The AFLATOX® Project: Approaching the Development of New Generation,Natural-Based Compounds for the Containment of the Mycotoxigenic Phytopathogen Aspergillus flavus and Aflatoxin Contamination

The control of the fungal contamination on crops is considered a priority by the sanitary authorities of an increasing number of countries, and this is also due to the fact that the geographic areas interested in mycotoxin outbreaks are widening. Among the different pre- and post-harvest strategies that may be applied to prevent fungal and/or aflatoxin contamination, fungicides still play a prominent role; however, despite of countless efforts, to date the problem of food and feed contamination remains unsolved, since the essential factors that affect aflatoxins production are various and hardly to handle as a whole. In this scenario, the exploitation of bioactive natural sources to obtain new agents presenting novel mechanisms of action may represent a successful strategy to minimize, at the same time, aflatoxin contamination and the use of toxic pesticides. The Aflatox^® Project was aimed at the development of new-generation inhibitors of aflatoxigenic Aspergillus spp. proliferation and toxin production, through the modification of naturally occurring molecules: a panel of 177 compounds, belonging to the thiosemicarbazones class, have been synthesized and screened for their antifungal and anti-aflatoxigenic potential. The most effective compounds, selected as the best candidates as aflatoxin containment agents, were also evaluated in terms of cytotoxicity, genotoxicity and epi-genotoxicity to exclude potential harmful effect on the human health, the plants on which fungi grow and the whole ecosystem.     

Single-phase gold/palladium catalyst: The nature of synergistic effect

The liquid phase oxidation of glycerol with oxygen has been studied using mono and bimetallic catalysts based on Au and Pd metals supported on activated carbon, in order to study the effect of the metal on the distribution of the products and on activity of catalysts. It was found that by using bimetallic catalysts (Au–Pd) a strong synergistic effect was shown. By using a preformed nucleating centre we were able to obtain a single alloyed phase, which allowed us to address the synergistic effect to the presence of alloyed Au/Pd. The advantage of using this latter catalyst lies not only in the high activity but also in a prolonged catalyst life. Although a partial leaching of palladium and assembling of the particles have been revealed by ICP and HRTEM respectively, activity after 10 re-cycles decreased less than expected (about 10%).     

Electrochemical anticorrosion performance evaluation of Al2O3 coatings deposited by MOCVD on an industrial brass substrate

Alumina (Al₂O₃) coatings of different thickness were deposited on OT59 brass substrate (BS) using the metal organic chemical vapour deposition (MOCVD) technique to evaluate the corrosion performance by EIS measurements. The used precursor was dimethyl-aluminium-isopropoxide. Electrochemical characterizations of the deposited films were performed in a standard very aggressive acidic solution (aerated 1N H₂SO₄ at 25 °C up to 168 h of immersion time) by means of direct current method (Tafel curves) and electrochemical impedance spectroscopy (EIS). The Rutherford backscattering spectroscopy (RBS) indicated that the films are very pure with the correct Al₂O₃ stoichiometry, while the IR absorption spectra showed that the films did not contain any OH groups. The surface film morphology was investigated by atomic force microscopy (AFM) and displayed a globular texture. The films were very smooth, with a maximum root mean square roughness, for example, of 14 nm for a 0.96 μm thick coating. The EIS data confirmed, as expected, that a 2.40 μm Al₂O₃ layer ensures the best corrosion protection after 168 h of immersion in the very acidic environment used.     

Robust event discovery from photo collections using Signature Image Bases (SIBs)

Analyzing personal photo albums for understanding the related events is an emerging trend. A reliable event recognition tool could suggest appropriate annotation of pictures, provide the context for single image classification and tagging, achieve automatic selection and summarization, ease organization and sharing of media among users. In this paper, a novel method for fast and reliable event-type classification of personal photo albums is presented. Differently from previous approaches, the proposed method does not process photos individually but as a whole, exploiting three main features, namely Saliency, Gist, and Time, to extract an event signature, which is characteristic for a specific event type. A highly challenging database containing more than 40.000 photos belonging to 19 diverse event-types was crawled from photo-sharing websites for the purpose of modeling and performance evaluation. Experimental results showed that the proposed approach meets superior classification accuracy with limited computational complexity.     

Collective decision making: a great opportunity for constraint reasoning

Collective decision making is an area of increasingly growing interest, mainly due to the rise of many IT-enabled environments where people connect and share information with others. We believe that constraint reasoning can have a major impact in this field, by providing general and flexible frameworks to model agents’ preferences over the alternative decisions, efficient algorithms to compute the best individual and collective decisions, and innovative approaches to deal with missing information. However, in order to do this, we claim that constraint reasoning should increase its efforts to open up to other research areas, such as voting and game theory, multi-agent systems, machine learning, and reasoning under uncertainty.     

Accurate determination of hexanal in beef bouillons by headspace solid‐phase microextraction gas‐chromatography mass‐spectrometry

Lipid oxidation has great impact on the quality of food products through flavor and taste deterioration, reduction in nutritive value, and potential toxicity of the oxidized food components. Flavor and taste deterioration can be easily perceived and it represents one of the major causes of consumer complaints in the food industry. The deterioration of sensory properties is due to the decomposition products of hydroperoxides that easily isomerize and degrade into volatile compounds. Volatile products are responsible for flavor and taste deterioration. In this study, we present the development of the solid‐phase microextraction gas chromatography‐mass spectrometry (SPME‐GC‐MS) technique to quantify low amounts (μg/g range) of secondary oxidation products, i.e. hexanal. The optimization of SPME parameters is a difficult task because of the possibility of further formation of volatile products during analysis. Different parameters such as type of fiber, exposure time of the fiber to the sample headspace and the optimal temperature of absorption have also been investigated. The complete validation of the method was achieved by the determination of linearity, limits of detection and quantification and repeatability. We demonstrated that the SPME method is a valuable tool for the quantification of low amounts of secondary oxidation products such as hexanal. Therefore, this technique can be used to detect early formation of volatiles.     

Stay with the flow: How macroinvertebrate communities recover during the rewetting phase in Alpine streams affected by an exceptional drought

Droughts are affecting an increasing number of lotic ecosystems worldwide due to the combined effects of climatic and anthropogenic pressures. Unlike naturally intermittent rivers, where the drying phase is a part of the annual flow regime, water scarcity in Alpine rivers represents a relatively recent phenomenon and, therefore, a major threat for the biodiversity of these lotic ecosystems. However, Alpine stream community response to drought is still poorly investigated. Here, we assess the recovery of macroinvertebrates in two Alpine streams after a supraseasonal drought. As water returned, a total of 10 sampling sessions were carried out, and temporal patterns in diversity, density, and taxonomic composition of benthic communities, as well as in the percentage of functional feeding groups, were investigated. We found that the resistance of invertebrate communities in Alpine streams is generally low: drought markedly reduced the diversity and density of macroinvertebrates. Conversely, our results suggest that the passive dispersal by drift from the upstream river sections seems the most probable mechanism promoting the post‐drought recovery. Nevertheless, this resilience ability appears to be stream specific and influenced by intrinsic stream characteristics, including the flow permanence and distance from the nearest upstream perennial reach. This work sheds light on the ecological consequences of droughts on macroinvertebrate communities. As flow intermittency in Alpine areas is expected to intensify under current global change scenarios, results of this study provide important information to predict changes in the taxonomic composition and diversity of macroinvertebrate communities.     

Compatibilization of an immiscible blend of EPDM and POM with an Ionomer

Immiscible blends of ethylene-propylene-diene-monomer (EPDM) and polyoxymethylene (POM), when EPDM is the major phase were compatibilized on the addition of an ionomer, poly(ethylene-co-methacrylic acid). The inclusion of the ionomer reduced the interfacial tension between the two phases, such that the diameter of the POM domains were significantly reduced to between 0.5 and 2 μm, typical of that required to toughen ductile polymers. The mechanical properties of the resultant compatibilized blends were significantly enhanced with increases in Young's modulus (↑54%), tensile strength (σ, ↑139%), elongation at break (ε, ↑97%), and tensile toughness (↑500%) with increasing ionomer content, relative to EPDM rubber alone. The ShoreA hardness of the compatibilized blend was 70.1 compared with 56.8 for the immiscible binary blend and, 50.2 for neat EPDM rubber.     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.