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arXiv 2020年

作者： Jin, Tangxin Chazirakis, Anthony Kalligiannaki, Evangelia Harmandaris, Vagelis Katsoulakis, Markos A. Department of Mathematics and Statistics University of Massachusetts Amherst United States Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Crete Greece Department of Mathematics and Applied Mathematics University of Crete Heraklion Crete Greece

In this work, we present methodologies for the quantification of confidence in bottom-up coarse-grained models for molecular and macromolecular systems. Coarse-graining methods have been extensively used in the past decades in order to extend the length and time scales accessible by simulation methodologies. The quantification, though, of induced errors due to the limited availability of fine-grained data is not yet established. Here, we employ rigorous statistical methods to deduce guarantees for the optimal coarse models obtained via approximations of the multi-body potential of mean force, with the relative entropy, the relative entropy rate minimization, and the force matching methods. Specifically, we present and apply statistical approaches, such as bootstrap and jackknife, to infer confidence sets for a limited number of samples, i.e., molecular configurations. Moreover, we estimate asymptotic confidence intervals assuming adequate sampling of the phase space. We demonstrate the need for non-asymptotic methods and quantify confidence sets through two applications. The first is a two-scale fast/slow diffusion process projected on the slow process. With this benchmark example, we establish the methodology for both independent and time-series data. Second, we apply these uncertainty quantification approaches on a polymeric bulk system. We consider an atomistic polyethylene melt as the prototype system for developing coarse-graining tools for macromolecular systems. For this system, we estimate the coarse-grained force field and present confidence levels with respect to the number of available microscopic data. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Optimizing Variational Representations of Divergences and Accelerating their Statistical Estimation

arXiv

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arXiv 2020年

作者： Birrell, Jeremiah Katsoulakis, Markos A. Pantazis, Yannis The Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA01003 United States The Institute of Applied and Computational Mathematics Foundation for Research and Technology Hellas GR Heraklion70013 Greece

Variational representations of divergences and distances between high-dimensional probability distributions offer significant theoretical insights and practical advantages in numerous research areas. Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions. Their advantages include: 1) They can be estimated from data as statistical averages. 2) Such representations can leverage the ability of neural networks to efficiently approximate optimal solutions in function spaces. However, a systematic and practical approach to improving the tightness of such variational formulas, and accordingly accelerate statistical learning and estimation from data, is currently lacking. Here we develop such a methodology for building new, tighter variational representations of divergences. Our approach relies on improved objective functionals constructed via an auxiliary optimization problem. Furthermore, the calculation of the functional Hessian of objective functionals unveils the local curvature differences around the common optimal variational solution;this quantifies and orders the tightness gains between different variational representations. Finally, numerical simulations utilizing neural network optimization demonstrate that tighter representations can result in significantly faster learning and more accurate estimation of divergences in both synthetic and real datasets (of more than 1000 dimensions), often accelerated by nearly an order of magnitude. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

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RETHINKING CASE FATALITY RATIOS FOR COVID-19 FROM A DATA-DRIVEN VIEWPOINT

arXiv

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arXiv 2020年

作者： Rosakis, Phoebus Marketou, Maria E. Department of Mathematics and Applied Mathematics University of Crete Heraklion70013 Greece Institute of Applied and Computational Mathematics Foundation of Research and Technology-Hellas Heraklion70013 Greece Department of Cardiology Heraklion University Hospital Heraklion70013 Greece

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Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data 23rd

Pathway Activity Score Learning for Dimensionality Reduction...

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23rd International Conference on Discovery Science, DS 2020

作者： Karagiannaki, Ioulia Pantazis, Yannis Chatzaki, Ekaterini Tsamardinos, Ioannis Department of Computer Science University of Crete Heraklion Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece Laboratory of Pharmacology Medical School Democritus University of Thrace Alexandroupoli Greece Institute of Agri-food and Life Sciences University Research Center Hellenic Mediterranean University HeraklionCrete71410 Greece Gnosis Data Analysis PC HeraklionCrete Greece

ISBN: (纸本)9783030615260

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://***/mensxmachina/PASL. © 2020, The Author(s).

关键词： Activation analysis

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Functional Imaging of the Cerebellum during Action Execution and Observation

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Cerebral cortex communications 2021年第3期2卷 tgab041页

作者： Vassilis Raos Helen E Savaki Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Heraklion Crete 70013 Greece.

We employed the C-deoxyglucose autoradiographic method to map the activity in the cerebellar cortex of rhesus monkeys that performed forelimb movements either in the light or in the dark and of monkeys that observed forelimb movements executed by a human experimenter. The execution of forelimb movements, both in the light and in the dark, activated the forelimb representations in the cerebellar hemispheric extensions of 1) vermian lobules IV-VI and 2) vermian lobule VIIIB, ipsilaterally to the moving forelimb. Activations in the former forelimb representation involved both a paravermal and a lateral hemispheric region. Also, Crus II posterior in the ansiform lobule (the hemispheric expansion of lobule VIIB) was activated bilaterally by execution of movements in the light but not in the dark. Action observation activated the lateral-most region of the forelimb representation in the lateral hemispheric extension of vermian lobules IV-VI, as well as the crus II posterior, bilaterally. Our results demonstrate that the cerebellar cortex, in addition to its involvement in the generation of movement, is also recruited in the perception of observed movements. Moreover, our findings suggest a modularity gradient in the primate cerebellar cortex, which progresses from unimodal (medially) to multimodal (laterally) functional areas.

关键词： action execution action observation cerebellum mental simulation monkey

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Inter-species spin-noise correlations in hot atomic vapors

arXiv

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arXiv 2023年

作者： Mouloudakis, K. Vouzinas, F. Margaritakis, A. Koutsimpela, A. Mouloudakis, G. Koutrouli, V. Skotiniotis, M. Tsironis, G.P. Loulakis, M. Mitchell, M.W. Vasilakis, G. Kominis, I.K. Department of Physics University of Crete Heraklion71003 Greece ICFO - Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain School of Applied Mathematical and Physical Sciences National Technical University of Athens Athens15780 Greece Institute of Electronic Structure and Laser Foundation for Research and Technology Heraklion71110 Greece Física Teòrica: Informació i Fenòmens Quàntics Departament de Física Universitat Autònoma de Barcelona Bellaterra08193 Spain Departamento de Electromagnetismo y Física de la Materia Universidad de Granada Granada18010 Spain Institute of Theoretical and Computational Physics University of Crete Heraklion70013 Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology Heraklion70013 Greece ICREA - Institució Catalana de Recerca i Estudis Avançats Barcelona08010 Spain Quantum Biometronics Heraklion71409 Greece

We report an experimental and theoretical study of spin noise correlations in a 87Rb-133Cs unpolarized alkali-metal vapor dominated by spin-exchange collisions. We observe strong unequal-time inter-species correlations and account for these with a first-principles theoretical model. Since the two atomic species have different spin precession frequencies, the dual-species vapor enables the use of an additional experimental handle, the applied magnetic field, for untangling various subtypes of spin correlations. In particular, the measured cross-correlation and auto-correlation spectra shed light on a number of spin-dynamic effects involving intra-atom, inter-atom, intra-species and inter-species correlations. Cross-correlation coefficients exceeding 60% have been observed at low-magnetic fields, where the two spin species couple strongly via spin-exchange collisions. The understanding of such spontaneously generated correlations can motivate the design of quantum-enhanced measurements with single or multi-species spin-polarized alkali-metal vapors used in quantum sensing applications. © 2023, CC BY.

关键词： Atoms

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Publisher Correction: Symmetry-governed dynamics of magnetic skyrmions under field pulses

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Communications Physics 2025年第1期8卷 1页

作者： Vladyslav M. Kuchkin Bruno Barton-Singer Pavel F. Bessarab Nikolai S. Kiselev Department of Physics and Materials Science University of Luxembourg Luxembourg Luxembourg Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece Science Institute University of Iceland Reykjavík Iceland Department of Physics and Electrical Engineering Linnaeus University Kalmar Sweden Peter Grünberg Institute Forschungszentrum Jülich and JARA Jülich Germany

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Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions

Latent Feature Representations for Human Gene Expression Dat...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yannis Pantazis Christos Tselas Kleanthi Lakiotaki Vincenzo Lagani Ioannis Tsamardinos Foundation for Research and Technology - Hellas Inst. of Applied and Computational Mathematics Heraklion Greece University of Crete Heraklion Greece Institute of Chemical Biology Ilia State University Tbilisi Georgia

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces of the human genome, by utilizing three dimensionality reduction approaches and a diverse set of curated datasets. We applied standard Principal Component Analysis (PCA), kernel PCA and Autoencoder Neural Networks on 1360 datasets from four different measurement technologies. The latent feature spaces are tested for their ability to (a) reconstruct the original data and (b) improve predictive performance on validation datasets not used during the creation of the feature space. While linear techniques show better reconstruction performance, nonlinear approaches, particularly, neural-based models seem to be able to capture non-additive interaction effects, and thus enjoy stronger predictive capabilities. Despite the limited sample size of each dataset and the biological / technological heterogeneity across studies, our results show that low dimensional representations of the human transcriptome can be achieved by integrating hundreds of datasets. The created space is two to three orders of magnitude smaller compared to the raw data, offering the ability of capturing a large portion of the original data variability and eventually reducing computational time for downstream analyses.

关键词： Dimensionality reduction Gene expression Principal component analysis Biology Training Neural networks Bioinformatics

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Public Goods Games in Disease Evolution and Spread

arXiv

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arXiv 2024年

作者： Morison, Christo Fic, Mallgorzata Marcou, Thomas Mohamadichamgavi, Javad Antón, Javier Redondo Sayyar, Golsa Stein, Alexander Bastian, Frank Krakovská, Hana Krishnan, Nandakishor Pires, Diogo L. Satouri, Mohammadreza Thomsen, Frederik J. Tjikundi, Kausutua Ali, Wajid School of Mathematical Sciences Queen Mary University of London London United Kingdom Department of Theoretical Biology Max Planck Institute for Evolutionary Biology Plön Germany Center for Computational and Theoretical Biology University of Würzburg Würzburg Germany Centre for Mathematical Biology University of South Bohemia České Budějovice Czech Republic Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Department of Computer Science University of Turin Turin Italy Bolyai Institute University of Szeged Szeged Hungary Evolutionary Dynamics Group Centre for Cancer Genomics and Computational Biology Barts Cancer Institute Queen Mary University of London London United Kingdom School of Mathematical Sciences University College Cork Cork Ireland Section for Science of Complex Systems Medical University of Vienna Vienna Austria Complexity Science Hub Vienna Vienna Austria HUN-REN Centre for Ecological Research Institute of Evolution Budapest Hungary Department of Mathematics City University of London London United Kingdom Institute for Health Systems Science Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Institute of Applied Mathematics Delft University of Technology Delft Netherlands ISI Foundation Turin Italy Department of Mathematical Sciences University of Liverpool Liverpool United Kingdom

Cooperation arises in nature at every scale, from within cells to entire ecosystems. In the framework of evolutionary game theory, public goods games (PGGs) are used to analyse scenarios where individuals can cooperate or defect, and can predict when and how these behaviours emerge. However, too few examples motivate the transferal of knowledge from one application of PGGs to another. Here, we focus on PGGs arising in disease modelling of cancer evolution and the spread of infectious diseases. We use these two systems as case studies for the development of the theory and applications of PGGs, which we succinctly review and compare. We also posit that applications of evolutionary game theory to decision-making in cancer, such as interactions between a clinician and a tumour, can learn from the PGGs studied in epidemiology, where cooperative behaviours such as quarantine and vaccination compliance have been more thoroughly investigated. Furthermore, instances of cellular-level cooperation observed in cancers point to a corresponding area of potential interest for modellers of other diseases, be they viral, bacterial or otherwise. We aim to demonstrate the breadth of applicability of PGGs in disease modelling while providing a starting point for those interested in quantifying cooperation arising in healthcare. © 2024, CC BY.

关键词： Diseases

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Is the Insular Coastal Tourism of Western Greece at Risk Due to Climate Induced Sea Level Rise?

SSRN

引用

SSRN 2023年

作者： Karditsa, Katerina A. Niavis, Spyros Paramana, Theodora Monioudi, Isavela Poulos, Serafeim Hatzaki, Maria Department of Ports Management and Shipping National and Kapodistrian University of Athens Evripos Campus Greece Department of Planning and Regional Development University of Thessaly Volos Greece Laboratory of Environmental Chemistry Department of Chemistry National and Kapodistrian University of Athens Greece Department of Marine Sciences University of the Aegean Mytilene Greece Laboratory of Physical Geography Section of Geography and Climatology Department of Geology and Geoenvironment National and Kapodistrian University of Athens Greece Laboratory of Climatology and Atmospheric Environment Section of Geography and Climatology Department of Geology and Geoenvironment National and Kapodistrian University of Athens Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Heraklion70013 Greece

Beaches are among the most common areas for tourism activity constituting valuable economic resources. However, due to their location in the coastal zone they are particularly exposed to the impacts of Climate Change (CC), which is expected to have broad implications for the development prospects of the economy and human welfare, as a result of, amongst other, extreme events and sea-level rise. Looking beyond 2050, it is considerably important for beach tourism managers to have developed proper adaptation strategies. The main scope of the paper is to support tourism adaptation planning, in response to sea level rise impacts on beach zones at a regional scale, and in specific in the touristic beaches of Ionian Islands (Greece). The methodology follows the AR6 (IPCC, 2022) approach for Risk Assessment along with the concept of the Impact Chain according to which Risk is expressed as a function of factors (i) Hazard (H), (ii) Vulnerability (V) (sensitivity and adaptive capacity) and (iii) Exposure (E). Adaptation prospects consider both physico-geographical and tourism information to respond to climatic risk and highlight the priorities to indicate case-by-case proper adaptation measures. © 2023, The Authors. All rights reserved.

关键词： Beaches

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