Epigallocatechin-3-gallate (EGCG) has been demonstrated to inhibit cancer in experimental studies through its antioxidant activity and modulations on cellular functions by binding specific proteins. We demonstrated previously that EGCG upregulates the expression of microRNA (i.e. miR-210) by binding HIF-1α, resulting in reduced cell proliferation and anchorage-independent growth. However, the binding affinities of EGCG to HIF-1α and many other targets are higher than the EGCG plasma peak level in experimental animals administered with high dose of EGCG, raising a concern whether the microRNA regulation by HIF-1α is involved in the anti-cancer activity of EGCG in vivo. We employed functional genomic approaches to elucidate the role of microRNA in the EGCG inhibition of tobacco carcinogen-induced lung tumors in A/J mice. By analysing the microRNA profiles, we found modest changes in the expression levels of 21 microRNAs. By correlating these 21 microRNAs with the mRNA expression profiles using the computation methods, we identified 26 potential targeted genes of the 21 microRNAs. Further exploration using pathway analysis revealed that the most impacted pathways of EGCG treatment are the regulatory networks associated to AKT, NF-κB, MAP kinases, and cell cycle, and the identified miRNA targets are involved in the networks of AKT, MAP kinases and cell cycle regulation These results demonstrate that the miRNA-mediated regulation is actively involved in the major aspects of the anti-cancer activity of EGCG in vivo.

Early and accurate detection of plant diseases is essential for ensuring food security and maintaining sustainable agricultural productivity. However, most deep learning models for plant disease classification rely heavily on large-scale annotated datasets, which are expensive, labor-intensive, and often impractical to obtain in real-world farming environments. To address this limitation, we propose a unified self-supervised learning (SSL) framework that leverages unlabeled plant imagery to learn meaningful and transferable visual representations. Our method integrates three complementary objectives—Bootstrap Your Own Latent (BYOL), Masked Image Modeling (MIM), and contrastive learning—within a ResNet101 backbone, optimized through a hybrid loss function that captures global alignment, local structure, and instance-level distinction. GPU-based data augmentations are used to introduce stochasticity and enhance generalization during pretraining. Experimental results on the challenging PlantDoc dataset demonstrate that our model achieves an accuracy of 77.82%, with macro-averaged precision, recall, and F1-score of 80.00%, 78.24%, and 77.48%, respectively—on par with or exceeding most state-of-the-art supervised and self-supervised approaches. Furthermore, when fine-tuned on the PlantVillage dataset, the pretrained model attains 99.85% accuracy, highlighting its strong cross-domain generalization and practical transferability. These findings underscore the potential of self-supervised learning as a scalable, annotation-efficient, and robust solution for plant disease detection in real-world agricultural settings, especially where labeled data is scarce or unavailable.

Green tea polyphenol epigallocatechin-3-gallate (EGCG) has been demonstrated to inhibit cancer in experimental studies through its antioxidant activity and modulations on cellular functions by binding specific proteins. By means of computational analysis and functional genomic approaches, we previously identified a set of protein coding genes and microRNAs whose expressions were significantly modulated in response to the EGCG treatment in tobacco carcinogen-induced lung adenocarcinoma in A/J mice. However, to what degree these genes are involved in the cancer inhibition of EGCG remains unclear. In this study, we further employed statistical methods and literature research to analyze these data in combination with The Cancer Genome Atlas (TCGA) lung adenocarcinoma datasets for additional data mining. Under the assumption that, if a gene mediates EGCG’s cancer inhibition, its expression level change caused by EGCG should be opposite to what occurred in the carcinogenesis, we identified Myb and Peg3 as the primary putative genes involved in the cancer inhibitory activity. Further analysis suggested that the regulation of Myb could be mediated through an EGCG-upregulated microRNA, miR-449c-5p. Although the actions of EGCG involve multiple targets/pathways, further analysis by mining the existing genomic datasets revealed that the upregulations of Myb and Peg3 are likely the key anti-cancer events of EGCG in vivo.

The beauty and power of the genome editing mechanism, CRISPR Cas9 endonuclease system, lies in the fact that it is RNA-programmable such that Cas9 can be guided to any genomic loci complementary to a 20-nt RNA, single guide RNA (sgRNA), to cleave double stranded DNA, allowing the introduction of wanted mutations. Unfortunately, it has been reported repeatedly that the sgRNA can also guide Cas9 to off-target sites where the DNA sequence is homologous to sgRNA. Using human genome and Streptococcus pyogenes Cas9 (SpCas9) as an example, this article mathematically analyzed the probabilities of off-target homologies of sgRNAs and discovered that for large genome size such as human genome, potential off-target homologies are inevitable for sgRNA selection. A highly efficient computationl algorithm was developed for whole genome sgRNA design and off-target homology searches. By means of a dynamically constructed sequence-indexed database and a simplified sequence alignment method, this algorithm achieves very high efficiency while guaranteeing the identification of all existing potential off-target homologies. Via this algorithm, 1,876,775 sgRNAs were designed for the 19,153 human mRNA genes and only two sgRNAs were found to be free of off-target homology. By means of the novel and efficient sgRNA homology search algorithm introduced in this article, genome wide sgRNA design and off-target analysis were conducted and the results confirmed the mathematical analysis that for a sgRNA sequence, it is almost impossible to escape potential off-target homologies. Future innovations on the CRISPR Cas9 gene editing technology need to focus on how to eliminate the Cas9 off-target activity.

Gait analysis is a critical tool for diagnosing and assessing the severity of Parkinson’s disease (PD). This study introduces a novel parallel hybrid architecture combining Conv1D, Efficient Transformers, and Bidirectional GRU layers to analyze gait data for both PD detection and severity staging. Conv1D layers extract local spatial features, Efficient Transformers capture contextual dependencies, and Bidirectional GRUs model temporal patterns in VGRF signals. Designed to balance computational efficiency and scalability, the model demonstrates state-of-the-art performance, achieving 95.7% accuracy in PD detection and 87.3% accuracy in severity staging on the PhysioNet gait dataset. Additionally, the architecture is highly versatile, offering potential for application in other 1D signal analysis tasks.