Antibacterial surface modification of titanium implants in orthopaedics

The use of biomaterials in orthopaedics for joint replacement, fracture healing and bone regeneration is a rapidly expanding field. Infection of these biomaterials is a major healthcare burden, leading to significant morbidity and mortality. Furthermore, the cost to healthcare systems is increasing dramatically. With advances in implant design and production, research has predominately focussed on osseointegration; however, modification of implant material, surface topography and chemistry can also provide antibacterial activity. With the increasing burden of infection, it is vitally important that we consider the bacterial interaction with the biomaterial and the host when designing and manufacturing future implants. During this review, we will elucidate the interaction between patient, biomaterial surface and bacteria. We aim to review current and developing surface modifications with a view towards antibacterial orthopaedic implants for clinical applications

An evaluation of the factors affecting ‘poacher’ detection with drones and the efficacy of machine-learning for detection

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies

Optimising observing strategies for monitoring animals using drone-mounted thermal infrared cameras

The proliferation of relatively affordable off-the-shelf drones offers great opportunities for wildlife monitoring and conservation. Similarly the recent reduction in cost of thermal infrared cameras also offers new promise in this field, as they have the advantage over conventional RGB cameras of being able to distinguish animals based on their body heat and being able to detect animals at night. However, the use of drone-mounted thermal infrared cameras comes with several technical challenges. In this paper we address some of these issues, namely thermal contrast problems due to heat from the ground, absorption and emission of thermal infrared radiation by the atmosphere, obscuration by vegetation, and optimizing the flying height of drones for a best balance between covering a large area and being able to accurately image and identify animals of interest. We demonstrate the application of these methods with a case study using field data, and make the first ever detection of the critically endangered riverine rabbit (Bunolagus monticularis) in thermal infrared data. We provide a web-tool so that the community can easily apply these techniques to other studies (http://www.astro.ljmu.ac.uk/~aricburk/uav_calc/)

Counting crocodiles from the sky: Monitoring the critically endangered gharial (Gavialis gangeticus) population with an Unmanned Aerial Vehicle (UAV).

Technology is rapidly changing the methods in the field of wildlife monitoring. Unmanned aerial vehicle (UAV) is an example of a new technology that allows biologists to take to the air to monitor wildlife. Fixed Wing UAV was used to monitor critically endangered gharial population along 46 km of the Babai River in Bardia National Park. The UAV was flown at an altitude of 80 m along 12 pre-designed missions with a search effort of 2.72 hours of flight time acquired a total of 11,799 images covering an effective surface area of 8.2 km2 of river bank habitat. The images taken from the UAV could differentiate between gharial and muggers. A total count of 33 gharials and 31 muggers with observed density (per km2) of 4.64 and 4.0 for gharial and mugger respectively. Comparison of count data between one-time UAV and multiple conventional visual encounter rate surveys data showed no significant difference in the mean. Basking season and turbidity were important factors for monitoring crocodiles along the river bank habitat. Efficacy of monitoring crocodiles by UAV at the given altitude can be replicated in high priority areas with less operating cost and acquisition of high resolution data

Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. - a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world

A global risk assessment of primates under climate and land use/cover scenarios

Primates are facing an impending extinction crisis, driven by extensive habitat loss, land use change, and hunting. Climate change is an additional threat, which alone or in combination with other drivers, may severely impact those taxa unable to track suitable environmental conditions. Here, we investigate the extent of climate and land use/cover (LUC) change-related risks for primates. We employed an analytical approach to objectively select a subset of climate scenarios, for which we then calculated changes in climatic and LUC conditions for 2050 across primate ranges (N=426 species) under a best- and a worst-case scenario. Generalised linear models were used to examine whether these changes varied according to region, conservation status, range extent, and dominant habitat. Finally, we reclassified primate ranges based on different magnitudes of maximum temperature change, and quantified the proportion of ranges overall and of primate hotspots in particular that are likely to be exposed to extreme temperature increases. We found that, under the worst-case scenario, 74% of Neotropical forest-dwelling primates are likely to be exposed to maximum temperature increases up to 7°C. In contrast, 38% of Malagasy savanna primates will experience less pronounced warming of up to 3.5°C. About one quarter of Asian and African primates will face up to 50% crop expansion within their range. Primary land (undisturbed habitat) is expected to disappear across species’ ranges, whereas secondary land (disturbed habitat) will increase by up to 98%. With 86% of primate ranges likely to be exposed to maximum temperature increases >3°C, primate hotspots in the Neotropics are expected to be particularly vulnerable. Our study highlights the fundamental exposure risk of a large percentage of primate ranges to predicted climate and LUC changes. Importantly, our findings underscore the urgency with which climate change mitigation measures need to be implemented to avert primate extinctions on an unprecedented scale

Small room for compromise between oil palm cultivation and primate conservation in Africa

Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent’s primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge

Assessment of Chimpanzee Nests Detectability on Drone-Acquired Images

As with other species of great apes, chimpanzee numbers have declined during the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km2), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones to detect wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50x500m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability on drone-acquired images. We discuss the potential, but also the limitations of this technology for determining chimpanzee distribution and density and provide guidance for future investigation on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection on animal distribution and habitat connectivity that has important implications for apes conservation in an increasingly anthropogenically disturbed landscape