7results about How to "Improve extraction ability" patented technology

The application relates to the technical field of archaea DNA extraction, in particular to a preparation method and application of auxiliary wall-breaking nanometer magnetic beads for archaea DNA extraction. The method comprises the following steps: (1) constructing an inverse emulsion droplet microfluidic chip: washing, drying and hydrophobic modification treatment are performed on a PDMS base material to prepare a microfluidic chip; (2) preparing an inverse microemulsion system: the continuous phase and the dispersed phase are respectively pumped into the microfluidic chip at a predetermined flow rate, so that Fe 2+ and Fe 3+ undergo a coprecipitation reaction to generate Fe3O4 nanoparticles; (3) maturation crystallization; (4) demulsification separation and washing; and (5) drying to obtain auxiliary wall-breaking nanometer magnetic beads. The method has the beneficial effect of solving the problems in the prior art that the cell wall is difficult to effectively break, the DNA release rate is low, the extraction purity is poor, the repeatability is insufficient and the standardization and continuous preparation are difficult to realize when DNA extraction is performed on archaea samples in extreme environments.

The application discloses a method for extracting antidepression active Euphausia superba oil by adopting subcritical combination solvent, and belongs to the technical field of deep processing of Euphausia superba. The method is a three-stage gradient extraction method of "subcritical butane-acetone-ethanol", and realizes differential separation and enrichment of lipid components by using solvent polarity difference. The first-stage subcritical butane extraction is carried out under low temperature, closed and oxygen isolation conditions, and high-efficiency desolventizing is realized by using reduced pressure gasification, so that the heat-sensitive active ingredients are effectively reserved, and meanwhile, oxidation and metamorphosis of raw materials are avoided, thereby laying a foundation for preparation of subsequent high-quality extracts. The method successfully prepares three Euphausia superba oil products with significant component differences, and especially, the third-stage extract and the enzymatic modification product thereof exhibit excellent antidepression activity. The application realizes functional separation of Euphausia superba lipid components, and provides strong support for development of personalized and diversified antidepression products.

The invention belongs to the technical field of high-performance polymer materials and advanced micro-packaging, and relates to a hyperbranched polyimide embedding material as well as a preparation method and application thereof. The method comprises the following steps: dropwise adding a prepared hyperbranched polyimide solution with an active group as a terminal group into a functional material to be embedded, and carrying out suction filtration and drying to obtain embedded powder; the obtained embedding powder is placed in a supercritical carbon dioxide device, the hyperbranched polyimide embedding material generates a large number of pores by regulating and controlling the temperature, pressure and time and utilizing the swelling, plasticizing and extremely strong permeation and diffusion capabilities of supercritical carbon dioxide to hyperbranched polyimide with active groups, the specific surface area of the material is increased, and the hyperbranched polyimide embedding material is prepared. Finally, the composite material with a porous structure, high embedding rate and well protected activity of functional substances is obtained. According to the hyperbranched polyimide embedding material prepared by the method, the stability of functional substances can be improved, and the compatibility with an addition system can also be improved.

The invention discloses a behavior recognition method and device based on a video text model and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring video data to be identified, and performing frame extraction on the video data to obtain multiple video frames; inputting the multiple video frames into a preset video text model to obtain a behavior recognition result of the target object in the video data; wherein the video text model comprises an intra-frame attention module, a cross-frame attention module and a video text feature fusion module. According to the method, the time sequence relation between the video frames can be captured, the information between different frames can be integrated, semantic information is provided for behavior recognition, a video text model can comprehensively judge the behavior action of the target object from the two aspects of vision and semantics, and therefore the accuracy of behavior action recognition in the open set video is improved.

The invention relates to an unmanned aerial vehicle multi-scale target detection method based on a DINO framework, and belongs to the field of unmanned aerial vehicle target detection, and the method comprises the steps: constructing a hybrid Transform backbone network which comprises an image preprocessing layer, a first-stage DA-STB structure, a second-stage DA-STB structure, a third-stage DA-STB structure and a fourth-stage CGTB structure; an image to be processed is input into a Patch Partition image preprocessing layer to be divided; sequentially inputting DA-STB structures in the first stage, the second stage and the third stage; inputting the features into a fourth-stage CGTB structure to perform feature enhancement; and inputting the multi-scale features output by the hybrid Transform backbone network into an original Neck module of the DINO model, and outputting a target category and bounding box prediction result. According to the method, the precision and efficiency of multi-scale target detection can be remarkably improved, and particularly, the method has higher target sensing and extraction capability in small target detection and complex background interference environments.

This invention discloses a WTNet pediatric mandibular wisdom tooth germ segmentation network architecture method, mainly comprising an input enhancement module with a region feature enhancement module and a bone feature separation module. Independent scale-specific feature fusion modules are provided in the tooth segmentation branch and the bone segmentation branch of the bone feature separation module, respectively. For an input and its corresponding ground truth label, the input is first fed into the input enhancement module to generate a mask and supervised using the ground truth label; then, the input and mask are fed into the region feature enhancement module to obtain enhanced input; in the bone feature separation module, the enhanced input is fed into the scale-specific feature fusion modules of the tooth segmentation branch and the bone segmentation branch respectively after passing through a shared encoder, and then fed into the decoders of the two branches. The tooth and bone ground truth labels are used to complete the supervision of the two branches respectively, obtaining tooth segmentation masks and bone segmentation masks. The two masks are fused to obtain the final segmentation result.

The application belongs to the field of image fusion, and discloses a kind of image fusion method based on double decoder, for solving the problem that the feature extraction capability and fusion effect of complex multi-modal image processing of camera shooting of different imaging modes based on deep learning-based image fusion method are poor, the application includes: multi-modal image A1, A2 is extracted through large receptive field feature extraction module, then respectively through two interactive decoding modules, and in the decoding process, the decoding information between two different modal decoding modules is spliced on the channel to interact and fuse, finally reconstructs the fusion image C, calculates the loss of fusion image C and multi-modal image A1, A2, updates network model parameters.The application can effectively realize the fusion of complex multi-modal image, has the characteristics of good feature information extraction, small parameter quantity, high reconstruction precision, strong robustness and the like.