Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information

Solving materials engineering tasks is often hindered by limited information, such as in inverse problems with only boundary data information or design tasks with a simple objective but a vast search space. To address these challenges, multiple deep learning (DL) architectures are leveraged to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recovered mechanical fields for 2D and 3D complex microstructures. In 2D, a conditional generative adversarial network (GAN) is utilized to complete partially masked field maps and predict the composite geometry with convolutional models with great accuracy and generality by making precise predictions on field data with mixed stress/strain components, hierarchical geometries, distinct materials properties and various types of microstructures including ill-posed inverse problems. In 3D, a Transformer-based architecture is implemented to predict complete 3D mechanical fields from input field snapshots. The model manifests excellent performance regardless of microstructural complexity and recovers the entire bulk field even from a single surface field image, allowing internal structural characterization from only boundary measurements. The whole frameworks provide efficient ways for analysis and design with incomplete information and allow the direct inverse translation from properties back to materials structures.

Intracranial alternating current stimulation facilitates neurogenesis in a mouse model of Alzheimer's disease

BACKGROUND

Neurogenesis is significantly impaired in the brains of both human patients and experimental animal models of Alzheimer's disease (AD). Although deep brain stimulation promotes neurogenesis, it is an invasive technique that may damage neural circuitry along the path of the electrode. To circumvent this problem, we assessed whether intracranial electrical stimulation to the brain affects neurogenesis in a mouse model of Alzheimer's disease (5xFAD).

METHODS AND RESULTS

We used Ki67, Nestin, and doublecortin (DCX) as markers and determined that neurogenesis in both the subventricular zone (SVZ) and hippocampus were significantly reduced in the brains of 4-month-old 5xFAD mice. Guided by a finite element method (FEM) computer simulation to approximately estimate current and electric field in the mouse brain, electrodes were positioned on the skull that were likely to deliver stimulation to the SVZ and hippocampus. After a 4-week program of 40-Hz intracranial alternating current stimulation (iACS), neurogenesis indicated by expression of Ki67, Nestin, and DCX in both the SVZ and hippocampus were significantly increased compared to 5xFAD mice who received sham stimulation. The magnitude of neurogenesis was close to the wild-type (WT) age-matched unmanipulated controls.

CONCLUSION

Our results suggest that iACS is a promising, less invasive technique capable of effectively stimulating the SVZ and hippocampus regions in the mouse brain. Importantly, iACS can significantly boost neurogenesis in the brain and offers a potential treatment for AD.