Young men utilise limited neuromuscular prepartion to regulate post-impact knee mechanics during step landing

Purpose The neuromuscular mechanisms determining the mechanical behaviour of the knee during landing impact remain poorly understood. It was hypothesised that neuromuscular preparation is subject-specific and ranges along a continuum from passive to active. Methods A group of healthy men (N = 12) stepped-down from a knee-high platform for 60 consecutive trials. Surface EMG of the quadriceps and hamstrings was used to determine pre-impact onset timing, activation amplitude and cocontraction for each trial. Partial least squares regression was used to associate pre-impact preparation with post-impact knee stiffness and coordination. Results The group analysis revealed few significant changes in pre-impact preparation across trial blocks. Single-subject analyses revealed changes in muscle activity that varied in size and direction between individuals. Further, the association between pre-impact preparation and post-impact knee mechanics was subject-specific and ranged along a continuum of strategies. Conclusion The findings suggest that neuromuscular preparation during step landing is subject-specific and its association to post-impact knee mechanics occurs along a continuum, ranging from passive to active control strategies. Further work should examine the implications of these strategies on the distribution of knee forces in-vivo.

Plasma metabolomics reveals biomarkers of the atherosclerosis

Atherosclerotic cardiovascular disease remains the leading cause of morbidity and mortality in industrialized societies. The lack of metabolite biomarkers has impeded the clinical diagnosis of atherosclerosis so far. In this study, stable atherosclerosis patients (n=16) and age- and sex-matched non-atherosclerosis healthy subjects (n=28) were recruited from the local community (Harbin, P. R. China). The plasma was collected from each study subject and was subjected to metabolomics analysis by GC/MS. Pattern recognition analyses (principal components analysis, orthogonal partial least-squares discriminate analysis, and hierarchical clustering analysis) commonly demonstrated plasma metabolome, which was significantly different from atherosclerotic and non-atherosclerotic subjects. The development of atherosclerosis-induced metabolic perturbations of fatty acids, such as palmitate, stearate, and 1-monolinoleoylglycerol, was confirmed consistent with previous publication, showing that palmitate significantly contributes to atherosclerosis development via targeting apoptosis and inflammation pathways. Altogether, this study demonstrated that the development of atherosclerosis directly perturbed fatty acid metabolism, especially that of palmitate, which was confirmed as a phenotypic biomarker for clinical diagnosis of atherosclerosis.

Urinary biomolecular indicators of exercise-induced over exertion injury

Poor health and injury represent major obstacles to the future economic security of Australia. The national economic cost of work-related injury is estimated at $57.5 billion p/a. Since exposure to high physical demands is a major risk factor for musculoskeletal injury, monitoring and managing such physical activity levels in workers is a potentially important injury prevention strategy. Current injury monitoring practices are inadequate for the provision of clinically valuable information about the tissue specific responses to physical exertion. Injury of various soft tissue structures can manifest over time through accumulation of micro-trauma. Such micro-trauma has a propensity to increase the risk of acute injuries to soft-tissue structures such as muscle or tendon. As such, the capacity to monitor biomarkers that result from the disruption of these tissues offers a means of assisting the pre-emptive management of subclinical injury prior to acute failure or for evaluation of recovery processes. Here we have adopted an in-vivo exercise induced muscle damage model allowing the application of laboratory controlled conditions to assist in uncovering biochemical indicators associated with soft-tissue trauma and recovery. Importantly, urine was utilised as the diagnostic medium since it is non-invasive to collect, more acceptable to workers and less costly to employers. Moreover, it is our hypothesis that exercise induced tissue degradation products enter the circulation and are subsequently filtered by the kidney and pass through to the urine. To test this hypothesis a range of metabolomic and proteomic discovery-phase techniques were used, along with targeted approaches. Several small molecules relating to tissue damage were identified along with a series of skeletal muscle-specific protein fragments resulting from exercise induced soft-tissue damage. Each of the potential biomolecular markers appeared to be temporally present within urine. Moreover, the regulation of abundance seemed to be associated with functional recovery following the injury. This discovery may have important clinical applications for monitoring of a variety of inflammatory myopathies as well as novel applications in monitoring of the musculoskeletal health status of workers, professional athletes and/or military personnel to reduce the onset of potentially debilitating musculoskeletal injuries within these professions.

Genetic response to combined family selection for improved mean harvest weight in giant freshwater prawn (Macrobrachium rosenbergii) in Vietnam

We estimated genetic changes in body and carcass weight traits in a giant freshwater prawn (GFP) (Macrobrachium rosenbergii) population selected for increased body weight at harvest in Vietnam. The data set consisted of 18,387 individual body and 1730 carcass weight records, as well as full pedigree information collected over four generations. Average selection response (per generation) in body weight at harvest (transformed to square root) estimated as the difference between the Selection line and the Control group was 7.4% calculated from least squares mean (LSMs), 7.0% from estimated breeding values (EBVs) and 4.4% calculated from EBVs between two consecutive generations. Favorable correlated selection responses (estimated from LSMs) were found for other body traits including: total length, cephalothorax length, abdominal length, cephalothorax width, and abdominal width (12.1%, 14.5%, 10.4%, 15.5% and 13.3% over three selection generations, respectively). Data in the second generation of selection showed positive correlated responses for carcass weight traits including: abdominal weight, exoskeleton-off weight, and telson-off weight of 8.8%, 8.6% and 8.8%, respectively. We conclude that body weight at harvest responded well to the application of combined (between and within) family selection and correlated responses in carcass weight traits were favorable.

Organisational change stressor and nursing job satisfaction : the mediating effect of coping strategies

Aim To examine the mediating effect of coping strategies on the consequences of nursing and non-nursing (administrative) stressors on the job satisfaction of nurses during change management. Background Organisational change can result in an increase in nursing and nonnursing- related stressors, which can have a negative impact on the job satisfaction of nurses employed in health-care organisations. Method Matched data were collected in 2009 via an online survey at two timepoints (six months apart). Results Partial least squares path analysis revealed a significant causal relationship between Time 1 administrative and role stressors and an increase in nursing-specific stressors in Time 2. A significant relationship was also identified between job-specific nursing stressors and the adoption of effective coping strategies to deal with increased levels of change-induced stress and strain and the likelihood of reporting higher levels of job satisfaction in Time 2. Conclusions The effectiveness of coping strategies is critical in helping nurses to deal with the negative consequences of organisational change. Implications for nursing management This study shows that there is a causal relationship between change, non-nursing stressors and job satisfaction. Senior management should implement strategies aimed at reducing nursing and nonnursing stress during change in order to enhance the job satisfaction of nurses. Keywords: Australia, change management, job satisfaction, nursing and non-nursing stressors, public and non-profit sector

Application of near infrared (NIR) spectroscopy for determining the thickness of articular cartilage

The determination of the characteristics of articular cartilage such as thickness, stiffness and swelling, especially in the form that can facilitate real-time decisions and diagnostics is still a matter for research and development. This paper correlates near infrared spectroscopy with mechanically measured cartilage thickness to establish a fast, non-destructive, repeatable and precise protocol for determining this tissue property. Statistical correlation was conducted between the thickness of bovine cartilage specimens (n = 97) and regions of their near infrared spectra. Nine regions were established along the full absorption spectrum of each sample and were correlated with the thickness using partial least squares (PLS) regression multivariate analysis. The coefficient of determination (R2) varied between 53 and 93%, with the most predictive region (R2 = 93.1%, p < 0.0001) for cartilage thickness lying in the region (wavenumber) 5350–8850 cm−1. Our results demonstrate that the thickness of articular cartilage can be measured spectroscopically using NIR light. This protocol is potentially beneficial to clinical practice and surgical procedures in the treatment of joint disease such as osteoarthritis.

Rapid nutrient determination of sugarcane milling by-products using near infrared spectroscopy

This thesis investigates the use of near infrared (NIR) spectroscopic methods for rapid measurement of nutrient elements in mill mud and mill ash. Adoption of NIR-based analyses for carbon, nitrogen, phosphorus, potassium and silicon will allow Australian sugarcane farmers to comply with recent legislative changes, and act within recommended precision farming frameworks. For these analyses, NIR spectroscopic methods surpass several facets of traditional wet chemistry techniques, dramatically reducing costs, required expertise and chemical exposure, while increasing throughput and access to data. Further, this technology can be applied in various modes, including laboratory, at-line and on-line installations, allowing targeted measurement.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Generalised gamma bidding model

A generalised gamma bidding model is presented, which incorporates many previous models. The log likelihood equations are provided. Using a new method of testing, variants of the model are fitted to some real data for construction contract auctions to find the best fitting models for groupings of bidders. The results are examined for simplifying assumptions, including all those in the main literature. These indicate no one model to be best for all datasets. However, some models do appear to perform significantly better than others and it is suggested that future research would benefit from a closer examination of these.

A Near-Infrared Reflectance Spectroscopy Method for Direct Analysis of Several Chemical Components and Properties of Fruit, for Example, Chinese Hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

The association between socioeconomic status and depression in Vietnamese adults : pilot study

This study examined the prevalence of depressive symptoms and elucidated the causal pathway between socioeconomic status and depression in a community in the central region of Vietnam. The study used a combination of qualitative and quantitative research methods. Indepth interviews were applied with two local psychiatric experts and ten residents for qualitative research. A cross sectional survey with structured interview technique was implemented with 100 residents in the pilot quantitative survey. The Center for Epidemiological Studies-Depression Scale (CES-D) was applied to valuate depressive symptoms ( CES-D score over 21) and depression ( CESD core over 25). Ordinary Least Squares Regression following the three steps of Baron and Kenny’s framework was employed for testing mediation models. There was a strong social gradient with respect to depressive symptoms. People with higher education levels reported fewer depressive symptoms (lower CES-D scores). Incomes were also inversely associated with depressive symptoms, but only the ones at the bottom of the quartile income. Low level and unstable individuals in terms of occupation were associated with higher depressive symptoms compared with the highest occupation group. Employment status showed the strongest gradient with respect to its impact on the burden of depressive symptoms compared with other indicators of SES. Findings from this pilot study suggest a pattern on the negative association between socioeconomic status and depression in Vietnamese adults.

Context-specific stressors, work-related social support and work-family conflict : a mediation study

Understanding the antecedents of work-family conflict is important as it allows organisations to effectively engage in work design for professional employees. This study examines the impact of sources of social support as antecedents of work-family conflict. The hypotheses were tests using Partial Least Squares modelling on a sample of 366 professional employees. The path model showed that context-specific stressors impacted positively on job demand, which led to higher levels of work-family conflict. Contrary to our expectation, non-work related social support did not have any statistical relationship with job demand and work-family conflict. In addition, individuals experiencing high job demands were found to obtain more social support from both work and non-work-related sources. Individuals with more work-related social support were less likely to have less work-family conflict. Surprisingly, non-work social support sources had no statistically significant relationship with work-family conflict.

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

Taxonomic analysis of marine phytoplankton

Samples of sea water contain phytoplankton taxa in varying amounts, and marine scientists are interested in the relative abundance of each taxa. Their relative biomass can be ascertained indirectly by measuring the quantity of various pigments using high performance liquid chromatography. However, the conversion from pigment to taxa is mathematically non trivial as it is a positive matrix factorisation problem where both matrices are unknown beyond the level of initial estimates. The prior information on the pigment to taxa conversion matrix is used to give the problem a unique solution. An iteration of two non-negative least squares algorithms gives satisfactory results. Some sample analysis of data indicates prospects for this type of analysis. An alternative more computationally intensive approach using Bayesian methods is discussed.

A Satellite Selection Algorithm for Achieving High Reliability of Ambiguity Resolution with GPS and Beidou Constellations

Reliability of carrier phase ambiguity resolution (AR) of an integer least-squares (ILS) problem depends on ambiguity success rate (ASR), which in practice can be well approximated by the success probability of integer bootstrapping solutions. With the current GPS constellation, sufficiently high ASR of geometry-based model can only be achievable at certain percentage of time. As a result, high reliability of AR cannot be assured by the single constellation. In the event of dual constellations system (DCS), for example, GPS and Beidou, which provide more satellites in view, users can expect significant performance benefits such as AR reliability and high precision positioning solutions. Simply using all the satellites in view for AR and positioning is a straightforward solution, but does not necessarily lead to high reliability as it is hoped. The paper presents an alternative approach that selects a subset of the visible satellites to achieve a higher reliability performance of the AR solutions in a multi-GNSS environment, instead of using all the satellites. Traditionally, satellite selection algorithms are mostly based on the position dilution of precision (PDOP) in order to meet accuracy requirements. In this contribution, some reliability criteria are introduced for GNSS satellite selection, and a novel satellite selection algorithm for reliable ambiguity resolution (SARA) is developed. The SARA algorithm allows receivers to select a subset of satellites for achieving high ASR such as above 0.99. Numerical results from a simulated dual constellation cases show that with the SARA procedure, the percentages of ASR values in excess of 0.99 and the percentages of ratio-test values passing the threshold 3 are both higher than those directly using all satellites in view, particularly in the case of dual-constellation, the percentages of ASRs (>0.99) and ratio-test values (>3) could be as high as 98.0 and 98.5 % respectively, compared to 18.1 and 25.0 % without satellite selection process. It is also worth noting that the implementation of SARA is simple and the computation time is low, which can be applied in most real-time data processing applications.