4194 papers
This study reveals that isolating glioblastoma cells from their native microenvironment triggers a compensatory, NPC2-driven neuronal mimicry program via contact-dependent synaptic rewiring, which serves as a survival strategy with adverse prognostic implications and underscores the need for microenvironmental reconstitution in organoid models.
This study demonstrates the generation of functional human adrenomedullary organoids from both neural crest-derived sympathoadrenal progenitors and Schwann cell precursors, revealing that these distinct developmental origins dictate specific cell fates and differential catecholamine release patterns under stress.
This study demonstrates that by correcting a modeling error regarding regulatory mechanisms, an evolutionary simulation confirms that a generational memory window functions as a critical engine/brake system for temporal credit assignment, enabling stable multi-peak coexistence and a sharp transition in binding rates analogous to eligibility traces in spiking neural networks.
This meta-analysis of 47 public sequencing studies involving over 6,800 individuals reveals that autoimmune disorders share a common signature of reduced gut microbial diversity and depleted butyrate-producing bacteria, alongside disease-specific microbial alterations, suggesting a potential link between gut dysbiosis and autoimmune pathogenesis.
The paper introduces STEPHY, a rapid and scalable graph neural inference toolkit that transforms large viral phylogenies into actionable regional epidemic intelligence by accurately estimating key epidemiological dynamics and source-sink roles, as demonstrated through extensive simulations and real-world SARS-CoV-2 data.
This paper presents MCGRO, a novel confidence-guided graph reinforcement framework that integrates heterogeneous multi-omics data through adaptive weighting and iterative edge optimization to generate robust patient similarity networks for improved cancer subtyping and clinical stratification.
This paper introduces a conservation–divergence spectrum framework that identifies subtype-specificity determinants across protein superfamilies by mapping positions onto a plane constrained by information-theoretic boundaries, revealing that specificity is encoded as a sparse, distributed, probabilistic code consistent with Boltzmann-competition principles rather than localized deterministic interactions.
This paper establishes a prospective, two-dimensional criterion to predict when the conservation–divergence spectrum can successfully identify specificity-determining positions, demonstrating through blind validation across seven protein families that the method's reliability depends on the phylogenetic independence and locality of the functional grouping rather than the detection algorithm itself.
Through a comparative genomic analysis of over 720,000 protein sequences, this study identifies capsid, replicase, and RNA-dependent RNA polymerase as unique viral signatures, providing strong evidence that viruses represent distinct evolutionary lineages rather than degenerate cellular life.
This paper proposes a biologically inspired artificial neural network framework that utilizes convolutional and Hopfield networks to simulate human visual cortex functions, including feature extraction, migraine-related photophobia, and audio-enhanced image recall, thereby offering new insights into neural mechanisms.