Results 121 to 130 of about 1,563,284 (329)

A Review of the `BMS' Package for R [PDF]

This paper describes the relative merits and attractiveness of the newest Bayesian model averaging package, BMS, available in the statistical software R to implement a Bayesian model averaging exercise. This package provides the user with a wide range of
Christopher F. Parmeter, Shahram Amini
core  

Single‐Cell Metabolic Imaging and Digital Scoring of Fat Tissue Remodeling by Label‐Free Metabolic Microscopy

Advanced Science, EarlyView.
Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim, Constantin Berger, Alexander Wolf, Alina Peteranderl, Martin Klingenspor, Vasilis Ntziachristos, Yongguo Li, Miguel A. Pleitez   +7 more
wiley   +1 more source

Lipid‐Facilitated Opening of the ADAM10 Sheddase Revealed by Enhanced Sampling Simulations

Advanced Science, EarlyView.
Phosphatidylserine acts as a lipid trigger to enhance activation of the sheddase ADAM10. By integrating fluorescence spectroscopy assays with enhanced sampling molecular dynamics simulations, this study shows that phosphatidylserine promotes ADAM10 catalytic activity along with expansion of its extracellular domains, enhancing accessibility to scaffold
Adrien Schahl, Nandan Haloi, Marta Carroni, Shengpan Zhang, Quentin James Sattentau, Erdinc Sezgin, Lucie Delemotte, Rebecca J. Howard   +7 more
wiley   +1 more source

Hydrological Model Diversity Enhances Streamflow Forecast Skill at Short‐ to Medium‐Range Timescales

Water Resources Research, 2019
We investigate the ability of hydrological multimodel ensemble predictions to enhance the skill of streamflow forecasts at short‐ to medium‐range timescales.
Sanjib Sharma, Ridwan Siddique, Seann Reed, Peter Ahnert, Alfonso Mejia   +4 more
doaj   +1 more source

A Bayesian Approach for Predicting with Polynomial Regresión of Unknown Degree. [PDF]

This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging.
Daniel Peña, Irwin Guttman, M Dolores Redondas   +2 more
core  

Modern and Ancient Genomes Reveal Neolithic Paternal Expansions of Millet and Rice Farmers and Demic Diffusion from China into Mainland Southeast Asia

Advanced Science, EarlyView.
This study clarifies the genetic patterns of paternal lineages across East Asia and Mainland Southeast Asia. Han populations are relatively homogeneous, whereas southern ethnolinguistic minorities display regional structures. Shared Y‐chromosome lineages indicate Neolithic expansions and extensive north‐south gene flow, supporting demic diffusion ...
Yunhui Liu, Lintao Luo, Yutong Jiang, Minzhu Zhao, Ting Yang, Zhiyong Wang, Lisiteng Luo, Yuhang Feng, Zihao Zhu, Yuzhu Wang, Limei Zhang, Bofeng Zhu, Chao Liu, Renkuan Tang, Mengge Wang, Guanglin He   +15 more
wiley   +1 more source

Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes [PDF]

Economic forecasts and policy decisions are often informed by empirical analysis based on econometric models. However, inference based upon a single model, when several viable models exist, limits its usefulness.
Herman K. van Dijk, Rodney W. Strachan
core   +3 more sources

Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging

Journal of Hydrology, 2019
Uncertainty in a hydrodynamic model originates from input data, model structure and parameters. In order to provide the robust model predictions, the Bayesian model averaging (BMA) approach could be used as a multi-model combining method to account for ...
Zhuo Liu, V. Merwade
semanticscholar   +1 more source

Bayesian Model Averaging in the Instrumental Variable Regression Model [PDF]

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors.
Gary Koop, Roberto Leon-Gonzalez, Rodney Strachan   +2 more
core  

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source