Results 61 to 70 of about 1,350 (213)

BIN1 and ALDH1B1 Deficiency in Colonic Smooth Muscle Drives Mitochondrial Dysfunction and Fibrosis in Slow‐Transit Constipation

Advanced Science, EarlyView.
Slow‐transit constipation (STC) is a disabling motility disorder with unclear smooth‐muscle mechanisms. Spatial proteomic analysis of STC patient colon reveals both the central pathogenic role of smooth muscle cells (SMCs) in STC and novel regulators of intestinal motility, BIN1 and ALDH1B1.
Jianbo Liu, Hao Zhang, Wenhao Qiao, Ran Liu, Jie Sun, Xiaopei Li, Qin Li, Dongbo Zhao, Dawei Chen, Jingxin Li, Shuxiao Dong   +10 more
wiley   +1 more source

Tangent Exponentiated Odd Log-Logistic Weibull Quantile Regression with Applications to Complete and Censored Data [PDF]

The Egyptian Statistical Journal
A contemporary quantile regression model is formulated utilizing the tan exponentiated odd log-logistic Weibull distribution proposed in this study. The novel regression model is capable of modeling both complete and censored data, making it desirable ...
Mohammed Hashim Mustpha, Dioggban Jakperik, Suleman Nasiru   +2 more
doaj   +1 more source

On estimating conditional quantiles and distribution functions [PDF]

Computational Statistics & Data Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Distribution-Function-Based Bivariate Quantiles

Journal of Multivariate Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, L-A, Welsh, Alan
openaire   +2 more sources

Trends in the Quantiles of the Life Table Survivorship Function [PDF]

European Journal of Population, 2018
We offer a new approach for modeling past trends in the quantiles of the life table survivorship function. Trends in the quantiles are estimated, and the extent to which the observed patterns fit the unit root hypothesis or, alternatively, an innovative outlier model, are conducted.
Chuliá Soler, Helena, Guillén, Montserrat, Uribe Gil, Jorge Mario   +2 more
openaire   +3 more sources

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source