Browsing by Author "Shi, Yu"

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    Differential involvement of Wnt signaling in Bmp regulation of cancellous versus periosteal bone growth
    (Springer Nature, 2017-06-06) He, Guangxu; Shi, Yu; Lim, Joohyun; Bellido, Teresita; Ni, Jiangdong; Long, Fanxin; Anatomy and Cell Biology, School of Medicine
    Bone morphogenetic proteins (Bmp) are well-known to induce bone formation following chondrogenesis, but the direct role of Bmp signaling in the osteoblast lineage is not completely understood. We have recently shown that deletion of the receptor Bmpr1a in the osteoblast lineage with Dmp1-Cre reduces osteoblast activity in general but stimulates proliferation of preosteoblasts specifically in the cancellous bone region, resulting in diminished periosteal bone growth juxtaposed with excessive cancellous bone formation. Because expression of sclerostin (SOST), a secreted Wnt antagonist, is notably reduced in the Bmpr1a-deficient osteocytes, we have genetically tested the hypothesis that increased Wnt signaling might mediate the increase in cancellous bone formation in response to Bmpr1a deletion. Forced expression of human SOST from a Dmp1 promoter fragment partially rescues preosteoblast hyperproliferation and cancellous bone overgrowth in the Bmpr1a mutant mice, demonstrating functional interaction between Bmp and Wnt signaling in the cancellous bone compartment. To test whether increased Wnt signaling can compensate for the defect in periosteal growth caused by Bmpr1a deletion, we have generated compound mutants harboring a hyperactive mutation (A214V) in the Wnt receptor Lrp5. However, the mutant Lrp5 does not restore periosteal bone growth in the Bmpr1a-deficient mice. Thus, Bmp signaling restricts cancellous bone accrual partly through induction of SOST that limits preosteoblast proliferation, but promotes periosteal bone growth apparently independently of Wnt activation.
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    Intensified Structural Overshoot Aggravates Drought Impacts on Dryland Ecosystems
    (AGU, 2024-01) Zhang, Yixuan; Liu, Liu; Cheng, Yongming; Kang, Shaozhong; Li, Hao; Wang, Lixin; Shi, Yu; Liu, Xingcai; Cheng, Lei; Earth and Environmental Sciences, School of Science
    A favorable environment can induce vegetation overgrowth to exceed the ecosystem carrying capacity, exacerbating water resource depletion and increasing the risk of lagged effects on vegetation degradation. This phenomenon is defined as structural overshoot, which can lead to large-scale forest mortality and grassland deterioration. However, the current understanding of structural overshoot remains incomplete due to the complex time-varying interactions between vegetation and climate. Here, we used a dynamic learning algorithm to decompose the contributions of vegetation and climate to drought occurrence, trace the connection between antecedent and concurrent vegetation dynamics, thus effectively capturing structural overshoot. This study focused on the climate-sensitive hotspot in Northwest China drylands, where significant vegetation greening induced by a warming and wetting climate was detected during 1982–2015, leading to soil moisture deficit and aggravating vegetation degradation risks during droughts. We found that during this period, structural overshoot induced approximately 34.6% of the drought events, and lagged effects accounted for 16.7% of the vegetation degradation for these overshoot drought events. The occurrence of overshoot droughts exhibited an increasing trend over time, which was primarily driven by vegetation overgrowth followed by precipitation variation. Although the severity of overshoot and non-overshoot droughts were generally comparable in spatial distribution, the impact of overshoot droughts is still becoming increasingly obvious. Our results indicate that the expected intensified overshoot droughts cannot be ignored and emphasize the necessity of sustainable agroecosystem management strategies.
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    Predicting Pancreatic Ductal Adenocarcinoma Occurrence Up to 10 Years in Advance Using Features of the Main Pancreatic Duct in Pre-Diagnostic CT Scans
    (MDPI, 2025-06-04) Wang, Lixia; Shi, Yu; Qureshi, Touseef Ahmad; Xie, Yibin; Gaddam, Srinivas; Azab, Linda; Wu, Chaowei; He, Yimeng; Deng, Zengtian; Javed, Sehrish; Diwan, Garima; Lopes Vendrami, Camila; Rodriguez, Alex; Specht, Katherine; Jeon, Christie Y.; Chaudhry, Humaira; Buxbaum, James L.; Pisegna, Joseph R.; Yaghmai, Vahid; Goessling, Wolfram; Hernandez-Barco, Yasmin G.; Miller, Frank H.; Tirkes, Temel; Pandol, Stephen J.; Li, Debiao; Radiology and Imaging Sciences, School of Medicine
    Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) prediction in high-risk individuals is essential for early detection and improved outcome. While prior studies have utilized pancreatic radiomics for PDAC prediction, the added value of main pancreatic duct (MPD) features remains unclear. This study aims to assess the additional value of features of the main pancreatic duct (MPD) for predicting PDAC occurrence across different timeframes in advance. Methods: In total, 321 contrast-enhanced CT scans of the MPD and pancreas carried out across control, pre-diagnostic, and diagnostic cohorts were segmented, and radiomics were extracted. A support vector machine (SVM) classifier was used to classify the control and pre-diagnostic cohorts, with model performance assessed using area under the receiver operating characteristic (ROC) curves (AUCs) Results: The MPD diameter and volume significantly increased from the control to the pre-diagnostic and diagnostic CT scans (p < 0.05). The addition of features of the MPD to the pancreas improved the PDAC prediction AUC from 0.83 to 0.96 for subjects 6 months to 3 years in advance, from 0.81 to 0.94 for 3-6 years in advance, and 0.75 to 0.84 for 6-10 years in advance of diagnosis. Additionally, integrating MPD radiomics with diameter and volume significantly improved the AUC from 0.81 to 0.88 for subjects 6 months to 3 years in advance. Conclusions: Radiomic features from abdominal CT scans allow PDAC prediction up to 10 years in advance. Integrating MPD features, including diameter and volume, significantly improves PDAC prediction compared to using radiomics of the pancreas alone.