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In this review, we survey a number of experiments over the last few decades, that specifically study the destruction of the 7 Be nucleus, in search for a solution to the long standing cosmological lithium problem. The destruction of 7 Be by both neutrons and charged particles are discussed. However, the reduction in the abundance of the primordial 7 Li is found to be negligible and thus the lithium anomaly remains. The second lithium problem involving 6 Li is still controversial. Overall, it appears that the solution to the lithium problems may not reside in nuclear physics.

This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities. The physiological detection using electrocardiogram (ECG) signals has been the most popular means and widely accepted automated detection system to monitor heart health. Additionally, arrhythmia beat classification plays a prominent role in electrocardiogram (ECG) analysis dedicated elucidate cardiac health status while analyzing heart rhythm. The authors aim to classify ECG samples into major arrhythmia classes precisely by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT). The QRS complex plays a crucial role in ECG signal identification. Therefore, the position and amplitude of R-peaks are determined to detect the QRS complex. The feature vectors of the QRS complex are further optimized with cuckoo search (CS) optimization algorithm in addition to denoising signals using DWT to select the most relevant set of features. The Support vector machine (SVM)-trained support vector contains the best training information used to train feed-forward back-propagation neural network (FFBPNN) to propose the variant DWT + CS + SVM-FFBPNN to classify signals among five classes. MIT-BIH arrhythmia database is utilized for different types of heartbeats. The classification analysis based on a variant with optimized feature vector using cuckoo search algorithm and SVM-FFBPNN determines heart rate with an accuracy of 98.319%. In contrast, the variant FFBPNN without optimization obtains 97.95% accuracy. The improved performance of the novel combination of classifiers resulted in overall classification accuracy of 98.53% with precision and recall of 98.247% and 95.68%, respectively. The simulation analysis comprising 3600 samples and 1160 heartbeats also outperformed the existing arrhythmia classifications performed based on neural networks. This illustrates the success of the proposed ECG classification model in accurately categorizing ECG signals for arrhythmia classification.

Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier’s generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.

The surface plasmon resonance (SPR) technique is a remarkable tool, with applications in almost every area of science and technology. Sensing is the foremost and majorly explored application of SPR technique. The last few decades have seen a surge in SPR sensor research related to sensitivity enhancement and innovative target materials for specificity. Nanotechnological advances have augmented the SPR sensor research tremendously by employing nanomaterials in the design of SPR-based sensors, owing to their manifold properties. Carbon-based nanomaterials, like graphene and its derivatives (graphene oxide (GO)), (reduced graphene oxide (rGO)), carbon nanotubes (CNTs), and their nanocomposites, have revolutionized the field of sensing due to their extraordinary properties, such as large surface area, easy synthesis, tunable optical properties, and strong compatible adsorption of biomolecules. In SPR based sensors carbon-based nanomaterials have been used to act as a plasmonic layer, as the sensitivity enhancement material, and to provide the large surface area and compatibility for immobilizing various biomolecules, such as enzymes, DNA, antibodies, and antigens, in the design of the sensing layer. In this review, we report the role of carbon-based nanomaterials in SPR-based sensors, their current developments, and challenges.

In this review, I give a brief introduction to Bose Institute and describe the research work pursued in nuclear astrophysics. The experiments are carried out at ISOLDE, CERN while Monte Carlo simulations and data analysis are done at Bose Institute. The review ends with future plans and an outlook.

The centrosome is the main microtubule organization centre of animal cells. It is composed of a centriole pair surrounded by pericentriolar material (PCM). Traditionally described as amorphous, the architecture of the PCM is not known, although its intricate mode of assembly alludes to the presence of a functional, hierarchical structure. Here we used subdiffraction imaging to reveal organizational features of the PCM. Interphase PCM components adopt a concentric toroidal distribution of discrete diameter around centrioles. Positional mapping of multiple non-overlapping epitopes revealed that pericentrin (PCNT) is an elongated molecule extending away from the centriole. We find that PCM components occupy separable spatial domains within mitotic PCM that are maintained in the absence of microtubule nucleation complexes and further implicate PCNT and CDK5RAP2 in the organization and assembly of PCM. Globally, this work highlights the role of higher-order PCM organization in the regulation of centrosome assembly and function. Centrosomes consist of two centrioles surrounded by pericentriolar material (PCM) that nucleates microtubules. The PCM has been considered as amorphous but, using subdiffraction fluorescence imaging, Pelletier and colleagues now reveal the organized structure of human PCM.

For efficient clearance of Mycobacterium tuberculosis (Mtb), macrophages tilt towards M1 polarization leading to the activation of transcription factors associated with the production of antibacterial effector molecules such as nitric oxide (NO) and proinflammatory cytokines such as interleukin 1 β (IL-1β) and tumor necrosis factor α (TNF-α). At the same time, resolution of inflammation is associated with M2 polarization with increased production of arginase and cytokines such as IL-10. The transcriptional and post-transcriptional mechanisms that govern the balance between M1 and M2 polarization, and bacteria-containing processes such as autophagy and trafficking of Mtb to lysosomes, are incompletely understood. Here we report for the first time, that the transcription factor KLF4 is targeted by microRNA-26a (miR-26a). During Mtb infection, downregulation of miR-26a (observed both ex vivo and in vivo) facilitates upregulation of KLF4 which in turn favors increased arginase and decreased iNOS activity. We further demonstrate that KLF4 prevents trafficking of Mtb to lysosomes. The CREB-C/EBPβ signaling axis also favors M2 polarization. Downregulation of miR-26a and upregulation of C/ebpbeta were observed both in infected macrophages as well as in infected mice. Knockdown of C/ebpbeta repressed the expression of selected M2 markers such as Il10 and Irf4 in infected macrophages. The importance of these pathways is substantiated by observations that expression of miR-26a mimic or knockdown of Klf4 or Creb or C/ebpbeta, attenuated the survival of Mtb in macrophages. Taken together, our results attribute crucial roles for the miR-26a/KLF4 and CREB-C/EBPβsignaling pathways in regulating the survival of Mtb in macrophages. These studies expand our understanding of how Mtb hijacks host signaling pathways to survive in macrophages, and open up new exploratory avenues for host-targeted interventions.

Chitin is the second most plenteous polysaccharide in nature after cellulose, present in cell walls of several fungi, exoskeletons of insects, and crustacean shells. Chitin does not accumulate in the environment due to presence of bacterial chitinases, despite its abundance. These enzymes are able to degrade chitin present in the cell walls of fungi as well as the exoskeletons of insect. They have shown being the potential agents for biological control of the plant diseases caused by various pathogenic fungi and insect pests and thus can be used as an alternative to chemical pesticides. There has been steady increase in demand of chitin derivatives, obtained by action of chitinases on chitin polymer for various industrial, clinical, and pharmaceutical purposes. Hence, this review focuses on properties and applications of chitinases starting from bacteria, followed by fungi, insects, plants, and vertebrates. Designing of chitinase by applying directed laboratory evolution and rational approaches for improved catalytic activity for cost-effective field applications has also been explored.

Glaciers and snow cover area (SCA) plays an important role in river runoff in Himalayan region. There is a need to monitor SCA on spatio-temporal basis for better and efficient utilization of water resources. Moderate Resolution Imaging Spectroradiometer (MODIS) provides less cloudy data due to high temporal resolution as compared to other optical sensors for high elevation regions, and its 8-day snow cover product is globally used for snow cover estimation. The main objective of the present paper is to estimate annual and seasonal SCA in Chandra basin, Western Himalaya, and analysis of its variation with elevation, aspect, and slope during 2001 to 2017 using MODIS Terra (MOD10A2) and Aqua (MYD10A2) snow cover product as well as to correlate the same with temperature and precipitation using fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the global climate (ERA5) data. The total average SCA observed is 84.94% of basin area during the study period. The maximum annual average SCA was found as 91.23% in 2009 with minimum being 76.37% in 2016. Strong correlation is observed in annual and seasonal SCA with temperature which indicate that SCA variability is highly sensitive to temperature.