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Article ; Online: Tensor-SqRA: Modeling the transition rates of interacting molecular systems in terms of potential energies.

Sikorski, Alexander / Niknejad, Amir / Weber, Marcus / Donati, Luca

2024 Volume 160, Issue 10

Abstract: Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based ...

Abstract	Estimating the rate of rare conformational changes in molecular systems is one of the goals of molecular dynamics simulations. In the past few decades, a lot of progress has been done in data-based approaches toward this problem. In contrast, model-based methods, such as the Square Root Approximation (SqRA), directly derive these quantities from the potential energy functions. In this article, we demonstrate how the SqRA formalism naturally blends with the tensor structure obtained by coupling multiple systems, resulting in the tensor-based Square Root Approximation (tSqRA). It enables efficient treatment of high-dimensional systems using the SqRA and provides an algebraic expression of the impact of coupling energies between molecular subsystems. Based on the tSqRA, we also develop the projected rate estimation, a hybrid data-model-based algorithm that efficiently estimates the slowest rates for coupled systems. In addition, we investigate the possibility of integrating low-rank approximations within this framework to maximize the potential of the tSqRA.
Language	English
Publishing date	2024-03-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/5.0187792
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Article ; Online: Girsanov reweighting for metadynamics simulations.

Donati, Luca / Keller, Bettina G

The Journal of chemical physics

2018 Volume 149, Issue 7, Page(s) 72335

Abstract: Metadynamics is a computational method to explore the phase space of a molecular system. Gaussian functions are added along relevant coordinates on the fly during a molecular-dynamics simulation to force the system to escape from minima in the potential ... ...

Abstract	Metadynamics is a computational method to explore the phase space of a molecular system. Gaussian functions are added along relevant coordinates on the fly during a molecular-dynamics simulation to force the system to escape from minima in the potential energy function. The dynamics in the resulting trajectory are however unphysical and cannot be used directly to estimate dynamical properties of the system. Girsanov reweighting is a recent method used to construct the Markov State Model (MSM) of a system subjected to an external perturbation. With the combination of these two techniques-metadynamics/Girsanov-reweighting-the unphysical dynamics in a metadynamics simulation can be reweighted to obtain the MSM of the unbiased system. We demonstrate the method on a one-dimensional diffusion process, alanine dipeptide, and the hexapeptide Val-Gly-Val-Ala-Pro-Gly (VGVAPG). The results are in excellent agreement with the MSMs obtained from direct unbiased simulations of these systems. We also apply metadynamics/Girsanov-reweighting to a
MeSH term(s)	Bacterial Proteins/chemistry ; Dipeptides/chemistry ; Markov Chains ; Molecular Dynamics Simulation ; Oligopeptides/chemistry ; Protein Conformation, beta-Strand ; Streptococcus/chemistry ; Thermodynamics
Chemical Substances	Bacterial Proteins ; Dipeptides ; IgG Fc-binding protein, Streptococcus ; Oligopeptides ; alanylalanine (2867-20-1) ; valyl-glycyl-valyl-alanyl-prolyl-glycine (92899-39-3)
Language	English
Publishing date	2018-08-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/1.5027728
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Article ; Online: Markov models from the square root approximation of the Fokker-Planck equation: calculating the grid-dependent flux.

Donati, Luca / Weber, Marcus / Keller, Bettina G

Journal of physics. Condensed matter : an Institute of Physics journal

2020 Volume 33, Issue 11, Page(s) 115902

Abstract: Molecular dynamics (MD) are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the MD as a stochastic process and analyses the dominant ... ...

Abstract	Molecular dynamics (MD) are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the MD as a stochastic process and analyses the dominant eigenfunctions of the associated Fokker-Planck operator, or of closely related transfer operators. So far, the calculation of the discretized operators requires extensive MD simulations. The square-root approximation of the Fokker-Planck equation is a method to calculate transition rates as a ratio of the Boltzmann densities of neighboring grid cells times a flux, and can in principle be calculated without a simulation. In a previous work we still used MD simulations to determine the flux. Here, we propose several methods to calculate the exact or approximate flux for various grid types, and thus estimate the rate matrix without a simulation. Using model potentials we test computational efficiency of the methods, and the accuracy with which they reproduce the dominant eigenfunctions and eigenvalues. For these model potentials, rate matrices with up to [Formula: see text] states can be obtained within seconds on a single high-performance compute server if regular grids are used.
Language	English
Publishing date	2020-12-22
Publishing country	England
Document type	Journal Article
ZDB-ID	1472968-4
ISSN	1361-648X ; 0953-8984
ISSN (online)	1361-648X
ISSN	0953-8984
DOI	10.1088/1361-648X/abd5f7
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Article ; Online: MetalGAN: Multi-domain label-less image synthesis using cGANs and meta-learning.

Fontanini, Tomaso / Iotti, Eleonora / Donati, Luca / Prati, Andrea

Neural networks : the official journal of the International Neural Network Society

2020 Volume 131, Page(s) 185–200

Abstract: Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and ... ...

Abstract	Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset.
MeSH term(s)	Automated Facial Recognition/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Machine Learning
Language	English
Publishing date	2020-08-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2020.07.031
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Article ; Online: Dynamical reweighting methods for Markov models.

Kieninger, Stefanie / Donati, Luca / Keller, Bettina G

Current opinion in structural biology

2020 Volume 61, Page(s) 124–131

Abstract: Conformational dynamics is essential to biomolecular processes. Markov State Models (MSMs) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for ... ...

Abstract	Conformational dynamics is essential to biomolecular processes. Markov State Models (MSMs) are widely used to elucidate dynamic properties of molecular systems from unbiased Molecular Dynamics (MD). However, the implementation of reweighting schemes for MSMs to analyze biased simulations is still at an early stage of development. Several dynamical reweighing approaches have been proposed, which can be classified as approaches based on (i) Kramers rate theory, (ii) rescaling of the probability density flux, (iii) reweighting by formulating a likelihood function, (iv) path reweighting. We present the state-of-the-art and discuss the methodological differences of these methods, their limitations and recent applications.
MeSH term(s)	Algorithms ; Markov Chains ; Models, Theoretical ; Molecular Dynamics Simulation ; Protein Binding ; Protein Conformation
Language	English
Publishing date	2020-01-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2019.12.018
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Book ; Online: Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation

Li, Runze / Fontanini, Tomaso / Donati, Luca / Prati, Andrea / Bhanu, Bir

2021

Abstract: Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the training of generative adversarial networks (GANs) is an unexplored area ... ...

Abstract	Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the training of generative adversarial networks (GANs) is an unexplored area in computer vision research. Indeed, we argue that this kind of information can be used to influence GANs training in a positive way. For this reason, in this paper, it is shown how gradient based attentions can be used as knowledge to be conveyed in a teacher-student paradigm for multi-domain image-to-image translation tasks in order to improve the results of the student architecture. Further, it is demonstrated how "pseudo"-attentions can also be employed during training when teacher and student networks are trained on different domains which share some similarities. The approach is validated on multi-domain facial attributes transfer and human expression synthesis showing both qualitative and quantitative results. Comment: Preprint
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	004
Publishing date	2021-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Historical eye on IPF: a cohort study redefining the mortality scenario.

Tomassetti, Sara / Ravaglia, Claudia / Piciucchi, Sara / Ryu, Jay / Wells, Athol / Donati, Luca / Dubini, Alessandra / Klersy, Catherine / Luzzi, Valentina / Gori, Leonardo / Rosi, Elisabetta / Lavorini, Federico / Poletti, Venerino

Frontiers in medicine

2023 Volume 10, Page(s) 1151922

Abstract: Rationale: Therapies that slow idiopathic pulmonary fibrosis (IPF) progression are now available and recent studies suggest that the use of antifibrotic therapy may reduce IPF mortality.: Objectives: The aim of the study was to evaluate whether, to ... ...

Abstract	Rationale: Therapies that slow idiopathic pulmonary fibrosis (IPF) progression are now available and recent studies suggest that the use of antifibrotic therapy may reduce IPF mortality. Objectives: The aim of the study was to evaluate whether, to what extent, and for which factors the survival of IPF in a real-life setting has changed in the last 15 years. Methods: Historical eye is an observational study of a large cohort of consecutive IPF patients diagnosed and treated in a referral center for ILDs with prospective intention. We recruited all consecutive IPF patients seen at GB Morgagni Hospital, Forlì, Italy between January 2002 and December 2016 (15 years). We used survival analysis methods to describe and model the time to death or lung transplant and Cox regression to model prevalent and incident patient characteristics (time-dependent Cox models were fitted). Measurements and main results: The study comprised 634 patients. The year 2012 identifies the time point of mortality shift (HR 0.58, CI 0.46-0.63, Conclusion: Antifibrotic drugs significantly impact hospitalizations, acute exacerbations, and IPF survival. After the introduction of cryobiopsy and antifibrotic drugs, the prognosis of IPF patients has significantly improved together with our ability to detect IPF at an earlier stage.
Language	English
Publishing date	2023-06-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2775999-4
ISSN	2296-858X
ISSN	2296-858X
DOI	10.3389/fmed.2023.1151922
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Book ; Online: MetalGAN

Fontanini, Tomaso / Iotti, Eleonora / Donati, Luca / Prati, Andrea

Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning

2019

Abstract	Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain problem and it is validated on facial attribute transfer, using CelebA dataset.
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
Subject code	006 ; 004
Publishing date	2019-12-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimation of the infinitesimal generator by square-root approximation.

Donati, Luca / Heida, Martin / Keller, Bettina G / Weber, Marcus

Journal of physics. Condensed matter : an Institute of Physics journal

2018 Volume 30, Issue 42, Page(s) 425201

Abstract: In recent years, for the analysis of molecular processes, the estimation of time-scales and transition rates has become fundamental. Estimating the transition rates between molecular conformations is-from a mathematical point of view-an invariant ... ...

Abstract	In recent years, for the analysis of molecular processes, the estimation of time-scales and transition rates has become fundamental. Estimating the transition rates between molecular conformations is-from a mathematical point of view-an invariant subspace projection problem. We present a method to project the infinitesimal generator acting on function space to a low-dimensional rate matrix. This projection can be performed in two steps. First, we discretize the conformational space in a Voronoi tessellation, then the transition rates between adjacent cells is approximated by the geometric average of the Boltzmann weights of the Voronoi cells. This method demonstrates that there is a direct relation between the potential energy surface of molecular structures and the transition rates of conformational changes. We will show also that this approximation is correct and converges to the generator of the Smoluchowski equation in the limit of infinitely small Voronoi cells. We present results for a two dimensional diffusion process and alanine dipeptide as a high-dimensional system.
MeSH term(s)	Alanine/chemistry ; Dipeptides/chemistry ; Kinetics ; Molecular Conformation ; Molecular Dynamics Simulation
Chemical Substances	Dipeptides ; Alanine (OF5P57N2ZX)
Language	English
Publishing date	2018-09-07
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1472968-4
ISSN	1361-648X ; 0953-8984
ISSN (online)	1361-648X
ISSN	0953-8984
DOI	10.1088/1361-648X/aadfc8
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Article ; Online: Clinical, radiological and pathological findings in patients with persistent lung disease following SARS-CoV-2 infection.

Ravaglia, Claudia / Doglioni, Claudio / Chilosi, Marco / Piciucchi, Sara / Dubini, Alessandra / Rossi, Giulio / Pedica, Federica / Puglisi, Silvia / Donati, Luca / Tomassetti, Sara / Poletti, Venerino

The European respiratory journal

2022 Volume 60, Issue 4

Abstract: Some patients experience pulmonary sequelae after SARS-CoV-2 infection, ranging from self-limited abnormalities to major lung diseases. Morphological analysis of lung tissue may help our understanding of pathogenic mechanisms and help to provide ... ...

Abstract	Some patients experience pulmonary sequelae after SARS-CoV-2 infection, ranging from self-limited abnormalities to major lung diseases. Morphological analysis of lung tissue may help our understanding of pathogenic mechanisms and help to provide consistent personalised management. The aim of this study was to ascertain morphological and immunomolecular features of lung tissue. Transbronchial lung cryobiopsy was carried out in patients with persistent symptoms and computed tomography suggestive of residual lung disease after recovery from SARS-CoV-2 infection. 164 patients were referred for suspected pulmonary sequelae after COVID-19; 10 patients with >5% parenchymal lung disease underwent lung biopsy. The histological pattern of lung disease was not homogeneous and three different case clusters could be identified, which was mirrored by their clinical and radiological features. Cluster 1 ("chronic fibrosing") was characterised by post-infection progression of pre-existing interstitial pneumonias. Cluster 2 ("acute/subacute injury") was characterised by different types and grades of lung injury, ranging from organising pneumonia and fibrosing nonspecific interstitial pneumonia to diffuse alveolar damage. Cluster 3 ("vascular changes") was characterised by diffuse vascular increase, dilatation and distortion (capillaries and venules) within otherwise normal parenchyma. Clusters 2 and 3 had immunophenotypical changes similar to those observed in early/mild COVID-19 pneumonias (abnormal expression of STAT3 in hyperplastic pneumocytes and PD-L1, IDO and STAT3 in endothelial cells). This is the first study correlating histological/immunohistochemical patterns with clinical and radiological pictures of patients with post-COVID lung disease. Different phenotypes with potentially different underlying pathogenic mechanisms have been identified.
MeSH term(s)	B7-H1 Antigen ; COVID-19/complications ; Endothelial Cells ; Humans ; Lung/diagnostic imaging ; Lung/pathology ; SARS-CoV-2
Chemical Substances	B7-H1 Antigen
Language	English
Publishing date	2022-10-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639359-7
ISSN	1399-3003 ; 0903-1936
ISSN (online)	1399-3003
ISSN	0903-1936
DOI	10.1183/13993003.02411-2021
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