Search for: All records

Aging cells functionally decline and accumulate damage through poorly understood mechanisms. In this issue, Antentor et al. (https://doi.org/10.1083/jcb.202412064) find that increased vacuolar pH in older yeast cells slows clathrin-mediated endocytosis. These findings have broad implications in aging-related plasma membrane protein quality control. 

Industry-funded research poses a threat to the validity of scientific inference on carcinogenic hazards. Scientists require tools to better identify and characterize industry sponsored research across bodies of evidence to reduce the possible influence of industry bias in evidence synthesis reviews. We applied a novel large language model (LLM)-based tool named InfluenceMapper to demonstrate and evaluate its performance in identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame. MethodsAll epidemiological, animal cancer, and mechanistic studies included in systematic reviews on the carcinogenicity of the three agents by theIARC Monographsprogramme. Selected agents were recently evaluated by theMonographsand are of commercial interest by major industries. InfluenceMapper extracted disclosed entities in study publications and classified up to 40 possible disclosed relationship types between each entity and the study and between each entity and author. A human classified entities as ‘industry or industry-funded’ and assessed relationships with industry for potential conflicts of interest. Positive predictive values described the extent of true positive relationships identified by InfluenceMapper compared to human assessment. ResultsAnalyses included 2,046 studies for all three agents. We identified 320 disclosed industry or industry-funded entities from InfluenceMapper output that were involved in 770 distinct study-entity and author-entity relationships. For each agent, between 4 and 8% of studies disclosed funding by industry and 1–4% of studies had at least one author who disclosed receiving industry funding directly. Industry trade associations for all three agents funded 22 studies published in 16 journals over a 37-year span. Aside from funding, the most prevalent disclosed relationships with industry were receiving data, holding employment, paid consulting, and providing expert testimony. Positive predictive values were excellent (≥ 98%) for study-entity relationships but declined for relationships with individual authors. ConclusionsLLM-based tools can significantly expedite and bolster the detection of disclosed conflicts of interest from industry sponsored research in cancer prevention. Possible use cases include facilitating the assessment of bias from industry studies in evidence synthesis reviews and alerting scientists to the influence of industry on scientific inference. Persistent challenges in ascertaining conflicts of interest underscore the urgent need for standardized, transparent, and enforceable disclosures in biomedical journals. 

Here we provide percent contribution of mineral associated (i.e., heavy fraction - HF) and relatively more labile (i.e., light fraction - LF) organic matter through soil profiles and along hillslope catena within sites in the Critical Zone Network (CZNet) Geomicrobiology cluster. Each sample is separated into a HF an a LF utilizing a 1.85 g cm-3 sodium polytungstate (3Na2WO4·9WO3·H2O or Na6 [H2W12O40]) solution. The resultant fractions are run for percent carbon (C) and nitrogen (N) and their associated stable isotopes (δ13C and δ15N) to offer novel insights in soil organic matter processes. Samples that were either too small for analytical analysis or below instrument detection limit are labeled with BDL. 

We revise the millipede genus Apheloria Chamberlin, 1921—a colorful and often encountered group of millipedes in eastern North America. With molecular phylogenetics, we estimate the evolutionary history of the genus, and use it in combination with morphology to understand species diversity. We describe a new species, Apheloria uwharrie sp. nov. from North and South Carolina, synonymize Apheloria tigana Chamberlin, 1939 syn. nov. with Apheloria virginiensis (Drury, 1770), and remove Apheloria luminosa (Kenyon, 1893) syn. nov. from the genus and place it in synonymy with Pleuroloma flavipes Rafinesque, 1820. Currently there are six species of Apheloria: Apheloria corrugata (Wood, 1864) stat. nov.; Apheloria montana (Bollman, 1887); Apheloria polychroma Marek, Means & Hennen, 2018; Apheloria uwharrie sp. nov.; Apheloria virginiensis (Drury, 1770); and Apheloria whiteheadi (Shelley, 1986). 

The Change Hawaii (Change(HI)) project is fundamentally addressing the existential threat of climate change in Hawaii by integrating data and climate science to foster statewide resilience, enhance decision science, and support workforce development in critical fields. A cornerstone of this initiative is the \textbf{Hawaii Climate Data Portal (HCDP)}, which operates as a vital science gateway and data hub \cite. The HCDP's primary objective is to build capacity through advanced data science and artificial intelligence (AI), serving as a robust resource for monitoring, visualizing, and communicating environmental change \cite{longman_hawaii_2024}. Its critical role is highlighted by its extensive provision of climate data and its Application Programming Interface (API), which is instrumental in the development and functionality of diverse decision support tools tailored for various stakeholders across the state. This paper details the HCDP's integration with the Tapis API platform, and its successful application in developing actionable climate science outcomes for Hawaii. 

Recent work characterized shifts in preparatory activity of the motor cortex during motor learning. The specific shift geometry during learning, washout, and relearning blocks was hypothesized to implement the acquisition, retention, and retrieval of motor memories. We sought to train recurrent neural network (RNN) models that could be used to study these motor learning phenomena. We built an environment for a curl field (CF) motor learning task and trained RNNs with reinforcement learning (RL) with novel regularization terms to perform behaviorally realistic reaching trajectories over the course of learning. Our choice of RL over supervised learning was motivated by the idea that motor adaptation, in the absence of demonstrations, is a process of reoptimization. We find these models, despite lack of supervision, reproduce many behavioral findings from monkey CF adaptation experiments. These models also captured key neurophysiological findings.We found that the model’s preparatory activity existed in a force-predictive subspace that remained stable across learning, washout, and relearning. Additionally, preparatory activity shifted uniformly, independently of the distance to the CF trained target. Finally, we found that the washout shift became more orthogonal to the learning shift, and hence more brain-like, when the RNNs were pretrained to have prior experience with CF dynamics. We argue the increased fit to neurophysiological recordings is driven by more generalizable and structured dynamical motifs in the model with more prior experience. This suggests that prior experience could organize preparatory neural activity underlying motor memory to have more orthogonal characteristics, by forming structured dynamical motifs in the motor cortex circuitry. 

This paper introduces a method to synthesize a 3D tensor field within a constrained geometric domain represented as a tetrahedral mesh. Whereas previous techniques optimize forisotropicfields, we focus onanisotropictensor fields that are smooth and aligned with the domain boundary or user guidance. The key ingredient of our method is a novel computational design framework, built on top of thesymmetric orthogonally decomposable(odeco) tensor representation, to optimize the stretching ratios and orientations for each tensor in the domain. In contrast to past techniques designed only forisotropictensors, we demonstrate the efficacy of our approach in generating smooth volumetric tensor fields with highanisotropyand shape conformity, especially for the domain with complex shapes. We apply these anisotropic tensor fields to various applications, such as anisotropic meshing, structural mechanics, and fabrication. 

Different actin nucleation-promoting factors (NPFs) orchestrate different patterns of cell protrusions, likely reflecting their distinct patterns of self-organization. Here, we leveraged in vivo biochemical approaches to investigate how the WAVE complex instructs the formation of sheet-like lamellipodia. We show that the WAVE complex is a core constituent of a linear multilayered protein array at the plasma membrane, expected for an NPF that builds sheet-like actin-based protrusions. Negative membrane curvature is both necessary and sufficient for WAVE complex linear membrane association in the presence of upstream activators (Rac, Arf1/6, and PIP3) and the PRDs of both WAVE2 and Abi2, providing a potential mechanistic basis for templating of lamellipodia and their emergent behaviors, including barrier avoidance. Through computational modeling, we demonstrate that WAVE complex’s linear organization and preference for negative curvature both play important roles in robust lamellipodia formation. Our data reveal key features of mesoscale WAVE complex patterning and highlight an integral relation between NPF self-organization and cell morphogenesis.