To further understand the potential anti-angiogenesis mechanism of T7, the effect of T7 on the proliferation of HUVECs was determined at 48 h. T7 decreased HUVEC cells survival and showed inhibition in a dose-dependent manner (Fig. 2A). The IC50 value of T7 on HUVECs was 14.52 mol/l. The migration of HUVECs was observed by using a wound healing assay. Compared with the control group, T7 significantly inhibited migration of HUVECs at 2.5, 5, 10 mol/l concentrations (Fig. 2B). A549 cells, H1299 cells and H460 cells were seeded to the 12well plate (200 cells/well) with regular growth medium.

In Activity 4.1b, we designed two puzzle cube designs made of 5 of the puzzle pieces that we designed in Activity 4.1a. In Activity 4.1c, we learned about statistics based on collected data and uploaded it to the Excel spreadsheet. To learn about the rest of my Activity 4.1 go to the projects tab, then to the portfolio tab and to Activity 4.1. In addition to this, similarly to the self.add_loss() method, you have access to an self.add_metric() method on layers.

In most practical scenarios you won’t have access to sensor data. For example, if you want to detect Illegal Activity at a place then when should you check to see that fuel lines, connections, and fuel vents are in good condition? you may have to rely on just video feeds from CCTV cameras. A model is then trained on this sensor data to output these six classes.

This would effectively get rid of that flickering. The issue is that the model will not always be fully confident about each video frame’s prediction, so the predictions will change rapidly and fluctuate. So with enough examples, the model learns that a person with a running pose on a football field is most likely to be playing football, and if the person with that pose is on a track or a road then he’s probably running. Now that we have established the need for Video Classification models to solve the problem of Human Activity Recognition, let us discuss the most basic and naive approach for Video Classification.

On the other hand, the stream on the bottom, also known as the fast branch, has low channels and operates on a high temporal frame rate version of the same video. Optical flow is the pattern of visible motion of objects, edges and helps calculate the motion vector of every pixel in a video frame. It is effectively used in motion tracking applications.