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   https://doi.org/10.1093/sysbio/syac068  

   Martin, Bruce S.; Bradburd, Gideon S.; Harmon, Luke J.; Weber, Marjorie G.; López-Fernández, ed., Hernán (November 2022, Systematic Biology)

   Abstract Rates of phenotypic evolution vary markedly across the tree of life, from the accelerated evolution apparent in adaptive radiations to the remarkable evolutionary stasis exhibited by so-called “living fossils.” Such rate variation has important consequences for large-scale evolutionary dynamics, generating vast disparities in phenotypic diversity across space, time, and taxa. Despite this, most methods for estimating trait evolution rates assume rates vary deterministically with respect to some variable of interest or change infrequently during a clade’s history. These assumptions may cause underfitting of trait evolution models and mislead hypothesis testing. Here, we develop a new trait evolution model that allows rates to vary gradually and stochastically across a clade. Further, we extend this model to accommodate generally decreasing or increasing rates over time, allowing for flexible modeling of “early/late bursts” of trait evolution. We implement a Bayesian method, termed “evolving rates” (evorates for short), to efficiently fit this model to comparative data. Through simulation, we demonstrate that evorates can reliably infer both how and in which lineages trait evolution rates varied during a clade’s history. We apply this method to body size evolution in cetaceans, recovering substantial support for an overall slowdown in body size evolution over time with recent bursts among some oceanic dolphins and relative stasis among beaked whales of the genus Mesoplodon. These results unify and expand on previous research, demonstrating the empirical utility of evorates. [cetacea; macroevolution; comparative methods; phenotypic diversity; disparity; early burst; late burst] 

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2. The Evolution of Topic Modeling

   https://doi.org/10.1145/3507900  

   Churchill, Rob; Singh, Lisa (January 2022, ACM Computing Surveys)

   Topic models have been applied to everything from books to newspapers to social media posts in an effort to identify the most prevalent themes of a text corpus. We provide an in-depth analysis of unsupervised topic models from their inception to today. We trace the origins of different types of contemporary topic models, beginning in the 1990s, and we compare their proposed algorithms, as well as their different evaluation approaches. Throughout, we also describe settings in which topic models have worked well and areas where new research is needed, setting the stage for the next generation of topic models. 

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3. The evolution of macrosystems biology

   https://doi.org/10.1002/fee.2288  

   LaRue, Elizabeth A; Rohr, Jason; Knott, Jonathan; Dodds, Walter K; Dahlin, Kyla M; Thorp, James H; Johnson, Jeremy S; Rodríguez González, Mayra I; Hardiman, Brady S; Keller, Michael; et al (February 2021, Frontiers in Ecology and the Environment)

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4. The raw material of evolution

   https://doi.org/10.1126/science.adk0121  

   Hoelzel, A Rus; Lynch, Michael (September 2023, Science)

   Estimates of whale mutation rates contribute to understanding evolutionary processes 

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5. Evolution of the nanoHUB Tool

   Clark, Steven (April 2021, BOINC Workshop)

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   This presentation describes the implementation of the BOINC infrastructure in the nanoHUB science gateway. BOINC is being used to support uncertainty quantification, machine learning, and interactive educational use of nanoHUB simulation tools. 

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