Antiviral Plants from Marajó Island, Brazilian Amazon: A Narrative Review.

Diseases caused by viruses are a global threat, resulting in serious medical and social problems for humanity. They are the main contributors to many minor and major outbreaks, epidemics, and pandemics worldwide. Over the years, medicinal plants have been used as a complementary treatment in a range of diseases. In this sense, this review addresses promising antiviral plants from Marajó island, a part of the Amazon region, which is known to present a very wide biodiversity of medicinal plants. The present review has been limited to articles and abstracts available in Scopus, Web of Science, Science Direct, Scielo, PubMed, and Google Scholar, as well as the patent offices in Brazil (INPI), United States (USPTO), Europe (EPO) and World Intellectual Property Organization (WIPO). As a result, some plants from Marajó island were reported to have actions against HIV-1,2, HSV-1,2, SARS-CoV-2, HAV and HBV, Poliovirus, and influenza. Our major conclusion is that plants of the Marajó region show promising perspectives regarding pharmacological potential in combatting future viral diseases.

AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.

Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2-9% and 6-7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences' input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.