In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent

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Nanobodies, also known as VHHs, originate from the serum of Camelidae. Nanobodies have considerable advantages over conventional antibodies, including smaller size, more modifiable, and deeper tissue penetration, making them promising tools for immunotherapy and antibody-drug development. A high-throughput nanobody screening platform is critical to the rapid development of nanobodies. To date, droplet-based microfluidic systems have exhibited improved performance compared to the traditional phage display technology in terms of time and throughput. In realistic situations, however, it is difficult to directly apply the technology to the screening of nanobodies. Requirements of plasma cell enrichment and high cell viability, as well as a lack of related commercial reagents, are leading causes for impeding the development of novel methods. We overcame these obstacles by constructing a eukaryotic display system that secretes nanobodies utilizing homologous recombination and eukaryotic transformation technologies, and the significant advantages are that it is independent of primary cell viability and it does not require plasma cell enrichment in advance. Next, a signal capture system of "SA-beads + Biotin-antigen + nanobody-6 × His + fluorescence-labeled anti-6 × His (secondary antibody)" was designed for precise localization of the eukaryotic-expressed nanobodies in a droplet. Based on this innovation, we screened 293T cells expressing anti-PD-L1 nanobodies with a high positive rate of targeted cells (up to 99.8%). Then, single-cell transcriptomic profiling uncovered the intercellular heterogeneity and BCR sequence of target cells at a single-cell level. The complete complementarity determining region (CDR3) structure was obtained, which was totally consistent with the BCR reference. This study expanded the linkage between microfluidic technology and nanobody applications and also showed potential to accelerate the rapid transformation of nanobodies in the large-scale market.

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Nowadays, the society has entered the digital information age, and the information contained in the image is far more than the sum of the information contained in other media. In the Internet industry, image processing technology can be used to quickly find the required picture information. Other applications include disaster prevention, industrial automation production lines, semiconductor, electronics, tobacco, and food industries. After the meter glyph spot image is collected, there are several spots in the image, and the corresponding pixel values are stored in memory. In order to process images, they should be distinguished and marked so that the spot has definite eigenvalues. To this end, this paper proposes an image processing method. Firstly, an image denoising method combining self-snake model and P-M model is introduced. Secondly, the recursive HOSVD dimensionality reduction algorithm based on tensor product model is used to further process the image. The center of the Hartmann aperture image is solved by the centroid of all the spots, and the center overlap algorithm for determining the centroid distance of the aperture image by the symmetry of the centroid of the spot centroid can reduce the number of calculations. The experimental results show that this method can effectively identify and process the spot of the image and greatly reduce the time complexity and computational complexity of the algorithm.

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