Temporal trends of synthetic musk compounds in mother's milk and associations with personal use of perfumed products

We analyzed two nitro musks (musk xylene and musk ketone) and five polycyclic musks (HHCB, AHTN, ADBI, ATII, and AHDI) in mother's milk from primiparae women (N = 101) living in Uppsala County, Sweden, 1996-2003. Possible temporal trends in musk concentrations and associations with lifestyle/medical factors, such as use of perfumed products during pregnancy were studied. HHCB showed the highest median concentration (63.9 ng/g lipid) followed by AHTN (10.4 ng/g) and musk xylene (MX) (9.5 ng/g). Concentrations of the other substances were, in most cases, below the quantification limit (2.0-3.0 ng/g). Women with a high use of perfume during pregnancy had elevated milk concentrations of HHCB, and elevated concentrations of AHTN were observed among women reporting use of perfumed laundry detergent. This strongly suggests that perfumed products are important sources of musk exposure both among the mothers and the nursed infants. Concentrations of AHTN and MX declined significantly between 1996 and 2003, suggesting a decline in the industrial use of the compounds in consumer products, or alterations in the consumer use pattern of perfumed products. No temporal trend in HHCB concentrations was seen. The lack of toxicity data makes it difficult to generalize about the safety of musk exposure of breast-fed infants.     

Motion synthesis and editing for the generation of new sign language content

Machine Translation - Existing work on the animation of signing avatars often relies on pure procedural techniques or on the playback of Motion Capture (MoCap) data. While the first solution...     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Membrane automata with priorities

In this paper the one-way P automata with priorities are introduced. Such automata are P systems where the membranes are only allowed to consume objects from parent membranes, under the given conditions. The result of computation of these systems is the set of multiset sequences consumed by skin membrane into the system. The rules associated in some order with each membrane cannot modify any objects, they can only move them through membrane. We show that P automata with priorities and two membranes can accept every recursively enumerated language.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

Imaging of mouse experimental melanoma in vivo and ex vivo by combination of confocal and nonlinear microscopy

We investigated possibilities of the combination of the one- and two-photon excitation microscopy for examination of the experimental melanoma tissue in vivo, in mice under general anesthesia, and ex vivo on freshly harvested specimens. Our aim was to obtain sufficiently informative images of unstained tumor tissues and their modifications after hyperthermia treatment. The mouse experimental melanoma structure was studied and compared with normal tissue from the same animal by using confocal and nonlinear microscopy techniques based on (i) one-photon excitation (1PE) fluorescence, (ii) 1PE reflectance, (iii) second harmonic generation imaging, and (iv) two-photon excitation autofluorescence. We checked different spectral conditions and other settings of image acquisition, as well as combinations of the above imaging modalities, to fully exploit the potential of these techniques in the evaluation of treated and untreated cancer tissue morphology. Our approach enabled to reveal the collagen fiber network in relation with the other tissues, and to identify invasive tumor cells. It also proved to be useful for the examination of interrelationships between functional and morphological aspects based on optical properties of the tissues, especially in studies of changes between the tumor and control tissue, as well as changes induced by physical treatments, e.g., delivery of microwave hyperthermia treatment. These differences were also evaluated quantitatively, when we found out that the maximum Euler–Poincaré characteristic reflects well the melanoma morphological structure. The results showed that the proposed investigative approach could be suitable also for a direct evaluation of tissue modifications induced by clinical interventions. Microsc. Res. Tech., 2009. © 2009 Wiley-Liss, Inc.     

Thermal Analysis and Cone Calorimeter Study of Engineered Wood with an Emphasis on Fire Modelling

Fire Technology - Engineered wood products (EWPs) are a group of materials having a very similar chemical composition but having different and non-uniform thermo-physical properties throughout...