Falls and mobility in Parkinson’s disease : protocol for a randomised controlled clinical trial

Background Although physical therapy and falls prevention education are argued to reduce falls and disability in people with idiopathic Parkinson's disease, this has not yet been confirmed with a large scale randomised controlled clinical trial. The study will investigate the effects on falls, mobility and quality of life of (i) movement strategy training combined with falls prevention education, (ii) progressive resistance strength training combined with falls prevention education, (iii) a generic life-skills social program (control group).
Methods/Design People with idiopathic Parkinson's disease who live at home will be recruited and randomly allocated to one of three groups. Each person shall receive therapy in an out-patient setting in groups of 3-4. Each group shall be scheduled to meet once per week for 2 hours for 8 consecutive weeks. All participants will also have a structured 2 hour home practice program for each week during the 8 week intervention phase. Assessments will occur before therapy, after the 8 week therapy program, and at 3 and 12 months after the intervention. A falls calendar will be kept by each participant for 12 months after outpatient therapy. Consistent with the recommendations of the Prevention of Falls Network Europe group, three falls variables will be used as the primary outcome measures: the number of fallers, the number of multiple fallers and the falls rate. In addition to quantifying falls, we shall measure mobility, activity limitations and quality of life as secondary outcomes.
Discussion This study has the potential to determine whether outpatient movement strategy training combined with falls prevention education or progressive resistance strength training combined with falls prevention education are effective for reducing falls and improving mobility and life quality in people with Parkinson's disease who live at home.

Osteopaenia - a marker of low bone mass and fracture risk

Areal bone mineral density is commonly categorised into normal bone mineral density, osteopaenia and osteoporosis on the basis of nominal thresholds recommended by the World Health Organization. However, bone mineral density is a continuous variable and there is a strong association between lower bone mineral density and greater risk for fracture. Fracture risk is not negligible in persons with moderate deficits in bone mineral density. Although absolute fracture risk is greatest for individuals with osteoporosis, more than half of the fractures arise from those with osteopaenia, and even normal bone mineral density, a probable consequence of greater numbers of individuals at risk in these categories. However, areal bone mineral density measurements used commonly in clinical practice do not detect differences in bone tissue properties, geometry and microarchitecture, which contribute to bone strength. Newer technologies such as high-resolution peripheral computed tomography have the advantage of assessing trabecular and cortical components of bone separately, in addition to geometric characteristics of the skeleton. Quantifying these parameters and considering clinical risk factors that affect fracture risk independent of bone quantity and quality, may better discriminate between high- and low-risk individuals. This would improve the decision-making for targeting appropriate interventions, either lifestyle or medication, to reduce thepublic health burden of fractures.

The energetic and survival costs of growth in free-ranging chipmunks

The growth/survival trade-off is a fundamental aspect of life-history evolution that is often explained by the direct energetic requirement for growth that cannot be allocated into maintenance. However, there is currently no empirical consensus on whether fast-growing individuals have higher resting metabolic rates at thermoneutrality (RMRt) than slow growers. Moreover, the link between growth rate and daily energy expenditure (DEE) has never been tested in a wild endotherm. We assessed the energetic and survival costs of growth in juvenile eastern chipmunks (Tamias striatus) during a year of low food abundance by quantifying post-emergent growth rate (n = 88), RMRt (n = 66), DEE (n = 20), and overwinter survival. Both RMRt and DEE were significantly and positively related to growth rate. The effect size was stronger for DEE than RMRt, suggesting that the energy cost of growth in wild animals is more likely to be related to the maintenance of a higher foraging rate (included in DEE) than to tissue accretion (included in RMRt). Fast growers were significantly less likely to survive the following winter compared to slow growers. Juveniles with high or low RMRt were less likely to survive winter than juveniles with intermediate RMRt. In contrast, DEE was unrelated to survival. In addition, botfly parasitism simultaneously decreased growth rate and survival, suggesting that the energetic budget of juveniles was restricted by the simultaneous costs of growth and parasitism. Although the biology of the species (seed-storing hibernator) and the context of our study (constraining environmental conditions) were ideally combined to reveal a direct relationship between current use of energy and future availability, it remains unclear whether the energetic cost of growth was directly responsible for reduced survival.

Connectivity, online social capital, and mood : a Bayesian nonparametric analysis

Social capital indicative of community interaction and support is intrinsically linked to mental health. Increasing online presence is now the norm. Whilst social capital and its impact on social networks has been examined, its underlying connection to emotional response such as mood, has not been investigated. This paper studies this phenomena, revisiting the concept of “online social capital†in social media communities using measurable aspects of social participation and social support. We establish the link between online capital derived from social media and mood, demonstrating results for different cohorts of social capital and social connectivity. We use novel Bayesian nonparametric factor analysis to extract the shared and individual factors in mood transition across groups of users of different levels of connectivity, quantifying patterns and degree of mood transitions. Using more than 1.6 million users from Live Journal, we show quantitatively that groups with lower social capital have fewer positive moods and more negative moods, than groups with higher social capital. We show similar effects in mood transitions. We establish a framework of how social media can be used as a barometer for mood. The significance lies in the importance of online social capital to mental well-being in overall. In establishing the link between mood and social capital in online communities, this work may suggest the foundation of new systems to monitor online mental well-being.

Crowd detection from still images

The analysis of human crowds has widespread uses from law enforcement to urban engineering and traffic management. All of these require a crowd to first be detected, which is the problem addressed in this paper. Given an image, the algorithm we propose segments it into crowd and non-crowd regions. The main idea is to capture two key properties of crowds: (i) on a narrow scale, its basic element should look like a human (only weakly so, due to low resolution, occlusion, clothing variation etc.), while (ii) on a larger scale, a crowd inherently contains repetitive appearance elements. Our method exploits this by building a pyramid of sliding windows and quantifying how “crowd-like” each level of the pyramid is using an underlying statistical model based on quantized SIFT features. The two aforementioned crowd properties are captured by the resulting feature vector of window responses, describing the degree of crowd-like appearance around an image location as the surrounding spatial extent is increased.

Learning aggregation weights from 3-tuple comparison sets

An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs. © 2013 IEEE.

Bioconversion of α-Linolenic Acid into n-3 Long-Chain polyunsaturated fatty acid in hepatocytes and ad hoc cell culture optimisation

This study aimed to establish optimal conditions for a cell culture system that would allow the measurement of 18:3n-3 (ALA) bioconversion into n-3 long-chain polyunsaturated fatty acid (n-3 LC-PUFA), and to determine the overall pathway kinetics. Using rat hepatocytes (FaO) as model cells, it was established that a maximum 20:5n-3 (EPA) production from 50 mM ALA initial concentration was achieved after 3 days of incubation. Next, it was established that a gradual increase in the ALA concentration from 0 up to 125mM lead to a proportional increase in EPA, without concomitant increase in further elongated or desaturated products, such as 22:5n-3 (DPA) and 22:6n-3 (DHA) in 3 day incubations. Of interest, ALA bioconversion products were observed in the culture medium. Therefore, in vitro experiments disregarding the medium fatty acid content are underestimating the metabolism efficiency. The novel application of the fatty acid mass balance (FAMB) method on cell culture system (cells with medium) enabled quantifying the apparent enzymatic activities for the biosynthesis of n-3 LC-PUFA. The activity of the key enzymes was estimated and showed that, under these conditions, 50% (Km) of the theoretical maximal (Vmax = 3654 mmol.g21 of cell protein.hour21) Fads2 activity on ALA can be achieved with 81 mM initial ALA. Interestingly, the apparent activity of Elovl2 (20:5n-3 elongation) was the slowest amongst other biosynthesis steps. Therefore, the possible improvement of Elovl2 activity is suggested toward a more efficient DHA production from ALA. The present study proposed and described an ad hoc optimised cell culture conditions and methodology towards achieving a reliable experimental platform, using FAMB, to assist in studying the efficiency of ALA bioconversion into n-3 LC-PUFA in vitro. The FAMB proved to be a powerful and inexpensive method to generate a detailed description of the kinetics of n-3 LC-PUFA biosynthesis enzymes activities in vitro.

Machinability and phase transformation study of nanobainite steels

There is an increasing demand for high strength materials with the development of technology and critical applications. Nano materials are newly developed materials with extremely high strength for this purpose. Nanobainite is a dual phase material containing alternate layers of bainitic ferrite in nano dimensions and retained austenite. Nanobainite is produced by isothermally holding austenitized steel at a temperature of 200°C or less, depending on the chemical composition, for 6 10 days until bainite forms and then cooling to room temperature using austempering. The experimental design consisted of face milling under 12 combinations of Depth of Cut (DOC)-1, 2 and 3mm; cutting speed-100 and 150m/min; constant feed-0.15mm/rev and coolant on/off. The machinability of the material is assessed by means of analysis, such as surface texture and microhardness. The assessment also involves microstructural comparisons before and after milling. Future work involves quantifying the microstructural phase before and after milling using XRD. The results obtained are used to assess the most favorable condition to cut this new variety of steel.

A preliminary study on machinability assessment of nanobainite steel

The demand for high strength materials and improvements in heat treatment techniques has given rise to this new form of high strength steel known as nanobainite steel. The production of nanobainite steel involves slow isothermal holding of austenitic steel around 200oC for 10 days, in order to obtain a carbon enriched austenite and cooling to room temperature using austempering. The microstructure of nanobainite steel is dual phase consisting of alternate layers of bainitic ferrite and austenite. The experimental design consists of face milling under 12 combination of Depth of Cut (DoC)-1, 2 and 3mm; cutting speed-100 and 150m/min; constant feed- 0.15mm/rev and coolant on/off. The machinability of the material is assessed by means of analysis such as metallography and cutting force analysis. The results obtained are used to assess the most favorable condition to machine this new variety of steel. Future work involves study on phase transformation by quantifying the microstructural phase before and after milling using XRD.

Australian SMEs waste to landfill

Landfill waste has a negative impact on the environment and small and medium sized enterprises (SMEs) are believed to be significant contributors. There is little government or scholarly research, however, quantifying the collective volume of waste SMEs send to landfill. Where studies do exist they measure total volumes (landfill and recycling combined) and/or do not distinguish between specific waste streams (e.g. wood) and subcategories (e.g. dust). This paper contributes to knowledge by giving insight into the collective volume of waste of 404 SMEs, reconceptualising SME waste into subcategories and by measuring landfill volumes. It presents findings from these 404 Australian SMEs which found that, in descending order, cardboard, paper, plastic wrap, wood dust and particleboard were the subcategories these SMEs sent to landfill in the greatest volumes. It also argues that this reconceptualisation, and associated data collection protocols, have the potential to enable scholars and policy makers to determine the waste subcategories to which SMEs contribute most, formulate targeted interventions and research or evaluate environmental outcomes. © 2014 © 2014 Environment Institute of Australia and New Zealand Inc.

Suzhi development: Indigenous approaches to enhancing the quality of human resources

Purpose- This paper aims to examine the Chinese indigenous concept of suzhi ((ProQuest: Non US-ASCII text omitted)) by analyzing its historical evolution and its contemporary implications for human resource management (HRM) research and practice at the national and organizational levels.Design/methodology/approach- An integrated review of literatures in sinology, political science, anthropology and sociology concerned with suzhi-related research, combined with recent incidents associated with suzhi.Findings-Suzhi is an indigenous concept embedded in the centuries-long historical context of China.Suzhi development has been focused on three key dimensions, moral, physical and mental, as a way of building quality employees and citizens. Yet developing and quantifying the moral aspects ofsuzhi is more challenging than measuring its physical and mental dimensions. Linkingsuzhi development to human capital theory enriches the understanding of this indigenous concept at both organizational and national levels.Research limitations/implications- By analyzing a three-dimensionalsuzhi composite, the article offers an example of howsuzhi may be linked to human capital theory and identifies directions for future research.Originality/value- By analyzingsuzhi at organizational and national levels for HRM purposes, this article broadens thesuzhi literature from its place in the political sciences and social anthropology to encompass a theoretical analysis in HRM and development for the benefit of organizations and the society.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Winter foraging site fidelity of king penguins breeding at the Falkland Islands

Foraging site fidelity has profound consequences for individual fitness, population processes and the effectiveness of species conservation measures. Accordingly, quantifying site fidelity has become increasingly important in animal movement and habitat selection studies. To assess foraging site fidelity in king penguins (Aptenodytes patagonicus) breeding at the Falkland Islands (51.48°S, 57.83°W), we measured overlap in time spent in foraging areas (at a 0.1° × 0.1° grid resolution) between successive foraging trips and foraging route consistency during the crèche period. In total, 30 complete foraging trips from seven king penguins were recorded between April and October 2010. King penguins predominantly foraged on the highly productive Patagonian slope, to the north of the Falkland Islands [median foraging trip distance 213 km (SD = 215 km) and duration 12.8 days (SD = 14.7 days)]. Overlap in time spent in an area on consecutive foraging trips ranged between 2 and 73 % (mean 27 %, SD = 22 %). Bearing during the outbound portion of foraging trips was typically highly repeatable for individual birds, but foraging trip duration and distance were not. Travel during the outbound phase of foraging trips was consistent with the direction of the northward-flowing Falkland Current that may act as a directional cue or facilitate rapid transit to foraging areas. Flexibility in foraging trip distances and durations may be a response to changes in resource availability and changes in the energetic requirements of adults and chicks over an extended breeding cycle.

Automated quantification of neurite outgrowth orientation distributions on patterned surfaces

Objective. We have developed an image analysis methodology for quantifying the anisotropy of neuronal projections on patterned substrates. Approach. Our method is based on the fitting of smoothing splines to the digital traces produced using a non-maximum suppression technique. This enables precise estimates of the local tangents uniformly along the neurite length, and leads to unbiased orientation distributions suitable for objectively assessing the anisotropy induced by tailored surfaces. Main results. In our application, we demonstrate that carbon nanotubes arrayed in parallel bundles over gold surfaces induce a considerable neurite anisotropy; a result which is relevant for regenerative medicine. Significance. Our pipeline is generally applicable to the study of fibrous materials on 2D surfaces and should also find applications in the study of DNA, microtubules, and other polymeric materials.

Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR

Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments. © 2014 by the authors; licensee MDPI, Basel, Switzerland.