Blackwellbasse1455

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. XL184 supplier Computational fluid dynamic (CFD) simulations are conducted to determine the characteristics of the flow and design thmuscle tissue engineering purposes.Critical understanding of the complex metastatic cascade of prostate cancer is necessary for the development of a therapeutic interventions for treating metastatic prostate cancer. Increasing evidence supports the synergistic role of biochemical and biophysical cues in cancer progression at metastases. The biochemical factors such as cytokines have been extensively studied in relation to prostate cancer progression to the bone; however, the role of shear stress-induced by interstitial fluid around bone extracellular matrix has not been fully explored as a driving factor for prostate cancer metastasis. Shear stress governs various cellular processes, including cell proliferation and migration. Thus, it is essential to understand the impact of fluid-derived shear stress on the aggressiveness of prostate cancer at the metastatic stage. Here, we report development of a three-dimensional (3D) in-vitro dynamic cell culture system to recapitulate the microenvironment of prostate cancer bone metastasis, to understand the cause of modulation in cell response under fluid-derived shear stress. We observed an increased human mesenchymal stem cells (hMSCs) proliferation and differentiation rate under dynamic culture. We observed that hMSCs under static culture form cell agglutinates, whereas under dynamic culture, hMSCs exhibited a directional alignment with broad and flattened morphology. Next, we observed increased expression of mesenchymal to epithelial transition (MET) biomarkers in bone metastasized prostate cancer models as well as large changes in cellular and tumoroid morphologies with shear stress. Evaluation of cell adhesion proteins indicated that the altered cancer cell morphologies resulted from the constant force pulling due to increased E-Cadherin and phosphorylated Focal adhesion kinase (FAK) proteins under shear stress. Collectively, we have successfully developed a 3D in-vitro dynamic model to recapitulate the behavior of bone metastatic prostate cancer under dynamic conditions.Objective In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer-interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements. Approach High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size. Main results Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5x5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3x3 electrodes, an inter-electrode distance of 8mm, and an electrode size of 3mm radius (performing at ~70% 3-class classification accuracy). Significance Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.Objective The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality reduction algorithms select them. This data is then treated as visual input for 2D and 3D CNNs which then further extract 'features of features'. Approach Statistical features derived from three electroencephalography datasets are presented in visual space and processed in 2D and 3D space as pixels and voxels respectively. Three datasets are benchmarked, mental attention states and emotional valences from the four TP9, AF7, AF8 and TP10 10-20 electrodes and an eye state data from 64 electrodes. 729 features are selected through three methods of selection in order to form 27x27 images and 9x9x9 cubes from the same datasets. CNNs engineered for the 2D and 3D preprocessing representations learn to convolve useful graphical features from the data. Main results A 70/30 split method shows that the strongest methods for classification accuracy of feature selection are One Rule for attention state and Relative Entropy for emotional state both in 2D.