Quantification of mutant versus wild-type myosin in human muscle biopsies using nano-LC/ESI-MS

A liquid chromatography/electrospray ionization mass spectrometry (nano-LC/ESI-MS) approach is described by which abundance of proteins (e.g., of beta-myosin heavy chain; MW 223 kDa) carrying a point mutation can be determined in tissue samples where the mutant protein is coexpressed with its wild-type forms. After enzymatic cleavage of the extracted parent protein, mutant and wild-type species of the peptide with the locus of the point mutation were quantified. Synthetic peptides, identical to wild-type and mutant peptides but labeled with stable isotopes ((13)C, (15)N), were added in known amounts as internal standards. The peak areas obtained by MS for the stable-isotope-labeled peptides and for the native peptides were used for quantification. To demonstrate the suitability of this approach we determined the relative abundance of beta-myosin with the Arg723Gly exchange in muscle biopsies of patients with Familial Hypertrophic Cardiomyopathy (HCM). For two such patients the fraction of mutated myosin was 62%, i.e., significantly different from 50%, which is quite unexpected for an autosomal dominant disease in heterozygous patients. Correlation between abundance of mutant myosin and clinical malignancy seen for several mutations in the myosin head domain emphasizes the relevance of such quantification. The approach for quantification described here is generally applicable for quantification of proteins with single point mutations even if only small amounts of tissue are available.     

NEVA: Visual Analytics to Identify Fraudulent Networks

Trust-ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well-defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill-defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false-negative and false-positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance-enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.     

Metal-Free Hydrogen-Bonded Polymers Mimic Noble Metal Electrocatalysts

The most active and efficient catalysts for the electrochemical hydrogen evolution reaction (HER) rely on platinum, a fact that increases the cost of producing hydrogen and thereby limits the widespread adoption of this fuel. Here, a metal-free organic electrocatalyst that mimics the platinum surface by implementing a high work function and incorporating hydrogen-affine hydrogen bonds is introduced. These motifs, inspired from enzymology, are deployed here as selective reaction centres. It is shown that the keto-amine hydrogen-bond motif enhances the rate-determining step in proton reduction to molecular hydrogen. The keto-amine-functionalized polymers reported herein evolve hydrogen at an overpotential of 190 mV. They share certain key properties with platinum: a similar work function and excellent electrochemical stability and chemical robustness. These properties allow the demonstration of one week of continuous HER operation without notable degradation nor delamination from the carrier electrode. Scaled continuous-flow electrolysis is reported and 1 L net molecular hydrogen is produced within less than 9 h using 2.3 mg of polymer electrocatalyst.     

A food matrix reduces digestion and absorption of food allergens in vivo

Scope: Food allergy is caused by primary (class 1) food allergens, e.g. Bos d 5 (cow's milk) and Cor a 8 (hazelnut) or secondary (class 2) food allergens, e.g. Mal d 1 (apple). The latter cannot sensitize susceptible individuals but can cause allergy due to immunological cross‐reactivity with homologous respiratory allergens. Here, we studied the effects of food matrix on gastrointestinal proteolysis, epithelial transport and in vivo absorption of class 1 and class 2 food allergens. Methods and results: Mal d 1 lost its IgE‐reactivity immediately after simulated gastric digestion whereas Bos d 5 and Cor a 8 did not. Only Cor a 8 maintained IgE‐binding capacity after simulated intestinal proteolysis. The presence of hazelnut and peanut extracts, which served as protein‐rich model food matrices, delayed gastrointestinal degradation and reduced epithelial transport rates of all allergens through CaCo‐2 monolayers. Finally, IgE‐reactive allergens were assessed at different time points in sera from rats fed with all three allergens with or without hazelnut extract. The levels of all allergens peaked 2 h after animals were fed without matrix and increased over 8 h after feeding. Conclusions: A protein‐rich food matrix delays gastrointestinal digestion and epithelial transport of food allergens and thereby may affect their sensitizing capacity and clinical symptoms.     

The use of sensory evaluation and consumer preference for the selection of sweetpotato cultivars in East Africa

Sweetpotato is an important staple crop in East Africa with great potential for introducing improved cultivars, but little is known about which sensory characteristics are desirable. Over a 2 year period, 600 consumers were interviewed at three locations (urban and rural) in the Lake Zone of Tanzania and their preference of 14 locally available sweetpotato cultivars in cooked form was evaluated. A simple questionnaire based on consumers first‐choice preference was used followed by socio‐economic questions. A trained sensory panel profiled the cooked sweetpotato samples, enabling comparisons with preference, location and season. Cluster analysis based on the sensory attributes was used to classify the cultivars into three groups; one cluster comprised the most preferred cultivars and another contained the least. Some cultivars were consistently preferred over the 2 year period while others were not. The location where the cultivars were grown also influenced preference. Stepwise regression indicated that the most discriminating sensory attributes were starch and stickiness. Target levels based on the mean intensity scores of these attributes are suggested as a means of screening new cultivars. The implications of these findings are discussed. Copyright © 2004 Society of Chemical Industry     

Clustering clinical departments for wards to achieve a prespecified blocking probability

OR Spectrum - When the number of available beds in a hospital is limited, it can be beneficial to cluster several clinical departments such that the probability of not being able to admit a patient...