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Background: Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.

Methods: This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.

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The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a mechanism-agnostic representation of nucleotide sequences.

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Neotropical regions near the equator are recognized as speciation "hot spots" reflecting their abundant biodiversity. In western South America, the coasts of Panama, Colombia, Ecuador, the Galápagos Archipelago, and northern Peru form the Tropical Eastern Pacific biome. This area has the greatest heterogeneity of sympatric fiddler crab species of any portion of the planet.

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Antarctic Geothermal Soils Exhibit an Absence of Regional Habitat Generalist Microorganisms.

Environ Microbiol

January 2025

Thermophile Research Unit, Te Aka Mātuatua, School of Science, Te Whare Wānanga o Waikato, University of Waikato, Hamilton, Aotearoa-New Zealand.

Active geothermal systems are relatively rare in Antarctica and represent metaphorical islands ideal to study microbial dispersal. In this study, we tested the macro-ecological concept that high dispersal rates result in communities being dominated by either habitat generalists or specialists by investigating the microbial communities on four geographically separated geothermal sites on three Antarctic volcanoes (Mts. Erebus, Melbourne, and Rittman).

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