A common epitope of beta-lactoglobulin and serum retinol-binding proteins: Elucidation of its core sequence using synthetic peptides

One of the monoclonal antibodies raised against bovine beta-lactoglobulin reacted with human serum retinol binding protein. The finding that this monoclonal antibody also reacted with the serum retinol binding proteins isolated from other animals, suggested that this epitopic conformation is conserved among these proteins. Using ELISA and various synthetic peptides of defined sequence, we show in this paper that the epitope defined by this monoclonal antibody comprises of the highly conserved core sequence of DTDY present in beta-lactoglobulin and retinol binding proteins.

Free energy calculations of the interactions of c-Jun-based synthetic peptides with the c-Fos protein

The c-Fos–c-Jun complex forms the activator protein 1 transcription factor, a therapeutic target in the treatment of cancer. Various synthetic peptides have been designed to try to selectively disrupt the interaction between c-Fos and c-Jun at its leucine zipper domain. To evaluate the binding affinity between these synthetic peptides and c-Fos, polarizable and nonpolarizable molecular dynamics (MD) simulations were conducted, and the resulting conformations were analyzed using the molecular mechanics generalized Born surface area (MM/GBSA) method to compute free energies of binding. In contrast to empirical and semiempirical approaches, the estimation of free energies of binding using a combination of MD simulations and the MM/GBSA approach takes into account dynamical properties such as conformational changes, as well as solvation effects and hydrophobic and hydrophilic interactions. The predicted binding affinities of the series of c-Jun-based peptides targeting the c-Fos peptide show good correlation with experimental melting temperatures. This provides the basis for the rational design of peptides based on internal, van der Waals, and electrostatic interactions.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

RF Sputtered Er2O3 Thin Films as High-k Gate Dielectrics for Germanium MOS Devices

We report the material and electrical properties of Erbium Oxide (Er2O3) thin films grown on n-Ge (100) by RF sputtering. The properties of the films are correlated with the processing conditions. The structural characterization reveals that the films annealed at 550 degrees C, has densified as compared to the as-grown ones. Fixed oxide charges and interface charges, both of the order of 10(13)/cm(2) is observed.

Synthetic arabinomannan glycolipids and their effects on growth and motility of the Mycobacterium smegmatis

Arabinomannan-containing glycolipids, relevant to the mycobacterial cell-wall component lipoarabinomannan, were synthesized by chemical methods. The glycolipids were presented with tri- and tetrasaccharide arabinomannans as the sugar portion and a double alkyl chain as the lyophilic portion. Following synthesis, systematic biological and biophysical studies were undertaken in order to identify the effects of the glycolipids during mycobacterium growth. The studies included mycobacterial growth, biofilm formation and motility assays. From the studies, it was observed that the synthetic glycolipid with higher arabinan residues inhibited the mycobacterial growth, lessened the biofilm formation and impaired the motility of mycobacteria. A surface plasmon resonance study involving the immobilized glycan surface and the mycobacterial crude lysates as analytes showed specificities of the interactions. Further, it was found that cell lysates from motile bacteria bound oligosaccharide with higher affinity than non-motile bacteria.

Racemization at proline residues during peptide bond formation : A study of diastereomeric mixtures of synthetic alamethicin fragments by 270 MHz 1H NMR

The stepwise synthesis of amino terminal pentapeptide of alamethicin, Z-Aib-Pro-Aib-Ala-Aib-OMe, by the dicyclohexylcarbodiimide mediated couplings leads to extensive racemization at the Ala and Pro residues. Racemization is largely suppressed by the use of additives like N-hydroxysuccinimide and 1-hydroxybenzotriazole. The presence of diastereomeric peptides may be detected by the observation of additional methyl ester and benzylic methylene signals in the 270 MHz 1H NMR spectra. Unambiguous spectral assignment of the signals to the diastereomers has been carried out by the synthesis and NMR studies of the D-Ala tetra and pentapeptides. The racemization at Pro is of particular relevance in view of the reported lack of inversion at C-terminal Pro on carboxyl activation.

Functional Imagery Training to reduce snacking: Testing a novel motivational intervention based on Elaborated Intrusion theory

Functional Imagery Training (FIT) is a new theory-based, manualized intervention that trains positive goal imagery. Multisensory episodic imagery of proximal personal goals is elicited and practised, to sustain motivation and compete with less functional cravings. This study tested the impact of a single session of FIT plus a booster phone call on snacking. In a stepped-wedge design, 45 participants who wanted to lose weight or reduce snacking were randomly assigned to receive a session of FIT immediately or after a 2-week delay. High-sugar and high-fat snacks were recorded using timeline follow back for the previous 3 days, at baseline, 2 and 4 weeks. At 2 weeks, snacking was lower in the immediate group than in the delayed group, and the reduction after FIT was replicated in the delayed group between 2 and 4 weeks. Frequencies of motivational thoughts about snack reduction rose following FIT for both groups, and this change correlated with reductions in snacking and weight loss. By showing that FIT can support change in eating behaviours, these findings show its potential as a motivational intervention for weight management.

Two-step self-assembly of hierarchically-ordered nanostructures

Due to their unique size- and shape-dependent physical and chemical properties, highly hierarchically-ordered nanostructures have attracted great attention with a view to application in emerging technologies, such as novel energy generation, harvesting, and storage devices. The question of how to get controllable ensembles of nanostructures, however, still remains a challenge. This concept paper first summarizes and clarifies the concept of the two-step self-assembly approach for the synthesis of hierarchically-ordered nanostructures with complex morphology. Based on the preparation processes, two-step self-assembly can be classified into two typical types, namely, two-step self-assembly with two discontinuous processes and two-step self-assembly completed in one-pot solutions with two continuous processes. Compared to the conventional one-step self-assembly, the two-step self-assembly approach allows the combination of multiple synthetic techniques and the realization of complex nanostructures with hierarchically-ordered multiscale structures. Moreover, this approach also allows the self-assembly of heterostructures or hybrid nanomaterials in a cost-effective way. It is expected that widespread application of two-step self-assembly will give us a new way to fabricate multifunctional nanostructures with deliberately designed architectures. The concept of two-step self-assembly can also be extended to syntheses including more than two chemical/physical reaction steps (multiple-step self-assembly).

Rational design of 3D dendritic TiO2 nanostructures with favorable architectures

Controlling the morphology and size of titanium dioxide (TiO2) nanostructures is crucial to obtain superior photocatalytic, photovoltaic, and electrochemical properties. However, the synthetic techniques for preparing such structures, especially those with complex configurations, still remain a challenge because of the rapid hydrolysis of Ti-containing polymer precursors in aqueous solution. Herein, we report a completely novel approach-three- dimensional (3D) TiO2 nanostructures with favorable dendritic architectures-through a simple hydrothermal synthesis. The size of the 3D TiO2 dendrites and the morphology of the constituent nano-units, in the form of nanorods, nanoribbons, and nanowires, are controlled by adjusting the precursor hydrolysis rate and the surfactant aggregation. These novel configurations of TiO2 nanostructures possess higher surface area and superior electrochemical properties compared to nanoparticles with smooth surfaces. Our findings provide an effective solution for the synthesis of complex TiO2 nano-architectures, which can pave the way to further improve the energy storage and energy conversion efficiency of TiO 2-based devices.