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User interaction with digital platforms: A consumer protection perspective
The rapid growth of digital platforms has transformed how consumers interact with the marketplace and led to new challenges for consumer protection. This Perspective describes how the Federal Trade Commission’s Bureau of Economics uses behavioral insights and empirical methods to support efforts to prevent unfair or deceptive business practices in digital markets. We highlight two studies that address critical knowledge gaps in digital consumer protection, including how consumers perceive and respond to advertising disclosures and how digital design choices affect the accessibility and effectiveness of consumer reporting tools. The studies illustrate how behavioral research can be used both prospectively, to evaluate potential remedies, and retrospectively, to assess the effects of past Federal Trade Commission (FTC) actions. The first study is a laboratory experiment that examines how the salience of advertising disclosures influences consumer recognition of and engagement with advertising content, where engagement is defined as the time users spend viewing ads. The second is a retrospective analysis of a redesigned consumer complaint website, using a natural experiment to evaluate the effect of usability improvements on complaint submission rates and information quality. Together, these studies demonstrate how applied research on user behavior can inform marketplace oversight and the design of consumer-facing tools. We conclude by outlining ongoing efforts to expand the role of empirical research at the FTC to strengthen the foundation for evidence-based policymaking in an increasingly complex digital environment.
A practical and accessible workflow for bulk TCR sequencing from buffy coat samples without T cell enrichment
Vimentin promotes actin assembly by stabilizing ATP-actin subunits at the barbed end
Vimentin intermediate filaments play essential roles in maintaining cell integrity and regulating numerous cellular functions. In particular, vimentin cooperates with the actin cytoskeleton in key cellular processes that rely on actin dynamics, such as migration, division, and mechanosensing. While there is evidence that these two cytoskeletal components interact in cells, the underlying molecular mechanisms are only partially understood. Actin and vimentin can interact through biochemical signaling pathways or via cross-linkers, but whether they engage in a direct protein–protein interaction has remained controversial, in part because such interactions are difficult to isolate and characterize in cells. Using in vitro reconstitution coupled to theoretical modeling, and total internal reflection fluorescence microscopy to monitor the elongation of single actin filaments, we show that vimentin promotes actin elongation by stabilizing actin subunits at the barbed end in a dose-dependent manner. Strikingly, this effect depends on the nucleotide state of actin, as the acceleration is only observed for the elongation from adenosine triphosphate (ATP)-actin, and not adenosine diphosphate (ADP)-actin monomers. We further establish that neither the vimentin tail nor head domains are required for this effect, and both filamentous and nonfilamentous vimentin enhance actin elongation. Finally, we find that vimentin promotes the nucleation of actin filaments. Consistently, magnetic pull-down assays demonstrate a direct interaction between vimentin and ATP-actin monomers. Altogether, these findings identify vimentin as an unexpected new actor in the regulation of actin dynamics at the barbed end and bring new insights into the functional role of vimentin through cytoskeletal crosstalk.
Ethiopia’s green legacy initiative enhances carbon stock and carbon dioxide sequestration across diverse landscapes
Structural characterization of human neutralizing antibodies against JC and BK polyomaviruses
The human JC polyomavirus (JCPyV) causes the fatal demyelinating disease progressive multifocal leukoencephalopathy (PML) in immunocompromised individuals. JCPyV frequently undergoes mutations in PML patients, and these variants are thought to establish “antibody recognition holes” that enable the virus to circumvent the antibody response of infected individuals. Many of these PML-associated mutations cluster in the glycan receptor-binding site of the virus. Using X-ray crystallography, we investigated the binding modes of JCPyV VP1-specific human monoclonal antibodies (mAbs) that were isolated from healthy donors and individuals who recovered from PML, and that can recognize a panel of PML-associated JCPyV variants. Our structural analyses show that three out of four of these mAbs bind epitopes that overlap with the glycan–receptor binding site at the surface of the virus particle. The observed interactions explain how PML-associated mutations strategically interfere with antibody recognition, resulting in immune evasion. In contrast, mAb 29B1 engages a region of the capsid that is distant from the glycan site and does not feature mutations associated with PML. The binding site is conserved in the closely related BK polyomavirus (BKPyV), and we show that mAb 29B1 binds both viruses with high affinity and blocks infection. Our findings form an excellent platform for the development of therapeutic Ab approaches and potential vaccination strategies that could protect at-risk patients from infections with both JCPyV and BKPyV. Moreover, small molecules that target the mAb 29B1 binding site could be potentially effective against both viruses.
Explainable deep learning for understanding the influence of urban characteristics and atmospheric parameters on surface urban heat islands
Abstract Identifying the key drivers of Surface Urban Heat Islands (SUHIs) is crucial for understanding urban thermal dynamics and informing mitigation strategies. This study models SUHI, examines the contribution of often-neglected atmospheric parameters, identifies key drivers, and pinpoints the spatial regions that most strongly influence SUHI intensity. An explainable deep learning framework integrating multi-source surface and atmospheric data is applied. The framework combines satellite-derived indicators of gray, green, and blue infrastructure, population density, topography from the Digital Elevation Model (DEM), and atmospheric variables from Weather Research and Forecasting (WRF) outputs to predict SUHI intensity. A residual attention-based Convolutional Neural Network (CNN), trained on a 13-year dataset, achieves robust predictive performance, with a Root Mean Square Error (RMSE) of 0.33 °C under normal conditions and 0.39 °C during heatwaves. Results show that atmospheric parameters, especially wind, are strongly associated with variations in SUHI intensity and contribute to enhanced rural cooling, which is linked to stronger urban–rural temperature contrasts. Precipitation and water vapor influence SUHI by altering evaporative and radiative cooling, while mean sea level pressure has a secondary role. Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM), indicate that urbanization intensity, vegetation, and population density are the primary contributors to SUHI variability, while wind and moisture are associated with changes in thermal contrasts. Spatial analysis further shows that densely built-up urban cores are consistently emphasized in the model’s predictions of SUHI. Grad-CAM highlights spatial interactions between urban and atmospheric parameters. By quantifying surface and atmospheric factors and identifying influential locations, this framework provides accurate SUHI estimates and actionable insights for urban planners, supporting urban ecosystem management and targeted heat adaptation strategies.
Applied behavioral and decision sciences in support of US FDA’s drug regulatory mission
The US Food and Drug Administration (FDA) makes and communicates decisions that directly affect the health choices and well-being of the US public. Behavioral and decision scientists have long supported FDA’s pharmaceutical (medical drug) regulatory and public health mission, working alongside clinical and pharmaceutical scientists. This Perspective describes four interrelated cases illustrating these collaborations: a) creating the Benefit–Risk Framework to guide regulatory decisions regarding new drug approvals; b) launching an internal decision support service to facilitate specific regulatory decisions; c) implementing the Patient-Focused Drug Development initiative to strengthen the use of patient input to inform regulatory decision making; and d) developing FDA SOURCE, a dynamic systems simulation model to assess potential strategies to address the US opioid overdose crisis. Key to the success of these efforts has been their behavioral and decision science foundations, strategically implemented by an expert team dedicated to fostering trusted collaborations with internal and external partners. These cases offer a model for other agencies facing complex decisions of public impact.
Phenomics-assisted sparse testing for potato breeding
Abstract In recent decades, global weather patterns have shifted dramatically, introducing greater unpredictability into agriculture. A major challenge in plant breeding is developing selection strategies that remain accurate under such uncertainty. Sparse testing is a well-established approach to increase the number of genotypes evaluated in field trials while keeping costs manageable. However, incorporating image-based data into sparse testing remains challenging. We developed a strategy to integrate high-throughput phenotyping data into sparse testing in potato breeding to improve predictive performance in multi-environment trials. Our approach involved constructing an environmental kernel derived from the covariance matrix of image-based data. We assessed the predictive performance of several regression models under sparse testing, including those based on genomic or phenomic data alone and in combination. Models using only the proposed environmental kernel achieved predictive accuracies comparable to, or exceeding, those of genomic prediction models in various sparse testing scenarios. The best results were observed for tuber yield, a key trait in potato breeding. These findings highlight the potential of image-based environmental kernels to improve the efficiency and accuracy of sparse testing. This approach is cost-effective and scalable, particularly useful for breeding programs with limited resources.
Stress granules as RNA triage hubs suppress extracellular vesicle secretion under oxidative stress in cancer
Cancer cells confronting oxidative stress must coordinate their extracellular vesicle (EV) secretion to balance intercellular signaling with the intracellular programs required for survival, yet how these decisions are integrated remains poorly understood. Here, we identify a stress-adaptive mechanism in which stress granules (SGs) selectively suppress CD63 + EV release. Using a bioluminescent EV–reporter screen, we found that the clinical compound YM155 selectively inhibits CD63 + EV secretion across diverse tumor cells. Mechanistically, YM155 rapidly inactivates the antioxidant transcription factor FOXO3a, diminishing expression of key detoxifying enzymes and leading to delayed but sustained accumulation of reactive oxygen species (ROS). Elevated ROS drives SG formation, and these SGs function not as passive storage sites but as RNA triage hubs that exclude and destabilize a subset of transcripts. Among them, Rab27A mRNA—encoding a GTPase essential for multivesicular-body docking to the plasma membrane—is selectively excluded and degraded, resulting in loss of Rab27A protein and suppression of CD63 + EV secretion. Forced Rab27A expression restores EV release but paradoxically reduces proliferation under oxidative stress, indicating that EV suppression is prosurvival. The same FOXO3a–ROS–SG–Rab27A axis operates during physiological glucose deprivation and is evident in vivo, where SGs form in xenograft tumors and circulating CD63 + EVs decline. Pancancer transcriptomic analyses further show that Rab27A expression correlates with FOXO3a-dependent antioxidant programs, underscoring clinical relevance. These findings reveal that SGs actively reprogram RNA fate to tune vesicle output, establishing a redox-responsive mechanism by which cancer cells transiently suppress EV secretion to enhance survival.
Road infrastructure drives habitat fragmentation and connectivity loss for large mammals in central iranian protected area
Abstract While Protected Areas shield wildlife from many external threats, existing road infrastructure within their boundaries often remains a persistent source of disturbance. These road networks fragment habitats and reduce connectivity, ultimately compromising population viability and survival. This study investigates the effects of road networks on the habitat suitability and connectivity of the goitered gazelle ( Gazella subgutturosa ), wild goat ( Capra aegagrus ), and grey wolf ( Canis lupus ) in Kolah Ghazi National Park, Iran. These species occupy different ecological niches as key herbivores and a top predator, making them valuable indicators of how road infrastructure affects wildlife across trophic levels. We identified core habitats using MaxEnt, and assessed landscape connectivity and critical corridors using resistance kernels. For goitered gazelle, distance to agriculture, roads, and rangelands were most influential. For grey wolf, distance to roads, agriculture, and rangelands dominated. Core habitats for all three species occur largely within the park, yet roads sever key patches: about 12 km for wild goat, 11 km for goitered gazelle, and nearly 10 km for grey wolf. Corridor coverage within conservation areas was high (goitered gazelle 91%, wild goat 94%, grey wolf 98.9%), but gazelle corridors intersect roads repeatedly and wild goat corridors intersect 5.5 km of roads. The findings suggest substantial habitat fragmentation and connectivity loss due to roads. We recommend conservation actions that prioritize the identified core areas and corridors, enforce slower traffic in crossing zones, and provide species-appropriate crossing structures to preserve connectivity and minimize wildlife-vehicle collisions.
Asymmetric and intermittent supershear rupture mediated by local fault complexity during the 2025 <i> M <sub>W</sub> </i> 7.7 Myanmar earthquake
We determine the detailed rupture process of the 2025 M W 7.7 Myanmar earthquake by joint inversion of near-fault strong-motion, geodetic, and teleseismic data. The rupture initiated with significant slip of up to ~5 m near the epicenter. Localized fault geometric complexity and stress heterogeneity might have led to small-scale slip segmentation and intermittent episodes of supershear rupture. Northward rupture propagation was subshear overall and terminated rapidly after extending ~80 km within the partial stress shadow of a 2012 earthquake. In contrast, southward rupture propagated ~380 km, fully traversing a predefined seismic gap that had been unruptured since 1839, and undergoing cycles of acceleration and deceleration that included at least two episodes of intermittent supershear rupture before gradually stopping after traversing a segment that ruptured in 1930. Slip exceeding 3 m is confined to the shallow fault above a 15 km locking depth and the average static stress drop is 4.7 MPa. The long-term stress accumulation on multiple asperities along the central Sagaing fault promotes localized ruptures in discrete events but sometimes leads to rupture cascades with intermittent supershear behavior as in 2025. Local slip duration also displays a north–south asymmetry, being longer near the epicenter and progressively shorter toward the south before the end. This pattern may be associated with lower rigidity near the epicenter and rupture dynamics being modulated by fault edges for long, narrow strike-slip faults. Our findings highlight the roles of localized fault structure and stress heterogeneity in controlling slip distribution and rupture speed.
Socioeconomic determinants of overweight and obesity in Ghana: evidence from the Annual Household Income and Expenditure Survey panel
Ion-disrupted hydrogen-bond networks enable fast water transport under two-dimensional confinement
A vision-based MobileNet–GRU framework for continuous monitoring of student engagement and emotional states
Evaluating bias in target trial emulation for heart failure across statistical and deep learning methods
Abstract Target trial emulation (TTE) is increasingly used for causal inference from observational data, but remains vulnerable to confounding by indication, and whether advanced adjustment methods mitigate this bias is unclear. Using Clinical Practice Research Datalink Aurum, we emulate target trials of beta-blockers (positive control) and digoxin (negative control) versus usual care on two-year all-cause mortality in patients with heart failure with reduced ejection fraction. We apply four adjustment strategies: propensity score matching, inverse probability of treatment weighting, targeted maximum likelihood estimation, and a Transformer-based deep learning approach. No method reproduces the randomised controlled trial (RCT) benchmarks: all suggest neutral or harmful effects for beta-blockers and elevated mortality for digoxin. In semi-synthetic simulation, all methods recover the true effects when confounders are observed, yet fail in real-world data. TTE, even with advanced adjustment, may not yield trial-equivalent estimates when confounding is strong; randomised evidence remains essential for clinical and policy decisions.