Accurate and fast clade assignment via deep learning and frequency chaos game representation.

BACKGROUND: Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. RESULTS: In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. CONCLUSIONS: By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY: The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Disease Surveillance of Cougars (Puma concolor) in Utah, USA.

Adult, free-ranging cougars (Puma concolor) were sampled in three regions of Utah, US, from 2018 to 2021. A total of 68% (23/34) of the sampled cougars had antibodies to feline parvovirus, 15% (5/33) to canine distemper virus, 18% (6/34) to calicivirus, and 22% (8/37) to Yersinia pestis. Forty-one percent (13/32) had IgG antibodies to Toxoplasma gondii and 6% (2/33) to feline immunodeficiency virus, and 3% (1/32) were positive for Dirofilaria immitis (heartworm) antigen. All were seronegative for Toxoplasma gondii IgM, feline enteric coronavirus, SARS-CoV-2, feline leukemia virus, feline herpesvirus, and Francisella tularensis. Tapeworms and Toxascaris leonina eggs were detected in the feces. The disease exposures detected were similar to what has been reported from cougar populations in other western US states, and the current level of exposures is unlikely to have a negative impact on the state's population.

COVID-19 suppression of human mobility releases mountain lions from a landscape of fear.

Humans have outsized effects on ecosystems, in part by initiating trophic cascades that impact all levels of the food chain.1,2 Theory suggests that disease outbreaks can reverse these impacts by modifying human behavior,3,4 but this has not yet been tested. The COVID-19 pandemic provided a natural experiment to test whether a virus could subordinate humans to an intermediate link in the trophic chain, releasing a top carnivore from a landscape of fear. Shelter-in-place orders in the Bay Area of California led to a 50% decline in human mobility, which resulted in a relaxation of mountain lion aversion to urban areas. Rapid changes in human mobility thus appear to act quickly on food web functions, suggesting an important pathway by which emerging infectious diseases will impact not only human health but ecosystems as well.