Two Optimal Value Functions in Parametric Conic Linear Programming

Journal of Optimization Theory and Applications - We consider the conic linear program given by a closed convex cone in an Euclidean space and a matrix, where vector on the right-hand side of the...     

Necessary and Sufficient Conditions for Qualitative Properties of Infinite Dimensional Linear Programming Problems

Necessary and sufficient conditions for qualitative properties of infinite dimensional linear programing problems such as solvability, duality, and complementary slackness conditions are studied in this article. As illustrations for the results, we investigate the parametric version of Gale’s example.     

A method of constructing 2-resolvable t-designs for $$t=3,4$$ t = 3 , 4

Designs, Codes and Cryptography - The paper introduces a method for constructing 2-resolvable t-designs for $$t=3,4$$ . The main idea is based on the assumption that there exists a partition of a...     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

A metabolically engineered spin-labeling approach for studying glycans on cells

Metabolic glycan engineering (MGE) coupled with nitroxide spin-labeling (SL) was utilized to investigate the heterogeneous environment of cell surface glycans in select cancer and normal cells. This approach exploited the incorporation of azides into cell surface glycans followed by a click reaction with a new nitroxide spin label. Both sialic acid and N-acetylglucosamine (GlcNAc) were targeted for spin labelling. Although each of these moieties experiences a diverse and heterogeneous glycan environment, their EPR spectra and hence mobility are both characterized as a linear combination of two distinct spectra where one component reflects a highly mobile or uncrowded micro-environment with the second component reflecting more restricted motion, reflective of increased crowding and packing within the glycocalyx. What differs among the spectra of the targeted glycans is the relative percentage of each component, with sialic acid moieties experiencing on average an ∼80% less crowded environment, where conversely GlcNAc/GalNAz labeled sites reported on average a ∼50% more crowded environment. These distinct environments are consistent with the organization of sugar moieties within cellular glycans where some residues occur close to the cell membrane/protein backbone (i.e. more restricted) and others are more terminal in the glycan (i.e. more mobile). Strikingly, different cell lines displayed varied relative populations of these two components, suggesting distinctive glycan packing, organization, and composition of different cells. This work demonstrates the capability of SDSL EPR to be a broadly useful tool for studying glycans on cells, and interpretation of the results provides insights for distinguishing the differences and changes in the local organization and heterogeneity of the cellular glycocalyx.

Metabolic glycan engineering (MGE) coupled with nitroxide spin-labeling (SL) was utilized to investigate the heterogeneous environment of cell surface glycans in select cancer and normal cells.