Mild methods to assemble and pattern organic monolayers on hydrogen-terminated Si(111)

Mild methods to assemble well-ordered organic monolayers of olefins on Si(111) using 2,2,6,6-tetramethyl-1-piperidinyloxy and to pattern these monolayers on the micrometer-size scale using soft lithography are reported.     

On robust optimization of two-stage systems

Robust-optimization models belong to a special class of stochastic programs, where the traditional expected cost minimization objective is replaced by one that explicitly addresses cost variability. This paper explores robust optimization in the context of two-stage planning systems. We show that, under arbitrary measures for variability, the robust optimization approach might lead to suboptimal solutions to the second-stage planning problem. As a result, the variability of the second-stage costs may be underestimated, thereby defeating the intended purpose of the model. We propose sufficient conditions on the variability measure to remedy this problem. Under the proposed conditions, a robust optimization model can be efficiently solved using a variant of the L-shaped decomposition algorithm for traditional stochastic linear programs. We apply the proposed framework to standard stochastic-programming test problems and to an application that arises in auctioning excess electric power. Mathematics Subject Classification (1991):90C15, 90C33, 90B50, 90A09, 90A43Supported in part by NSF Grants DMI-0099726 and DMI-0133943     

Assessment of unmixing approaches for the quantitation of SERS nanoparticles in highly multiplexed spectral images

Surface-enhanced Raman scattering nanoparticles (SERS NPs) offer powerful optical contrast features for imaging assays. Their gold core enhances the inelastic scattering cross section, allowing highly sensitive and rapid detection, and their characteristic sets of narrow spectral bands give them unsurpassed multiplexing capabilities. Multiplexed hyperspectral images are commonly unmixed using a compensation matrix of reference spectra to produce quantitative image channels illustrating the distribution of each material. It is these unmixed channels that are fit for interpretation from assays utilizing SERS NP contrast agents. Some factors that may impact SERS NP quantitative and dynamic range capabilities may include endogenous background heterogeneity, the ability of unmixing algorithms to account for signal variances, and linear system conditioning imposed by contrast agent signals. We report on hyperspectral Raman imaging of mixtures of SERS NPs from an expanded library of contrast agents. We study increasing plexity and varying degrees of system conditioning as inputs to a diverse set of classical, non-negatively constrained, and regularized regression algorithms to investigate which signal features and unmixing methods deliver the most promising quantitation performance with the least error. Raman imaging of SERS NP mixtures is performed on controlled substrates and representative biological specimens, and experimental results are compared against ground truth data. We evaluate spectral fitting fidelity, quantitation, and specificity correlations with system conditioning. Spectral unmixing with a regularized hybrid of least squares regression with principal component analysis (HLP) algorithm approximated spectra with 3.5× better fitting fidelity and 3× better quantitation robustness with tissue background compared with simpler unmixing routines.     

Metamodels for role-driven agent-based modelling

A major challenge in agent-based modelling is the management of the process to generate executable simulations from the initial conceptual models. This process is complex and usually involves several roles, which may raise communication problems due to the diverse backgrounds and perspectives of participants and the use of non-explicit knowledge. This situation demands a clear separation and precise definition of the multiple aspects of the process, in order to facilitate their understanding, grasp their relationships and develop them. This paper addresses this goal with a fine-step refinement process for information based on the use of domain-specific languages. It considers analysis contexts that include a particular theoretical framework, domain, type of problem and target platform. For a given context, the process formally defines modelling languages conceptually close to the different aspects relevant to it. It also defines mappings between concepts in those languages. Researchers develop simulations by specifying models with the languages, and share and refine information by using mappings between these models. This infrastructure provides guidance throughout the process and makes the information involved explicit. A case study of continuous double auctions illustrates the approach.     

Algorithms for propositional model counting

We present algorithms for the propositional model counting problem #SAT. The algorithms utilize tree decompositions of certain graphs associated with the given CNF formula; in particular we consider primal, dual, and incidence graphs. We describe the algorithms coherently for a direct comparison and with sufficient detail for making an actual implementation reasonably easy. We discuss several aspects of the algorithms including worst-case time and space requirements.     

Determination of mercury and methylmercury in hair samples by neutron activation

Mercury and methylmercury in hair samples were determined by neutron activation analysis. Samples were digested in 10M NaOH, and methylmercury was then isolated by solvent extraction with toluene. The isolated methylmercury was then absorbed onto cysteine paper. The dried cysteine paper was activated for six hours in a TRIGA reactor and methylmercury was analysed via 279.2 keV of²⁰³Hg. Methylmercury and total mercury in some standard reference materials were also analysed, and the results were in good agreement with those reported in the literature. Results for hair samples showed that the methylmercury concentration ranged 14–40% of the total mercury. Gas chromatogram showed that methylmercury was only present in the samples analysed. In samples where methylmercury and other organic mercury are presented, the NAA method is good for the determination of the total organic mercury only.     

Natural Compound 3β,7β,25-trihydroxycucurbita-5,23(E)-dien-19-al from Momordica charantia Acts as PPARγ Ligand

Momordica charantia is a popular vegetable associated with effective complementary and alternative diabetes management in some parts of the world. However, the molecular mechanism is less commonly investigated. In this study, we investigated the association between a major cucurbitane triterpenoid isolated from M. charantia, 3β,7β,25-trihydroxycucurbita-5,23(E)-dien-19-al (THCB) and peroxisome proliferator activated receptor gamma (PPARγ) activation and its related activities using cell culture and molecular biology techniques. In this study, we report on both M. charantia fruit crude extract and THCB in driving the luciferase activity of Peroxisome Proliferator Response Element, associated with PPARγ activation. Other than that, THCB also induced adipocyte differentiation at far less intensity as compared to the full agonist rosiglitazone. In conjunction, THCB treatment on adipocytes also resulted in upregulation of PPAR gamma target genes expression; AP2, adiponectin, LPL and CD34 at a lower magnitude compared to rosiglitazone’s induction. THCB also induced glucose uptake into muscle cells and the mechanism is via Glut4 translocation to the cell membrane. In conclusion, THCB acts as one of the many components in M. charantia to induce hypoglycaemic effect by acting as PPARγ ligand and inducing glucose uptake activity in the muscles by means of Glut4 translocation.     

Eugenol-Functionalized Magnetite Nanoparticles Modulate Virulence and Persistence in Pseudomonas aeruginosa Clinical Strains

Efficient antibiotics to cure Pseudomonas aeruginosa persistent infections are currently insufficient and alternative options are needed. A promising lead is to design therapeutics able to modulate key phenotypes in microbial virulence and thus control the progression of the infectious process without selecting resistant mutants. In this study, we developed a nanostructured system based on Fe₃O₄ nanoparticles (NPs) and eugenol, a natural plant-compound which has been previously shown to interfere with microbial virulence when utilized in subinhibitory concentrations. The obtained functional NPs are crystalline, with a spherical shape and 10–15 nm in size. The subinhibitory concentrations (MIC 1/2) of the eugenol embedded magnetite NPs (Fe₃O₄@EUG) modulate key virulence phenotypes, such as attachment, biofilm formation, persister selection by ciprofloxacin, and the production of soluble enzymes. To our knowledge, this is the first report on the ability of functional magnetite NPs to modulate P. aeruginosa virulence and phenotypic resistance; our data highlights the potential of these bioactive nanostructures to be used as anti-pathogenic agents.