Results 51 to 60 of about 549,387 (258)
Generating Synthetic Missing Data: A Review by Missing Mechanism
The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different ...
Miriam Seoane Santos +5 more
doaj +1 more source
Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM
Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley +1 more source
Two-stage multiple imputation with a longitudinal composite variable
Background Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation.
Xuzhi Wang, Martin G. Larson, Chunyu Liu
doaj +1 more source
Mapping the evolution of mitochondrial complex I through structural variation
Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Background Tobacco use is a major contributor to chronic illnesses worldwide, leading to significant morbidity and mortality. Effective tobacco cessation programs are crucial in reducing the health risks associated with smoking.
Mengting Zhao, Benjamin Langworthy
doaj +1 more source
A survey on missing data in machine learning
Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or ...
Tlamelo Emmanuel +5 more
doaj +1 more source
Function‐driven design of a surrogate interleukin‐2 receptor ligand
Interleukin (IL)‐2 signaling can be achieved and precisely fine‐tuned through the affinity, distance, and orientation of the heterodimeric receptors with their ligands. We designed a biased IL‐2 surrogate ligand that selectively promotes effector T and natural killer cell activation and differentiation. Interleukin (IL) receptors play a pivotal role in
Ziwei Tang +9 more
wiley +1 more source
In situ molecular organization and heterogeneity of the Legionella Dot/Icm T4SS
We present a nearly complete in situ model of the Legionella Dot/Icm type IV secretion system, revealing its central secretion channel and identifying new components. Using cryo‐electron tomography with AI‐based modeling, our work highlights the structure, variability, and mechanism of this complex nanomachine, advancing understanding of bacterial ...
Przemysław Dutka +11 more
wiley +1 more source
Real‐world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses.
Arjun Sondhi +6 more
doaj +1 more source
The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source

