Underwater salient object detection (USOD) poses a significantly greater challenge than traditional terrestrial scenes, due to both the complex image degradation and the absence of multimodal information in underwater environments. Existing image enhancement methods are not specifically optimized for USOD, while current USOD approaches rarely consider effective extraction and utilization of multimodal information, leading to limited performance. This paper proposes HydroDepthAwareNet (HDANet), which addresses these challenges through developing targeted designs to enhance USOD performance. It first integrates a task-driven underwater image enhancement module, named HydroDepthEnhanceModule (HDEM), which is based on physical models to provide enhanced images and multimodal information optimized for USOD tasks. Furthermore, we develop a physics-inspired three-way unsupervised learning strategy, leveraging the complementary effects of re-enhancement and re-degradation to improve HDEM’s generalization across diverse underwater image degradation scenarios. Additionally, we design a robust cross-attention (RCA) module to effectively fuse multimodal features while mitigating noise and blurring by exploiting channel and spatial cross-attention mechanisms. Extensive experiments on various USOD datasets demonstrate that the proposed HDANet significantly outperforms existing state-of-the-art methods. The source code will be made available at https://github.com/mikurules/USOD-HDANet.

Objective: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice. Methods: First, a kinematic model of a dual-arm patch clamp system combined with Monte Carlo method is developed to calculate the workspaces of recording micropipette and stimulation micropipette, and optimize the length of end effector for reducing collision incidences during operation. Then, a quantitative stimulation method to cells using one micropipette is developed based on pressing depth control. Finally, a fast robotic dual-arm patch clamp operation process is proposed based on a three-stage motion control of dual micropipettes to approach target cells and form whole-cell recording with quantitative mechanical stimulation. Results: Experimental results on 50 pyramidal neurons in the primary visual cortex of mouse brain slices demonstrate that this system achieves a threefold throughput with a 37% improvement in the success rate of the contact process and a 42% improvement in the success rate of whole-cell recording in comparison to manual operation. With these advantages, a mechanical stimulation-regulated increase in neuron excitability is observed in primary visual cortex. The experimental results also show that the sodium ion current may be more sensitive to mechanical stimulation than potassium ion current. Conclusion: Our system significantly improves the efficiency of mechanical stimulation induced excitability research of neurons in brain slices. Significance: Our methods have the potential to investigate pathological and pathogenic mechanisms of mechanosensitive ion channel dysfunction-induced diseases in the future.

The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a live brain and their intercellular connectivity in comparison to cultured single neurons. However, the narrow working space, small scope, and depth of the field of view make the positioning of the operation’s micropipette to the target neuron a time-consuming task reliant on a high level of experience, significantly slowing down operation of the patch clamp technique in brain slices. Further, the current poor controllability in gigaseal formation, which is the key to electrophysiology signal recording, significantly lowers the patch clamp success rate. In this paper, a stepwise navigation of the micropipette is conducted to accelerate the positioning process of the micropipette tip to the target neuron in the brain slice. Then, a fuzzy proportional–integral–derivative controller is designed to control the gigaseal formation process along a designed resistance curve. The experimental results demonstrate an almost doubled patch clamp technique speed, with a 25% improvement in the success rate compared to the conventional manual method. The above advantages may promote the application of our method in brain science research based on brain slice platforms.

The inside-out patch clamp technique has been widely applied in brain science and neuroscience research due to its ability to detect extremely weak currents flowing through a single ion channel. The current manual inside-out patch clamp operations are highly expertise-requisite and low efficient. Meanwhile, the existing robotic systems are only applicable for suspended cells due to their new system setups. For the first time, this letter proposed a robotic inside-out patch clamp system for adherent cells based on vesicle rupture control. Firstly, impedance models were established to detect the vesicle rupture state. Then, a force analysis that combines the defocusing imaging model was developed to precisely control the exposure time of the vesicle in the air, which is a key factor in the rupture process of the vesicle. Based on the above works, a robotic inside-out patch clamp process for adherent cells was established. Experimental results demonstrate that the proposed robotic system can detect vesicle rupture state with a 100% success rate, control exposure time with an average error of 0.02$\,\text{s}$ and operate adherent HEK-293 cells with a success rate of 70% at an average operation speed of 61.3$\,$seconds/cell. The success rate of our system is more than three times that of manual operation results, laying a solid foundation for subsequent single ion channel functionality research.