Dartmouth Hospital

Close social relationships are critical for emotional well-being. The COVID-19 pandemic severely disrupted in-person contact with friends, particularly among young adults, for whom friendships support key developmental goals. In a longitudinal study of U.S. college students (N = 205; 10,088 observations), we examined how close friendship networks related to emotional well-being during the early months of the pandemic (May-October 2020). Leveraging prepandemic social network data and 28 days of ecological momentary assessments of affect and social interactions, we found that students with more close college friends reported higher positive affect and lower negative affect in daily life, even while physically separated from those friends. These individuals were buffered from the emotional toll of pandemic-related stressors, a pattern not explained by personality, interaction frequency, or living conditions. Rather, participants with more close friends experienced higher quality online interactions. Additionally, personal disclosures, whether in-person or online, were consistently associated with greater feelings of closeness. Notably, individuals with fewer close friends showed the largest boost in closeness following partner disclosures, suggesting that emotional sharing may play a compensatory role for those with limited social ties. These findings illustrate how friendships can continue to shape affective experiences from afar and highlight disclosure as a key mechanism through which closeness and its emotional benefits can be cultivated. Integrating social network structure, daily affect, and interaction-level processes, this work advances affective science by providing evidence of how the social regulation of emotion extends beyond physical proximity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

In its present geometric configuration, the Thwaites Ice Shelf exerts only a limited influence on the upstream dynamics of Thwaites Glacier. Previous studies have demonstrated that ice flux across the grounding line is largely insensitive to the presence or absence of the ice shelf. However, these assessments have predominantly relied on diagnostic stress‑balance modelling using the contemporary geometry. In this study, we extend that analysis by evaluating the role—if any—of ocean‑induced basal melting in shaping the near‑future (decadal‑scale) evolution of Thwaites Glacier. First, we systematically quantify the sensitivity of upstream ice‑flow velocities to changes in ice rheology and ice‑shelf thickness across a suite of geometries generated through transient ice‑flow simulations. Second, we incorporate new coupled transient ice–ocean simulations to examine how varying ocean conditions influence projected glacier evolution within this integrated modelling framework. Our results indicate that both the current state and near‑term evolution of Thwaites Glacier are largely decoupled from oceanic forcing. This finding reinforces the view that present‑day mass loss from Thwaites Glacier is not primarily driven by ocean conditions and that this situation is unlikely to change in the foreseeable future.

We study how the design of testing institutions, encompassing both the tests themselves and the procedures used to administer them, shapes selection outcomes in environments with multiple criteria and strategic agents. We model the testing agency as either a set of independent bureaucracies (each test administered separately) or a joint bureaucracy (where test order and personalization can be coordinated). Our mechanism design analysis shows that under a joint bureaucracy, fixed-order sequential mechanisms with stringent tests are optimal for maximizing the probability mass of qualified candidates selected. Furthermore, we demonstrate that personalizing tests through upfront communication, now increasingly feasible via AI and automation, can select all qualified candidates. Finally, we compare institutional settings and quantify the value of controlling test order, showing that the benefit depends critically on the distribution of testees and the stringency of optimal tests. Our results contribute to the design of robust, efficient, and fair testing systems in both human and AI-mediated environments.

tRNAs play a critical role in protein synthesis, influencing mRNA translation dynamics to shape proteomes. Emerging evidence links dysregulated tRNA activity to cancer progression, with tRNA-Arg-TCT identified as an oncogenic driver when ectopically overexpressed in non-malignant cells. The requirement of endogenous tRNA-Arg-TCT in cancer biology, however, remains untested. Moreover, considering that the tRNA-Arg-TCT family comprises six genes in humans, the importance of an individual tRNA isodecoder in cancer remains unknown. Here, we find elevated levels of tRNA-Arg-TCT-4-1 isodecoder are associated with poor patient prognosis across multiple cancer types. We demonstrate that, using different antisense RNA strategies, specific inhibition of tRNA-Arg-TCT-4-1 suppresses the growth of glioblastoma (GBM) and liposarcoma (LPS) cancer cells. Mechanistically, we find that tRNA-Arg-TCT-4-1 inhibition leads to a codon-biased remodeling of mRNA translation and the proteome, preferentially suppressing expression of growth-promoting genes and pathways encoded by mRNAs enriched in arginine AGA codons. Strikingly, intratumoral delivery of an antisense oligonucleotide (ASO) targeting tRNA-Arg-TCT-4-1 suppresses tumor growth and extends survival in mouse xenograft experiments performed using either a human LPS cell line or a patient-derived soft tissue sarcoma model. This study provides a foundation for targeting tRNA dysregulation as a novel therapeutic approach for cancer.

A bstract Current generative modeling of single-cell transcriptomics relies on continuous latent representations, transforming inherently discrete and sparse gene counts into continuous space. We propose Discrete Cell Models (DCM), a diffusion-based framework that learns cellular representations directly in the discrete domain. Our framework supports both unconditional and conditional generation, allowing for precise modeling of complex biological scenarios such as cell-type-specific transcriptional responses to genetic perturbations. We demonstrate that DCM scales effectively and achieves strong performance against current state-of-the-art methods, including scVI, CPA, STATE, scGPT, and scLDM. On the Dentate Gyrus benchmark, DCM achieves a 5-fold improvement in MMD 2 RBF and a nearly 2-fold improvement in W 2 distance, over the leading continuous diffusion baseline (scLDM). On the conditional Replogle perturbation benchmark, DCM sets a new state of the art on W 2 distance while remaining competitive on MMD 2 RBF . Together, these results establish discrete diffusion as a promising direction for foundational models of cellular biology.

Achieving bright and long-lived emission with π-conjugated organic chromophores in the solid state presents a significant challenge but also an opportunity for applications ranging from nanoscale electronics to heterogeneous photocatalysis. Tetraryl[n]cumulenes, organic molecules consisting of three or more cumulative double bonds, are highly modular systems with tunable redox and optoelectronic properties, yet their utility remains limited due to rapid quenching from aryl group planarization or excimer formation. Herein, we demonstrate that the incorporation of a tetraryl[3]cumulene-based organic linker within the metal-organic framework (MOF) CORN-MOF-9 (CORN = Cornell University) suppresses these quenching pathways, allowing for bright and long-lived emission. Extensive transient absorption (TA) and photoluminescence (PL) spectroscopic studies support the unique properties of the MOF and also confirm it exhibits spectral diffusion, leading to red-shifted steady-state emission compared to molecular analogs in solution. Overall, our findings represent a new avenue to unlock the promising photochemical properties of cumulenes within materials platforms, paving the way for their use in various applications. Introduction.

Abstract The use of neural networks has shown significant potential to reduce the computational costs associated with the dynamics of industrial computational fluids. Weak adversarial networks (WAN) leverage weak solution theory to transform the problem of solving PDEs into a Min-Max optimization problem, which is then solved by training a generative adversarial network. Although this method has been successfully applied to two-dimensional (2D) Navier-Stokes (NS) equations, previous work says nothing about the Navier-Stokes-Brinkman (NSB) equations. In this study, we first leverage the stream function to introduce the biharmonic formulation of NSB equations. Then, we extend the WAN framework to solve NS equations in porous media (WAN2DNSB) and provide free surface flow as a numerical experiment. Our results demonstrate the stability and accuracy of the proposed method, highlighting its advantages over the traditional Physic-Informed Neural Networks (PINNs) algorithm, particularly for problems lacking strong solutions. This work contributes to the growing research on AI-driven numerical methods for complex fluid dynamics problems, offering a promising approach for industrial applications.

This paper explores the design and fabrication of a robotic airfoil based on a tensegrity morphing structure. We begin by introducing a family of tensegrity morphing airfoil designs that convert a continuous airfoil shape into a discrete configuration. The airfoil structure is divided into two main components: a rigid section (the D-section head) and a flexible section (the tensegrity morphing tail). We compute aerodynamic forces using the panel method and, based on the drag and lift analyses, propose a morphing airfoil design and develop its CAD model. This model integrates all essential electronics, including the battery, PCB board, and motors, within the rigid D-section. The flexible tail is actuated by strings, enabling adaptive morphing. Our approach integrates lightweight tensegrity principles with adaptive design to create efficient morphing airfoil structures. The methodology is also applicable to bio-inspired wings, robotic fingers, grippers, and other soft robotic systems.