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Neural and Behavioral Correlates of Rapid Familiarization to Novel Taste
Gustatory cortex (GC) plays a pivotal role in taste perception, expressing neural ensemble dynamics that reflect, in sequence, taste quality and hedonics, and that influence taste-guided behavior. The circuitry underlying these responses has traditionally been described as fixed and hardwired in adult rodents, although GC taste responses are known to change across multiple timescales of experience, including between sessions of novel taste exposure. Here, we show that responses change on an even smaller timescale—that in fact rapid, experience-dependent changes in GC responses occur across the first handful of taste exposure trials. Specifically, we show that the first 6–9 responses to novel taste stimuli in experimentally naive female lab rats are distinct from later trials in the same session: these earliest responses show less “stereotypical” dynamics and encode both taste identity and palatability less reliably than later responses; “normal” rat taste responses are not preexisting but rather develop across the first several minutes of taste exposure. This phenomenon, which is not taste-specific, is nonexistent (or nearly so) in later sessions. Furthermore, this rapid neural response plasticity is paralleled in changing licking behavior in response to the same stimuli. Together, these results provide novel insights into the real-time dynamics of sensory processing across novel-taste familiarization, essentially demonstrating that the taste system, rather than being hardwired, becomes “tuned up” by use.
Molecular dynamics and free energy analysis of curcumin binding near the DNA-recognition interface of NF-κB
Geospatial mapping of malaria risk in flood-prone zones of Sub-Saharan Africa
Abstract The World Health Organisation (WHO) aims to eliminate malaria by 2030; yet, the disease remains endemic in Sub-Saharan Africa. Stagnant floodwaters provide ideal breeding grounds for mosquitoes. Previous estimates of potential malaria risk in flood zones have been limited due to insufficient large-scale geospatial data. Here, we integrate high-resolution flood maps (2000–2018) from the Global Flood Database, malaria incidence data from the Malaria Atlas Project, and geospatial population data across 492 flood-prone zones in 38 countries. We used a geospatial statistical models to assess malaria relative risk and drivers. We found that in East and West Africa, malaria relative risk is elevated in flood-prone regions compared to national baselines. We estimate that $$\sim$$ 12 million individuals diagnosed with Plasmodium falciparum (Pf )were exposed to flooding events, representing one-third of the population affected by floods. Our analyses find that flood exposure is one of the main drivers of the malaria burden in flood zones. These findings identify critical malaria hotspots and key drivers in flood-prone zones, and can help inform WHO’s malaria eradication strategies by guiding policymakers on the geographic distribution of vulnerable areas.
Cd-Rich Shell-Engineered ZnCdS Nanocrystals for Ultrahigh-Performance Adjustable Shielding of Ultraviolet–Blue Light
Research on the pathways through which rural large-scale sporting events enhance the well-being of local residents—a grounded theory study based on Guizhou, China
Accelerating Proton Exchange in 1,8-Bis(dialkylamino)naphthalene Proton Sponges through Intramolecular Catalysis for CEST MRI
Similar Response Dynamics Represent Opposite Behaviors and Rewards in the Frontal Cortex
The frontal cortex (FC) plays a pivotal role in adaptively controlling actions and their dynamics in response to incoming sensory signals. We explored FC encoding of identical stimuli and their behavioral consequences when they signified diametrically opposite responses depending on task context. Two groups of female ferrets performed Go-NoGo auditory categorization tasks with opposite contingencies and rewards and diverse stimuli. Remarkably, despite opposite stimulus-action associations, single-unit responses were similar across all tasks, being more sustained and stronger to Target sounds (signaling a change in action) than to Reference sounds (indicating maintenance of ongoing actions) especially during task engagement. Overall activity was composed of three distinct dynamic response profiles. Each corresponded to a separate neuronal cluster and exhibited a different role in relation to the succession of task events. Decoding based on the temporal structure of population responses revealed distinct decoders that were aligned to different task events. Similar to single-unit findings, the β-band power extracted from the FC local field potentials (LFPs) was strongly and similarly modulated during Target stimuli across all tasks despite opposite behavioral actions. In contrast, power in all other LFP frequency bands varied significantly across task stimuli and actions. Based on these findings, we propose the FC encodes a common, highly abstract representation of all the different behavioral tasks. We further outline a hypothetical model of pathway-specific functional projections from the tripartite FC neuronal clusters to the basal ganglia, consistent with previous evidence for the conjoint roles of the FC and striatum in adaptive motor control.
A multi-view autoencoder architecture with self-adaptive feature recalibration and confidence-aware ensemble for heart disease classification
Abstract Heart disease continues to pose a major challenge to global health, underscoring the need for early, accurate prediction models. In this study, we introduce a new hybrid intelligent framework designed to significantly improve heart disease classification. Our approach combines multi-view deep feature extraction, self-adaptive feature recalibration, and dynamic ensemble learning to deliver more reliable predictions. The process begins with a multi-view autoencoder that separately captures latent features from demographic, clinical, and diagnostic data. This separation preserves the unique information each data type offers, leading to richer and more meaningful feature representations. Next, we apply a self-adaptive feature recalibration mechanism that assigns importance weights to each feature based on the data itself. This ensures that features with stronger clinical relevance play a greater role in the model’s decision-making. Finally, we integrate a confidence-aware ensemble of three powerful classifiers—Extra Trees, Random Forest, and XGBoost. This ensemble dynamically adjusts the influence of each model depending on how confident they are at the instance level. We tested the proposed framework across five well-known heart disease datasets, using 10-fold cross-validation to ensure robustness. The results are promising: the model achieved an accuracy of 92.45%, sensitivity of 93.2%, specificity of 91.4%, and an F1-score of 91.4%. It consistently outperformed traditional machine learning methods, recent hybrid ensembles, and even state-of-the-art deep learning models like TabNet, SAINT, NODE, and TabTransformer. Statistical significance was confirmed via Friedman and Wilcoxon signed-rank tests ( p < 0.001). To support interpretability, we used SHAP analysis, which highlighted key medical predictors such as chest pain type, number of major vessels, and ST depression. In summary, our results demonstrate that combining multi-view representation learning with self-adaptive feature recalibration and dynamic ensemble strategies leads to a highly effective, interpretable, and clinically relevant tool for early heart disease prediction. This framework holds strong promise for integration into smart clinical decision support systems, with future research aimed at validating it on larger and more diverse patient populations. Not applicable. This research does not involve a clinical trial or any prospective human experimentation.