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Loading individual oocytes is a critical step for injecting RNA, expressing heterologous proteins, performing electrophysiological measurements, and so on. However, existing methods remain a challenge for automatically loading multiple single oocytes into different locations. Herein, a novel microfluidic chip on a robotic manipulator (chip‐on‐robot) with feedback control for flexible manipulation of multiple oocytes within a large spatial range is proposed. The manipulator automatically controls the microfluidic chip to reach different locations based on imaging feedback. The microfluidic chip then utilizes the hydrodynamic focusing effect of the main channel to separate oocytes for individual loading or reloading under capacitive sensor feedback. The separation distance reaches approximately 16 times the oocyte diameter. Moreover, capacitive signal feedback on the number of oocytes for flow direction control ensures the separation of all oocytes. For close‐loop control of the loading/reloading process, image‐based oocyte detection is combined using deep learning to calculate the target position of the oocyte. Finally, an automatic sequence is achieved to load multiple single oocytes into a well chip by using the chip‐on‐robot. As a demonstration, the oocytes are reloaded into a specified location based on the conditions. The proposed chip‐on‐robot with feedback control has significant advantages in the micromanipulation of oocytes. [ABSTRACT FROM AUTHOR]

Laser transmission welding is a highly accurate method for joining plastics, but its diverse process parameters require effective modeling for optimal results. Traditional artificial neural networks (ANNs) typically establish predictive models between laser processing parameters and welding strength, neglecting the crucial role of welding morphology in feature extraction, thus diminishing accuracy. To address this, we developed a serial ANN model based on statistically evident correlations, which predicts joint morphology and strength sequentially, resulting in a 47% improvement in predictive accuracy and a mean error of just 7.13%. This two-layered approach effectively reduces the stepwise propagation of errors in ANNs, allowing the first layer to provide a refined data representation for the second layer to predict welding strength. Furthermore, finding the optimal laser parameter set is time-consuming and computationally demanding with traditional ANN-based optimization methods. To address this, we integrated the Markov decision process with the serial ANN for the first time and proposed a novel varying step strategy for the model, enabling a balance of swift convergence and avoidance of suboptimal solutions. Notably, the Markov-serial ANN model attained enhanced optimization results using only 15.5% of the computational resources required by a standard parameter interval optimization methodology. Welding experiments verified the reliability of the Markov-serial ANN, achieving a mean error of 4.54% for welding strength. [ABSTRACT FROM AUTHOR]

The osmotic stress imposed on microorganisms by hypotonic conditions is perceived to regulate water and solute flux via cell membranes, which are crucial for survival. Some cells that fail to perceive osmotic stress die because this results in the rupture of the cell membrane. The flux through the membrane is characterized by the membrane permeability, which is measured using a stopped-flow apparatus in response to a millisecond-order osmolarity change. However, the obtained data are an ensemble average of each cell response. Additionally, the measurement of permeability, considering cellular viability, contributes to a more accurate evaluation of osmoadaptation. Here, we present a novel on-chip instantaneous extracellular solution exchange method using an air–liquid interface. The presented method provides a concurrent evaluation at the single-cell level in response to a millisecond-order osmotic shock, considering cellular viability by solution exchange. This method utilizes a liquid bridge with a locally formed droplet on the surface of a micropillar fabricated inside a microchannel. We evaluated a solution exchange time of 3.6 ms and applied this method to Synechocystis PCC 6803 under two different osmolarity conditions. The live/dead ratio of 1 M to 0.5 M osmotic down shock condition was 78.8/21.2% while that of 1 M to 0.25 M osmotic down shock condition was 40.0/60.0%. We evaluated the water permeability of two groups: cells that were still live before and after osmotic shock (hereafter named cell type 1), and cells that were live before but were dead 10 minutes after osmotic shock (hereafter named cell type 2). The results indicated that the water permeability of cell type 2 was higher than that of cell type 1. The results obtained using the presented methods confirmed that the effect of osmotic stress can be accurately evaluated using single-cell analysis. [ABSTRACT FROM AUTHOR]