Analysis of complex molecular systems: The impact of multivariate analysis for resolving the interactions of small molecules with biopolymers - a review

This review is focused on the impact of chemometrics for resolving data sets collected from investigations of the interactions of small molecules with biopolymers. These samples have been analyzed with various instrumental techniques, such as fluorescence, ultraviolet–visible spectroscopy, and voltammetry. The impact of two powerful and demonstrably useful multivariate methods for resolution of complex data—multivariate curve resolution–alternating least squares (MCR–ALS) and parallel factor analysis (PARAFAC)—is highlighted through analysis of applications involving the interactions of small molecules with the biopolymers, serum albumin, and deoxyribonucleic acid. The outcomes illustrated that significant information extracted by the chemometric methods was unattainable by simple, univariate data analysis. In addition, although the techniques used to collect data were confined to ultraviolet–visible spectroscopy, fluorescence spectroscopy, circular dichroism, and voltammetry, data profiles produced by other techniques may also be processed. Topics considered including binding sites and modes, cooperative and competitive small molecule binding, kinetics, and thermodynamics of ligand binding, and the folding and unfolding of biopolymers. Applications of the MCR–ALS and PARAFAC methods reviewed were primarily published between 2008 and 2013.

Pathogens penetrating the central nervous system: Infection pathways and the cellular and molecular mechanisms of invasion

The brain is well protected against microbial invasion by cellular barriers, such as the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB). In addition, cells within the central nervous system (CNS) are capable of producing an immune response against invading pathogens. Nonetheless, a range of pathogenic microbes make their way to the CNS, and the resulting infections can cause significant morbidity and mortality. Bacteria, amoebae, fungi, and viruses are capable of CNS invasion, with the latter using axonal transport as a common route of infection. In this review, we compare the mechanisms by which bacterial pathogens reach the CNS and infect the brain. In particular, we focus on recent data regarding mechanisms of bacterial translocation from the nasal mucosa to the brain, which represents a little explored pathway of bacterial invasion but has been proposed as being particularly important in explaining how infection with Burkholderia pseudomallei can result in melioidosis encephalomyelitis.

Multiview gait biometrics for human identity recognition

We propose a novel multiview fusion scheme for recognizing human identity based on gait biometric data. The gait biometric data is acquired from video surveillance datasets from multiple cameras. Experiments on publicly available CASIA dataset show the potential of proposed scheme based on fusion towards development and implementation of automatic identity recognition systems.

What triggers problem recognition? An exploration on young Australian male problematic online gamers

Help-seeking is a complex decision-making process that first begins with problem recognition. However, little is understood about the conceptualisation of the helpseeking process and the triggers of problem recognition. This research proposes the use of the Critical Incident Technique (CIT) to examine and classify incidents that serve as key triggers of problem recognition among young Australian male problematic online gamers. The research provides a classification of five different types of triggers that will aid social marketers into developing effective early detection, prevention and treatment focused social marketing interventions.

Fractal and complex network analyses of protein molecular dynamics

Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h(q)h(q) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h(2)h(2) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H≈0.5H≈0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h(2)h(2) of MF-DFA on the time series, exponent λλ of the exponential degree distribution and fractal dimension dBdB of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between 〈h(2)〉〈h(2)〉 (from MF-DFA on time series) and 〈dB〉〈dB〉 of the converted HVGs for different energy, pressure and volume.

Molecular orientational order of nitroxide radicals in liquid crystalline media

The orientational distribution of a set of stable nitroxide radicals in aligned liquid crystals 5CB (nematic) and 8CB (smectic A) was studied in detail by numerical simulation of EPR spectra. The order parameters up to the 10th rank were measured. The directions of the principal orientation axes of the radicals were determined. It was shown that the ordering of the probe molecules is controlled by their interaction with the matrix molecules more than the inherent geometry of the probes themselves. The rigid fused phenanthrene-based (A5) and 2-azaphenalene (A4) nitroxides as well as the rigid core elongated C11 and 5α-cholestane (CLS) nitroxides were found to be most sensitive to the orientation of the liquid crystal matrixes.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

On robust face recognition via sparse coding: The good, the bad and the ugly

In the field of face recognition, sparse representation (SR) has received considerable attention during the past few years, with a focus on holistic descriptors in closed-set identification applications. The underlying assumption in such SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such an assumption is easily violated in the face verification scenario, where the task is to determine if two faces (where one or both have not been seen before) belong to the same person. In this study, the authors propose an alternative approach to SR-based face verification, where SR encoding is performed on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which then form an overall face descriptor. Owing to the deliberate loss of spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment and various image deformations. Within the proposed framework, they evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN) and an implicit probabilistic technique based on Gaussian mixture models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, on both the traditional closed-set identification task and the more applicable face verification task. The experiments also show that l1-minimisation-based encoding has a considerably higher computational cost when compared with SANN-based and probabilistic encoding, but leads to higher recognition rates.

Synthesis of a multimodal molecular imaging probe based on a hyperbranched polymer architecture

Molecular imaging is utilised in modern medicine to aid in the diagnosis and treatment of disease by allowing its spatiotemporal state to be examined in vivo. This study focuses on the development of novel multimodal molecular imaging agents based on hyperbranched polymers that combine the complementary capabilities of optical fluorescence imaging and positron emission tomography-computed tomography (PET/CT) into one construct. RAFT-mediated polymerisation was used to prepare two hydrophilic hyperbranched polymers that were differentiated by their size and level of branching. The multiple functional end-groups facilitated covalent attachment of both near infrared fluorescent dyes for optical imaging, as well as a copper chelator allowing binding of 64Cu as a PET radio nuclei. In vivo multimodal imaging of mice using PET/CT and planar optical imaging was first used to assess the biodistribution of the polymeric materials and it was shown that the larger and more branched polymer had a significantly longer circulation time. The larger constructs were also shown to exhibit enhanced accumulation in solid tumours in a murine B16 melanoma model. Importantly, it was demonstrated that the PET modality gave rise to high sensitivity immediately after injection of the agent, while the optical modality facilitated extended longitudinal studies, thus highlighting how the complementary capabilities of the molecular imaging agents can be useful for studying various diseases, including cancer.

Molecular imaging with polymers

Polymers open up new possibilities in the field of molecular imaging, allowing sensitive and robust agents that can be imaged over long periods of time. This review highlights some recent advances in polymeric molecular imaging agents in both (pre)clinical and emerging applications.

The function of the RNA-binding protein TEL1 in moss reveals ancient regulatory mechanisms of shoot development

The shoot represents the basic body plan in land plants. It consists of a repeated structure composed of stems and leaves. Whereas vascular plants generate a shoot in their diploid phase, non-vascular plants such as mosses form a shoot (called the gametophore) in their haploid generation. The evolution of regulatory mechanisms or genetic networks used in the development of these two kinds of shoots is unclear. TERMINAL EAR1-like genes have been involved in diploid shoot development in vascular plants. Here, we show that disruption of PpTEL1 from the moss Physcomitrella patens, causes reduced protonema growth and gametophore initiation, as well as defects in gametophore development. Leafy shoots formed on ΔTEL1 mutants exhibit shorter stems with more leaves per shoot, suggesting an accelerated leaf initiation (shortened plastochron), a phenotype shared with the Poaceae vascular plants TE1 and PLA2/LHD2 mutants. Moreover, the positive correlation between plastochron length and leaf size observed in ΔTEL1 mutants suggests a conserved compensatory mechanism correlating leaf growth and leaf initiation rate that would minimize overall changes in plant biomass. The RNA-binding protein encoded by PpTEL1 contains two N-terminus RNA-recognition motifs, and a third C-terminus non-canonical RRM, specific to TEL proteins. Removal of the PpTEL1 C-terminus (including this third RRM) or only 16–18 amino acids within it seriously impairs PpTEL1 function, suggesting a critical role for this third RRM. These results show a conserved function of the RNA-binding PpTEL1 protein in the regulation of shoot development, from early ancestors to vascular plants, that depends on the third TEL-specific RRM.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

Conceptualising recognition of prior learning processes in the age of open learning

This chapter interrogates what recognition of prior learning (RPL) can and does mean in the higher education sector—a sector in the grip of the widening participation agenda and an open access age. The chapter discusses how open learning is making inroads into recognition processes and examines two studies in open learning recognition. A case study relating to e-portfolio-style RPL for entry into a Graduate Certificate in Policy and Governance at a metropolitan university in Queensland is described. In the first instance, candidates who do not possess a relevant Bachelor degree need to demonstrate skills in governmental policy work in order to be eligible to gain entry to a Graduate Certificate (at Australian Qualifications Framework Level 8) (Australian Qualifications Framework Council, 2013, p. 53). The chapter acknowledges the benefits and limitations of recognition in open learning and those of more traditional RPL, anticipating future developments in both (or their convergence).

Crystal structures of the endoplasmic reticulum aminopeptidase-1 (ERAP1) reveal the molecular basis for N-terminal peptide trimming

Endoplasmatic reticulum aminopeptidase 1 (ERAP1) is a multifunctional enzyme involved in trimming of peptides to an optimal length for presentation by major histocompatibility complex (MHC) class I molecules. Polymorphisms in ERAP1 have been associated with chronic inflammatory diseases, including ankylosing spondylitis (AS) and psoriasis, and subsequent in vitro enzyme studies suggest distinct catalytic properties of ERAP1 variants. To understand structure-activity relationships of this enzyme we determined crystal structures in open and closed states of human ERAP1, which provide the first snapshots along a catalytic path. ERAP1 is a zinc-metallopeptidase with typical H-E-X-X-H-(X)18-E zinc binding and G-A-M-E-N motifs characteristic for members of the gluzincin protease family. The structures reveal extensive domain movements, including an active site closure as well as three different open conformations, thus providing insights into the catalytic cycle. A K 528R mutant strongly associated with AS in GWAS studies shows significantly altered peptide processing characteristics, which are possibly related to impaired interdomain interactions.

Molecular Origin of the Intrinsic Bending Force for Helical Morphology Observed in Chiral Amphiphilic Assemblies: Concentration and Size Dependence

The formation of the helical morphology in monolayers and bilayers of chiral amphiphilic assemblies is believed to be driven at least partly by the interactions at the chiral centers of the amphiphiles. However, a detailed microscopic understanding of these interactions and their relation with the helix formation is still not clear. In this article a study of the molecular origin of the chirality-driven helix formation is presented by calculating, for the first time, the effective pair potential between a pair of chiral molecules. This effective potential depends on the relative sizes of the groups attached to the two chiral centers, on the orientation of the amphiphile molecules, and also on the distance between them. We find that for the mirror-image isomers (in the racemic modification) the minimum energy conformation is a nearly parallel alignment of the molecules. On the other hand, the same for a pair of molecules of one kind of enantiomer favors a tilt angle between them, thus leading to the formation of a helical morphology of the aggregate. The tilt angle is determined by the size of the groups attached to the chiral centers of the pair of molecules considered and in many cases predicted it to be close to 45 degrees. The present study, therefore, provides a molecular origin of the intrinsic bending force, suggested by Helfrich (J. Chem. Phys. 1986, 85, 1085-1087), to be responsible for the formation of helical structure. This effective potential may explain many of the existing experimental results, such as the size and the concentration dependence of the formation of helical morphology. It is further found that the elastic forces can significantly modify the pitch predicted by the chiral interactions alone and that the modified real pitch is close to the experimentally observed value. The present study is expected to provide a starting point for future microscopic studies.