216 research outputs found
Bridge helix bending promotes RNA polymerase II backtracking through a critical and conserved threonine residue.
The dynamics of the RNA polymerase II (Pol II) backtracking process is poorly understood. We built a Markov State Model from extensive molecular dynamics simulations to identify metastable intermediate states and the dynamics of backtracking at atomistic detail. Our results reveal that Pol II backtracking occurs in a stepwise mode where two intermediate states are involved. We find that the continuous bending motion of the Bridge helix (BH) serves as a critical checkpoint, using the highly conserved BH residue T831 as a sensing probe for the 3'-terminal base paring of RNA:DNA hybrid. If the base pair is mismatched, BH bending can promote the RNA 3'-end nucleotide into a frayed state that further leads to the backtracked state. These computational observations are validated by site-directed mutagenesis and transcript cleavage assays, and provide insights into the key factors that regulate the preferences of the backward translocation
Intraband and interband spin-orbit torques in non-centrosymmetric ferromagnets
Intraband and interband contributions to the current-driven spin-orbit torque
in magnetic materials lacking inversion symmetry are theoretically studied
using Kubo formula. In addition to the current-driven field-like torque (
being a unit vector determined by the symmetry of the spin-orbit coupling), we
explore the intrinsic contribution arising from impurity-independent interband
transitions and producing an anti-damping-like torque of the form . Analytical
expressions are obtained in the model case of a magnetic Rashba two-dimensional
electron gas, while numerical calculations have been performed on a dilute
magnetic semiconductor (Ga,Mn)As modeled by the Kohn-Luttinger Hamiltonian
exchanged coupled to the Mn moments. Parametric dependences of the different
torque components and similarities to the analytical results of the Rashba
two-dimensional electron gas in the weak disorder limit are described.Comment: 10 pages, 5 figure
基于视觉识别的钢包底吹氩控制模型开发
This paper briefly describes the application of visual recognition in the process of refining molten steel by blowing argon at the bottom of ladle. Starting from the basis of metallurgy, cross information science, to a point to try to extend the exploration of a facet of practice
Controllable Mind Visual Diffusion Model
Brain signal visualization has emerged as an active research area, serving as
a critical interface between the human visual system and computer vision
models. Although diffusion models have shown promise in analyzing functional
magnetic resonance imaging (fMRI) data, including reconstructing high-quality
images consistent with original visual stimuli, their accuracy in extracting
semantic and silhouette information from brain signals remains limited. In this
regard, we propose a novel approach, referred to as Controllable Mind Visual
Diffusion Model (CMVDM). CMVDM extracts semantic and silhouette information
from fMRI data using attribute alignment and assistant networks. Additionally,
a residual block is incorporated to capture information beyond semantic and
silhouette features. We then leverage a control model to fully exploit the
extracted information for image synthesis, resulting in generated images that
closely resemble the visual stimuli in terms of semantics and silhouette.
Through extensive experimentation, we demonstrate that CMVDM outperforms
existing state-of-the-art methods both qualitatively and quantitatively.Comment: 16 pages, 11 figure
Implicit Diffusion Models for Continuous Super-Resolution
Image super-resolution (SR) has attracted increasing attention due to its
wide applications. However, current SR methods generally suffer from
over-smoothing and artifacts, and most work only with fixed magnifications.
This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity
continuous image super-resolution. IDM integrates an implicit neural
representation and a denoising diffusion model in a unified end-to-end
framework, where the implicit neural representation is adopted in the decoding
process to learn continuous-resolution representation. Furthermore, we design a
scale-controllable conditioning mechanism that consists of a low-resolution
(LR) conditioning network and a scaling factor. The scaling factor regulates
the resolution and accordingly modulates the proportion of the LR information
and generated features in the final output, which enables the model to
accommodate the continuous-resolution requirement. Extensive experiments
validate the effectiveness of our IDM and demonstrate its superior performance
over prior arts.Comment: 8 pages, 9 figures, published to CVPR202
La torre de doña Urraca en Covarrubias (Burgos)
La torre de doña Urraca en Covarrubias (Burgos
Strong but intermittent spatial covariations in tropical land temperature
Surface temperature variations across the tropics exhibit different levels of spatial coherence, yet this is poorly characterized. Years of high temperature anomalies occurring simultaneously across large geographical regions have the potential to adversely impact food production and societal well‐being. Using cluster analysis of correlations between extensive temperature measurements from the last six decades, we find a major change occurs in the late 1970s. Two spatial clusters merge to a single dominant one, and therefore, warmer years are experienced at the same time across most tropical land regions. Noting this change occurs at the same time as the Pacific Decadal Oscillation shifts a warm phase, we investigate this potential driver by a range of coupled ocean‐atmosphere‐land climate models. These simulations verify that stronger spatial tropical land temperature coherence tends to occur in Pacific Decadal Oscillation warm phases, although model differences exist in projections of how climate change may modulate this dependence
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