Pancreatic amyloid deposits, composed primarily of the 37-residue islet amyloid polypeptide (IAPP), are a characteristic feature found in more than 90% of patients with type II diabetes. Although IAPP amyloid deposits are associated with areas of pancreatic islet beta-cell dysfunction and depletion and are thought to play a role in disease, their structure is unknown. We used electron paramagnetic resonance spectroscopy to analyze eight spin-labeled derivatives of IAPP in an effort to determine structural features of the peptide. In solution, all eight derivatives gave rise to electron paramagnetic resonance spectra with sharp lines indicative of rapid motion on the sub-nanosecond time scale. These spectra are consistent with a rapidly tumbling and highly dynamic peptide. In contrast, spectra for the fibrillar form exhibit reduced mobility and the presence of strong intermolecular spin-spin interactions. The latter implies that the peptide subunits are ordered and that the same residues from neighboring peptides are in close proximity to one another. Our data are consistent with a parallel arrangement of IAPP peptides within the amyloid fibril. Analysis of spin label mobility indicates a high degree of order throughout the peptide, although the N-terminal region is slightly less ordered. Possible similarities with respect to the domain organization and parallelism of Alzheimer's amyloid beta peptide fibrils are discussed.
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http://dx.doi.org/10.1074/jbc.M406853200 | DOI Listing |
Nat Commun
December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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December 2024
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China.
Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images.
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December 2024
Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, China.
While circular RNAs (circRNAs) exhibit lower abundance compared to corresponding linear RNAs, they demonstrate potent biological functions. Nevertheless, challenges arise from the low concentration and distinctive structural features of circRNAs, rendering existing methods operationally intricate and less sensitive. Here, we engineer an intelligent tetrahedral DNA framework (TDF) possessing precise spatial pattern-recognition properties with exceptional sensing speed and sensitivity for circRNAs.
View Article and Find Full Text PDFThe proximity ligation-based Hi-C and derivative methods are the mainstream tools to study genome-wide chromatin interactions. These methods often fragment the genome using enzymes functionally irrelevant to the interactions per se, restraining the efficiency in identifying structural features and the underlying regulatory elements. Here we present Footprint-C, which yields high-resolution chromatin contact maps built upon intact and genuine footprints protected by transcription factor (TF) binding.
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December 2024
Condensed Matter Theory Group, School of Studies in Physics, Jiwaji University, Gwalior, 474 011, India.
This study presents a comprehensive investigation into the intrinsic properties of RNiP (where R = Sm, Eu) filled skutterudite, employing the full-potential linearized augmented plane wave method within density functional theory (DFT) simulations using the WIEN2k framework. Structural, phonon stability, mechanical, electronic, magnetic, transport, thermal, and optical properties are thoroughly explored to provide a holistic understanding of these materials. Initially, the structural stability of SmNiP and EuNiP is rigorously evaluated through ground-state energy calculations obtained from structural optimizations, revealing a preference for a stable ferromagnetic phase over competing antiferromagnetic and non-magnetic phases.
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