Ground-based gamma-ray astronomy has had a major breakthrough with the impressive results obtained using systems of imaging atmospheric Cherenkov telescopes. Ground-based gamma-ray astronomy has a huge potential in astrophysics, particle physics and cosmology. CTA is an international initiative to build the next generation instrument, with a factor of 5-10 improvement in sensitivity in the 100 GeV-10 TeV range and the extension to energies well below 100 GeV and above 100 TeV. CTA will consist of two arrays (one in the north, one in the south) for full sky coverage and will be operated as open observatory. The design of CTA is based on currently available technology. This document reports on the status and presents the major design concepts of CTA.

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Infrared signals of microorganisms are highly specific fingerprint-like patterns that can be used for probing the identity of microorganisms. The simplicity and versatility of Fourier-transform infrared spectroscopy (FT-IR) makes it a versatile technique for rapid differentiation, classification, identification and large-scale screening at the subspecies level.

The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. ?? 2013 Elsevier B.V. All rights reserved

This study describes a computer-based technique for classifying and identifying bacterial samples using Fouriertransform infrared spectroscopy (FT-IR) patterns. Classification schemes were tested for selected series of bacterial strains and species from a variety of different genera. Dissimilarities between bacterial IR spectra were calculated using modified correlation coefficients. Dissimilarity matrices were used for cluster analysis, which yielded dendrograms broadly equated with conventional taxonomic classification schemes. Analyses were performed with selected strains of the taxa Staphylococcus, Streptococcus, Clostridium, Legionella and Escherichia coli in particular, and with a database containing 139 bacterial reference spectra. The latter covered a wide range of Gram-negative and Gram-positive bacteria. Unknown specimens could be identilied when included in an established cluster analysis. Thirty-six clinical isolates of Staphylococcus aureus and 24 of Streptococcus faecdis were tested and all were assigned to the correct species cluster. It is concluded that: (1) FT-IR patterns can be used to type bacteria; (2) FT-IR provides data which can be treated such that classifications are similar and/or complementary to conventional classification schemes; and (3) FT-IR can be used as an easy and safe method for the rapid identification of clinical isolates.

Infrared (IR) spectra of intact microbial cells are highly specific fingerprint‐like signatures which are used to differentiate, classify, and identify diverse microbial species and strains. Microbial IR spectra are also useful to (1) detect in situ intracellular compounds or structures such as inclusion bodies, storage materials, and endospores, (2) monitor and quantify metabolically released CO 2 in response to various different substrates, and (3) characterize growth‐dependent phenomena and cell–drug interactions. The characteristic information, useful for microbial characterizations, is generally distributed over the entire IR region of the electromagnetic spectrum, i.e. over the near‐infrared (NIR), mid‐infrared (MIR), and far‐infrared (FIR). The spectral traits can be systematically extracted from the typically broad and complex spectral contours applying resolution enhancement techniques, difference spectroscopy, and pattern recognition methods such as factor‐analysis and cluster‐analysis, and artificial neural networks (ANNs). Additional applications arise by means of a light microscope coupled to the IR spectrometer. IR spectra of microcolonies containing less than 10 3 cells are obtained from colony replica by a stamping technique that transfers microcolonies growing on culture plates to a special IR‐sample holder. Using a computer‐controlled x , y ‐stage together with mapping and video techniques, the fundamental tasks of microbiological analysis, namely detection, enumeration, and differentiation of microorganisms, are perspectively integrated in one single apparatus.

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In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.