Reactive Trajectories Research Articles

Maltreatment type differences in cortisol stress response trajectories across adolescence.

This study examined cortisol stress response trajectories across adolescence in 454 maltreated and comparison youth recruited from Los Angeles County between 2002 and 2005 (66.7% maltreated; 46.7% girls; 39.0% Latino; 37.7% Black; 12.3% Mixed or Biracial; 11.0% White; Mage = 10.9 years, SD = 1.2). Adolescents' peak activation and cortisol reactivity and recovery slopes following the Trier Social Stress Test for Children were calculated at four waves, then used to model peak activation and cortisol reactivity and recovery trajectories arrayed by age. Maltreated youth had blunted cortisol reactivity at age 9 relative to comparison youth (b = -.19, p = .02). Sexually and physically abused youth showed blunted cortisol reactivity and recovery trajectories relative to emotionally abused and neglected youth.

Charting Nanocluster Structures via Convolutional Neural Networks.

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes.

Effects of Barefoot and Shod Conditions on the Kinematics and Kinetics of the Lower Extremities in Alternating Jump Rope Skipping-A One-Dimensional Statistical Parameter Mapping Study.

To explore the difference in the biomechanics of the lower extremity during alternating jump rope skipping (AJRS) under barefoot and shod conditions. Fourteen experienced AJRS participants were randomly assigned to wear jump rope shoes or be barefoot (BF) during the AJRS at a self-selected speed. The Qualisys motion capture system and Kistler force platform were used to synchronously collect the ground reaction forces and trajectory data of the hip, knee, ankle, and metatarsophalangeal (MTP) joints. One-dimensional statistical parameter mapping was used to analyze the kinematics and kinetics of the lower extremity under both conditions using paired t-tests. Wearing shoes resulted in a significant decrease in the ROM (p < 0.001) and peak angular velocity (p < 0.001) of the MTP joint during the landing phase. In addition, the MTP joint power (p < 0.001) was significantly larger under shod condition at 92-100% of the landing phase. Moreover, wearing shoes reduced the peak loading rate (p = 0.002). The findings suggest that wearing shoes during AJRS could provide better propulsion during push-off by increasing the MTP plantarflexion joint power. In addition, our results emphasize the significance of the ankle and MTP joint by controlling the ankle and MTP joint angle.

Wireless pressure insoles for measuring ground reaction forces and trajectories of the centre of pressure during functional activities

Wireless pressure insoles may enable the assessment of movement biomechanics in a real-world setting, and thus play an important role in the recommendation of clinical management, but they are not yet a gold standard due to the unknown accuracy and reliability with respect to different functional activities. Here, we compare novel wireless pressure insoles with force plates and examine the test–retest reliability of the insoles for measuring vertical ground reaction forces (vGRFs) and trajectories of the center of pressure (COP). In this observational study, healthy adults underwent two data collection sessions during one day. The Bland–Altman analysis was used to compare the outcomes measured with the two instruments during squats, jumps, and the sit-to-stand test. Test–retest reliability was assessed by the interclass correlation coefficient and the standard error of measurement for the outcomes during squats, jumps, walking, and stair ambulation. Trajectories of the COP in the anterior–posterior direction were comparable between the two systems during all activities. The insoles consistently measured shorter trajectories of the COP in the medial–lateral direction (except jumps) and lower vGRFs than the force plates. Test–retest reliability of the insoles was fair to high or excellent for all outcomes during all activities. In conclusion, the insoles provide reliable measures of vGRFs and trajectories of the COP during multiple functional activities in healthy adults. Although the insoles do not produce identical results to the force plate, the qualitative similarity and consistency between the two systems confirm the insoles can be used to measure these outcomes, based on the purpose and accuracy required.

Regular reaction dynamics in analytical form in the vicinity of symmetrical transition states. Central barrier crossings in SN2 reactions.

When an activated complex, as defined in transition state theory (TST), has a polyhedral shape, its kinetic energy is found to be diagonal in a system of spherical polar coordinates. If, in addition, the polyhedron is characterized by a high symmetry, then its dynamics considerably simplifies. An application of this approach to the most symmetrical TS known to date, i.e., that which controls the Cl- + CH3Cl → ClCH3 + Cl- SN2 nucleophilic substitution, is presented and an analytical expression of its potential energy surface is provided. In a substantial range around the saddle point, approximate equations of motion for the two components of the reaction coordinate, i.e., the antisymmetrical stretching motion of the ClCCl core and the wagging motion of the hydrogen triad, can be derived in an analytical form. During an extensive period of time, the main component of the reaction coordinate is governed by an unexpectedly simple equation of motion that depends on a single initial condition, irrespective of the other ones and of the internal energy. Reactive trajectories are observed to form a perfectly collimated bundle characterized by undetectable dispersion, thereby giving a spectacular example of regular dynamics in an anharmonic potential. Regularity and collimation are brought about by local symmetry, which is a widespread feature of potential energy surfaces. Anharmonicity is observed to influence the dynamics only at a late stage. As energy increases, trajectories tend to fan out and to deviate from the analytical equation. For the wagging motion, chaos sets in at much lower energies.

The complex-formation dynamics of O + H(D)2+ (v = 0,1; j = 0) reactions on 12A'' and 12A′

The reactions O + H(D)2+ are studied based on the ground state potential energy surface (PES) 12A″ and the first excited PES 12A′. Both two potential energy surfaces comprise deep wells during the minimum energy reaction path. The average lifetimes of the complex-formation of the title reactions as a function of translational energy are calculated. The results indicated that the average lifetime of the complex-formation decreases with the increasing of collision energy for the two potential energy surfaces. There are two kinds of reaction mechanisms, one is direct reaction mechanism; another is indirect one. The indirect mechanism is dominated for all the collision energies studied. However, with the increasing of the collision energy, the direct reaction mechanism becomes more and more pronounced. Besides, the influences of reagent vibrational excitation and isotopic effect on the scalar properties and vector properties are studied in detail respectively in this work. The reagent vibrational excitation enhances the number of the direct reactive trajectories remarkably on 12A″, though it has nearly no influence on the average lifetime of the complex formation. In strong contrast, the reagent vibrational excitation suppresses the number of indirect reactive trajectories and reduces the average lifetime of the complex formation on the 12A′. The isotopic substitution has no influence on the scalar properties; however, it affects the vector properties strongly.

Navigating phase space transport with the origin-fate map.

We introduce and demonstrate the usage of the origin-fate map (OFM) as a tool for the detailed investigation of phase space transport in reactant-product-type systems. For these systems, which exhibit clearly defined start and end states, it is possible to build a comprehensive picture of the lobe dynamics by considering backward and forward integration of sets of initial conditions to index their origin and fate. We illustrate the method and its utility in the study of a two degrees of freedom caldera potential with four exits, demonstrating that the OFM not only recapitulates results from classical manifold theory but even provides more detailed information about complex lobe structures. The OFM allows the detection of dynamically significant transitions caused by the creation of new lobes and is also able to guide the prediction of the position of unstable periodic orbits (UPOs). Further, we compute the OFM on the periodic orbit dividing surface (PODS) associated with the transition state of a caldera entrance, which allows for a powerful analysis of reactive trajectories. The intersection of the manifolds corresponding to this UPO with other manifolds in the phase space results in the appearance of lobes on the PODS, which are directly classified by the OFM. This allows computations of branching ratios and the exploration of a fractal cascade of lobes as the caldera is stretched, which results in fluctuations in the branching ratio and chaotic selectivity. The OFM is found to be a simple and very useful tool with a vast range of descriptive and quantitative applications.

Optimal Reaction Coordinates and Kinetic Rates from the Projected Dynamics of Transition Paths.

Finding optimal reaction coordinates and predicting accurate kinetic rates for activated processes are two of the foremost challenges of molecular simulations. We introduce an algorithm that tackles the two problems at once: starting from a limited number of reactive molecular dynamics trajectories (transition paths), we automatically generate with a Monte Carlo approach a sequence of different reaction coordinates that progressively reduce the kinetic rate of their projected effective dynamics. Based on a variational principle, the minimal rate accurately approximates the exact one, and it corresponds to the optimal reaction coordinate. After benchmarking the method on an analytic double-well system, we apply it to complex atomistic systems: the interaction of carbon nanoparticles of different sizes in water.

Dynamical matching in a three-dimensional Caldera potential-energy surface.

In a previous paper, we used a recent extension of the periodic-orbit dividing surfaces method to distinguish the reactive and nonreactive parts in a three-dimensional (3D) Caldera potential-energy surface. Furthermore, we detected the phenomenon of dynamical matching in a 3D Caldera potential-energy surface. This happened for a specific value of the radius r of the periodic orbit dividing surfaces (r=0.25). In this paper, we demonstrated that the chemical ratios of the number of reactive and nonreactive trajectories to the total number of trajectories converges for a range of the radius r of the periodic-orbit dividing surfaces. This is important not only for validating the previous paper and to confirm that the method can detect the phenomenon of dynamical matching independently of the chosen radius of the construction of the dividing surface but also for investigating the application of the method to other Hamiltonian models.

Autonomic reactivity during acute social stress: exploratory investigation of an interaction by threat interpretation bias and emotion regulation difficulties

ABSTRACT Objective: This preliminary study examined the interactive influences of interpretation bias and emotion dysregulation in predicting autonomic stress response. Methods: Participants (N = 72 college students; ages 18–25, 70% female, 47% Hispanic/Latinx), completed a performance-based assessment of threat interpretation bias and self-report measures of emotion regulation difficulties. Electrodermal activity (EDA; skin conductance level) was collected while participants completed a standardized psychosocial stressor task. Results: Interpretation bias and emotion regulation difficulties interacted to predict EDA trajectories (β = 0.054, SE = .001, CI:.007, .002, p < .001) during acute stress: presence of either interpretation bias toward threat (β = 6.950, SE = 2.826, CI: 1.407, 12.488, p = .013) or emotion regulation difficulties (β = 9.387, SE = 3.685, CI: 2.141, 16.636, p = .011) was associated with greater reactivity and poorer recovery; presence of both was associated with the lowest resting state EDA and blunted reactivity (β = 1.340, SE = 3.085, CI: −4.721, 7.407, p = .66). Conclusions: Preliminary findings support interpretation bias and emotion regulation difficulties, and their distinct interactive patterns, as predictors of autonomic reactivity trajectories during stress. Interpretation bias modification and emotion regulation skills may be important intervention targets for common psychological conditions that are influenced by aberrant psychophysiological processes.

Molecular Dynamics of Iron Porphyrin-Catalyzed C-H Hydroxylation of Ethylbenzene.

Quasi-classical molecular dynamics (MD) simulations were carried out to study the mechanism of iron porphyrin-catalyzed hydroxylation of ethylbenzene. The hydrogen atom abstraction from ethylbenzene by iron-oxo species is the rate-determining step, which generates the radical pair of iron-hydroxo species and the benzylic radical. In the subsequent radical rebound step, the iron-hydroxo species and benzylic radical recombine to form the hydroxylated product, which is barrierless on the doublet energy surface. In the gas-phase quasi-classical MD study on the doublet energy surface, 45% of the reactive trajectories lead directly to the hydroxylated product, and this increases to 56% in implicit solvent model simulations. The percentage of reactive trajectories leading to the separated radical pair is 98-100% on high-spin (quartet/sextet) energy surfaces. The low-spin state reactivity dominates in the hydroxylation of ethylbenzene, which is dynamically both concerted and stepwise, since the time gap between C-H bond cleavage and C-O bond formation ranges from 41 to 619 fs. By contrast, the high-spin state catalysis is an energetically stepwise process, which has a negligible contribution to the formation of hydroxylation products.

Are Reactions to Ego Vehicles Predictable Without Data?: A Semi-Supervised Approach

To make intelligent decisions in an autonomous vehicle, the system must predict the future reactions of surrounding vehicles for any given action plan of the ego vehicle. However, learning reactive trajectories is challenging due to scant action-reaction pair data. That is, building a dataset with multiple action-reaction pairs for an identical scene history is impossible in reality. Here, we propose a semi-supervised learning framework with auxiliary structures to handle this problem. The proposed training framework has two modules: <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Action Reconstructor</i> </b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Identifier</i> </b> modules with corresponding loss functions referred to as the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Reconstruction Loss</i> </b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Association Loss</i> </b> . In addition to the conventional supervised approach pertaining to readily available data, the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Action Reconstructor</i> </b> module is employed to learn the dependencies on the ego vehicle in an unsupervised manner. Furthermore, reaction trajectory data corresponding to the augmented future trajectories of the ego vehicle are not available, meaning that the model must be trained in an unsupervised manner as well. The main idea of the proposed unsupervised learning method is to find the identity feature vector from both history and future trajectories and associate these features for each vehicle. This idea is realized by introducing the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Identifier</i> </b> network and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Association Loss</i> </b> , which are used only during the training process. Interestingly, experimental results show that plausible reaction can be predicted for the augmented future trajectory of the ego vehicle, which indicates that the network can generalize the interactive behavior of vehicles from a partially labelled dataset.

Deep learning collective variables from transition path ensemble.

The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples.

Accelerated Entropic Path Sampling with a Bidirectional Generative Adversarial Network.

The role of entropy in mediating the dynamic outcomes of chemical reactions remains largely unknown. To evaluate the change of entropy along post-transition state paths, we have previously developed entropic path sampling that computes configurational entropy from an ensemble of reaction trajectories. However, one major caveat of this approach lies in its high computational demand: about 2000 trajectories are needed to converge the computation of an entropic profile. Here, by leveraging a deep generative model, we developed an accelerated entropic path sampling approach that evaluates entropic profiles using merely a few hundred reaction dynamic trajectories. The new method, called bidirectional generative adversarial network-entropic path sampling, can enhance the estimation of probability density functions of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from the true data. The method was established using cyclopentadiene dimerization, in which we reproduced the reference entropic profiles (derived from 2480 trajectories) using merely 124 trajectories. The method was further benchmarked using three reactions with symmetric post-transition-state bifurcation, including endo-butadiene dimerization, 5-fluoro-1,3-cyclopentadiene dimerization, and 5-methyl-1,3-cyclopentadiene dimerization. The results indicate the existence of a "hidden entropic intermediate", which is a dynamic species that binds to a local entropic maximum where no free energy minimum is formed.

Interactive Organic Reaction Trajectory Animation iPhone Application (iORA) and Web Site (webORA)

Organic reaction trajectories show atomic motion and molecular reorganization of reactants during a single product-forming collision. iORA is a free (available from the Apple App Store), easy to use, highly interactive iPhone application that provides on-the-fly 3D rendering of organic reaction trajectories calculated with density functional theory (DFT) direct dynamics simulations. Visualization of trajectories using iORA enables qualitative and quantitative learning about organic reaction pathways. iORA users can interactively zoom, rotate, and translate the reaction animations. Users can track distances, angles, and dihedral angles. Other iORA features include buttons to jump to the transition state, control the animation speed, and 3D image display. Currently available reaction classes include Bronsted acid–base, alkyl substitution, alkene addition, alkyne addition, diene addition, rearrangement, oxidation, reduction, carbonyl addition, radical, pericyclic, and aromatic substitution. WebORA is a web-based site that also displays interactive DFT-based organic reaction trajectory simulations from these reaction classes. It can be used by all web browsers and operating systems. iORA and webORA also offer brief trajectory descriptions.

Kinetic Mechanism for Simulating the Temperature and Pressure Effect on the Explosive Decomposition of Acetylene by ReaxFF Molecular Dynamics

AbstractIn the utilization of acetylene as the raw material in chemical engineering, understanding its pyrolysis mechanism is a vital issue in avoiding its explosion. The ReaxFF MD simulation was adopted to investigate the pyrolytic behavior of acetylene at various temperatures and pressures. The simulation results revealed that the pyrolysis mechanism of acetylene could be divided into three temperature ranges: (i) T&lt;1200 K, where homogenous molecular polymerization of acetylene occurs, producing polymerization products primarily C4H4 and C6H6; (ii) 1200&lt;T&lt;1800 K, where the free radical path competes with the molecular path, and free radicals such as C2H3⋅ and C4H3⋅ participate in the polymerization reaction; (iii) T&gt;1800 K, where the acetylene molecule is cracked, and H⋅ and C2H⋅ drive the formation of polyacetylene and hydrogen. Finally, a reaction network for the pyrolysis of acetylene to yield compounds below C10 is generated through the identification, quantification, and evaluation of the reaction trajectory. The kinetic model of acetylene pyrolysis has been dramatically improved and supplemented, which is in satisfactory agreement with experimental values.

Concepts Relevant for the Kinetic Analysis of Reversible Reaction Systems.

The net rate of a reversible chemical reaction is the difference between unidirectional rates of traversal along forward and reverse reaction paths. In a multistep reaction sequence, the forward and reverse trajectories, in general, are not the microscopic reverse of one another; rather, each unidirectional route is comprised of distinct rate-controlling steps, intermediates, and transition states. Consequently, traditional descriptors of rate (e.g., reaction orders) do not reflect intrinsic kinetic information but instead conflate unidirectional contributions determined by (i) the microscopic occurrence of forward/reverse reactions (i.e., unidirectional kinetics) and (ii) the reversibility of reaction (i.e., nonequilibrium thermodynamics). This review aims to provide a comprehensive resource of analytical and conceptual tools which deconvolute the contributions of reaction kinetics and thermodynamics to disambiguate unidirectional reaction trajectories and precisely identify rate- and reversibility-controlling molecular species and steps in reversible reaction systems. The extrication of mechanistic and kinetic information from bidirectional reactions is accomplished through equation-based formalisms (e.g., De Donder relations) grounded in principles of thermodynamics and interpreted in the context of theories of chemical kinetics developed in the past 25 years. The aggregate of mathematical formalisms detailed herein is general to thermochemical and electrochemical reactions and encapsulates a diverse body of scientific literature encompassing chemical physics, thermodynamics, chemical kinetics, catalysis, and kinetic modeling.

Elucidation of the mechanism and the free energy of a Morita-Baylis-Hillman type reaction catalyzed by enzymes using transition path sampling.

Transition path sampling (TPS) is a Monte-Carlo importance sampling technique in the space of trajectories which is used to elucidate transition states, reaction coordinates and kinetic parameters of complex biomolecular reactions. Within TPS an ensemble of truly dynamic reactive trajectories are obtained with a Monte-Carlo type acceptance criterion in combination with the shooting and shifting algorithms. To sample the equilibrium properties such as free energies, the inclusion of non-reactive trajectories in the TPS ensemble is essential since these trajectories sample the long lived reactant and product states. With a modified acceptance criterion and in combination with a window based sampling algorithm, TPS can be used to obtain unbiased free energy profiles for chemical reactions that are catalyzed by enzymes. Here we use TPS to study the proton transfer mechanism and the corresponding reaction free energy of enzymes (MBHases) that catalyze a Morita-Baylis-Hillman (MBH) type reaction. These enzymes were developed through directed evolution and we aim to map the quantum mechanical tunneling (QMT) contribution in the proton transfer mechanism to laboratory evolution of the MBHases to determine the role played by QMT to rate improvements of evolved variants. The calculated free energies would also provide insights into the role of QMT in the reaction mechanism as the width and height of the free energy barrier determines the importance of the QMT contributions.

A kinetic mechanism for U6 snRNP biogenesis obtained by single molecule fluorescence colocalization microscopy.

Pre-mRNA splicing is the process of removing introns and forming a mature mRNA product by the spliceosome. The U6 small nuclear RNA ribonucleoprotein (snRNP) is an essential component of the splicing machinery and is composed of the U6 snRNA, Prp24, and Lsm2-8 heteroheptameric ring. In the complex, there are both RNA-protein and protein-protein interactions that play critical roles in snRNP assembly. Here, we are studying how the Lsm2-8 ring is recruited to the U6 snRNA in real-time using single-molecule fluorescence colocalization microscopy (CoSMoS). By using fluorescently labeled RNAs and proteins, we have analyzed single molecule reaction trajectories to determine the initial kinetic parameters for snRNP formation and how these change due to post-transcriptional modification of the RNA. We have also elucidated the function of highly-conserved protein domains in Prp24 in both splicing factor recruitment and stabilization of the RNP. Overall, our single-molecule results allow us to propose a kinetic mechanism for snRNP assembly and elucidate how assembly can proceed efficiently in both the presence and absence of RNA modifications.

Insights into the Mechanism of the Cardiac Drug Omecamtiv Mecarbil─A Computational Study.

Omecamtiv mecarbil (OM) is a positive inotrope that is thought to bind directly to an allosteric site of the β-cardiac myosin. The drug is under investigation for the treatment of systolic heart failure. The drug is classified as a cardiac myosin modulator and has been observed to affect multiple vital steps of the cross-bridge cycle including the recovery stroke and the chemical step. We explored the free-energy surface of the recovery stroke of the human cardiac β-myosin in the presence of OM to determine its influence on this process. We also investigated the effects of OM on the recovery stroke in the presence of genetic cardiomyopathic mutations R712L, F764L, and P710R using metadynamics. We also utilized the method of transition path sampling to generate an unbiased ensemble of reactive trajectories for the ATP hydrolysis step in the presence of OM that were able to provide insight into the differences observed due to OM in the dynamics and mechanism of the decomposition of ATP to ADP and HPO42-, a central part of the power generation in cardiac muscle. We studied chemistry in the presence of the same three mutations to further elucidate the effect of OM, and its use in the treatment of cardiac disease.