Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper

Flea‘in Around: A Look at the Identification, Preservation, Clearing, and Mounting of Siphonaptera

Fleas are remarkable and highly specialized insects, with no part of their external anatomy being easily mistaken for that of any other insect. Due to their small size, the subtle differences among the distinguishing morphological characteristics of each species, and complexities of preparing specimens, identifying, and working with fleas is challenging. Various documents and taxonomic keys are available that discuss mounting procedures and the identification of medically important fleas for large regions of the world including the United States; however, many of these have become antiquated over time. Some of the distinguishing specialized characteristics exhibited among flea species, as presented in older keys, come in the form of line drawings, which are accurate but can be difficult to use when comparing it to structures on a whole specimen when viewed through a microscope. This paper presents a guide which describes in detail previously developed, but obscure techniques covering the preservation, preparation, clearing, and mounting of Siphonaptera specimens. In addition, we are also presenting an easy-to-use photographic key of twelve flea species collected from back yard wildlife, as well as pet cats and dogs in Orange County, CA. This key, which is freely available online at the Orange County Mosquito and Vector Control District’s website, is an effective tool for the identification of common flea species found in southern California. Using the key in conjunction with the mounting guide will provide users with a full-circle guide to preserving, identifying, and mounting flea specimens. Keyed flea genera include Cediopsylla, Ctenocephalides, Diamanus, Echidinophaga, Hoplopsyllus, Leptopsylla, Nosopsyllus, Orchopoeas, Pulex, and Xenopsylla. Examined hosts include cats, coyotes, dogs, mice, opossums, rabbits, raccoons, rats, skunks, squirrels, and woodrats

Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots

Delimiting Cryptic Morphological Variation among Human Malaria Vector Species using Convolutional Neural Networks

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiaecomplex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance

NEBLINE, August 2017

CONTENTS Feature: Back-to-School Nutrition Food & Health Farm & Acreage Pests & Wildlife Horticulture Early Childhood 4-H & Youth and other extension news and events Special pullout section: Lancaster County Super Fair 2017 Schedule & Ma

Town of Newington, New Hampshire annual report 2018.

This is an annual report containing vital statistics for a town/city in the state of New Hampshire

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Identifying success factors in crowdsourced geographic information use in government

Crowdsourcing geographic information in government is focusing on projects that are engaging people who are not government officials and employees in collecting, editing and sharing information with governmental bodies. This type of projects emerged in the past decade, due to technological and societal changes - such as the increased use of smartphones, combined with growing levels of education and technical abilities to use them by citizens. They also flourished due to the need for updated data in relatively quick time when financial resources are low. They range from recording the experience of feeling an earthquake to recording the location of businesses during the summer time. 50 cases of projects in which crowdsourced geographic information was used by governmental bodies across the world are analysed. About 60% of the cases were examined in 2014 and in 2017, to allow for comparison and identification of success and failure. The analysis looked at different aspects and their relationship to success: the drivers to start a project; scope and aims; stakeholders and relationships; inputs into the project; technical and organisational aspect; and problems encountered. The main key factors of the case studies were analysed with the use of Qualitative Comparative Analysis (QCA) which is an analytical method that combines quantitative and qualitative tools in sociological research. From the analysis, we can conclude that there is no “magic bullet” or a perfect methodology for a successful crowdsourcing in government project. Unless the organisation has reached maturity in the area of crowdsourcing, identifying a champion and starting a project that will not address authoritative datasets directly is a good way to ensure early success and start the process of organisational learning on how to run such projects. Governmental support and trust is undisputed. If the choice is to use new technologies, this should be accompanied by an investment of appropriate resources within the organisation to ensure that the investment bear fruits. Alternatively, using an existing technology that was successful elsewhere and investing in training and capacity building is another path for success. We also identified the importance of intermediary Non-Governmental Organizations (NGOs) with the experience and knowledge in working with crowdsourcing within a partnership. These organizations have the knowledge and skills to implement projects at the boundary between government and the crowd, and therefore can offer the experience to ensure better implementation. Changes and improvement of public services, or a focus on environmental monitoring can be a good basis for a project. Capturing base mapping is a good point to start, too. The recommendation of the report address organisational issues, resources, and legal aspects