), Prague), according to the clinical history and the results of diagnostic tests of each patient. 2. Use of the available standard pollen mixtures. 3. Improvement of the specificity of these mixtures by eliminating the redundant pollen types (i.e. pollen of the species not spread in the area). Intentions for the future, including the establishment of a pollen forecasting service, are mentioned.     

Aliphatic chain length by isotropic mixing (ALCHIM): determining composition of complex lipid samples by 1H NMR spectroscopy

Quantifying the amounts and types of lipids present in mixtures is important in fields as diverse as medicine, food science, and biochemistry. Nuclear magnetic resonance (NMR) spectroscopy can quantify the total amounts of saturated and unsaturated fatty acids in mixtures, but identifying the length of saturated fatty acid or the position of unsaturation by NMR is a daunting challenge. We have developed an NMR technique, aliphatic chain length by isotropic mixing, to address this problem. Using a selective total correlation spectroscopy technique to excite and transfer magnetization from a resolved resonance, we demonstrate that the time dependence of this transfer to another resolved site depends linearly on the number of aliphatic carbons separating the two sites. This technique is applied to complex natural mixtures allowing the identification and quantification of the constituent fatty acids. The method has been applied to whole adipocytes demonstrating that it will be of great use in studies of whole tissues.     

Reconstruction of molecular network evolution from cross‐sectional omics data

Cross‐sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene–gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage‐wise mixtures of Gaussian graphical models (GGMs) from high‐dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross‐validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down‐stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene–gene interaction network changes in the transition from normal to cancer prostate tissue.     

Independent component analysis reveals new and biologically significant structures in micro array data

Background  

An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes.     

A semi-parametric hybrid neural model for nonlinear blind signal separation

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.     

Stochastic ICA contrast maximisation using OJA's nonlinear PCA algorithm

Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.     

A possible approach to the analysis of protease mixtures

A problem of quantitative assay of proteases in complex mixtures is solved by using a set of peptide substrates with detectable (chromogenic or fluorogenic) groups (DGs). Quantitation of separate DGs released in reaction of enzyme mixture with a set of substrates is carried out in chromatographic analysis of reaction products. Reaction of peptide derivatives of aminonaphthalene sulfamides with a mixture of thrombin and proteases from viper venom shows the amounts of produced DGs to be proportional to the amounts of both thrombin and venom proteases, confirming the validity of proposed approach. There are cases of mutual influence of some components in proteases mixtures as illustrated by inhibition of trypsin activity in presence of viper venom; one determines enzyme activities in this specific mixture rather than their amounts.     

Probits of mixtures

The tolerances of individuals (insects, parasites) in a population have a frequency or probability distribution called a tolerance distribution. Many tolerance distributions in bioassay studies can be the result of a rather heterogeneous population of individuals and can often be modelled as a mixture of a number of standard unimodal distributions. A probit analysis can be generalized to the case where the tolerance distribution is a mixture of location and scale parameter distributions. In this article, the existence and determination of the maximum likelihood estimates are investigated. An expectation-maximization (EM) algorithm for probits of mixtures is developed and it is shown that by application of the EM algorithm, the problem of probits of mixtures can be separated into a series of probits of individual component tolerance distributions.     

Analyzing gene expression time-courses

Measuring gene expression over time can provide important insights into basic cellular processes. Identifying groups of genes with similar expression time-courses is a crucial first step in the analysis. As biologically relevant groups frequently overlap, due to genes having several distinct roles in those cellular processes, this is a difficult problem for classical clustering methods. We use a mixture model to circumvent this principal problem, with hidden Markov models (HMMs) as effective and flexible components. We show that the ensuing estimation problem can be addressed with additional labeled data partially supervised learning of mixtures - through a modification of the expectation-maximization (EM) algorithm. Good starting points for the mixture estimation are obtained through a modification to Bayesian model merging, which allows us to learn a collection of initial HMMs. We infer groups from mixtures with a simple information-theoretic decoding heuristic, which quantifies the level of ambiguity in group assignment. The effectiveness is shown with high-quality annotation data. As the HMMs we propose capture asynchronous behavior by design, the groups we find are also asynchronous. Synchronous subgroups are obtained from a novel algorithm based on Viterbi paths. We show the suitability of our HMM mixture approach on biological and simulated data and through the favorable comparison with previous approaches. A software implementing the method is freely available under the GPL from http://ghmm.org/gql.     

Genotoxicological characterisation of complex mixtures. Genotoxic effects of a complex mixture of perhalogenated hydrocarbons

Toxicological and genotoxicological investigation of complex mixtures is one of the main focus of the recent research in toxicology. Testing complex mixtures present a formidable scientific problem since most recently available toxicological data has been obtained from single substance studies and is not simply transferable to mixtures of chemicals. Although there are no special strategies and standard protocols available for determining toxic and genotoxic effects of complex mixtures, the fundamental concepts of evaluation are the same as those for single substances.The focus of interest of the submitted paper is the genotoxicological characterisation of a complex mixture of mostly perhalogenated hydrocarbons which is generated as a waste product from the plasma etching process in the semiconductor industry. By use of several in vitro test systems (comet assay and micronucleus test), the clastogenic potency of the mixture was tested in various human cell types (lymphocytes and normal bronchial epithelial cells) and in rat hepatocytes. Results demonstrated that the complex perhalogenated hydrocarbons mixture causes DNA single-strand breaks and micronuclei formation, and direct concentration-to-effect correlations were proved in all experiments. The presence of an external metabolising system (S9 mix from rat hepatocytes) in human cell culture systems did not cause any change of the observed effects when compared to experiments performed in the absence of the S9 mix. Therefore, we conclude that the mixture acts as direct genotoxicant and that there is no detoxification by the external enzyme system.Further, convincing and reproducible results of the in vitro comet assay and the micronucleus assay in primary human cell cultures indicated these tests may be utilized for the genotoxicological analyses of complex mixtures with concern to human health hazard.     

Contribution of hydrolyzed nucleic acids and their constituents to the apparent amino acid composition of biological compounds.

Acid hydrolysis of protein-free mixtures of nucleotides, nucleosides, and nucleic acids yields amino acids, free bases, and possibly other unidentified fragments when analyzed by thin-layer chromatography and by standard amino acid analysis. Glycine is the predominant amino acid detected, which may constitute 47–97% of the apparent amino acid composition, depending on the type of material subjected to hydrolysis. Obviously, hydrolyzed nucleic acids or their constituents can therefore contribute to the apparent amino acid composition of a supposedly pure peptide or of other more complex mixtures of compounds mistakenly believed to contain only protein. To circumvent this problem, we suggest that nucleotides or nucleic acid moieties should be removed from any product for which the amino acid composition is desired, and that whenever a large glycine peak is noted in a hydrolyzed sample, the presence of nucleic acids or their constituents should be suspected.     

Effect of temperature and some common metals on the stability of volatile anaesthetic–Entonox mixtures

Thermal stability of pressurised ready-to-use volatile liquid anaesthetic mixtures (halothane, isoflurane and enflurane) in Entonox (commercially available premixed 50% N₂O, 50% O₂ mixture) were investigated at temperatures of 20, 258, 400, 503 and 602°C on glass, stainless steel, copper and aluminium by gas chromatography and GC–MS. It was found that most of the decomposition products formed were halogenated compounds and the observed thermal stabilities in glass, stainless steel and copper allowed a thermal treatment up to 250°C without any decomposition problem. Aluminium was found to be the most effective metal at causing decomposition of the anaesthetic mixtures even at lower temperatures.     

Multiple phase equilibria in binary mixtures of phospholipids

Approximate phase diagrams describing lateral phase separations are given for binary mixtures of dimyristoyl phosphatidylcholine with dipalmitoyl phosphatidylcholine, distearoyl phosphatidylcholine, and dipalmitoyl phosphatidylethanolamine. These diagrams are based in part on freeze-fracture electron microscopic data presented here, as well as other earlier spin-label, calorimetric, and X-ray data. These phase diagrams represent an improvement over previous studies in that both solid phases (P_β' and L_β') of the phosphatidylcholines are included. Further consideration is given to the problem of binary mixtures in which there are two P_β' phases that do not form a continuous range of solid solutions.     

Utilization of Recycled Asphalt Concrete with Warm Mix Asphalt and Cost-Benefit Analysis

The asphalt paving industries are faced with two major problems. These two important challenges are generated with an increase in demand for environmentally friendly paving mixtures and the problem of rapidly rising raw materials. Recycling of reclaimed asphalt pavement (RAP) is a critical necessity to save precious aggregates and reduce the use of costly bitumen. Warm Mix Asphalt (WMA) technology provides not only the option of recycling asphalt pavement at a lower temperature than the temperature maintained in hot mixtures but also encourages the utilization of RAP and therefore saves energy and money. This paper describes the feasibility of utilizing three different WMA additives (organic, chemical and water containing) at recommended contents with different percentages of RAP. The mechanical properties and cost-benefit analysis of WMA containing RAP have been performed and compared with WMA without RAP. The results indicated that, 30%, 10% and 20% can be accepted as an optimum RAP addition related to organic, chemical and water containing additives respectively and organic additive with 30% RAP content has an appreciable increase in tensile strength over the control mix. It was also concluded that the RAP with WMA technology is the ability to reduce final cost compared to HMA and WMA mixtures.     

A simple, continuous-flow ultrafiltration apparatus

A continuous-flow, inverted ultrafiltration apparatus has been made, which permits protein solutions to be concentrated rapidly and has allowed the fractionation of some binary model mixtures. A major problem, which prevents such fractionations in conventional apparatus, has been identified. The design avoids shearing and overheating of labile solutions and appears to be suitable for both micro- and macro-scale applications.     

Estimators of tissue proportions from X-ray CT images

Estimators are derived of tissue proportions from X-ray computed tomography (CT) images. These take into account that many pixels in such images are responses to mixtures of tissue types. The problem is motivated by an application involving estimation of sheep tissue weights. The standard estimator, a count of the number of pixels in a particular range of values, is compared with the maximum likelihood fit of a mixed-pixel distribution and a moment-based estimator. Both simulations and the application show the moment estimator to be best.