Two cases of occupational allergic contact dermatitis from a cycloaliphatic epoxy resin in a neat oil: Case Report

BACKGROUND: Metal-working fluids contain complex mixtures of chemicals and metal workers constitute a potential risk group for the development of allergic contact dermatitis. CASE PRESENTATION: Two metal workers developed allergic contact dermatitis on the hands and lower arms from exposure to a neat oil used in metal processing. Patch testing revealed that the relevant contact allergen was a cycloaliphatic epoxy resin, 1,2-cyclohexanedicarboxylic acid, bis(oxiranylmethyl) ester, added to the oil as a stabilizer. None of the patients had positive reactions to the bisphenol A-based epoxy resin in the standard series. CONCLUSIONS: These cases emphasize that well-known contact allergens may show up from unexpected sources of exposure. Further, it can be a long-lasting, laborious process to detect an occupational contact allergen and cooperation from the patient and the manufacturer of the sensitizing product is essential

Geometric Algebra Model of Distributed Representations

Formalism based on GA is an alternative to distributed representation models developed so far --- Smolensky's tensor product, Holographic Reduced Representations (HRR) and Binary Spatter Code (BSC). Convolutions are replaced by geometric products, interpretable in terms of geometry which seems to be the most natural language for visualization of higher concepts. This paper recalls the main ideas behind the GA model and investigates recognition test results using both inner product and a clipped version of matrix representation. The influence of accidental blade equality on recognition is also studied. Finally, the efficiency of the GA model is compared to that of previously developed models.Comment: 30 pages, 19 figure

Reusable ω-transaminase sol-gel catalyst for the preparation of amine enantiomers

Heterogeneous &omega;-transaminase sol-gel catalysts were prepared and characterized in terms of immobilization degree, loading capacity and catalytic behavior in the kinetic resolution of racemic 1-phenylethylamine (a model compound) with sodium pyruvate in phosphate buffer (pH 7.5). The catalyst obtained when &omega;-transaminase from Arthrobacter sp. was encapsulated from the aqueous solution of the enzyme, isopropyl alcohol and polyvinyl alcohol in the sol-gel matrices, consisting of the 1:5 mixture of tetramethoxysilane and methyltrialkoxysilane, proved to be optimal including the reuse and storage stabilities of the catalyst. &nbsp;The optimized immobilizate was shown to perform well in the kinetic resolution of four structurally different aromatic primary amines in aqueous DMSO (10 v/v-%). The enzyme preparation showed synthetic potential by enabling the catalyst reuse in five consecutive preparative scale kinetic resolutions using 100 mM 1-phenylethylamine in aqueous DMSO (10 v/v-%). It was typical to fresh catalyst preparations that the kinetic resolution tended to exceed 50% before the reaction stopped leaving the (S)-amine unreacted while thereafter in reuse the reactions stopped at 50% conversion as expectable to highly enantioselective reactions.</p

Incremental dimension reduction of tensors with random index

We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure

Learning and generalization of compositional representations of visual scenes

Complex visual scenes that are composed of multiple objects, each with attributes, such as object name, location, pose, color, etc., are challenging to describe in order to train neural networks. Usually,deep learning networks are trained supervised by categorical scene descriptions. The common categorical description of a scene contains the names of individual objects but lacks information about other attributes. Here, we use distributed representations of object attributes and vector operations in a vector symbolic architecture to create a full compositional description of a scene in a high-dimensional vector. To control the scene composition, we use artificial images composed of multiple, translated and colored MNIST digits. In contrast to learning category labels, here we train deep neural networks to output the full compositional vector description of an input image. The output of the deep network can then be interpreted by a VSA resonator network, to extract object identity or other properties of indiviual objects. We evaluate the performance and generalization properties of the system on randomly generated scenes. Specifically, we show that the network is able to learn the task and generalize to unseen seen digit shapes and scene configurations. Further, the generalisation ability of the trained model is limited. For example, with a gap in the training data, like an object not shown in a particular image location during training, the learning does not automatically fill this gap.Comment: 10 pages, 6 figure

Cartoon Computation: Quantum-like computing without quantum mechanics

We present a computational framework based on geometric structures. No quantum mechanics is involved, and yet the algorithms perform tasks analogous to quantum computation. Tensor products and entangled states are not needed -- they are replaced by sets of basic shapes. To test the formalism we solve in geometric terms the Deutsch-Jozsa problem, historically the first example that demonstrated the potential power of quantum computation. Each step of the algorithm has a clear geometric interpetation and allows for a cartoon representation.Comment: version accepted in J. Phys.A (Letter to the Editor

Expression of antizyme inhibitor 2 in mast cells and role of polyamines as selective regulators of serotonin secretion

Peer reviewe

Expression of ODC Antizyme Inhibitor 2 (AZIN2) in Human Secretory Cells and Tissues

Ornithine decarboxylase (ODC) antizyme inhibitor 2 (AZIN2), originally called ODCp, is a regulator of polyamine synthesis that we originally identified and cloned. High expression of ODCp mRNA was found in brain and testis. We reported that AZIN2 is involved in regulation of cellular vesicle transport and/or secretion, but the ultimate physiological role(s) of AZIN2 is still poorly understood. In this study we used a peptide antibody (K3) to human AZIN2 and by immunohistochemistry mapped its expression in various normal tissues. We found high expression in the nervous system, in type 2 pneumocytes in the lung, in megakaryocytes, in gastric parietal cells co-localized with H, K-ATPase beta subunit, in selected enteroendocrine cells, in acinar cells of sweat glands, in podocytes, in macula densa cells and epithelium of collecting ducts in the kidney. The high expression of AZIN2 in various cells with secretory or vesicle transport activity indicates that the polyamine metabolism regulated by AZIN2 is more significantly involved in these events than previously appreciated.Peer reviewe

Calcium Gluconate in Phosphate Buffered Saline Increases Gene Delivery with Adenovirus Type 5

Peer reviewe

Comprehensive characterisation of the compressive behaviour of hydrogels using a new modelling procedure and redefining compression testing

The aim of tissue engineering is the regeneration of damaged tissue or the production of representative tissue organoids in vitro. To achieve this, one approach is to use hydrogels, water-swollen hydrophilic and crosslinked polymer networks, that can accommodate encapsulation of living cells and help the regeneration process. Even though mechanically biomimicking target tissue is important for a favorable cell response, the mechanical characterisation of tissues or hydrogels is not yet a fully defined process with various possible models and methods existing. In this paper, for the first time, a specific procedure and model has been suggested for the discussion of the nonlinear stress-strain relationship in large deformations of hydrogels. Moreover, this approach has comprehensively characterised the compressive material performance of hydrogels in a theoretical framework. To present the performance and utility of the introduced procedure, it is used with two different compositions of bioamine crosslinked gellan gum hydrogel. In addition, a three-dimensional digital image correlation technique has been utilized together with compression testing to measure the actual force and deformation in unconfined compression. The material model parameters were obtained to represent nonlinear stress-strain behaviour and the viscoelastic response (relaxation) of gellan gum hydrogel in compression.acceptedVersionPeer reviewe