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Humans are inherently social species. Our behavior is heavily influenced by the social environment and context. While the traditional social neuroscience approach has made significant progress in mapping isolated social cognitive processes, it often fails to capture the complexity of real-world social environment—where perception, decision-making, and interaction unfold simultaneously and contextually. In this review, we introduce naturalistic social neuroscience as a paradigm shift that bridges this gap through incorporating multidisciplinary naturalistic measurements, which integrates lab-based simulation, such as movie watching and virtual reality, real-world embedded measures, such as digital phenotyping and wearables devices, as well as the strategic integration of multiple approaches. The framework also includes an artificial intelligence (AI)-powered multilevel data analysis to synthesize behavioral, computational, and neurobiological data to reveal the mechanism underlying real-world behaviors. Finally, we propose opportunities, critical considerations, and future directions for pushing forward naturalistic social neuroscience. This review broadens the view and equips researchers with an extended toolkit for understanding the richness of social behavior.
Metastatic breast cancer cells disseminate to organs with a soft microenvironment. Whether and how the mechanical properties of the local tissue influence their response to treatment remains unclear. Here we found that a soft extracellular matrix empowers redox homeostasis. Cells cultured on a soft extracellular matrix display increased peri-mitochondrial F-actin, promoted by Spire1C and Arp2/3 nucleation factors, and increased DRP1- and MIEF1/2-dependent mitochondrial fission. Changes in mitochondrial dynamics lead to increased production of mitochondrial reactive oxygen species and activate the NRF2 antioxidant transcriptional response, including increased cystine uptake and glutathione metabolism. This retrograde response endows cells with resistance to oxidative stress and reactive oxygen species-dependent chemotherapy drugs. This is relevant in a mouse model of metastatic breast cancer cells dormant in the lung soft tissue, where inhibition of DRP1 and NRF2 restored cisplatin sensitivity and prevented disseminated cancer-cell awakening. We propose that targeting this mitochondrial dynamics- and redox-based mechanotransduction pathway could open avenues to prevent metastatic relapse.
This study investigates the interplay between composition-dependent phase stability and deformation mechanisms in Co-Cr-Ni medium-entropy alloys (MEAs) of equiatomic and non-equiatomic compositions. Co-rich (Co2CrNi), Cr-rich (CoCr2Ni), and Ni-rich (CoCrNi2) MEAs were designed with each composition determined by selecting a constituent element present in the highest proportion compared to the equiatomic CoCrNi system, which served as the master composition. The composition tuning resulted in distinct microstructure evolution, influencing the mechanical properties and flow mechanisms, closely correlated with variations in stacking fault energy (SFE). Notably, the results highlighted the superior formability, attributed to the distinct deformation mechanisms present in single-phase MEAs (i.e., Co2CrNi, CoCrNi2, and CoCrNi). On the other hand, the introduction of similar to 50 at% Cr thermodynamically triggered the formation of sigma-phase upon homogenization treatment, negatively affecting the stability of the face-centered cubic (FCC) matrix and the alloy's mechanical behavior. Macro-to-nanoscale hierarchical microstructure analyses confirmed the progression of deformation via deformation-induced twinning (CoCrNi) and epsilon-martensite formation (Co2CrNi) in low-SFE alloys. In contrast, the higher-SFE CoCrNi2 alloy exhibited highly dense dislocation walls (HDDWs) with fewer planar dislocation arrays despite the presence of numerous annealing twins. This research provides a systematic alloy design strategy, integrating theoretical analyses with experimental observations to achieve an in-depth comprehension of composition tuning in the Co-Cr-Ni system.
Recently, fluid antenna system (FAS) exploiting flexible-location antennas within a given space has emerged as a key enabler for next-generation wireless communications and Internet-of-Things (IoT). In FAS, acquisition of precise channel state information (CSI) for all possible switchable locations, referred to as ports, is necessary, but demanding. Affirmatively, recent studies have revealed that by virtue of high spatial correlation among a number of ports, the CSI for all the ports can be acquired by estimating the CSI only for a small subset of the ports. However, an important and fundamental question still remains unanswered yet: then how much training is exactly required to estimate the CSI for all the ports in FAS? In this paper, we aim to rigorously answer this nontrivial question by developing a new channel estimation technique for FAS based on a latent domain representation of the CSI for the ports and by jointly optimizing training overhead, training sequences, and port switching. Our thorough analysis newly reveals that the training overhead required for estimating the CSI for all the ports is always less than the rank of spatial channel correlation matrix for all the ports and varies with signal-to-noise ratio (SNR). To alleviate the computational burden of the optimal solution, we also propose a low-complexity, yet near-optimal, solution for training design and port switching. Extensive simulation results confirm that in a practical situation with a large number of ports in a small size, the training overhead required for accurate CSI acquisition in FAS is within at most 10% of the number of ports at modest SNR, and the FAS outperforms the conventional fixed antenna system in terms of both the channel estimation accuracy and training overhead.
Frozen soils, including seasonally frozen ground and permafrost, are rapidly changing under a warming climate, with cascading effects on water, energy, and carbon cycles. We synthesize recent advances in the physics, observation, and modeling of frozen-soil hydrology, emphasizing freeze–thaw dynamics, infiltration regimes and preferential flow, groundwater–permafrost interactions (including talik development and advective heat), and resulting shifts in streamflow seasonality. Progress in in situ sensing, geophysics, and remote sensing now resolves unfrozen water, freezing fronts, and active-layer dynamics across scales, while land-surface and tracer-aided hydrological models increasingly represent phase change, macropore bypass, and vapor transport. Thaw-induced activation of subsurface pathways alters recharge and baseflow, influences vegetation and biogeochemistry, and modulates greenhouse-gas emissions. Key uncertainties persist in scaling micro-scale processes, parameterizing ice-impeded hydraulics, and representing abrupt thaw and wetland dynamics. We outline a tiered modeling framework, priority observations, and integration of vegetation–hydrology–carbon processes to improve projections of cold-region water resources and climate feedbacks.
