The unambiguous mass spectrometric identification and characterization of glycopeptides is crucial to elucidate the micro- and macroheterogeneity of glycoproteins. Here, combining lower and stepped collisional energy fragmentation for the in-depth and site-specific analysis of N- and O-glycopeptides is proposed. Using a set of four representative and biopharmaceutically relevant glycoproteins (IgG, fibrinogen, lactotransferrin, and ribonuclease B), the benefits and limitations of the developed workflow are highlighted and a state-of-the-art blueprint for conducting high-quality in-depth N- and O-glycoproteomic analyses is provided. Further, a modified and improved version of cotton hydrophilic interaction liquid chromatography-based solid phase extraction for glycopeptide enrichment is described. For the unambiguous identification of N-glycopeptides, the use of a conserved yet, rarely employed-fragmentation signature [M +H+ X GlcNAc] is proposed. It is shown for the first time that this fragmentation signature can consistently be found across all N-glycopeptides, but not on O-glycopeptides. Moreover, the use of the relative abundance of oxonium ions to retrieve glycan structure information, for example, differentiation of hybrid- and high-mannose-type N-glycans or differentiation between antenna GlcNAc and bisecting GlcNAc, is systematically and comprehensively evaluated. The findings may increase confidence and comprehensiveness in manual and software-assisted glycoproteomics.
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http://dx.doi.org/10.1002/pmic.201800282 | DOI Listing |
J Imaging
November 2024
Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia.
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Objectives: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.
Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data.
PNAS Nexus
January 2025
Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures.
View Article and Find Full Text PDFEnviron Sci Technol
December 2024
CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science & Technology of China, Hefei 230026, China.
Organic pollutants removal via a polymerization transfer (PT) pathway based on the use of single-atom catalysts (SACs) promises efficient water purification with minimal energy/chemical inputs. However, the precise engineering of such catalytic systems toward PT decontamination is still challenging, and the conventional SACs are plagued by low structural stability of carbon material support. Here, we adopted magnesium oxide (MgO) as a structurally stable alternative for loading single copper (Cu) atoms to drive peroxymonosulfate-based Fenton-like reactions.
View Article and Find Full Text PDFMed Phys
December 2024
University Clinic for Medical Radiation Physics, Medical Campus Pius Hospital, Carl von Ossietzky University, Oldenburg, Germany.
Background: Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming.
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