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Towards Unified Neural Decoding with Brain Functional Network Modeling
Di Wu, Linghao Bu, Yifei Jia, Lu Cao, Siyuan Li, Siyu Chen, Yueqian Zhou, Sheng Fan, Wenjie Ren, Dengchang Wu, Kang Wang, Yue Zhang, Yuehui Ma, Jie Yang, Mohamad Sawan
https://arxiv.org/abs/2506.12055

Towards Unified Neural Decoding with Brain Functional Network Modeling
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that co…

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https://samshelley.bandcamp.com/track/crown-heights-affair-galaxy-of-love-sam-shelley-re-edit
https://open.spotify.com/track/195mwsLM34pO9WOrA88mzt

On the Potential of Microtubules for Scalable Quantum Computation
Nick E. Mavromatos, Andreas Mershin, Dimitri V. Nanopoulos
https://arxiv.org/abs/2505.20364

On the Potential of Microtubules for Scalable Quantum Computation
We examine the quantum coherence properties of tubulin heterodimers in the Microtubule (MT) lattice. In the cavity-MT model proposed by the authors, according to which the MT interiors are modeled as high-Q quantum-electrodynamics cavities, decoherence-resistant entangled states have been argued to emerge under physiological conditions, with decoherence times of order $\mathcal{O}(10^{-6})$ s. The latter is the result of strong electric-dipole interactions of tubulin dimers with ordered-water d…

A DNA Methylation Classification Model Predicts Organ and Disease Site
Keng-Jung Lee, Dharanya Sampath, Konstantinos Mavrommatis
https://arxiv.org/abs/2506.00146

A DNA Methylation Classification Model Predicts Organ and Disease Site
Cell-free DNA (cfDNA) analysis is a powerful, minimally invasive tool for monitoring disease progression, treatment response, and early detection. A major challenge, however, is accurately determining the tissue of origin, especially in complex or heterogeneous disease contexts. To address this, we developed a machine learning framework that leverages tissue-specific DNA methylation signatures to classify both tissue and disease origin from cfDNA data. Our model integrates methylation datasets …