L1 sequence of a new human papillomavirus type-58 variant associated with cervical intraepithelial neoplasia

The present study on molecular characterization of a human papillomavirus (HPV) isolated in Central Brazil describes the L1 gene sequence from a new variant of HPV-58, the isolate Bsb-02. The sample was from a smear obtained from a woman with cervical intraepithelial neoplasia grade II. The whole L1 gene from isolate Bsb-02 was sequenced automatically, showing 99.1% nucleotide identity with the gene from the HPV-58 reference. The clustering between Bsb-02 and HPV-58 reference sequence was also supported by phylogenetic analysis. Fourteen nucleotide substitutions were observed: eight were synonymous and six were associated with amino acid substitutions. A10V and V144I have not been previously described. At GenBank, the only complete L1 sequence from HPV-58 in addition to the HPV-58 reference one is that of Bsb-02. These data provide information that may be relevant to HPV diagnosis and to rational vaccine strategies. HPV variants may also be associated with host immune responses and with the risk of cervical neoplasia

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost