An Improved Protocol for Agrobacterium-Mediated Transformation in Subterranean Clover (Trifolium subterraneum L.)

Subterranean clover (Trifolium subterraneum) is the most widely grown annual pasture legume in southern Australia. With the advent of advanced sequencing and genome editing technologies, a simple and efficient gene transfer protocol mediated by Agrobacterium tumefaciens was developed to overcome the hurdle of genetic manipulation in subterranean clover. In vitro tissue culture and Agrobacterium transformation play a central role in testing the link between specific genes and agronomic traits. In this paper, we investigate a variety of factors affecting the transformation in subterranean clover to increase the transformation efficiency. In vitro culture was optimised by including cefotaxime during seed sterilisation and testing the best antibiotic concentration to select recombinant explants. The concentrations for the combination of antibiotics obtained were as follows: 40 mg L⁻¹ hygromycin, 100 mg L⁻¹ kanamycin and 200 mg L⁻¹ cefotaxime. Additionally, 200 mg L⁻¹ cefotaxime increased shoot regeneration by two-fold. Different plant hormone combinations were tested to analyse the best rooting media. Roots were obtained in a medium supplemented with 1.2 µM IAA. Plasmid pH35 containing a hygromycin-resistant gene and GUS gene was inoculated into the explants with Agrobacterium tumefaciens strain AGL0 for transformation. Overall, the transformation efficiency was improved from the 1% previously reported to 5.2%, tested at explant level with Cefotaxime showing a positive effect on shooting regeneration. Other variables in addition to antibiotic and hormone combinations such as bacterial OD, time of infection and incubation temperature may be further tested to enhance the transformation even more. This improved transformation study presents an opportunity to increase the feeding value, persistence, and nutritive value of the key Australian pasture.     

Optimized Sensor Nodes Deployment in Wireless Sensor Network Using Bat Algorithm

Wireless Personal Communications - For the optimal performance of wireless sensor networks in different areas of applications needs to maximize the coverage area of sensor nodes. The coverage of...     

Deep learning approach for investigation of temporal radio frequency signatures of drones

The omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two-class classification, the presence along with identification of their make using four-class classification. And this is further extended to the determination of their flight modes using ten-class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two-class classification scenario, all considered classifiers perform near to idle, whereas in a four-class classification scenario, CNN performs best, followed by AE, CNN-LSTM, and LSTM, respectively. Moreover, in a ten-class classification scenario, AE far outperforms CNN, followed by LSTM and CNN-LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.     

Machine translation from text to sign language: a systematic review

Universal Access in the Information Society - An equal opportunity for all is the basic right of every human being. The deaf society of the world needs to have access to all the information just...     

Conventional and Non-conventional Edible Oils: An Indian Perspective

The Indian dietary pattern has undergone major changes over the past 40 years. Many of these changes involve modification in dietary intake of fats and oils. In developing countries like India, vegetable oils are replacing animal fats because of the cost and health concerns. A wide range of vegetable oils are available in the market but the choice of healthy cooking oil has been a controversial subject since ideas keep on changing as new evidence accumulates. One of the factors holding back the increased use of value-added healthy oils in India is that most potential consumers are genuinely not aware of their health benefits. Edible oil consumption is primarily a community-driven phenomenon in India. The purpose of this paper is to highlight the current scenario of edible oils in India, their composition, health benefits, food applications, stability issues and future aspects as buying the right oil for health has become of great importance.