Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236388 | PMC |
| http://dx.doi.org/10.1523/JNEUROSCI.3539-14.2014 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Global Protein Interactome Mapping in Rice Using Barcode-Indexed PCR Coupled with HiFi Long-Read Sequencing.
Adv Sci (Weinh)
January 2025
State Key Lab of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou, 311400, China.
Establishing the protein-protein interaction network sheds light on functional genomics studies by providing insights from known counterparts. However, the rice interactome has barely been studied due to the lack of massive, reliable, and cost-effective methodologies. Here, the development of a barcode-indexed PCR coupled with HiFi long-read sequencing pipeline (BIP-seq) is reported for high throughput Protein Protein Interaction (PPI)identification.
View Article and Find Full Text PDFReflecting on partnerships established and sustained over four cycles of a federally funded cancer prevention and control research program: lessons learned for community-academic networks.
Front Public Health
January 2025
Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
Introduction: The Centers for Disease Control and Prevention (CDC) funded Cancer Prevention and Control Research Network (CPCRN) is a national network which aims to accelerate the adoption and implementation of evidence-based cancer prevention and control strategies and interventions in communities, enhance large-scale efforts to reach underserved populations and reduce their cancer-related health disparities, and develop the capacity of the dissemination and implementation work force specifically in cancer prevention and control.
Methods: Our site has been a part of the CPCRN since its inception in 2002 with the exception of the 2004-2009 funding cycle. As community-based participatory research is a core value of our center, we examined the development and continued engagement of our community partners using a qualitative, inductive approach to identify emergent themes from focus group sessions with current and past investigators.
Artificial neural network detection of pancreatic cancer from proton (1H) magnetic resonance spectroscopy patterns of plasma metabolites.
Commun Med (Lond)
January 2025
Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: Routine screening to detect silent but deadly cancers such as pancreatic ductal adenocarcinoma (PDAC) can significantly improve survival, creating an important need for a convenient screening test. High-resolution proton (1H) magnetic resonance spectroscopy (MRS) of plasma identifies circulating metabolites that can allow detection of cancers such as PDAC that have highly dysregulated metabolism.
Methods: We first acquired 1H MR spectra of human plasma samples classified as normal, benign pancreatic disease and malignant (PDAC).
Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation.
Med Image Anal
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Big DataBased Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory of Kidney Diseases, Beijing, 100853, China. Electronic address:
Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.
View Article and Find Full Text PDFTime-Varying Spatial Propagation of Brain Networks in fMRI Data.
Hum Brain Mapp
February 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory, Atlanta, Georgia, USA.
Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!