Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

BackgroundSynovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.MethodsThe OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.ResultsOA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.ConclusionsWe developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy

A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning

A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement

How well do tree plantations comply with the twin targets of the clean development mechanism? : the case of tree plantations in Tanzania

Abstract: This paper studies the effect of a CDM tree-planting project on carbon sequestration and urban and rural income distribution, taking economy-wide impacts into account. Carbon sequestration in agricultural soil is considered in addition to the carbon in the tree farm itself. The study points to that project designs that raise the general investment level may add substantially to the project's carbon capture by stimulating the productivity of agriculture, thus binding more carbon in soil. As demand for crops is rising, the mode of agricultural production turns more intensive and improved plant growth leaves more plant residues for uptake as soil organic carbon. As for the income effect, the non-poor benefit more than the poor in economic terms, except when the project is hosted by the rural poorest. Foreign owned projects withdrawing the project surplus may turn out to reduce the income of urban poor and does not enhance agricultural productivity and beyond-project carbon sequestration. Keywords: CDM, afforestation, poverty reduction, CGE, Tanzani

How well do tree plantations comply with the twin targets of the Clean Development Mechanism? The case of tree plantations in Tanzania

This paper studies the effect of a CDM tree-planting project on carbon sequestration and urban and rural income distribution, taking economy-wide impacts into account. Carbon sequestration in agricultural soil is considered in addition to the carbon in the tree farm itself. The study points to that project designs that raise the general investment level may add substantially to the project's carbon capture by stimulating the productivity of agriculture, thus binding more carbon in soil. As demand for crops is rising, the mode of agricultural production turns more intensive and improved plant growth leaves more plant residues for uptake as soil organic carbon. As for the income effect, the non-poor benefit more than the poor in economic terms, except when the project is hosted by the rural poorest. Foreign owned projects withdrawing the project surplus may turn out to reduce the income of urban poor and does not enhance agricultural productivity and beyond-project carbon sequestration.CDM; afforestation; poverty reduction; CGE; Tanzania

How well do tree plantations comply with the twin targets of the Clean Development Mechanism? -- The case of tree plantations in Tanzania

The Clean Development Mechanism (CDM) of the Kyoto Protocol is supposed to provide both carbon mitigation and poverty reduction. This article reports from a model based study of market related carbon leakage and poverty reduction in the wake of a CDM tree-planting project in Tanzania. A tree plantation was incorporated in a computable general equilibrium (CGE) model with income differentiated household segments. The study focused on sensitivity of carbon leakage and income distribution to different project ownerships and carbon premium allocations. It turned out that the project value in terms of carbon premium has clear shortcomings as indicator of induced GDP growth and poverty alleviation. The non-poor rural and urban households benefit considerably more than the poor households. However, rising household income in all domestic project ownership arrangements increases demand for food, raises use of fertilizer and crop yields. A carbon cycle module for agricultural land use was incorporated in the CGE model, showing an increased carbon sequestration in agricultural soil, representing a negative leakage through markets in the range of 60-120% of the certified emissions reductions as registered in the CDM tree plantation project.Afforestation CDM CGE Leakage Poverty reduction Tanzania

Hypoxia Molecular Characterization in Hepatocellular Carcinoma Identifies One Risk Signature and Two Nomograms for Clinical Management

Hypoxia is a universal feature in the tumor microenvironment (TME). Nonetheless, the heterogeneous hypoxia patterns of TME have still not been elucidated in hepatocellular carcinoma (HCC). Using consensus clustering algorithm and public datasets, we identified heterogeneous hypoxia subtypes. We also revealed the specific biological and clinical characteristics via bioinformatic methods. The principal component analysis algorithm was employed to develop a hypoxia-associated risk score (HARS). We identified the two hypoxia subtypes: low hypoxia pattern (C1) and high hypoxia pattern (C2). C1 was less sensitive to immunotherapy compared to C2, consistent with the lack of immune cells and immune checkpoints (ICPs) in C1, whereas C2 was the opposite. C2 displayed worse prognosis and higher sensitivity to obatoclax relative to C1, while C1 was more sensitive to sorafenib. The two subtypes also demonstrated subtype-specific genomic variations including mutation, copy number alteration, and methylation. Moreover, we developed and validated a risk signature: HARS, which had excellent performance for predicting prognosis and immunotherapy. We revealed two hypoxia subtypes with distinct biological and clinical characteristics in HCC, which enhanced the understanding of hypoxia pattern. The risk signature was a promising biomarker for predicting prognosis and immunotherapy

Insight into the Vibrational and Thermodynamic Properties of Layered Lithium Transition-Metal Oxides LiMO₂ (M = Co, Ni, Mn): A First-Principles Study

Evaluation of the finite-temperature thermodynamic properties of the electrode materials generally helps to accurately describe the performance of Li-ion battery (LIBs). To know the characteristics of the layered lithium transition-metal oxides LiMO₂ (M = Co, Ni, Mn) comprehensively, herein, the vibrational and related thermodynamic quantities of these electrode materials are investigated by using density functional perturbation theory (DFPT). Local density approximation (LDA) and generalized gradient approximation with the Hubbard model correction (GGA+<i>U</i>) yield similar results, either for the phonon dispersion or for the thermodynamic functions. Among the three layered lithium transition-metal oxides, the vibrational and thermodynamic properties of LiNiO₂ is more close to that of LiMnO₂, while relatively far away from that of LiCoO₂, due to the same crystal structure of LiNiO₂ and LiMnO₂, which is different from that of LiCoO₂. In addition, the corrections of average intercalation voltage as a function of temperature for Li_0.75CoO₂ and Li_0.5CoO₂ are evaluated when considering the contribution of vibrational entropy. Since our theoretical results for LiCoO₂ agree well with those from experiments, we can provide the reliable thermodynamic data for the layered lithium transition-metal oxides