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Recent progress in the field of nanophotonics has opened up novel avenues for developing nanomaterial-based biosensing systems, which can detect various disease-specific biomarkers, including long noncoding RNAs (lncRNAs) known to circulate in biological fluids. Herein, we designed and developed a nanophotonic approach for rapid and specific capture of lncRNAs using oligonucleotide-conjugated graphene quantum-dot-nanoconjugates. The method offers accurate identification of the target lncRNAs with high selectivity, despite the presence of other molecules in the given sample. The observations also pointed toward the high feasibility and simplicity of the method in the selective determination of lncRNAs. Overall, the approach has the potential of assessing lncRNA expression as a function of disease initiation and progression.

Conceptualised at the conference organised in January 2020 titled “Contextualising Migration: Perspectives from Literature, Culture and Translation” by the School of Humanities and Social Sciences at GITAM, Hyderabad in collaboration with CIIL, the present volume aims to engage the emergent tendencies within the long histories of migration motivated by a renewed understanding of translated ideas and identities in the present order of world affairs. The volume also aims to trace the literary metamorphosis under the influence of the emerging transnational, transmedial world of literary exchange that has documented the complex negotiation of loss and recovery and methods of searching for one’s identity on one hand and on the other, made literature increasingly difficult to be tied down to one nation, one language. Consequently, the volume is divided into three interconnected sections. The first two sections are dedicated to account for the challenges thrown by the latest discourse and dynamics of migration and to document the theoretical as well as literary responses provided to such developments. These sections also attempt to bring out the significant role of translation in the life of immigrant communities. The final section is designed to substantiate the understanding of the emerging fictional and non-fictional worlds further by looking comparatively into the recent literary output coming from the diaspora and discussing its shifts and extensions with respect to the early writings.

Mitochondria play a central role in maintaining cellular and metabolic homeostasis during vital development cycles of foetal growth. Optimal mitochondrial functions are important not only to sustain adequate energy production but also for regulated epigenetic programming. However, these organelles are subtle targets of environmental exposures, and any perturbance in the defined mitochondrial machinery during the developmental stage can lead to the re-programming of the foetal epigenetic landscape. As these modifications can be transferred to subsequent generations, we herein performed a cross-sectional study to have an in-depth understanding of this intricate phenomenon. The study was conducted with two arms: whereas the first group consisted of in utero pro-oxidant exposed individuals and the second group included controls. Our results showed higher levels of oxidative mtDNA damage and associated integrated stress response among the exposed individuals. These disturbances were found to be closely related to the observed discrepancies in mitochondrial biogenesis. The exposed group showed mtDNA hypermethylation and changes in allied mitochondrial functioning. Altered expression of mitomiRs and their respective target genes in the exposed group indicated the possibilities of a disturbed mitochondrial-nuclear cross talk. This was further confirmed by the modified activity of the mitochondrial stress regulators and pro-inflammatory mediators among the exposed group. Importantly, the disturbed DNMT functioning, hypermethylation of nuclear DNA, and higher degree of post-translational histone modifications established the existence of aberrant epigenetic modifications in the exposed individuals. Overall, our results demonstrate the first molecular insights of in utero pro-oxidant exposure associated changes in the mitochondrial-epigenetic axis. Although, our study might not cement an exposure-response relationship for any particular environmental pro-oxidant, but suffice to establish a dogma of mito-epigenetic reprogramming at intrauterine milieu with chronic illness, a hitherto unreported interaction.

Air pollution has emerged as a serious threat to human health due to close association with spectrum of chronic ailments including cardiovascular disorders, respiratory diseases, nervous system dysfunctions, diabetes and cancer. Exposure to air-borne pollutants along with poor eating behaviours and inferior dietary quality irreversibly impacts epigenomic landscape, leading to aberrant transcriptional control of gene expression which is central to patho-physiology of non-communicable diseases. It is assumed that nutriepigenomic interventions such as vitamins can control such adverse effects through their immediate action on mitochondrial epigenomic-axis. Importantly, the exhaustive clinical utility of vitamins-interceded epigenetic synchronization is not well characterized. Therefore, improving the current limitations linked to stability and bioavailability issues in vitamin formulations is highly warranted. The present review not only sums up the available data on the role of vitamins as potential epigenetic modifiers but also discusses the importance of nano-engineered vitamins as potential epidrugs for dietary and pharmacological intervention to mitigate the long-term effects of air pollution toxicity.

Surface-enhanced Raman scattering (SERS) has emerged as one of the most promising platforms for various biosensing applications. These sensing systems encompass the advantages of specificity, ultra-high sensitivity, stability, low cost, repeatability, and easy-to-use methods. Moreover, their ability to offer a molecular fingerprint and identify the target analyte at low levels make SERS a promising technique for detecting circulating cancer biomarkers with greater sensitivity and reliability. Among the various circulating biomolecules, oncomiRs are emerging as prominent biomarkers for the early screening of breast cancers (BCs). In this review, we provide a comprehensive understanding of different SERS-based biosensors and their application to identify BC-specific oncomiRs. We also discuss different SERS-based sensing strategies, nano-analytical frameworks, and challenges to be addressed for effective clinical translation.

The dangerous COVID-19 (SARS-CoV-2) is rising steadily and globally, with more than 72,851,747 confirmed cases observed to WHO including 1,643,339 deaths till 17 December 2020. The country’s economy is now almost fully halted, people are stuck up and investment becomes deteriorating. So, this is turning to worry of the government for a development and health. Health organizations are often desperate for evolving decision-making innovations to overcome this viral virus and encourage people to receive rapid and effective responses in real-time. Thus, it is important to create auto-mechanisms as a preventive shield to ensure healthy humanity against SARS-CoV-2. Advanced analytics methods and other strategies could also empower researchers, learners and the pharmaceutical industry to acknowledge the hazardous COVID-19 and speed it up care procedures by efficiently testing vast volumes of research data. The prevention method consequence is being used to effectively manage, calculate, forecast and monitor current infected people and future potential cases. Therefore, we proposed CNN and VGG16 based deep learning models to incorporate and enforce AI-based precautionary measures to detect the face mask on Simulated Masked Face Dataset (SMFD). This technique is capable of recognizing masked and unmasked faces to help monitor safety breaches, facilitate the use of face masks, and maintain a secure working atmosphere.

In the agricultural sector, plant leaf diseases and harmful insects represent a major challenge. Faster and more reliable prediction of leaf diseases in crops may help develop an early treatment technique while reducing economic losses considerably. Current technological advances in deep learning have made it possible for researchers to improve the performance and accuracy of object detection and recognition systems significantly. In this chapter, using images of plant leaves, the authors introduced a deep-learning method with different datasets for detecting leaf diseases in different plants and concerned with a novel approach to plant disease recognition model, based on the classification of the leaf image, by the use of deep convolutional networks. Ultimately, the approach of developing deep learning methods on increasingly large and accessible to the public image datasets provides a viable path towards massive global diagnosis of smartphone-assisted crop disease.

Multidisciplinary initiatives in the new world of coronavirus were combined to limit the spread of the pandemic. Interestingly, the AI group was a part of those efforts. This result-based approach is used to help scan, assess, predict and track current patients and possibly potential patients. Developments for tracking social distances or recognizing face masks have made headlines in particular. Most current advanced approaches to face mask recognition are built based on deep learning which is dependent on a large number of face samples. Nearly everybody wears a mask during corona virus outbreak in order to effectively avoid the spread of COVID-19 virus. Our goal is to train a customized deep learning model that helps to detect even if or not a person wears a mask and study the concept of model pruning with Keras-Surgeon. Model pruning can be efficient in reducing model size, so that it can be easily implemented and inferred on embedded systems.

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences.

Biometrics play a crucial role in establishing an individuals identity. A signature is one of the most widely recognized way to authorize transactions and authenticate the human identity as compared to other electronic identification methods such as fingerprint and retina scans. Due to a huge demand for authentication, fast algorithms need to be assimilated for signature recognition and verification. Human signatures can be treated as an image and the techniques of neural networks can be applied to them for recognition and verification. This paper exploits a database of samples of offline signatures that are captured in an image format and this database is used to train the neural network.

Mesh and Torus are most popular interconnection topologies based on 2D-mesh.Comparison between Mesh and Torus will be considered and new interconnection topology will be proposed to provide better performance. The C2Mesh, is an enhanced mesh interconnected network. This paper enhances the torus network based on the theme of C2Mesh. Topological Properties of new network will be analyzed and implemented by simulation. The new routing Algorithm will be designed for new proposed network (C2Torus). This manuscript performs Comparison between C2Torus and C2Mesh.