Neuro-GPT: Developing A Foundation Model for EEG

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG

Machine learning coarse-grained potentials of protein thermodynamics

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics

Elevated circulating Hsp70 levels are correlative for malignancies in different mammalian species

Circulating Hsp70 levels were determined in feline and porcine cohorts using two different ELISA systems. These comparative animal models of larger organisms often reflect diseases, and especially malignant tumors, better than conventional rodent models. It is therefore essential to investigate the biology and utility of tumor biomarkers in animals such as cats and pigs. In this study, levels of free Hsp70 in the blood of cats with spontaneously occurring tumors were detected using a commercial Hsp70 ELISA (R&D Systems). Sub-analysis of different tumor groups revealed that animals with tumors of epithelial origin presented with significantly elevated circulating Hsp70 concentrations. In addition to free Hsp70 levels measured with the R&D Systems Hsp70 ELISA, levels of exosomal Hsp70 were determined using the compHsp70 ELISA in pigs. Both ELISA systems detected significantly elevated Hsp70 levels (R&D Systems: median 24.9 ng/mL; compHsp70: median 44.2 ng/mL) in the blood of a cohort of APC$^{1311/+}$ pigs diagnosed with high-grade adenoma polyps, and the R&D Systems Hsp70 ELISA detected also elevated Hsp70 levels in animals with low-grade polyps. In contrast, in flTP53$^{R167H}$ pigs, suffering from malignant osteosarcoma, the compHsp70 ELISA (median 674.32 ng/mL), but not the R&D Systems Hsp70 ELISA (median 4.78 ng/mL), determined significantly elevated Hsp70 concentrations, indicating that in tumor-bearing animals, the dominant form of Hsp70 is of exosomal origin. Our data suggest that both ELISA systems are suitable for detecting free circulating Hsp70 levels in pigs with high-grade adenoma, but only the compHsp70 ELISA can measure elevated, tumor-derived exosomal Hsp70 levels in tumor-bearing animals

Stay Tuned! Assessing the Effectiveness of Social Media Video Ads

Advanced web technologies, especially social media channels, provide branding opportunities for companies to promote their brands for superior customer appeal. The present work shows the result of a real use case regarding the visibility and interaction performance of auto-play vs. click-to-play ads in affecting customers' mind

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics

torchmd/torchmd-net: 0.4.0

What's Changed Make TensorNet CUDA-graph compatible by @RaulPPelaez in https://github.com/torchmd/torchmd-net/pull/195 Use update instead of |= so Trainer is compatible with Python 3.8 by @RaulPPelaez in https://github.com/torchmd/torchmd-net/pull/218 Full Changelog: https://github.com/torchmd/torchmd-net/compare/0.3.0...0.4.

Machine learning coarse-grained potentials of protein thermodynamics

Abstract A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics

torchmd/torchmd-net: 0.3.0

This release bumps all dependencies to be up to date with the latest conda-forge versions. Also changes the torch extension compilation so that it happens during installation. What's Changed Updating to Lightning 2.0 by @RaulPPelaez in https://github.com/torchmd/torchmd-net/pull/210 Compile torch extension in setup.py by @RaulPPelaez in https://github.com/torchmd/torchmd-net/pull/216 Full Changelog: https://github.com/torchmd/torchmd-net/compare/0.2.7...0.3.