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Abstract Traditional deep fluorescence imaging has primarily focused on red‐shifting imaging wavelengths into the near‐infrared (NIR) windows or implementation of multi‐photon excitation approaches. Here, the advantages of NIR and multiphoton imaging are combined by developing a dual‐infrared two‐photon microscope that enables high‐resolution deep imaging in biological tissues. This study first computationally identifies that photon absorption, as opposed to scattering, is the primary contributor to signal attenuation. A NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues labeled with fluorescent single‐walled carbon nanotubes (SWNTs). Spatial imaging resolutions are achieved close to the Abbe resolution limit and eliminate blur and background autofluorescence of biomolecules, 300 µm deep into brain slices and through the full 120 µm thickness of aNicotiana benthamianaleaf. NIR‐II two‐photon microscopy can also measure tissue heterogeneity by quantifying how much the fluorescence power law function varies across tissues, a feature this study exploits to distinguish Huntington's Disease afflicted mouse brain tissues from wildtype. These results suggest dual‐infrared two‐photon microscopy can accomplish in‐tissue structural imaging and biochemical sensing with a minimal background, and with high spatial resolution, in optically opaque or highly autofluorescent biological tissues. 

We derived equations and closed-form solutions of transit time for a viscous droplet squeezing through a small circular pore with a finite length at microscale under constant pressures. Our analyses were motivated by the vital processes of biological cells squeezing through small pores in blood vessels and sinusoids and droplets squeezing through pores in microfluidics. First, we derived ordinary differential equations (ODEs) of a droplet squeezing through a circular pore by combining Sampson flow, Poiseuille flow, and Young–Laplace equations and took into account the lubrication layer between the droplet and the pore wall. Second, for droplets wetting the wall with small surface tension, we derived the closed-form solutions of transit time. For droplets with finite surface tension, we solved the original ODEs numerically to predict the transit time. After validations against experiments and finite element simulations, we studied the effects of pressure, viscosity, pore/droplet dimensions, and surface tension on the transit time. We found that the transit time is inversely linearly proportional to pressure when the surface tension is low compared to the critical surface tension for preventing the droplet to pass and becomes nonlinear when it approaches the critical tension. Remarkably, we showed that when a fixed percentage of surface tension to critical tension is applied, the transit time is always inversely linearly proportional to pressure, and the dependence of transit time on surface tension is nonmonotonic. Our results provided a quick way of quantitative calculations of transit time for designing droplet microfluidics and understanding cells passing through constrictions. 

Many robot-delivered health interventions aim to support people longitudinally at home to complement or replace in-clinic treat- ments. However, there is little guidance on how robots can support collaborative goal setting (CGS). CGS is the process in which a person works with a clinician to set and modify their goals for care; it can improve treatment adherence and efficacy. However, for home-deployed robots, clinicians will have limited availability to help set and modify goals over time, which necessitates that robots support CGS on their own. In this work, we explore how robots can facilitate CGS in the context of our robot CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), which delivers neurorehabilitation to people with mild cognitive impairment (PwMCI). We co-designed robot behaviors for supporting CGS with clinical neuropsychologists and PwMCI, and prototyped them on CARMEN. We present feedback on how PwMCI envision these behaviors supporting goal progress and motivation during an intervention. We report insights on how to support this process with home-deployed robots and propose a framework to support HRI researchers interested in exploring this both in the context of cognitively assistive robots and beyond. This work supports design- ing and implementing CGS on robots, which will ultimately extend the efficacy of robot-delivered health interventions.