Predicting protein curvature sensing across membrane compositions with a bilayer continuum model.

Unlabelled: Cytoplasmic proteins must recruit to membranes to function in processes such as endocytosis and cell division. Many of these proteins recognize not only the chemical structure of the membrane lipids, but the curvature of the surface, binding more strongly to more highly curved surfaces, or 'curvature sensing'. Curvature sensing by amphipathic helices is known to vary with membrane bending rigidity, but changes to lipid composition can simultaneously alter membrane thickness, spontaneous curvature, and leaflet symmetry, thus far preventing a systematic characterization of lipid composition on such curvature sensing through either experiment or simulation.

Maf1 Cooperates with Progesterone Receptor to Repress RNA Polymerase III Transcription of Select tRNAs.

Progesterone receptors (PR) can regulate transcription by RNA Polymerase III (Pol III), which transcribes small non-coding RNAs, including all transfer RNAs (tRNAs). We have previously demonstrated that PR is associated with the Pol III complex at tRNA genes and that progestins downregulate tRNA transcripts in breast tumor models. To further elucidate the mechanism of PR-mediated regulation of Pol III, we studied the interplay between PR, the Pol III repressor Maf1, and TFIIIB, a core transcription component.

Joint EANM/EANO/RANO/SNMMI practice guideline/procedure standard for PET imaging of brain metastases: version 1.0.

This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neuro-Oncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). Brain metastases are the most common malignant central nervous system (CNS) tumors. PET imaging with radiolabeled amino acids and to lesser extent [F]FDG has gained considerable importance in the assessment of brain metastases, especially for the differential diagnosis between recurrent metastases and treatment-related changes which remains a limitation using conventional MRI.

Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.

Objective: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.

Materials And Methods: Cephalometric radiographs from 301 subjects, taken at pre-pubertal (T1, age 11) and post-pubertal stages (T2, age 18), were analysed. Three ML models-Lasso regression, Random Forest, and Support Vector Regression (SVR)-were trained on a subset of 240 subjects, while 61 subjects were used for testing.