Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264927 | PMC |
| http://dx.doi.org/10.1021/acs.jcim.5c01366 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
De novo crystal structure determination of L-alaninamide HCl by quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP).
Solid State Nucl Magn Reson
August 2025
Department of Chemistry & Biochemistry, Florida State University, Tallahassee, FL, 32306, USA; National High Magnetic Field Laboratory, Tallahassee, FL, 32310, USA. Electronic address:
Quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) is a method for determining the crystal structures of organic solids. To date, our two previous QNMRX-CSP studies have relied upon on Cl solid-state NMR (SSNMR) spectroscopy, powder X-ray diffraction (PXRD), Monte-Carlo simulated annealing (MC-SA), and dispersion-corrected density functional theory (DFT-D2∗) calculations for the determination of crystal structures for organic HCl salts with known crystal structures, in order to benchmark the method and subject it to blind tests. Herein, we apply QNMRX-CSP for the de novo crystal structure determination of L-alaninamide HCl (L-Ala-NH), for which no crystal structure has been reported, using Cl SSNMR and PXRD data for structural prediction and refinement, along with C and N SSNMR data for subsequent structural validation.
View Article and Find Full Text PDFToward universal immunofluorescence normalization for multiplex tissue imaging with UniFORM.
Cell Rep Methods
August 2025
Department of Biomedical Engineering and Computational Biology Program, OHSU, Portland, OR, USA; Knight Cancer Institute, OHSU, Portland, OR, USA. Electronic address:
We present UniFORM, a non-parametric, Python-based pipeline for normalizing multiplex tissue imaging (MTI) data at both the feature and pixel levels. UniFORM employs an automated rigid landmark registration method tailored to the distributional characteristics of MTI, with UniFORM operating without prior distributional assumptions and handling both unimodal and bimodal patterns. By aligning the biologically invariant negative populations, UniFORM removes technical variation while preserving tissue-specific expression patterns in positive populations.
View Article and Find Full Text PDFUnsupervised Visible-Infrared ReID via Pseudo-Label Correction and Modality-Level Alignment.
IEEE Trans Neural Netw Learn Syst
September 2025
Unsupervised visible-infrared person reidentification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intramodality clustering and cross-modality feature matching to achieve UVI-ReID. However, there exist two challenges: 1) noisy pseudo-labels might be generated in the clustering process and 2) the cross-modality feature alignment via matching the marginal distribution of visible and infrared modalities may misalign the different identities from the two modalities.
View Article and Find Full Text PDFGraph Clustering-guided Multi-view Neighborhood-enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction.
IEEE J Biomed Health Inform
September 2025
Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFEnhanced *COOH Adsorption over Edge-Rich Ni-N Sites for Efficient Acidic CO Electroreduction.
J Am Chem Soc
September 2025
Hunan Joint International Research Center for Carbon Dioxide Resource Utilization, School of Physics, Central South University, Changsha, Hunan 410083, China.
Single-atom Ni catalysts hold great promise for the acidic CO reduction reaction (CORR), owing to their high CO selectivity. However, their performance under industrially relevant high current conditions is limited by the weak interaction between isolated Ni-N sites and *COOH intermediates, restricting efficient CO conversion. Here, we introduced edge-rich Ni-N sites via support vacancy engineering to enhance *COOH adsorption, thereby boosting the CORR activity and selectivity in acidic media.
View Article and Find Full Text PDF