Resource Optimization for Quantum Dynamics with Tensor Networks: Quantum and Classical Algorithms.

The exponential scaling of the quantum degrees of freedom with the size of the system is one of the biggest challenges in computational chemistry and particularly in quantum dynamics. We present a tensor network approach for the time-evolution of the nuclear degrees of freedom of multiconfigurational chemical systems at a reduced storage and computational complexity. We also present quantum algorithms for the resultant dynamics. To preserve the compression advantage achieved via tensor network decompositions, we present an adaptive algorithm for the regularization of nonphysical bond dimensions, preventing the potentially exponential growth of these with time. While applicable to any quantum dynamical problem, our method is particularly valuable for dynamical simulations of nuclear chemical systems. Our algorithm is demonstrated using ab initio potentials obtained for a symmetric hydrogen-bonded system, namely, the protonated 2,2'-bipyridine, and compared to exact diagonalization numerical results.

Rare Events Sampling Methods for Quantum and Classical Ab Initio Molecular Dynamics.

We provide an approach to sample rare events during classical ab initio molecular dynamics and quantum wavepacket dynamics. For classical AIMD, a set of fictitious degrees of freedom are introduced that may harmonically interact with the electronic and nuclear degrees of freedom to steer the dynamics in a conservative fashion toward energetically forbidden regions. A similar approach when introduced for quantum wavepacket dynamics has the effect of biasing the trajectory of the wavepacket centroid toward the regions of the potential surface that are difficult to sample. The approach is demonstrated for a phenol-amine system, which is a prototypical problem for condensed phase-proton transfer, and for model potentials undergoing wavepacket dynamics. In all cases, the approach yields trajectories that conserve energy while sampling rare events.

Establishment of Age Specific Reference Interval for Aminoacids and Acylcarnitine in Dried Blood Spot by Tandem Mass Spectrometry.

The Extended Screening for Inborn Errors of Metabolism is done for aminoacidopathies, fatty acid oxidation disorders and organic acid disorders. In a single dried blood spot, the tandem mass spectrometry is capable of measuring multiple analytes like amino acids, acylcarnitines, nucleosides, succinylacetone and lysophosphatidylcholines. This study was proposed to establish age specific reference internal for aminoacids and acylcartinitine in dried blood spot by tandem mass spectrometry. A total of 480 apparently healthy children were enrolled for the study and sub classified into four groups as follows: Group A: 0-1 month, Group B: 1 month-1 year, Group C: 1-5 year and Group D: 5-12 years each having 120 participants. Sample size were calculated as per CLSI approved guidelines. Tables 1 and 2 presents the age-specific percentile distribution of aminoacids and acylcarnitines established from healthy subjects as per rank-based method recommended by the IFCC and CLSI. Tables 3, 4 and 5 presents the cut-off values of primary and secondary marker/ratios for screening of aminoacidopathies, fatty acid oxidation disorders and organic acid disorders respectively. As a general principle, the interpretation of extended newborn screening results should be based on age specific cut-off established by the laboratory for primary analyte concentration and secondary analyte concentration/ ratios. This study was useful in establishing age specific cut-off values for various amino acids and acylcarnitines in South Indian population. [Table: see text] [Table: see text] [Table: see text] [Table: see text] [Table: see text].

Reformulation of All ONIOM-Type Molecular Fragmentation Approaches and Many-Body Theories Using Graph-Theory-Based Projection Operators: Applications to Dynamics, Molecular Potential Surfaces, Machine Learning, and Quantum Computing.

We present a graph-theory-based reformulation of all ONIOM-based molecular fragmentation methods. We discuss applications to (a) accurate post-Hartree-Fock AIMD that can be conducted at DFT cost for medium-sized systems, (b) hybrid DFT condensed-phase studies at the cost of pure density functionals, (c) reduced cost on-the-fly large basis gas-phase AIMD and condensed-phase studies, (d) post-Hartree-Fock-level potential surfaces at DFT cost to obtain quantum nuclear effects, and (e) novel transfer machine learning protocols derived from these measures. Additionally, in previous work, the unifying strategy discussed here has been used to construct new quantum computing algorithms. Thus, we conclude that this reformulation is robust and accurate.

N95 Filtering Facepiece Respirators during the COVID-19 Pandemic: Basics, Types, and Shortage Solutions

@#The coronavirus disease 2019 (COVID-19) is highly infectious, with the current pandemic causing significant morbidity and mortality worldwide. As large numbers of frontline healthcare workers (HCWs) have also been infected and several have died, there is much global concern about protective measures for them, particularly those performing surgery or other procedures with close patient contact. Since the beginning of the pandemic, there has been and there remains a shortage in the supply of personal protective equipment (PPE), including the N95 filtering facepiece (FFP) respirator, for HCWs. N95 respirators have filtration efficiency of 95% of aerosol particles. Surgical N95 respirators are used where fluid resistance is also required together with respiratory protection, e.g. during surgery or interventional procedures. The shortage of N95 respirators may be overcome by extended use and reuse - comprising rotation and decontamination by approved techniques. The additional role of powered air-purifying respirators (PAPR) is also discussed.