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3,386 results on '"Singh, Vikram"'

23. Double magnetic transition, complex field-induced phases, and large magnetocaloric effect in the frustrated garnet compound Mn$_{3}$Cr$_{2}$Ge$_{3}$O$_{12}$

Author

Mohanty, S., Magar, A., Singh, Vikram, Islam, S. S., Guchhait, S., Jain, A., Yusuf, S. M., Tsirlin, A. A., and Nath, R.

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract

A detailed study of the magnetic and magnetocaloric properties of a garnet compound Mn$_{3}$Cr$_{2}$Ge$_{3}$O$_{12}$ is carried out using x-ray diffraction, magnetization, heat capacity, and neutron diffraction measurements as well as \textit{ab initio} band-structure calculations. This compound manifests two successive magnetic transitions at $T_{\rm N1} \simeq 4.5$ K and $T_{\rm N2} \simeq 2.7$ K. Neutron powder diffraction experiments reveal that these two transitions correspond to the collinear and non-collinear antiferromagnetic ordering of the nonfrustrated Cr$^{3+}$ and frustrated Mn$^{2+}$ sublattices, respectively. The interactions within each of the Cr and Mn sublattices are antiferromagnetic, while the inter-sublattice interactions are ferromagnetic. The $H-T$ phase diagram is quite complex and displays multiple phases under magnetic field, which can be attributed to the frustrated nature of the spin lattice. Mn$_{3}$Cr$_{2}$Ge$_{3}$O$_{12}$ shows a large magnetocaloric effect with a maximum value of isothermal entropy change $\Delta S_{\rm m} \simeq -23$ J/kg-K and adiabatic temperature change $\Delta T_{\rm ad} \simeq 9$ K for a field change of 7 T. Further, a large value of the relative cooling power ($RCP \simeq 360$ J/kg) demonstrates the promise of using this compound in magnetic refrigeration., Comment: Phys. Rev. B (Accepted) 12 pages, 10 figures, 54 references

Published
2024

27. Inferring interaction networks from transcriptomic data: methods and applications

Author

Singh, Vikram

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Biomolecules, Quantitative Biology - Genomics
Abstract

Transcriptomic data is a treasure-trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilised to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules, and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network based techniques of prioritising key genes, outlining the centrality based, diffusion based and subgraph based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains., Comment: 48 pages, 3 figures

Published
2023

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