Rashba-coupling modelling for two-dimensional and high-order Rashba Hamiltonian for one-dimensional confined heavy holes

Based on standard k.p (8 x 8) multiband Hamiltonian, we have deduced an explicit analytical expression for the Rashba-coupling parameter which clarifies its anomalous behavior for heavy holes (hh), gated in quasi-two-dimensional (Q2D) systems, by letting grow the density. Our modelling remarkable better agrees with experimental results in comparison with earlier theoretical models, while recovers the expected cubic dependence on the quasi-momentum. For quasi-one-dimensional (Q1D) hh systems, we have formally derived an effective Rashba Hamiltonian with two competitive terms on the quasi-momentum, a linear term and a cubic one as predicted from suitable approximations to the Q2D scope. The Rashba-coupling parameters also behave anomalously and qualitatively support recent experiments in core/shell nanowires. Furthermore, they exhibit an essential asymptotic discontinuity in the low density regime as a function of the lateral confinement length. For hh, we present closed schemes to accurately quote the Rashba-coupling parameters both for the Q2D and Q1D systems, which become unprecedented for holes.Comment: 6 pages, 4 figure

A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

<p>Background The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question.</p> <p>Results In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases.</p> <p>Conclusions With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.</p&gt

Optimization of diarylazines as anti-HIV agents with dramatically

Non-nucleoside inhibitors of HIV-1 reverse transcriptase are reported that have ca. 100-fold greater solubility than the structurally related drugs etravirine and rilpivirine, while retaining high anti-viral activity. The solubility enhancements come from strategic placement of a morpholinylalkoxy substituent in the entrance channel of the NNRTI binding site. Compound 4d shows low-nanomolar activity similar to etravirine towards wild-type HIV-1 and key viral variants.Fil: Bollini, Mariela. University of Yale; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cisneros, José A.. University of Yale; Estados UnidosFil: Spasov, Krasimir A.. University of Yale; Estados UnidosFil: Anderson, Karen S.. University of Yale; Estados UnidosFil: Jorgensen, William L.. University of Yale; Estados Unido