Synthesis of lipid A derivatives and their interactions with polymyxin B and polymyxin B nonapeptide

Lipid A is the causative agent of Gram-negative sepsis, a leading cause of mortality among hospitalized patients. Compounds that bind lipid A can limit its detrimental effects. Polymyxin B, a cationic peptide antibiotic, is one of the simplest molecules capable of selectively binding lipid A and may serve as a model for further development of lipid A binding agents. However, association of polymyxin B with lipid A is not fully understood, primarily due to the low solubility of lipid A in water and inhomogeneity of lipid A preparations. To better understand lipid A-polymyxin B interaction, pure lipid A derivatives were prepared with incrementally varied lipid chain lengths. These compounds proved to be more soluble in water than lipid A, with higher aggregation concentrations. Isothermal titration calorimetric studies of these lipid A derivatives with polymyxin B and polymyxin B nonapeptide indicate that binding stoichiometries (peptide to lipid A derivative) are less than 1 and that affinities of these binding partners correlate with the aggregation states of the lipid A derivatives. These studies also suggest that cooperative ionic interactions dominate association of polymyxin B and polymyxin B nonapeptide with lipid A.     

On the noise sensitivity of monotone functions

It is known that for all monotone functions f : {0, 1}ⁿ → {0, 1}, if x ∈ {0, 1}ⁿ is chosen uniformly at random and y is obtained from x by flipping each of the bits of x independently with probability ? = n^?α, then P[f(x) ≠ f(y)] < cn^?α+1/2, for some c > 0. Previously, the best construction of monotone functions satisfying P[f_n(x) ≠ f_n(y)] ≥ δ, where 0 < δ < 1/2, required ? ≥ c(δ)n^?α, where α = 1 ? ln 2/ln 3 = 0.36907 …, and c(δ) > 0. We improve this result by achieving for every 0 < δ < 1/2, P[f_n(x) ≠ f_n(y)] ≥ δ, with:

• ? = c(δ)n^?α for any α < 1/2, using the recursive majority function with arity k = k(α);
• ? = c(δ)n^?1/2log^tn for t = log₂ = .3257 …, using an explicit recursive majority function with increasing arities; and
• ? = c(δ)n^?1/2, nonconstructively, following a probabilistic CNF construction due to Talagrand.

We also study the problem of achieving the best dependence on δ in the case that the noise rate ? is at least a small constant; the results we obtain are tight to within logarithmic factors. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 23: 333–350, 2003     

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.     

Crystallization of Semiconductor Nanorods into Perfectly Faceted Hexagonal Superstructures

Super crystallization of ligand‐capped nanocrystals into defined periodic solids from solution is the definitive demonstration of their self‐organizing properties. To date, this has been mainly limited to spherical nanocrystals where organization emulates atom or molecule packing in regular crystals with the most thermodynamically stable arrangement being eventually preferred. Here, the crystallization of wurtzite CdS nanorods into micrometer‐sized CdS superstructures with regular hexagonal symmetry is demonstrated by fine‐tuning the nanorod dispersibility over time. It is shown that the supercrystals have a long nucleation stage to form monolayer hexagons followed by a relatively faster growth stage both occurring rod by rod (in‐plane) and layer by layer (out of plane). The perfectly symmetrical hexagon shape of the final structure is mapped from the wurtzite crystal structure of each individual nanorod where they pack in side by side and end to end arrangements. These well‐defined superstructures are highly attractive for applications that collectively exploit electronic or optical properties that are synthetically tunable through the size and shape of each nanorod building block.     

A lower bound for the optimal crossing-free Hamiltonian cycle problem

Consider a drawing in the plane ofK _n , the complete graph onn vertices. If all edges are restricted to be straight line segments, the drawing is called rectilinear. Consider a Hamiltonian cycle in a drawing ofK _n . If no pair of the edges of the cycle cross, it is called a crossing-free Hamiltonian cycle (cfhc). Let (n) represent the maximum number of cfhc's of any drawing ofK _n , and (n) the maximum number of cfhc's of any rectilinear drawing ofK _n . The problem of determining (n) and (n), and determining which drawings have this many cfhc's, is known as the optimal cfhc problem. We present a brief survey of recent work on this problem, and then, employing a recursive counting argument based on computer enumeration, we establish a substantially improved lower bound for (n) and (n). In particular, it is shown that (n) is at leastk × 3.2684 ⁿ . We conjecture that both (n) and (n) are at mostc × 4.5 ⁿ .This research, part of which was conducted at Queen's University, was supported by an N.S.E.R.C. postgraduate scholarship.     

Development of a cryopreservation protocol for the microcyclic rust-fungus Puccinia spegazzinii

The rust fungus Puccinia spegazzinii (Basidiomycotina: Uredinales) has been identified as a potential classical biological control agent for the invasive weed Mikania micrantha (Asteraceae). Long-term, live storage of this pathogen is required for reference. As biotrophs, almost all rusts species cannot be preserved by traditional cryopreservation protocols, which rely on in vitro culture techniques. In addition, the embedded teliospores and delicate basidiospores of this microcyclic rust are not amenable to direct plunge freezing. Continuous culture of the rust on living plants is both laborious and expensive, so a variety of approaches for cryopreservation and storage were tested. These methods included traditional approaches to fungal cryopreservation such as variation of cooling rate regime and alginate encapsulation techniques. However, an in situ cryopreservation technique was the only method identified as having any potential for the long-term cryopreservation of the 10 isolates tested. Material from either petiole or stem tissue remained viable after cryopreservation, determined by the ability of the material to produce basidiospores. However, despite great progress being made in developing an optimal cryopreservation method, infection of the host plant by basidiospores produced from previously cryopreserved teliospores, embedded in leaf petioles, was not achieved.