TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Master stability functions of networks of Izhikevich neurons.

Synchronization has attracted interest in many areas where the systems under study can be described by complex networks. Among such areas is neuroscience, where it is hypothesized that synchronization plays a role in many functions and dysfunctions of the brain. We study the linear stability of synchronized states in networks of Izhikevich neurons using master stability functions (MSFs), and to accomplish that, we exploit the formalism of saltation matrices. Such a tool allows us to calculate the Lyapunov exponents of the MSF properly since the Izhikevich model displays a discontinuity within its spikes. We consider both electrical and chemical couplings as well as global and cluster synchronized states. The MSF calculations are compared with a measure of the synchronization error for simulated networks. We give special attention to the case of electric and chemical coupling, where a riddled basin of attraction makes the synchronized solution more sensitive to perturbations.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for Tensor-Net models, with performance gains ranging from 2x to 10x over previous, non-optimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

Predicting the size and morphology of nanoparticle clusters driven by biomolecular recognition.

Nanoparticle aggregation is a driving principle of innovative materials and biosensing methodologies, improving transduction capabilities displayed by optical, electrical or magnetic measurements. This aggregation can be driven by the biomolecular recognition between target biomolecules (analytes) and receptors bound onto nanoparticle surface. Despite theoretical advances on modelling the entropic interaction in similar systems, predictions of the fractal morphologies of the nanoclusters of bioconjugated nanoparticles are lacking. The morphology of resulting nanoclusters is sensitive to the location, size, flexibility, average number of receptors per particle f̄, and the analyte-particle concentration ratio. Here we considered bioconjugated iron oxide nanoparticles (IONPs) where bonds are mediated by a divalent protein that binds two receptors attached onto different IONPs. We developed a protocol combining analytical expressions for receptors and linker distributions, and Brownian dynamics simulations for bond formation, and validated it against experiments. As more bonds become available (e.g., by adding analytes), the aggregation deviates from the ideal Bethe's lattice scenario due to multivalence, loop formation, and steric hindrance. Generalizing Bethe's lattice theory with a (not-integer) effective functionality feff leads to analytical expressions for the cluster size distributions in excellent agreement with simulations. At high analyte concentration steric impediment imposes an accessible limit value facc to feff, which is bounded by facc < feff < f̄. A transition to gel phase, is correctly captured by the derived theory. Our findings offer new insights into quantifying analyte amounts by assessing nanocluster size, and predicting nanoassembly morphologies accurately is a first step towards understanding variations of physical properties in clusters formed after biomolecular recognition.

Computing hydrodynamic interactions in confined doubly periodic geometries in linear time.

We develop a linearly scaling variant of the force coupling method [K. Yeo and M. R. Maxey, J. Fluid Mech. 649, 205-231 (2010)] for computing hydrodynamic interactions among particles confined to a doubly periodic geometry with either a single bottom wall or two walls (slit channel) in the aperiodic direction. Our spectrally accurate Stokes solver uses the fast Fourier transform in the periodic xy plane and Chebyshev polynomials in the aperiodic z direction normal to the wall(s). We decompose the problem into two problems. The first is a doubly periodic subproblem in the presence of particles (source terms) with free-space boundary conditions in the z direction, which we solve by borrowing ideas from a recent method for rapid evaluation of electrostatic interactions in doubly periodic geometries [Maxian et al., J. Chem. Phys. 154, 204107 (2021)]. The second is a correction subproblem to impose the boundary conditions on the wall(s). Instead of the traditional Gaussian kernel, we use the exponential of a semicircle kernel to model the source terms (body force) due to the presence of particles and provide optimum values for the kernel parameters that ensure a given hydrodynamic radius with at least two digits of accuracy and rotational and translational invariance. The computation time of our solver, which is implemented in graphical processing units, scales linearly with the number of particles, and allows computations with about a million particles in less than a second for a sedimented layer of colloidal microrollers. We find that in a slit channel, a driven dense suspension of microrollers maintains the same two-layer structure as above a single wall, but moves at a substantially lower collective speed due to increased confinement.

Relationality: The Role of Connectedness in the Social Ecology of Resilience.

Previous work has focused on the role of social capital on resilience. However, this research tends to search for civic and other organizations, often formal institutionalized groups which, when they are not found, leads to questions about how social networks are possibly governed. Without formal organizational structures to govern these networks, how is pro-environmental/pro-social behavior sustained. In this article, we focus on a diffused mechanism for collective action, which is referred to as relationality. Relationality is a theory that underscores how social connectedness, through mechanisms of empathy, foster collective action in noncentralized modes of network governance. The concept of relationality addresses important issues not considered by the literature on social capital --so being, we will refer to relational elements as relational capital. Relational capital constitutes a type of asset that communities can activate vis-a-vis environmental and other perturbation. As we describe, the evidence for relationality as an important mechanism for sustainability and resilience is accumulating.

Simulations of dynamically cross-linked actin networks: Morphology, rheology, and hydrodynamic interactions.

Cross-linked actin networks are the primary component of the cell cytoskeleton and have been the subject of numerous experimental and modeling studies. While these studies have demonstrated that the networks are viscoelastic materials, evolving from elastic solids on short timescales to viscous fluids on long ones, questions remain about the duration of each asymptotic regime, the role of the surrounding fluid, and the behavior of the networks on intermediate timescales. Here we perform detailed simulations of passively cross-linked non-Brownian actin networks to quantify the principal timescales involved in the elastoviscous behavior, study the role of nonlocal hydrodynamic interactions, and parameterize continuum models from discrete stochastic simulations. To do this, we extend our recent computational framework for semiflexible filament suspensions, which is based on nonlocal slender body theory, to actin networks with dynamic cross linkers and finite filament lifetime. We introduce a model where the cross linkers are elastic springs with sticky ends stochastically binding to and unbinding from the elastic filaments, which randomly turn over at a characteristic rate. We show that, depending on the parameters, the network evolves to a steady state morphology that is either an isotropic actin mesh or a mesh with embedded actin bundles. For different degrees of bundling, we numerically apply small-amplitude oscillatory shear deformation to extract three timescales from networks of hundreds of filaments and cross linkers. We analyze the dependence of these timescales, which range from the order of hundredths of a second to the actin turnover time of several seconds, on the dynamic nature of the links, solvent viscosity, and filament bending stiffness. We show that the network is mostly elastic on the short time scale, with the elasticity coming mainly from the cross links, and viscous on the long time scale, with the effective viscosity originating primarily from stretching and breaking of the cross links. We show that the influence of nonlocal hydrodynamic interactions depends on the network morphology: for homogeneous meshworks, nonlocal hydrodynamics gives only a small correction to the viscous behavior, but for bundled networks it both hinders the formation of bundles and significantly lowers the resistance to shear once bundles are formed. We use our results to construct three-timescale generalized Maxwell models of the networks.

A fast spectral method for electrostatics in doubly periodic slit channels.

We develop a fast method for computing the electrostatic energy and forces for a collection of charges in doubly periodic slabs with jumps in the dielectric permittivity at the slab boundaries. Our method achieves spectral accuracy by using Ewald splitting to replace the original Poisson equation for nearly singular sources with a smooth far-field Poisson equation, combined with a localized near-field correction. Unlike existing spectral Ewald methods, which make use of the Fourier transform in the aperiodic direction, we recast the problem as a two-point boundary value problem in the aperiodic direction for each transverse Fourier mode for which exact analytic boundary conditions are available. We solve each of these boundary value problems using a fast, well-conditioned Chebyshev method. In the presence of dielectric jumps, combining Ewald splitting with the classical method of images results in smoothed charge distributions, which overlap the dielectric boundaries themselves. We show how to preserve the spectral accuracy in this case through the use of a harmonic correction, which involves solving a simple Laplace equation with smooth boundary data. We implement our method on graphical processing units and combine our doubly periodic Poisson solver with Brownian dynamics to study the equilibrium structure of double layers in binary electrolytes confined by dielectric boundaries. Consistent with prior studies, we find strong charge depletion near the interfaces due to repulsive interactions with image charges, which points to the need for incorporating polarization effects in understanding confined electrolytes, both theoretically and computationally.

The potential role of sestrin 2 in liver regeneration.

Liver regeneration is a remarkably complex phenomenon conserved across all vertebrates, enabling the restoration of lost liver mass in a matter of days. Unfortunately, extensive damage to the liver may compromise this process, often leading to the death of affected individuals. Ischemia/reperfusion injury (IRI) is a common source of damage preceding regeneration, often present during liver transplantation, resection, trauma, or hemorrhagic shock. Increased oxidative stress and mitochondrial dysfunction are key hallmarks of IRI, which can jeopardize the liver's ability to regenerate. Therefore, a better understanding of both liver regeneration and IRI is of important clinical significance. In the current review, we discuss the potential role of sestrin 2 (SESN2), a novel anti-aging protein, in liver regeneration and ischemia/reperfusion preceding regeneration. We highlight its beneficial role in protecting cells from mitochondrial dysfunction and oxidative stress as key aspects of its involvement in liver regeneration. Additionally, we describe how its ability to promote the expression of Nrf2 bears significant importance in this context. Finally, we focus on a potential novel link between SESN2, mitohormesis and ischemic preconditioning, which could explain some of the protective effects of preconditioning.

Risk Communication for Empowerment: Interventions in a Rohingya Refugee Settlement.

There are many reasons that people, when warned of an impending extreme event, do not take proactive, self-defensive action. We focus on one possible reason, which is that, sometimes, people lack a sense of agency or even experience disempowerment, which can lead to passivity. This article takes up one situation where the possibility of disempowerment is salient, that of Rohingya refugees who were evicted from their homes in Myanmar and forced to cross the border into neighboring Bangladesh. In their plight, we see the twin elements of marginalization and displacement acting jointly to produce heightened vulnerability to the risks from extreme weather. Building on a relational model of risk communication, a consortium of researchers and practitioners designed a risk communication training workshop that featured elements of empowerment-based practice. The program was implemented in two refugee camps. Evaluation suggests that the workshop may have had an appreciable effect in increasing participants' sense of agency and hope, while decreasing their level of fatalism. The outcomes were considerably more positive for female than male participants, which has important implications. This work underscores the potential for participatory modes of risk communication to empower the more marginalized, and thus more vulnerable, members of society.

Maritornes y la genética / Maritornes and genetics

Maritornes es una de las figuras femeninas creadas por Cervantes en El Quijote. Aparece en varios capítulos de la primera parte, y es más importante su protagonismo en los capítulos XVIy XLIII. En la primera aparición del capítulo XVI, Cervantes relata a Maritornes con signos físicos compatibles con algún síndrome genético. De acuerdo con esta descripción, el objetivo del trabajo es presentar la posibilidad de que lo que describe Cervantes en el año 1605 en el personaje Maritornes haya sido una mujer con un trastorno genético.

Maritornes is a female character created by Cervantes for Don Quixote. She appears in several chapters of part one and takes on a greater role in chapters XVI and XLIII. In her first appearance in chapter XVI, Cervantes describes Maritornes as having physical signs compatible with a genetic disorder. According to this description, the objective of this article was to pose the possibility that, in 1605, Cervantes described Maritornes as a woman with a genetic disorder.

Maritornes and genetics. / Maritornes y la genética.

Maritornes is a female character created by Cervantes for Don Quixote. She appears in several chapters of part one and takes on a greater role in chapters XVI and XLIII. In her first appearance in chapter XVI, Cervantes describes Maritornes as having physical signs compatible with a genetic disorder. According to this description, the obj ective of this article was to pose the possibility that, in 1605, Cervantes described Maritornes as a woman with a genetic disorder.

Maritornes es una de las figuras femeninas creadas por Cervantes en El Quijote. Aparece en varios capítulos de la primera parte, y es más importante su protagonismo en los capítulos XVI y XLIII. En la primera aparición del capítulo XVI, Cervantes relata a Maritornes con signos físicos compatibles con algún síndrome genético. De acuerdo con esta descripción, el objetivo del trabajo es presentar la posibilidad de que lo que describe Cervantes en el año 1605 en el personaje Maritornes haya sido una mujer con un trastorno genético.

CDCA7 finely tunes cytoskeleton dynamics to promote lymphoma migration and invasion.

Metastases, the major cause of death from cancer, require cells' acquisition of the ability to migrate and involve multiple steps, including local tumor cell invasion and basement membrane penetration. Certain lymphoid tumors are highly metastatic, but the mechanisms of invasion by lymphoma cells are poorly understood. We recently showed that CDCA7, a protein induced by MYC, is overexpressed in lymphoid tumors and that its knockdown decreases lymphoid tumor growth without inhibiting the proliferation of normal cells. Here we show that CDCA7 is critical for invasion and migration of lymphoma cells. Indeed, CDCA7 knockdown in lymphoma cells limited tumor cell invasion in matrigel-coated transwell plates and tumor invasion of neighboring tissues in a mouse xenograft model and in a zebrafish model of cell invasion. CDCA7 silencing markedly inhibited lymphoma cell migration on fibronectin without modifying cell adhesion to this protein. Instead, CDCA7 knockdown markedly disrupted the precise dynamic reorganization of actomyosin and tubulin cytoskeletons required for efficient migration. In particular, CDCA7 silencing impaired tubulin and actomyosin cytoskeleton polarization, increased filamentous actin formation, and induced myosin activation. Of note, inhibitors of actin polymerization, myosin II, or ROCK reestablished the migration capacity of CDCA7-silenced lymphoma cells. Given the critical role of CDCA7 in lymphoma-genesis and invasion, therapies aimed at inhibiting its expression or activity might provide significant control of lymphoma growth, invasion, and metastatic dissemination.

Análisis descriptivo de los intentos autolíticos mediante intoxicación medicamentosa / Descriptive analysis of suicide attempts by drug poisoning

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Multidimensional Assessment of the Host Response in Mechanically Ventilated Patients with Suspected Pneumonia.

Rationale: The identification of informative elements of the host response to infection may improve the diagnosis and management of bacterial pneumonia. Objectives: To determine whether the absence of alveolar neutrophilia can exclude bacterial pneumonia in critically ill patients with suspected infection and to test whether signatures of bacterial pneumonia can be identified in the alveolar macrophage transcriptome. Methods: We determined the test characteristics of alveolar neutrophilia for the diagnosis of bacterial pneumonia in three cohorts of mechanically ventilated patients. In one cohort, we also isolated macrophages from alveolar lavage fluid and used the transcriptome to identify signatures of bacterial pneumonia. Finally, we developed a humanized mouse model of Pseudomonas aeruginosa pneumonia to determine if pathogen-specific signatures can be identified in human alveolar macrophages. Measurements and Main Results: An alveolar neutrophil percentage less than 50% had a negative predictive value of greater than 90% for bacterial pneumonia in both the retrospective (n = 851) and validation cohorts (n = 76 and n = 79). A transcriptional signature of bacterial pneumonia was present in both resident and recruited macrophages. Gene signatures from both cell types identified patients with bacterial pneumonia with test characteristics similar to alveolar neutrophilia. Conclusions: The absence of alveolar neutrophilia has a high negative predictive value for bacterial pneumonia in critically ill patients with suspected infection. Macrophages can be isolated from alveolar lavage fluid obtained during routine care and used for RNA-Seq analysis. This novel approach may facilitate a longitudinal and multidimensional assessment of the host response to bacterial pneumonia.

Upconverting Nanorockers for Intracellular Viscosity Measurements During Chemotherapy.

Chemicals capable of producing structural and chemical changes on cells are used to treat diseases (e.g., cancer). Further development and optimization of chemotherapies require thorough knowledge of the effect of the chemical on the cellular structure and dynamics. This involves studying, in a noninvasive way, the properties of individual cells after drug administration. Intracellular viscosity is affected by chemical treatments and it can be reliably used to monitor chemotherapies at the cellular level. Here, cancer cell monitoring during chemotherapeutic treatments is demonstrated using intracellular allocated upconverting nanorockers. A simple analysis of the polarized visible emission of a single particle provides a real-time readout of its rocking dynamics that are directly correlated to the cytoplasmic viscosity. Numerical simulations and immunodetection are used to correlate the measured intracellular viscosity alterations to the changes produced in the cytoskeleton of cancer cells by anticancer drugs (colchicine and Taxol). This study evidences the possibility of monitoring cellular properties under an external chemical stimulus for the study and development of new treatments. Moreover, it provides the biomedical community with new tools to study intracellular dynamics and cell functioning.

Collective colloid diffusion under soft two-dimensional confinement.

This work presents a numerical and theoretical investigation of the collective dynamics of colloids in an unbounded solution but trapped in a harmonic potential. Under strict two-dimensional confinement (infinitely stiff trap) the collective colloidal diffusion is enhanced and diverges at zero wave number (like k^{-1}), due to the hydrodynamic propagation of the confining force across the layer. The analytic solution for the collective diffusion of colloids under a Gaussian trap of width δ still shows enhanced diffusion for large wavelengths kδ<1, while a gradual transition to normal diffusion for kδ>1. At intermediate and short wavelengths, we illustrate to what extent the hydrodynamic enhancement of diffusion is masked by the conservative forces between colloids. At very large wavelengths, the collective diffusion becomes faster than the solvent momentum transport and a transition from Stokesian dynamics to inertial dynamics takes place. Using our inertial coupling method code (resolving fluid inertia), we study this transition by performing simulations at small Schmidt number. Simulations confirm theoretical predictions for the k→0 limit [Phys. Rev. E 90, 062314 (2014)PLEEE81539-375510.1103/PhysRevE.90.062314] showing negative density-density time correlations. However, at finite k simulations show deviations from the theory.

Load speed regulation in compliant mechanical transmission systems using feedback and feedforward control actions.

The problem of controlling the load speed of a mechanical transmission system consisting of a belt-pulley and gear-pair is considered. The system is modeled as two inertia (motor and load) connected by a compliant transmission. If the transmission is assumed to be rigid, then using either the motor or load speed feedback provides the same result. However, with transmission compliance, due to belts or long shafts, the stability characteristics and performance of the closed-loop system are quite different when either motor or load speed feedback is employed. We investigate motor and load speed feedback schemes by utilizing the singular perturbation method. We propose and discuss a control scheme that utilizes both motor and load speed feedback, and design an adaptive feedforward action to reject load torque disturbances. The control algorithms are implemented on an experimental platform that is typically used in roll-to-roll manufacturing and results are shown and discussed.