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Abstract Spectroscopic studies of elliptical galaxies show that their stellar population ages, mean metallicity, andαenhancement traced by [Mg/Fe] all increase with galaxy stellar mass or velocity dispersion. We use one-zone galactic chemical evolution (GCE) models with a flexible star formation history (SFH) to model the age, [Mg/H], and [Mg/Fe] inferred from simple stellar population (SSP) fits to observed ellipticals atz∼ 0 andz∼ 0.7. We show that an SSP fit to the spectrum computed from a full GCE model gives ages and abundances close to the light-weighted, logarithmically averaged values of the composite stellar population, 〈age〉, 〈[Mg/H]〉, and 〈[Mg/Fe]〉. With supernova Mg and Fe yields fixed to values motivated by Milky Way stellar populations, we find that predicted 〈[Mg/H]〉–〈age〉 and 〈[Mg/Fe]〉–〈age〉 relations are surprisingly insensitive to SFH parameters: Older galaxies have higher 〈[Mg/Fe]〉, but the detailed form of the SFH has limited impact. The star formation efficiency (SFE) and outflow efficiency affect the early and late evolution of 〈[Mg/H]〉, respectively; explaining observed trends requires higher SFE and lower outflows in more massive galaxies. With core-collapse supernova yields calibrated to the plateau [Mg/Fe]_cc≈ 0.45 observed in many Milky Way studies, our models underpredict the observed 〈[Mg/Fe]〉 ratios of ellipticals by 0.05–0.1 dex. Increasing the core-collapse yield ratio to [Mg/Fe]_cc= 0.55 improves the agreement, though the models remain below the data. We discuss potential resolutions of this discrepancy, including the possibility that many ellipticals terminate their star formation with a self-enriching, terminating burst that reduces the light-weighted age and boosts 〈[Mg/Fe]〉. 

Abstract Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials. 

Abstract BackgroundEcological barriers can shape the movement strategies of migratory animals that navigate around or across them, creating migratory divides. Wind plays a large role in facilitating aerial migrations and can temporally or spatially change the challenge posed by an ecological barrier, with beneficial winds potentially converting a barrier into a corridor. Here, we explore the role wind plays in shaping initial southbound migration strategy among individuals breeding at two sites along an ecological barrier. MethodsUsing GPS satellite transmitters, we tracked the southbound migrations of Short-billed Dowitchers(Limnodromus griseus caurinus)from two breeding sites in Alaska to nonbreeding sites in coastal Mexico. The breeding sites were positioned in distinct regions along an ecological barrier – the Gulf of Alaska. We investigated potential differences in migratory timing, wind availability, and tailwind supportenroute across the Gulf of Alaska between individuals breeding at the two sites. ResultsRoute choice and arrival timing to wintering sites differed markedly between the two breeding sites: individuals departing from the more westerly site left at the same time as those from further east but crossed the Gulf of Alaska farther west and arrived along the Pacific coast of Mexico an average of 19 days earlier than their counterparts. Dowitchers from both sites departed with slight tailwinds, but once aloft over the Gulf of Alaska, birds from the more westerly site had up to twelve times more tailwind assistance than birds from the more easterly one. ConclusionsThe distinct migration strategies and degree of wind assistance experienced by birds at these two breeding sites demonstrates how differences in wind availability along migratory routes can form the basis for intraspecific variation in migration strategies with potential carryover effects. Future changes in wind regimes may therefore interact with changes in habitat availability to influence migration patterns and migratory bird conservation. 

The development of efficient quantum communication technologies depends on the innovation in multiple layers of its implementation, a challenge we address from the fundamental properties of the physical system at the nano-scale to the instrumentation level at the macro-scale. We select a promising near infrared quantum emitter, the nitrogen-vacancy (NV) center in 4H-SiC, and integrate it, at an ensemble level, with nanopillar structures that enhance photon collection efficiency into an objective lens. Moreover, changes in collection efficiency in pillars compared to bulk can serve as indicators of color center orientation in the lattice. To characterize NV center properties at the unprecedented sub-2 Kelvin temperatures, we incorporate compatible superconducting nanowire single photon detectors inside the chamber of an optical cryostat and create the ICECAP, the Integrated Cryogenic system for Emission, Collection And Photon-detection. ICECAP measurements show no significant linewidth broadening of NV ensemble emission and up to 14-fold enhancement in collected emission. With additional filtering, we measure emitter lifetimes of NV centers in a basal (hk) and an axial (kk) orientation unveiling their cryogenic values of 2.2 ns and 2.8 ns. 

Abstract Lithium niobate is a promising material for developing quantum acoustic technologies due to its strong piezoelectric effect and availability in the form of crystalline thin films of high quality. However, at radio frequencies and cryogenic temperatures, these resonators are limited by the presence of decoherence and dephasing due to two-level systems. To mitigate these losses and increase device performance, a more detailed picture of the microscopic nature of these loss channels is needed. In this study, we fabricate several lithium niobate acoustic wave resonators and apply different processing steps that modify their surfaces. These treatments include argon ion sputtering, annealing, and acid cleans. We characterize the effects of these treatments using three surface-sensitive measurements: cryogenic microwave spectroscopy measuring density and coupling of TLS to mechanics, X-ray photoelectron spectroscopy and atomic force microscopy. We learn from these studies that, surprisingly, increases of TLS density may accompany apparent improvements in the surface quality as probed by the latter two approaches. Our work outlines the importance that surfaces and fabrication techniques play in altering acoustic resonator coherence, and suggests gaps in our understanding as well as approaches to address them. 

This paper investigates methods for training parameterized functions for guiding state-space search algorithms. Existing work commonly generates data for training such guiding functions by solving problem instances while leveraging the current version of the guiding function. As a result, as training progresses, the guided search algorithm can solve more difficult instances that are, in turn, used to further train the guiding function. These methods assume that a set of problem instances of varied difficulty is provided. Since previous work was not designed to distinguish the instances that the search algorithm can solve from those that cannot be solved with the current guiding function, the algorithm commonly wastes time attempting and failing to solve many of these instances. In this paper, we improve upon these training methods by generating a curriculum for learning the guiding function that directly addresses this issue. Namely, we propose and evaluate a Teacher-Student Curriculum (TSC) approach where the teacher is an evolutionary strategy that attempts to generate problem instances of ``correct difficulty'' and the student is a guided search algorithm utilizing the current guiding function. The student attempts to solve the problem instances generated by the teacher. We conclude with experiments demonstrating that TSC outperforms the current state-of-the-art Bootstrap Learning method in three representative benchmark domains and three guided search algorithms, with respect to the time required to solve all instances of the test set. 

Leveraging Innovation and Optimizing Nurturing in STEM (NSF S-STEM #2130022, known locally as LION STEM Scholars) is a program developed to serve low-income undergraduate Engineering students at Penn State Berks, a regional campus of the Pennsylvania State University. As part of the program, scholars participate in a four-year comprehensive multi- tiered mentoring program and cohort experience. The LION STEM curricular program includes Engineering Ahead (a 4-week summer residential math-intensive bridge program prior to entering college), a first semester First-Year Seminar, and a second semester STEM-Persistence Seminar. Co-curricular activities focus on professional communication skills, financial literacy, career readiness, undergraduate research, and community engagement. The program seeks to accomplish four goals: (1) adapt, implement, and analyze evidence-based curricular and co- curricular activities to support, retain, and graduate a diverse set of the project's engineering scholars, (2) implement, test, and study through research and project evaluation strategies for systematically supporting student academic and career pathways in STEM, including development of STEM identity, (3) contribute to the knowledge base through investigation of the project's four-year multi-modal program so that other colleges may successfully implement similar programs, and (4) disseminate outcomes and findings related to the supports and interventions that promote student success to other institutions working to support low-income STEM students. The purpose of this paper is to analyze data from a repeated-measures design to provide a holistic narrative about the effects that the academic and support activities offered to LION STEM Scholars have on the development of their future-engineer role identity throughout their first year as an undergraduate engineering student. This paper presents data collected from semi- structured (Smith & Osborn, 2007) audio-recorded interviews from the first cohort of LION STEM Scholars (n=7) at three different time points (pre-summer bridge, post-summer bridge, end of first semester) as well as data collected from a written survey at the end of scholars’ second semester. 

SUMMARY With rapid environmental change, shifts in migration timing are vitally important for maintaining population stability and have been widely documented. However, little remains known abouthowmigrants are driving these shifts and what factors may influence the effective utilization of these strategies, limiting our ability to accurately assess species- and population-level vulnerability to climate change. The Hudsonian godwit (Limosa haemastica) is an extreme long-distance migratory shorebird that has (1) previously shifted its population-level migration timing and (2) exhibits sex-specific morphological differences. Therefore, we combined over a decade of light-level geolocator tracking data from a single breeding population with a historical predictive model to assess on-going shifts in migration timing while determining the time-shifting strategies utilized by each sex. Surprisingly, we found that godwit departure and arrival timing rapidly shifted 6 days later from 2010-2023 with no differences in timing between the sexes. Despite this change in migration timing, the population has maintained an average migratory duration of 24 days, suggesting that godwits are driving shifts in arrival timing entirely by shifting their nonbreeding ground departure, something rarely documented in long-distance migrants. Yet, we also found that godwits are not shifting their migration timing in the direction predicted by our model, providing evidence that this response may not be adaptive. These results emphasize the urgent need for a more holistic approach to assessing the relative vulnerability of migratory species and the adaptiveness of changes in migration timing. 

The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.