Farmers’ Preferences for the Design of Fruit Fly Pest-Free Area (FF-PFA) in Kerio-Valley: A Latent-Class Approach

Fruit flies are a very important group of pests for many countries due to their potential to cause damage in fruits thus restricting access to international markets for plant products that can host fruit flies. The high probability of introduction of fruit flies associated with a wide range of hosts’ results in restrictions imposed by many importing countries to accept fruits from areas in which these pests are established. For these reasons, establishment and maintenance of pest free areas for fruit flies (FF-PFAs) is receiving considerable attention in the current policy debates. Kenya Plant Health Inspectorate Service (KEPHIS) has taken the lead to establish and help maintain FF-PFAs in the main mango production zones of Elgeyo-Marakwet County of Kenya. However, as the ultimate success of the programme depends on farmers’ judgment and acceptance, acquiring information about potential demand is of paramount importance for policy advice. In this paper, we assess the demand in terms of consumer preferences and willingness to pay for FF-PFAs using a stated choice experiment method (SCE). A novel feature of this paper is that it focuses on how the FF-PFA should be designed and presented. Results from the latent class model (LCM) reveal that farmers prefer FF-PFAs featuring training, market information with sales contract, large benefits to other mango value-chain actors and when they are recommended by officials. Keywords: FF-PFA, SCE, LCM, Farmers’ preference, Mang

Real‐time alerts from AI‐enabled camera traps using the Iridium satellite network: A case‐study in Gabon, Central Africa

Efforts to preserve, protect and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to create a ‘smart’ camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an ‘alert’ containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. All reference materials required to build the system are provided in open-source repositories. Results show the system can operate for a minimum of 3 months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network. We anticipate significant developments in this field and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

Sp1-regulated expression of p11 contributes to motor neuron degeneration by membrane insertion of TASK1

Disruption in membrane excitability contributes to malfunction and differential vulnerability of specific neuronal subpopulations in a number of neurological diseases. The adaptor protein p11, and background potassium channel TASK1, have overlapping distributions in the CNS. Here, we report that the transcription factor Sp1 controls p11 expression, which impacts on excitability by hampering functional expression of TASK1. In the SOD1-G93A mouse model of ALS, Sp1-p11-TASK1 dysregulation contributes to increased excitability and vulnerability of motor neurons. Interference with either Sp1 or p11 is neuroprotective, delaying neuron loss and prolonging lifespan in this model. Nitrosative stress, a potential factor in human neurodegeneration, stimulated Sp1 expression and human p11 promoter activity, at least in part, through a Sp1-binding site. Disruption of Sp1 or p11 also has neuroprotective effects in a traumatic model of motor neuron degeneration. Together our work suggests the Sp1-p11- TASK1 pathway is a potential target for treatment of degeneration of motor neurons

Robust ecological analysis of camera trap data labelled by a machine learning model

1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. 2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

Hypoexcitability of Motoneurons: An Early Pathological Sign in ALS

International audienc

Low seroprevalence of IgG antibodies to Ebola virus in an epidemic zone: Ogooué-Ivindo region, northeastern Gabon, 1997

A population-based serosurvey was performed to determine the seroprevalence of antibodies to Ebola virus (EBO) in a region that has experienced multiple epidemics of EBO hemorrhagic fever. Of 2533 residents in 8 villages, serum samples from 979 (38.6%) were tested by enzyme-linked immunosorbent assay for immunoglobulin (Ig) G and IgM antibodies to Ebola-Zaire (EBO-Z) virus. Fourteen samples (1.4%) were found positive for IgG antibodies, and 4 of these (.4%) were samples from survivors of an epidemic of EBO hemorrhagic fever. Seroprevalence based on the remaining 10 IgG-seropositive individuals with no history of exposure to EBO was 1.0% (exact binomial 95% confidence interval, 0.5%-1.9%). No serum samples were found positive for IgM antibodies to EBO-Z virus. The low seroprevalence suggests that, outside of recognized outbreaks, human exposure to EBO in this epidemic zone is rare. © 2005 by the Infectious Diseases Society of America. All rights reserved