Results 121 to 130 of about 6,228 (205)

Comparative Single‐Cell Transcriptomic Atlas Reveals the Genetic Regulation of Reproductive Traits

open access: yesAdvanced Science, EarlyView.
A cross‐species single‐cell transcriptomic atlas of reproductive and central nervous system tissues from sheep and humans reveals conserved cellular programs and regulatory networks that regulated fertility. Integration with GWAS for sheep lifetime average litter size identifies UNC5–SLIT–BMP signaling as a core pathway coordinating neuroendocrine ...
Bingru Zhao   +8 more
wiley   +1 more source

An integrated single-cell reference atlas of the human endometrium

open access: yes, 2023
Marečková M   +26 more
europepmc   +1 more source

Visualizing and Quantifying Impact with Mechanochromic Sensing Paints Based on Self‐Assembled Polydiacetylene‐Silk Core‐Shell Vesicles

open access: yesAdvanced Science, EarlyView.
Tracking physical impacts is important in many fields. Self‐assembled microparticles made from polydiacetylene and silk fibroin that change color from blue to red when hit can provide an alternative approach to traditional mechanical transducers, quantitatively visualizing impact with responses ranging from <100 to 770 N.
Marco Lo Presti   +4 more
wiley   +1 more source

Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. [PDF]

open access: yesNeuroimage, 2013
Gray KR   +5 more
europepmc   +1 more source

Machine Learning‐Enhanced Analysis of Exosomal Surface Sialic Acid Using Surface‐Enhanced Raman Spectroscopy for Ovarian Cancer Diagnosis and Therapeutic Monitoring

open access: yesAdvanced Science, EarlyView.
Machine learning‐assisted surface‐enhanced Raman spectroscopy analysis of exosomal sialic acid for ovarian cancer diagnosis, as well as independent monitoring of exosomal sialic acid expression levels across different treatment periods, reveals a potential correlation with treatment response.
Lili Cong   +6 more
wiley   +1 more source

Hierarchical Embedded Sphere Model: An Interpretable ML‐Guided Multiscale Descriptor Engineering Decodes OER Activity on TM@MO2 Catalysts

open access: yesAdvanced Science, EarlyView.
Hierarchical Embedded Sphere Model combines DFT and interpretable machine learning to decode catalytic activity on TM‐doped MO2. It disentangles global electronic, active‐site, and local coordination effects, revealing two activation mechanisms: dopant‐driven (Rh@MO2) and coordination‐mediated (Fe@ZrO2).
Ziyuan Li   +5 more
wiley   +1 more source

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