High nutrient utilization and resorption efficiency promote bamboo expansion and invasion.

Bamboos are fast-growing, aggressively-spreading, and invasive woody clonal species that often encroach upon adjacent tree plantations, forming bamboo-tree mixed plantations. However, the effects of bamboo invasion on leaf carbon (C) assimilation, and nitrogen (N) and phosphorus (P) utilization characteristics remains unclear. We selected four different stands of Pleioblastus amarus invading Chinese fir (Cunninghamia lanceolata) plantations to investigate the concentrations, stoichiometry, and allometric growth relationships of mature and withered leaves of young and old bamboos, analyzing N and P utilization and resorption patterns. The stand type, bamboo age, and their interaction affected the concentrations, stoichiometry and allometric growth patterns of leaf C, N, and P in both old and young bamboos, as well as the N and P resorption efficiency. Bamboo invasion into Chinese fir plantations decreased leaf C, N, and P concentrations, C:N and C:P ratios, N and P resorption efficiency, and allometric growth exponents among leaf C, N, and P, while it only slightly altered N:P ratios. PLS-PM analysis revealed that bamboo invasion negatively impacted leaf C, N, and P concentrations, as well as N and P utilization and resorption. The results indicate that high N and P utilization and resorption efficiency, along with the mutual sharing of C, N, and P among bamboos in interface zones, promote continuous bamboo expansion and invasion. Collectively, these findings highlight the significance of N and P utilization and resorption in bamboo expansion and invasion and provide valuable guidance for the establishment of mixed stands and the ecological management of bamboo forests.

CarSitePred: an integrated algorithm for identifying carbonylated sites based on KNDUA-LNDOT resampling technique.

Carbonylated sites are the determining factors for functional changes or deletions in carbonylated proteins, so identifying carbonylated sites is essential for understanding the process of protein carbonylated and exploring the pathogenesis of related diseases. The current wet experimental methods for predicting carbonylated modification sites ae not only expensive and time-consuming, but also have limited protein processing capabilities and cannot meet the needs of researchers. The identification of carbonylated sites using computational methods not only improves the functional characterization of proteins, but also provides researchers with free tools for predicting carbonylated sites. Therefore, it is essential to establish a model using computational methods that can accurately predict protein carbonylated sites. In this study, a prediction model, CarSitePred, is proposed to identify carbonylation sites. In CarSitePred, specific location amino acid hydrophobic hydrophilic, one-to-one numerical conversion of amino acids, and AlexNet convolutional neural networks convert preprocessed carbonylated sequences into valid numerical features. The K-means Normal Distribution-based Undersampling Algorithm (KNDUA) and Localized Normal Distribution Oversampling Technology (LNDOT) were firstly proposed and employed to balance the K, P, R and T carbonylation training dataset. And for the first time, carbonylation modification sites were transformed into the form of images and directly inputted into AlexNet convolutional neural network to extract features for fitting SVM classifiers. The 10-fold cross-validation and independent testing results show that CarSitePred achieves better prediction performance than the best currently available prediction models. Availability: https://github.com/zuoyun123/CarSitePred.Communicated by Ramaswamy H. Sarma.

MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma.

BACKGROUND: Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. PURPOSE: Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). METHOD: The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). RESULT: Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. CONCLUSION: This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

3D printed osteochondral scaffolds: design strategies, present applications and future perspectives.

Articular osteochondral (OC) defects are a global clinical problem characterized by loss of full-thickness articular cartilage with underlying calcified cartilage through to the subchondral bone. While current surgical treatments can relieve pain, none of them can completely repair all components of the OC unit and restore its original function. With the rapid development of three-dimensional (3D) printing technology, admirable progress has been made in bone and cartilage reconstruction, providing new strategies for restoring joint function. 3D printing has the advantages of fast speed, high precision, and personalized customization to meet the requirements of irregular geometry, differentiated composition, and multi-layered boundary layer structures of joint OC scaffolds. This review captures the original published researches on the application of 3D printing technology to the repair of entire OC units and provides a comprehensive summary of the recent advances in 3D printed OC scaffolds. We first introduce the gradient structure and biological properties of articular OC tissue. The considerations for the development of 3D printed OC scaffolds are emphatically summarized, including material types, fabrication techniques, structural design and seed cells. Especially from the perspective of material composition and structural design, the classification, characteristics and latest research progress of discrete gradient scaffolds (biphasic, triphasic and multiphasic scaffolds) and continuous gradient scaffolds (gradient material and/or structure, and gradient interface) are summarized. Finally, we also describe the important progress and application prospect of 3D printing technology in OC interface regeneration. 3D printing technology for OC reconstruction should simulate the gradient structure of subchondral bone and cartilage. Therefore, we must not only strengthen the basic research on OC structure, but also continue to explore the role of 3D printing technology in OC tissue engineering. This will enable better structural and functional bionics of OC scaffolds, ultimately improving the repair of OC defects.

The biological functions and related signaling pathways of SPON2.

Spondin-2 (SPON2), also referred to as M-spondin or DIL-1, is a member of the extracellular matrix protein family known as Mindin-F-spondin (FS). SPON2 can be used as a broad-spectrum tumor marker for more than a dozen tumors, mainly prostate cancer. Meanwhile, SPON2 is also a potential biomarker for the diagnosis of certain non-tumor diseases. Additionally, SPON2 plays a pivotal role in regulating tumor metastasis and progression. In normal tissues, SPON2 has a variety of biological functions represented by promoting growth and development and cell proliferation. This paper presents a comprehensive overview of the regulatory mechanisms, diagnostic potential as a broad-spectrum biomarker, diverse biological functions, involvement in various signaling pathways, and clinical applications of SPON2.