Results 51 to 60 of about 391,798 (266)
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Gene Selection for Classifications Using Multiple PCA with Sparsity
Tsinghua Science and Technology, 2012 A gene selection algorithm was developed using Multiple Principal Component Analysis with Sparsity (MSPCA). The MSPCA algorithm is used to analyze normal and disease gene expression samples and to set these component loadings to zero if they are smaller ...
Yanwei Huang, Liqing Zhang
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Indoor Pedestrian Tracking with Sparse RSS Fingerprints
Tsinghua Science and Technology, 2018 Indoor pedestrian localization is of great importance for diverse mobile applications. Many indoor localization approaches have been proposed; among them, Radio Signal Strength (RSS)-based approaches have the advantage of existing infrastructures and ...
Qiuxia Chen, Dongdong Ding, Yue Zheng
doaj +1 more source
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
Dictionary-Based Low-Rank Approximations and the Mixed Sparse Coding Problem
Frontiers in Applied Mathematics and Statistics, 2022 Constrained tensor and matrix factorization models allow to extract interpretable patterns from multiway data. Therefore crafting efficient algorithms for constrained low-rank approximations is nowadays an important research topic.
Jeremy E. Cohen
doaj +1 more source
FEBS Letters, EarlyView.Although too much iron in the brain promotes neurodegeneration, iron ion chelators have had mixed effects in clinical trials. This review explains why; some chelators do not render the iron redox‐inactive (e.g., L1) whereas others do (e.g., desferrioxamine).
Barry Halliwell
wiley +1 more source
EvAn: Neuromorphic Event-Based Sparse Anomaly Detection
Frontiers in Neuroscience, 2021 Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range ...
Lakshmi Annamalai +3 more
doaj +1 more source
Sparse Image Reconstruction using Sparse Priors [PDF]
2006 International Conference on Image Processing, 2006 Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a ...
Michael Ting +2 more
openaire +1 more source