Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Crystal Growth and Magnetic Properties of Topological Nodal-Line Semimetal GdSbTe with Antiferromagnetic Spin Ordering.

Abstract: We report crystal growth, AC and DC magnetic susceptibilities [χ(T, H)], magnetization [M(T, H)], and heat capacity [CP(T, H)] measurement results of GdSbTe single crystal. GdSbTe is isostructural to the confirmed nonmagnetic nodal-line semimetal ZrSiS of noncentrosymmetric tetragonal crystal structure in space group P4/nmm (No. 129), but it shows additional long-range antiferromagnetic spin ordering for the Gd spins of S = 7/2 below TN. Both χ(T, H) and CP(T, H) measurements confirm the existence of a long-range antiferromagnetic (AFM) spin ordering of Gd spins below TN ∼ 12 K, and an additional spin reorientation/recovery (sr) behavior is identified from the change of on-site spin anisotropy between Tsr1 ∼ 7 and Tsr2 ∼ 4 K. The anisotropic magnetic susceptibilities of χ(T, H) below TN clearly demonstrate that the AFM long-range spin ordering of GdSbTe has an easy axis parallel to the ab-plane direction. The field- and orientation-dependent magnetization of M(T, H) at 2 K shows two plateaus to indicate the spin-flop transition for H||ab near ∼2.1 T and a metamagnetic state near ∼5.9 T having ∼1/3 of the fully polarized magnetization by the applied field. The heat capacity measurement results yield Sommerfeld coefficient of γ ∼ 7.6(4) mJ/mol K2 and θD ∼ 195(2) K being less than half of that for the nonmagnetic ZrSiS. A three-dimensional (3D) AFM spin structure is supported by the ab initio calculations for Gd having magnetic moment of 7.1 μB and the calculated AFM band structure indicates that GdSbTe is a semimetal with bare density of states (0.36 states/eV fu) at the Fermi level, which is 10 times smaller than the measured one to suggest strong spin-fluctuation.

NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence.

Abstract: N-acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG-interacting residues in a protein from its primary sequence information. We extracted 231 NAG-interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG-interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG-interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG-interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG-interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/.

Bactericidal surfaces: An emerging 21st-century ultra-precision manufacturing and materials puzzle

Abstract: Progress made by materials scientists in recent years has greatly helped the field of ultra-precision manufacturing. Ranging from healthcare to electronics components, phenomena such as twinning, dislocation nucleation, and high-pressure phase transformation have helped to exploit plasticity across a wide range of metallic and semiconductor materials. One current problem at the forefront of the healthcare sector that can benefit from these advances is that of bacterial infections in implanted prosthetic devices. The treatment of implant infections is often complicated by the growth of bacterial biofilms on implant surfaces, which form a barrier that effectively protects the infecting organisms from host immune defenses and exogenous antibiotics. Further surgery is usually required to disrupt the biofilm, or to remove the implant altogether to permit antibiotics to clear the infection, incurring considerable cost and healthcare burdens. In this review, we focus on elucidating aspects of bactericidal surfaces inspired by the biological world to inform the design of implant surface treatments that will suppress bacterial colonization. Alongside manufacturing and materials related challenges, the review identifies the most promising natural bactericidal surfaces and provides representative models of their structure, highlighting the importance of the critical slope presented by these surfaces. The scalable production of these complex hierarchical structures on freeform metallic implant surfaces has remained a scientific challenge to date and, as identified by this review, is one of the many 21st-century puzzles to be addressed by the field of applied physics.