Several strategies have been developed for the production of peptide nucleic acid (PNA) microarrays by parallel probe synthesis and selective coupling of full-length molecules. Such microarrays were used for direct detection of the hybridisation of unlabelled DNA by time-of-flight secondary ion mass spectrometry. PNAs were synthesised by an automated process on filter-bottom microtitre plates. The resulting molecules were released from the solid support and attached without any purification to microarray surfaces via the terminal amino group itself or via modifications, which had been chemically introduced during synthesis. Thus, only full-length PNA oligomers were attached whereas truncated molecules, produced during synthesis because of incomplete condensation reactions, did not bind. Different surface chemistries and fitting modifications of the PNA terminus were tested. For an examination of coupling selectivity, bound PNAs were cleaved off microarray surfaces and analysed by MALDI-TOF mass spectrometry. Additionally, hybridisation experiments were performed to compare the attachment chemistries, with fully acetylated PNAs spotted as controls. Upon hybridisation of unlabelled DNA to such microarrays, binding events could be detected by visualisation of phosphates, which are an integral part of nucleic acids but missing entirely in PNA probes. Overall best results in terms of selectivity and sensitivity were obtained with thiol-modified PNAs on maleimide surfaces.
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http://dx.doi.org/10.1093/nar/gng120 | DOI Listing |
PLoS One
January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
View Article and Find Full Text PDFDesigning novel functional proteins crucially depends on accurately modeling their fitness landscape. Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. While initial protein representation learning studies solely focused on either sequence or structural features, recent hybrid architectures have sought to merge these modalities to harness their respective strengths.
View Article and Find Full Text PDFBrief Bioinform
November 2024
State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall.
View Article and Find Full Text PDFFront Hum Neurosci
October 2024
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.
Background: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions.
View Article and Find Full Text PDFAnalyst
December 2024
Department of Physics and Astronomy, University of Louisville, Louisville, Kentucky 40208, USA.
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