In language models of word meaning, directions in the embedding space often correspond to semantic features that can be reused across different words. For example, a single direction corresponding to gender may differentiate word pairs like boy/girl, uncle/aunt, and king/queen. Here we show that the same principle governs semantically driven neural responses in the human brain. We recorded populations of single neurons during podcast listening and identified word sets with consistent meaning differences. Across fifteen sets, including gender, plural, and negation, we observed consistent vectorial directions, resulting in parallelogram structures within the neural manifold. Deviation from parallelism in large language models (LLMs) predicted corresponding deviations in brain-derived parallelism. Among pronouns, vectors corresponding to case, number and person exhibited parallelogram structures individually and, collectively, obeyed the principle of commutativity, resulting in a prismatic structure. Finally, different semantic variables were preferentially associated with discrete groups of neurons, consistent with energy-efficiency theories. Together, these results establish a geometric foundation for the neural encoding of word meaning.
Abstract We describe a case of autochthonous acute Chagas disease in an infant with fulminant myocarditis requiring aggressive support. Peridomestic investigation revealed Trypanosoma cruzi-infected vectors and animals. This highlights the importance of (1) early diagnosis and treatment and (2) understanding the epidemiological risk factors for Chagas disease in the United States.
Mechanisms responsible for skeletal muscle kidney crosstalk have not been defined. We have determined that a circulating mediator, signal regulatory protein α (SIRPα), impairs intracellular insulin-mediated functions. To elucidate the effect of myokine SIRPα on diabetic kidney disease (DKD), flox mice and muscle-specific (m-specific) SIRPα-KO mice were subjected to an obesity-induced model of diabetes, high-fat diet (HFD; 60%) or insulin-deficient hyperglycemia model, streptozotocin (STZ), and were subsequently exposed to anti-SIRPα monoclonal antibodies. In the obesity-induced diabetic mice, serum SIRPα increased. Genetic deletion of muscle SIRPα protected against obesity and improved intracellular insulin signaling in muscle and adipose tissue, with reduced intramuscular fat deposition when compared with flox mice on HFD. Moreover, mSIRPα-KO mice displayed enhanced kidney tubular fatty acid oxidation (FAO) expression with suppressed intraorgan triglycerides deposition, and importantly, protection against DKD. Conversely, exogenous SIRPα impaired kidney proximal tubular cell FAO, ATP production, and exacerbated fibrosis. Finally, suppressing SIRPα in skeletal muscles or treatment with anti-SIRPα monoclonal antibodies in STZ-treated mice mitigated cachexia, hyperlipidemia, kidney triglyceride deposition, and renal dysfunction in spite of significant hyperglycemia. Importantly, serum SIRPα was upregulated in patients with DKD. In conclusion, SIRPα serves as a potential biomarker and therapeutic target in DKD.
Humans handle numbers nimbly, suggesting a richer neural manifold structure than the prevalent mental number line model. In populations of medial temporal lobe (MTL) neurons in humans performing two simple tasks (dot counting and arithmetic), we find robust neural coding of numerosity that results in high dimensional, simplex-shaped manifolds. This shape affords more flexibility than a linear manifold due to its high shattering dimensionality and expressibility. Dot arrays and Arabic numerals evoked distinct simplicial population codes, yet they were linked by a linearly transferable latent structure within the same task. We find similar simplicial geometry of number representations in large language models (LLMs). Moreover, subjects internally computed arithmetic results were decodable during the calculation period, with decoding accuracy correlating with individual mathematical capacity. Finally, linear transformations of simplicial operand representations modeled the brains conversion of operands into decodable results, suggesting that the brains arithmetic procedures have some resemblance to the attention architecture of LLMs. Together, these findings establish a high dimensional representational foundation for numerical cognition in the brain.
