Recent advancements in open-vocabulary 3D querying have achieved remarkable progress. However, existing approaches such as LERF and LangSplat still rely heavily on specific query text for accurate 3D target identification. They fail to accurately comprehend fuzzy query text (which describes a target's properties, functions, or traits rather than naming it directly), causing localization mistakes and poor 3D segmentation results. In this work, we introduce a novel task—open-vocabulary 3D fuzzy query—which aims to locate and generate precise 3D target masks based on fuzzy query text, a capability that is crucial for building a 3D intelligent system. To address this challenge, we proposeFuzzy Language Gaussian Splatting (FL-GS), a framework consisting of three key stages: We first leverage a multimodal large language model (MLLM) to identify and localize potential targets in representative views based on fuzzy query text, thereby generating initial masks using the segment anything model (SAM). Subsequently, we compute the pairwise similarity between all target masks and construct an undirected, unweighted graph, from which the maximum clique is identified, thus obtaining the most probable correct masks, referred to as refined masks. Finally, we propose single-dimensional mask encoding to efficiently achieve precise 3D target masks through supervision with refined masks. Furthermore, we manually annotated two key datasets and established a benchmark for this new task. Experimental results clearly demonstrate that FL-GS outperforms existing methods in open-vocabulary 3D fuzzy querying.

Cell rotation plays a crucial role in micromanipulation. Among manual cell rotation techniques, single-micropipette cell rotation is widely adopted due to its high efficiency and flexibility. However, there is currently no method capable of achieving automated single-micropipette cell rotation. In this study, we developed the first three-dimensional (3D) simulation system for single-micropipette cell rotation. Based on this simulation system, we successfully achieved single-micropipette cell rotation imitation learning (IL) for the first time. Specifically, we first analyze the forces acting on cells in the fluid, establishing a dynamic model that describes the cell's behavior in response to the flow velocity at the holding micropipette's orifice, the relative position of the micropipette, and time. We then developed the cell rotation simulation environment by discretizing the model and designing the simulation's cell and holding micropipette models based on real-world conditions. Finally, we designed a network architecture for IL using this model, achieving single-micropipette cell rotation in simulation. The results demonstrate that the simulation system exhibits a relative error range of 5.34% to 12.21% compared to real-world experiments, indicating a high degree of accuracy. Additionally, the single-micropipette cell rotation task achieved a success rate of 69% with an average completion time of 17.13 seconds, closely matching the expert data's average time of 17.69 seconds, confirming the feasibility of the simulation system.

Zebrafish are widely used in the biomedical field, as an ideal model for microinjection. In automated zebrafish microinjection, posture adjustment is the first and key step, which takes a lot of skill, and injection success assessment is a challenging task. Constrained by these two aspects, it is difficult to further enhance the efficiency and success rate of injection. In this study, we propose an automated dual-micropipette coordination microinjection system. Zebrafish are randomly arranged in our system, reducing the operational difficulty, and the yolk is positioned using a pose estimation algorithm, followed by injection accomplished with dual-micropipette. Due to the reduction of posture adjustment time by half, the proposed system achieves the shortest injection time of 15.2s. Moreover, the simplicity of the system and the ease of operation contribute to the clinical feasibility of our system.

During somatic cell nuclear transfer (SCNT), precise removal of the oocyte genetic material is critical. However, due to the invisibility of the genetic material (spindle) under brightfield observation and the potential displacement caused by the movement of micropipettes, accurately positioning the micropipette at the spindle poses a significant challenge. This study introduces an approach for optimal spindle removal by predicting its position. Initially, the polarization imaging system visualized the oocyte spindle, while a Multi-Feature Adaptive Kernel Correlation Filter (MFAKCF) algorithm tracked the spindle with 92.84% accuracy. Subsequently, enhancements were made to the Nonlinear Mass-Spring-Damper (NMSD) model to simulate live oocyte mechanical characteristics. Adjustments to NMSD model parameters simulated spindle displacement variations under diverse experimental conditions. Finally, the optimal spindle removal position was determined using NMSD model to simulate the micropipette approach to the spindle and the resulting position of spindle displacement. Experimental validation showed that the predictive accuracy of this model was 97.26%, with an average positional error of$0.4~\mu $m. Using this approach method can reduce cytoplasm loss to 4.5% and have a 100% enucleation success rate. Thus, the proposed prediction based optimal spindle removal position method can effectively anticipates spindle final positions, aiding in minimizing cytoplasmic loss during spindle removal. Note to Practitioners—Accurate oocyte nucleus removal is crucial for somatic cell nuclear transfer (SCNT) but challenging due to spindle invisibility under brightfield microscopy and displacement caused by micropipette movements. This study proposes a predictive method to address these issues. The polarization imaging system was used to visualize the spindle, and a MFAKCF tracking algorithm enabled real-time tracking. An enhanced NMSD model simulated spindle displacement, enabling accurate position prediction and reducing cytoplasmic loss during removal. This method offers a reliable tool for precise spindle removal in SCNT and broader applications in biomedical micromanipulation.

Zebrafish microinjection is widely used in vascular biology, neurology, and other research areas. The microinjection of zebrafish larvae is a complicated 3D task because zebrafish larvae are independent individuals with complex body structures compared with biological cells. In this study, we propose an automatic high-throughput injection system for zebrafish larvae under an optical inverted microscope. The proposed system combines the fixing device design, object detection, visual positioning, and 3D injection path planning to improve the survival rate of zebrafish after injection. Experimental results demonstrated the capability and efficiency of the proposed system, which achieved a success rate of 95.0% and a survival rate of 98.7% for zebrafish injection. The system can be applied to various biomedical and biochemical experiments.Note to Practitioners—Zebrafish larvae have long been an important model organism in neuroscience, biomedicine, and drug discovery. In various methods for external substance transportation into the zebrafish, microinjection is more efficient but also more difficult. At present, high-throughput injection of zebrafish larvae is mainly based on traditional manual operations. The injection speed and success rate of manual injection gradually decrease with the increase in zebrafish numbers. In this study, an automatic injection system is designed and implemented for high-throughput zebrafish larvae injection. We first use a simply designed mold to fix batch zebrafish larvae. Then we propose a coarse-fine two-step positioning method and perform a 3D injection path planning to achieve precise microinjection. The proposed injection system frees the operators from the repetitive and tedious injection work, providing technical support for the life science experiment.

Single-cell transportation is one of the most common cell operations. Transporting cells with micropipettes is convenient for a wide range of biomedical applications. For high-efficiency cell transportation, the cells must be aspirated into the orifice of a micropipette. However, this is very difficult to achieve, as there is relative movement between the cell and the culture medium when the fluid drives the cell in the culture medium. It is crucial to use cell dynamics rather than fluid dynamics as the control objects to improve control performance and stop the cell immediately when it approaches the micropipette. In this study, the cell dynamics were modeled using a second-order model by integrating the dynamic model between the fluid and the cell into a first-order fluid dynamic model. A backstepping controller-based extended state observer was proposed to control the cell movement inside the micropipette. Experiments demonstrated that the proposed controller could aspirate cells into the orifice of the micropipette with high accuracy and no overshoot. Furthermore, the proposed controller was applied to automated somatic cell nuclear transfer, and it significantly boosted operational efficiency. Note to Practitioners—The need to apply advanced automation methods to transfer cells in life sciences has increased at a steady pace. The key feature of such systems is the ability to select and transfer cells at a predetermined position in space and time for biological applications. We propose a cell positioning control method based on vision-guided robotics that can directly aspirate cells to specified positions near the orifice of a micropipette. In somatic cell nuclear transfer, the proposed method of transferring somatic cells into oocytes occurs at a faster pace than manual operation. This provides essential functionality for single-cell transfer and is an appropriate technology for practitioners with this functional requirement.

Cell rotation is widely used to adjust cell posture in sub-cellular micromanipulations. The trajectory planning of the injection micropipette is needed, so that the cells can be rotated with the minimum deformation to reduce cell damage and keep cell viability. Due to the uncertainty of cell properties and manipulation environment, it is difficult to identify the parameters of the mechanical models in traditional robotic cell rotation methods. In this paper, deep reinforcement learning is introduced into cell manipulation for the first time to perform trajectory planning of the micropipette. We first abstract the cell rotation process by using the mechanical model and microscopic vision techniques and build a cell rotation simulation environment. Then we design a reward function by combining various factors of cell rotation and implement a reinforcement learning framework based on deep Q-learning (DQL). Finally, we train the cell rotation process based on the deep reinforcement learning algorithm. The simulation results indicate the proposed DQL agent achieved an average success rate of 97% without useless exploration. Moreover, the proposed method rotated the cells in a way that causes less mechanical damage than humans, demonstrating the DRL ability for cell rotation with high efficiency and low cell damage.

Cell aspiration is a common technique in cell manipulation for cell transfer or intracellular property measurement. In this paper, we present a robotic micromanipulation system for cell aspiration and positioning by a micropipette. Considering the relative motion of the object and the fluid, we first establish an overall dynamic model of microbead motion inside and outside the micropipette based on computational fluid dynamics (CFD). Then we design an adaptive sliding mode controller (ASMC) for microbead aspiration outside the micropipette and positioning inside the pipette based on the dynamic model. The controller is proven to achieve asymptotic stability by Lyapunov techniques. Simulation and experimental results demonstrate the effectiveness of the fluid model and the performance of the designed control system. Note to Practitioners—Cell aspiration with a micropipette is a key technology in cell manipulation. Generally, there is relative motion between the aspirated object and the fluid, resulting in large overshoot even aspiration failure. In this paper, we set up an overall dynamic model of microbead motion inside and outside the micropipette, combining microbead motion dynamics, fluid dynamics and pneumatic pump modeling. Based on this model, we design an ASMC for microbead aspiration outside the micropipette and positioning inside the pipette. In simulations and experiments, the positioning errors of the microbead of different sizes converge to zero without overshoot, revealing the strong robustness of the controller. Applications for this technology include cell or sperm aspiration and injection.

Visual localization, which is a key technology to realize the automation of cell manipulation, has been widely studied. Since the depth of field of the microscope is narrow, the planar localization and depth estimation are usually coupled together. At present, most methods adopt the serial working mode of focusing first and then planar localization, but they usually do not have good real-time performance and stability. In this paper, a simultaneous depth estimation and localization network was developed for cell manipulation. The network takes a focused image and a defocus-offset image as inputs, and outputs the defocus in the depth direction and the offset in the plane at the same time after going through defocus-offset information extraction, defocus classification mapping and offset regression mapping. To train and test our network, we also create two datasets: An Adherent Cell dataset and an Injection Micropipette dataset. The experimental results demonstrated that the proposed method achieves the detection of all test samples with a frame rate of more than 40Hz, and the maximum errors of depth estimation and localization are$\boldsymbol{2.44\mu m}$and$\boldsymbol{0.49\mu m}$, respectively. The proposed method has good stability, which is mainly reflected in its strong generalization ability and anti-noise ability.

Somatic cell nuclear transfer (SCNT), which is an important procedure in cloning, has been conducted manually for decades. The operating efficiency drops sharply in batch SCNT because of the long-time observation under microscopy and the time-wasting traditional process. Though the operating time was reduced by robotic SCNT in previous studies, the traditional operating process was still used. In this article, we designed a new robotic batch SCNT process based on a microfluidic groove and two micropipettes in parallel. By using this new SCNT process, the operating area switching, objective lens conversing, and focusing on traditional SCNT process were eliminated, and oocyte localization was simplified, which saved much operating time. Experimental results showed that the new robotic batch process reduced about 50 s (41.7%) compared with the manual process (proposed 70 s versus manual 120 s). A success rate of 93.3% (n = 30) and a survival rate of 96.4% were achieved (n = 28), which were similar to manual process. The new robotic batch SCNT method demonstrated a high degree of efficiency and reproducibility.

Batch-cell manipulation is a key technology in biological applications. Robotic manipulation has important significance to improve the operation success rate and reduce the technical threshold, but the problem of inefficiency still exists in batch-cell experiments. In this paper, an automated cell transportation system is designed for batch-cell manipulation. It has some technical aspects such as a cell groove to contain the cells, the micromanipulator and motor stage control methods, and computer vision algorithms. Since the cells are arranged in a line in the groove, the transportation system improves the efficiency of finding the cells in the petri dish. Furthermore, the minimum pressure to drag and release the cell are analyzed theoretically, so that the other cells will not be affected when manipulating one cell. The visual algorithms to detect the cell position and cell holding state are evaluated by porcine oocyte. Experimental results show both algorithm has high success rates: 96% and 100%. Finally, cell rotating experiments are introduced to verify the effectiveness of the transportation system. The average transfer efficiency has been improved by 20% compared to manual operation. The results show that this system can be used in many manipulations.

In this paper, nn innovative and practical authentication system, Anonymous micropayments authentication (AMA), is designed for micropayments in mobile data network. Through AMA the customer and the merchant can authenticate each other indirectly, at the same time the merchant doesn't know the customer's real identity. A customer can get fast micropayemts not only from his local domain but also from a remote domain without increasing any burden on his mobile phone/smartcard. Furthermore, without increasing communication overheads in the air, computational overheads on the mobile phone/smartcard, which usually has limited computational capability and storage, is minimized.