Fining white wine with plant proteins: effects of fining on proanthocyanidins and aroma components

Plant-derived insoluble proteins (wheat gluten, and isolates from pea, lentil, and soybean) were used as fining agents in model white wine (made from Catalanesca grapes) after cold stabilization. Plant proteins were effective in giving a fast and remarkable decrease in turbidity. GC/MS and HPLC/MS approaches indicated that individual proteins had a different impact on the levels of compounds relevant to wine stability. Protein stability of wine was not affected by fining with plant proteins. Lentil proteins and gluten gave the best removal of monomeric and dimeric flavonol. Both caused a decrease in the total content of fermentative aroma compounds, such as ethyl esters, acetate esters, and alcohols. Lentil proteins had the highest impact on the aroma components, giving a marked decrease in aroma components. Gluten may thus be regarded as giving the best balance between fining efficacy and retention of aroma compounds. Also, gluten in the treated wines remained well below the suggested threshold for gluten-free foods. This study provides a methodological frame for thorough characterization of the impact of specific interventions on key wine components.     

Natural polysaccharides platforms for oral controlled release of ketoprofen lysine salt

Context: Ketoprofen lysinate (KL) is one of the most widely used non-steroidal anti-inflammatory drugs in the symptomatic treatment of some chronic inflammatory diseases. Compared to ketoprofen, KL shows better pharmacokinetics and tolerability. However, due to its short half-life of 1–2?h, a multiple dose regimen is required for oral administration. Thus, the present work deals with its encapsulation in a hydrogel-based system by prilling in order to prolong its activity.

Objective: In this paper, we propose alginate and pectin as carriers and release tailoring agent for the development of hydrogel-based beads for KL retarded and sustained release.

Materials and methods: Beads were produced by a Nisco Encapsulator® using alginate or pectin. Operative variables were optimized to produce beads with desired morphology and size. Solid state properties were analyzed by SEM and DSC. Drug release performance was studied by Pharmacopeia pH-change assay to simulate gastrointestinal environment.

Results and discussion: Prilling technique was successfully used to encapsulate high soluble drugs as KL in polysaccharides-based hydrogels. Pectin proved to be a proper polymer able to encapsulate ketoprofen lysine salt. Formulation (F8) showed good morphological properties and size, high drug content (15.6%) and encapsulation efficiency (93.5%) and promising drug release profiles. Hosting F8 in an acid-resistant capsule (DR^®caps) a delivery platform has been developed to control KL release in a delayed (90?min lag time) and prolonged way (270?min complete release).

Conclusion: The platform may be proposed as potentially useful in the oral administration of NSAIDs in chronic inflammatory diseases affected by circadian rhythm.     

Description and composition of bio-inspired design patterns: a complete overview

In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design pattern.     

Reversible Fragmentation and Self‐Assembling of Nematic Liquid Crystal Droplets on Functionalized Pyroelectric Substrates

Investigation on the behavior of nematic liquid crystals on functionalized polar dielectric crystal substrates is accomplished. Very interesting effects can be observed in maneuvering liquid crystal droplets on the substrate surface, driven by electric fields generated by pyroelectric effect. Reversible drops fragmentation and self‐assembling in different configurations can be achieved. The dynamics of the observed phenomena is studied and the repeatability of the process is full assessed.     

Process and terminations variations aware stability criteria for microwave amplifiers

Two novel process variations aware, necessary and sufficient conditions suitable for implementation in CAD optimizers are proposed to check amplifiers stability. Case studies are presented, showing that the new criteria allow robust amplifier design, under variation of active device immittance parameters in pre‐specified rectangular regions, due to manufacturing tolerances. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 23: 619–626, 2013.     

Efficient energy-based embedding models for link prediction in knowledge graphs

We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.     

Structure–composition relationships of the traditional balsamic vinegar of Modena close to jamming transition (part II): Threshold control parameters

The Traditional Balsamic Vinegar of Modena (TBVM) is a high-valuable Italian specialty that, for reasons not yet fully explained, may undergo non-equilibrium degrading phenomena involving phase separation and flow arrest caused by solidification with or without crystalline order. TBVM was probed for its microstructure and composition as well as for its flow ability under low- and high shear limits. Results indicated vinegar concentration, temperature and viscosity as three independent variables affecting the extent of solidification in TBVM. Polymer-mediated mechanisms and diffusion-limited kinetics were hypothesized for structure development. Three main experimental evidences offered a convincing proof unifying all solidification phenomena observed in TBVM under the concept of colloidal jamming transition: (i) simultaneous presence of fractal-like aggregated colloids and polydispersed biopolymers; (ii) non-linear shear dependence above a critical level of vinegar concentration; (iii) a modified Krieger–Dougherty model satisfactorily described scaling behavior of relative viscosity accounting for the fractal dimension of jammed structure. Threshold for jamming in TBVM was defined in terms of critical concentration of the overall structure-active constituents (corresponding to 72°Bx and 40% w/w of the main sugars) and maximum resistance to the Newtonian flow (the onset for shear-thinning flow was achieved with a low-shear limiting viscosity of about 0.95 Pa·s).     

Pattern recognition in stained HEp-2 cells: Where are we now?

Indirect Immunofluorescence (IIF) images are increasingly being used for the diagnosis of autoimmune diseases. However, the analysis of this kind of images has until now reached a comparatively low level of automation, if compared with other medical imaging techniques. The Special Issue on the Analysis and Recognition of Indirect Immunofluorescence Images of the Pattern Recognition journal aims at providing a comprehensive evaluation of the state of the art for the staining pattern classification problem, through the adoption of a common experimental protocol and the testing of all the methods on a publicly available dataset.