The optogenetic neuron ablation approach enables noninvasive remote decoding of specific neuron function within a complex living organism in high spatiotemporal resolution. However, it suffers from shallow tissue penetration of visible light with low ablation efficiency. This study reports a upconversion nanoparticle (UCNP)–based multiplex proteins activation tool to ablate deep‐tissue neurons for locomotion modulation. By optimizing the dopant contents and nanoarchitecure, over 300‐fold enhancement of blue (450–470 nm) and red (590–610 nm) emissions from UCNPs is achieved upon 808 nm irradiation. Such emissions simultaneously activate mini singlet oxygen generator and Chrimson, leading to boosted near infrared (NIR) light–induced neuronal ablation efficiency due to the synergism between singlet oxygen generation and intracellular Ca2+ elevation. The loss of neurons severely inhibits reverse locomotion, revealing the instructive role of neurons in controlling motor activity. The deep penetrance NIR light makes the current system feasible for in vivo deep‐tissue neuron elimination. The results not only provide a rapidly adoptable platform to efficient photoablate single‐ and multiple‐cells, but also define the neural circuits underlying behavior, with potential for development of remote therapy in diseases.

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Bottom drag coefficient is one of the key parameters in quantifying shelf hydrodynamics and sediment transport processes. It varies markedly due to dynamic forcing and bed type differences, so a set of empirical values have been used for beds of coarse material where bedforms are often present. In comparison, dramatically fewer such rule-of-thumb values are available for muddy beds. Here, we present results of variations in bottom drag as calculated from in situ measurements by bottom-mounted tripods that were placed across the top of a muddy deposit during two different deployments, one in summer and another in winter. A tidal asymmetry of bottom drag was observed, most likely caused by variations of local bed roughness. For hydrodynamically smooth (Re<2.3×105) flows, computed values of bottom drag coefficient were fairly scattered but still showed an overall decreasing trend with an increase in Reynolds number. The bottom drag coefficient for hydrodynamically rough or transitional flow was typically constant, while the averaged drag coefficient over all observation periods was 1.7×10−3. Smaller waves (bottom orbital velocity ub &lt;0.1 m/s) had a very limited impact on the bottom drag coefficient. However, with an increase in ub, the wave–current interactions can decrease the time-averaged near-bed velocity and enhance turbulent kinetic energy, thus leading to an increase in the drag coefficient.