Spin polarization versus lifetime effects at point contacts between superconducting niobium and normal metals

Point-contact Andreev reflection spectroscopy is used to measure the spin polarization of metals but analysis of the spectra has encountered a number of serious challenges, one of which is the difficulty to distinguish the effects of spin polarization from those of the finite lifetime of Cooper pairs. We have recently confirmed the polarization-lifetime ambiguity for Nb-Co and Nb-Cu contacts and suggested to use Fermi surface mismatch, the normal reflection due to the difference of Fermi wave vectors of the two electrodes, to solve this dilemma. Here we present further experiments on contacts between superconducting Nb and the ferromagnets Fe and Ni as well as the noble metals Ag and Pt that support our previous results. Our data indicate that the Nb - normal metal interfaces have a transparency of up to about 80 per cent and a small, if not negligible, spin polarization.Comment: 7 pages, 2 figures, submitted to Proceedings of the 26th Conference on Low Temperature Physic

Generation of large scale digital evaluation models via synthetic aperture radar interferometry

We investigate the possibility to generate a large-scale Digital Elevation Model by applying the Synthetic Aperture Radar interferometry technique and using tandem data acquired by the ERS-1/ERS-2 sensors. The presented study is mainly focused on the phase unwrapping step that represents the most critical point of the overall processing chain. In particular, we concentrate on the unwrapping problems related to the use of a large ERS tandem data set that, in order to be unwrapped, must be partitioned. The paper discusses the inclusion of external information (even rough) of the scene topography, the application of a region growing unwrapping technique and the insertion of possible constraints on the phase to be retrieved in order to minimize the global unwrapping errors. Our goal is the generation of a digital elevation model relative to an area of 300 km by 100km located in the southern part of Italy. Comparisons between the achieved result and a precise digital terrain model, relative to a smaller area, are also included

Nanostructures by Self-assembling Peptide Amphiphile as Potential Selective Drug Carriers

The self-assembling behaviour, at physiological pH, of the amphiphile peptide (C18)(2)L5CCK8 in nanostructures is reported. Stable aggregates presenting a critical micellar concentration of 2 X 10(-6) mol kg(-1), and characterized by water exposed CCK8 peptide in P-sheet conformation, are obtained. Small angle neutron scattering experiments are indicative for a 3D structure with dimensions >= 100 nm. AFM images confirm the presence of nanostructures. Fluorescence experiments indicating the sequestration of pyrene, chosen as drug model, and the anticancer Doxorubicin within the nanostructures are reported

Dental unit water content and antibiotic resistance of Pseudomonas aeruginosa and Pseudomonas species: a case study

Background Many studies consider the contamination of dental unit waterlines (DUWLs), but few of them have studied the possible presence of antibiotic resistant Pseudomonas aeruginosa in the DUWLs. Aims Investigation of the presence of P. aeruginosa and Pseudomonas spp. strains in DUWLs and evaluation of their resistance to six antibiotics (ceftazidime, netilmicin, piperacillin/tazobactam, meropenem, levofloxacin, colistin sulfate) at a public dental clinic in Milan, Italy. Results Dental units were contaminated by P. aeruginosa with loads of 2-1,000 CFU/L and were mainly located on the mezzanine floor, with a range of 46-54%, while Pseudomonas spp. were primarily found on the first and second floors, ranging from 50 to 91%. P. aeruginosa was antibiotic resistant in 30% of the strains tested, andPseudomonas spp. in 31.8% . Cold water from controls was also contaminated by these microorganisms. Conclusion Monitoring antibiotic resistance in the water and adopting disinfection procedures on DUs are suggested within the Water Safety Plan

A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Shear induced grain boundary motion for lamellar phases in the weakly nonlinear regime

We study the effect of an externally imposed oscillatory shear on the motion of a grain boundary that separates differently oriented domains of the lamellar phase of a diblock copolymer. A direct numerical solution of the Swift-Hohenberg equation in shear flow is used for the case of a transverse/parallel grain boundary in the limits of weak nonlinearity and low shear frequency. We focus on the region of parameters in which both transverse and parallel lamellae are linearly stable. Shearing leads to excess free energy in the transverse region relative to the parallel region, which is in turn dissipated by net motion of the boundary toward the transverse region. The observed boundary motion is a combination of rigid advection by the flow and order parameter diffusion. The latter includes break up and reconnection of lamellae, as well as a weak Eckhaus instability in the boundary region for sufficiently large strain amplitude that leads to slow wavenumber readjustment. The net average velocity is seen to increase with frequency and strain amplitude, and can be obtained by a multiple scale expansion of the governing equations

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

International audienceThe use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)) . We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) separate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)) , thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance

Ruthenium(III) complexes entrapped in liposomes with enhanced cytotoxic and anti-metastatic properties

Metal-based anticancer drugs are pivotal in the fight against cancer pathologies. Since 1978 cis-platin was licensed for medical treatment of a wide number of tumor pathologies(1). However its chemiotherapic use is strongly limited by many and severe side effects and acquired tumor resistance. Since these limitations could be overcome by other metal complexes, in the last thirty years ruthenium compounds have been tested showing a remarkable antitumoral and antimetastatic activity associated with a lower toxicity. A hexacoordinate Ru(III) complex (NAMI-A) is currently undergoing advanced clinical evaluation (2). All data indicate that NAMI-A acts as a pro-drug, but the integrity of ruthenium complexes is essential to store the cytotoxic activity. In this scenario the condition of administration of ruthenium drugs are crucial to exploit their anticancer activity (3). In the last years innovative strategies have been produced to vehicle ruthenium ions in tumor cells like aggregates. This study aims to incorporate the ruthenium complexes in the inner aqueous compartment of liposomes and to test biological properties of two NAMI-A like pyridine derivatives. Specifically, we have investigated the pyridine derivatives of the sodium-compensated analogue of NAMI-A, Na[trans-RuCl4(pyridine)(DMSO)] (NAMI-Pyr) and Na[trans-RuCl4(Pytri)(DMSO)] (NAMI-Pytri). In thelatter complex the pyridine ligand is functionalized with a sugar moiety so as to increase biocompatibility and the ability to cross the cell membrane. The stability of the complexes was studied and compared in solution at different pH following UV-VIS spectra. Lipid formulations based on Egg PC were prepared adding Cholesterol, DSPE-PEG2000 joining molar ratio 57/38 /5% w/w respectively in MeOH/CHCl3 (50/50 v/v) mixture and hydrated with 0.9% w/w of NaCl. This composition was selected to reproduce analog supramolecular aggregates in clinical use to vehicle doxorubicin (Doxil). Ruthenium complexes were loaded into liposomes using the passive equilibration loading method. Full drug containing liposomes were structurally characterized by dynamic light scattering (DLS) measurements. Data indicate the formation of stable aggregates with size and shape in the right range for in vivo applications. The amount of encapsulated ruthenium complexes was quantified by means of ICP-AES. Stability and drug release properties of ruthenium containing liposomes were confirmed in buffer. The growth inhibitory effects of both liposomal and free complexes drug were tested on prostate cancer cells (PC3). Preliminary results show high cytotoxic effect of ruthenium complexes delivered by supramolecular aggregates with respect to free complexes drug

Performance Enhancement of Deep Reinforcement Learning Networks using Feature Extraction

The combination of Deep Learning and Reinforcement Learning, termed Deep Reinforcement Learning Networks (DRLN), offers the possibility of using a Deep Learning Neural Network to produce an approximate Reinforcement Learning value table that allows extraction of features from neurons in the hidden layers of the network. This paper presents a two stage technique for training a DRLN on features extracted from a DRLN trained on a identical problem, via the implementation of the Q-Learning algorithm, using TensorFlow. The results show that the extraction of features from the hidden layers of the Deep Q-Network improves the learning process of the agent (4.58 times faster and better) and proves the existence of encoded information about the environment which can be used to select the best action. The research contributes preliminary work in an ongoing research project in modeling features extracted from DRLNs

An Appraisal of the Oleocanthal-Rich Extra Virgin Olive Oil (EVOO) and Its Potential Anticancer and Neuroprotective Properties

dietary consumption of olive oil represents a key pillar of the mediterranean diet, which has been shown to exert beneficial effects on human health, such as the prevention of chronic non-communicable diseases like cancers and neurodegenerative diseases, among others. these health benefits are partly mediated by the high-quality extra virgin olive oil (EVOO), which is produced mostly in mediterranean countries and is directly made from olives, the fruit of the olive tree (Olea europaea L.). preclinical evidence supports the existence of antioxidant and anti-inflammatory properties exerted by the polyphenol oleocanthal, which belongs to the EVOO minor polar compound subclass of secoiridoids (like oleuropein). this narrative review aims to describe the antioxidant and anti-inflammatory properties of oleocanthal, as well as the potential anticancer and neuroprotective actions of this polyphenol. based on recent evidence, we also discuss the reasons underlying the need to include the concentrations of oleocanthal and other polyphenols in the EVOO's nutrition facts label. finally, we report our personal experience in the production of a certified organic EVOO with a "protected designation of origin" (PDO), which was obtained from olives of three different cultivars (rotondella, frantoio, and Leccino) harvested in geographical areas located a short distance from one another (villages' names: gorga and camella) within the southern italy "cilento, vallo di diano and alburni national park" of the campania region (province of salerno, Italy)