Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative omics technology for cell type identification in cancer diagnostics, enabling high-throughput parallel generation of cellular-resolution data that revolutionizes precision medicine. However, scRNA-seq data are frequently compromised by prevalent drop-out events due to limitations in sample quality and technical bottlenecks, which cause a large number of the results lost. To address this challenge, we propose a nonlinear imputation via state transition process (NISP) method for the diffusion and imputation of missing values in single-cell sequencing data. Our results demonstrate that the NISP framework effectively preserves nonlinear characteristics inherent to biological state transitions, enabling to recover more than 50% of missing values and remove more than 98% of noise. Therefore, NISP exhibits superior sensitivity in missing value imputation and significantly enhances the structural clarity of post-imputation datasets. Finally, validation using spatiotemporal transcriptomic arrays derived from colorectal cancer organoids further corroborates the capability of NISP to accurately capture the intrinsic manifold structure of cellular states. The result shows the significant potential of NISP in biological applications, notably its pivotal role in elucidating the mechanisms driving tumorigenesis and cancer progression.

Engineered assembloids fabricated from tissue spheroids hold immense promise for developmental biology, disease modeling, and regenerative medicine. However, fabricating heterogeneous assembloids with precise spatial patterning remains a critical bottleneck, often reliant on manual pipetting that lacks scalability and reproducibility. While magnetic microrobotics, which transforms spheroids into controllable robots, offers a non-invasive alternative, it faces a fundamental challenge: the global input of magnetic actuation. A single command moves all robots simultaneously, leading to coupled motion and frequent assembly failures. Here, we present a collaborative control framework that overcomes this limitation by leveraging local constraints and a novel motion decoupling strategy. We reformulate the high-dimensional, coupled multi-robot planning problem into a low-dimensional aggregate space, effectively transforming the assembly task into a dynamic sequential decision problem. This framework, coupled with a graph-based dynamic path optimization algorithm, enables deterministic, collision-free assembly. Experimental validation demonstrates a 78.57% higher success rate and a 33.20% higher assembly efficiency compared to conventional strategies. This work establishes a foundational engineering principle for assembloid fabrication, transitioning the process from a qualitative experiments to a controllable and programmable engineering discipline, thereby unlocking the potential for deterministic construction of complex biological structures.

Organoids are cell clusters cultured in vitro that maintain the structure and function of the donor organs. They have found important applications in biomedicine, such as drug screening and personalized therapy. However, conventional organoid culture methods lack control of physical properties like size and distribution, leading to increased heterogeneity and very low batch-to-batch reproducibility, which significantly limits their widespread use. Controlling these properties at the microscale is challenging, particularly for fragile fragments, which are the main source for culturing organoids. To address this issue, we present a robotic micromanipulation system that allows operators to select fragments of particular sizes and automatically transfer them into a customized in-situ organoid chip (IOC) for culture. The chip was designed with microwell arrays to uniform the culture environment and facilitate imaging analysis. The transfer of fragments is modeled based on computational fluid dynamics (CFD) and is enabled by designing a robust model predictive control (RMPC) framework. Simulation and experiment results demonstrated the effectiveness of the model and controller. In colorectal cancer organoid culture experiments, our system significantly improved the morphological homogeneity of organoids. Note to Practitioners—Organoids have been demonstrated to be one of the most promising in vitro models. Lacking control of its size and distribution results in significant heterogeneity and low batch-to-batch reproducibility, which limits its wide uses. Here, we report a robotic micromanipulation system that allows operators to select fragments of particular sizes and morphologies and automatically transfer them into a customized organoid chip for culture. The results of colorectal cancer organoids culture experiments verified the effectiveness of our system in reducing the morphological heterogeneity among organoids.

In three-dimensional cell culture, cells have the potential to form multicellular tissue spheroids through self-organization, which can be used to study human development and disease in-vitro, and to screen drugs for toxicity and efficacy. Current approaches are limited by intensive manual manipulation, and the uneven cell distribution caused by sedi-mentation, aggregation, and compaction of cells in bioprinting will affect the consistency of spheroid. Here, we develop an automated micromanipulation platform to control the consistency of the initial state and generate uniform cell clusters. To control spheroid formation, the implementation of such a system is dominated by real-time count of moving cells in micropipette, and the dynamic control of cells moving with a randomly distributed probability density. As an example application, we seeded 293T cells in microcavity. The accuracy of total cell count of the closed-loop system reached 95.47%. Additionally, the system was shown to be effective in preventing cell overflow. F or formed cell spheroids, the system prevented the creation of companion spheroids and improved spheroid consistency from 68.74% to 91.04%. This platform facilitates the use of spheroids for a wide range of biomedical applications such as organ development and precision drug screening.

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects’ identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of$102~{\mathrm{\mu m}}$, and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.Note to Practitioners—This article aimed to tackle the problem of automated tracking groups of zebrafish larvae, an ideal vertebrate model organism for large-scale chemical and genetic screens. The task of group tracking is to record each individual’s movement and calculate their position, velocity, direction, and other parameters for further analysis, where the correct identity of each individual must be maintained. Existing algorithms either switch larvae’ identities easily or are unable to achieve online tracking due to the limitations of their methods to address individuals’ intersections. DanioSense (DS) adopts a convolutional neural network to identify larval heads whenever they intersect and uses an adaptive Kalman filter to calculate the movement parameters optimally. Besides, a range of visualization options is designed to bring insight into underlying patterns through massive amounts of data. Theoretically, this algorithm’s approach to solving intersections and calculating movement statistics can also apply to other fish-like animals. Its visualization options are applicable to other tracking systems. The key advantage of Daniosense over existing trackers is the capability to track each larva within a group and output detailed quantitative data in nearly real time. The tracking performance of DS is based on the quality of image segmentation and the success rate of classifying samples. Many state-of-the-art image segmentation and classification neural networks can be adopted to extend this system’s applications to more complex environments but at a higher computation and time cost, which is a tradeoff between efficiency and capability. Some applications require a higher video sampling rate, so the system’s processing speed needs to be further improved with better hardware and software framework optimization. The next steps include improving the processing efficiency, providing more tracking modules and visualization options, and extending its application fields.

Predators in nature grip their prey in different ways, which give innovational ideas of gripping approaches in industrial applications. Octopus performs flexible gripping with the help of vacuum grippers, suction cups, which inspired a new type of microgripper for biological sample micromanipulation. The proposed gripper consists of a glass pipette and a pump driven by a step-motor. The step-motor is controlled with adaptive robust control to adjust the gripping pressure applied on the biological sample. A dynamic model is developed for the biological sample aiming for better deformation control performance. A visual detection algorithm is developed for data processing to identify the parameters in the dynamic model and the detection result of visual algorithm is also used as feedback of adaptive robust control, which diminishes the negative influence of parameter and model uncertainties. Zebrafish larva was used as the testing sample for experiment and the corresponding parameters were identified experimentally. The experimental results correlated well with the model predicted deformation curve and visual detection algorithm provided promising accuracy, which is less than [Formula: see text]. Adaptive robust control provides fast and accuracy response in point-to-point deformation testing, and the average responding time is less than 30 s and the average error is no larger than 1 pixel.

This article presents an automated strategy to touch the injection site on zebrafish larva skin with the injection pipette tip accurately in the presence of water-depth variation, which is a crucial problem to automate zebrafish larva microinjection. The presented method consists of two parts: adaptive coordinate transformation and curve evolution for edge detection. In the first part, the impact of refraction is taken into consideration. An adaptive calibration method is developed, which enables the coordinate transformation matrix to adapt to the changing water depth. In the second part, the abovementioned calibration result is used to keep the injection pipette tip descending along the desired route. A curve-evolution-based edge detection algorithm is introduced to detect the deformation of larva skin caused by contact with the injection pipette tip. Experimental results demonstrate that high accuracy and success rates are achieved. The effect of uncertainties caused by water-depth variation and the skill requirement in manual manipulation are eliminated. The proposed contact detection strategy can be extended to microinjection for other organisms.Note to Practitioners—As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. For studying drug toxicity and disease models, exogenous substances need to be injected into zebrafish larvae. However, for both manual and automated injection, a fatal problem is that the camera on the microscope only provides 2-D positional information. It is laborious to align the pipette tip with the injection site along the$z$-axis. Moreover, due to the characteristic of stereomicroscopes, the impact of refraction at the water surface cannot be ignored. In order to address these issues, in this article, we present an adaptive calibration method and an edge detection algorithm for zebrafish larva heart injection to avoid contact failure in practical implementations.

In the image based visual servo, image Jacobian is vital to the system performance because it is the bridge that warps the velocity in feature space to camera velocity in Cartesian space. However, image Jacobian is sensitive to the camera intrinsic parameters, while the camera intrinsic parameter calibration error is almost unavoidable, which heavily affects the image Jacobian and visual servo process. In this paper, we find the camera intrinsic parameter invariance of the image Jacobian in our 4 DOF visual servo system. The image Jacobian of some geometry features is invariant to the camera intrinsic parameters, indicating that the disturbance in camera intrinsic parameters will not affect the convergence trajectory of those features. To further analyse camera intrinsic invariance, we proposed camera intrinsic parameter Jacobian of Image Jacobian, which can fully describe the camera intrinsic parameter invariance of the image Jacobian. The work in this paper can be used to analyse the system sensitivity to the camera intrinsic parameters. The camera intrinsic invariance is also significant for choosing the visual servo features when designing the visual servo system.

Pneumatic actuators gain much popularity in many industries where there is great demand for a safety working environment and dynamic performance of a system. But the nonlinear characteristics such as friction and air compressibility add to difficulty of controlling so that constrain its wider application. In this paper, in order to overcome the disadvantage like the inaccuracy of parameters, uncertainty of the model and disturbance, a sliding mode observer with RBF neural network is proposed. The RBF neural network is designed to appropriate the nonlinear parts of the model, and the robustness of sliding mode control can guarantee the stability of control system under perturbation and model uncertainty. The stability of this algorithm is proved by Lyapunov theory. Finally, simulations done with Simulink is designed to examine the effectiveness of our algorithm. The result shows this algorithm has good performance.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

In this paper, we propose a force feedback scheme for Delta device to improve precision and stability in master-slave teleoperation. A simple and exact dynamical equation is created with principle of virtual work, which is easy to calculate in real-time. After analysing three items deep in dynamical equation, a reasonable strategy is designed to identify the mass of Delta mechanism. The identified parameters and dynamical equation are verified correct in ADAMS and MATLAB softwares. Simultaneously, following previous work on haptic interface, a suitable Kalman Filter algorithm is proposed to attain smooth contact force in real-time based on the impedance of environment, and simplified due to the short period of a cycle. Finally, a whole master-slave system is set up with CHAI3D toolkit, which consists of Phantom Omni, Computer and Delta device. With a force sensor mounted on the end, the contact force is measured in practice and then filtered with proposed algorithm. The final result shows that the estimated curve followed measured data well and lied at the center of original curve.