Annals of the New York Academy of Sciences最新文献

On the cover: The cover image is based on the Original Article Students' associations with the STEM acronym and their impact on value beliefs and STEM choices by Heidrun Stoeger et al., https://doi.org/10.1111/nyas.70018.

On the cover: The cover image is based on the Perspective Chromatin-associated condensates as an inspiration for the system architecture of future DNA computers by Lennart Hilbert et al., https://doi.org/10.1111/nyas.15415.

Human bitter taste plays an important role in the quality assessment of food. The presence of the corresponding receptors, the taste receptor 2 family (TAS2Rs), in nongustatory tissues without direct contact to the environment suggested that, apart from food compounds, putative endogenous agonists may also exist. Recent studies on bitter taste receptors of vertebrates, including humans, identified occasional steroid hormones as agonists for these receptors; therefore, steroid hormones represent relevant, potentially endogenous agonists for TAS2Rs. In this study, a comprehensive analysis of 19 steroid hormones, cholesterol, and two plant-derived hormones was performed using functional assays to assess the activation of TAS2Rs. Two TAS2Rs, TAS2R14 and TAS2R46, were found to be differentially activated by the test compounds, with TAS2R46 being in almost all cases the more sensitive receptor. Some steroid hormones activated TAS2R46 with extraordinarily high potencies. Comparison with a human metabolite database revealed that several steroid hormone levels reach activating concentrations for TAS2Rs, suggesting that TAS2Rs indeed could act as sensors for circulating steroid hormones.

On the cover: The cover image is based on the Original Article A hairy situation: The diversity of bat wing pelage and its functional implications by Alexandria C. Shockney et al., https://doi.org/10.1111/nyas.15411.

Despite how frequently adults are alone with children, we know little about children's ability to corroborate alibis. In two studies, we investigated children's ability to act as alibi corroborators. In both studies, two visitors (one male, one female) attended children's summer camps to present science activities to the children. In the pilot study (N = 83; M_age = 7.1 years), for half of the children, the female researcher left the room for one of the activities. Children were then interviewed about the adults' whereabouts either immediately or 1 day later. In the main study (N = 147; M_age = 9.40), the female researcher left the room for one activity in a more salient manner, and all children were interviewed 3 days later. Across both studies, though there was substantial variability, many children did not report that the female researcher left (pilot study, 82%; main study, 32%), despite direct questions about her presence. All inaccurate reports of an adult leaving were in response to the most direct (yes/no) question. These findings suggest that children are largely accurate in corroborating an alibi for someone who did not leave, but many children err when someone does leave. The present studies have implications for how to question children about an adult's whereabouts.

On the cover: The cover image is based on the Original Article Spatial metabolic and phenotypic characterization of the germ-free mouse model by Lauren Adams et al., https://doi.org/10.1111/nyas.70002.

The rapid proliferation of automotive augmented-reality head-up displays (AR-HUDs) in intelligent-driving systems has increased the need for rigorous multidimensional performance evaluation-a task complicated by data heterogeneity and cognitive uncertainty. To address this dual challenge, we propose a comprehensive decision-making method that combines Pythagorean fuzzy set and Hamacher confidence-weighted aggregation operator (PyHa-CWAO) and embeds it in a novel hierarchical evaluation framework that separately appraises the static basic information layer (SBIL) and the dynamic AR fusion layer (DAFL) before synthesizing the results. We further propose a hyperbolic tendency-confidence fused scoring model for the first time, which simultaneously captures subjective hesitation and confidence weights while amplifying interalternative discrimination. Combining DEMATEL for causal-relationship elicitation with VIKOR for multicriteria ranking yields the integrated PyHa-CWAO-DEMATEL-VIKOR decision model. A driving-simulation and eye-tracking experiment evaluated 10 AR-HUD prototypes (six DAFL, four SBIL) across 12 objective and subjective indicators. Compared with three established multicriteria decision-making methods, the proposed model improves ranking consistency by 14%, confirming its robustness and practical value. Overall, this work offers an accurate, resilient, and extensible decision tool for AR-HUD and broader extended reality interfaces, enriching fuzzy-decision theory and providing a methodological foundation for future virtual human-computer interaction optimization.

To address the challenge that existing lung nodule segmentation algorithms face in balancing high accuracy with a lightweight design, we propose LLNS-Net, a compact yet effective lung nodule segmentation network. In the feature-mining encoder, convolutional residual blocks operating at multiple scales extract both shallow and deep nodule information from CT images, while an efficient multiscale attention mechanism enriches semantic representations. A subsequent feature enhancement module explores and leverages correlations among the outputs of the encoder's submodules. Within this module, we introduce an enhanced mixed local channel attention (E-MLCA) mechanism and a reinforced multiscale feature module to further strengthen cross-scale feature learning. The decoder aggregates features from four decoding layers and applies subchannel enhancement to refine the segmentation map. This design improves boundary smoothness and more accurately preserves the true morphology of nodule regions. Experimental results demonstrate that, compared with mainstream methods such as HmsUnet, MSA-Unet, and H-vmunet, the intersection over union of LLNS-Net improves by 1.86%, 0.27%, and 7.62%, respectively. Additionally, the generated feature maps exhibit smoother boundaries and superior visual quality compared to those produced by leading medical image segmentation algorithms.