DNA supercoiling plays an important role in a variety of cellular processes, including transcription, replication, and DNA compaction. To fully understand these processes, we must uncover and characterize the dynamics of supercoiled DNA. However, supercoil dynamics are difficult to access because of the wide range of relevant length and timescales. In this work, we present an algorithm to reconstruct the arrangement of identical fluorescent particles distributed around a circular DNA molecule, given their three-dimensional trajectories through time. We find that this curve reconstruction problem is analogous to solving the traveling salesman problem. We demonstrate that our approach converges to the correct arrangement with a sufficiently long observation time. In addition, we show that the time required to accurately reconstruct the fluorophore arrangement is reduced by increasing the fluorophore density or reducing the level of supercoiling. This curve reconstruction algorithm, when paired with next-generation super-resolution imaging systems, could be used to access and thereby advance our understanding of supercoil dynamics.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822745 | PMC |
http://dx.doi.org/10.1016/j.bpj.2020.10.044 | DOI Listing |
Plast Reconstr Surg
December 2024
Department of Plastic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, South Korea.
Background: Despite the recent steep rise in the use of prepectoral direct-to-implant (DTI) breast reconstruction, concerns remain regarding the potentially risk of complications, resulting in the selective application of the technique; however, the selection process was empirically based on the operator's decision. Using patient and operation-related factors, this study aimed to develop a nomogram for predicting postoperative complications following prepectoral DTI reconstruction.
Methods: Between August 2019 and March 2023, immediate prepectoral DTI was performed for all patients deemed suitable for one-stage implant-based reconstruction.
Background: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Alzheimers Dement
December 2024
Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
Background: Trisomy 21 in Down syndrome (DS) is associated with an earlier accumulation of beta-amyloid (Aβ) plaques and a higher rate of Alzheimer's Disease due to the triplication of the amyloid precursor protein gene. In this study we compare accumulation rates of Aβ measured with [C-11]PiB PET between large longitudinal cohorts of DS and neurotypical (NT) participants at a single site.
Methods: Participants imaged at the University of Wisconsin with ≥2 PiB scans and ≥2 years between scans were included in this study.
Clin Appl Thromb Hemost
January 2025
Department of Nursing, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Introduction: Preoperative patients with knee osteoarthritis have a significantly increased risk of venous thromboembolism (VTE). While the Caprini risk assessment model offers some clinical guidance in predicting deep vein thrombosis (DVT), it has a relatively low predictive accuracy. Enhancing the model by integrating biomarkers, such as D-dimers, can potentially improve its accuracy.
View Article and Find Full Text PDFBackground: Predictive biomarkers characterizing disease progression are called for in the context of emerging treatments for Alzheimer's disease. We implemented a link prediction model on morphometric correlation networks(MCN) generated from structural MRI.
Method: High-resolution T1MPRAGE images were retrospectively collected at two timepoints (interval 2.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!