Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.     

Deep Learning Unravels a Dynamic Hierarchy While Empowering Molecular Dynamics Simulations

Molecular dynamics (MD) provide predictive understanding of the behavior of condensed matter. However, its true potential remains largely untested because relevant timescales are often inaccessible, limited portions of conformation space get sampled, and infrequent events are usually irreproducible. A culprit is the huge informational burden required to iterate integration steps. To address the problem, deep learning is applied to encode the dynamics into a shorthand embodiment retaining only essential topological features of the vector field that steers MD integration. The flow is simplified via an equivalence relation that identifies conformations within basins of attraction in potential energy and encodes the dynamics onto a modulo-basin “quotient space” where fast motions are averaged out. The quotient space projection enables coverage of realistic timescales while unraveling the underlying dynamic hierarchy. Deep learning is exploited to propagate the simplified trajectory beyond MD-accessible timescales and to reconstruct it at atomistic level. As shown, the quotient-encoding-propagating-decoding scheme generates within a few hours protein folding pathways with experimentally verified outcomes. By contrast, MD computations covering comparable timespans would take over a hundred days on special-purpose supercomputers. Thus, quotient space constitutes a model for hierarchical understanding of MD simulation while enabling access to realistic timescales.     

Modelling Photosynthesis with ZnII-Protoporphyrin All-DNA G-Quadruplex/Aptamer Scaffolds

All-DNA scaffolds act as templates for the organization of photosystem I model systems. A series of DNA templates composed of Zn^II-protoporphyrin IX (Zn^IIPPIX)-functionalized G-quadruplex conjugated to the 3′- or 5′-end of the tyrosinamide (TA) aptamer and Zn^IIPPIX/G-quadruplex linked to the 3′- and 5′-ends of the TA aptamer through a four-thymidine bridge. Effective photoinduced electron transfer (ET) from Zn^IIPPIX/G-quadruplex to bipyridinium-functionalized tyrosinamide, TA-MV²⁺, bound to the TA aptamer units is demonstrated. The effectiveness of the primary ET quenching of Zn^IIPPIX/G-quadruplex by TA-MV²⁺ controls the efficiency of the generation of TA-MV^+.. The photosystem-controlled formation of TA-MV^+. by the different photosystems dictates the secondary activation of the ET cascade corresponding to the ferredoxin-NADP⁺ reductase (FNR)-catalysed reduction of NADP⁺ to NADPH by TA-MV^+., and the sequestered alcohol dehydrogenase catalysed reduction of acetophenone to 1-phenylethanol by NADPH.     

The Hexameric Resorcinarene Capsule at Work: Supramolecular Catalysis in Confined Spaces

The hexameric resorcinarene capsule reported by Atwood in 1997 is able to act as a supramolecular catalyst. Its inner cavity provides a unique environment, in which organic reactions can be efficiently catalyzed, thanks to the confinement effect of the substrates. In addition, different stereo- and regiochemical outcomes can be observed with respect to reactions in the bulk solvent. The hexameric capsule shows some catalytic features reminiscent of natural enzymes. In particular, highlights of the capsule discussed herein include 1) its ability to recognize the substrates (substrate selectivity), 2) the possibility of stabilizing the transition states and intermediates through secondary interactions, 3) an inherent Brønsted acidity, and 4) its ability to act as a hydrogen-bond catalyst. In addition, it is also shown how the catalytic activity of the hexameric capsule can be modulated in the presence of competitive alkylammonium guests, which show high affinities for its internal cavity. These aspects are discussed through a critical examination of data reported in the literature in recent years.     

Matrix representation of a binary relation using fuzzy and artificial learning theory. An algorithm which uses the potential functions learning rule

One of the most significant problems in economic domain is the dispose of human preference and choice forecasting. Recently, the economists have focused their researches to use the fuzzy concepts and the artificial learning procedures in the theory of economic choice. This paper extends the work done in this direction and offers a new algorithm for finding the matrix representation of the fuzzy binary relation which describes a preference relation.     

Variable volume piezometer for measurement of volumetric properties of materials under high pressure

A piezometer was designed to measure volume change of foods and other biomaterials as a function of pressure, and calibrated and tested up to 700 MPa at 25°C. The sensor was constructed from a polycarbonate tube, sealed with a plug on one end and a removable piston on the other. Samples were placed inside the tube. Upon pressurization, the piston displaced inside the tube, transmitting pressure to the sample; impedance in a magnet wire coil on the outside of the tube was measured to characterize volume change. Sensor calibration with water yielded a polynomial that characterized volume as a function of impedance. Sensor precision is 0.3% of measured volume; its resolution allows measurement of sub-microliter volume changes. Testing with ethanol and sucrose solutions yielded data within 1% of previously reported values. The sensor's applications include measurement of volumetric properties of liquid and solid biomaterials, and chemical reaction volumes.     

Dissipation matrix and artificial heat conduction for Godunov‐type schemes of compressible fluid flows

This paper aims to reassess the Riemann solver for compressible fluid flows in Lagrangian frame from the viewpoint of modified equation approach and provides a theoretical insight into dissipation mechanism. It is observed that numerical dissipation vanishes uniformly for the Godunov‐type schemes in the sense that associated dissipation matrix has zero determinant if an exact or approximate Riemann solver is used to construct numerical fluxes in the Lagrangian frame. This fact connects to some numerical defects such as the wall‐heating phenomenon and start‐up errors. To cure these numerical defects, a traditional numerical viscosity is added, as well as the artificial heat conduction is introduced via a simple passage of the Lax–Friedrichs type discretization of internal energy. Copyright © 2016 John Wiley & Sons, Ltd.     

Levenberg-Marquardt based artificial physics method for mobile robot oscillation alleviation

This paper proposed a modified artificial physics (AP) method to solve the autonomous navigation problem for mobile robots in complex environments. The basic AP method tends to cause oscillations in the presence of obstacles and in narrow passages, which can result in time consumption. To alleviate oscillation, we modified the AP method using the Levenbery-Marquardt (LM) algorithm. In the modified AP method, we altered the original directions of AP forces to the Newton direction, and adjust the parameter by the LM algorithm. A series of comparative experimental results show that the modified AP method can achieve smoother trajectories with less time consumption. This demonstrates the feasibility and effectiveness of our proposed approach.