Spatiotemporal characterization of the field-induced insulator-to-metal transition.

Many correlated systems feature an insulator-to-metal transition that can be triggered by an electric field. Although it is known that metallization takes place through filament formation, the details of how this process initiates and evolves remain elusive. We use in-operando optical reflectivity to capture the growth dynamics of the metallic phase with space and time resolution. We demonstrate that filament formation is triggered by nucleation at hotspots, with a subsequent expansion over several decades in time. By comparing three case studies (VO2, V3O5, and V2O3), we identify the resistivity change across the transition as the crucial parameter governing this process. Our results provide a spatiotemporal characterization of volatile resistive switching in Mott insulators, which is important for emerging technologies, such as optoelectronics and neuromorphic computing.

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks.

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Glucose transport: meeting the metabolic demands of cancer, and applications in glioblastoma treatment.

GLUT1, and to a lesser extent, GLUT3, appear to be interesting targets in the treatment of glioblastoma multiforme. The current review aims to give a brief history of the scientific community's understanding of these glucose transporters and to relate their importance to the metabolic changes that occur as a result of cancer. One of the primary changes that occurs in cancer, the Warburg Effect, is characterized by an extreme shift toward glycolysis from the usual reliance on oxidative phosphorylation and is currently being investigated to target the upstream and downstream factors responsible for Warburg-induced changes. Further, it aims to explain the differential expression of GLUT1 and GLUT3 in glioblastoma tissue, and how these modulations in expression can serve as targets to restore a more normal metabolism. Additionally, hypoxia-induced factor-1α's (HIF1α) role in a number of transcriptional changes typical to GBM will be discussed, including its role in GLUT upregulation. Finally, the four known subtypes of GBM [proneural, neural, mesenchymal, and classical] will be characterized in order to discuss how metabolic changes differ in each subtype. These changes have the potential to be selectively targeted in order to provide specificity to the clinical treatment options in GBM.