Lumpectomy, also called breast-conserving surgery, has become the standard surgical treatment for early-stage breast cancer. However, accurately locating the tumor during a lumpectomy, especially when the lesion is small and nonpalpable, is a challenge. Such difficulty can lead to either incomplete tumor removal or prolonged surgical time, which result in high re-operation rates (~25%) and increased surgical costs. Here, we report a fiber optoacoustic guide (FOG) with augmented reality (AR) for sub-millimeter tumor localization and intuitive surgical guidance with minimal interference. The FOG is preoperatively implanted in the tumor. Under external pulsed light excitation, the FOG omnidirectionally broadcasts acoustic waves through the optoacoustic effect by a specially designed nano-composite layer at its tip. By capturing the acoustic wave, three ultrasound sensors on the breast skin triangulate the FOG tip’s position with 0.25-mm accuracy. An AR system with a tablet measures the coordinates of the ultrasound sensors and transforms the FOG tip’s position into visual feedback with <1-mm accuracy, thus aiding surgeons in directly visualizing the tumor location and performing fast and accurate tumor removal. We further show the use of a head-mounted display to visualize the same information in the surgeons’ first-person view and achieve hands-free guidance. Towards clinical application, a surgeon successfully deployed the FOG to excise a “pseudo tumor” in a female human cadaver. With the high-accuracy tumor localization by FOG and the intuitive surgical guidance by AR, the surgeon performed accurate and fast tumor removal, which will significantly reduce re-operation rates and shorten the surgery time.

The process of leaf senescence consists of the final stage of leaf development. It has evolved as a mechanism to degrade macromolecules and micronutrients and remobilize them to other developing parts of the plant; hence it plays a central role for the survival of plants and crop production. During senescence, a range of physiological, morphological, cellular, and molecular events occur, which are generally referred to as the senescence syndrome that includes several hallmarks such as visible yellowing, loss of chlorophyll and water content, increase of ion leakage and cell death, deformation of chloroplast and cell structure, as well as the upregulation of thousands of so-called senescence-associated genes (SAGs) and downregulation of photosynthesis-associated genes (PAGs). This chapter is devoted to methods characterizing the onset and progression of leaf senescence at the morphological, physiological, cellular, and molecular levels. Leaf senescence normally progresses in an age-dependent manner but is also induced prematurely by a variety of environmental stresses in plants. Focused on the hallmarks of the senescence syndrome, a series of protocols is described to asses quantitatively the senescence process caused by developmental cues or environmental perturbations. We first briefly describe the senescence process, the events associated with the senescence syndrome, and the theories and methods to phenotype senescence. Detailed protocols for monitoring senescence in planta and in vitro, using the whole plant and the detached leaf, respectively, are presented. For convenience, most of the protocols use the model plant species Arabidopsis and rice, but they can be easily extended to other plants.

Conventional methods for breast tumor margins assessment need a long turnaround time, which may lead to re-operation for patients undergoing lumpectomy surgeries. Photoacoustic tomography (PAT) has been shown to visualize adipose tissue in small animals and human breast. Here, we demonstrate a customized multimodal ultrasound and PAT system for intraoperative breast tumor margins assessment using fresh lumpectomy specimens from 66 patients. The system provides the margin status of the entire excised tissue within 10 minutes. By subjective reading of three researchers, the results show 85.7% [95% confidence interval (CI), 42.0% - 99.2%] sensitivity and 84.6% (95% CI, 53.7% - 97.3%) specificity, 71.4% (95% CI, 30.3% - 94.9%) sensitivity and 92.3% (95% CI, 62.1% - 99.6%) specificity, and 100% (95% CI, 56.1% - 100%) sensitivity and 53.9% (95% CI, 26.1% - 79.6%) specificity respectively when cross-correlated with post-operational histology. Furthermore, a machine learning-based algorithm is deployed for margin assessment in the challenging ductal carcinoma in situ tissues, and achieved 85.5% (95% CI, 75.2% - 92.2%) sensitivity and 90% (95% CI, 79.9% - 95.5%) specificity. Such results present the potential of using mutlimodal ultrasound and PAT as a high-speed and accurate method for intraoperative breast tumor margins evaluation.

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