The National Academies of Sciences, Engineering, and Medicine (also known as NASEM or the National Academies) is the collective scientific national academy of the United States. The name is used interchangeably in two senses: (1) as an umbrella term for its three quasi-independent honorific member organizations the National Academy of Sciences (NAS), the National Academy of Engineering (NAE), and the National Academy of Medicine (NAM); and (2) as the brand for studies and reports issued by the operating arm of the three academies, the National Research Council (NRC). The NRC was first formed in 1916 as an activity of the NAS. Now jointly governed by all three academies, the NRC produces some 200 publications annually which are published by the National Academies Press. The reports produced by the National Academies have been characterized as reflective of scientific consensus.
The Nectriaceae includes major plant and human pathogens, yet the genomic foundation underpinning its taxonomy remains uneven and largely unassessed. We analysed 1,530 genome sequence assemblies to quantify metadata completeness, geographic and taxonomic bias, and assembly quality across the family. One-third of the assemblies lacked essential metadata, sequencing was heavily skewed toward a few agriculturally important lineages, and sampling of many genera was limited or nonexistent. BUSCO and QUAST metrics revealed striking heterogeneity in assembly quality, with widespread fragmentation and a substantial subset of genomes falling outside the expected quality thresholds. From orthologous protein sequences of 763 single-copy genes in 576 high-quality genomes, we reconstructed a phylogenomic backbone for the Nectriaceae and quantified gene- and site-level concordance. While major clades broadly match current concepts, extensive gene-tree discordance and a polyphyletic Nisikadoi complex highlight unresolved evolutionary and taxonomic boundaries. Our study delivers the first integrated, family-wide evaluation of Nectriaceae genomic resources and outlines a framework for quality standards, curated metadata, and stable phylogenomic inference to support future taxonomic and comparative work. ### Competing Interest Statement The authors have declared no competing interest.
Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particularly well-suited for developing large Visual Foundation Models (VFMs). These models serve as powerful feature extractors, leveraging extensive RS data for pretraining and subsequent fine-tuning in various geoscientific applications. However, existing VFMs in the RS domain often concentrate on specific image characteristics, neglecting the full season-aware potential of RS data. To bridge this gap, we introduce SeaMo, a novel VFM that effectively integrates multimodal and multi-seasonal RS information. SeaMo leverages a masked image modeling framework to fully exploit the spatial, spectral, and seasonal dimensions of RS data. Specifically, we employ unaligned spatial region selection to capture spatial heterogeneity, incorporate multi-source inputs for enhanced multimodal integration, and introduce temporal-multimodal fusion blocks to assimilate seasonal variations effectively. By explicitly modeling the complex, season-dependent attributes of RS data, SeaMo enhances generalization, robustness, and adaptability across geoscientific tasks. Extensive experiments and ablation studies demonstrate its superior performance, underscoring its potential as a foundational model for Earth observation.
The fusion of infrared and visible images is essential in remote sensing applications, as it combines the thermal information of infrared images with the detailed texture of visible images for more accurate analysis in tasks like environmental monitoring, target detection, and disaster management. The current fusion methods based on Transformer techniques for infrared and visible (IV) images have exhibited promising performance. However, the attention mechanism of the previous Transformer-based methods was prone to extract common information from source images without considering the discrepancy information, which limited fusion performance. In this paper, by reevaluating the cross-attention mechanism, we propose an alternate Transformer fusion network (ATFusion) to fuse IV images. Our ATFusion consists of one discrepancy information injection module (DIIM) and two alternate common information injection modules (ACIIM). The DIIM is designed by modifying the vanilla cross-attention mechanism, which can promote the extraction of the discrepancy information of the source images. Meanwhile, the ACIIM is devised by alternately using the vanilla cross-attention mechanism, which can fully mine common information and integrate long dependencies. Moreover, the successful training of ATFusion is facilitated by a proposed segmented pixel loss function, which provides a good trade-off for texture detail and salient structure preservation. The qualitative and quantitative results on public datasets indicate our ATFusion is effective and superior compared to other state-of-the-art methods.
The experience of pain, like other interoceptive processes, has recently been conceptualized in terms of predictive coding and free energy frameworks. In these views, the brain integrates sensory, proprioceptive, and interoceptive signals to generate probabilistic inferences about upcoming events, which shape both the state and the perception of our inner body. Here, we ask whether it is possible to induce pain expectations by providing false faster (vs. slower) acoustic cardiac feedback before administering electrical cutaneous shocks. We test whether these expectations will shape both the perception of pain and the body’s physiological state toward prior predictions. Results confirmed that faster cardiac feedback elicited pain expectations that affected both perceptual pain judgments and the body’s physiological response. Perceptual pain judgments were biased toward the expected level of pain, such that participants illusorily perceived identical noxious stimuli as more intense and unpleasant. Physiological changes mirrored the predicted level of pain, such that participants’ actual cardiac response in anticipation of pain stimuli showed a deceleration in heart rate, in line with the well-known orienting cardiac response in anticipation of threatening stimuli (Experiment 1). In a control experiment, such perceptual and cardiac modulations were dramatically reduced when the feedback reproduced an exteroceptive, instead of interoceptive, cardiac feedback (Experiment 2). These findings show that cardiac perception can be understood as interoceptive inference that modulates both our perception and the physiological state of the body, thereby actively generating the interoceptive and autonomic consequences that have been predicted.
Trees play a key role in subsurface water dynamics through stemflow and throughfall, affecting runoff and soil infiltration. However, a comprehensive understanding of their role in hydraulic responses across spatial scales requires integrated approaches and further research. In this study, we investigated the effects of rainfall partitioning on subsurface water dynamics across multiple spatial scales on a forested hillslope (30 degrees slope) in the Re della Pietra catchment of Central Italy. Four hierarchical spatial scales were considered to capture subsurface water dynamics: the point scale (via single-ring infiltration tests at three depths), the single-tree scale (through artificial stemflow events), the plot scale (covering a 100 m2 area, assessed either by an artificial throughfall event alone or in conjunction with multiple artificial stemflow events), and the hillslope scale (through piezometers data). Geophysical surveys were also conducted at the plot and single-tree spatial scales using both Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) to improve interpretation of hydrological functioning. Our results showed that the soil exhibits dual-permeability behavior, with throughfall promoting infiltration primarily into the matrix, while stemflow enhances fast-flow infiltration. Infiltrometer tests indicated lower infiltrability between tree stems (mean Ks values ranging from 177.8 to 237.4 mm h-1, depending on depth), consistent with the artificial throughfall events. In contrast, at tree bases, artificial stemflow produced rapid infiltration through macropores and fractures, reaching a mean steady-state rate of 1031.9 mm h-1. Numerical model inversions yielded consistent hydraulic parameters, and dual-permeability modeling showed that, at the plot scale, the stemflow-driven fast-flow region had higher Ks than the matrix (1047.4 vs. 217.5 mm h-1). Geophysical surveys confirmed these dynamics: ERT detected vertical wetting beneath stems, while GPR revealed lateral subsurface flow at depth. Piezometer data further identified fast-flow pathways at the hillslope scale, where rapid water table rises could be explained by flow connectivity between vertical and deeper lateral pathways. By combining controlled infiltration experiments, hydrological modeling, and geophysics, this study explicitly links point-, tree-, and plot-scale infiltration processes with hillslope-scale responses. These findings clearly demonstrate how stemflow-driven fast-flow pathways connect with lateral subsurface domains, thereby providing new insight into the mechanisms controlling rapid groundwater fluctuations on forested hillslopes.
