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Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods. 

Calculus has long been known as a “gateway course” to STEM fields in postsecondary education. To address this issue, researchers in the Math Department at Mountain State University (pseudonym) designed a model of complementary instruction that features peer-facilitated workshops where Calculus I students work in groups on inquiry-oriented, groupworthy tasks. The purpose of this multiple-case study is to seek answers to the question, "How do undergraduate Calculus I students experience and navigate their learning of calculus in the parallel spaces of coursework and inquiry-oriented complementary instruction?" The findings of one case study are presented here and include characterizations of the different forms of agentive participation afforded to students in each of the two spaces, as well as their complementary nature relative to learning calculus with understanding. Implications for dismantling the persistent barriers imposed by calculus on access to postsecondary STEM fields are also discussed. 

It has been recently discovered that the thermosetting matrix of engineering composites can be fully depolymerized in organic solvents through covalent bond exchange reactions (BERs) between the polymer network and solvent molecules. This breakthrough enables the eco-friendly and sustainable recovery of valuable fiber reinforcements using mild processing conditions. However, current investigations have been limited to proof-of-concept experimental demonstrations, leaving unanswered questions regarding the influence of temperature, solvent choice, and fiber arrangement on composite depolymerization performance. These factors are crucial for the commercialization and widespread industrial implementation of this technique. To address this significant knowledge gap, this study aims to establish the relationship between composite depolymerization speed and various material and processing conditions. A multiscale diffusion-reaction computational model is defined based on the finite element method, which links the microscale BER rate to the continuum-level composite depolymerization kinetics. Specifically, it reveals how the processing temperature, solvent diffusivity, fiber content, and fiber arrangement affect the overall composite depolymerization speed. The study enhances our understanding of the underlying mechanisms of composite recycling using organic solvents. As a result, it provides valuable insights for industrial stakeholders, allowing them to optimize depolymerization conditions, make informed material selections, and develop suitable business models for waste management. 

Abstract MotivationSite concordance factors (sCFs) have become a widely used way to summarize discordance in phylogenomic datasets. However, the original version of sCFs was calculated by sampling a quartet of tip taxa and then applying parsimony-based criteria for discordance. This approach has the potential to be strongly affected by multiple hits at a site (homoplasy), especially when substitution rates are high or taxa are not closely related. ResultsHere, we introduce a new method for calculating sCFs. The updated version uses likelihood to generate probability distributions of ancestral states at internal nodes of the phylogeny. By sampling from the states at internal nodes adjacent to a given branch, this approach substantially reduces—but does not abolish—the effects of homoplasy and taxon sampling. Availability and implementationUpdated sCFs are implemented in IQ-TREE 2.2.2. The software is freely available at https://github.com/iqtree/iqtree2/releases. Supplementary informationSupplementary information is available at Bioinformatics online. 

Abstract Soft-elasticity in monodomain liquid crystal elastomers (LCEs) is promising for impact-absorbing applications where strain energy is ideally absorbed at constant stress. Conventionally, compressive and impact studies on LCEs have not been performed given the notorious difficulty synthesizing sufficiently large monodomain devices. Here, we use direct-ink writing 3D printing to fabricate bulk (>cm 3 ) monodomain LCE devices and study their compressive soft-elasticity over 8 decades of strain rate. At quasi-static rates, the monodomain soft-elastic LCE dissipated 45% of strain energy while comparator materials dissipated less than 20%. At strain rates up to 3000 s −1 , our soft-elastic monodomain LCE consistently performed closest to an ideal-impact absorber. Drop testing reveals soft-elasticity as a likely mechanism for effectively reducing the severity of impacts – with soft elastic LCEs offering a Gadd Severity Index 40% lower than a comparable isotropic elastomer. Lastly, we demonstrate tailoring deformation and buckling behavior in monodomain LCEs via the printed director orientation. 

Isoprene emitted by vegetation is an important precursor of secondary organic aerosol (SOA), but the mechanism and yields are uncertain. Aerosol is prevailingly aqueous under the humid conditions typical of isoprene-emitting regions. Here we develop an aqueous-phase mechanism for isoprene SOA formation coupled to a detailed gas-phase isoprene oxidation scheme. The mechanism is based on aerosol reactive uptake coefficients (γ) for water-soluble isoprene oxidation products, including sensitivity to aerosol acidity and nucleophile concentrations. We apply this mechanism to simulation of aircraft (SEAC⁴RS) and ground-based (SOAS) observations over the southeast US in summer 2013 using the GEOS-Chem chemical transport model. Emissions of nitrogen oxides (NO_x  ≡  NO + NO₂) over the southeast US are such that the peroxy radicals produced from isoprene oxidation (ISOPO₂) react significantly with both NO (high-NO_x pathway) and HO₂ (low-NO_x pathway), leading to different suites of isoprene SOA precursors. We find a mean SOA mass yield of 3.3 % from isoprene oxidation, consistent with the observed relationship of total fine organic aerosol (OA) and formaldehyde (a product of isoprene oxidation). Isoprene SOA production is mainly contributed by two immediate gas-phase precursors, isoprene epoxydiols (IEPOX, 58 % of isoprene SOA) from the low-NO_x pathway and glyoxal (28 %) from both low- and high-NO_x pathways. This speciation is consistent with observations of IEPOX SOA from SOAS and SEAC⁴RS. Observations show a strong relationship between IEPOX SOA and sulfate aerosol that we explain as due to the effect of sulfate on aerosol acidity and volume. Isoprene SOA concentrations increase as NO_x emissions decrease (favoring the low-NO_x pathway for isoprene oxidation), but decrease more strongly as SO₂ emissions decrease (due to the effect of sulfate on aerosol acidity and volume). The US Environmental Protection Agency (EPA) projects 2013–2025 decreases in anthropogenic emissions of 34 % for NO_x (leading to a 7 % increase in isoprene SOA) and 48 % for SO₂ (35 % decrease in isoprene SOA). Reducing SO₂ emissions decreases sulfate and isoprene SOA by a similar magnitude, representing a factor of 2 co-benefit for PM_2.5 from SO₂ emission controls.