100 years on: a century of genetics

In 2006 we celebrated the centenary of a remarkable year that saw the birth of genetics as a scientific discipline. This birth had its origins in horticulture and was supervised by a remarkable Cambridge academic, accompanied by a loyal group of female colleagues who worked together in underfunded conditions with little institutional support. Despite this deprivation, they established the foundations of an ongoing revolution, with huge academic and commercial consequences that we can recognize today in the shape of genomics and its application to biomedicine.

Soy isoflavones increase preprandial peptide YY (PYY), but have no effect on ghrelin and body weight in healthy postmenopausal women

Background: Soy isoflavones show structural and functional similarities to estradiol. Available data indicate that estradiol and estradiol-like components may interact with gut "satiety hormones" such as peptide YY (PYY) and ghrelin, and thus influence body weight. In a randomized, double-blind, placebo-controlled, cross-over trial with 34 healthy postmenopausal women (59 ± 6 years, BMI: 24.7 ± 2.8 kg/m2), isoflavone-enriched cereal bars (50 mg isoflavones/day; genistein to daidzein ratio 2:1) or non-isoflavone-enriched control bars were consumed for 8 weeks (wash-out period: 8-weeks). Seventeen of the subjects were classified as equol producers. Plasma concentrations of ghrelin and PYY, as well as energy intake and body weight were measured at baseline and after four and eight weeks of each intervention arm. Results: Body weight increased in both treatment periods (isoflavone: 0.40 ± 0.94 kg, P < 0.001; placebo: 0.66 ± 0.87 kg, P = 0.018), with no significant difference between treatments. No significant differences in energy intake were observed (P = 0.634). PYY significantly increased during isoflavone treatment (51 ± 2 pmol/L vs. 55 ± 2 pmol/L), but not during placebo (52 ± 3 pmol/L vs. 50 ± 2 pmol/L), (P = 0.010 for treatment differences, independent of equol production). Baseline plasma ghrelin was significantly lower in equol producers (110 ± 16 pmol/L) than in equol non-producers (162 ± 17 pmol/L; P = 0.025). Conclusion: Soy isoflavone supplementation for eight weeks did not significantly reduce energy intake or body weight, even though plasma PYY increased during isoflavone treatment. Ghrelin remained unaffected by isoflavone treatment. A larger and more rigorous appetite experiment might detect smaller differences in energy intake after isoflavone consumption. However, the results of the present study do not indicate that increased PYY has a major role in the regulation of body weight, at least in healthy postmenopausal women.

Self-assembly of amphiphilic peptides

The self-assembly of amphiphilic peptides is reviewed. The review covers surfactant-like peptides with amphiphilicity arising from the sequence of natural amino acids, and also peptide amphiphiles (PAs) in which lipid chains are attached to hydrophilic peptide sequences containing charged residues. The influence of the secondary structure on the self-assembled structure and vice versa is discussed. For surfactant-like peptides structures including fibrils, nanotubes, micelles and vesicles have been reported. A particularly common motif for PAs is beta-sheet based fibrils, although other structures have been observed. In these structures, the peptide epitope is presented at the surface of the nanostructure, providing remarkable bioactivity. Recent discoveries of potential, and actual, applications of these materials in biomedicine and bionanotechnology are discussed.

Biomimetic soft matter

Biomaterials are often soft materials. There is now growing interest in designing, synthesizing and characterising soft materials that mimic the properties of biological materials such as tissue, proteins, DNA or cells. Research on biomimetic soft matter is therefore a developing theme with important emerging applications in biomedicine including tissue engineering, diagnostics, gene therapy, drug delivery and many others. There are also important basic science questions concerning the use of concepts from colloid and polymer science to understand the self-assembly of biomimetic soft materials. This issue of Soft Matter presents a selection of extremely topical articles on a diversity of biomimetic soft matter systems. I thank the contributors for this quite remarkable collection of papers, which report many fascinating discoveries and insights.

Influence of elastase on alanine-rich peptide hydrogels

The self-assembly of the alanine-rich amphiphilic peptides Lys(Ala)6Lys (KA6K) and Lys(Ala)6Glu (KA6E)with homotelechelic or heterotelechelic charged termini respectively has been investigated in aqueous solution. These peptides contain hexa-alanine sequences designed to serve as substrates for the enzyme elastase. Electrostatic repulsion of the lysine termini in KA6K prevents self-assembly, whereas in contrast KA6E is observed, through electron microscopy, to form tape-like fibrils, which based on X-ray scattering contain layers of thickness equal to the molecular length. The alanine residues enable efficient packing of the side-chains in a beta-sheet structure, as revealed by circular dichroism, FTIR and X-ray diffraction experiments. In buffer, KA6E is able to form hydrogels at sufficiently high concentration. These were used as substrates for elastase, and enzyme-induced de-gelation was observed due to the disruption of the beta-sheet fibrillar network. We propose that hydrogels of the simple designed amphiphilic peptide KA6E may serve as model substrates for elastase and this could ultimately lead to applications in biomedicine and regenerative medicine.

Self-assembling amphiphilic peptides

The self-assembly of several classes of amphiphilic peptides is reviewed, and selected applications are discussed. We discuss recent work on the self-assembly of lipopeptides, surfactant-like peptides and amyloid peptides derived from the amyloid-β peptide. The influence of environmental variables such as pH and temperature on aggregate nanostructure is discussed. Enzyme-induced remodelling due to peptide cleavage and nanostructure control through photocleavage or photo-cross-linking are also considered. Lastly, selected applications of amphiphilic peptides in biomedicine and materials science are outlined.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.