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The maggot cycle. From Rossetto et al., 2014.  

The maggot cycle. From Rossetto et al., 2014.  

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Figure 1. Clostridium botulinum bacteria.  
Figure 2. Human form of botulism. Intoxination due to consumption of...
Figure 3. Flaccid paralysis characteristic of botulism in cow and turkey.  
Figure 4. The maggot cycle. From Rossetto et al., 2014.  
+20 Figure 6. Structure of the precursor toxin complex.  
Clostridium botulinum, from toxin and flagellin genotyping to Whole Genome Sequencing: An insight into the genetic diversity of human and animal botulism associated Clostridia
Thesis
Full-text available
  • Mar 2016
Clostridium botulinum is the etiologic agent of botulism, a deadly paralytic disease that can affect both human and animals. Different bacteria, producing neurotoxins type A to G, are responsible for the disease. They are separated into different groups (I to VI) on the basis of their phenotypical and biological characteristics. My PhD work was str...
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Context 1
... established, a botulism outbreak is self-perpetuating. This is described as the wildlife carcass-maggot cycle of avian botulism (Figure 4). Environmental conditions that favour botulism outbreaks in wildlife include warm temperatures (optimum growth temperature between 25°C and 42°C), shallow alkaline waters that contain abundant invertebrate populations, and decomposing carcasses ( Soos and Wobeser, 2006). ...
Context 2
... and C. baratii BoNTs producing Group V and VI respectively are related to their own non toxigenic species sequences ( Figure 14) ( Poehlein et al., 2015). ...

Citations

Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)
Preprint
  • May 2021
Avian botulism caused by a bacterium, Clostridium botulinum, causes a paralytic disease in birds often leading to high fatality, and is usually diagnosed using molecular techniques. Diagnostic techniques for Avian botulism include: Mouse Bioassay, ELISA, PCR, all of which are time-consuming, laborious and require invasive sample collection from affected sites. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of water-body sites important for avian fauna) backed by on-ground reporting evidence of outbreaks. In the current version, BAWD covers a total ground area of 904 sq.km from two open source satellite projects (Sentinel and Landsat). BAWD consists of 17 topographically diverse global sites spanning across 4 continents, with locations monitored over a time-span of 3 years (2016-2020). Using BAWD and state-of-the-art deep-learning techniques we propose a first-ever Artificial Intelligence based (AI) model to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol, Visible, Infra-red (NIR/SWIR) and Bands of Thermal). AVI-BoT uses fused multi-spectral satellite images of water-bodies (10-bands) as input to generate a spatial prediction map depicting probability of potential Avian botulism outbreaks. We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature as conducive for outbreak) to predict Avian botulism. Using AVI-BoT, we achieve a training accuracy of 0.94 and validation accuracy of 0.96 on BAWD, far superior in comparison to our Causative factors model. The proposed technique presents a scale-able, low-cost, non-invasive methodology for continuous monitoring of bird-habitats against botulism outbreaks with the potential of saving valuable fauna lives.
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