Article
Multidisciplinary Sciences
Alicia N. M. Kraay, Kristin N. Nelson, Conan Y. Zhao, David Demory, Joshua S. Weitz, Benjamin A. Lopman
Summary: The study found that widespread serological testing during the COVID-19 pandemic could not only reduce the number of deaths, but also slow down subsequent waves of transmission, increase social interaction levels, and remain relevant in the face of emerging new variants.
NATURE COMMUNICATIONS
(2021)
Review
Engineering, Industrial
Giray Okten, Yaning Liu
Summary: Randomized quasi-Monte Carlo methods are becoming more popular in applications due to their faster convergence rate and the availability of simple statistical tools for analyzing estimation errors.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Multidisciplinary
Leon Otis, Eric Neuscamman
Summary: We discuss recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo and show how combining methods from these fields can predict excited states accurately. Important advances in both fields include improved optimization methods, handling of complicated wave function forms, and balancing the quality of wave functions for ground and excited states. Demonstrations using a combination of specific quantum chemistry and variational Monte Carlo show that this approach is more reliable and accurate than other high-level methods and can provide clarity in cases where existing methods do not agree.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2023)
Article
Chemistry, Physical
Tina N. Mihm, William Z. Van Benschoten, James J. Shepherd
Summary: A new approach using low-cost calculations was developed to find a twist angle that matches the coupled cluster doubles energy in a finite unit cell. The method was shown to have comparable accuracy with exact methods beyond coupled cluster doubles theory. Additionally, for small system sizes, the same twist angle can be found by comparing energies directly, suggesting a potential route towards twist angle selection.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Multidisciplinary Sciences
Mahmoud E. Bakr
Summary: This study proposes a nonparametric technique to determine the distribution type of data and investigates its application in maximum life expectancy prediction and analysis. The efficiency and statistical power of the test are calculated using alternative asymmetric probability models, and real-world datasets are used for validation.
Article
Management
Eunji Lim, Peter W. Glynn
Summary: This paper discusses the use of simulation in computing predictors when real-world observations are collected. The challenge is that the simulation's state description often includes unobserved information from the real system. The authors propose an estimation methodology that involves launching multiple simulations from states closely aligned with the most recent real-world observation.
OPERATIONS RESEARCH
(2022)
Article
Computer Science, Theory & Methods
Marwa Thabet, Mouhebeddine Berrima, Brahim Hnich
Summary: This paper addresses the issue of co-location attack in cloud computing, using secure VM placement strategies and controlling the rate of insecure placements through Monte Carlo simulation and hypothesis testing. It evaluates the correctness in terms of security approximation and performance in terms of energy consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Management
Joseph L. Breeden, Yevgeniya Leonova
Summary: This article develops a Monte Carlo method that estimates the loss distribution for a single loan, allowing for a better understanding of the risk distribution due to modelling and macroeconomic uncertainty. The study finds that the Monte Carlo simulation results fit well with a Lognormal distribution. Additionally, the feasibility of using quantum computers for the calculations is explored, showing potential for significant speed enhancement, albeit with the need for further quantum algorithm development for the full analysis of competing risks.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Physics, Multidisciplinary
Yuliya Shapovalova
Summary: This study empirically illustrates the performance of different classes of Bayesian inference methods in estimating stochastic volatility models, comparing their adaptability to various model specifications and dimensions. The research emphasizes the importance of considering various data-generating processes for a fair assessment of the methods used in comparing models.
Article
Physics, Multidisciplinary
Minghui Hu, Youjin Deng, Jian-Ping Lv
Summary: The concept of logarithmic universality presents a new model for describing critical phenomena, characterized by distinct features from the standard scenario. Monte Carlo simulations on the three-dimensional XY model provide strong evidence for the emergence of logarithmic universality. Discussions on finite-size scaling and critical exponent offer a new perspective on understanding critical universality.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Timothy Foldes, Antony Lesage, Maria Barbi
Summary: This study reexamines the coil-globule transition of a polymer from a spectral perspective, introducing a new possibility and reintroducing overlooked mature spectral methods. This method not only allows for the determination of the polymer state without information about polymer length or interaction strength, but also proposes an experimental implementation.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Indrajit Sau, Arnab Sen, Debasish Banerjee
Summary: Matter-free lattice gauge theories provide an ideal model for studying the confinement to deconfinement transitions at finite temperatures, and the critical exponents can change continuously with varying coupling while maintaining a fixed ratio. This phenomenon, known as weak universality, has been demonstrated for the first time in lattice gauge theories. Using an efficient cluster algorithm, we show that the finite temperature phase transition of U(1) quantum link lattice gauge theory belongs to the 2D XY universality class, and the occurrence of weak universality is observed when higher charged matter fields are added.
PHYSICAL REVIEW LETTERS
(2023)
Review
Engineering, Biomedical
Hyojun Park, Harald Paganetti, Jan Schuemann, Xun Jia, Chul Hee Min
Summary: Monte Carlo simulations are important in radiotherapy for evaluating physical properties that are difficult to measure, such as aiding in the design of radiotherapy devices. This article reviews the MC method for device simulations in radiation therapy, including modeling treatment heads and in-room devices for imaging and therapy purposes. The impact of MCSs in the field and the role of MC in future device design are also discussed.
PHYSICS IN MEDICINE AND BIOLOGY
(2021)
Article
Chemistry, Physical
Matthew Schmidt, Pierre-Nicholas Roy
Summary: The Raman vibrational frequency shifts of pure parahydrogen and orthodeuterium clusters are calculated using three different methods. The results are compared to experiment and it is found that, for large enough system sizes, the perturbation theory method has the strongest balance between accuracy and precision when weighing against computational cost.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Immunology
M. Fleur du Pre, Jana Blazevski, Alisa E. Dewan, Jorunn Stamnaes, Chakravarthi Kanduri, Geir Kjetil Sandve, Marie K. Johannesen, Christian B. Lindstad, Kathrin Hnida, Lars Fugger, Gerry Melino, Shuo-Wang Qiao, Ludvig M. Sollid
JOURNAL OF EXPERIMENTAL MEDICINE
(2020)
Correction
Genetics & Heredity
Sam D. Molyneux, Paul D. Waterhouse, Dawne Shelton, Yang W. Shao, Christopher M. Watling, Qing-Lian Tang, Isaac S. Harris, Brendan C. Dickson, Pirashaanthy Tharmapalan, Geir K. Sandve, Xiaoyang Zhang, Swneke D. Bailey, Hal Berman, Jay S. Wunder, Zsuzsanna Izsvak, Mathieu Lupien, Tak W. Mak, Rama Khokha
Article
Cardiac & Cardiovascular Systems
Anna Helgadottir, Gudmar Thorleifsson, Kristjan F. Alexandersson, Vinicius Tragante, Margret Thorsteinsdottir, Finnur F. Eiriksson, Solveig Gretarsdottir, Eythor Bjornsson, Olafur Magnusson, Gardar Sveinbjornsson, Ingileif Jonsdottir, Valgerdur Steinthorsdottir, Egil Ferkingstad, Brynjar O. Jensson, Hreinn Stefansson, Isleifur Olafsson, Alex H. Christensen, Christian Torp-Pedersen, Lars Kober, Ole B. Pedersen, Christian Erikstrup, Erik Sorensen, Soren Brunak, Karina Banasik, Thomas F. Hansen, Mette Nyegaard, Gudmundur Eyjolfssson, Olof Sigurdardottir, Bjorn L. Thorarinsson, Stefan E. Matthiasson, Thora Steingrimsdottir, Einar S. Bjornsson, Ragnar Danielsen, Folkert W. Asselbergs, David O. Arnar, Henrik Ullum, Henning Bundgaard, Patrick Sulem, Unnur Thorsteinsdottir, Gudmundur Thorgeirsson, Hilma Holm, Daniel F. Gudbjartsson, Kari Stefansson
EUROPEAN HEART JOURNAL
(2020)
Editorial Material
Biochemical Research Methods
Gabriel Balaban, Ivar Grytten, Knut Dagestad Rand, Lonneke Scheffer, Geir Kjetil Sandve
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biology
Steven Bell, Andreas S. Rigas, Magnus K. Magnusson, Egil Ferkingstad, Elias Allara, Gyda Bjornsdottir, Anna Ramond, Erik Sorensen, Gisli H. Halldorsson, Dirk S. Paul, Hannes P. Eggertsson, Kristoffer S. Burgdorf, Joanna M. M. Howson, Lise W. Thorner, Snaedis Kristmundsdottir, William J. Astle, Christian Erikstrup, Jon K. Sigurdsson, Dragana Vuckovic, Khoa M. Dinh, Vinicius Tragante, Praveen Surendran, Ole B. Pedersen, Brynjar Vidarsson, Tao Jiang, Helene M. Paarup, Pall T. Onundarson, Parsa Akbari, Kaspar R. Nielsen, Sigrun H. Lund, Kristinn Juliusson, Magnus Magnusson, Michael L. Frigge, Asmundur Oddsson, Isleifur Olafsson, Stephen Kaptoge, Henrik Hjalgrim, Gudmundur Runarsson, Angela M. Wood, Ingileif Jonsdottir, Thomas F. Hansen, Olof Sigurdardottir, Hreinn Stefansson, David Rye, James E. Peters, David Westergaard, Hilma Holm, Nicole Soranzo, Karina Banasik, Gudmar Thorleifsson, Willem H. Ouwehand, Unnur Thorsteinsdottir, David J. Roberts, Patrick Sulem, Adam S. Butterworth, Daniel F. Gudbjartsson, John Danesh, Soren Brunak, Emanuele Di Angelantonio, Henrik Ullum, Kari Stefansson
Summary: The meta-analysis of three genome-wide association studies revealed 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. These variants are associated with iron deficiency anemia and iron overload, highlighting their significant role in regulating iron homeostasis.
COMMUNICATIONS BIOLOGY
(2021)
Article
Biology
Joseph Dowsett, Egil Ferkingstad, Line Jee Hartmann Rasmussen, Lise Wegner Thorner, Magnus K. Magnusson, Karen Sugden, Gudmar Thorleifsson, Mike Frigge, Kristoffer Solvsten Burgdorf, Sisse Rye Ostrowski, Erik Sorensen, Christian Erikstrup, Ole Birger Pedersen, Thomas Folkmann Hansen, Karina Banasik, Soren Brunak, Vinicius Tragante, Sigrun Helga Lund, Lilja Stefansdottir, Bjarni Gunnarson, Richie Poulton, Louise Arseneault, Avshalom Caspi, Terrie E. Moffitt, Daniel Gudbjartsson, Jesper Eugen-Olsen, Hreinn Stefansson, Kari Stefansson, Henrik Ullum
Summary: The study found a 60% heritability factor for suPAR variation and identified 13 independently genome-wide significant sequence variants associated with suPAR levels across 11 distinct loci. These findings provide new insight into the causes of variation in suPAR plasma levels, which may clarify suPAR's potential role in associated diseases.
COMMUNICATIONS BIOLOGY
(2021)
Article
Biology
Thjodbjorg Eiriksdottir, Steinthor Ardal, Benedikt A. Jonsson, Sigrun H. Lund, Erna V. Ivarsdottir, Kristjan Norland, Egil Ferkingstad, Hreinn Stefansson, Ingileif Jonsdottir, Hilma Holm, Thorunn Rafnar, Jona Saemundsdottir, Gudmundur L. Norddahl, Gudmundur Thorgeirsson, Daniel F. Gudbjartsson, Patrick Sulem, Unnur Thorsteinsdottir, Kari Stefansson, Magnus O. Ulfarsson
Summary: The study developed predictors for all-cause mortality using large-scale proteomics datasets, indicating that the plasma proteome may be valuable in assessing overall health status and estimating the risk of death.
COMMUNICATIONS BIOLOGY
(2021)
Article
Genetics & Heredity
Egil Ferkingstad, Patrick Sulem, Bjarni A. Atlason, Gardar Sveinbjornsson, Magnus I. Magnusson, Edda L. Styrmisdottir, Kristbjorg Gunnarsdottir, Agnar Helgason, Asmundur Oddsson, Bjarni V. Halldorsson, Brynjar O. Jensson, Florian Zink, Gisli H. Halldorsson, Gisli Masson, Gudny A. Arnadottir, Hildigunnur Katrinardottir, Kristinn Juliusson, Magnus K. Magnusson, Olafur Th. Magnusson, Run Fridriksdottir, Saedis Saevarsdottir, Sigurjon A. Gudjonsson, Simon N. Stacey, Solvi Rognvaldsson, Thjodbjorg Eiriksdottir, Thorunn A. Olafsdottir, Valgerdur Steinthorsdottir, Vinicius Tragante, Magnus O. Ulfarsson, Hreinn Stefansson, Ingileif Jonsdottir, Hilma Holm, Thorunn Rafnar, Pall Melsted, Jona Saemundsdottir, Gudmundur L. Norddahl, Sigrun H. Lund, Daniel F. Gudbjartsson, Unnur Thorsteinsdottir, Kari Stefansson
Summary: Genome-wide association studies of plasma protein levels in Icelanders have identified numerous associations with diseases and other traits, providing valuable insights into disease pathogenesis and potential drug targets. Through integration of proteomics, genomics, and transcriptomics, this research offers a resource for improving disease understanding and aiding drug discovery and development.
Article
Biochemical Research Methods
Torbjorn Rognes, Lonneke Scheffer, Victor Greiff, Geir Kjetil Sandve
Summary: In this study, CompAIRR was developed for fast computation of AIRR overlap, achieving a 1000-fold improvement in computational speed compared to existing methods. CompAIRR has been integrated with immuneML, a machine learning ecosystem for AIRR analysis.
Letter
Biochemical Research Methods
Geir Kjetil Sandve, Victor Greiff
Article
Multidisciplinary Sciences
Marketa Chlubnova, Asbjorn O. Christophersen, Geir Kjetil F. Sandve, Knut E. A. Lundin, Jorgen Jahnsen, Shiva Dahal-Koirala, Ludvig M. Sollid
Summary: 42 wheat gluten-reactive T cell clones with different phenotypes and no reactivity to known epitopes were screened. Synthetic peptides were identified bioinformatically from a wheat gluten protein database and tested against the T cell clones. Reactivity of 10 T cell clones was assigned, and 5 previously uncharacterized gliadin/glutenin epitopes with a 9-nucleotide oligomer core region were identified. This work represents an advance in identifying CeD-driving gluten epitopes.
Article
Psychiatry
Emilie Willoch Olstad, Hedvig Marie Egeland Nordeng, Geir Kjetil Sandve, Robert Lyle, Kristina Gervin
Summary: This study investigated the associations between prenatal exposure to citalopram or escitalopram, maternal depression, and offspring DNA methylation (DNAm). The researchers also examined the interaction effect of (es)citalopram exposure and DNAm on neurodevelopmental outcomes, as well as the correlation between DNAm at birth and neurodevelopmental trajectories in childhood.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Computer Science, Artificial Intelligence
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Geir Kjetil Sandve, Victor Greiff, Dag Trygve Truslew Haug
Summary: Language models trained on proteins can predict functions from sequences but lack insight into underlying mechanisms. Extracting rules from these models can make them interpretable and help explain biological mechanisms.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Biochemical Research Methods
Ping-Han Hsieh, Camila Miranda Lopes-Ramos, Manuela Zucknick, Geir Kjetil Sandve, Kimberly Glass, Marieke Lydia Kuijjer
Summary: Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns. However, certain normalization methods can introduce false-positive associations between genes, hindering downstream co-expression network analysis. In this study, a normalization method called SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links) is developed to avoid false-positive associations and retain associations to genes expressed in small subgroups of samples. This method has the potential to impact network modeling and association-based approaches in large-scale heterogeneous data.
Article
Biology
Chakravarthi Kanduri, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Maria Chernigovskaya, Victor Greiff, Geir K. Sandve
Summary: This article presents a study aimed at determining the effectiveness of baseline machine learning (ML) methods in the classification of adaptive immune receptor repertoires (AIRRs). The study generated a series of synthetic AIRR benchmark datasets and found that even when the immune signal occurs only in 1 out of 50,000 AIR sequences, the baseline L1-penalized logistic regression model can achieve high prediction accuracy.