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In models of visual spatial attention control, it is commonly held that top–down control signals originate in the dorsal attention network, propagating to the visual cortex to modulate baseline neural activity and bias sensory processing. However, the precise distribution of these top–down influences across different levels of the visual hierarchy is debated. In addition, it is unclear whether these baseline neural activity changes translate into improved performance. We analyzed attention-related baseline activity during the anticipatory period of a voluntary spatial attention task, using two independent functional magnetic resonance imaging datasets and two analytic approaches. First, as in prior studies, univariate analysis showed that covert attention significantly enhanced baseline neural activity in higher-order visual areas contralateral to the attended visual hemifield, while effects in lower-order visual areas (e.g., V1) were weaker and more variable. Second, in contrast, multivariate pattern analysis (MVPA) revealed significant decoding of attention conditions across all visual cortical areas, with lower-order visual areas exhibiting higher decoding accuracies than higher-order areas. Third, decoding accuracy, rather than the magnitude of univariate activation, was a better predictor of a subject's stimulus discrimination performance. Finally, the MVPA results were replicated across two experimental conditions, where the direction of spatial attention was either externally instructed by a cue or based on the participants’ free choice decision about where to attend. Together, these findings offer new insights into the extent of attentional biases in the visual hierarchy under top–down control and how these biases influence both sensory processing and behavioral performance. 

Inverse molecular generation is an essential task for drug discovery, and generative models offer a very promising avenue, especially when diffusion models are used. Despite their great success, existing methods are inherently limited by the lack of a semantic latent space that can not be navigated and perform targeted exploration to generate molecules with desired properties. Here, we present a property-guided diffusion model for generating desired molecules, which incorporates a sophisticated diffusion process capturing intricate interactions of nodes and edges within molecular graphs and leverages a time-dependent molecular property classifier to integrate desired properties into the diffusion sampling process. Furthermore, we extend our model to a multi-property-guided paradigm. Experimental results underscore the competitiveness of our approach in molecular generation, highlighting its superiority in generating desired molecules without the need for additional optimization steps. 

Hydrotropic solvents are a promising solvent in biomass processing due to their unique amphiphilic structure. This review summarizes recent advances in hydrotropic solvent systems with their chemical structure, amphiphilicity, roles, and mechanism.