Article
Engineering, Mechanical
Yichao Yang, Mayank Chadha, Zhen Hu, Michael D. Todd
Summary: This paper introduces a novel framework for optimal sensor placement design in structural health monitoring using Bayes risk as the objective function. The framework considers external and internal costs, making it applicable to various SHM designs. Through an example problem, the effectiveness and comprehensiveness of the framework are demonstrated, along with discussions on challenges such as computationally expensive models and uncertainty quantification.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Biophysics
Jiajie Chen, Youjun Zeng, Jie Zhou, Xueliang Wang, Boliang Jia, Ruibiao Miyan, Teliang Zhang, Wei Sang, Ying Wang, Haixia Qiu, Junle Qu, Ho-Pui Ho, Bruce Zhi Gao, Yonghong Shao, Ying Gu
Summary: The widely used surface-based biomolecule sensing scheme has greatly facilitated the investigation of protein-protein interactions in lab-on-a-chip microfluidic systems. However, in most biosensing schemes, the interactions are driven in a passive way, hindering their efficiency. To break this limitation, an all-optical active method termed optothermophoretic flipping (OTF) was developed. The method achieved a 23.6-fold sensitivity increment in biomolecule interactions sensing compared to Brownian diffusion, opening new opportunities for high sensitivity biosensing platforms.
BIOSENSORS & BIOELECTRONICS
(2022)
Article
Engineering, Mechanical
Jia-Hua Yang, Wen-Yue Liu, Yong-Hui An, Heung-Fai Lam
Summary: This paper develops an enhanced adaptive sequential Monte Carlo (ASMC) method to solve Bayesian model updating and model class selection for complex engineering structures. The study reveals the difficulties of sampling from complex probability density functions (PDFs) during sequential sampling process, leading to the approximation of PDF using incremental weights of samples and the adaptive sampling scheme. The research also presents new formulations to calculate model class evidence, enabling the separate quantification of data fit and information gain of a model class.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Mathematics, Applied
Keyi Wu, Peng Chen, Omar Ghattas
Summary: Bayesian optimal experimental design is important for reducing model uncertainty in a Bayesian framework. In this study, we focus on goal-oriented design and propose an efficient method based on maximizing expected information gain to solve linear Bayesian inverse problems governed by computationally expensive partial differential equations.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Stephanie A. Valenzuela, Hannah S. N. Crory, Chao-Yi Yao, James R. Howard, Gabriel Saucedo, A. Prasanna de Silva, Eric V. Anslyn
Summary: A colorimetric indicator displacement assay was developed for high-throughput experimentation to determine the percentage of cis and trans alkenes. The method showed high accuracy in determining the percentage of alkene configurations and can be optimized for human eye response by tuning the color.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2021)
Article
Biodiversity Conservation
James D. Hagy III, Betty J. Kreakie, Marguerite C. Pelletier, Farnaz Nojavan, John A. Kiddon, Autumn J. Oczkowski
Summary: One of the goals of coastal ecological research is to describe, quantify and predict human effects on coastal ecosystems. By using previous data and information, we have developed a predictive approach to assess the condition of coastal ecosystems. This method can help us understand and interpret the impact of environmental variables on ecosystem condition, and predict future changes.
ECOLOGICAL INDICATORS
(2022)
Article
Biochemical Research Methods
Thomas K. F. Wong, Teng Li, Louis Ranjard, Steven H. Wu, Jeet Sukumaran, Allen G. Rodrigo
Summary: AFPhyloMix is a novel method that reconstructs the phylogeny of homologous sequences from a mixed sample without the need for barcoding. By aligning short reads to a reference alignment and identifying variable sites, AFPhyloMix computes the phylogenetic tree and haplotype relative abundances using a Bayesian inference model. Results show that AFPhyloMix works well on both simulated and real data sets.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Agriculture, Multidisciplinary
Yan Li, Songhua Yan, Jianya Gong
Summary: This study proposes a new Bayesian neural network framework that quantifies the uncertainty in retrieving soil moisture (SM) and ultimately reduces the uncertainty and improves accuracy using techniques such as Monte-Carlo dropout and Deep Ensembles.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Engineering, Environmental
Zheng Zong, Tao Wang, Jiajue Chai, Yue Tan, Pengfei Liu, Chongguo Tian, Jun Li, Yunting Fang, Gan Zhang
Summary: This study developed a novel method using stable nitrogen and oxygen isotopes to identify the sources and formation processes of HONO in an urban area. The results showed that secondary formation, particularly the NO2 heterogeneous reaction, was the dominant process contributing to HONO formation during both day and night. Bayesian simulation demonstrated the contributions of coal combustion, biomass burning, vehicle exhaust, and soil emissions to HONO.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Materials Science, Multidisciplinary
Braulio Beltran-Pitarch, Benny Guralnik, Neetu Lamba, Andreas R. Stilling-Andersen, Lars Norregaard, Torben M. Hansen, Ole Hansen, Nini Pryds, Peter F. Nielsen, Dirch H. Petersen
Summary: In order to develop materials with higher thermoelectric efficiency, a new method based on micro four-point probe (M4PP) was developed to determine the thermal diffusivity of a bulk material using the phase delay of the second harmonic voltage. The method was tested on two relevant thermoelectric materials, skutterudite and bismuth telluride, and showed good agreement with independent estimates. The M4PP method also demonstrated the ability to characterize materials with nonuniform and erratic electrical resistivity, such as bismuth telluride.
MATERIALS TODAY PHYSICS
(2023)
Article
Engineering, Civil
Aike Steentoft, Bu-Sung Lee, Markus Schlapfer
Summary: Understanding and predicting the flows of people in cities is crucial for urban infrastructure planning. Existing deep-learning approaches lack the ability to quantify uncertainty in predictions, limiting their practical use. To address this, we propose a Bayesian deep-learning approach that provides uncertainty estimates and identifies important geographic features in mobility patterns. We demonstrate the application of our method using large-scale taxi trip data from New York City.
Article
Engineering, Industrial
Melissa De Iuliis, Omar Kammouh, Gian Paolo Cimellaro, Solomon Tesfamariam
Summary: The resilience of infrastructure is crucial in reducing disaster risk and evaluating recovery time after catastrophic events, with Downtime (DT) being a key parameter to measure infrastructural seismic resilience. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate infrastructure DT post-earthquake, with three scenarios demonstrating the methodology's effectiveness despite uncertain parameters. The methodology can support decision-makers in managing and minimizing earthquake impacts, as well as in promptly recovering damaged infrastructure.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Green & Sustainable Science & Technology
M. Gholami, D. Torreggiani, P. Tassinari, A. Barbaresi
Summary: This study introduces a framework for forecasting building energy demand, utilizing automated calibration and known and unknown variables. By defining 11 archetypes to represent buildings in different neighborhoods of Bologna, Italy, and employing Gaussian Process and NUTS algorithm for simulation and sampling, the method accelerates the prediction and calibration of building energy demand.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Oncology
Anisha Das, Shengxian Ding, Rongjie Liu, Chao Huang
Summary: This study proposed a time-independent model to predict the ultimate volume of a high malignancy tumor and determine if the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.
Article
Engineering, Geological
Liang Han, Lin Wang, Wengang Zhang, Boming Geng, Shang Li
Summary: In this study, the conditional random field (CRF) was improved to simulate rockhead profiles. With the assistance of Bayesian theory, the proposed method utilizes measurement data and prior information to handle uncertainty. The method provides reasonable estimations of rockhead depth at various locations and reduces subjectivity in determining prior mean.
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Alexander S. Serov, Francois Laurent, Charlotte Floderer, Karen Perronet, Cyril Favard, Delphine Muriaux, Nathalie Westbrook, Christian L. Vestergaard, Jean-Baptiste Masson
SCIENTIFIC REPORTS
(2020)
Article
Genetics & Heredity
Jean-Baptiste Masson, Francois Laurent, Albert Cardona, Chloe Barre, Nicolas Skatchkovsky, Marta Zlatic, Tihana Jovanic
Article
Biochemistry & Molecular Biology
Mohamed El Beheiry, Charlotte Godard, Clement Caporal, Valentin Marcon, Cecilia Ostertag, Oumaima Sliti, Sebastien Doutreligne, Stephane Fournier, Bassam Hajj, Maxime Dahan, Jean-Baptiste Masson
JOURNAL OF MOLECULAR BIOLOGY
(2020)
Article
Biochemistry & Molecular Biology
Marie Locard-Paulet, Guillaume Voisinne, Carine Froment, Marisa Goncalves Menoita, Youcef Ounoughene, Laura Girard, Claude Gregoire, Daiki Mori, Manuel Martinez, Herve Luche, Jerome Garin, Marie Malissen, Odile Burlet-Schiltz, Bernard Malissen, Anne Gonzalez de Peredo, Romain Roncagalli
MOLECULAR SYSTEMS BIOLOGY
(2020)
Article
Cardiac & Cardiovascular Systems
Francesca Raimondi, Vladimiro Vida, Charlotte Godard, Francesco Bertelli, Elena Reffo, Nathalie Boddaert, Mohamed El Beheiry, Jean-Baptiste Masson
Summary: This study evaluated the appropriateness of a new virtual reality technology, DIVA, for cardiac anatomy renderings compared to standard 3D rendering techniques. Results showed that VR models created using DIVA had shorter and more consistent post-processing times, providing better visualization of cardiac structures and details.
JOURNAL OF CARDIAC SURGERY
(2021)
Article
Physics, Multidisciplinary
Hippolyte Verdier, Maxime Duval, Francois Laurent, Alhassan Casse, Christian L. Vestergaard, Jean-Baptiste Masson
Summary: Single particle tracking is used to study how biomolecules interact physically in their natural environments. However, analyzing single particle trajectories can be challenging due to the difficulty in inferring the physical model of underlying random walks, as well as the stochastic nature of motion and experimental noise. A new approach based on graph neural networks has been introduced to reliably learn models of random walks and their anomalous exponents.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2021)
Article
Immunology
Philippe Nicolas, Jocelyn Ollier, Daiki Mori, Guillaume Voisinne, Javier Celis-Gutierrez, Claude Gregoire, Jeanne Perroteau, Regine Vivien, Mylene Camus, Odile Burlet-Schiltz, Anne Gonzalez de Peredo, Beatrice Clemenceau, Romain Roncagalli, Henri Vie, Bernard Malissen
Summary: Using traceable gene tagging, we investigated the composition and dynamics of TCR-induced signalosomes in human T cells. We found a high degree of conservation in the proximal TCR-signaling network between human CD4(+) and CD8(+) T cells, as well as between human and mouse T cells. Our study suggests that drugs targeting the proximal TCR signaling network should behave similarly when applied to human and mouse T cells. However, differences likely exist in the distal TCR-signaling pathway, and our fast-track AP-MS approach can be favored to determine the mechanism of action of drugs targeting human T cell activation.
JOURNAL OF EXPERIMENTAL MEDICINE
(2022)
Article
Biochemical Research Methods
Francois Laurent, Hippolyte Verdier, Maxime Duval, Alexander Serov, Christian L. Vestergaard, Jean-Baptiste Masson
Summary: Single-molecule localization microscopy is an important tool for studying the dynamics and cellular function of biomolecules. We introduce TRamWAy, a modular Python library that provides various functions including data tracking, meshing, inverse model solving, and analysis, along with a simple web-based interface.
Article
Immunology
Guillaume Voisinne, Marie Locard-Paulet, Carine Froment, Emilie Maturin, Marisa Goncalves Menoita, Laura Girard, Valentin Mellado, Odile Burlet-Schiltz, Bernard Malissen, Anne Gonzalez de Peredo, Romain Roncagalli
Summary: This study uses time-resolved high-throughput proteomic analyses to investigate the impact of ligand affinity on early T cell receptor signaling. By identifying and quantifying phosphorylation events and protein-protein interactions, the study reveals differences in signalosome formation between low and high-affinity ligands.
Article
Biochemical Research Methods
Hippolyte L. Verdier, Francois Laurent, Alhassan L. Casse, Christian L. Vestergaard, Christian G. L. Specht, Jean-Baptiste Masson
Summary: Numerous models have been developed to account for the complex properties of biomolecule random walks, but it is difficult to identify the model when analyzing experimental data due to various influencing factors and short trajectories. In this study, a two-step statistical testing scheme was developed to compare biomolecule dynamics observed in different experimental conditions without assuming a specific model. This approach involves training a graph neural network to learn summary statistics of individual trajectories and performing a statistical test using these statistics. The method was validated using simulated trajectories and successfully applied to detect differences in alpha-synuclein dynamics in cortical neurons. This approach provides a means of interpreting differences in biomolecule dynamics without relying on model-specific assumptions.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Francesco Bertelli, Francesca Raimondi, Charlotte Godard, Emma Bergonzoni, Claudia Cattapan, Elisa Gastino, Francesco Galliotto, Nathalie Boddaert, Mohamed El Beheiry, Jean-Baptiste Masson, Alvise Guariento, Vladimiro L. Vida
Summary: In this study, a new tool called DIVA software was developed to simplify and validate the use of 3-dimensional reconstruction and virtual reality systems for exploring medical images. Five inexperienced volunteers were able to create accurate 3D models of patients' hearts using this software, showing improvement in quality and time with increasing experience.
INTERDISCIPLINARY CARDIOVASCULAR AND THORACIC SURGERY
(2023)
Article
Physics, Fluids & Plasmas
Hippolyte Verdier, Francois Laurent, Alhassan Casse, Christian L. Vestergaard, Jean -Baptiste Masson
Summary: We propose a simulation-based, amortized Bayesian inference scheme for inferring the parameters of random walks. Our approach learns the posterior distribution of the parameters through a likelihood-free method. It utilizes a graph neural network to learn optimized low-dimensional summary statistics of the random walk from simulated data. An invertible neural network then generates the posterior distribution of the parameters using variational inference. The method is applied to infer the parameters of the fractional Brownian motion model and shows good computational complexity and precision.
Article
Medicine, General & Internal
Enora Laas, Mohamed El Beheiry, Jean-Baptiste Masson, Caroline Malhaire
Summary: Oncoplastic surgery expands the indications for conservative breast cancer treatments, and with the help of virtual reality software DIVA, precise visualization of tumors and breast volumes based on patient's MRI can be achieved to rapidly confirm and secure the indication for surgery, minimizing disfigurement for patients.
Article
Pediatrics
Francois Ruiz, Cecilia Neiva-Vaz, Marie-Paule Vazquez, Jean-Baptiste Masson, Mohammed El Beheiry, Stephanie Pannier, Marine De Tienda, Laureline Berteloot, Pauline Lopez, Thomas Blanc, Roman Hossein Khonsari
Summary: This report describes the skin incision design strategies for ischiopagus twin separation, aiming to minimize morbidity related to coverage issues, especially in the abdominal and perineal regions. Two out of three sets of twins achieved complete coverage with the incision design, while specific strategies were defined for optimizing grafting procedures in the remaining set.
JOURNAL OF PEDIATRIC SURGERY CASE REPORTS
(2021)
Article
Immunology
Daiki Mori, Claude Gregoire, Guillaume Voisinne, Javier Celis-Gutierrez, Rudy Aussel, Laura Girard, Mylene Camus, Marlene Marcellin, Jeremy Argenty, Odile Burlet-Schiltz, Frederic Fiore, Anne Gonzalez de Peredo, Marie Malissen, Romain Roncagalli, Bernard Malissen
Summary: The study used a CRISPR/Cas9-based platform to investigate the roles of LAT, CD5, and CD6 in TCR signal propagation in mouse T cells. It was found that LAT and CD5 signalosomes have positive and negative functions, respectively, while the CD6 signalosome contains both positive and negative regulators of T cell activation. Furthermore, CD6 was shown to play a role in inflammatory pathologies and autoimmune diseases, with potential as a therapeutic target.
JOURNAL OF EXPERIMENTAL MEDICINE
(2021)
