Results 121 to 130 of about 290,361 (291)

Rapid Remodeling of the Human Gut Microbiome in Response to Short‐Term Animal Product Restriction and Associations with Host Molecular Phenotypes

Advanced Science, EarlyView.
A real‐world model of structured animal product restriction practiced for religious reasons reveals the dynamic adaptability of the human gut microbiome to dietary change and uncovers reductions in diversity and rare taxa loss. Integrated microbiome, metabolomic, and proteomic analyses uncover coordinated taxonomic and molecular shifts and identify ...
Christina Emmanouil, Maria Anezaki, Alexandros Simistiras, Stavros Glentis, Nikolaos Scarmeas, Pantelis Hatzis, Konstantinos Rouskas, Antigone S. Dimas   +7 more
wiley   +1 more source

Missing covariates in logistic regression, estimation and distribution selection. [PDF]

We derive explicit formulae for estimation in logistic regression models where some of the covariates are missing. Our approach allows for modeling the distribution of the missing covariates either as a multivariate normal or multivariate t-distribution.
Claeskens, Gerda, Consentino, Fabrizio
core  

A Protein‐Centric Strategy Coupled with Match‐Between‐Run Glycoproteomics Enables Discovery of Robust Site‐Specific Glycan Biomarkers for Hepatocellular Carcinoma

Advanced Science, EarlyView.
In this study, a highly robust N‐glycoproteomics (HRN) platform with a newly developed match‐between‐run scheme is used for biomarker discovery. This study proposes a protein‐centric strategy to prioritize proteins susceptible to aberrant glycosylation.
Lei Liu, Taiheng Ma, Qi Liu, He Zhu, Zheng Fang, Jiahong Ma, Ting Yu, Yan Wang, Jiahua Zhou, Xiaoyan Liu, Yaqian Li, Zhimou Guo, Xinmiao Liang, Mingming Dong, Deguang Sun, Mingliang Ye   +15 more
wiley   +1 more source

Multiple Imputation by Chained Equations (MICE): Implementation in Stata

Journal of Statistical Software, 2011
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random ...
Patrick Royston, Ian R. White
doaj  

Non-Bayesian multiple imputation [PDF]

, 2005
Abstract: Multiple imputation is a method specifically designed for variance estimation in the presence of missing data. Rubin’s combination formula requires that the imputation method is “proper” which essentially means that the imputations are random draws from a posterior distribution in a Bayesian framework.
openaire   +2 more sources

Cohort‐Scale Spatial Autocorrelation for Tumor Prediction in Mid‐Infrared Pathology and Spatial Biomarker Discovery Using MALDI Imaging Lipidomics

Advanced Science, EarlyView.
Computational annotation of various tissue types in heterogeneous samples such as colorectal cancer liver metastasis (CRLM) using spatial autocorrelation analysis on non‐destructive mid‐infrared (MIR) imaging data enabled correlative multimodal mass spectrometry imaging (MSI) for spatial investigation of lipid tumor marker candidates. The method can be
Miriam F. Rittel, Nikolas Ebert, Denis Abu Sammour, Sebastian Graf, Björn C. Fröhlich, Emrullah Birgin, Shad A. Mohammed, Nuh N. Rahbari, Axel Wellmann, Oliver Wasenmüller, Cleo‐Aron Weis, Stefan Schmidt, Carsten Hopf   +12 more
wiley   +1 more source

Maternal Preconception Antibiotic Exposure Disrupts Microbial Succession: A Transgenerational Risk for Offspring Gut Mucosal Immaturity and Colitis Susceptibility

Advanced Science, EarlyView.
This study reveals that maternal antibiotic exposure prior to conception disrupts intergenerational gut microbial succession. By enhancing maternal‐offspring microbial transmission, altering microbial developmental trajectories and increasing selective pressures during community assembly, these disturbances lead to persistent gut mucosal immaturity and
Yuzhu Chen, Ruqiao Duan, Cunzheng Zhang, Gaonan Li, XiaoLin Ji, Qi Zhang, Fei Pei, Kun Wang, Liping Duan   +8 more
wiley   +1 more source

Unsupervised data imputation with multiple importance sampling variational autoencoders

Scientific Reports
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data.
Shenfen Kuang, Yewen Huang, Jie Song
doaj   +1 more source

Alphaviral Capsid Proteins Inhibit Stress Granule Assembly via Competitive RNA Binding With G3BP1

Advanced Science, EarlyView.
Stress granules exert antiviral functions. This study illustrates a conserved function of alphaviral capsid proteins in modulating stress granules. Oligomerization mediated by a helical motif coupled with a positively charged intrinsically disordered region (IDR) directly competes with G3BP1 for RNA binding, thereby disrupting G3BP1‐RNA liquid–liquid ...
Yun Zhang, Yi Liu, Zhiying Yao, Haolong Lai, Xiaoxin Chen, Ziqiu Wang, Yiqiong Bao, Tingting Li, Xiaoming Zhou, Xiabin Chen, Peiguo Yang   +10 more
wiley   +1 more source

Mapping Genetic Regulation of Transcription to Identify Functional Variants and Genes Associated with Pancreatic Cancer Risk

Advanced Science, EarlyView.
Integration of a pancreatic eQTL map with a GWAS meta‐analysis identifies 82 putative functional variants and 15 genes. The association between rs11102484 and pancreatic cancer risk is observed in a total of 5699 cases and 8467 controls. The G allele of rs11102484 weakens ZNF263 binding and the silencer‐promoter interaction, thereby increasing ST7L ...
Xiaoyang Wang, Hui Geng, Zhengyan Yao, Yuan Jiang, Can Chen, Zequn Lu, Shuangshuang Tian, Ming Zhang, Ruiyan Liu, Chenxi Feng, Bin Li, Xiaoping Miao, Jianbo Tian, Shaokai Zhang, Ying Zhu   +14 more
wiley   +1 more source