Princeton University

The Simons Observatory (SO) is a new suite of cosmic microwave background telescopes in the Chilean Atacama Desert with an extensive science program spanning cosmology, Galactic and extragalactic astrophysics, and particle physics. SO will survey the millimeter-wave sky over a wide range of angular scales using six spectral bands across three types of dichroic, polarization-sensitive transition-edge sensor (TES) detector modules: Low-Frequency (LF) modules with bandpasses centered near 30 and 40 GHz, Mid-Frequency (MF) modules near 90 and 150 GHz, and Ultra-High-Frequency (UHF) modules near 220 and 280 GHz. Twenty-five UHF detector modules, each containing 1720 optically-coupled TESs connected to microwave SQUID multiplexing readout, have now been produced. This work summarizes the pre-deployment characterization of these detector modules in laboratory cryostats. Across all UHF modules, we find an average operable TES yield of 83%, equating to over 36,000 devices tested. The distributions of (220, 280) GHz saturation powers have medians of (24, 26) pW, near the centers of their target ranges. For both bands, the median optical efficiency is 0.6, the median effective time constant is 0.4 ms, and the median dark noise-equivalent power (NEP) is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>40 aW<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$/\sqrt{\mathbf {Hz}}$</tex-math></inline-formula>. The expected photon NEPs at (220, 280) GHz are (64, 99) aW<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$/\sqrt{\mathbf {Hz}}$</tex-math></inline-formula>, indicating these detectors will achieve background-limited performance on the sky. Thirty-nine UHF and MF detector modules are currently operating in fielded SO instruments, which are transitioning from the commissioning stage to full science observations.

Second generation antipsychotics (SGAs) are widely used clinical tools; yet, they often cause negative side effects and take weeks to become effective, leading to poor patient compliance. The effect/side effect profile of individual SGAs is highly variable, and the mechanisms that underlie this variability are not well understood. Here, we identify a role of type 3 dopamine receptor (D3R) neurons in the Nucleus Accumbens (NAc) that mimics the aversive effects of quetiapine. Using single-nucleus RNA sequencing, we show that D3R is expressed in a subpopulation of D1R neurons and defines a distinct NAc cell type. We found that both clozapine and quetiapine cause acute conditioned place aversion in mice, but aversion subsides only after 21 days of treatment with quetiapine, a drug characterized as an arrestin-biased agonist at D3R. We provide evidence at both the cellular and population level that quetiapine inhibits D3R-expressing neurons in the lateral shell (LatSh) of the NAc. We also demonstrate that local injection of quetiapine into LatSh NAc is sufficient to cause place aversion. Selective optogenetic inhibition of D3R-neurons in the LatSh produces real time place aversion in mice, correlating this cell type to the aversive effects of quetiapine. These findings suggest a cell-type-specific mechanism underlying quetiapine-induced aversion and its potential attenuation with chronic treatment, offering insight into the cell types and circuitry that shape the quetiapine side effect profile.

Cancer cachexia is a complex metabolic syndrome characterized by involuntary weight loss, skeletal muscle atrophy, adipose tissue remodeling, anorexia, and systemic inflammation. Affecting up to 80% of patients with advanced cancer, it is associated with poor prognosis and lacks effective therapies. This review focuses on metabolic reprogramming occurring between organs, integrating recent mechanistic insights into cross talk between central and peripheral tissues. Key mediators, including CCL2, IL-6, LIF, and GDF-15, are discussed in the context of neuroimmune and metabolic pathways that drive tissue wasting. Particular attention is given to the CCR2/CCL2 axis, which mediates immune cell infiltration into the brain, liver, and tumors, contributing to anorexia, muscle loss, and hepatic dysfunction. Understanding these integrated interorgan pathways highlights potential therapeutic targets to preserve metabolic homeostasis, improve functional outcomes, and extend survival in patients with cancer-associated cachexia.

Abstract Understanding the weather and climate of Mesoamerica and the Caribbean remains challenging due to complex hydroclimate interactions, limited observations, and poor representation of regional processes in global models. We introduce the Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative that fosters research collaboration to advance weather and climate science, develop convection-permitting datasets, and promote knowledge exchange. MAAG’s first major contribution is a 2-week convection-permitting simulation of Hurricane Maria (2017) using Model for Prediction Across Scales–Atmosphere (MPAS-A), featuring a novel regional 15- to 3-km variable-resolution mesh over the region. Initial evaluation shows that MPAS-A captures key features like precipitation patterns, the intertropical convergence zone, and low-level jets. Some biases remain, particularly in enhanced land convection and slight deviations in Maria’s track. This novel dataset, now publicly available through NCAR’s Data Archive, supports studies of other extreme events and mesoscale convective systems active during the same period. It offers a valuable resource for the research community. MAAG is a new but rapidly growing initiative achieving notable milestones in a short time. It serves as a collaborative platform for codesigning high-resolution modeling experiments aimed at producing actionable weather and climate information. We invite the community to join MAAG, explore this initial dataset, and advance regional weather and climate research. Significance Statement The Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative, aims at addressing the complex challenges of understanding weather and climate in Mesoamerica and the Caribbean. MAAG’s first major achievement is a 2-week convection-permitting simulation of Hurricane Maria (2017) using a novel 15- to 3-km variable-resolution mesh. This dataset accurately captures key regional features and is publicly available through NCAR’s Data Archive. By fostering collaboration through data production and sharing, monthly group meetings that serve as a platform for networking and knowledge exchange, and the development of advanced high-resolution datasets, MAAG provides a vital resource for advancing regional weather and climate science. The initiative is rapidly growing, serving as a platform for codesigned modeling experiments aimed at producing actionable climate information for academia and different sectors. We invite the scientific community to join MAAG and advance research in this critical region.

Empirical research on sensory processing often focuses on group averages to uncover how sensory input translates into behavior. While this population-level approach has revealed important general principles of genetic and environmental control of behavior, it masks the rich and potentially meaningful variation observed at the level of individuals. Key features of sensory processing are reactivity, the degree to which a stimulus elicits a response, and habituation, a fundamental learning process that serves as a cognitive filter by reducing the response to repeated, irrelevant stimuli. We developed a model-based approach to quantify individual sensory processing dynamics, using a visual escape paradigm in Drosophila. We collected light-off jump responses in more than two hundred flies and quantified them individually via Bayesian inference of a dynamical model's parameters. Considering both reactivity and habituation, we found that quantitative properties of individual responses vary greatly even in an isogenic population raised under identical environmental conditions. By moving from population averages to individual-based analyses, we uncover and quantify pronounced stochasticity in individual responses. Furthermore, we found that individual reactivity and habituation parameters display substantial temporal stability over a two-week interval, indicating that these behaviors are stable, intrinsic properties of an individual. The presented framework enables robust stratification of individual behavioral phenotypes and provides a quantitative platform for modeling interindividual differences in sensory processing, including frequently observed atypical responses in neurogenetic diseases.