One Question,Multiple Answers: Biochemical and Biophysical Screening Methods Retrieve Deviating Fragment Hit Lists

Fragment‐based lead discovery is gaining momentum in drug development. Typically, a hierarchical cascade of several screening techniques is consulted to identify fragment hits which are then analyzed by crystallography. Because crystal structures with bound fragments are essential for the subsequent hit‐to‐lead‐to‐drug optimization, the screening process should distinguish reliably between binders and non‐binders. We therefore investigated whether different screening methods would reveal similar collections of putative binders. First we used a biochemical assay to identify fragments that bind to endothiapepsin, a surrogate for disease‐relevant aspartic proteases. In a comprehensive screening approach, we then evaluated our 361‐entry library by using a reporter‐displacement assay, saturation‐transfer difference NMR, native mass spectrometry, thermophoresis, and a thermal shift assay. While the combined results of these screening methods retrieve 10 of the 11 crystal structures originally predicted by the biochemical assay, the mutual overlap of individual hit lists is surprisingly low, highlighting that each technique operates on different biophysical principles and conditions.     

Traffic sign recognition system with β -correction

Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.     

Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes

Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.     

Natural antioxidants from herbs and spices

There is a growing interest in natural antioxidants found in plants because of the world‐wide trend toward the use of natural additives in food and cosmetics. Herbs and spices are one of the most important targets to search for natural antioxidants from the point of view of safety. This review presents the results on stabilization of lipids and lipid‐containing foods with different herbs and spices (ground materials or extracts) and reports the structure of the main antioxidatively acting compounds isolated from them. The review presents information about the antioxidative effects of rosemary, sage, oregano, thyme, ginger, summer savory, black pepper, red pepper, clove, marjoram, basil, peppermint, spearmint, common balm, fennel, parsley, cinnamon, cumin, nutmeg, garlic, coriander, etc. Among the herbs of the Labiatae family, rosemary has been more extensively studied and its extracts are the first marketed natural antioxidants. Sage and oregano, which belong to the same family, have gained the interest of many research groups as potential antioxidants.     

ECOC-DRF: Discriminative random fields based on error correcting output codes

We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.     

Anti-oxidant activity and mechanism of action of trans-resveratrol in different lipid systems

The anti-oxidative properties of trans -resveratrol were studied in the concentration range 0.02–0.20% (8.8 × 10⁻⁴–8.8 × 10⁻³  M ) during autoxidation of pure triacylglycerols of lard (TGL) and of pure triacylglycerols of sunflower oil (TGSO), as well as of commercial sunflower oil at 100 °C. In sunflower oil trans -resveratrol showed no anti-oxidative activity. In pure lipid systems trans -resveratrol considerably retarded the oxidation process, the effect being stronger in the less oxidizable substrate (TGL). The effectiveness (stabilization factor F ), strength (oxidation rate ratio, ORR) and activity ( A = F /ORR) of the different anti-oxidant concentrations were determined. The activity A was found to increase dose dependency as follows: A =3750–15425 for TGL, A =79–207 for TGSO. The participation of trans -resveratrol in the side reactions of inhibited oxidation was discussed.     

Rayleigh mixture model for plaque characterization in intravascular ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.     

Reliable networks with unreliable sensors

Wireless sensor networks (WSNs) deployed in hostile environments suffer from a high rate of node failure. We investigate the effect of such failure rate on network connectivity. We provide a formal analysis that establishes the relationship between node density, network size, failure probability, and network connectivity. We show that large networks can maintain connectivity despite a significantly high probability of node failure. We derive mathematical functions that provide lower bounds on network connectivity in WSNs. We compute these functions for some realistic values of node reliability, area covered by the network, and node density, to show that, for instance, networks with over a million nodes can maintain connectivity with a probability exceeding 95% despite node failure probability exceeding 53%.