Results 71 to 80 of about 3,113,242 (314)

Thermostable neutral metalloprotease from Geobacillus sp. EA1 does not share thermolysin's preference for substrates with leucine at the P1′ position

FEBS Letters, EarlyView.
Knowing how proteases recognise preferred substrates facilitates matching proteases to applications. The S1′ pocket of protease EA1 directs cleavage to the N‐terminal side of hydrophobic residues, particularly leucine. The S1′ pocket of thermolysin differs from EA's at only one position (leucine in place of phenylalanine), which decreases cleavage ...
Grant R. Broomfield, Torsten Kleffmann, David J. Saul, Sigurd M. Wilbanks   +3 more
wiley   +1 more source

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification

Italian National Conference on Sensors, 2022
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing.
Jinsong Yang, Yizhen Peng, Jingsong Xie, Pengxi Wang   +3 more
semanticscholar   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.14 User's Manual.

, 2020
The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling ...
B. Adams, W. Bohnhoff, K. Dalbey, Mohamed S. Ebeida, John P. Eddy, M. Eldred, Russell Hooper, P. Hough, Kenneth T. Hu, J. Jakeman, Mohammad Khalil, Kathryn Maupin, Jason Monschke, Elliott Ridgway, A. Rushdi, Daniel Seidl, J. Stephens, J. Winokur   +17 more
semanticscholar   +1 more source

Calibration after bootstrap for accurate uncertainty quantification in regression models

npj Computational Materials, 2022
Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.
Glenn Palmer, Siqi Du, A. Politowicz, Joshua Paul Emory, Xiyu Yang, Anupraas Gautam, Grishma Gupta, Zhelong Li, R. Jacobs, Dane Morgan   +9 more
semanticscholar   +1 more source

Tonic signaling of the B‐cell antigen‐specific receptor is a common functional hallmark in chronic lymphocytic leukemia cell phosphoproteomes at early disease stages

Molecular Oncology, EarlyView.
B‐cell chronic lymphocytic leukemia (B‐CLL) and monoclonal B‐cell lymphocytosis (MBL) show altered proteomes and phosphoproteomes, analyzed using mass spectrometry, protein microarrays, and western blotting. Identifying 2970 proteins and 316 phosphoproteins, including 55 novel phosphopeptides, we reveal BCR and NF‐kβ/STAT3 signaling in disease ...
Paula Díez, Pablo Juanes‐Velasco, Marina L. García‐Vaquero, Conrad Droste, Alicia Landeira‐Viñuela, Miguel Alcoceba, Helena Fidalgo‐Gómez, Sara Misiego‐Herrero, Almudena Navarro‐Bailón, Mónica Baile, José M. Bastida, Jose Manuel Sanchez‐Santos, Rafael Góngora, Julia Almeida, Marcos Gonzalez‐Diaz, Alberto Orfao, Javier De Las Rivas, Manuel Fuentes   +17 more
wiley   +1 more source

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

, 2018
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by ...
Gehler, Peter, Nowozin, Sebastian, Prokudin, Sergey   +2 more
core   +1 more source

Decrypting cancer's spatial code: from single cells to tissue niches

Molecular Oncology, EarlyView.
Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik, Shi Pan, Eloise Withnell, Hou Wang Lam, Maria Secrier   +4 more
wiley   +1 more source

Strategies for robust renovation of residential buildings in Switzerland

Nature Communications
Building renovation is urgently required to reduce the environmental impact associated with the building stock. Typically, building renovation is performed by envelope insulation and/or changing the fossil-based heating system.
Alina Galimshina, Maliki Moustapha, Alexander Hollberg, Sébastien Lasvaux, Bruno Sudret, Guillaume Habert   +5 more
doaj   +1 more source

Uncertainty quantification and Heston model

Journal of Mathematics in Industry, 2018
In this paper, we study the impact of the parameters involved in Heston model by means of Uncertainty Quantification. The Stochastic Collocation Method already used for example in computational fluid dynamics, has been applied throughout this work in ...
María Suárez-Taboada, Jeroen A. S. Witteveen, Lech A. Grzelak, Cornelis W. Oosterlee   +3 more
doaj   +1 more source