Electrospun microporous gelatin–polycaprolactone blend tubular scaffold as a potential vascular biomaterial

The work reported involved the fabrication of an electrospun tubular conduit of a gelatin and polycaprolactone (PCL) blend as an adventitia‐equivalent construct. Gelatin was included as the matrix for increased biocompatibility with the addition of PCL for durability. This is contrary to most of the literature available for biomaterials based on blends of gelatin and PCL where PCL is the major matrix. The work includes the assiduous selection of key electrospinning parameters to obtain smooth bead‐free fibres with a narrow distribution of pore size and fibre diameter. Few reports elucidate the optimization of all electrospinning parameters to fabricate tubular conduits with a focus on obtaining homogeneous pores and fibres. This stepwise investigation would be unique for the fabrication of gelatin–PCL electrospun tubular constructs. The fabricated microfibrous gelatin–PCL constructs had pores of size ca 50–100 μm reportedly conducive for cell infiltration. The measured value of surface roughness of 57.99 ± 17.4 nm is reported to be favourable for protein adhesion and cell adhesion. The elastic modulus was observed to be similar to that of the tunica adventitia of the native artery. Preliminary in vitro and in vivo biocompatibility tests suggest safe applicability as a biomaterial. Minimal cytotoxicity was observed using MTT assay. Subcutaneous implantation of the scaffold demonstrated acute inflammation which decreased by day 15. The findings of this study could enable the fabrication of smooth bead‐free microfibrous gelatin–PCL tubular construct as viable biomaterial which can be included in a bilayer or a trilayer scaffold for vascular tissue engineering. © 2019 Society of Chemical Industry     

Synergistic antimicrobial effect of lactocin AL705 and nisin combined with organic acid salts against Listeria innocua 7 in broth and a hard cheese

The effectiveness of antimicrobial mixtures against Listeria innocua 7, used as a L. monocytogenes surrogate, was investigated in broth and a food system. Synergistic effects were found for nisin (Nis), potassium sorbate (PS), calcium propionate (CP) and sodium lactate (SL), Nis + PS being the most effective binary mixture that exhibited listericidal activity in broth. To assess the effect of adding lactocin AL705 (AL705) to Nis + organic acid salt combinations, tridimensional isobolograms were generated. Sub-MIC combinations of the antimicrobials exerted bactericidal activity against L. innocua 7 after AL705 addition to the binary mixtures. However, when applied on Sardo cheese contaminated with L. innocua 7 (initial inoculum 4.45 ± 0.06 CFU g⁻¹), only Nis + PS + AL705 produced count reductions respect to the control, reaching 3.04 ± 0.35 CFU g⁻¹ counts after 15 days at 15 °C. Ternary combinations containing AL705 showed potential to reduce antimicrobial usages for L. innocua 7 inhibition.     

Long-term energy scenarios for Mexico: Policy options for carbon savings and main barriers

This paper shows that possibilities exist for maintaining current energy-related carbon emissions per capita in Mexico at almost constant levels in the long term. It is argued, however, that the identified carbon emissions reductions will not be achieved easily or rapidly, as they require a restructuring of Mexico's current technological base and an unusual degree of coordination among the government, lending institutions, equipment manufacturers, and final users. Such changes also will entail surmounting major institutional and financial barriers and creating a better international environment for technology transfer and capital lending.     

Modelling the rate of secondary succession after farmland abandonment in a Mediterranean mountain area

Secondary succession after farmland abandonment has become a common process in north Mediterranean countries, especially in mountain areas. In this paper, a methodology is tested which combines Markov chains and logistic multivariate regression to model secondary succession after farmland abandonment in environments where abiotic constraints play a major role, like mountain areas. In such landscapes, a decay in the succession rate with time is usually found, as the best locations are progressively occupied. This is frequently addressed using non-stationary Markov chains. Here, we test if the combination of logistic multivariate regression with Markov chains, however, allows for spatially distributed transitions probabilities based on abiotic factors and therefore, it is able to reproduce the preferential colonization of the most favourable locations. The model is tested in the Ijuez Valley in the Spanish Pyrenees, which underwent generalized land abandonment during the 1950s. Results confirm a substantial improvement in the prediction success of the Markov-logistic model when compared to the standard Markov chain approach. As a result, the decay in the succession rate can be successfully modelled. The specific results for our study area are discussed further in an ecological context. The methodology proposed is applicable to any landscape where vegetation dynamics are constrained by environmental factors. However, the inclusion of land use as an explanatory factor would be necessary in human-managed landscapes.     

Link between Lipid Second Messengers and Osmotic Stress in Plants

Plants are subject to different types of stress, which consequently affect their growth and development. They have developed mechanisms for recognizing and processing an extracellular signal. Second messengers are transient molecules that modulate the physiological responses in plant cells under stress conditions. In this sense, it has been shown in various plant models that membrane lipids are substrates for the generation of second lipid messengers such as phosphoinositide, phosphatidic acid, sphingolipids, and lysophospholipids. In recent years, research on lipid second messengers has been moving toward using genetic and molecular approaches to reveal the molecular setting in which these molecules act in response to osmotic stress. In this sense, these studies have established that second messengers can transiently recruit target proteins to the membrane and, therefore, affect protein conformation, activity, and gene expression. This review summarizes recent advances in responses related to the link between lipid second messengers and osmotic stress in plant cells.     

Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

Guidelines for the incremental identification of aspects in requirements specifications

The desired principle of separation of concerns in software development can be jeopardized by the so-called crosscutting concerns, which tend to be scattered over (and tangled with) the functionality of the modular units of a system. The correct identification of such concerns (and their encapsulation into separate artifacts) is thereby considered a way to improve software understanding and evolution. Pursuing a proper management of concerns from the requirements engineering stage can greatly benefit the entire software life-cycle. In this paper, we propose conceptual guidelines on how to perform the identification of crosscutting concerns in the process of building requirements specifications. We argue that the identification must be carried out in an incremental way, to encapsulate apart the crosscutting concerns even if they have not emerged completely yet.