Visualizing drug effects over time in live animals using optical pharmacodynamics
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Optical sorting: past, present and future
Optical sorting combines optical tweezers with diverse techniques, including optical spectrum, artificial intelligence (AI) and immunoassay, to endow unprecedented capabilities in particle sorting. In comparison to other methods such as microfluidics, acoustics and electrophoresis, optical sorting offers appreciable advantages in nanoscale precision, high resolution, non-invasiveness, and is becoming increasingly indispensable in fields of biophysics, chemistry, and materials science. This review aims to offer a comprehensive overview of the history, development, and perspectives of various optical sorting techniques, categorised as passive and active sorting methods. To begin, we elucidate the fundamental physics and attributes of both conventional and exotic optical forces. We then explore sorting capabilities of active optical sorting, which fuses optical tweezers with a diversity of techniques, including Raman spectroscopy and machine learning. Afterwards, we reveal the essential roles played by deterministic light fields, configured with lens systems or metasurfaces, in the passive sorting of particles based on their varying sizes and shapes, sorting resolutions and speeds. We conclude with our vision of the most promising and futuristic directions, including AI-facilitated ultrafast and bio-morphology-selective sorting. It can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.
Airborne optical imaging technology: a road map in CIOMP
Airborne optical imaging can flexibly obtain the intuitive information of the observed scene from the air, which plays an important role of modern optical remote sensing technology. Higher resolution, longer imaging distance, and broader coverage are the unwavering pursuits in this research field. Nevertheless, the imaging environment during aerial flights brings about multi-source dynamic interferences such as temperature, air pressure, and complex movements, which forms a serious contradiction with the requirements of precision and relative staticity in optical imaging. As the birthplace of Chinese optical industry, the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) has conducted the research on airborne optical imaging for decades, resulting in rich innovative achievements, completed research conditions, and exploring a feasible development path. This article provides an overview of the innovative work of CIOMP in the field of airborne optical imaging, sorts out the milestone nodes, and predicts the future development direction of this discipline, with the aim of providing inspiration for related research.
All-optical superconducting qubit readout
The rapid development of superconducting quantum hardware is expected to run into substantial restrictions on scalability because error correction in a cryogenic environment has stringent input–output requirements. Classical data centres rely on fibre-optic interconnects to remove similar networking bottlenecks. In the same spirit, ultracold electro-optic links have been proposed and used to generate qubit control signals, or to replace cryogenic readout electronics. So far, these approaches have suffered from either low efficiency, low bandwidth or additional noise. Here we realize radio-over-fibre qubit readout at millikelvin temperatures. We use one device to simultaneously perform upconversion and downconversion between microwave and optical frequencies and so do not require any active or passive cryogenic microwave equipment. We demonstrate all-optical single-shot readout in a circulator-free readout scheme. Importantly, we do not observe any direct radiation impact on the qubit state, despite the absence of shielding elements. This compatibility between superconducting circuits and telecom-wavelength light is not only a prerequisite to establish modular quantum networks, but it is also relevant for multiplexed readout of superconducting photon detectors and classical superconducting logic.
Dopamine in the tail of the striatum facilitates avoidance in threat–reward conflicts
Responding appropriately to potential threats before they materialize is critical to avoiding disastrous outcomes. Here we examine how threat-coping behavior is regulated by the tail of the striatum (TS) and its dopamine input. Mice were presented with a potential threat (a moving object) while pursuing rewards. Initially, the mice failed to obtain rewards but gradually improved in later trials. We found that dopamine in TS promoted avoidance of the threat, even at the expense of reward acquisition. Furthermore, the activity of dopamine D1 receptor-expressing neurons promoted threat avoidance and prediction. In contrast, D2 neurons suppressed threat avoidance and facilitated overcoming the potential threat. Dopamine axon activation in TS not only potentiated the responses of dopamine D1 receptor-expressing neurons to novel sensory stimuli but also boosted them acutely. These results demonstrate that an opponent interaction of D1 and D2 neurons in the TS, modulated by dopamine, dynamically regulates avoidance and overcoming potential threats.
Innovating beyond electrophysiology through multimodal neural interfaces
Neural circuits distributed across different brain regions mediate how neural information is processed and integrated, resulting in complex cognitive capabilities and behaviour. To understand dynamics and interactions of neural circuits, it is crucial to capture the complete spectrum of neural activity, ranging from the fast action potentials of individual neurons to the population dynamics driven by slow brain-wide oscillations. In this Review, we discuss how advances in electrical and optical recording technologies, coupled with the emergence of machine learning methodologies, present a unique opportunity to unravel the complex dynamics of the brain. Although great progress has been made in both electrical and optical neural recording technologies, these alone fail to provide a comprehensive picture of the neuronal activity with high spatiotemporal resolution. To address this challenge, multimodal experiments integrating the complementary advantages of different techniques hold great promise. However, they are still hindered by the absence of multimodal data analysis methods capable of providing unified and interpretable explanations of the complex neural dynamics distinctly encoded in these modalities. Combining multimodal studies with advanced data analysis methods will offer novel perspectives to address unresolved questions in basic neuroscience and to develop treatments for various neurological disorders.
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