Production of geranyl propionate by enzymatic esterification of geraniol and propionic acid in solvent‐free system

Background: This work reports the optimization of geranyl propionate production by esterification of geraniol and propionic acid in a solvent‐free system using a commercial lipase as catalyst. For this purpose, a sequential strategy was performed applying two experimental designs. Results: The operating conditions that optimized geranyl propionate production were determined to be 40 °C, geraniol to propionic acid molar ratio of 3:1, 150 rpm and 10 wt% of enzyme, with a resulting reaction conversion of about 93%. After determining the optimum reaction parameters, a kinetic study was carried out evaluating the influence of substrates molar ratio, enzyme concentration and temperature on reaction conversion. Results obtained in this step allow one to conclude that an excess of alcohol (acid to alcohol molar ratio of 1:6), relatively low enzyme concentration (5 wt%), temperature of 40 °C and substrates molar ratio of 1:1 afforded nearly complete reaction conversion after 30 min of reaction. Conclusion: New experimental data on enzymatic esterification of geraniol and propionic acid for geranyl propionate production are reported in this work, showing a promising perspective of the technique to overcome the well‐known drawbacks of the chemical‐catalyzed route. Copyright © 2010 Society of Chemical Industry     

The State‐of‐the‐Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net .     

Dynamic selection of generative–discriminative ensembles for off-line signature verification

In practice, each writer provides only a limited number of signature samples to design a signature verification (SV) system. Hybrid generative–discriminative ensembles of classifiers (EoCs) are proposed in this paper to design an off-line SV system from few samples, where the classifier selection process is performed dynamically. To design the generative stage, multiple discrete left-to-right Hidden Markov Models (HMMs) are trained using a different number of states and codebook sizes, allowing the system to learn signatures at different levels of perception. To design the discriminative stage, HMM likelihoods are measured for each training signature, and assembled into feature vectors that are used to train a diversified pool of two-class classifiers through a specialized Random Subspace Method. During verification, a new dynamic selection strategy based on the K-nearest-oracles (KNORA) algorithm and on Output Profiles selects the most accurate EoCs to classify a given input signature. This SV system is suitable for incremental learning of new signature samples. Experiments performed with real-world signature data (composed of genuine samples, and random, simple and skilled forgeries) indicate that the proposed dynamic selection strategy can significantly reduce the overall error rates, with respect to other EoCs formed using well-known dynamic and static selection strategies. Moreover, the performance of the SV system proposed in this paper is significantly greater than or comparable to that of related systems found in the literature.     

A classifier fusion system for bearing fault diagnosis

In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together – by means of the Iterative Boolean Combination (IBC) technique – they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.     

Lineage‐Specific Commitment of Stem Cells with Organic and Graphene Oxide–Functionalized Nanofibers

Controlling the differentiation to certain lineages is the main goal of current stem cell research, which might exploit new routes based on the interaction of cells with nanomaterials. Here it is shown that primary neurospheres from dental pulp stem cells grown on combinatorial surfaces with different fibrous morphology and graphene oxide functionalization exhibit different differentiation propensity. The developed materials strongly influence the stem cell fate, as highlighted by morphological, immunofluorescence, molecular biology, and functional analyses. Instructive cues lead to the increased expression of markers that are characteristic of selective differentiation into osteoblasts, glial cells, fibroblasts, and neurons even in basal medium conditions, and randomly oriented fibers are found to revert neuronal precommitment and to trigger osteoblastic differentiation. Graphene oxide coatings lead instead to the relatively enhanced expression of genes typical of either glial or neuronal commitment, depending on the underlying nanofibrous morphology. The mechanisms addressing cell fate are investigated, highlighting the correlation of wetting anisotropy and protein adsorption capacity of different surfaces, ultimate cell conformational changes reflected by skeletal and nuclear elongation, and directed cell commitment. Cues from the different surfaces are therefore lineage‐specific, unveiling remarkable potentialities for cellular programming by means of nanomaterials.     

Antimicrobial activity of rhamnolipids against Listeria monocytogenes and their synergistic interaction with nisin

The rhamnolipids (RL) are biodegradable biosurfactants which have low toxicity and surface active properties that can be useful for food processing industries. The objective of this study was to evaluate the antimicrobial potential of rhamnolipids against Listeria monocytogenes. Susceptibility tests were performed by the minimal inhibitory concentration (MIC) using the micro-broth dilution technique. The MIC values varied from 78.1 μg/mL to 2500 μg/mL with the 2500 μg/mL being the predominant value. Among the 32 tested cultures, 90.6% were susceptible to RL. Results showed that the rhamnolipid activity was primarily bacteriostatic. The interaction of rhamnolipid with nisin was also investigated. The combined effect of nisin and RL was evaluated against two wild-type isolates of L. monocytogenes, L12 sensitive to RL (MIC 156.2 μg/mL) and L17 less sensitive to RL (2500 μg/mL). The FIC indexes for the isolates were 0.18 and 0.078 for L12 and L17 respectively, indicating a strong synergistic effect. The survival curve of isolates L12 and L17 showed that the combination between nisin and RL was bactericidal at lower concentration than for the individual antimicrobials. For the L12 isolate 78.1 μg/mL of RL and 160 IU/mL of nisin eliminated the population after 30 min of incubation. The combination of 156.2 μg/mL of RL and 320 IU/mL of nisin reduced completely L17 population after 2 h of incubation. Rhamnolipids showed antimicrobial activity against L. monocytogenes and presented a synergistic effect when combined with nisin.     

Metal-C60 Interface Interaction: Effects on Metal Dispersion in Co-Deposited Films

Abstract

In case of a metal finely dispersed in a C₆₀ matrix, the metal-to-C₆₀ charge transfer lowers the interface energy. The total cohesive energy per metal atom E_tot was estimated for Au clusters in fullerite. The results show that, for a charge Q_f ≥ 1 electron (e) transferred from the metal clusters to each C₆₀ cage, a minimum appears in E_tot, defining a most stable cluster size. Thereby, the equilibrium dispersion state of the metal in the fullerite matrix depends on Q_f.