Strategies to Modulate Specialized Metabolism in Mediterranean Crops: From Molecular Aspects to Field

Plant specialized metabolites (SMs) play an important role in the interaction with the environment and are part of the plant defense response. These natural products are volatile, semi-volatile and non-volatile compounds produced from common building blocks deriving from primary metabolic pathways and rapidly evolved to allow a better adaptation of plants to environmental cues. Specialized metabolites include terpenes, flavonoids, alkaloids, glucosinolates, tannins, resins, etc. that can be used as phytochemicals, food additives, flavoring agents and pharmaceutical compounds. This review will be focused on Mediterranean crop plants as a source of SMs, with a special attention on the strategies that can be used to modulate their production, including abiotic stresses, interaction with beneficial soil microorganisms and novel genetic approaches.     

Synthesis of Invar 36 type alloys from elemental and prealloyed powders by mechanical alloying

Invar 36 (Fe₆₄Ni₃₆) nanocrystalline powders were successfully obtained by the mechanical alloying process. The mechanically alloyed Invar 36 powders were obtained from both, Fe–Ni elemental and Fe–Ni₃Fe prealloyed powders. XRD, DSC and magnetic measurements were used to characterise the Invar 36 powders. The lattice parameter evolution versus temperature of Invar 36 powders was investigated by in-situ high-temperature X-ray diffraction (HT-XRD). For both, Invar 36 (Fe, Ni) and Invar 36 (Fe, Ni₃Fe) powders, the lattice parameter values are constant up to about 350°C. The magnetic measurement also indicated that the Invar 36-type alloys are formed after 16?h of milling.     

Variational inequality in classical plasticity. Applications to Armstrong–Frederick elasto-plastic model

In this paper the quasi-static initial and boundary value problem for an elasto-plastic mixed hardening material is reformulated within the constitutive framework of small strains. The plastic factor plays the basic role in describing the rate independent evolution equations for the plastic strain and hardening variables. The plastic factor is equivalently represented as the solution of an appropriate local inequality involving the yield function. The main idea was to introduce the variational inequality at any time $t$ to be solved for the velocity field and the complementary plastic factor. There is the plastic factor in a strain-driven process. The solution procedure proposed here to solve the initial and boundary value problem is based on the solutions of the variational inequality at time $t$, coupled with an update algorithm in order to evaluate the current state of the material for an incremental deformation process. This time the return mapping algorithm is avoided as the values of the plastic factor and the velocity are known at time $t.$ As we developed a procedure to simultaneously solve the equilibrium equation coupled with the rate-independent evolution equations, no necessity to compute the algorithmic elasto-plastic tangent moduli occurs. The numerical simulations are done for the mixed hardening elasto-plastic model involving Armstrong–Frederick kinematic hardening. To validate the proposed numerical algorithms, we compare the solutions based on the variational inequality and those based on return mapping algorithm, computed for the same Prager kinematic hardening law.     

Experiences in building a Grid-based platform to serve Earth observation training activities

Earth observation data processing and storing can be done nowadays only using distributed systems. Experiments dealing with a large amount of data are possible within the timeframe of a lesson and can give trainees the freedom to innovate. Following these trends and ideas, we have built a proof-of-the-concept platform, named GiSHEO, for Earth observation educational tasks. It uses Grid computing technologies to analyze and store remote sensing data, and combines them with eLearning facilities. This paper provides an overview of the GiSHEO's platform architecture and of its technical and innovative solutions.     

Modulation of the antioxidant/pro-oxidant balance,cytotoxicity and antiviral actions of grape seed extracts

Grape seed extracts (GSEs) were investigated in yeast cells harbouring defects in their antioxidant system (regarding the cellular growth and growth recovery from H₂O₂ insult). GSEs antioxidant activity was detected in wild-type and mutant strains Δcta1, Δgsh1 and Δoye2glr1, while pro-oxidant activity in Δsod1 cells was seen. Assessment of proliferation of prostate cancer PC3 and HBV-replicating HepG2 2.2.15 cells treated with GSEs has shown higher cytotoxicity of red grape seed extract (RW) than white grape seed extract (WW) subjective to dose and period of administration. No antiviral effect was detected by measuring the secreted virion particles in HepG2 2.2.15 cells treated with GSEs. The GSEs play a dual antioxidant/pro-oxidant role in vivo according with the cellular antioxidant system deficiencies and exhibit cytotoxic properties in PC3 and HepG2 2.2.15 cell lines, but no antiviral action against HBV.     

Genetic nearest feature plane

The problem addressed in this paper concerns the complexity reduction of the nearest feature plane classifier, so that it may be applied also in dataset where the training set contains many patterns. This classifier considers, to classify a test pattern, the subspaces created by each combination of three training patterns. The main problem is that in dataset of high cardinality this method is unfeasible.A genetic algorithm is here used for dividing the training patterns in several clusters which centroids are used to build the feature planes used to classify the test set.The performance improvement with respect to other nearest neighbor based classifiers is validated through experiments with several benchmark datasets.