Abstract RNA interference (RNAi) refers to the process of post‐transcriptional silencing of cellular mRNA by the application of double‐stranded RNA (dsRNA). RNAi strategies have been widely employed to regulate gene expression in plants and animals including insects. With the availability of the full genome sequences of major vector mosquitoes, RNAi has been increasingly used to conduct genetic studies of human pathogens in mosquito vectors and to study the evolution of insecticide resistance in mosquitoes. This review summarizes the recent progress in our understanding of mosquito–pathogen interactions using RNAi and various methods of dsRNA delivery in mosquitoes at different stages. We also discuss potential applications of this technology to develop novel tools for vector control.
AI, or artificial intelligence, is precise simulation of human intelligence by machines. AI and computer-aided diagnosis are routinely used in medical imaging. In the context of the COVID-19 pandemic, early and accurate diagnosis of COVID-19 cases reduce the spread and mortality caused by SARS-CoV-2, the causative agent of COVID-19. AI has been used to improve the precise detection of COVID-19 cases. The gold standard for the detection of SARS-CoV-2 is by reverse transcription polymerase chain reaction (RT-PCR). However, RT-PCR is a time-consuming process that can give false-negative results and requires skillful laboratory technicians, which may not be possible in some resource- constrained regions. Contrastingly, AI-based screening and detection of specific changes in X-ray and CT scan images of suspected COVID-19 patients can offer a costeffective, speedy, and accurate diagnosis of clinical cases. Convolutional neural network (CNN), a deep learning algorithm, can improve the accuracy and reliability of diagnosis of COVID-19 from chest X-rays. An enormous amount of training data is required to make accurate predictions of clinical cases via CNN. Several pretrained models such as GoogLe- Net, AlexNet, VGG, and open databases such as GitHub can help the training of deep learning networks. This chapter will provide an overview of AI, machine learning, and deep learning-based strategies to improve the specificity, sensitivity, and accuracy for diagnosing SARS-CoV-2.
Sustainable Development Goals of the United Nations require global partnerships among developing and developed nations to achieve the overarching goals of ending poverty and improving the equity, health, and environmental balance and sustenance. Wastewater treatment can play a critical role in achieving these goals. Release or discharge of untreated or poorly treated wastewater in the environment can increase the concentration of pollutants such as organic and non-organic matter, heavy metals, pathogens, pesticides, drugs, plastics, and other chemicals depending on the wastewater source. These environmental pollutants disrupt the ecological balance and affect plants, animals, and human health. Sustainable wastewater treatment is in alignment with many of the sustainable development goals. Besides, wastewater is also a resource for generating energy, fertilizers, nutrients, and clean water and can generate revenue which can then sustain wastewater treatment, making the process circular. Isolation and converting waste to resources is a step toward sustainability and a circular economy for a better planet. This chapter will summarize wastewater treatment strategies in line with sustainable development goals and a circular approach.
The quorum sensing molecule Autoinducer-2 (AI-2) is generated as a byproduct of activated methyl cycle by the action of LuxS in Escherichia coli. AI-2 is synthesized, released and later internalized in a cell-density dependent manner. Here, by mutational analysis of the genes, uvrY and csrA, we describe a regulatory circuit of accumulation and uptake of AI-2. We constructed a single-copy chromosomal luxS-lacZ fusion in a luxS + merodiploid strain and evaluated its relative expression in uvrY and csrA mutants. At the entry of stationary phase, the expression of the fusion and AI-2 accumulation was positively regulated by uvrY and negatively regulated by csrA respectively. A deletion of csrA altered message stability of the luxS transcript and CsrA protein exhibited weak binding to 5' luxS regulatory region. DNA protein interaction and chromatin immunoprecipitation analysis confirmed direct interaction of UvrY with the luxS promoter. Additionally, reduced expression of the fusion in hfq deletion mutant suggested involvement of small RNA interactions in luxS regulation. In contrast, the expression of lsrA operon involved in AI-2 uptake, is negatively regulated by uvrY and positively by csrA in a cell-density dependent manner. The dual role of csrA in AI-2 synthesis and uptake suggested a regulatory crosstalk of cell signaling with carbon regulation in Escherichia coli. We found that the cAMP-CRP mediated catabolite repression of luxS expression was uvrY dependent. This study suggests that luxS expression is complex and regulated at the level of transcription and translation. The multifactorial regulation supports the notion that cell-cell communication requires interaction and integration of multiple metabolic signals.
General wisdom is, mathematical operation is needed to generate number by numbers. It is pointed out that without any mathematical operation true random numbers can be generated by numbers through algorithmic process. It implies that human brain itself is a living true random number generator. Human brain can meet the enormous human demand of true random numbers.
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