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Abstract We explore the stability of isotropic, spherical, self-gravitating systems with a double power-law density profile. Systems with rapid transitions between the inner and outer slopes are shown to have an inflection in their isotropic distribution function (DF), wheredf/dE > 0, thereby violating Antonov’s stability criterion. Using high-resolutionN-body simulations, we show that the resulting instability causes the growth of a rotating dipole (orl= 1) mode. The inflection feature in the DF responds to the mode by promoting its growth, driving the instability. The growth of the dipole results in a torque that dislodges the original cusp from its central location and sets it in motion throughout the central region. Once the mode goes nonlinear, it saturates, together with the cusp, into a long-lived soliton (thel= 1 equivalent of a bar in a disk galaxy), which maintains its sloshing motion through the center of the halo along a slowly precessing, elliptical orbit. Concurrently, the soliton traps increasingly more particles into libration, and the exchange of energy and angular momentum with these trapped particles works toward eroding the bump in the distribution function. We point out similarities between the dipole mode and the bump-on-tail instability in electrostatic plasmas, and highlight a potential connection with core stalling and dynamical buoyancy in systems with a cored density profile. Finally, we discuss the astrophysical implications in terms of lopsidedness and off-center nuclei in galaxies. 

Abstract The SMC orbits within the LMC’s dark matter (DM) halo in a ∼1:10 mass-ratio encounter. The LMC–Milky Way (MW) interaction is also ∼1:10, and is expected to perturb the MW’s DM distribution. However, no framework exists to quantify the severity of these perturbations over multiple pericenters and longer periods of time, such as the LMC–SMC interaction history. We construct basis function expansions of a high-resolutionN-body simulation of the Clouds interacting in isolation and analyze their DM distributions at an epoch approximating the time of their infall to the MW. Our goal is to quantify how the Clouds distort each other’s DM distributionswithoutthe MW. The LMC halo’s response to the SMC includes a ∼20 kpc long dynamical friction wake and the displacement of the LMC’s density center during each SMC pericenter, which produces two overdensities in the LMC halo (at ∼60 and ∼100 kpc) at MW infall. The SMC’s tidal radius at infall is just ∼4 kpc, at which point the SMC has lost two-thirds of its initial DM mass to the LMC. The distortions to the Clouds’ halos produce a highly asymmetric acceleration field. Accurate orbit integration in the LMC–SMC system must account for the time-dependent shapes of both halos. The SMC-induced perturbations in the LMC DM halo resemble the MW–LMC system, and persist over multiple SMC pericenters. We conclude that 1:10 satellite–host encounters induce characteristic deformations in both DM halos across host-mass scales, with implications for merger rates and tests of DM models. 

Traceability is critical for achieving seafood sustainability goals. However, trade restrictions highlight challenges for identifying basic information, including country of harvest. We use new seafood trade data to illustrate how trade can elude enforcement using the case of responses following Russian sanctions and quantify pathways through which the US imports Russian-harvested products. We then discuss the current policy landscape for enforcing trade restrictions and highlight priorities for improving seafood traceability. 

Abstract The dynamics of star-forming gas can be affected by many physical processes, such as turbulence, gravity, supernova explosions, and magnetic fields. In this paper, we investigate several nearby star-forming regions (Orion, Upper Sco, Taurus, and Perseus) for kinematic imprints of these influences on the newly formed stars. Using Gaia DR3 astrometry and APOGEE DR17 radial velocities, we compute first-order velocity structure functions (VSFs) of young stars in galactic Cartesian coordinates in both 6D (3D positions and 3D velocities) and 4D (3D positions and each 1D velocity) to identify signatures of turbulence and anisotropic motion. We also construct 3D and 1D radial velocity profiles to identify coherent expansion trends, and compare stellar proper motions to plane-of-sky magnetic field orientations in Taurus and Perseus. We find that the VSFs are mildly anisotropic, with slightly different amplitudes, slopes, or features in different directions in several groups, but in general, they are all consistent with Larson’s Relation at intermediate length scales, especially in less compact groups. In several cases, the VSFs exhibit features suggestive of local energy injection from supernovae. Radial velocity profiles reveal clear anisotropic expansion in multiple groups, with the most extreme cases corresponding to those with the most anisotropic VSFs. In Perseus, we find that the motions of young stars are preferentially perpendicular to the local magnetic field. We find multiple, overlapping causes in each group for the observed kinematics. Our findings support that young stars remember more than just the turbulent state of their natal clouds. 

The discovery of new chemical functions is being accelerated by blocc chemistry for iterative carbon–carbon bond formation coupled to artificial intelligence (AI) and machine learning (ML) techniques; yet, these technologies are largely absent from introductory undergraduate courses. This lab emphasizes the utility of data science tools through the context of discovering chemical function. We propose that teaching blocc chemistry provides student scientists the ability to explore functional data from frontier research generated for the purpose of this labeven with minimal knowledge of organic chemistry. Here, we report the first laboratory experiment in a sequence designed to illuminate the AI-Chemistry interface. This first experiment teaches students the fundamental principles of K-Nearest Neighbor (KNN) analysis and K-Medoids clustering to predict unknown chemical functions through the lens of blocc chemistry. 

The intersection of automatable blocc chemistry for iterative carbon–carbon bond formation with artificial intelligence is amplifying molecular innovation in new and exciting ways. In this lab, students are introduced to concepts and tools that help them gain familiarity and confidence with this emerging area of chemistry. Students specifically learn about four automated synthesis platforms, each of which stitches together a bounded set of molecular building blocks using just one type of robust bond-forming reaction. Students then analyze a variety of small molecules and biopolymers to identify the most redundant types of bonds in each molecule that are compatible with iterative formation from bifunctional building blocks. Based on their analysis, students then select an appropriate iterative automated synthesis platform and determine which molecular building blocks would be required to assemble the desired targets. Students next investigate two case studies where blocc chemistry was used used in concert with artificial intelligence to discover new molecular functions. In the first case, students identify high-performing blocks from selected data sets to optimize a single objective function related to organic laser properties. In the second case, students engage with a new online platform inspired by Scratch, dubbed the Digital Molecule Maker (DMM), to perform a multi-objective optimization of organic photovoltaic candidates (OPV). To conclude the lab, students manually perform the blocc chemistry that is foundational for the DMM. This activity series is the second installment of a sequence of undergraduate laboratories designed to illuminate the functional discovery-enabling interface of AI and automated blocc chemistry. 

Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N‐methyl iminodiacetic acid) MIDA or (tetramethyl N‐methyl iminodiacetic acid) TIDA boronate building blocks via iterative C─C bond formation, an approach we call “block chemistry”, alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration‐enabling group, CbzT (p‐TIDA boronate‐substituted carboxybenzyl), that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch‐and‐release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N─C and C─C bonds. This work substantially expands the types of small molecules that can be iteratively assembled in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do.