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In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.

The influence of Al(2)O(3) nanoparticles on the curing of an epoxy thermoset based on diglycidyl ether of bisphenol A was investigated using temperature-modulated differential scanning calorimetry (TMDSC) and rheology. Diethylene triamine was used as a hardener. TMDSC not only allows for a systematic study of the kinetics of cure but simultaneously gives access to the evolution of the specific heat capacities of the thermosets. The technique thus provides insight into the glass transition behaviour of the nanocomposites and hence makes it possible to shed some light on the interaction between the nanoparticles and the polymer matrix. The Al(2)O(3) fillers are shown to accelerate the growth of macromolecules upon isothermal curing. Several mechanisms which possibly could be responsible for the acceleration are described. As a result of the faster network growth chemical vitrification occurs at earlier times in the filled thermosets and the specific reaction heat decreases with increasing nanoparticle concentration. Rheologic measurements of the zero-shear viscosity confirm the faster growth of the macromolecules in the presence of the nanoparticles.

An application of FTIR spectroscopic imaging for the identification and visualization of early micrometastasis from breast cancer to lungs in a murine model is shown. Spectroscopic and histological examination is focused on lung cross-sections derived from animals at the early phase of metastasis (early micrometastasis, EM) as compared to healthy control (HC) and late phase of metastasis (advanced macrometastasis, AM) using murine model of metastatic breast cancer with 4T1 cells orthotopically inoculated. FTIR imaging allows for a detailed, objective and label-free differentiation and visualization of EM foci including large and small micrometastases as well as single cancer cells grouped in clusters. An effect of the EM phase on the entire lung tissue matrix as well as characteristic biochemical profiles for HC and advanced macrometastasis were determined from morphological and spectroscopic points of view. The extraordinary sensitivity of FTIR imaging toward EM detection and discrimination of AM borders confirms its applicability as a complementary tool for the histopathological assessment of the metastatic cancer progression.

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.

Many phenomenological properties of reactive polymers like polyurethanes increase or decrease continuously in the course of the curing process before saturating at the end of the chemical reaction. This holds true for instance for the mass density, the refractive index, the chemical turnover and the hypersonic properties. The reason for this monotone behaviour is that the chemical reaction behaves like a continuous succession of irreversible phase transitions. These transitions are superposed by the sol-gel transition and possibly by the chemically induced glass transition, with the drawback that the latter two highlighted transitions are often hidden by the underlying curing process. In this work we propose generalized mode Grüneisen parameters as an alternative probe for elucidating the polymerization process itself and the closely related transition phenomena. As a model system we use polyurethane composed of a diisocyanate and varying ratios of difunctional and trifunctional alcohols.

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