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Achieving a comprehensive understanding of the behaviour of proteins is greatly facilitated by the knowledge of their structures, thermodynamics and dynamics. All this information can be provided in an effective manner in terms of…

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims…

Understanding complex biological macromolecules, especially proteins, is vital for grasping their diverse chemical functions with direct impact in biology and pharmacology. While techniques like X-ray crystallography and cryo-electron…

We construct posterior distributions of the equation of state (EoS) for matter beyond the inner crust of neutron stars by incorporating finite nuclei (FN) constraints within relativistic mean field models. These constraints are implemented…

This work proposes a multimodal diffeomorphic registration method using Neural Ordinary Differential Equations (Neural ODEs). Nonrigid registration algorithms exhibit tradeoffs between their accuracy, the computational complexity of their…

This study proposes a self-optimization physics-informed Fourier-features randomized neural network (SO-PIFRNN) framework, which significantly improves the numerical solving accuracy of PDEs through hyperparameter optimization mechanism.…

We use powder neutron diffraction to study the spin and lattice structures of polycrystalline samples of nonsuperconducting PrFeAsO and superconducting PrFeAsO0.85F0.15 and PrFeAsO0.85. We find that PrFeAsO exhibits an abrupt structural…

We present an investigation on the structural and magnetic properties of the interfaces of $\mathrm{Fe_3O_4/MgO(001)}$ and $\mathrm{Fe_3O_4/NiO/MgO(001)}$ by extracting cation-selective magnetooptical depth profiles by means of x-ray…

Thresholding--the pruning of nodes or edges based on their properties or weights--is an essential preprocessing tool for extracting interpretable structure from complex network data, yet existing methods face several key limitations.…

Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (NODEs) represent two distinct machine learning frameworks for modeling nonlinear neuronal dynamics. This study systematically evaluates their performance…

Increased ability to predict protein structures is moving research focus towards understanding protein dynamics. A promising approach is to represent protein dynamics through networks and take advantage of well-developed methods from…

This article reports the optimized experimental requirements to determine neutrino mass hierarchy using electron antineutrinos ($\bar{\nu}_e$) generated in a nuclear reactor. The features of the neutrino mass hierarchy can be extracted from…

Over the past decade, an abundance of information from neutron-star observations, nuclear experiments and theory has transformed our efforts to elucidate the properties of dense matter. However, at high densities relevant to the cores of…

The 8He(d,p) reaction was studied in inverse kinematics at 15.4A MeV using the MUST2 Si-CsI array in order to shed light on the level structure of 9He. The well known 16O(d,p)17O reaction, performed here in reverse kinematics, was used as a…

The discovery of magnetic protein provides a new understanding of a biocompass at the molecular level. However, the mechanism by which magnetic protein enables a biocompass is still under debate, mainly because of the absence of permanent…

Protein interaction networks (PINs) are often used to "learn" new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses…

We report 75As-NQR/NMR studies on the oxygen-deficient iron(Fe)-based oxypnictide superconductors LaFeAsO_{0.6} (T_c=28 K) along with the results on LaFeAsO, LaFeAsO_{0.75}(T_c=20 K) and NdFeAsO_{0.6}(T_c=53 K). Nuclear spin-lattice…

The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods…

Structure-informed protein representation learning is essential for effective protein function annotation and \textit{de novo} design. However, the presence of inherent noise in both crystal and AlphaFold-predicted structures poses…

We show that cluster expansions (CE), previously used to model solid-state materials with binary or ternary configurational disorder, can be extended to the protein design problem. We present a generalized CE framework, in which properties…