Results 101 to 110 of about 547,274 (236)
Lipid‐Facilitated Opening of the ADAM10 Sheddase Revealed by Enhanced Sampling Simulations
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 +7 more
wiley +1 more source
Unveil Fundamental Graph Properties for Neural Architecture Search
This paper proposes NASGraph, a graph‐based framework that represents neural architectures as graphs whose structural properties determine performance. By revealing structure–performance relationships, NASGraph enables efficient neural architecture search with significantly reduced computation.
Zhenhan Huang +4 more
wiley +1 more source
cuteHap: Haplotype‐Aware Structural Variant Detection in Phased Long‐Read Sequencing Data
cuteHap is a haplotype‐aware structural variant detection method designed for phased long‐read sequencing. By employing self‐adaptive clustering and credibility‐prioritized beam search algorithms, cuteHap generates accurate haplotype‐resolved calls and outperforms state‐of‐the‐art tools.
Shuqi Cao +7 more
wiley +1 more source
Low‐frequency noise fingerprints in hafnia ferroelectrics provide a quantitative handle to resolve the long‐standing debate between polarization‐mediated and defect‐mediated switching. By tuning oxygen vacancy density via ALD O3 dose time and applying a physically constrained deconvolution, we extract bias‐resolved current fractions for both mechanisms
Ryun‐Han Koo +8 more
wiley +1 more source
Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics
Weak nonlinearities critically shape the long term behavior of oscillatory systems but are difficult to identify from data. A data‐driven framework is introduced to infer governing equations of weakly nonlinear oscillators from sparse and noisy observations.
Teng Ma +5 more
wiley +1 more source
Bayesian Estimation of Unknown Heteroscedastic Variances [PDF]
We propose a Bayesian procedure to estimate possibly heteroscedastic variances of the regression error term, without assuming any structure on them. What we propose in this paper, may be construed as a Conditional Bayesian procedure that is conditioned ...
Hiroaki Chigira, Tsunemasa Shiba
core
This study firstly presents a comprehensive and high‐resolution pan‐3D genome resource in chicken. Our findings reveal the role of structural variations in 3D genome architectures, and how they influence the domestication process and production traits at the 3D genome level.
Zhen Zhou +19 more
wiley +1 more source
Bayesian Inference on Dynamic Models with Latent Factors [PDF]
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply.
Domenico Sartore +2 more
core
Intrinsic PPG–ECG Coupling for Accurate and Low‐Power Blood Pressure Monitoring
A PPG–ECG coupling strategy for continuous blood pressure monitoring that intrinsically synchronizes signals within a single waveform is demonstrated, minimizing synchronization errors and hardware complexity. This approach halves power consumption while maintaining high accuracy, enabling compact, energy‐efficient wearable devices for personalized ...
Sitong Chen +5 more
wiley +1 more source
Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang +11 more
wiley +1 more source

