A nanopore-based device provides single-molecule detection and analytical capabilities that are achieved by electrophoretically driving molecules in solution through a nano-scale pore. The nanopore provides a highly confined space within which single nucleic acid polymers can be analyzed at high throughput by one of a variety of means, and the perfect processivity that can be enforced in a narrow pore ensures that the native order of the nucleobases in a polynucleotide is reflected in the sequence of signals that is detected. Kilobase length polymers (single-stranded genomic DNA or RNA) or small molecules (e.g., nucleosides) can be identified and characterized without amplification or labeling, a unique analytical capability that makes inexpensive, rapid DNA sequencing a possibility. Further research and development to overcome current challenges to nanopore identification of each successive nucleotide in a DNA strand offers the prospect of `third generation' instruments that will sequence a diploid mammalian genome for ~$1,000 in ~24 h.
As a novel class of dynamic and non-covalent polymers, supramolecular polymers not only display specific structural and physicochemical properties, but also have the ability to undergo reversible changes of structure, shape, and function in response to diverse external stimuli, making them promising candidates for widespread applications ranging from academic research to industrial fields. By an elegant combination of dynamic/reversible structures with exceptional functions, functional supramolecular polymers are attracting increasing attention in various fields. In particular, functional supramolecular polymers offer several unique advantages, including inherent degradable polymer backbones, smart responsiveness to various biological stimuli, and the ease for the incorporation of multiple biofunctionalities (e.g., targeting and bioactivity), thereby showing great potential for a wide range of applications in the biomedical field. In this Review, the trends and representative achievements in the design and synthesis of supramolecular polymers with specific functions are summarized, as well as their wide-ranging biomedical applications such as drug delivery, gene transfection, protein delivery, bio-imaging and diagnosis, tissue engineering, and biomimetic chemistry. These achievements further inspire persistent efforts in an emerging interdisciplin-ary research area of supramolecular chemistry, polymer science, material science, biomedical engineering, and nanotechnology.
Most existing deep learning-based sentiment classification methods need large human-annotated data, but labeling large amounts of high-quality emotional texts is labor-intensive. Users on various social platforms generate massive amounts of tagged opinionated text (e.g., tweets, customer reviews), providing a new resource for training deep models. However, some of the tagged instances have sentiment tags that are diametrically opposed to their true semantics. We cannot use this tagged data directly because the noisy labeled instances have a negative impact on the training phase. In this paper, we present a novel Simple Weakly-supervised Contrastive Learning framework (SWCL). We use the contrastive learning strategy to pre-train the deep model on the large user-tagged data (referred to as weakly-labeled data) and then the pre-trained model is fine-tuned on the small human-annotated data. We refine the contrastive loss function to better exploit inter-class contrastive patterns, making contrastive learning more applicable to the weakly-supervised setting. Besides, multiple sampling on different sentiment pairs reduces the negative impact of label noises. SWCL captures the diverse sentiment semantics of weakly labeled data and improves their suitability for downstream sentiment classification tasks. Our method outperforms the other baseline methods in experiments on the Amazon review, Twitter, and SST-5 datasets. Even when fine-tuned on 0.5 percent of the training data (i.e. 32 instances), our framework significantly boosts the deep models’ performance, demonstrating its robustness in a few-shot learning scenario.
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