In Drosophila, pheromones play a crucial role in regulating courtship behaviors. In males, female aphrodisiac pheromones promote male‐female courtship, and male antiaphrodisiac pheromones inhibit male‐male courtship. Previous studies have reported that receptor proteins belonging to the pickpocket (ppk) family, ionotropic receptor family and gustatory receptor family are required for pheromone detection and normal courtship. However, none of them has been shown to be sufficient for sensing pheromones after ectopic expression in originally unresponsive cells. “M” cells are activated by male antiaphrodisiac pheromones but not female aphrodisiac pheromones, and the activated cells inhibit male‐male courtship. In our study, male flies with ectopic expression of ppk25, ppk29 and ppk23 in “M” cells showed decreased male‐female courtship. Using an in vivo calcium imaging approach, we found that the “M” cells expressing these three ppks were significantly activated by the female aphrodisiac pheromone 7,11‐heptacosadiene (7,11‐HD). Our results indicate that a sodium channel consisting, at minimum, of ppk25, ppk29 and ppk23, can sense 7,11‐HD, most likely as a receptor. Our findings may help us gain insights into the molecular mechanisms of pheromonal functions.

In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning (DL), such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms.