Search

Byline: Ngairangbam. Sanjeeta, Sumita. Banerjee, Saikat. Mukherjee, T. Devi, D. Nandini, P. Aparnadevi Context: Areca nut (AN) is a potent cytotoxic and genotoxic agent. Oxidative stress-induced by chewing of AN [...]

This study investigates velocity adjustment directions after hopping in surface hopping dynamics. Using fulvene and a protonated Schiff base (PSB4) as case studies, we investigate the population decay and reaction yields of different sets of dynamics with the velocity adjusted in either the nonadiabatic coupling, gradient difference, or momentum directions. For the latter, in addition to the conventional algorithm, we investigated the performance of a reduced kinetic energy reservoir approach recently proposed. Our evaluation also considered velocity adjustment in the directions of approximate nonadiabatic coupling vectors. While results for fulvene are susceptible to the adjustment approach, PSB4 is not. We correlated this dependence to the topography near the conical intersections. When nonadiabatic coupling vectors are unavailable, the gradient difference direction is the best adjustment option. If the gradient difference is also unavailable, a semiempirical vector direction or the momentum direction with a reduced kinetic energy reservoir becomes an excellent option to prevent an artificial excess of back hoppings. The precise velocity adjustment direction is less crucial for describing the nonadiabatic dynamics than the kinetic energy reservoir’s size.

Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. This paper describes the main features of these methods and how they are implemented in Newton-X. It emphasizes the newest developments, including zero-point-energy leakage correction, dynamics on complex-valued potential energy surfaces, dynamics induced by incoherent light, dynamics based on machine-learning potentials, exciton dynamics of multiple chromophores, and supervised and unsupervised machine learning techniques. Newton-X is interfaced with several third-party quantum-chemistry programs, spanning a broad spectrum of electronic structure methods.

The problem associated with the zero-point energy (ZPE) leak in classical trajectory calculations is well known. Since ZPE is a manifestation of the quantum uncertainty principle, there are no restrictions on energy during the classical propagation of nuclei. This phenomenon can lead to unphysical results, such as forming products without the ZPE in the internal vibrational degrees of freedom (DOFs). The ZPE leakage also permits reactions below the quantum threshold for the reaction. We have developed a new Hessian-free method, inspired by the Lowe-Andersen thermostat model, to prevent energy dipping below a threshold in the local-pair (LP) vibrational DOFs. The idea is to pump the leaked energy to the corresponding local vibrational mode taken from the other vibrational DOFs. We have applied the new correction protocol on the ab-initio ground-state molecular dynamics simulation of the water dimer (H2O)2, which dissociates due to unphysical ZPE spilling from high-frequency OH modes. The LP-ZPE method has been able to prevent the ZPE spilling of the OH stretching modes by pumping back the leaked energy into the corresponding modes, while this energy is taken from the other modes of the dimer itself, keeping the system as a microcanonical ensemble.

Fusion frames are widely studied for their applications in recovering signals from large data. These are proved to be very useful in many areas, for example, wireless sensor networks. In this paper, we discuss a generalization of fusion frames, K-fusion frames. K-fusion frames provide decompositions of a Hilbert space into atomic subspaces with respect to a bounded linear operator. This article studies various kinds of properties of K-fusion frames. Several perturbation results on K-fusion frames are formulated and analyzed.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Liquid–vapor phase change in the presence of non-condensable gases is a classical problem, which continues to challenge continuum modeling. Here, we propose a new model based on the phase field method, which describes the dynamics of the non-condensable gas, phase change and flow simultaneously. The model is built by extending van der Waals and Korteweg's theory for phase-transforming mixtures. The model equations have fourth order partial differential operators which presents significant challenges to standard spatial discretization methods. In addition, the isentropic form of the model equations is not hyperbolic at the liquid–gas interface, which inhibits the direct application of most algorithms for systems of hyperbolic equations. We propose a novel numerical scheme based on Isogeometric Analysis and the Taylor–Galerkin method, and also implement a residual based discontinuity capturing scheme. We show numerical results, at micron length scales, to study the accuracy and stability of our algorithm. We study the flow of a shock wave past a liquid droplet to test our numerical algorithm when steep gradients are present in the solution. Finally, we use the model to study the problem of gas–vapor bubble collapse near a solid wall. • We propose a residual-based numerical scheme using Taylor–Galerkin discretization. • We show simulations of bubble collapse near a wall and shock wave past a droplet. [ABSTRACT FROM AUTHOR]

The major bottleneck of first principle based beyond Born–Oppenheimer (BBO) treatment originates from large number and complicated expressions of adiabatic to diabatic transformation (ADT) equations for higher dimensional sub-Hilbert spaces. In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. It is noteworthy to mention that the nonadiabatic coupling terms (NACTs) often become singular (removable) at degenerate point(s) or along a seam in the nuclear configuration space (CS) and thereby, a unitary transformation is required to convert the kinetically coupled (adiabatic) Hamiltonian to a potentially (diabatic) one to avoid such singularity(ies). The “ADT” program can be efficiently used to (a) formulate analytic functional forms of differential equations for ADT angles and diabatic potential energy matrix and (b) solve the set of coupled differential equations numerically to evaluate ADT angles, residue due to singularity(ies), ADT matrices, and finally, diabatic potential energy surfaces (PESs). For the numerical case, user can directly provide ab initiodata (adiabatic PESs and NACTs) as input files to this software or can generate those input files through in-built python codes interfacing MOLPRO followed by ADT calculation. In order to establish the workability of our program package, we selectively choose six realistic molecular species, namely, NO2radical, H3+, F + H2, NO3radical, C6H6+radical cation, and 1,3,5-C6H3F3+radical cation, where two, three, five and six electronic states exhibit profound nonadiabatic interactions and are employed to compute diabatic PESs by using ab initiocalculated adiabatic PESs and NACTs. The “ADT” package released under the GNU General Public License v3.0 (GPLv3) is available at https://github.com/AdhikariLAB/ADT-Programand also as the Supporting Information of this article.

Chikungunya fever is a major public health issue in India. Re-emergence of chikungunya virus (CHIKV) in West Bengal was detected after 32 years in 2006. After 2010, this infection was in apparent decline, but in 2016 a massive outbreak affected the country. Present study was carried out to understand CHIKV infection dynamics during recent outbreaks in West Bengal, Eastern India and its implication on disease manifestations. Blood was collected from 641 symptomatic patients. Patients’ sera were serologically diagnosed to detect presence of anti-chikungunya-IgM antibodies. Viral RNA was extracted; presence of CHIKV genome and its respective viral load was determined by real time quantitative reverse transcription-PCR (real-time qRT-PCR). Statistical analysis was performed using EPI INFO software. CHIKV infection was detected in 24.64% of symptomatic patients. Middle-aged patients (31–40 years) were predominantly affected; clinically, both arthralgia and joint-swelling were significantly prevalent among CHIKV-infected patients. Myalgia, joint-swelling, and arthralgic manifestation were found in significantly higher frequency among patients with high chikungunya viral load (> 10,000 copies/ml). Thus, this study clearly indicated the re-emergence of CHIKV in Eastern India. Significant presence of myalgia, joint swelling, and arthralgia among chikungunya patients with high viral load implied association of disease severity with viral load; requiring vigilance for proper management of infected patients as this disease is highly morbid in nature. However, in addition to chikungunya virus, other viral, bacterial, and protozoal infections also occur during post-monsoon season in India, having overlapping symptoms. Hence, continuous monitoring of these infections is required for better clinical management of patients.

Nonadiabatic molecular dynamics plays an essential role in exploring the time evolution of molecular systems. Various methods have been developed for this study, with varying accuracy and computational cost. One very successful among them is trajectory surface hopping, which propagates nuclei as classical trajectories using forces from a quantum description of the electrons and incorporates nonadiabatic effects through stochastic state changes during each trajectory propagation. A statistical analysis of an ensemble of the independent trajectories recovers the simulated system’s behavior. This approach can give good results, but it is known to overlook nuclear quantum effects, leading to inaccurate predictions. Here, we present quantum dynamics from classical trajectories (QDCT), a new protocol to recover the quantum wavepacket from the classical trajectories generated by surface hopping. In this first QDCT implementation, we apply it to recover results at the multiple spawning level from postprocessing surface hopping precomputed trajectories. With a series of examples, we demonstrate QDCT’s potential to improve the accuracy of the dynamics, correct decoherence effects, and diagnose problems or increase confidence in surface hopping results. All that comes at virtually no computational cost since no new electronic calculation is required.

We present the molecular symmetry (MS) adapted treatment of nonadiabatic coupling terms (NACTs) for the excited electronic states (22E′ and 12A1′) of Na3cluster, where the adiabatic potential energy surfaces (PESs) and the NACTs are calculated at the MRCI level by using an ab initio quantum chemistry package (MOLPRO). The signs of the NACTs at each point of the configuration space (CS) are determined by employing appropriate irreducible representations (IREPs) arising due to MS group, and such terms are incorporated into the adiabatic to diabatic transformation (ADT) equations to obtain the ADT angles. Since those sign corrected NACTs and the corresponding ADT angles demonstrate the validity of curl condition for the existence of three-state (22E′ and 12A1′) sub-Hilbert space, it becomes possible to construct the continuous, single-valued, symmetric, and smooth 3 × 3 diabatic Hamiltonian matrix. Finally, nuclear dynamics has been carried out on such diabatic surfaces to explore whether our MS-based treatment of diabatization can reproduce the pattern of the experimental spectrum for system Bof Na3cluster.

Amyloid fibrils are misfolded, protease-resistant forms of normal proteins. They are infectious such as prions or noninfectious such as β-amyloid (Aβ) fibrils causing Alzheimer's disease (AD). Prions and amyloids are structurally similar, possessing cross β-pleated sheet-like structures. As microbial keratinase could degrade prions, we tested keratinase activity on Aβ fibrils.

Theranostic-based nanomedicine plays a crucial role in the field of cancer therapy. This is due to having the capability to combine both therapy and diagnosis together in a single system. Herein a new class of metal–ligand-based nanocarrier in a norbornene backbone has been designed as a theranostic system. Fe3+-terpyridine complex (Fe-Tpy)has been used here as T1contrast agent for high-resolution MR imaging, and hydrazone-linked doxorubicin is used for effective pH-responsive delivery. Polyethylene glycol functionalized with a folic acid (peg folate) motif is used to make the entire polymeric system dispersible in water for longer retention and site-specific therapy. All these specialty functional groups are anchored in a single system by using the ring-opening metathesis polymerization (ROMP) technique under the norbornene backbone. Relaxivity study and 1D image experiments have shown the utility of Fe-Tpycomplex as an effective T1contrast agent. In vitrostudies are performed to confirm the promising potentiality of the nanocarrier as the efficient nanotheranostic system in prostate cancer.

AbstractThis article addresses various properties and different methods of estimation of the parameters of exponentiated Gumbel distribution from the frequentist point of view. Various mathematical and statistical properties of the exponentiated Gumbel distribution, such as quantiles, moments, conditional moments, hazard rate function, mean residual lifetime, mean deviation about mean and median, entropies and order statistics, are derived. We briefly describe different frequentist approaches, namely, maximum likelihood estimation, method of moments, percentile-based estimation method, least squares estimation, method of maximum product of spacings, method of Cramér-von-Mises and methods based on Anderson-Darling statistic. Monte Carlo simulations are performed to compare the performance of the estimation methods for small and large samples. The application of the model is studied using a flood data example. Bootstrap method was used to obtain bias and standard error of the estimates as well as the percentile confidence intervals. Further, confidence regions for the parameters are obtained using likelihood ratio based method. Finally, the concept of return period is used to predict the occurrence of flood in the future.

In signal processing and image science, capturing the phase of measurements is often difficult. In such cases, one needs to reconstruct signal/image from intensity measurements, that is, perform phase retrieval. Like phase retrieval, norm retrieval restores the norm of a signal from its intensity measurements. In the last few decades, phase retrieval and norm retrieval have become active research areas. In this paper, we study phase retrieval sequences and norm retrieval sequences in the tensor product of Hilbert spaces. We explore various methods of construction of phase (norm) retrieval sequences in tensor product spaces by means of tensor product of vectors and tensor product of operators.

Phase retrieval was introduced for Hilbert space frames by Balan et al. in 2006. In 2016, Bemrose et al. instigated weaving frames for Hilbert spaces. In this paper, we introduce the notion of weaving phase retrieval sequences and weaving norm retrieval sequences. We begin with the simplest way of constructing weaving phase (norm) retrieval sequences from a given phase (norm) retrieval sequence. Given a bounded linear operator T, we study under what conditions, a phase retrieval sequence, φ, is woven with Tφ. We investigate whether the property of weaving phase retrieval is preserved under the action of T. Further, we explore whether wovenness is preserved under perturbation.

The article deals with visualization of heatlines and isotherms during cooling of a hot moving steel plate numerically. The cooling of the plate is assumed using single spray-water jet. The visualization process is carried out by forming and discretizing the governing energy equation based on finite volume method. The linear algebraic equations are solved by tri-diagonal matrix algorithm (TDMA). Accordingly, a numerical code is developed on FORTRAN platform. In the computational domain, a suitable heat transfer region for cooling is identified analyzing the heatline distribution in the domain and depends on the process parameters. Accordingly a parametric study is performed and reveals that effective heat transfer region increases with increasing jet velocity and cooling methods, and decreases with increasing plate velocity.

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.