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https://www.selleckchem.com/products/4-hydroxytamoxifen-4-ht-afimoxifene.html HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders. By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.Multiple human tissues exhibit fibrous nature. Therefore, the fabrication of hydrogel filaments for tissue engineering is a trending topic. Current tissue models are made of materials that often require further enhancement for appropriate cell attachment, proliferation and differentiation. Here we present a simple strategy, based on the use surface chaotic flows amenable of mathematical modeling, to fabricate continuous, long and thin filaments of gelatin methacryloyl (GelMA). The fabrication of these filaments is achieved by chaotic advection in a finely controlled and miniaturized version of the journal bearing (JB) system. A drop of GelMA pregel was injected on a higher-density viscous fluid (glycerin) and a chaotic flow is applied through an iterative process. The hydrogel drop is exponentially deformed and elongated to generate a fiber, which was then polymerized under UV-light exposure.

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