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

Detection and molecular characterisation of Cryptosporidium parvum in British European hedgehogs (Erinaceus europaeus)

Surveillance was conducted for the occurrence of protozoan parasites of the genus Cryptosporidium in European hedgehogs (Erinaceus europaeus) in Great Britain. In total, 108 voided faecal samples were collected from hedgehogs newly admitted to eight wildlife casualty treatment and rehabilitation centres. Terminal large intestinal (LI) contents from three hedgehog carcasses were also analysed. Information on host and location variables, including faecal appearance, body weight, and apparent health status, was compiled. Polymerase Chain Reaction (PCR) targeting the 18S ribosomal RNA gene, confirmed by sequencing, revealed an 8% (9/111) occurrence of Cryptosporidium parvum in faeces or LI contents, with no significant association between the host or location variables and infection. Archived small intestinal (SI) tissue from a hedgehog with histological evidence of cryptosporidiosis was also positive for C. parvum by PCR and sequence analysis of the 18S rRNA gene. No other Cryptosporidium species were detected. PCR and sequencing of the glycoprotein 60 gene identified three known zoonotic C. parvum subtypes not previously found in hedgehogs: IIdA17G1 (n=4), IIdA19G1 (n=1) and IIdA24G1 (n=1). These subtypes are also known to infect livestock. Another faecal sample contained C. parvum IIcA5G3j which has been found previously in hedgehogs, and for which there is one published report in a human, but is not known to affect livestock. The presence of zoonotic subtypes of C. parvum in British hedgehogs highlights a potential public health concern. Further research is needed to better understand the epidemiology and potential impacts of Cryptosporidium infection in hedgehogs

Small-Scale Fluidized Bed Bioreactor for Long-Term Dynamic Culture of 3D Cell Constructs and in vitro Testing

With the increasing interest in three-dimensional (3D) cell constructs that better represent native tissues, comes the need to also invest in devices, i.e., bioreactors, that provide a controlled dynamic environment similar to the perfusion mechanism observed in vivo. Here a laboratory-scale fluidized bed bioreactor (sFBB) was designed for hydrogel (i.e., alginate) encapsulated cells to generate a dynamic culture system that produced a homogenous milieu and host substantial biomass for long-term evolution of tissue-like structures and “per cell” performance analysis. The bioreactor design, conceptualized through scale-down empirical similarity rules, was initially validated through computational fluid dynamics analysis for the distributor capacity of homogenously dispersing the flow with an average fluid velocity of 4.596 × 10–4 m/s. Experimental tests then demonstrated a consistent fluidization of hydrogel spheres, while maintaining shape and integrity (606.9 ± 99.3 μm diameter and 0.96 shape factor). It also induced mass transfer in and out of the hydrogel at a faster rate than static conditions. Finally, the sFBB sustained culture of alginate encapsulated hepatoblastoma cells for 12 days promoting proliferation into highly viable (>97%) cell spheroids at a high final density of 27.3 ± 0.78 million cells/mL beads. This was reproducible across multiple units set up in parallel and operating simultaneously. The sFBB prototype constitutes a simple and robust tool to generate 3D cell constructs, expandable into a multi-unit setup for simultaneous observations and for future development and biological evaluation of in vitro tissue models and their responses to different agents, increasing the complexity and speed of R&D processes

The Deuterator: software for the determination of backbone amide deuterium levels from H/D exchange MS data

<p>Abstract</p> <p>Background</p> <p>The combination of mass spectrometry and solution phase amide hydrogen/deuterium exchange (H/D exchange) experiments is an effective method for characterizing protein dynamics, and protein-protein or protein-ligand interactions. Despite methodological advancements and improvements in instrumentation and automation, data analysis and display remains a tedious process. The factors that contribute to this bottleneck are the large number of data points produced in a typical experiment, each requiring manual curation and validation, and then calculation of the level of backbone amide exchange. Tools have become available that address some of these issues, but lack sufficient integration, functionality, and accessibility required to address the needs of the H/D exchange community. To date there is no software for the analysis of H/D exchange data that comprehensively addresses these issues.</p> <p>Results</p> <p>We have developed an integrated software system for the automated analysis and representation of H/D exchange data that has been titled "The Deuterator". Novel approaches have been implemented that enable high throughput analysis, automated determination of deuterium incorporation, and deconvolution of overlapping peptides. This has been achieved by using methods involving iterative theoretical envelope fitting, and consideration of peak data within expected <it>m/z </it>ranges. Existing common file formats have been leveraged to allow compatibility with the output from the myriad of MS instrument platforms and peptide sequence database search engines.</p> <p>A web-based interface is used to integrate the components of The Deuterator that are able to analyze and present mass spectral data from instruments with varying resolving powers. The results, if necessary, can then be confirmed, adjusted, re-calculated and saved. Additional tools synchronize the curated calculation parameters with replicate time points, increasing throughput. Saved results can then be used to plot deuterium buildup curves and 3D structural overlays. The system has been used successfully in a production environment for over one year and is freely available as a web tool at the project home page <url>http://deuterator.florida.scripps.edu</url>.</p> <p>Conclusion</p> <p>The automated calculation and presentation of H/D exchange data in a user interface enables scientists to organize and analyze data efficiently. Integration of the different components of The Deuterator coupled with the flexibility of common data file formats allow this system to be accessible to the broadening H/D exchange community.</p

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

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

Video Analysis for the Detection of Animals Using Convolutional Neural Networks and Consumer-Grade Drones

Determining animal distribution and density is important in conservation. The process is both timeconsuming and labour-intensive. Drones have been used to help mitigate human-intensive tasks by covering large geographical areas over a much shorter timescale. In this paper we investigate this idea further using a proof of concept to detect rhinos and cars from drone footage. The proof of concept utilises off-the-shelf technology and consumer grade drone hardware. The study demonstrates the feasibility of using machine learning (ML) to automate routine conservation tasks such as animal detection and tracking. The prototype has been developed using a DJI Mavic Pro 2 and tested over a Global System for Mobile Communications (GSM) network. The Faster RCNN Resnet 101 architecture is used for transfer learning. Inference is performed with a frame sampling technique to address the required trade-off between precision, processing speed and live video feed synchronisation. Inference models are hosted on a web platform and video streams from the drone (using OcuSync) are transmitted to a Real-Time Messaging Protocol (RTMP) server for subsequent classification. During training, the best model achieves a Mean Average Precision (mAP) of 0.83, Intersection Over Union @(IOU) 0.50 and 0.69 @IOU 0.75, respectively. On testing the system in Knowsley Safari our prototype was able to achieve the following: Sensitivity (Sen): 0.91(0.869,094), Specificity (Spec): 0.78(0.74,0.82) and an Accuracy (ACC): 0.84 (0.81,0.87) when detecting rhinos, and Sen: 1.00(1.00,1.00), Spec: 1.00(1.00,1.00) and an ACC:1.00(1.00,1.00) when detecting cars. © Canadian Science Publishin

Obstetric anal sphincter injury: a systematic review of information available on the internet.

OBJECTIVE: There is no systematic evaluation of online health information pertaining to obstetric anal sphincter injury. Therefore, we evaluated the accuracy, credibility, reliability, and readability of online information concerning obstetric anal sphincter injury. MATERIALS AND METHODS: Multiple search engines were searched. The first 30 webpages were identified for each keyword and considered eligible if they provided information regarding obstetric anal sphincter injury. Eligible webpages were assessed by two independent researchers for accuracy (prioritised criteria based upon the RCOG Third and Fourth Degree Tear guideline); credibility; reliability; and readability. RESULTS: Fifty-eight webpages were included. Seventeen webpages (30%) had obtained Health On the Net certification, or Information Standard approval and performed better than those without such approvals (p = 0.039). The best overall performing website was http://www.pat.nhs.uk (score of 146.7). A single webpage (1%) fulfilled the entire criteria for accuracy with a score of 18: www.tamesidehospital.nhs.uk . Twenty-nine webpages (50%) were assessed as credible (scores ≥7). A single webpage achieved a maximum credibility score of 10: www.meht.nhs.uk . Over a third (21 out of 58) were rated as poor or very poor. The highest scoring webpage was http://www.royalsurrey.nhs.uk (score 62). No webpage met the recommended Flesch Reading Ease Score above 70. The intra-class coefficient between researchers was 0.98 (95% CI 0.96-0.99) and 0.94 (95% CI 0.89-0.96) for accuracy and reliability assessments. CONCLUSION: Online information concerning obstetric anal sphincter injury often uses language that is inappropriate for a lay audience and lacks sufficient accuracy, credibility, and reliability

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years

Size and shape constancy in consumer virtual reality

With the increase in popularity of consumer virtual reality headsets, for research and other applications, it is important to understand the accuracy of 3D perception in VR. We investigated the perceptual accuracy of near-field virtual distances using a size and shape constancy task, in two commercially available devices. Participants wore either the HTC Vive or the Oculus Rift and adjusted the size of a virtual stimulus to match the geometric qualities (size and depth) of a physical stimulus they were able to refer to haptically. The judgments participants made allowed for an indirect measure of their perception of the egocentric, virtual distance to the stimuli. The data show under-constancy and are consistent with research from carefully calibrated psychophysical techniques. There was no difference in the degree of constancy found in the two headsets. We conclude that consumer virtual reality headsets provide a sufficiently high degree of accuracy in distance perception, to allow them to be used confidently in future experimental vision science, and other research applications in psychology