“Complex Prediction in the Absence of Attention: Examining the Brain’s Implicit Ability to Recognize Qualitative Differences in Real Scenes”

Evan Center, Ph.D. student in psychology, Beckman Institute Graduate Fellow, and member of Beckman’s Mechanisms of Cognitive Control Group

The human brain is the most powerful known inferential computing structure. Much evidence supports the claim that we process stimuli and make inferences not only at the level of conscious awareness, but even at pre-conscious or largely unconscious levels. Despite considerable progress in understanding how attention helps us process the environment, our knowledge regarding unattended processing remains underexplored. To this end we take advantage of the N300 and vMMN (visual mismatch negativity) ERP (event-related potential) components to gauge brain activity initiated by unattended stimuli. The N300 and vMMN are thought to index global stimulus structure and violation of expectancy, respectively. Previous work in our lab shows that the N300 is sensitive to statistical regularity (e.g. exemplar representativeness) under conditions of full attention, but the degree to which this effect depends on attention remains unknown. Similarly, others have shown that the vMMN is sensitive to deviance in pattern structure even without attention, but have largely relied on repetitive, overly simplistic stimuli. We address both gaps by presenting unattended natural scene stimuli which are novel and richly detailed. Pilot results from 12 subjects evidence an N300 in response to ignored stimuli, indicating that full attention is not necessary to assess statistical regularity in scene structure. Moreover, this N300 difference is further exaggerated when we combine temporally global statistical regularity (exemplar representativeness) with temporally local statistical regularity (predictability within the experiment). Finally, we also observe a potential vMMN effect hinting that perhaps attention is not necessary for abstract rule construction and deviance detection, though more data is required to rule conclusively.

“Monomer Design in Frontal Ring-opening Metathesis Polymerization”

Huiying Liu, Ph.D. student in chemistry, graduate research assistant at the Beckman Institute, and a member of Beckman’s Autonomous Materials Systems Group

The traditional manufacturing of high-performance thermosets and fiber-reinforced polymer composites (FRPC) requires a large amount of energy and involves significant capital investment. Frontal ring-opening metathesis polymerization (FROMP) is a promising alternative strategy that substantially reduces manufacturing burdens by employing the enthalpy of polymerization to rapidly transform liquid monomers to fully cured polymers. To obtain FRPC with better mechanical properties and hotter monomers (i.e., more heat generated per unit time/volume) is highly desirable to compensate heat loss while increasing the fiber volume fraction. Thus, we investigated thermodynamics, kinetics, and frontal properties of 30-plus strained monomers to establish a quantitative structure-property relationship. We also collaborated with machine learning experts to develop a predictive model for FROMP monomers. A correlation between frontal properties and molecular parameters was constructed as a design rule, and several structural characteristics that mainly contribute to the heat release rate have also been identified. This systematic study not only expands the toolbox of FROMP, but also guides new monomer design that further advances our current system.

“Optical Control of Zn²⁺ Detection in Cells and Zebrafish Using Deoxyribozyme-conjugated Upconversion Nanoparticles"

Zhenglin Yang, Ph.D. student in biochemistry, member of Beckman’s Integrated Imaging theme

Spatial and temporal distributions of metal ions in vitro and in vivo are crucial in our understanding of the roles of metal ions in biological systems and health, and yet there is a very limited number of methods to probe metal ions with high space and time resolution, especially in vivo. To overcome this limitation, we report a Zn²⁺-specific near infrared (NIR) DNAzyme nanoprobe for real-time metal ion tracking with spatiotemporal control in early embryos and larvae of zebrafish. By conjugating photocaged DNAzymes onto lanthanide-doped upconversion nanoparticles (UCNPs), we have achieved upconversion of a deep tissue penetrating NIR 980 nm light into 365 nm UV emission. The UV photon then efficiently photo-decages a substrate strand, allowing enzymatic cleavage by a complementary DNA strand containing a Zn²⁺-selective DNAzyme. The product containing a visible FAM fluorophore is released after cleavage from quenchers, resulting in higher fluorescent signals. The DNAzyme-UCNP probe enables Zn²⁺ sensing by exciting in the NIR biological imaging window in both living cells and zebrafish embryos, and detecting in the visible region for biomedical research. This report introduces a platform that can be used to understand the Zn²⁺ distribution with spatiotemporal control, thereby giving insights into the dynamical Zn²⁺ ion distribution in intracellular and in vivo models.