Mycotoxin occurrence in beer produced in several European countries

The occurrence of ochratoxin A, trichothecenes, fumonisins and aflatoxins in a sample of 106 beers produced in several European countries, was investigated. The analyses were conducted using high-performance liquid chromatography with fluorimetric detection for ochratoxin A and aflatoxins, gas-chromatography and high-performance liquid chromatography, both coupled with mass spectrometer, for trichothecenes and fumonisins, respectively. Aflatoxins were not detected in any samples, whereas ochratoxin A, deoxynivalenol and fumonisins were found in a relatively high number of samples. Their presence was at low levels in all samples; however, some differences were observed between the European countries. As regards ochratoxin A, beer samples from southern Europe showed levels always lower than 0.040 μg l^?1, while the samples from other European countries showed significantly higher values, up to 0.189 μg l^?1 (P < 0.001). For fumonisins, the levels of Italian beers were significantly higher compared to the samples from other countries (P = 0.006).     

Molecular Dynamics Simulations of Bromodomains Reveal Binding-Site Flexibility and Multiple Binding Modes of the Natural Ligand Acetyl-Lysine

Experimental protein structures provide spatial information at the atomic level. A further dimension, time, is supplemented by molecular dynamics. Since the pioneering work on the 58-residue inhibitor of bovine pancreatic trypsin in the group of Martin Karplus in the seventies, molecular dynamics simulations have shown that the intrinsic flexibility of proteins is essential for their function. Here, we review simulation studies of bromodomains. These protein modules are involved in the recognition of acetylated lysine side chains, a post-translational modification frequently observed in histone tails. The molecular dynamics simulations have unmasked: (i) the large plasticity of the loops lining the acetyl-lysine binding site (coupled to its self-occlusion), and (ii) multiple binding modes of acetyl-lysine. These simulation results suggest that recognition of histone tails by bromodomains is modulated by their intrinsic flexibility, and further corroborate the utility of molecular dynamics in understanding (macro)molecular recognition.     

Small-Molecule Inhibitors of METTL3, the Major Human Epitranscriptomic Writer

The RNA methylase METTL3 catalyzes the transfer of a methyl group from the cofactor S-adenosyl-L-methionine (SAM) to the N⁶ atom of adenine. We have screened a library of 4000 analogues and derivatives of the adenosine moiety of SAM by high-throughput docking into METTL3. Two series of adenine derivatives were identified in silico, and the binding mode of six of the predicted inhibitors was validated by protein crystallography. Two compounds, one for each series, show good ligand efficiency. We propose a route for their further development into potent and selective inhibitors of METTL3.     

A fast compound algorithm for mining generators,closed itemsets,and computing links between equivalence classes

In pattern mining and association rule mining, there is a variety of algorithms for mining frequent closed itemsets (FCIs) and frequent generators (FGs), whereas a smaller part further involves the precedence relation between FCIs. The interplay of these three constructs and their joint computation have been studied within the formal concept analysis (FCA) field yet none of the proposed algorithms is scalable. In frequent pattern mining, at least one suite of efficient algorithms has been designed that exploits basically the same ideas and follows the same overall computational schema. Based on an in-depth analysis of the aforementioned interplay that is rooted in a fundamental duality from hypergraph theory, we propose a new schema that should enable for a more parsimonious computation. We exemplify the new schema in the design of Snow-Touch, a concrete FCI/FG/precedence miner that reuses an existing algorithm, Charm, for mining FCIs, and completes it with two original methods for mining FGs and precedence, respectively. The performance of Snow-Touch and of its closest competitor, Charm-L, were experimentally compared using a large variety of datasets. The outcome of the experimental study suggests that our method outperforms Charm-L on dense data while on sparse one the trend is reversed. Furthermore, we demonstrate the usefulness of our method and the new schema through an application to the analysis of a genome dataset. The initial results reported here confirm the capacity of the method to focus on significant associations.     

Considerations about learning Word2Vec

The Journal of Supercomputing - Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real...     

Generating Rare Association Rules Using the Minimal Rare Itemsets Family

Rare association rules correspond to rare, or infrequent, itemsets, as opposed to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities of local, rather than global, scope that can nevertheless provide valuable insights to an expert, especially in areas such as genetics and medical diagnosis where some specific deviations/illnesses occur only in a small number of cases. The work presented here is motivated by the long-standing open question of efficiently mining strong rare rules, i.e., rules with high confidence and low support. We also propose an efficient solution for finding the set of minimal rare itemsets. This set serves as a basis for generating rare association rules.     

DCOP FOR SMART HOMES: A CASE STUDY

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employ Adopt-N , a state-of-the-art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP-based approach by describing a case study taken from the RoboCare project. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguish Adopt-N from other algorithms for DCOP represent a strong asset in the smart home domain.     

Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.     

Biclustering meets triadic concept analysis

Biclustering numerical data became a popular data-mining task at the beginning of 2000’s, especially for gene expression data analysis and recommender systems. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So-called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address a complete, correct and non-redundant enumeration of such patterns, a well-known intractable problem, while no formal framework exists. We introduce important links between biclustering and Formal Concept Analysis (FCA). Indeed, FCA is known to be, among others, a methodology for biclustering binary data. Handling numerical data is not direct, and we argue that Triadic Concept Analysis (TCA), the extension of FCA to ternary relations, provides a powerful mathematical and algorithmic framework for biclustering numerical data. We discuss hence both theoretical and computational aspects on biclustering numerical data with triadic concept analysis. These results also scale to n-dimensional numerical datasets.