Peptidyl Vinyl Ketone Irreversible Inhibitors of Rhodesain: Modifications of the P2 Fragment

In this paper, we report the design, synthesis and biological investigation of a series of peptidyl vinyl ketones obtained by modifying the P2 fragment of previously reported highly potent inhibitors of rhodesain, the main cysteine protease of Trypanosoma brucei rhodesiense. Investigation of the structure–activity relationship led us to identify new rhodesain inhibitors endowed with an improved selectivity profile (a selectivity index of up to 22 000 towards the target enzyme), and/or an improved antitrypanosomal activity in the sub-micromolar range.     

The impact of different hop compounds on the growth of selected beer spoilage bacteria in beer

Beer spoiling lactic acid bacteria are a major reason for quality complaints in breweries around the world. Spoilage by a variety of these bacteria can result in haze, sediment, slime, off-flavours and acidity. As these bacteria occur frequently in the brewing environment, using certain hop products that inhibit the growth of these spoilers could be a solution to prevent problems. To investigate the impact of seven different hop compounds (α-acids, iso-α-acids, tetrahydro-iso-α-acids, rho-iso-α-acids, xanthohumol, iso-xanthohumol and humulinones) on the growth of six major beer spoilage bacteria (Lactobacillus brevis. L. backi, L. coryniformis, L. lindneri, L. buchneri, Pediococcus damnosous), two concentrations (10 and 25 mg/L) of each hop substance were added to unhopped beer. The potential growth of the spoilage bacteria was investigated over 56 consecutive days. A comparison of the results shows a strong inhibition of growth of all spoilage bacteria at 25 mg/L of tetrahydro-iso-α-acids closely followed by α-acids as the second most inhibitory substance. The results showed a high resistance of L. brevis to all hop compounds as well as an inhibition of L. coryniformis and L. buchneri at low concentrations of most hop components. In comparison with the control sample, L. lindneri showed increased growth in the presence of some hop compounds (rho-iso-α-acids, xanthohumol, iso-xanthohumol, humulinones). © 2020 The Authors. Journal of the Institute of Brewing published by John Wiley & Sons Ltd on behalf of The Institute of Brewing & Distilling     

Cognitive radio signal classification based on subspace decomposition and RBF neural networks

Spectrum sensing is one of the major challenges for commercial development of cognitive radio systems, since the detection of the presence of a primary user is a complex task that requires high reliability. This work proposes a signal classifier capable of detecting and identifying a primary user signal on a given channel of the radio spectrum. The proposed approach combines eigen-decomposition techniques and neural networks not only to decide about the presence of a primary user, but also to identify the primary user signal type, a feature that is not encountered in the current approaches proposed in literature. Besides the advantage of identifying the primary user type, the proposed method also considerably reduces the computational cost of the detection process. The proposed classification method has been applied to the development of five primary user signal Classification Modules, which includes wireless microphone, orthogonal frequency-division multiplexing and Digital Video Broadcasting-Terrestrial signals. The results show that the proposed classifier correctly detects and identifies the primary users, even under low signal to noise ratio and multipath scenarios.     

Canonical kernel representations for behaviors over finite Abelian groups

In this paper the problem of constructing canonical kernel representations for systems whose alphabet set is an Abelian group is addressed. In standard linear system theory, there exist two important canonical kernel representations: minimal and row reduced kernel representations. In this paper we propose an extension of such canonical kernel representations to the group case. This topic has applications in the theory of convolutional codes over groups where kernel representations correspond to syndrome formers which are essential in decoding.     

Semantic image interpretation of gamma ray profiles in petroleum exploration

This paper presents the S-Chart framework, an approach for semantic image interpretation of line charts; and the InteliStrata system, an application for semantic interpretation of gamma ray profiles. The S-Chart framework is structured as a set of knowledge models and algorithms that can be instantiated to accomplish chart interpretation in all sorts of domains. The knowledge models are represented in three semantic levels and apply the concept of symbol grounding in order to map the representation primitives between the levels. The interpretation algorithms carry out the interaction between the high-level symbolic reasoning, and the low-level signal processing. In order to demonstrate the applicability of the S-Chart framework, we developed the InteliStrata system, an application in Geology for the semantic interpretation of gamma ray profiles. Using the developed application, we have interpreted the charts of two gamma ray profiles captured in petroleum exploration wells, indicating the position of stratigraphic sequences and maximum flooding surfaces. The results were compared with the interpretation produced by an experienced geologist using the same data input. The system carried out interpretation that were compatible with the geologist interpretation over the data. Our framework has the advantage of allowing the integration of existing domain ontologies with domain independent visual knowledge models and also the ability of grounding domain concepts in low-level data.     

Towards a core ontology for robotics and automation

In this paper, we present the current results of the newly formed IEEE-RAS Working Group, named Ontologies for Robotics and Automation. In particular, we introduce a core ontology that encompasses a set of terms commonly used in Robotics and Automation along with the methodology we have adopted. Our work uses ISO/FDIS 8373 standard developed by the ISO/TC184/SC2 Working Group as a reference. This standard defines, in natural language, some generic terms which are common in Robotics and Automation such as robot, robotic device, etc. Furthermore, we discuss the ontology development process employed along with the problems and decisions taken.     

Managing requirements uncertainty with partial models

Models are good at expressing information that is known but do not typically have support for representing what information a modeler does not know at a particular phase in the software development process. Partial models address this by being able to precisely represent uncertainty about model content. In previous work, we developed a general approach for defining partial models and using them to reason about design models containing uncertainty. In this paper, we show how to apply our approach to managing uncertainty in requirements by providing support for uncertainty capture, elaboration, and change. In particular, we address the problem of specifying uncertainty within a requirements model, refining a model as uncertainty reduces, providing meaning to traceability relations between models containing uncertainty, and propagating uncertainty-reducing changes between related models. We describe the implementation of uncertainty management within the Model Management Tool Framework and illustrate our approach using two examples.