Competitive adsorption of plasma proteins at solid—liquid interfaces

The competitive adsorption of human serum albumin (HSA), human immuno-γ-globulin (HIgG) and human fibrinogen (HFb) onto polystyrene (PS) at 20° C and a pH of 7.35 (phosphate-buffered saline) was studied. Protein adsorption was studied using enzyme immunoassay. The results obtained with the immunoassay were compared with those obtained using radiolabelled proteins. Recent studies revealed that the adsorption behaviour of radiolabelled proteins onto surfaces differs from that of the non-labelled proteins, which may lead to misinterpretation of adsorption data. Differences in the adsorption behaviour of the labelled proteins as compared to non-labelled proteins can possibly be explained by the formation of modified proteins during the labelling procedure as shown by ion-exchange high-performance liquid chromatography (HPLC). The competitive adsorption of HSA, HIgG and HFb onto a PS latex was studied by measuring the depletion of proteins in solution. The decrease in protein concentration in solution was determined by HPLC techniques. A strong preferential adsorption of HFb was observed with maximum adsorption values of 0.6 μg/cm2

Mining Feature Relationships in Data

When faced with a new dataset, most practitioners begin by performing exploratory data analysis to discover interesting patterns and characteristics within data. Techniques such as association rule mining are commonly applied to uncover relationships between features (attributes) of the data. However, association rules are primarily designed for use on binary or categorical data, due to their use of rule-based machine learning. A large proportion of real-world data is continuous in nature, and discretisation of such data leads to inaccurate and less informative association rules. In this paper, we propose an alternative approach called feature relationship mining (FRM), which uses a genetic programming approach to automatically discover symbolic relationships between continuous or categorical features in data. To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features. Empirical testing on a variety of real-world datasets shows the proposed method is able to find high-quality, simple feature relationships which can be easily interpreted and which provide clear and non-trivial insight into data.Comment: 16 pages, accepted in EuroGP '2

Love In Lilac Time

https://digitalcommons.library.umaine.edu/mmb-vp/5921/thumbnail.jp

High-performance liquid chromatography as a technique to measure the competitive adsorption of plasma proteins onto latices

Isotherms of human serum albumin (HSA), human immunoglobulin G (HIgG), and human fibrinogen (HFb) onto a polystyrene (PS)-latex were determined by depletion of protein in the solution, which was either followed by radioactivity measurements or by UV spectroscopy. Different adsorption isotherms for the same protein were obtained when either radioactivity measurements or UV spectroscopy was used as a detection technique. In order to obtain reliable results from competitive protein adsorption experiments, a method based on the use of high-performance liquid chromatography was developed. A strong preferential adsorption of HFb was observed when adsorption studies were carried out with mixtures of HSA, HFb, and HIgG. When adsorption studies were carried out with solutions containing HSA monomer and dimer, a strong preferential adsorption of HSA dimer was also observed

Volume Holography: Novel Materials, Methods and Applications

This chapter aims to establish a link between material compositions, analytical methods and advanced applications for volume holography. It provides basics on volume holography, serving as a compendium on volume holographic grating formation, specific material requirements for volume holography and diffractive properties of the different types of volume holographic gratings. The particular significance of three‐dimensional optical structuring for the final optical functionality is highlighted. In this context, the interrelation between function and structure of volume holograms is investigated with view to research on and development of novel materials, methods and applications. Particular emphasis will be placed on analytical methods, assuming that they provide access for a deeper understanding of volume holographic grating formation, which appears to be prerequisite for the design of novel material systems for advanced applications

Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

Clustering is a difficult and widely-studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g. Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally pre-defined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved similarity functions can be used to perform clustering using a graph-based representation. The results of a variety of experiments across a range of large, high-dimensional datasets show that the proposed approach can achieve higher and more consistent performance than the benchmark methods. We further extend the proposed approach to automatically produce multiple complementary similarity functions by using a multi-tree approach, which gives further performance improvements. We also analyse the interpretability and structure of the automatically evolved similarity functions to provide insight into how and why they are superior to standard distance metrics.Comment: 29 pages, accepted by Evolutionary Computation (Journal), MIT Pres

Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming

Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.Comment: 16 pages, preprint for EuroGP '1

Nano-contact transfer with gold nanoparticles on PEG hydrogels and using wrinkled PDMS-stamps

In the present work, a soft lithographic process is used to create nanometer-sized line patterns of gold nanoparticles (Au NPs) on PEG-based hydrogels. Hereby nanometer-sized wrinkles on polydimethylsiloxane (PDMS) are first fabricated, then functionalized with amino-silane and subsequently coated with Au NPs. The Au NPs are electrostatically bound to the surface of the wrinkled PDMS. In the next step, these relatively loosely bound Au NPs are transferred to PEG based hydrogels by simple contacting, which we denote “nano-contact transfer”. Nano-patterned Au NPs lines on PEG hydrogels are thus achieved, which are of interesting potential in nano-photonics, biosensor applications (using SERS) and to control nanoscopic cell adhesion events.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli