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1.
J Chem Phys ; 156(17): 174114, 2022 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-35525642

RESUMO

Performing full-resolution atomistic simulations of nucleic acid folding has remained a challenge for biomolecular modeling. Understanding how nucleic acids fold and how they transition between different folded structures as they unfold and refold has important implications for biology. This paper reports a theoretical model and computer simulation of the ab initio folding of DNA inverted repeat sequences. The formulation is based on an all-atom conformational model of the sugar-phosphate backbone via chain closure, and it incorporates three major molecular-level driving forces-base stacking, counterion-induced backbone self-interactions, and base pairing-via separate analytical theories designed to capture and reproduce the effects of the solvent without requiring explicit water and ions in the simulation. To accelerate computational throughput, a mixed numerical/analytical algorithm for the calculation of the backbone conformational volume is incorporated into the Monte Carlo simulation, and special stochastic sampling techniques were employed to achieve the computational efficiency needed to fold nucleic acids from scratch. This paper describes implementation details, benchmark results, and the advantages and technical challenges with this approach.


Assuntos
Ácidos Nucleicos , Simulação por Computador , Entropia , Conformação de Ácido Nucleico , Ácidos Nucleicos/química , Física , Termodinâmica
2.
Sci Rep ; 12(1): 7557, 2022 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-35534639

RESUMO

Physics-informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of modeling a large variety of differential equations. PINNs are based on simple architectures, and learn the behavior of complex physical systems by optimizing the network parameters to minimize the residual of the underlying PDE. Current network architectures share some of the limitations of classical numerical discretization schemes when applied to non-linear differential equations in continuum mechanics. A paradigmatic example is the solution of hyperbolic conservation laws that develop highly localized nonlinear shock waves. Learning solutions of PDEs with dominant hyperbolic character is a challenge for current PINN approaches, which rely, like most grid-based numerical schemes, on adding artificial dissipation. Here, we address the fundamental question of which network architectures are best suited to learn the complex behavior of non-linear PDEs. We focus on network architecture rather than on residual regularization. Our new methodology, called physics-informed attention-based neural networks (PIANNs), is a combination of recurrent neural networks and attention mechanisms. The attention mechanism adapts the behavior of the deep neural network to the non-linear features of the solution, and break the current limitations of PINNs. We find that PIANNs effectively capture the shock front in a hyperbolic model problem, and are capable of providing high-quality solutions inside the convex hull of the training set.


Assuntos
Redes Neurais de Computação , Física
3.
Philos Trans A Math Phys Eng Sci ; 380(2226): 20210057, 2022 Jun 27.
Artigo em Inglês | MEDLINE | ID: mdl-35527635

RESUMO

Fluid dynamics is a research area lying at the crossroads of physics and applied mathematics with an ever-expanding range of applications in natural sciences and engineering. However, despite decades of concerted research efforts, this area abounds with many fundamental questions that still remain unanswered. At the heart of these problems often lie mathematical models, usually in the form of partial differential equations, and many of the open questions concern the validity of these models and what can be learned from them about the physical problems. In recent years, significant progress has been made on a number of open problems in this area, often using approaches that transcend traditional discipline boundaries by combining modern methods of modelling, computation and mathematical analysis. The two-part theme issue aims to represent the breadth of these approaches, focusing on problems that are mathematical in nature but help to understand aspects of real physical importance such as fluid dynamical stability, transport, mixing, dissipation and vortex dynamics. This article is part of the theme issue 'Mathematical problems in physical fluid dynamics (part 2)'.


Assuntos
Hidrodinâmica , Física , Matemática , Modelos Teóricos
5.
Methods Mol Biol ; 2449: 263-280, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35507267

RESUMO

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.


Assuntos
Ecossistema , Física , Biologia
6.
Phys Rev Lett ; 128(11): 111103, 2022 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-35363003

RESUMO

Recent work applying the notion of pseudospectrum to gravitational physics showed that the quasinormal mode spectrum of black holes is unstable, with the possible exception of the longest-lived (fundamental) mode. The fundamental mode dominates the expected signal in gravitational wave astronomy, and there is no reason why it should have privileged status. We compute the quasinormal mode spectrum of two model problems where the Schwarzschild potential is perturbed by a small "bump" consisting of either a Pöschl-Teller potential or a Gaussian, and we show that the fundamental mode is destabilized under generic perturbations. We present phase diagrams and study a simple double-barrier toy problem to clarify the conditions under which the spectral instability occurs.


Assuntos
Elefantes , Sifonápteros , Animais , Física
7.
Philos Trans A Math Phys Eng Sci ; 380(2224): 20210162, 2022 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-35400179

RESUMO

The first part of this paper is a brief survey of the approaches to economic inequality based on ideas from statistical physics and kinetic theory. These include the Boltzmann kinetic equation, the time-reversal symmetry, the ergodicity hypothesis, entropy maximization and the Fokker-Planck equation. The origins of the exponential Boltzmann-Gibbs distribution and the Pareto power law are discussed in relation to additive and multiplicative stochastic processes. The second part of the paper analyses income distribution data in the USA for the time period 1983-2018 using a two-class decomposition. We present overwhelming evidence that the lower class (more than 90% of the population) is described by the exponential distribution, whereas the upper class (about 4% of the population in 2018) by the power law. We show that the significant growth of inequality during this time period is due to the sharp increase in the upper-class income share, whereas relative inequality within the lower class remains constant. We speculate that the expansion of the upper-class population and income shares may be due to increasing digitization and non-locality of the economy in the last 40 years. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.


Assuntos
Renda , Física , Entropia , Cinética , Processos Estocásticos , Estados Unidos
8.
PLoS One ; 17(4): e0265929, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35363795

RESUMO

The main goal of this paper is to examine Nobel Prize data by studying the association among the laureate's country of birth or residence, discipline, time period in which the Nobel Prize was awarded, and gender of the recipient. Multiple correspondence analysis is used as a tool to examine the association between these four categorical variables by cross classifying them in the form of a four-way contingency table. The data that we examine comprise Nobel Prize recipients from 1901 to 2018 (inclusive) from eight-developed countries, with a total sample of 785 Nobel Prize recipients. The countries include Canada, France, Germany, Italy, Japan, Russia, the British Isles, and the USA and the disciplines in which the individuals were awarded the prizes include chemistry, physics, physiology or medicine, literature, economics, and peace.


Assuntos
Distinções e Prêmios , Medicina , Alemanha , História do Século XX , Humanos , Prêmio Nobel , Física
9.
J Anal Psychol ; 67(1): 33-44, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35417602

RESUMO

Starting with the current pandemic, a potent symbol of death for all, the author explores the need to transform our vision of, and position in, the world. The author describes the world in which we live in terms of accelerated transformation and extreme imbalance: environmental suicide, consumerism, environmental pollution, global warming and intense polarization brought about by techno-populism. She reflects on the enantiodromia of our current times in which the focus has shifted from extreme spirituality to the opposite extreme of materialism. She returns to the concepts of the psychoid archetype and unus mundus, and analogous concepts in quantum physics, examining the need to review the polarities of psyche and matter in the search for a new synthesis. The author then concludes by highlighting the importance of consciousness in the elaboration and construction of a new way of being in the world.


Prenant pour point de départ la pandémie actuelle - un puissant symbole de mort pour chacun - l'auteur explore le besoin de transformer notre vision du monde et notre position dans le monde. L'auteur décrit le monde dans lequel nous vivons en termes de transformation accélérée et de déséquilibre extrême: suicide environnemental, consumérisme, pollution environnementale, réchauffement climatique et polarisation intense, conséquence du techno-populisme. Elle réfléchit à l'énantiodromie de notre époque dans laquelle l'accent a glissé d'une spiritualité extrême à l'extrême opposé avec le matérialisme. Elle revient au concept de l'archétype du psychoïde et d'unus mundus, et à des concepts analogues en physique quantique, examinant le besoin de reconsidérer les polarités de la psyché et de la matière dans la recherche d'une synthèse nouvelle. L'auteur conclut ensuite en soulignant l'importance de la conscience dans l'élaboration et la construction d'une nouvelle manière d'être dans le monde.


Comenzando con la pandemia actual, un símbolo potente de muerte para todos, la autora explora la necesidad de transformar nuestra visión de, y posición en, el mundo. La autora describe el mundo en el cual vivimos en términos de una transformación acelerada y un desbalance extremo: suicidios ambientales, consumismo, contaminación ambiental, calentamiento global e intensa polarización producidos por el tecno-populismo. Ella reflexiona acerca de la enantiodromia de nuestros tiempos actuales, en los cuales el foco ha cambiado desde una espiritualidad extrema al opuesto de un materialismo extremo. Retorna a los conceptos de arquetipo psicoide y unus mundus, y conceptos análogos en la física cuántica, examinando la necesidad de revisar las polaridades de psique y materia en búsqueda de una nueva síntesis. La autora concluye destacando la importancia de la consciencia en la elaboración y construcción de un nuevo modo de estar en el mundo.


Começando com a pandemia atual, um símbolo potente de morte para todos, a autora explora a necessidade de transformar nossa visão e posição no mundo. A autora descreve o mundo em que vivemos em termos de transformação acelerada e desequilíbrio extremo: suicídio ambiental, consumismo, poluição ambiental, aquecimento global e intensa polarização provocada pelo tecnopopulismo. Ela reflete sobre a enantiodromia de nossos tempos atuais, em que o foco mudou da espiritualidade extrema para o extremo oposto do materialismo. Ela retorna aos conceitos do arquétipo psicóide e unus mundus, e conceitos análogos em física quântica, examinando a necessidade de revisar as polaridades psique e matéria em busca de uma nova síntese. A autora conclui então destacando a importância da consciência na elaboração e construção de um novo modo de ser no mundo.


Assuntos
Física , Psicoterapia , Feminino , Humanos
10.
Philos Trans A Math Phys Eng Sci ; 380(2225): 20210056, 2022 Jun 13.
Artigo em Inglês | MEDLINE | ID: mdl-35465715

RESUMO

Fluid dynamics is a research area lying at the crossroads of physics and applied mathematics with an ever-expanding range of applications in natural sciences and engineering. However, despite decades of concerted research efforts, this area abounds with many fundamental questions that still remain unanswered. At the heart of these problems often lie mathematical models, usually in the form of partial differential equations, and many of the open questions concern the validity of these models and what can be learned from them about the physical problem. In recent years, significant progress has been made on a number of open problems in this area, often using approaches that transcend traditional discipline boundaries by combining modern methods of modelling, computation and mathematical analysis. The two-part theme issue aims to represent the breadth of these approaches, focusing on problems that are mathematical in nature but help to understand aspects of real physical importance such as fluid dynamical stability, transport, mixing, dissipation and vortex dynamics. This article is part of the theme issue 'Mathematical problems in physical fluid dynamics (part 1)'.


Assuntos
Hidrodinâmica , Modelos Teóricos , Matemática , Física
11.
Sensors (Basel) ; 22(8)2022 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-35458898

RESUMO

The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is one of the most important and challenging problems in the area of intelligent sensor fault diagnostics. Within this frame of reference, we extended the physics-informed transfer learning framework, first presented previously for a fault cause assignment, to the level of sensor fault diagnostics for a range of different fault scenarios. Hence, the framework is utilized to perform intelligent sensor fault diagnostics for the first time. The underlying dynamics of the reference system are extracted using a completely data-driven methodology and dynamic mode decomposition with control (DMDc) in order to generate time-frequency illustrations of each sample with continuous wavelet transform (CWT). Then, sensor fault diagnostics for bias, drift over time, sine disturbance and increased noise sensor fault scenarios are achieved using the idea of transfer learning with a pre-trained image classification algorithm. The classification results yields a good performance on sensor fault diagnostics with 91.5% training and 84.7% test accuracy along with a fair robustness level with a set of reference benchmark system parameters.


Assuntos
Redes Neurais de Computação , Análise de Ondaletas , Algoritmos , Aprendizado de Máquina , Física
13.
Nat Commun ; 13(1): 2298, 2022 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-35484120

RESUMO

Evanescent illumination has been widely used to detect single biological macromolecules because it can notably enhance light-analyte interaction. However, the current evanescent single-molecule detection system usually requires specially designed microspheres or nanomaterials. Here we show that single protein detection and imaging can be realized on a plain glass surface by imaging the interference between the evanescent lights scattered by the single proteins and by the natural roughness of the cover glass. This allows us to quantify the sizes of single proteins, characterize the protein-antibody interactions at the single-molecule level, and analyze the heterogeneity of single protein binding behaviors. In addition, owing to the exponential distribution of evanescent field intensity, the evanescent imaging system can track the analyte axial movement with high resolution, which can be used to analyze the DNA conformation changes, providing one solution for detecting small molecules, such as microRNA. This work demonstrates a label-free single protein imaging method with ordinary consumables and may pave a road for detecting small biological molecules.


Assuntos
Física , Cinética , Microscopia de Fluorescência/métodos , Conformação de Ácido Nucleico , Ligação Proteica
14.
Nat Commun ; 13(1): 1708, 2022 Mar 31.
Artigo em Inglês | MEDLINE | ID: mdl-35361759

RESUMO

Guest capture and release are important properties of self-assembling nanostructures. Over time, a significant fraction of guests might engage in short-lived states with different symmetry and stereoselectivity and transit frequently between multiple environments, thereby escaping common spectroscopy techniques. Here, we investigate the cavity of an iron-based metal organic polyhedron (Fe-MOP) using spin-hyperpolarized 129Xe Chemical Exchange Saturation Transfer (hyper-CEST) NMR. We report strong signals unknown from previous studies that persist under different perturbations. On-the-fly delivery of hyperpolarized gas yields CEST signatures that reflect different Xe exchange kinetics from multiple environments. Dilute pools with ~ 104-fold lower spin numbers than reported for directly detected hyperpolarized nuclei are readily detected due to efficient guest turnover. The system is further probed by instantaneous and medium timescale perturbations. Computational modeling indicates that these signals originate likely from Xe bound to three Fe-MOP diastereomers (T, C3, S4). The symmetry thus induces steric effects with aperture size changes that tunes selective spin manipulation as it is employed in CEST MRI agents and, potentially, impacts other processes occurring on the millisecond time scale.


Assuntos
Imageamento por Ressonância Magnética , Física , Cinética , Espectroscopia de Ressonância Magnética/métodos , Metais
15.
J Chem Phys ; 156(14): 144903, 2022 Apr 14.
Artigo em Inglês | MEDLINE | ID: mdl-35428388

RESUMO

In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we investigate the feasibility and effect of desynchronous learning in a recently introduced decentralized, physics-driven learning network. We show that desynchronizing the learning process does not degrade the performance for a variety of tasks in an idealized simulation. In experiment, desynchronization actually improves the performance by allowing the system to better explore the discretized state space of solutions. We draw an analogy between desynchronization and mini-batching in stochastic gradient descent and show that they have similar effects on the learning process. Desynchronizing the learning process establishes physics-driven learning networks as truly fully distributed learning machines, promoting better performance and scalability in deployment.


Assuntos
Aprendizagem , Redes Neurais de Computação , Simulação por Computador , Aprendizagem/fisiologia , Neurônios , Física
16.
Analyst ; 147(9): 1881-1891, 2022 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-35420079

RESUMO

Electrochemical problems are widely studied in flowing systems since the latter offer improved sensitivity notably for electro-analysis and the possibility of steady-state measurements for fundamental studies even with macro-electrodes. We report the exploratory use of Physics-Informed Neural Networks (PINNs) as potentially simpler, and easier way to implement alternatives to finite difference or finite element simulations to predict the effect of flow and electrode geometry on the currents observed in channel electrodes where the flow is constrained to a rectangular duct with the electrode embedded flush with the wall of the cell. Several problems are addressed including the evaluation of the transport limited current at a micro channel electrode, the transport of material between two adjacent electrodes in a channel flow and the response of an electrode where the electrode reaction follows a preceding chemical reaction. The approach is shown to give quantitative agreement in the limits for which existing solutions are known whilst offering predictions for the case of the previously unexplored CE reaction at a micro channel electrode.


Assuntos
Hidrodinâmica , Redes Neurais de Computação , Eletrodos , Física
17.
Nature ; 604(7905): 252-253, 2022 04.
Artigo em Inglês | MEDLINE | ID: mdl-35418627

Assuntos
Aprendizagem , Física
18.
Protein Sci ; 31(5): e4299, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-35481654

RESUMO

When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.


Assuntos
Física , Proteínas , Concentração de Íons de Hidrogênio , Estabilidade Proteica , Proteínas/química , Proteínas/genética , Solubilidade
19.
PLoS Comput Biol ; 18(4): e1010019, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35377875

RESUMO

Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine.


Assuntos
Glioblastoma , Glioblastoma/patologia , Humanos , Aprendizado de Máquina , Processos Neoplásicos , Redes Neurais de Computação , Física
20.
Sci Rep ; 12(1): 5900, 2022 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-35393511

RESUMO

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Simulação por Computador , Hidrodinâmica , Física
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