Evaluation of alternative preservation treatments (water heat treatment, ultrasounds, thermosonication and UV-C radiation) to improve safety and quality of whole tomato

Previously optimised postharvest treatments were compared to conventional chlorinated water treatment in terms of their effects on the overall quality of tomato (‘Zinac’) during storage at 10 °C. The treatments in question were water heat treatment (WHT = 40 °C, 30 min), ultrasounds (US = 45 kHz, 80 %, 30 min), thermosonication (TS =40 °C, 30 min, 45 kHz, 80 %) and ultraviolet irradiation (UV-C: 0.97 kJ m−2). The quality factors evaluated were colour, texture, sensorial analysis, mass loss, antioxidant capacity, total phenolic content, peroxidase and pectin methylesterase enzymatic activities, and microbial load reduction. The results demonstrate that all treatments tested preserve tomato quality to some extent during storage at 10 °C. WHT, TS and UV-C proved to be more efficient on minimising colour and texture changes with the additional advantage of microbial load reduction, leading to a shelf life extension when compared to control trials. However, at the end of storage, with exception of WHT samples, the antioxidant activity and phenolic content of treated samples was lower than for control samples. Moreover, sensorial results were well correlated with instrumental colour experimental data. This study presents alternative postharvest technologies that improve tomato (Zinac) quality during shelf life period and minimise the negative impact of conventional chlorinated water on human safety, health and environment.info:eu-repo/semantics/publishedVersio

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Weak Keys in the Faure-Loidreau Cryptosystem

Some types of weak keys in the Faure-Loidreau (FL) cryptosystem are presented. We show that from such weak keys the private key can be reconstructed with a computational effort that is substantially lower than the security level. The proposed key-recovery attack is based on ideas of generalized minimum distance (GMD) decoding for rank-metric codes

A stochastic context free grammar based framework for analysis of protein sequences

<p>Abstract</p> <p>Background</p> <p>In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm.</p> <p>Results</p> <p>This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins.</p> <p>Conclusion</p> <p>A new Stochastic Context Free Grammar based framework has been introduced allowing the production of binding site descriptors for analysis of protein sequences. Experiments have shown that not only is this new approach valid, but produces human-readable descriptors for binding sites which have been beyond the capability of current machine learning techniques.</p

Identity and European integration : diversity as a source of integration

This article explores the concept of European Union identity and its significance for European integration by drawing upon insights from theories of nationalism and national identity. European Union identity is viewed as an ongoing process which is banal, contingent and contextual. The central hypothesis is that: European integration facilitates the flourishing of diverse national identities rather than convergence around a single homogeneous European Union identity. The role of the EU as facilitator for diverse understandings of collective identities encourages the enhabitation of the EU at an everyday level and the reinforcement of a sense of banal Europeanism which is a crucial aspect of the European integration process. Facilitating diversity may thus provide a vital source of dynamism for the integration process