6 results about "Biological modeling" patented technology

The invention provides a marine environment multi-dimensional time sequence prediction method and system based on a deep learning neural network. According to the invention, the deep learning neural network is utilized to predict the multi-dimensional time sequence change of the marine environment. And constructing a ship trajectory and collecting water body biological displacement information. And the neural network fuses the ship trajectory and the marine ecological parameters to generate interaction evolution characteristics. And mapping the sound pulse reflection intensity and the phase difference of the biological displacement into a three-dimensional motion vector, and constructing a biological activity intensity model after processing. And quantifying ship density gradient and course conflict as a space pressure coefficient, and combining with multi-ship disturbance superposition to form an ecological pressure diagram. Iteratively calculating the pressure time-varying law and the divergent characteristics of the biological model, and outputting an ocean disturbance matrix; and through adaptive weight fusion pressure propagation and biological response, generating spatial-temporal distribution prediction of marine ecological disturbance. According to the method, interaction between ship activities and marine organisms is quantified, and spatial-temporal distribution of marine ecological disturbance is predicted by fusing multi-dimensional data.

The invention discloses a growth animation generation method, apparatus and device, and a readable storage medium. The method comprises the steps of determining attribute information of point clouds based on a spatial relationship of the point clouds in a point cloud space; the attribute information comprises an offset vector for indicating density distribution and an offset length for indicating point cloud density; determining a target point cloud based on the attribute information; generating a target line segment based on the target point cloud; and on the basis of the sequence indicated by the offset length, sequentially generating branch sub-models indicated by the target line segment in the generation process of the target model to obtain a growth animation of the target model. According to the method provided by the invention, the L-system fractal algorithm can be fitted based on the data processing of the point cloud to simulate the most important branch sequence growth effect in the growth process, so that the complex iterative analysis in the traditional L-system fractal algorithm is avoided, and the animation expression effect of the biological model with the complex branch effect is effectively improved.

Embodiments of the present invention relate to a device for implementing high-speed drug screening based on a three-dimensional biological model, and the device according to one aspect is an analysis device for a three-dimensional biological model, the device comprising: a well plate; a probe provided in the well plate; and a plunger located to be spaced apart from the well plate, wherein: the well plate includes a well configured to accommodate the three-dimensional biological model, the well being a region in which at least a portion of the upper surface of the well plate is recessed along a first direction; at least a portion of the probe is located in the well; and the plunger is movable in the well along the first direction.

The application belongs to the technical field of micromanipulation biological modeling, and is based on a cell stretching tensor calculation method of dissipative particle dynamics, and the specific steps are as follows: step 1), a cell membrane model of dissipative particle dynamics is established; step 2), a simulation boundary condition and a type of ensemble integrator are determined; step 3), a cell membrane and a cytoskeleton model are aggregated; step 4), a microneedle penetration experiment platform is built in a simulation environment and the microneedle penetration experiment is started; and step 5), a strain of the cell in a penetration direction is calculated, the microneedle penetration simulation data are processed, finite strain theory is used for analysis and calculation, and a normal strain is output. The method can calculate the internal strain of the cell model under different parameters, and the effectiveness of the method is verified by comparison with the results in a real experiment.

The invention discloses a device suitable for simulating a weightlessness model to carry out magnetic resonance examination, and relates to the technical field of simulation experiment devices. The device suitable for simulating the weightlessness model to perform the magnetic resonance examination comprises a base and a supporting frame rotationally connected with the top of the base, an angle adjusting mechanism is installed on the supporting frame, the angle adjusting mechanism is used for binding a biological model and detecting the moving angle of the biological model, and the angle adjusting mechanism can be rotationally folded at the top of the base; the biological model is bound through the angle adjusting mechanism, the angle deflection of the biological model in a weightless state can be detected, then the posture change of a human body or an animal in the weightless state is simulated, the biological model can be bound to different positions on the swing rod, and then the coordinate position of a sample can be adjusted according to experimental requirements; in addition, the device optimizes a precise calibration gauge for angle detection, changes of the longitudinal swing angle are converted into the rotation angle of the scale wheel disc, and then the precision of angle detection is accurate to 0.1 degree.

Systems, methods, computer-readable media, and techniques for locating a scope tip of an endoscope within a patient's organ include: (a) acquiring (i) a biological model of the patient's organ and (ii) electromagnetic (EM) data generated by the endoscope within the organ; (b) generating a plurality of scope tip location hypotheses based on the biological model and the EM data; (c) generating a plurality of transformations, each transformation of the plurality of transformations respectively mapping each of the plurality of scope tip location hypotheses to the EM data; (d) determining a scope tip location from the plurality of scope tip location hypotheses, the location corresponding to an assumed transformation of the plurality of transformations that satisfies a threshold; and (e) presenting the scope tip location on a graphical display.