First catalog of spiders of Egypt (Araneae)

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  First catalog of spiders of Egypt (Araneae) Abstract This study provides a comprehensive catalog of the spider fauna of Egypt, comprising 389 species assigned to 197 genera and 40 families. For each species, all published locality records are compiled and accompanied by complete bibliographic references. The dataset has been carefully reviewed and critically evaluated in light of the most recent taxonomic and faunistic information. Among the recorded species, 55 (14.13%) are currently known only from Egypt and are thus considered endemic. Based on the World Spider Catalog (2026), 28 species are treated as nomina dubia, 23 species lack confirmed records from Egypt, which is not correct, 10 have to be omitted from the Egyptian list and 8 are species inquirenda. Despite the relatively high number of documented species, the Egyptian araneofauna remains insufficiently explored. Given the country’s ecological diversity and biogeographical position, further research is expected to signi...

Optimizing Scorpion Toxin Processing through Artificial Intelligence

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Optimizing Scorpion Toxin Processing through Artificial Intelligence

Abstract

Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt the opening/closing mechanisms in cell ion channels. These peptides are widely studied for several reasons including their use in drug discovery. Although improvements in RNAseq have greatly expedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due to their small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomes using a neural network approach. This pipeline implements basic neural networks to sort amino acid sequences to find those that are likely toxins and thereafter predict the type of toxin represented by the sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins in forthcoming scorpion genome sequencing projects and potentially serve a useful role in identifying targets for drug development.

Psenicnik, A., A., A., Graham, M. R., Hassan, M. K., A., M., Sharma, P. P., & E., C. (2024). Optimizing Scorpion Toxin Processing through Artificial Intelligence. Toxins, 16(10), 437. https://doi.org/10.3390/toxins16100437