Health aspects of Spirulina (Arthrospira) microalga food supplement

Spirulina, now named Arthrospira, is a microscopic and filamentous cyanobacterium that has a long history of use as a safe food lacking toxicity. It is commercially produced in large outdoor ponds under controlled conditions. The aim of this review article is to summarize available recent information concerning human clinical potential and applications of Spirulina, as well as clinical data related to the safety and side effects of Spirulina. Potential health benefits of Spirulina are mainly due to its chemical composition, which includes proteins (the highest protein content of any natural food, 55%-70%), carbohydrates, essential amino acids, minerals (especially iron), essential fatty acids, vitamins, and pigments. In this respect, three major bioactive components of Spirulina, the protein phycocyanin (a biliprotein pigment), sulfated polysaccharides and gamma linolenic acid seem to play significant role in imparting improved human body functions. Furthermore, new experimental evidence supports the immunomodulation and antiviral effects of Spirulina supplementation. According to the Dietary Supplements Information Expert Committee of United States Pharmacopeial Convention the available clinical evidence does not indicate a serious risk to health or other public health concerns for Spirulina. However, a few cases of severe side-effects have been reported

Combinations of QT-prolonging drugs: towards disentangling pharmacokinetic and pharmaco-dynamic effects in their potentially additive nature.

Background: Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG) QT-prolonging properties are combined is generally supposed but not well studied. Based on available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT) classification defines the risk of QT prolongation for exposure to single drugs. We aimed to investigate how combining AZCERT drug categories impacts QT duration and how relative drug exposure affects the extent of pharmacodynamic drug–drug interactions. Methods: In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other QTc-prolonging risk factors. We concurrently considered administered drug doses and pharmacokinetic interactions modulating drug clearance to calculate individual weights of relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we estimated individual drug exposure with these drugs and included this information as weights in weighted regression analyses. Results: Drugs attributing a ‘known’ risk for clinical consequences were associated with the largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with 95% confidence intervals for ‘known’ risk drugs + 0.93 ms (–8.88;10.75)]. Estimates for the ‘conditional’ risk class increased upon refinement with relative drug exposure and coadministration of a ‘known’ risk drug as a further risk factor. Conclusions: These observations indicate that indiscriminate combinations of QTc-prolonging drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation caused by drug combinations strongly depends on the nature of the combination partners and individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also for the risk prediction of combination therapies with QT-prolonging drugs

Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Genetic programming with semantic equivalence classes

Ruberto, S., Vanneschi, L., & Castelli, M. (2019). Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. DOI: 10.1016/j.swevo.2018.06.001In this paper, we introduce the concept of semantics-based equivalence classes for symbolic regression problems in genetic programming. The idea is implemented by means of two different genetic programming systems, in which two different definitions of equivalence are used. In both systems, whenever a solution in an equivalence class is found, it is possible to generate any other solution in that equivalence class analytically. As such, these two systems allow us to shift the objective of genetic programming: instead of finding a globally optimal solution, the objective is now to find any solution that belongs to the same equivalence class as a global optimum. Further, we propose improvements to these genetic programming systems in which, once a solution that belongs to a particular equivalence class is generated, no other solution in that class is accepted in the population during the evolution anymore. We call these improved versions filtered systems. Experimental results obtained via seven complex real-life test problems show that using equivalence classes is a promising idea and that filters are generally helpful for improving the systems' performance. Furthermore, the proposed methods produce individuals with a much smaller size with respect to geometric semantic genetic programming. Finally, we show that filters are also useful to improve the performance of a state-of-the-art method, not explicitly based on semantic equivalence classes, like linear scaling.authorsversionpublishe

Ecophysiological characterization of cultivable Antarctic psychrotolerant marine bacteria able to degrade hydrocarbons

The basic understanding of both the physiology and ecology of psychrotolerant Antarctic bacteria is a crucial step for the optimization of their biodegradative activity in cold environments. The detection of cold-adapted hydrocarbon-degrading bacteria in Antarctic seawaters is certainly of great interest for bioremediative purpose in oil polluted marine Antarctic systems, where the introduction of non native species is not allowed. This study focused on psychrotolerant marine bacteria inhabiting an Antarctic coastal area directly influenced by the human activity at the Italian Research Station (Terra Nova Bay). Fifty bacterial strains were isolated from hydrocarbon-degrading enrichment cultures obtained from seawater samples collected in the inlet Road Bay (Ross Sea). A preliminary Restriction Fragment Length Polymorphism (RFLP) analysis, carried out on 16S rDNA amplified via PCR using RSAI and AluI restriction enzymes, was applied to cluster the isolates according to the restriction profile they showed. One representative isolate per cluster was selected for further characterization; to elucidate their taxonomic position, conventional phenotypic and phylogenetic analyses were performed. Results led to the identification of the isolates as members of ten genera belonging to four phylogenetic groups: the alfa- and gamma-proteobacteria subdivisions, the gram-positive branch and the Cytophaga-Flexibacter-Bacteroides (CFB) phylum. Results indicate a high degree of biodiversity within the peculiar ecophysiological group of the hydrocarbon-degrading marine bacteria

A multiple expression alignment framework for genetic programming

Vanneschi, L., Scott, K., & Castelli, M. (2018). A multiple expression alignment framework for genetic programming. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming: 21st European Conference, EuroGP 2018, Proceedings, pp. 166-183. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_11Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analyzing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on four real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.authorsversionpublishe

An improved lumped model for freezing of a freely suspended supercooled water droplet in air stream

This work deals with the mathematical modeling of the transient freezing process of a supercooled water droplet in a cold air stream. The aim is to develop a simple yet accurate lumped-differential model for the energy balance for a freely suspended water droplet undergoing solidification, that allows for cost effective computations of the temperatures and freezing front evolution along the whole process. The complete freezing process was described by four distinct stages, namely, supercooling, recalescence, solidification, and cooling. At each stage, the Coupled Integral Equations Approach (CIEA) is employed, which reduces the partial differential equation for the temperature distribution within the spherical droplet into coupled ordinary differential equations for dimensionless boundary temperatures and the moving interface position. The resulting lumped-differential model is expected to offer improved accuracy with respect to the classical lumped system analysis, since boundary conditions are accounted for in the averaging process through Hermite approximations for integrals. The results of the CIEA were verified using a recently advanced accurate hybrid numerical-analytical solution through the Generalized Integral Transform Technique (GITT), for the full partial differential formulation, and comparisons with numerical and experimental results from the literature. After verification and validation of the proposed model, a parametric analysis is implemented, for different conditions of airflow velocity and droplet radius, which lead to variations in the Biot numbers that allow to inspect for their influence on the accuracy of the improved lumped-differential formulation

Microstructural Evolution of Cr-Rich ODS Steels as a Function of Heat Treatment at 475°C

In the current research, the effect of heat treatment on the morphology of the dispersoids and their phase composition were investigated in three Cr-rich ferritic oxide dispersion strengthened (ODS) steels: PM2000, MA956, and ODM751. The steels were aged at 475°C for times ranging from 100 to 1,000 h. The microstructure was characterized using transmission electron microscopy. Study of the as-recrystallized samples revealed nano-scale Y–Al–O complex-oxide particles dispersed in the ferritic matrix. These dispersoids, which differ in size (10–160 nm) and geometry (polygonal and spherical), were identified as Y4Al2O9, YAlO3, and Y3Al5O12. After heat treatment, a significant change in the morphology, size, and distribution of the dispersoids was observed. Changes in the phase composition of the oxide dispersoids were also observed: YAlO3 (with perovskite structure) was identified as the most dominant phase, indicating that it is probably the most stable phase in the Cr-rich ferritic ODS steels.JRC.F.4-Nuclear Reactor Integrity Assessment and Knowledge Managemen