397 research outputs found

    Superlong Salicylideneaniline Semiconductor Nanobelts Prepared by a Magnetic Nanoparticle-Assisted Self-Assembly Process for Luminescence Thermochromism

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    Controlling the molecular assembling and nanomorphology of organic semiconductors is crucial to obtain high-performance electronic devices. In this work, we have first reported novel superlong salicylideneaniline nanobelts (mHBA) using the magnetic nanoparticle-assisted self-assembly process. Our results show that magnetic nanoparticles will obviously influence the self-assembly behavior, nanomorphology, and crystal structure of molecular HBA. Moreover, the intensity of fluorescence mHBA exhibits decreasing and increasing patterns, with the increase in temperature over a wide temperature range of 8 to 295 K. To elucidate the origin of tautomer forms, the ground and excited states of mHBA were experimentally and theoretically studied. Our results suggest that superlong HBA nanobelts provide a promising intelligent fluorescent thermometer and an organic field-effect transistor

    DataSheet1_CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.docx

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    Compound-protein interaction (CPI) prediction is a foundational task for drug discovery, which process is time-consuming and costly. The effectiveness of CPI prediction can be greatly improved using deep learning methods to accelerate drug development. Large number of recent research results in the field of computer vision, especially in deep learning, have proved that the position, geometry, spatial structure and other features of objects in an image can be well characterized. We propose a novel molecular image-based model named CAT-CPI (combining CNN and transformer to predict CPI) for CPI task. We use Convolution Neural Network (CNN) to learn local features of molecular images and then use transformer encoder to capture the semantic relationships of these features. To extract protein sequence feature, we propose to use a k-gram based method and obtain the semantic relationships of sub-sequences by transformer encoder. In addition, we build a Feature Relearning (FR) module to learn interaction features of compounds and proteins. We evaluated CAT-CPI on three benchmark datasets—Human, Celegans, and Davis—and the experimental results demonstrate that CAT-CPI presents competitive performance against state-of-the-art predictors. In addition, we carry out Drug-Drug Interaction (DDI) experiments to verify the strong potential of the methods based on molecular images and FR module.</p

    Additional file 10: of Correction to: SALP, a new single-stranded DNA library preparation method especially useful for the high-throughput characterization of chromatin openness states

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    Figure S6. Comparison of the distribution of Hind III digestion library reads density and Hind III restriction sites through the whole genome. (DOCX 444 kb

    Additional file 9: of SALP, a new single-stranded DNA library preparation method especially useful for the high-throughput characterization of chromatin openness states

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    Figure S5. Construction of NGS library of gDNAs sheared by sonication and restriction endonuclease digestion with SALP method. (DOCX 15 kb
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