Neuronal Population Transitions Across a Quiescent-to-Active Frontier and Bifurcation.

The mechanistic understanding of why neuronal population activity hovers on criticality remains unresolved despite the availability of experimental results. Without a coherent mathematical framework, the presence of power-law scaling is not straightforward to reconcile with findings implying epileptiform activity. Although multiple pictures have been proposed to relate the power-law scaling of avalanche statistics to phase transitions, the existence of a phase boundary in parameter space is until now an assumption. Herein, a framework based on differential inclusions, which departs from approaches constructed from differential equations, is shown to offer an adequate consolidation of evidences apparently connected to criticality and those linked to hyperexcitability. Through this framework, the phase boundary is elucidated in a parameter space spanned by variables representing levels of excitation and inhibition in a neuronal network. The interpretation of neuronal populations based on this approach offers insights on the role of pharmacological and endocrinal signaling in the homeostatic regulation of neuronal population activity.

Predictive Chromatography of Leaf Extracts Through Encoded Environmental Forcing on Phytochemical Synthesis.

Environment fluctuations can influence a plant's phytochemical profile via phenotypic plasticity. This adaptive response ensures a plant's survival under fluctuating growth conditions. However, the resulting plant extract composition becomes unpredictable, which is a problem for highly standardized medicinal applications. Here we demonstrate, for the first time, the feasibility of tracking the changes in the phytochemical profile based on real-time measurements of a few environment and extract-preparation variables. As a result, we predicted the chromatograms of Blumea balsamifera extracts through an imputation-augmented convolutional neural network, which uses the image-transformed temporal measurements of the variables. We developed a sensor network that collected data in a greenhouse and a training algorithm that concurrently generated a data representation of the implicit plant-environment interactions leading to the mutable chromatograms of leaf extracts. We anticipate the generic applicability of the method for any plant and recognize its potential for addressing the standardization problems in plant therapeutics.