Search

This article discusses Earth's Rights as an environmental justice mechanism of reparation, protection, and justice for indigenous communities, environmental defenders, and other populations in Latin America. We argue that Earth's rights encompass and include the right to health and can be integrated into international human rights frameworks to protect all forms of life, responding to colonial legacies of discrimination and violence. We respond to the scarcity of literature discussing Earth's rights in relation to situations where human rights and Earth's rights are violated. We ground our argument in the theoretical conceptualization of Latin American proposals of Earth's rights and its potential for actionable policy approaches that include human health as inevitably interconnected to our planet's well-being. We address the environmental injustices that affect the right to health and argue that an Earth's rights framework can support reparations for historically marginalized communities. [ABSTRACT FROM AUTHOR]

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic rates can in principle be obtained by different techniques after projecting the high-dimensional atomic dynamics on one (or a few) collective variable. Even though it is well-known that the projected dynamics approximately follows – in a statistical sense – the generalized, underdamped or overdamped Langevin equations (depending on the time resolution), to date it is nontrivial to parameterize such equations starting from a limited, practically accessible amount of non-ergodic trajectories. In this work we focus on Markovian, underdamped Langevin equations, that arise naturally when considering, e.g., numerous water-solution processes at sub-picosecond resolution. After contrasting the advantages and pitfalls of different numerical approaches, we present an efficient parametrization strategy based on a limited set of molecular dynamics data, including equilibrium trajectories confined to minima and few hundreds transition path sampling-like trajectories. Employing velocity autocorrelation or memory kernel information for learning the friction and likelihood maximization for learning the free-energy landscape, we demonstrate the possibility to reconstruct accurate barriers and rates both for a benchmark system and for the interaction of carbon nanoparticles in water. [ABSTRACT FROM AUTHOR]

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes. [ABSTRACT FROM AUTHOR]

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins. [ABSTRACT FROM AUTHOR]

To investigate the impact of non-linear interactions on dynamic coarse graining, we study a simplified model system featuring a tracer particle in a complex environment. Using a projection operator formalism and computer simulations, we systematically derive generalized Langevin equations (GLEs) describing the dynamics of this particle. We compare different kinds of linear and non-linear coarse-graining procedures to understand how non-linearities enter reconstructed GLEs and how they influence the coarse-grained dynamics. For non-linear external potentials, we show analytically and numerically that the non-Gaussian parameter and the incoherent intermediate scattering function will not be correctly reproduced by the GLE if a linear projection is applied. This, however, can be overcome by using non-linear projection operators. We also study anharmonic coupling between the tracer and the environment and demonstrate that the reconstructed memory kernel develops an additional trap-dependent contribution. Our study highlights some open challenges and possible solutions in dynamic coarse graining. [ABSTRACT FROM AUTHOR]

Whether single-molecule trajectories, observed experimentally or in molecular simulations, can be described using simple models such as biased diffusion is a subject of considerable debate. Memory effects and anomalous diffusion have been reported in a number of studies, but directly inferring such effects from trajectories, especially given limited temporal and/or spatial resolution, has been a challenge. Recently, we proposed that this can be achieved with information-theoretical analysis of trajectories, which is based on the general observation that non-Markov effects make trajectories more predictable and, thus, more "compressible" by lossless compression algorithms. Toy models where discrete molecular states evolve in time were shown to be amenable to such analysis, but its application to continuous trajectories presents a challenge: the trajectories need to be digitized first, and digitization itself introduces non-Markov effects that depend on the specifics of how trajectories are sampled. Here we develop a milestoning-based method for information-theoretical analysis of continuous trajectories and show its utility in application to Markov and non-Markov models and to trajectories obtained from molecular simulations. [ABSTRACT FROM AUTHOR]

Local diffusivity of a protein depends crucially on the conformation, and the conformational fluctuations are often non-Markovian. Here, we investigate the Langevin equation with non-Markovian fluctuating diffusivity, where the fluctuating diffusivity is modeled by a generalized Langevin equation under a double-well potential. We find that non-Markovian fluctuating diffusivity affects the global diffusivity, i.e., the diffusion coefficient obtained by the long-time trajectories when the memory kernel in the generalized Langevin equation is a power-law form. On the other hand, the diffusion coefficient does not change when the memory kernel is exponential. More precisely, the global diffusivity obtained by a trajectory whose length is longer than the longest relaxation time in the memory kernel is not affected by the non-Markovian fluctuating diffusivity. We show that these non-Markovian effects are the consequences of an everlasting effect of the initial condition on the stationary distribution in the generalized Langevin equation under a double-well potential due to long-term memory. [ABSTRACT FROM AUTHOR]

Background: The need to determine physical compatibility of intravenous admixtures is directly related to patient safety and patient outcomes. While the provision of multi-modal analgesic strategies has increased over the past decade, a paucity of data exists regarding physical compatibility of select medications. Objectives: To evaluate the physical compatibility of methocarbamol in Lactated Ringer's (LR), 0.45% normal saline (0.45% NaCl), and Plasma-Lyte A (PLA) at concentrations of 4, 10, and 20 mg/mL. Methods: Admixtures were prepared and evaluated using previously validated methods under a laminar flow hood using aseptic technique. Samples were prepared in a triplicate manner, 3 mL aliquots were placed into polymethyl methacrylate cuvettes for evaluation at time points 0, 1, 5, 8, and 24 hours. Visual inspection of samples included assignment of a number: 0—no precipitation, 1—trace evidence of precipitation, 2—slight haze, 3—medium haze, and 4—heavy precipitation. Any evidence of precipitation was considered significant. A variable wavelength spectrophotometer set at 547 nanometers was used to measure absorbance. A change in absorbance of ±0.010 was considered significant. A change in pH of ±0.1 was considered significant. Results: No significant changes occurred relating to visual inspection or absorbance across all concentrations and time points for LR; however, there was a significant change in pH across all concentrations at hour 5. In 0.45% NaCl and PLA, no remarkable changes occurred across all concentrations and time points regarding visual observation, spectrophotometric absorbance, and pH analysis. Conclusions: Methocarbamol at concentrations of 4, 10, and 20 mg/mL is physically compatible for up to 1 hour in LR. Methocarbamol is physically compatible in 0.45% NaCl and PLA for up to 24 hours. Chemical stability tests are warranted to confirm these findings. [ABSTRACT FROM AUTHOR]

Memory effects are ubiquitous in a wide variety of complex physical phenomena, ranging from glassy dynamics and metamaterials to climate models. The Generalized Langevin Equation (GLE) provides a rigorous way to describe memory effects via the so-called memory kernel in an integro-differential equation. However, the memory kernel is often unknown, and accurately predicting or measuring it via, e.g., a numerical inverse Laplace transform remains a herculean task. Here, we describe a novel method using deep neural networks (DNNs) to measure memory kernels from dynamical data. As a proof-of-principle, we focus on the notoriously long-lived memory effects of glass-forming systems, which have proved a major challenge to existing methods. In particular, we learn the operator mapping dynamics to memory kernels from a training set generated with the Mode-Coupling Theory (MCT) of hard spheres. Our DNNs are remarkably robust against noise, in contrast to conventional techniques. Furthermore, we demonstrate that a network trained on data generated from analytic theory (hard-sphere MCT) generalizes well to data from simulations of a different system (Brownian Weeks–Chandler–Andersen particles). Finally, we train a network on a set of phenomenological kernels and demonstrate its effectiveness in generalizing to both unseen phenomenological examples and supercooled hard-sphere MCT data. We provide a general pipeline, KernelLearner, for training networks to extract memory kernels from any non-Markovian system described by a GLE. The success of our DNN method applied to noisy glassy systems suggests that deep learning can play an important role in the study of dynamical systems with memory. [ABSTRACT FROM AUTHOR]

Nanomaterial, due to its unique physical, chemical and biological properties compared to its bulk counterparts, has the potential to provide a product superior to its bulk predecessor. In this study, a novel alumina nanofiber filter was assessed for its removal and retention capability for MS2 aerosol. Its physical removal efficiency in the 10-400 nm range was 94.35%, while its viable removal efficiency was 98.87%, which was slightly lower than three conventional HEPA filters tested. However, its pressure drop was much lower than HEPA filters, yielding a higher filter quality than HEPA filters. The average extracted fraction from the nanofiber filter was 8.64×10 -2 ±7.00×10 -2 , which is three orders lower than other HEPA filters, demonstrating that the viruses were effectively retained in the nanofiber filter. Furthermore, the performance of the nanofiber filter showed no dependence on relative humidity. In conclusion, this novel alumina nanofiber filter presents advantageous potential for removal and retention of viral aerosol agents., (Copyright © 2008 Elsevier Ltd. All rights reserved.)

The article discusses the emerging field of environmental restorative justice, focusing on addressing environmental damage, pollution, and the role of restorative justice in these issues. It reviews recent publications on the topic, highlighting the challenges and opportunities in this area. The text explores the limitations of restorative justice in dealing with large corporations and governments responsible for environmental harm, suggesting alternative approaches like civil disobedience and public forums for truth-telling and restoration. The article also delves into the need for broader public recognition of environmental harm and the importance of including nature's voice in these discussions. [Extracted from the article]

Alumina is a material that has been used in both dental and orthopedic applications. It is with these uses in mind that osteoblast (bone-forming cell) function on alumina of varying particulate size, chemistry, and phase was tested in order to determine what formulation might be the most beneficial for bone regeneration. Specifically, in vitro osteoblast adhesion, proliferation, intracellular alkaline phosphatase activity, and calcium deposition was observed on delta-phase nanospherical, alpha-phase conventional spherical, and boehmite nanofiber alumina. Results showed for the first time increased osteoblast functions on the nanofiber alumina. Specifically, a 16% increase in osteoblast adhesion over nanophase spherical alumina and a 97% increase over conventional spherical alumina were found for nanofiber alumina after 2 h. A 29% increase in cell number after 5 days and up to a 57% greater amount of calcium was found on the surface of the nanofiber alumina compared with other alumina surfaces. Some of the possible explanations for such enhanced osteoblast behavior on nanofiber alumina may be attributed to chemistry, crystalline phase, and topography. Increased osteoblast function on nanofiber alumina suggests that it may be an ideal material for use in orthopedic and dental applications., (Copyright 2003 Wiley Periodicals, Inc. J Biomed Mater Res 67A: 1284-1293, 2003)

Single-molecule and single-particle tracking experiments are typically unable to resolve fine details of thermal motion at short timescales where trajectories are continuous. We show that, when a diffusive trajectory x t is sampled at finite time intervals δt, the resulting error in measuring the first passage time to a given domain can exceed the time resolution of the measurement by more than an order of magnitude. Such surprisingly large errors originate from the fact that the trajectory may enter and exit the domain while being unobserved, thereby lengthening the apparent first passage time by an amount that is larger than δt. Such systematic errors are particularly important in single-molecule studies of barrier crossing dynamics. We show that the correct first passage times, as well as other properties of the trajectories such as splitting probabilities, can be recovered via a stochastic algorithm that reintroduces unobserved first passage events probabilistically. [ABSTRACT FROM AUTHOR]

Generalized master equations provide a theoretically rigorous framework to capture the dynamics of processes ranging from energy harvesting in plants and photovoltaic devices to qubit decoherence in quantum technologies and even protein folding. At their center is the concept of memory. The explicit time-nonlocal description of memory is both protracted and elaborate. When physical intuition is at a premium, one would desire a more compact, yet complete, description. Here, we demonstrate how and when the time-convolutionless formalism constitutes such a description. In particular, by focusing on the dissipative dynamics of the spin-boson and Frenkel exciton models, we show how to: easily construct the time-local generator from reference reduced dynamics, elucidate the dependence of its existence on the system parameters and the choice of reduced observables, identify the physical origin of its apparent divergences, and offer analysis tools to diagnose their severity and circumvent their deleterious effects. We demonstrate that, when applicable, the time-local approach requires as little information as the more commonly used time-nonlocal scheme, with the important advantages of providing a more compact description, greater algorithmic simplicity, and physical interpretability. We conclude by introducing the discrete-time analog and a straightforward protocol to employ it in cases where the reference dynamics have limited resolution. The insights we present here offer the potential for extending the reach of dynamical methods, reducing both their cost and conceptual complexity. [ABSTRACT FROM AUTHOR]

The generalized Langevin equation (GLE) provides an attractive theoretical framework for investigating the dynamics of conformational fluctuations of polymeric systems. While the memory kernel is a central function in the GLE, explicit analytical forms for this function have been challenging to obtain, even for the simple models of polymer dynamics. Here, we achieve an explicit analytical expression for the memory kernel in the GLE for the end-to-end vector of Rouse chains in the overdamped limit. Our derivation takes advantage of the finding that the dynamics of the end-to-end vector of Rouse chains with both free ends are equivalent to those of Rouse chains with one free end and the other fixed. For the latter model, we first show that the equations of motion of the Rouse modes as well as their statistical properties can be obtained under the boundary conditions where the free end is held fixed temporarily. We then analytically solve the terms associated with intrachain interactions in the GLE. By formally comparing these terms with the GLE based on the Rouse modes, we obtain an explicit expression for the memory kernel, along with analytical forms for the potential field and the random colored noise force. Our analytical memory kernel is confirmed by numerical calculations in the Laplace space and is shown to yield asymptotic behaviors that are consistent with previous studies. Finally, we utilize our analytical result to simulate the cyclization dynamics of Rouse chains and discuss the scaling of the cyclization time with chain length. [ABSTRACT FROM AUTHOR]

We present a method to estimate the transition rates of molecular systems under different environmental conditions that cause the formation or the breaking of bonds and require the sampling of the Grand Canonical Ensemble. For this purpose, we model the molecular system in terms of probable "scenarios," governed by different potential energy functions, which are separately sampled by classical MD simulations. Reweighting the canonical distribution of each scenario according to specific environmental variables, we estimate the grand canonical distribution, then use the Square Root Approximation method to discretize the Fokker-Planck operator into a rate matrix and the robust Perron Cluster Cluster Analysis method to coarse-grain the kinetic model. This permits efficiently estimating the transition rates of conformational states as functions of environmental variables, for example, the local pH at a cell membrane. In this work, we formalize the theoretical framework of the procedure, and we present a numerical experiment comparing the results with those provided by a constant-pH method based on non-equilibrium Molecular Dynamics Monte Carlo simulations. The method is relevant for the development of new drug design strategies that take into account how the cellular environment influences biochemical processes. [ABSTRACT FROM AUTHOR]

We present a numerical method to produce stochastic dynamics according to the generalized Langevin equation with a non-stationary memory kernel. This type of dynamics occurs when a microscopic system with an explicitly time-dependent Liouvillian is coarse-grained by means of a projection operator formalism. We show how to replace the deterministic fluctuating force in the generalized Langevin equation by a stochastic process, such that the distributions of the observables are reproduced up to moments of a given order. Thus, in combination with a method to extract the memory kernel from simulation data of the underlying microscopic model, the method introduced here allows us to construct and simulate a coarse-grained model for a driven process. [ABSTRACT FROM AUTHOR]