Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation

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Thesis (Ed.M.)--Boston Universit

Role of cephalosporins in the era of Clostridium difficile infection

The incidence of Clostridium difficile infection (CDI) in Europe has increased markedly since 2000. Previous meta-analyses have suggested a strong association between cephalosporin use and CDI, and many national programmes on CDI control have focused on reducing cephalosporin usage. Despite reductions in cephalosporin use, however, rates of CDI have continued to rise. This review examines the potential association of CDI with cephalosporins, and considers other factors that influence CDI risk. EUCLID (the EUropean, multicentre, prospective biannual point prevalence study of CLostridium difficile Infection in hospitalized patients with Diarrhoea) reported an increase in the annual incidence of CDI from 6.6 to 7.3 cases per 10 000 patient bed-days from 2011–12 to 2012–13, respectively. While CDI incidence and cephalosporin usage varied widely across countries studied, there was no clear association between overall cephalosporin prescribing (or the use of any particular cephalosporin) and CDI incidence. Moreover, variations in the pharmacokinetic and pharmacodynamic properties of cephalosporins of the same generation make categorization by generation insufficient for predicting impact on gut microbiota. A multitude of additional factors can affect the risk of CDI. Antibiotic choice is an important consideration; however, CDI risk is associated with a range of antibiotic classes. Prescription of multiple antibiotics and a long duration of treatment are key risk factors for CDI, and risk also differs across patient populations. We propose that all of these are factors that should be taken into account when selecting an antibiotic, rather than focusing on the exclusion of individual drug classes