Results 151 to 160 of about 2,190,995 (307)
State-space modelling is a powerful tool to study ecological systems. The direct inclusion of uncertainty, unification of models and data, and ability to model unobserved, hidden states increases our knowledge about the environment and provides new ...
New, Leslie Frances
core
The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
Decomposition of state-space Model with inputs: The theory and an application to estimate the ROI of advertising [PDF]
This paper shows how to compute the in-sample effect of exogenous inputs on the endogenous variables in any linear model written in state-space form. Estimating this component may be, either interesting by itself, or a previous step before decomposing a ...
Sonia Sotoca López +2 more
core
RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour +2 more
wiley +1 more source
In a murine model of myocardial ischemia and reperfusion (MI/R), the CD36 azapeptide ligand MPE‐298 reduces cardiac injury and transiently lowers left ventricular long‐chain fatty acids (LCFAs) accumulation 3 h after reperfusion, accompanied by a decrease of oxidative stress and inflammation‐associated genes' expression in the heart and adipose tissue.
Jade Gauvin +12 more
wiley +1 more source
A Subspace Based Instrumental Variable Method For State-Space System Identification
. Traditional prediction-error techniques for multivariable system identification require canonical descriptions using a large number of parameters.
Bj Orn Ottersten +4 more
core
FSSM: Frequency-Enhanced State Space Modeling with FFT-Based Two-Sided Non-Causal Convolution for Image Dehazing. [PDF]
Zeng L, Huang Y.
europepmc +1 more source
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová +2 more
wiley +1 more source
Bootstrap prediction intervals in State Space models [PDF]
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates.
Esther Ruiz, Alejandro Rodriguez
core
Hydrostatic pressure activates HIF‐1α via β‐catenin to promote stemness in breast cancer cells
To mimic the elevated intestinal fluid pressure in breast cancers, we loaded human breast cancer cells (MCF‐7, MDA‐MB‐453, and BT‐474) to 50 mmHg hydrostatic pressure. Hydrostatic pressure exposure upregulated HIF‐1α and induced stemness in MCF‐7 and BT‐474 cells.
Da Zhai +8 more
wiley +1 more source

