Macrophage Extracellular Traps: Current Opinions and the State of Research regarding Various Diseases

Macrophages are an important component of the human immune system and play a key role in the immune response, which can protect the body against infection and regulate the development of tissue inflammation. Some studies found that macrophages can produce extracellular traps (ETs) under various conditions of stimulation. ETs are web-like structures that consist of proteins and DNA. ETs are thought to immobilize and kill microorganisms, as well as play an important role in tissue damage, inflammatory progression, and autoimmune diseases. In this review, the structure, identification, mechanism, and research progress of macrophage extracellular traps (METs) in related diseases are reviewed.

Malicious node identification routing and protection mechanism for VANET against various attack

With the help of road side unit vehicles communicate among themselves. This technique termed as VANET. This network helps us to improve the safety and efficiency of the occupants during travelling in vehicles. The basic idea of this technique is to send information about the traffic information to the road side unit or other vehicles. These vehicles get safe from attacks and misuse of their private data. The objective of this paper to secure the communication among the vehicles and the road side unit. In this technique the communication mainly dependant on the safety of the road such as vehicles tracking, emergency situations and message monitoring. There are various attacks like Sybil and Gray hole attack are vulnerable to VANET. To protect from these attacks our technique provide malicious node identification mechanism that help us to provide better facility to send data to vehicles safely. To avoid these types of attacks, our propose technique include feature like key management system to prevent the communication among the vehicles. Our proposed system mostly focus on Bandwidth, packet loss and packet delivery ratio [12].

Automated Plant Disease Detection using Deep Learning Architectures with Autonomous rover

Agriculture is the backbone and plays a vital role in many Asian countries. Farmers mainly depend on their agricultural produce for their living. A report says one-third of the farmers income account’s for the agricultural loss which is primarily due to plant diseases. To combat this farmers are in need of a early plant disease identification mechanism. Observation of individual plants in the farm for detecting the disease is labor-intensive and time consuming work, if the farm is vast and multiple plants are cultivated then it’s even worse. To solve such issues, current technologies like the Internet of Things (IoT) and artificial intelligence (AI) and Machine Learning (ML) are used to predict the diseases more effectively. Farmers usually detect plant diseases with the help of images captured manually and analyzed separately by experts. The proposed system renders an efficient solution for detecting multiple diseases in several plant varieties. The system is designed to detect and recognize several plant varieties, specifically pepper, grapes, and strawberry. The proposed system discovers various plant’s various diseases based on the inputs obtained by capturing images from a built-in camera present in the Autonomous rover. The rover also record’s it’s GPS location and makes a map of the entire farm traced and checked by the robot. The images are processed and are classified into their respective categories using deep learning algorithms. Convolutional neural networks the powerful methodology for image classification is the underlying principle applied. The deep learning model’s architecture namely, VGG16 and InceptionResNetV2, are used to train the model. These models are primarily made of convolutional layers. On testing, we recorded am accuracy of 93.21% was obtained from VGG16, and 95.24% from InceptionResNetV2.