Publications by authors named "Nayi Wang"

Article Synopsis
  • - A new remote sensing classification framework called HyFormer is introduced, addressing limitations of convolutional neural networks (CNNs) in pixelwise input and spectral representation by integrating fully connected layers and CNNs.
  • - The framework enhances feature extraction by combining CNN outputs with linearly transformed spectral data and utilizes a Transformer encoder for better information fusion and pixel-level classification.
  • - Experimental results demonstrate that HyFormer outperforms traditional Transformer models, achieving overall accuracies of 95.37% in Changxing County and 95.4% in Nanxun District, using Sentinel-2 multispectral images.
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MicroRNAs are a class of small RNA molecules that regulate the expression of mRNAs in a wide range of biological processes and are implicated in human health and disease such as cancers. How to measure microRNA profiles in single cells with high throughput is essential to the development of cell-based assays for interrogating microRNA-mediated intratumor heterogeneity and the design of new lab tests for diagnosis and monitoring of cancers. Here, we report on an in situ hybridization barcoding workflow implemented in a sub-nanoliter microtrough array chip for high-throughput and multiplexed microRNA detection at the single cell level.

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Measuring multiple omics profiles from the same single cell opens up the opportunity to decode molecular regulation that underlies intercellular heterogeneity in development and disease. Here, we present co-sequencing of microRNAs and mRNAs in the same single cell using a half-cell genomics approach. This method demonstrates good robustness (~95% success rate) and reproducibility (R = 0.

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MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level via a complex regulatory network that requires genome-wide miRNA profiling to dissect. The patterns of miRNA expression at the genome scale are rich in diagnostic and prognostic information for human diseases such as cancers. This analysis, however, requires multi-step purification of RNAs from large quantities of cells, which is not only time consuming and costly but also challenging in situations where cell numbers are limited.

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The hematopoietic stem cell-enriched family microRNAs (miRNAs) are critical regulators of hematopoiesis. Overexpression of or is frequent in human acute myeloid leukemia (AML), and the overexpression of these miRNAs in mice leads to expansion of hematopoietic stem cells accompanied by perturbed hematopoiesis with mostly myeloproliferative phenotypes. However, whether and how family miRNAs cooperate with known AML oncogenes in vivo, and how the resultant leukemia is dependent on overexpression, are not well understood.

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Since the early days of microRNA (miRNA) research, miRNA expression profiling technologies have provided important tools toward both better understanding of the biological functions of miRNAs and using miRNA expression as potential diagnostics. Multiple technologies, such as microarrays, next-generation sequencing, bead-based detection system, single-molecule measurements, and quantitative RT-PCR, have enabled accurate quantification of miRNAs and the subsequent derivation of key insights into diverse biological processes. As a class of ~22 nt long small noncoding RNAs, miRNAs present unique challenges in expression profiling that require careful experimental design and data analyses.

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