Mosquito detection with low-cost smartphones: data acquisition for malaria research

Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vectors, vegetation, standing water and human populations is crucial to the deployment of more effective interventions. Typically the presence and detection of malaria-vectoring mosquitoes is only quantified by hand-operated insect traps or signified by the diagnosis of malaria. If we are to gather timely, large-scale data to improve this situation, we need to automate the process of mosquito detection and classification as much as possible. In this paper, we present a candidate mobile sensing system that acts as both a portable early warning device and an automatic acoustic data acquisition pipeline to help fuel scientific inquiry and policy. The machine learning algorithm that powers the mobile system achieves excellent off-line multi-species detection performance while remaining computationally efficient. Further, we have conducted preliminary live mosquito detection tests using low-cost mobile phones and achieved promising results. The deployment of this system for field usage in Southeast Asia and Africa is planned in the near future. In order to accelerate processing of field recordings and labelling of collected data, we employ a citizen science platform in conjunction with automated methods, the former implemented using the Zooniverse platform, allowing crowdsourcing on a grand scale.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl

Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201

Indigenous uses of wild and tended plant biodiversity maintain ecosystem services in agricultural landscapes of the Terai Plains of Nepal

BACKGROUND: Despite a rapidly accumulating evidence base quantifying ecosystem services, the role of biodiversity in the maintenance of ecosystem services in shared human-nature environments is still understudied, as is how indigenous and agriculturally dependent communities perceive, use, and manage biodiversity. The present study aims to document traditional ethnobotanical knowledge of the ecosystem service benefits derived from wild and tended plants in rice-cultivated agroecosystems, compare this to botanical surveys, and analyze the extent to which ecosystem services contribute social-ecological resilience in the Terai Plains of Nepal. METHOD: Sampling was carried out in four landscapes, 22 Village District Committees, and 40 wards in the monsoon season. Data collection was based on transects walks to collect plant specimens, structured and semi-structured interviews, and participatory fieldwork in and around home gardens, farms, and production landscapes. We asked 180 farmers to free-list vernacular names and describe use-value of wild and tended plants in rice-cultivated agroecosystems. Uses were categorized into eight broad groupings, and 61 biomedical ailment classifications. We assessed if knowledge of plant species diversity and abundance differed with regard to caste, age, and gender. RESULTS: Nepalese farmers have a deep knowledge of the use and management of the 391 vascular plant specimens identified, which provide key provisioning, regulating, supporting, and cultural ecosystem services. Altogether, plants belong to 76 distinct plant species from 49 phylogenetic families: 56 are used to cure 61 ailments, 27 for rituals, 25 for food, 20 for timber, 17 for fuel, 17 for fodder, 11 for soil enhancement, and eight for pesticides. Four caste groups have statistically different knowledge, and younger informants report a lower average number of useful plants. CONCLUSION: Agricultural landscapes in Nepal are reservoirs of biodiversity. The knowledge of the use of wild and tended plant species in and around these farms differs by the caste and age group of land manager. Conducting research on agroecosystems will contribute to a deeper understanding of how nature is perceived by locals, to more efficient management and conservation of the breadbasket of Nepal, and to the conservation of valuable, but disappearing traditional knowledge and practice

Radiometer for the Investigation of Infrared Emissions from Flames and Rocket Plumes

A prototypical radiometer using standard one inch interference filters and a lead selenide detector was constructed for use in flame and rocket plume studies. This radiometer was designed to employ a 600 Hz chopper and chopper frequency/phase reference circuit for signal processing. Bandpass filters centered for either 2.7 mum or 4.45 mum were placed in the optical path. The passed carbon dioxide or water vapor band energy irradiated the lead selenide detector, resulting in an output voltage. This signal was then fed into a dedicated synchronous detector. The signal was then recorded by a computer system equipped with an analog-to-digital converter board. Infrared emission data was collected from two inch rocket motors and from a special burner based flame

The misdiagnosis of epilepsy in people with intellectual disabilities: A systematic review

AbstractPurposeEpilepsy is common in people with intellectual disabilities. Epilepsy can be difficult to diagnose and may be misdiagnosed in around 25% of cases. A systematic review was conducted to explore:(i)How common the misdiagnosis of epilepsy is amongst people with intellectual disabilities.(ii)Reasons for misdiagnosis of epilepsy.(iii)Implications of misdiagnosis.(iv)Improving diagnosis.MethodsPrimary studies and systematic reviews published in the English language between 1998 and 2008 were identified from electronic databases, experts, the Internet, grey literature, and citation tracking. Included studies were critically appraised by team members using the appraisal tools produced by the Critical Appraisal Skills Programme (CASP) at the Public Health Resource Unit, Oxford.ResultsEight studies were included in the review and critically appraised: six cohort studies and two case studies. Where data was provided in the cohort studies between 32% and 38% of people with intellectual disabilities were diagnosed as not having epilepsy or as having nonepileptic events. The main reason for misdiagnosis was the misinterpretation of behavioural, physiological, syndrome related, medication related or psychological events by parents, paid carers and health professionals.ConclusionsThose working in epilepsy and intellectual disability services and families must be made more aware of the possibility of misdiagnosis. Future research is needed about the misdiagnosis of epilepsy amongst people with intellectual disabilities and carer knowledge

Clinical Topic Review 2013 - Behavioral Health Screening Among MassHealth Children and Adolescents

Results from the 2013 evaluation suggest that the Children’s Behavioral Health Initiative had a large impact on formal behavioral health screening and treatment utilization among children and adolescents enrolled in MassHealth

Can Regenerative Agriculture increase national soil carbon stocks? : Simulated country scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC

ACKNOWLEDGMENTS We would like to thank Dr Andrew C. Martin for advice on our modelling framework. The authors would like to acknowledge the use of the University of Oxford Advanced Research Computing facility in carrying out this work. This work was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. PCB would like to acknowledge funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy [EXC 2075 – 390740016]. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Peer reviewedPostprin

Temperate Regenerative Agriculture practices increase soil carbon but not crop yield—a meta-analysis

We would like to thank R D Armstrong, S J Crittenden, J Deru, B Dumont, J Eriksen, C Garbisu, A Jacobs, T Kautz, H J Koch, B Mary, J Peigne and F Schulz for responding to requests for additional information on their studies, and Leo Petrokofsky for generating the online evidence map. Funding Information: This work was supported by funding from the Biotechnology and Biological Sciences Research Council (BBSRC) (Grant No. BB/M011224/1). PCB would like to acknowledge funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC 2075–390740016). Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd.Peer reviewedPublisher PD

Past vegetation dynamics to infer holocene climate changes in Tenerife and La Gomera, Canary Islands

Oceanic islands in the low latitudes, as the Canary Islands, are generally considered to have been well buffered from the climate change of the Quaternary period. However, questions remain about whether past climatic changes on Atlantic islands are synchronic with those occurring in Africa and the Mediterranean coast or if the climate remained stable during the Holocene. Here we used fossil pollen and charcoal time series on Tenerife and La Gomera in order to: 1) provide the first inter-island picture of vegetation dynamics through the last 9600 years of this important biodiverse region of Europe; 2) detect the vegetation sensitivity, mainly tree communities, to past climatic changes; and, 3) provide evidences for human-induced changes at this potentially highly informative point. Preliminary analyses suggest very little climate change for the period 4000 years to present, but this requires confirmation by reference to additional coring sites. In La Gomera, we found strong evidences of a shift towards drier conditions at around 5500 years ago. The general vegetation pattern observed was a decrease in hygrophilous trees (Canarian palm and willow) and an expansion of Morella-Erica woody heath. Our results provide the first evidence to suggest that the general Northern Africa and Mediterranean shift towards drier conditions may be traced in the Canary Islands