Publications by authors named "S Y Ling"

Spodoptera frugiperda is a significant agricultural pest, severely impacting the yield and quality of grain. Chitin is the momentous component of exoskeletons, which has a significant impact on the growth and development of insects. Our previous study found that exposure to lufenuron can reduce the expression of chitinase gene (SfCHT5) and increase the expression of chitin synthase gene (SfCHSB), two key genes for chitin synthesis in S.

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The neural processes underlying attentional processing are typically lateralized in adults, with spatial attention associated with the right hemisphere (RH) and object-based attention with the left hemisphere (LH). Using a modified two-rectangle attention paradigm, we compared the lateralization profiles of individuals with childhood hemispherectomy (either LH or RH) and age-matched, typically developing controls. Although patients exhibited slower reaction times (RTs) compared to controls, both groups benefited from valid attentional cueing.

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Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with high rates of postoperative recurrence. Identifying reliable biomarkers for predicting recurrence is critical for improving patient outcomes. This study investigates the predictive value of m6A methylation-related genes, METTL4 and METTL5, on HCC recurrence after surgery.

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Polycystic ovary syndrome (PCOS) is among the most prevalent endocrine and metabolic disorders affecting women of reproductive age. Multiple factors, including genetic predisposition, environmental influences, and lifestyle choices, are considered significant contributors to the development of PCOS. A kind of long noncoding RNA-C-Terminal binding protein 1 antisense (lncRNA CTBP1-AS) has been proven to be a new androgen receptor regulator.

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Background: The accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging. This study adopts a novel approach, using computed tomography (CT) radiomics to analyze the quantitative features in CT images to reveal the characteristics of lung nodules, and then employs diversity-weighted ensemble learning to enhance the accuracy of classification by integrating the predictive results of multiple models.

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