Beyond the challenge to research integrity: imposter participation in incentivised qualitative research and its impact on community engagement.

Participant recruitment for qualitative research often offers incentives (honoraria; financial compensation) to increase participation and to recognise lived expertise and time involved in research. While not necessarily a new concern for survey and other quantitative based research, 'spam', 'bot', and other inauthentic forms of research participation has rarely been an apparent issue for qualitative research, given it often involves levels of interaction with potential participants prior to the conduct of in-depth interviews and other methods of data generation. This is no longer the case. A troubling new occurrence has meant that recruitment calls for qualitative research with incentives on public-facing social media have attracted 'imposter' expressions of interest and research participation. In this commentary, we explore this challenge that goes beyond research integrity. In particular, we consider the risks of employing strategies to screen for legitimate participants and the importance of building trust and maintaining community engagement.

Earthworm-induced shifts in microbial diversity in soils with rare versus established invasive earthworm populations.

European earthworms have colonised many parts of Australia, although their impact on soil microbial communities remains largely uncharacterised. An experiment was conducted to contrast the responses to Aporrectodea trapezoides introduction between soils from sites with established (Talmo, 64 A. trapezoides m-2) and rare (Glenrock, 0.6 A. trapezoides m-2) A. trapezoides populations. Our hypothesis was that earthworm introduction would lead to similar changes in bacterial communities in both soils. The effects of earthworm introduction (earthworm activity and cadaver decomposition) did not lead to a convergence of bacterial community composition between the two soils. However, in both soils, the Firmicutes decreased in abundance and a common set of bacteria responded positively to earthworms. The increase in the abundance of Flavobacterium, Chitinophagaceae, Rhodocyclaceae and Sphingobacteriales were consistent with previous studies. Evidence for possible soil resistance to earthworms was observed, with lower earthworm survival in Glenrock microcosms coinciding with A. trapezoides rarity in this site, lower soil organic matter and clay content and differences in the diversity and abundance of potential earthworm mutualist bacteria. These results suggest that while the impacts of earthworms vary between different soils, the consistent response of some bacteria may aid in predicting the impacts of earthworms on soil ecosystems.

Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters.

Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function.

Optimal camera planning under versatile user constraints in multi-camera image processing systems.

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing.

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Quantifying functional mobility progress for chronic disease management.

A method for quantifying improvements in functional mobility is presented based on patient-worn accelerometer devices. For patients with cardiovascular, respiratory, or other chronic disease, increasing the amount of functional mobility is a large component of rehabilitation programs. We have conducted an observational trial on the use of accelerometers for quantifying mobility improvements in a small group of chronic disease patients (n=15, 48 - 86 yrs). Cognitive impairments precluded complex instrumentation of patients, and movement data was obtained from a single 2-axis accelerometer device worn at the hip. In our trial, movement data collected from accelerometer devices was classified into Lying vs Sitting/Standing vs Walking/Activity movements. This classification enabled the amount of walking to be quantified and graphically presented to clinicians and carers for feedback on exercise efficacy. Presenting long term trends in this data to patients also provides valuable feedback for self managed care and assisting with compliance.

A framework for linking gait characteristics of patients with accelerations of the waist.

This paper describes a framework for the analysis of accelerometer data as part of research undertaken in preparation for a clinical trial involving ambulatory monitoring of elderly rehabilitation patients. In particular we examine the response of side-mounted accelerometers to various gait patterns and attempt to establish a relationship with a biomechanical model for human gait. We explore the use of a linear predictive (LP) model as a basis for identifying key harmonic frequencies in the accelerometer response signals and use these harmonics to relate measured data back to harmonic predictions from the biomechanical model.