The gut microbial community of midas cichlid fish in repeatedly evolved limnetic-benthic species pairs

Gut bacterial communities are now known to influence a range of fitness related aspects of organisms. But how different the microbial community is in closely related species, and if these differences can be interpreted as adaptive is still unclear. In this study we compared microbial communities in two sets of closely related sympatric crater lake cichlid fish species pairs that show similar adaptations along the limnetic-benthic axis. The gut microbial community composition differs in the species pair inhabiting the older of two crater lakes. One major difference, relative to other fish, is that in these cichlids that live in hypersaline crater lakes, the microbial community is largely made up of Oceanospirillales (52.28%) which are halotolerant or halophilic bacteria. This analysis opens up further avenues to identify candidate symbiotic or co-evolved bacteria playing a role in adaptation to similar diets and life-styles or even have a role in speciation. Future functional and phylosymbiotic analyses might help to address these issues.

Bat species comparisons based on external morphology: A test of traditional versus geometric morphometric approaches

External morphology is commonly used to identify bats as well as to investigate flight and foraging behavior, typically relying on simple length and area measures or ratios. However, geometric morphometrics is increasingly used in the biological sciences to analyse variation in shape and discriminate among species and populations. Here we compare the ability of traditional versus geometric morphometric methods in discriminating between closely related bat species – in this case European horseshoe bats (Rhinolophidae, Chiroptera) – based on morphology of the wing, body and tail. In addition to comparing morphometric methods, we used geometric morphometrics to detect interspecies differences as shape changes. Geometric morphometrics yielded improved species discrimination relative to traditional methods. The predicted shape for the variation along the between group principal components revealed that the largest differences between species lay in the extent to which the wing reaches in the direction of the head. This strong trend in interspecific shape variation is associated with size, which we interpret as an evolutionary allometry pattern.

Identifying vertebral end plate lesions (schmorl's nodes) using a semi-automatic method

INTRODUCTION There is a large range in the reported prevalence of end plate lesions (EPLs), sometimes referred to as Schmorl's nodes in the general population (3.8-76%). One possible reason for this large range is the differences in definitions used by authors. Previous research has suggested that EPLs may potentially be a primary disturbance of growth plates that leads to the onset of scoliosis. The aim of this study was to develop a technique to measure the size, prevalence and location of EPLs on Computed Tomography (CT) images of scoliosis patients in a consistent manner. METHODS A detection algorithm was developed and applied to measure EPLs for five adolescent females with idiopathic scoliosis (average age 15.1 years, average major Cobb 60°). In this algorithm, the EPL definition was based on the lesion depth, the distance from the edge of the vertebral body and the gradient of the lesion edge. Existing low-dose, CT scans of the patients' spines were segmented semi-automatically to extract 3D vertebral endplate morphology. Manual sectioning of any attachments between posterior elements of adjacent vertebrae and, if necessary, endplates was carried out before the automatic algorithm was used to determine the presence and position of EPLs. RESULTS EPLs were identified in 15 of the 170 (8.8%) endplates analysed with an average depth of 3.1mm. 73% of the EPLs were seen in the lumbar spines (11/15). A sensitivity study demonstrated that the algorithm was most sensitive to changes in the minimum gradient required at the lesion edge. CONCLUSION An imaging analysis technique for consistent measurement of the prevalence, location and size of EPLs on CT images has been developed. Although the technique was tested on scoliosis patients, it can be used to analyse other populations without observer errors in EPL definitions.

Nanostructured hydroxyapatite surfaces-mediated adsorption alters recognition of BMP receptor IA and bioactivity of bone morphogenetic protein-2

Highly efficient loading of bone morphogenetic protein-2 (BMP-2) onto carriers with desirable performance is still a major challenge in the field of bone regeneration. Till now, the nanoscaled surface-induced changes of the structure and bioactivity of BMP-2 remains poorly understood. Here, the effect of nanoscaled surface on the adsorption and bioactivity of BMP-2 was investigated with a series of hydroxyapatite surfaces (HAPs): HAP crystal-coated surface (HAP), HAP crystal-coated polished surface (HAP-Pol), and sintered HAP crystal-coated surface (HAP-Sin). The adsorption dynamics of recombinant human BMP-2 (rhBMP-2) and the accessibility of the binding epitopes of adsorbed rhBMP-2 for BMP receptors (BMPRs) were examined by a quartz crystal microbalance with dissipation. Moreover, the bioactivity of adsorbed rhBMP-2 and the BMP-induced Smad signaling were investigated with C2C12 model cells. A noticeably high mass-uptake of rhBMP-2 and enhanced recognition of BMPR-IA to adsorbed rhBMP-2 were found on the HAP-Pol surface. For the rhBMP-2-adsorbed HAPs, both ALP activity and Smad signaling increased in the order of HAP-Sin < HAP < HAP-Pol. Furthermore, hybrid molecular dynamics and steered molecular dynamics simulations validated that BMP-2 tightly anchored on the HAP-Pol surface with a relative loosened conformation, but the HAP-Sin surface induced a compact conformation of BMP-2. In conclusion, the nanostructured HAPs can modulate the way of adsorption of rhBMP-2, and thus the recognition of BMPR-IA and the bioactivity of rhBMP-2. These findings can provide insightful suggestions for the future design and fabrication of rhBMP-2-based scaffolds/implants.

Grass pollen allergens globally: The contribution of subtropical grasses to burden of allergic respiratory diseases

Grass pollens of the temperate (Pooideae) subfamily and subtropical subfamilies of grasses are major aeroallergen sources worldwide. The subtropical Chloridoideae (e.g. Cynodon dactylon; Bermuda grass) and Panicoideae (e.g. Paspalum notatum; Bahia grass) species are abundant in parts of Africa, India, Asia, Australia and the Americas, where a large and increasing proportion of the world's population abide. These grasses are phylogenetically and ecologically distinct from temperate grasses. With the advent of global warming, it is conceivable that the geographic distribution of subtropical grasses and the contribution of their pollen to the burden of allergic rhinitis and asthma will increase. This review aims to provide a comprehensive synthesis of the current global knowledge of (i) regional variation in allergic sensitivity to subtropical grass pollens, (ii) molecular allergenic components of subtropical grass pollens and (iii) allergic responses to subtropical grass pollen allergens in relevant populations. Patients from subtropical regions of the world show higher allergic sensitivity to grass pollens of Chloridoideae and Panicoideae grasses, than to temperate grass pollens. The group 1 allergens are amongst the allergen components of subtropical grass pollens, but the group 5 allergens, by which temperate grass pollen extracts are standardized for allergen content, appear to be absent from both subfamilies of subtropical grasses. Whilst there are shared allergenic components and antigenic determinants, there are additional clinically relevant subfamily-specific differences, at T- and B-cell levels, between pollen allergens of subtropical and temperate grasses. Differential immune recognition of subtropical grass pollens is likely to impact upon the efficacy of allergen immunotherapy of patients who are primarily sensitized to subtropical grass pollens. The literature reviewed herein highlights the clinical need to standardize allergen preparations for both types of subtropical grass pollens to achieve optimal diagnosis and treatment of patients with allergic respiratory disease in subtropical regions of the world. © 2014 John Wiley & Sons Ltd.

Unique and cross-reactive T cell epitope peptides of the major bahia grass pollen allergen, pas n 1

Background Bahia grass pollen (BaGP) is a major cause of allergic rhinitis. Subcutaneous allergen-specific immunotherapy is effective for grass pollen allergy, but is unsuitable for patients with moderate to severe asthma due to the risk of anaphylaxis. T cell-reactive but IgE nonreactive peptides provide a safer treatment option. This study aimed to identify and characterize dominant CD4+ T cell epitope peptides of the major BaGP allergen, Pas n 1. Methods Pas n 1-specific T cell lines generated from the peripheral blood of BaGP-allergic subjects were tested for proliferative and cytokine response to overlapping 20-mer Pas n 1 peptides. Cross-reactivity to homologous peptides from Lol p 1 and Cyn d 1 of Ryegrass and Bermuda grass pollen, respectively, was assessed using Pas n 1 peptide-specific T cell clones. MHC class II restriction of Pas n 1 peptide T cell recognition was determined by HLA blocking assays and peptide IgE reactivity tested by dot blotting. Results Three Pas n 1 peptides showed dominant T cell reactivity; 15 of 18 (83%) patients responded to one or more of these peptides. T cell clones specific for dominant Pas n 1 peptides showed evidence of species-specific T cell reactivity as well as cross-reactivity with other group 1 grass pollen allergens. The dominant Pas n 1 T cell epitope peptides showed HLA binding diversity and were non-IgE reactive. Conclusions The immunodominant T cell-reactive Pas n 1 peptides are candidates for safe immunotherapy for individuals, including those with asthma, who are allergic to Bahia and possibly other grass pollens.

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.

VIP Barcoding: Composition vector-based software for rapid species identification based on DNA barcoding

Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.

Chlamydial infections of fish: Diverse pathogens and emerging causes of disease in aquaculture species

Chlamydial infections of fish are emerging as an important cause of disease in new and established aquaculture industries. To date, epitheliocystis, a skin and gill disease associated with infection by these obligate intracellular pathogens, has been described in over 90 fish species, including hosts from marine and fresh water environments. Aided by advances in molecular detection and typing, recent years have seen an explosion in the description of these epitheliocystis-related chlamydial pathogens of fish, significantly broadening our knowledge of the genetic diversity of the order Chlamydiales. Remarkably, in most cases, it seems that each new piscine host studied has revealed the presence of a phylogenetically unique and novel chlamydial pathogen, providing researchers with a fascinating opportunity to understand the origin, evolution and adaptation of their traditional terrestrial chlamydial relatives. Despite the advances in this area, much still needs to be learnt about the epidemiology of chlamydial infections in fish if these pathogens are to be controlled in farmed environments. The lack of in vitro methods for culturing of chlamydial pathogens of fish is a major hindrance to this field. This review provides an update on our current knowledge of the taxonomy and diversity of chlamydial pathogens of fish, discusses the impact of these infections on the health, and highlights further areas of research required to understand the biology and epidemiology of this important emerging group of fish pathogens of aquaculture species.

Evaluation of the relationship between Chlamydia pecorum sequence types and disease using a species-specific multi-locus sequence typing scheme (MLST)

Chlamydia pecorum is globally associated with several ovine diseases including keratoconjunctivitis and polyarthritis. The exact relationship between the variety of C. pecorum strains reported and the diseases described in sheep remains unclear, challenging efforts to accurately diagnose and manage infected flocks. In the present study, we applied C. pecorum multi-locus sequence typing (MLST) to C. pecorum positive samples collected from sympatric flocks of Australian sheep presenting with conjunctivitis, conjunctivitis with polyarthritis, or polyarthritis only and with no clinical disease (NCD) in order to elucidate the exact relationships between the infecting strains and the range of diseases. Using Bayesian phylogenetic and cluster analyses on 62 C. pecorum positive ocular, vaginal and rectal swab samples from sheep presenting with a range of diseases and in a comparison to C. pecorum sequence types (STs) from other hosts, one ST (ST 23) was recognised as a globally distributed strain associated with ovine and bovine diseases such as polyarthritis and encephalomyelitis. A second ST (ST 69) presently only described in Australian animals, was detected in association with ovine as well as koala chlamydial infections. The majority of vaginal and rectal C. pecorum STs from animals with NCD and/or anatomical sites with no clinical signs of disease in diseased animals, clustered together in a separate group, by both analyses. Furthermore, 8/13 detected STs were novel. This study provides a platform for strain selection for further research into the pathogenic potential of C. pecorum in animals and highlights targets for potential strain-specific diagnostic test development.

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.

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).

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.