How climate change displaces Pacific Island settlements and the public’s perception of large scale migration

Low lying Pacific Island countries are coming under threat from climate change. Island nations are suffering from the impacts of inundation, severe storms, droughts and shortages in fresh water. Climate change impacts are affecting daily life activities and are prompting Island nations to plan for the possibility of relocation. With climate change inducing human migration, this study has investigated how host nation people respond to possibly accommodating large Pacific Island communities. A literature review was carried out to research climate change impacts in the Pacific islands, migration theories and cultural interaction. To support this, Semi structured interviews were conducted to gain insight from the host population. Upon conducting interviews, three major themes were found. 1) Host population want the migrant population to integrate with them and be part of their communities. They do not want migrants to isolate and segregate themselves and create cultural factions. 2) With facing the possibility of large numbers of migrants relocating they could create a welfare burden on New Zealand economy. This is because migrants could be unskilled or the New Zealand economy may struggle to find sufficient employment. Migrants may also group together in an area to create support networks and host population are apprehensive that this could create ethnic enclaves amongst communities. 3) Migrants should be treated like everyone else and should relocate to wherever they can find work. The maintenance of cultural traditions and identity for migrant groups would be difficult to keep from disintegrating when migrant communities are dispersed around New Zealand and relocated away from coastal environments

Magnetic domain-wall velocity enhancement induced by a transverse magnetic field

Spin dynamics of field-driven domain walls (DWs) guided by Permalloy nanowires are studied by high-speed magneto-optic polarimetry and numerical simulations. DW velocities and spin configurations are determined as functions of longitudinal drive field, transverse bias field, and nanowire width. Nanowires having cross-sectional dimensions large enough to support vortex wall structures exhibit regions of drive-field strength (at zero bias field) that have enhanced DW velocity resulting from coupled vortex structures that suppress oscillatory motion. Factor of ten enhancements of the DW velocity are observed above the critical longitudinal drive-field (that marks the onset of oscillatory DW motion) when a transverse bias field is applied. Nanowires having smaller cross-sectional dimensions that support transverse wall structures also exhibit a region of higher mobility above the critical field, and similar transverse-field induced velocity enhancement but with a smaller enhancement factor. The bias-field enhancement of DW velocity is explained by numerical simulations of the spin distribution and dynamics within the propagating DW that reveal dynamic stabilization of coupled vortex structures and suppression of oscillatory motion in the nanowire conduit resulting in uniform DW motion at high speed.Comment: 8 pages, 5 figure

Study of diffusion weighted MRI as a predictive biomarker of response during radiotherapy for high and intermediate risk squamous cell cancer of the oropharynx: The MeRInO study

Introduction and background: A significant proportion of patients with intermediate and high risk squamous cell cancer of the oropharynx (OPSCC) continue to relapse locally despite radical chemoradiotherapy (CRT). The toxicity of the current combination of intensified dose per fraction radiotherapy and platinum based chemotherapy limits further uniform intensification. If a predictive biomarker for outcomes from CRT can be identified during treatment then individualised and adaptive treatment strategies may be employed. Methods/design: The MeRInO study is a prospective observational imaging study of patients with intermediate and high risk, locally advanced OPSCC receiving radical RT or concurrent CRT Patients undergo diffusion weighted MRI prior to treatment (MRI_1) and during the third week of RT (MRI_2). Apparent diffusion coefficient (ADC) measurements will be made on each scan for previously specified target lesions (primary and lymph nodes) and change in ADC calculated. Patients will be followed up and disease status for each target lesion noted. The primary aim of the MeRInO study is to determine the threshold change in ADC from baseline to week 3 of RT that may identify the sub-group of non-responders during treatment. Discussion: The use of DW-MRI as a predictive biomarker during RT for SCC H&N is in its infancy but studies to date have found that response to treatment may indeed be predicted by comparison of DW-MRI carried out before and during treatment. However, previous studies have included all sub-sites and biological sub-types. Establishing ADC thresholds that predict for local failure is an essential step towards using DW-MRI to improve the therapeutic ratio in treating SCC H&N. This would be done most robustly in a specific H&N sub-site and in sub-types with similar biological behaviour. The MeRInO study will help establish these thresholds in OPSCC

MUSCLE : automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis:Automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis

Summary: Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation) for the robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis, which was demonstrated on two different manufacturer’s LC-MS/MS instruments. Availability and implementation: Available at http://www.muscleproject.org. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Computed tomography metrological examination of additive manufactured acetabular hip prosthesis cups

Additive manufacturing (AM) is uniquely suitable for healthcare applications due to its design flexibility and cost effectiveness for creating complex geometries. Successful arthroplasty requires integration of the prosthetic implant with the bone to replace the damaged joint. Bone-mimetic biomaterials are utilised due to their mechanical properties and porous structure that allows bone ingrowth and implant fixation. The predictability of predetermined interconnected porous structures produced by AM ensures the required shape, size and properties that are suitable for tissue ingrowth and prevention of the implant loosening. The quality of the manufacturing process needs to be established before the utilisation of the parts in healthcare. This paper demonstrates a novel examination method of acetabular hip prosthesis cups based on X-ray computed tomography (CT) and image processing. The method was developed based on an innovative hip prosthesis acetabular cup prototype with a prescribed non-uniform lattice structure forming struts over the surface, with the interconnected porosity encouraging bone adhesion. This non-destructive, non-contact examination method can provide information of the interconnectivity of the porous structure, the standard deviation of the size of the pores and struts, the local thickness of the lattice structure in its size and spatial distribution. In particular, this leads to easier identification of weak regions that could inhibit a successful bond with the bone

Rapid UHPLC-MS metabolite profiling and phenotypic assays reveal genotypic impacts of nitrogen supplementation in oats

IntroductionOats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels.ObjectivesOats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated.MethodsFour winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha-1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach.ResultsApplication of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed.ConclusionsAlthough the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism

Exercise and high-fat feeding remodel transcript-metabolite interactive networks in mouse skeletal muscle

Abstract Enhanced coverage and sensitivity of next-generation ‘omic’ platforms has allowed the characterization of gene, metabolite and protein responses in highly metabolic tissues, such as, skeletal muscle. A limitation, however, is the capability to determine interaction between dynamic biological networks. To address this limitation, we applied Weighted Analyte Correlation Network Analysis (WACNA) to RNA-seq and metabolomic datasets to identify correlated subnetworks of transcripts and metabolites in response to a high-fat diet (HFD)-induced obesity and/or exercise. HFD altered skeletal muscle lipid profiles and up-regulated genes involved in lipid catabolism, while decreasing 241 exercise-responsive genes related to skeletal muscle plasticity. WACNA identified the interplay between transcript and metabolite subnetworks linked to lipid metabolism, inflammation and glycerophospholipid metabolism that were associated with IL6, AMPK and PPAR signal pathways. Collectively, this novel experimental approach provides an integrative resource to study transcriptional and metabolic networks in skeletal muscle in the context of health and disease

Assessing the impact of nitrogen supplementation in oats across multiple growth locations and years with targeted phenotyping and high-resolution metabolite profiling approaches

Oats (Avena sativa L.) are a healthy food, being high in dietary fibre (e.g. β-glucans), antioxidants, minerals, and vitamins. Understanding the effect of variety and crop management on nutritional quality is important. The response of four oat varieties to increased nitrogen levels was investigated across multiple locations and years with respect to yield, grain quality and metabolites (assessed via GC- and LC- MS). A novel high-resolution UHPLC-PDA-MS/MS method was developed, providing improved metabolite enrichment, resolution, and identification. The combined phenotyping approach revealed that, amino acid levels were increased by nitrogen supplementation, as were total protein and nitrogen containing lipid levels, whereas health-beneficial avenanthramides were decreased. Although nitrogen addition significantly increased grain yield and β-glucan content, supporting increasing the total nitrogen levels recommended within agricultural guidelines, oat varietal choice as well as negative impacts upon health beneficial secondary metabolites and the environmental burdens associated with nitrogen fertilisation, require further consideration

Non-targeted UHPLC-MS metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling

INTRODUCTION: The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner. OBJECTIVES: Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow. METHODS: We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples. RESULTS: Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance. CONCLUSION: The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-016-1030-9) contains supplementary material, which is available to authorized users