Results 211 to 220 of about 15,466 (273)

Genome-Wide Analysis of the <i>FAR1</i>/<i>FHY3</i> (<i>FRS</i>) Gene Family and Expression Responses of <i>PbFRS</i> Genes to PEG-Induced Osmotic Stress, Light, and Shade in <i>Phoebe bournei</i>. [PDF]

Int J Mol Sci
Feng Y, Liu R, Ying R, Ding Z, Guan H, Tang X, Zheng K, Zhang Z, Cao S.   +8 more
europepmc   +1 more source

Convergent Evolution towards High Net Carbon Gain Efficiency Contributes to the Shade Tolerance of Palms (Arecaceae). [PDF]

PLoS One, 2015
Ma RY, Zhang JL, Cavaleri MA, Sterck F, Strijk JS, Cao KF.   +5 more
europepmc   +1 more source

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Pairing Spruce Budworm Control and Minimal Understory Perturbations: Effects of Btk Spraying Frequency in Boreal Forests. [PDF]

Ecol Evol
Robitaille M, Pothier D, Pellerin S.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs

Advanced Intelligent Discovery, EarlyView.
A vision‐based add‐on transforms commercial spin coaters into autonomous modules of Self‐Driving Labs. Combining a width‐scaled U‐Net with classical geometric analysis, the system simultaneously measures contact angles and estimates substrate pose using a single camera.
Sven Fischer, Micha Hiegle, Holger Röhm
wiley   +1 more source

Global analysis of soybean bZIP gene family under stress and identification of salt-stress-responsive candidate genes. [PDF]

Front Plant Sci
Zhou E, Yao M, Wang Y, Xue D, Zhou Y, Wang X, Shi Y, Zhu Y, Li Z, Zhao N, Wang K, Li B, Gu C, Miao Y, Wei L.   +14 more
europepmc   +1 more source

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

Shade Avoidance Restricts Soybean Breeding Progress and Increases Herbivore Susceptibility. [PDF]

Evol Appl
Vonmetz L, Kopp EB, Jäggi S, Niklaus PA, Wuest SE.   +4 more
europepmc   +1 more source