Although there are many new applications for hybridizing short, synthetic oligonucleotide probes to DNA, such applications have not included determining unknown sequences of DNA. The lack of clear discrimination in hybridization of oligo probes shorter than 11 nucleotides and the lack of a theoretical understanding of factors influencing hybridization of short oligos have hampered the development of their use. We have found conditions for reliable hybridization of oligonucleotides as short as seven nucleotides to cloned DNA or to oligonucleotides attached to filters. Low-temperature hybridization and washing conditions, in contrast to the high stringency conditions currently used in hybridization experiments, have the potential for allowing the simple use of all oligos of six nucleotides or longer in meaningful hybridizations. We also present the hybridization discrimination theory that provides the conceptual framework for understanding these results.
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http://dx.doi.org/10.1089/dna.1990.9.527 | DOI Listing |
Appl Neuropsychol Adult
January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
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January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
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January 2025
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
We present a hybrid semiempirical density functional tight-binding (DFTB) model with a machine learning neural network potential as a correction to the repulsive term. This hybrid model, termed machine learning tight-binding (MLTB), employs the standard self-consistent charge (SCC) DFTB formalism as a baseline, enhanced by the HIP-NN potential as an effective many-body correction for short-range pairwise repulsive interactions. The MLTB model demonstrates significantly improved transferability and extensibility compared to the SCC-DFTB and HIP-NN models.
View Article and Find Full Text PDFSci Rep
January 2025
Amal Jyothi College of Engineering (Autonomous), Kanjirappally, Kerala, India.
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants.
View Article and Find Full Text PDFPLoS One
January 2025
Business School, University of Shanghai for Science and Technology, Shanghai, China.
As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information.
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