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Long-range NMR data, namely residual dipolar couplings (RDCs) from external alignment and paramagnetic data, are becoming increasingly popular for the characterization of conformational heterogeneity of multidomain biomacromolecules and protein complexes. The question addressed here is how much information is contained in these averaged data. We have analyzed and compared the information content of conformationally averaged RDCs caused by steric alignment and of both RDCs and pseudocontact shifts caused by paramagnetic alignment, and found that, despite the substantial differences, they contain a similar amount of information. Furthermore, using several synthetic tests we find that both sets of data are equally good towards recovering the major state(s) in conformational distributions.
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http://dx.doi.org/10.1007/s10858-015-9951-6 | DOI Listing |
Stroke
September 2025
Brain Language Laboratory, Freie Universität Berlin, Germany (A.-T.P.J., M.R.O., A.S., F.P.).
Background: Intensive language-action therapy treats language deficits and depressive symptoms in chronic poststroke aphasia, yet the underlying neural mechanisms remain underexplored. Long-range temporal correlations (LRTCs) in blood oxygenation level-dependent signals indicate persistence in brain activity patterns and may relate to learning and levels of depression. This observational study investigates blood oxygenation level-dependent LRTC changes alongside therapy-induced language and mood improvements in perisylvian and domain-general brain areas.
View Article and Find Full Text PDFFEBS Lett
September 2025
Institute of Biochemical Plant Physiology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Germany.
Ethylene (CH) regulates plant processes, such as germination, fruit ripening, and stress responses, impacting nutrition and food quality. The membrane-bound receptor ETR1 from Arabidopsis thaliana is a model for ethylene signaling, but both full-length and the soluble cytoplasmic domain have resisted crystallization. We present high-resolution NMR spectra of full-length ETR1 reconstituted in lipid nanodiscs, overcoming limitations and enhancing sample uniformity.
View Article and Find Full Text PDFPLoS One
August 2025
Information Department, Jilin Qianwei Hospital, Changchun, China.
In medical imaging diagnosis, accurate segmentation of the knee joint can help doctors better observe and diagnose lesions, thereby improving diagnostic accuracy and treatment effectiveness. Vision Mamba mainly relies on the State Space Model (SSM) for feature modeling, which excels at capturing global contextual information but cannot capture local texture features. Moreover, features of different scales are not effectively integrated, resulting in the model's weak segmentation ability on small-scale tissues (such as cartilage areas).
View Article and Find Full Text PDFSensors (Basel)
August 2025
College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, China.
The hippocampus is a key structure involved in the early pathological progression of Alzheimer's disease. Accurate segmentation of this region is vital for the quantitative assessment of brain atrophy and the support of diagnostic decision-making. To address limitations in current MRI-based hippocampus segmentation methods-such as indistinct boundaries, small target size, and limited feature representation-this study proposes an enhanced segmentation framework called FED-UNet++.
View Article and Find Full Text PDFPLoS One
August 2025
Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, Kuwait.
Knee Ailments, such as meniscus injuries, bother millions globally, with research showing that more than 14% of the population above 40 years lives with meniscus-related conditions. Conventional diagnosis techniques, like manual MRI interpretation, are labour-intensive, error-prone, and dependent on skilled radiologists, making an automatic and more accurate alternative indispensable. Current deep-learning solutions heavily depend on CNNs, which perform poorly in long-range dependencies and global contextual info.
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