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Intraoperative imaging of slide-free specimens is crucial for oncology surgeries, allowing surgeons to quickly identify tumor margins for precise surgical guidance. While high-resolution ultraviolet photoacoustic microscopy has been demonstrated for slide-free histology, the imaging speed is insufficient, due to the low laser repetition rate and the limited depth of field. To address these challenges, we present parallel ultraviolet photoacoustic microscopy (PUV-PAM) with simultaneous scanning of eight optical foci to acquire histology-like images of slide-free fresh specimens, improving the ultraviolet PAM imaging speed limited by low laser repetition rates. The PUV-PAM has achieved an imaging speed of 0.4 square millimeters per second (i.e., 4.2 minutes per square centimeter) at 1.3-micrometer resolution using a 50-kilohertz laser. In addition, we demonstrated the PUV-PAM with eight needle-shaped beams for an extended depth of field, allowing fast imaging of slide-free tissues with irregular surfaces. We believe that the PUV-PAM approach will enable rapid intraoperative photoacoustic histology and provide prospects for ultrafast optical-resolution PAM. 

Micro- and nanorobots excel in navigating the intricate and often inaccessible areas of the human body, offering immense potential for applications such as disease diagnosis, precision drug delivery, detoxification, and minimally invasive surgery. Despite their promise, practical deployment faces hurdles, including achieving stable propulsion in complex in vivo biological environments, real-time imaging and localization through deep tissue, and precise remote control for targeted therapy and ensuring high therapeutic efficacy. To overcome these obstacles, we introduce a hydrogel-based, imaging-guided, bioresorbable acoustic microrobot (BAM) designed to navigate the human body with high stability. Constructed using two-photon polymerization, a BAM comprises magnetic nanoparticles and therapeutic agents integrated into its hydrogel matrix for precision control and drug delivery. The microrobot features an optimized surface chemistry with a hydrophobic inner layer to substantially enhance microbubble retention in biofluids with multiday functionality and a hydrophilic outer layer to minimize aggregation and promote timely degradation. The dual-opening bubble-trapping cavity design enables a BAM to maintain consistent and efficient acoustic propulsion across a range of biological fluids. Under focused ultrasound stimulation, the entrapped microbubbles oscillate and enhance the contrast for real-time ultrasound imaging, facilitating precise tracking and control of BAM movement through wireless magnetic navigation. Moreover, the hydrolysis-driven biodegradability of BAMs ensures its safe dissolution after treatment, posing no risk of long-term residual harm. Thorough in vitro and in vivo experimental evidence demonstrates the promising capabilities of BAMs in biomedical applications. This approach shows promise for advancing minimally invasive medical interventions and targeted therapeutic delivery. 

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED , leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE . In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN. 

Abstract The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.