World Health Organization (WHO) declared COVID-19 as a pandemic disease onMarch 11, 2020. Comparison of genome sequences from diverse locations allows us to identify the genetic diversity among viruses which would help in ascertaining viral virulence, disease pathogenicity, origin and spread of the SARS-CoV-2 between countries. The aim of this study is to ascertain the genetic diversity among Indian SARS-CoV-2 isolates. Initial examination of the phylogenetic data of SARS-CoV-2 genomes (n=3123) from different continents deposited at GISAID (Global Initiative on Sharing All Influenza Data) revealed multiple origin for Indian isolates. An in-depth analysis of 449 viral genomes derived from samples representing countries from USA, Europe, China, East Asia, South Asia, Oceania, Middle East regions and India revealed that most Indian samples are divided into two clusters (A and B) with cluster A showing more similarity to samples from Oceania and Kuwait and the cluster B grouping with countries from Europe, Middle East and South Asia. Diversity analysis of viral clades, which are characterized by specific non-synonymous mutations in viral proteins, discovered that the cluster A Indian samples belong to I clade (V378I in ORF1ab), which is an Oceania clade with samples having Iran connections and the cluster B Indian samples belong to G clade (D614G in Spike protein), which is an European clade. Thus our study identifies that the Indian SARS-CoV-2 viruses belong to I and G clades with potential origin to be countries mainly from Oceania, Europe, Middle East and South Asia regions, which strongly implying the spread of virus through most travelled countries. The study also emphasizes the importance of pathogen genomics through phylogenetic analysis to discover viral genetic diversity and understand the viral transmission dynamics with eventual grasp on viral virulence and disease pathogenesis.

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.

Optical contrast is the most common preliminary method to identify layer number of two-dimensional (2D) materials, but it is seldom used as a confirmatory technique. We explain the reason for variation of optical contrast between imaging systems, motivating system-independent measurement of optical contrast as a critical need. We describe a universal method to quantify the layer number using the RGB (red–green–blue) and RAW optical images. For RGB images, the slope of 2D flake (MoS2, WSe2, graphene) intensity vs substrate intensity is extracted from optical images with varying lamp power. The intensity slope identifies layer number and is system independent. For RAW images, intensity slopes and intensity ratios are completely system and intensity independent. Intensity slope (for RGB) and intensity ratio (for RAW) are thus universal parameters for identifying layer number. The RAW format is not present in all imaging systems, but it can confirm layer number using a single optical image, making it a rapid and system-independent universal method. A Fresnel-reflectance-based optical model provides an excellent match with experiments. Furthermore, we have created a MATLAB-based graphical user interface that can identify layer number rapidly. This technique is expected to accelerate the preparation of heterostructures and to fulfill a prolonged need for universal optical contrast method.

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Technological advancement towards the quantum era requires secure communication, quantum computation and ultra-sensitive sensing capabilities. Layered quantum materials (LQMs) have remarkable optoelectronic and quantum properties that can usher us into the quantum era. Electron microscopy is the tool of choice for measuring these LQMs at an atomic and nanometre scale. On the other hand, electron-irradiation of LQMs can modify various material properties, including the creation of structural defects. We review different types of structural defects, as well as electron elastic-and inelastic-scattering induced processes. Controlled modification of optoelectronic and quantum properties of LQMs using electron-irradiation, including creation of single-photon emitters is discussed. Protection of electron-irradiation induced damage of LQMs via encapsulation by other layered materials is encouraged. We finally give insights into challenges and opportunities, including creating novel structures using an electron beam.