Toward a unified framework for interpreting machine-learning models in neuroimaging.

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds.

Enhanced Electrochemical Stability of a Zwitterionic-Polymer-Functionalized Electrode for Capacitive Deionization.

In capacitive deionization, the salt-adsorption capacity of the electrode is critical for the efficient softening of brackish water. To improve the water-deionization capacity, the carbon electrode surface is modified with ion-exchange resins. Herein, we introduce the encapsulation of zwitterionic polymers over activated carbon to provide a resistant barrier that stabilizes the structure of electrode during electrochemical performance and enhances the capacitive deionization efficiency.

Interpretation of seasonal water quality variation in the Yeongsan Reservoir, Korea using multivariate statistical analyses.

The Yeongsan (YS) Reservoir is an estuarine reservoir which provides surrounding areas with public goods, such as water supply for agricultural and industrial areas and flood control. Beneficial uses of the YS Reservoir, however, are recently threatened by enriched non-point and point source inputs. A series of multivariate statistical approaches including principal component analysis (PCA) were applied to extract significant characteristics contained in a large suite of water quality data (18 variables monthly recorded for 5 years); thereby to provide the important phenomenal information for establishing effective water resource management plans for the YS Reservoir.