Fluorescent probes in the second near-infrared window (NIR-II) allow high-resolution bioimaging with deep-tissue penetration. However, existing NIR-II materials often have poor signal-to-background ratios because of the lack of target specificity. Herein, an activatable NIR-II nanoprobe for visualizing colorectal cancers was devised. This designed probe displays H S-activated ratiometric fluorescence and light-up NIR-II emission at 900-1300 nm. By using this activatable and target specific probe for deep-tissue imaging of H S-rich colon cancer cells, accurate identification of colorectal tumors in animal models were performed. It is anticipated that the development of activatable NIR-II probes will find widespread applications in biological and clinical systems.

Near-infrared (NIR)-II fluorescence agents hold great promise for deep-tissue photothermal therapy (PTT) of cancers, which nevertheless remains restricted by the inherent nonspecificity and toxicity of PTT. In response to this challenge, we herein develop a hydrogen sulfide (H2S)-activatable nanostructured photothermal agent (Nano-PT) for site-specific NIR-II fluorescence-guided PTT of colorectal cancer (CRC). Our in vivo studies reveal that this theranostic Nano-PT probe is specifically activated in H2S-rich CRC tissues, whereas it is nonfunctional in normal tissues. Activation of Nano-PT not only emits NIR-II fluorescence with deeper tissue penetration ability than conventional fluorescent probes but also generates high NIR absorption resulting in efficient photothermal conversion under NIR laser irradiation. Importantly, we establish NIR-II imaging-guided PTT of CRC by applying the Nano-PT agent in tumor-bearing mice, which results in complete tumor regression with minimal nonspecific damages. Our studies thus shed light on the development of cancer biomarker-activated PTT for precision medicine.

H2S-activatable probes showed an extremely fast and highly selective photoacoustic response to H2S, permitting real-time photoacoustic trapping in living mice.

NIR light responsive nanoplatforms hold great promise for on‐demand drug release in precision cancer medicine. However, currently available systems utilize “always‐on” photothermal transducers that lack target specificity, and thus inaccurately differentiate tumors from normal tissues. Developed here is a theranostic nanoplatform featuring H2S‐mediated in situ production of NIR photothermal agents for imaging‐guided and photocontrolled drug release. The system targets H2S‐rich cancers. This nanoplatform shows H2S‐activatable NIR‐II emission and NIR light controllable release of the drug Camptothecin‐11. Upon administering the system to HCT116 tumor‐bearing mice, the tumor is greatly suppressed with minimal side effects, arising from the synergy of the cancer‐specific and NIR light activated therapy. This theranostic nanoplatform thus sheds light on precision medicine with guidance through NIR‐II imaging.

We address the problem of American Sign Language fingerspelling recognition "in the wild", using videos collected from websites. We introduce the largest data set available so far for the problem of fingerspelling recognition, and the first using naturally occurring video data. Using this data set, we present the first attempt to recognize fingerspelling sequences in this challenging setting. Unlike prior work, our video data is extremely challenging due to low frame rates and visual variability. To tackle the visual challenges, we train a special-purpose signing hand detector using a small subset of our data. Given the hand detector output, a sequence model decodes the hypothesized fingerspelled letter sequence. For the sequence model, we explore attention-based recurrent encoder-decoders and CTC-based approaches. As the first attempt at fingerspelling recognition in the wild, this work is intended to serve as a baseline for future work on sign language recognition in realistic conditions. We find that, as expected, letter error rates are much higher than in previous work on more controlled data, and we analyze the sources of error and effects of model variants.Index Terms-American Sign Language, fingerspelling, connectionist temporal classification, attention models 2 Two-handed fingerspelling occasionally occurs, including in our data.

Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It outperforms prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.

Activatable molecular probes hold great promise for targeted cancer imaging. However, the hydrophobic nature of most conventional probes makes them generate precipitated agglomerate in aqueous media, thereby annihilating their responsiveness to analytes and precluding their practical applications for bioimaging. This study reports the development of two small molecular probes with unprecedented aggregation enhanced responsiveness to H 2 S for in vivo imaging of H 2 S-rich cancers. The subtle modulation of the equilibrium between hydrophilicity and lipophilicity by N-methylpyridinium endows these designed probes with the capability of spontaneously self-assembling into nanoprobes under physiological conditions. Such probes in an aggregated state, rather than a molecular dissolved state, show NIR fluorescence light up and photoacoustic signals turn on upon H 2 S specific activation, allowing in vivo visualization and differentiation of cancers based on differences in H 2 S content. Thus, our study presents an effective design strategy which should pave the way to molecular design of optimized probes for precision cancer diagnostics.

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