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The nucleolus is a multiphasic biomolecular condensate that facilitates ribosome biogenesis, a complex process involving hundreds of proteins and RNAs. The proper execution of ribosome biogenesis likely depends on the material properties of the nucleolus. However, these material properties remain poorly understood due to the challenges of in vivo measurements. Here, we use micropipette aspiration (MPA) to directly characterize the viscoelasticity and interfacial tensions of nucleoli within transcriptionally activeXenopus laevisoocytes. We examine the major nucleolar subphases, the outer granular component (GC) and the inner dense fibrillar component (DFC), which itself contains a third small phase known as the fibrillar center (FC). We show that the behavior of the GC is more liquid-like, while the behavior of the DFC/FC is consistent with that of a partially viscoelastic solid. To determine the role of ribosomal RNA in nucleolar material properties, we degrade RNA using RNase A, which causes the DFC/FC to become more fluid-like and alters interfacial tension. Together, our findings suggest that RNA underlies the partially solid-like properties of the DFC/FC and provide insights into how material properties of nucleoli in a near-native environment are related to their RNA-dependent function. 

Abstract Polarization observations of the Milky Way and many other spiral galaxies have found a close correspondence between the orientation of spiral arms and magnetic field lines on scales of hundreds of parsecs. This paper presents polarization measurements at 214μm toward 10 filamentary candidate “bones” in the Milky Way using the High-resolution Airborne Wide-band Camera on the Stratospheric Observatory for Infrared Astronomy. These data were taken as part of the Filaments Extremely Long and Dark: A Magnetic Polarization Survey and represent the first study to resolve the magnetic field in spiral arms at parsec scales. We describe the complex yet well-defined polarization structure of all 10 candidate bones, and we find a mean difference and standard deviation of −74° ± 32° between their filament axis and the plane-of-sky magnetic field, closer to a field perpendicular to their length rather than parallel. By contrast, the 850μm polarization data from Planck on scales greater than 10 pc show a nearly parallel mean difference of 3° ± 21°. These findings provide further evidence that magnetic fields can change orientation at the scale of dense molecular clouds, even along spiral arms. Finally, we use a power law to fit the dust polarization fraction as a function of total intensity on a cloud-by-cloud basis and find indices between −0.6 and −0.9, with a mean and standard deviation of −0.7 ± 0.1. The polarization, dust temperature, and column density data presented in this work are publicly available online. 

Abstract Stars primarily form in galactic spiral arms within dense, filamentary molecular clouds. The largest and most elongated of these molecular clouds are referred to as “bones,” which are massive, velocity-coherent filaments (lengths ∼20 to >100 pc, widths ∼1–2 pc) that run approximately parallel and in close proximity to the Galactic plane. While these bones have been generally well characterized, the importance and structure of their magnetic fields (B-fields) remain largely unconstrained. Through the Stratospheric Observatory for Infrared Astronomy Legacy program FIlaments Extremely Long and Dark: a Magnetic Polarization Survey (FIELDMAPS), we mapped the B-fields of 10 bones in the Milky Way. We found that their B-fields are varied, with no single preferred alignment along the entire spine of the bones. At higher column densities, the spines of the bones are more likely to align perpendicularly to the B-fields, although this is not ubiquitous, and the alignment shows no strong correlation with the locations of identified young stellar objects. We estimated the B-field strengths across the bones and found them to be ∼30–150μG at parsec scales. Despite the generally low virial parameters, the B-fields are strong compared to the local gravity, suggesting that B-fields play a significant role in resisting global collapse. Moreover, the B-fields may slow and guide gas flow during dissipation. Recent star formation within the bones may be due to high-density pockets at smaller scales, which could have formed before or simultaneously with the bones. 

Semiheavy water (HOD) is one of the simplest molecules in which the bonds are labeled by isotope. We demonstrate that a pair of intense few-femtosecond infrared laser pulses can be used to selectively tunnel ionize along one of the two bonds. The first pulse doubly ionizes HOD, inducing rapid bond stretching and unbending. Femtoseconds later, the second pulse arrives and further ionization is selectively enhanced along the OH bond. These conclusions arise from 3D time-resolved measurements of ${\mathrm{H}}^{+}, {\mathrm{D}}^{+}$, and ${\mathrm{O}}^{+}$momenta following triple ionization. Published by the American Physical Society2025 

Surface-attached cells can sense and respond to shear flow, but planktonic (free-swimming) cells are typically assumed to be oblivious to any flow that carries them. Here, we find that planktonic bacteria can transcriptionally respond to flow, inducing expression changes that are beneficial in flow. Specifically, we use microfluidic experiments and quantitative modeling to show that in the presence of flow, planktonicPseudomonas aeruginosainduce shear rate–dependent genes that promote growth in low-oxygen environments. Untangling this mechanism revealed that in flow, motileP. aeruginosaspatially redistribute, leading to cell density changes that activate quorum sensing, which in turn enhances the oxygen uptake rate. In diffusion-limited environments, including those commonly encountered by bacteria, flow-induced cell density gradients also independently generate oxygen gradients that alter gene expression. Mutants deficient in this flow-responsive mechanism exhibit decreased fitness in flow, suggesting that this dynamic coupling of biological and mechanical processes can be physiologically significant. 

In fiber spinning of photopolymers, surface tension limits the diameter of the fiber that can be produced due to the Rayleigh–Plateau instability. Submerging a pre-fiber jet in a miscible environment liberates the system from capillary effects, thus allowing the jet to be stretched into thin threads without instability. In this work, we systematically investigated a spinning method using miscible liquids, called jet-assisted wet spinning (JAWS), where stretching is achieved by a nearby submerged liquid jet. The diameter of the pre-fiber jet is a function of its flow rate and position relative to the assisting submerged liquid jet. A particular case where the main jet is modeled as the Landau–Squire jet is used to demonstrate the tracer-like thinning behavior of the pre-fiber jet. Experiments show that buoyancy has a significant impact on the pre-fiber jet diameter because of its influence on the entrainment trajectory. Overall, our results demonstrate the potential for the parallelization of JAWS for high-throughput fiber production. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling.