Double patch clamp technique, using two micropipette electrodes to patch and measure electrophysiological signals of two neurons, is essential for investigating the functional connections between neurons in brain. However, the interactive mechanical disturbances from the dual-micropipette motions inside viscoelastic brain tissue cause dynamic drifts of neurons, making double patching low efficiency and challenging. In this paper, an interactive mechanical modeling of two micropipettes approaching two cells in elastic environment was established to estimate the dynamic drift of the cells. Based on that, a synchronous descent strategy, an appropriate relative position of two cells, and an online trajectory plan of two micropipettes were determined to improve double patch clamp efficiency. Finally, a robotic double patch clamp operation process was established for functional connectivity measurement between neurons in brain slice. The effectiveness of the proposed work is validated through both finite element modeling and experiments. The double patch clamp experiments on neurons in visual cortex demonstrate that our method achieves a 40% improvement in success rate and a 38% improvement in speed in comparison to the traditional manual method. With the above advantages, diverse functional connectivity activities between neurons were found using our system, paving a solid ground for further research.

Physical parameters of the intracellular environment such as mass density, intracellular pressure and elasticity have significant effects on the physiological activities of the cell and intracellular operation results. However, the significantly different measurement principles of the above parameters make it a challenging task for in situ measurement of them for the same cell, which significantly limits the study of their comprehensive regulation mechanisms to cell physiological activities and intracellular operation results. For the first time, a robotic in situ measurement system of multiple intracellular physical parameters is proposed based on a self-developed three-micropipettes system in this paper. Using this system, the mass density, elasticity and intracellular pressure of the same cell are measured automatically in sequence, according to a robotic in situ measurement process. Experimental results on sheep oocytes demonstrate an 83.3% measurement success rate at an average speed of 97.75 s/cell. The measurement results of the above three parameters are close to the reported results of individual, while with a significantly shorter operation time than theirs combined in references. Our system lays a solid foundation for the future research on the comprehensive regulation mechanism of these parameters to cell physiological activities and intracellular operation results.

Polar body biopsy has been widely applied in preimplantation genetic diagnosis for assisted reproductive technology. The key step in the polar body biopsy is picking up the polar body from the oocyte/embryo using a micropipette. Unfortunately, the almost transparent appearance of the polar body as well as its dynamic drift when the micropipette approaches it inside the cell makes it a challenging task to pick it up with less cytoplasm loss for the cell. The unnecessary cytoplasm loss in the picking up process of the polar body easily causes damage to the development competence of the cell and may lead to disturbances to the biopsy results of the polar body. This paper proposes a precise robotic picking up method of polar bodies with less cytoplasm loss for biopsy purposes. First, a defocus imaging method is proposed to locate the polar body with an almost transparent appearance. Then, the dynamic drift of the polar body with the micropipette moving inside the cell is modeled online based on force analysis to determine an appropriate trajectory for the micropipette to approach the polar body. Further, an Active Disturbance Rejection Controller (ADRC) is designed to move the micropipette along the desired trajectory to approach the polar body and then aspirate it into the micropipette. The experimental results on porcine oocytes demonstrate that our system is capable of localizing the polar body with a success rate of 95% and an average error of$1.12\pm 0.14~\mu $m. Moving along the determined trajectory, the micropipette is capable of approaching the edge of the polar body with an average error of$1.84\pm 0.31~\mu $m (n =20), which is only 11% of the results obtained without dynamic drift estimation of the polar body. With this advantage, our system picks up the polar body with a close 60% improvement in success rate (95% vs 60%) and only half of the average cytoplasm loss (5% vs 10%) in comparison to operation results without dynamic drift estimation. Note to Practitioners—Picking up of polar body from the oocyte/embryo using a micropipette is a vital operation in the polar body biopsy. Precisely picking up the polar body with less cytoplasm is vital to maintaining the developmental competence of the embryo/oocyte and reducing disturbances to biopsy result. This article presented a precise robotic picking up process of polar body. This process introduced defocus imaging method for polar body localization, dynamic drift estimation of polar body, and micropipette trajectory design and motion control by Active Disturbance Rejection Controller (ADRC). Experimental results have demonstrated the efficiency of the proposed robotic picking up process. Application of this process may provide an economical and practical method to carry polar body biopsy for practitioners.

Saturation and porosity are important indicators for the quantitative assessment of reservoir fluids. With the progress of seismic inversion methods, reconstructing saturation and porosity from seismic elastic parameters has become one of the most effective approaches. Theoretically, full waveform inversion (FWI) is the highest resolution method to reconstruct elastic parameters, and selecting appropriate parameters can enhance the accuracy of fluid monitoring. For partially saturated rocks, both P-wave velocity (Vp) and attenuation are sensitive to variations in fluid saturation and porosity. As a result, we can estimate the fluid state by inverting Vp, quality factor (Q), fluid saturation, and porosity. We propose a rock physics model constrained viscoacoustic FWI (RP-QFWI) method to estimate the fluid saturation and porosity directly from seismic records. Specifically, we use the extended Gassmann equation to constrain viscoacoustic FWI (QFWI). We evaluate the performance of RP-QFWI using the classic Frio-II CO2 injection model, a CO2 injection model based on an oil field, as well as a field seismic data. The results indicate that, for elastic parameters, the rock physics prior constraints improved the inversion accuracy and stabilized the inversion of weak parameters Q. For reservoir parameters, combining the two-step process into a single inversion method fully exploits the information in the seismic data, leading to more accurate inversion results.

The ocean bottom node (OBN) seismic acquisition system is designed to gather high-fidelity, wide-azimuth, and long-offset four-component (4C) data, which includes shear waves and enables the use of the elastic assumption in imaging and inversion. However, deploying geophysical instruments on the seafloor is difficult and costly, leading to the usual adoption of sparse node spacing. This can, however, lead to poor illumination and imaging challenges, especially in the shallow subsurface near the seafloor. To address these issues in the context of 4C elastic imaging, we propose a deep learning-based method using a multi-scale convolutional neural network (Ms-CNN) to improve the imaging quality of OBN surveys with sparse data acquisition. As an alternative to interpolating the sparse seismic data in the data domain, which can be a challenging task due to the limitations attributed to sampling theorem and the often larger amounts of data compared to the image, we train an Ms-CNN in a supervised fashion to map from sparse data images of PP and PS sections produced by 4C Gaussian beam migration to the equivalent dense data images, allowing for the direct processing of sparse data to improve imaging quality. Here, we combine the mean absolute error and multiscale structure similarity index measure in the loss function to optimize the network’s training process, and to help improve the performance. The effectiveness of the method is demonstrated through experiments on synthetic and field data, resulting in improved event continuity and reduced noise in migration results from sparse OBN acquisitions.

The ocean bottom nodes (OBNs) acquire seismic data at a challenging depth to explore the subsurface structures. The recorded body wave and Scholte wave are highly mixed and are difficult to be separated. The strong body wave would influence the high-order modes extraction using the Scholte wave, while the Scholte wave would degrade the imaging of sedimentary structures using body wave. The lacking of effective methods for separating both waves prevents their application. We developed a migration-based method to accurately separate the Scholte wave and body wave in the OBN data. First, we use high-pass filtering to divide the original OBN data into three parts: background noise, high-frequency body wave, and the mixture of Scholte wave and low-frequency body wave. Then, we separate the Scholte wave and low-frequency body wave using migration and demigration based on the fact that they have different limits of reversible-migration velocity. Finally, we generate the separated body wave by subtracting the Scholte wave from the denoised OBN data. For the off-line data, the local orthogonalization method is required to retrieve the weak leakage of Scholte wave around the apices. Theoretical analyses and numerical experiments show that the proposed method can accurately separate Scholte wave and body wave without any visible artifacts while retaining most of their inherent properties. The separated body wave provides a high-quality input for imaging sedimentary structures, and the separated Scholte wave enables the extraction of high-order modes of dispersion curve that are crucial for high-resolution surface-wave inversion.