Climate overrides fencing and soil mineral nutrients to affect plant diversity and biomass of alpine grasslands across North Tibet.

Introduction: Overgrazing and warming are thought to be responsible for the loss of species diversity, declined ecosystem productivity and soil nutrient availability of degraded grasslands on the Tibetan Plateau. Mineral elements in soils critically regulate plant individual's growth, performance, reproduction, and survival. However, it is still unclear whether plant species diversity and biomass production can be improved indirectly via the recovery of mineral element availability at topsoils of degraded grasslands, via grazing exclusion by fencing for years. Methods: To answer this question, we measured plant species richness, Shannow-Wiener index, aboveground biomass, and mineral element contents of Ca, Cu, Fe, Mg, Mn, Zn, K and P at the top-layer (0 - 10 cm) soils at 15 pairs of fenced vs grazed matched sites from alpine meadows (n = 5), alpine steppes (n = 6), and desert-steppes (n = 4) across North Tibet. Results: Our results showed that fencing only reduced the Shannon-Wiener index of alpine meadows, and did not alter aboveground biomass, species richness, and soil mineral contents within each grassland type, compared to adjacent open sites grazed by domestic livestock. Aboveground biomass first decreased and then increased along with the gradient of increasing Ca content but did not show any clear relationship with other mineral elements across the three different alpine grassland types. More than 45% of the variance in plant diversity indices and aboveground biomass across North Tibet can be explained by the sum precipitation during plant growing months. Structural equation modelling also confirmed that climatic variables could regulate biomass production directly and indirectly via soil mineral element (Ca) and plant diversity indices. Discussion: Overall, the community structure and biomass production of alpine grasslands across North Tibet was weakly affected by fencing, compared to the robst climatic control. Therefore, medium-term livestock exclusion by fencing might have limited contribution to the recovery of ecosystem structure and functions of degraded alpine grasslands.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma.

Background: Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC. Methods: The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients' overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells. Results: We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature. Conclusions: A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma.

Objective: Malignant melanoma (MM) is one of the most malignant types of skin cancer and its incidence and mortality rates are increasing worldwide. Aging is well recognized as a significant risk factor for cancer. However, few studies have analyzed in depth the association between aging-related genes (AGs) and malignant melanoma prognosis with tumor immune microenvironment. Methods: Here, we downloaded 471 MM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. 58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay. As a result, a gene signature for aging-related genes was created. The time-dependent ROC curve and Kaplan-Meier analysis were calculated to determine its predictive capability. Moreover, we created a nomogram for the clinicopathologic variables and the AGs gene signature to determine overall survival (OS). We also explored the association between three immune checkpoints, immune cell infiltration, and the aging-related gene signature. Results: We established an aging risk model to identify and predict the immune microenvironment in malignant melanoma. Then we developed and validated a prognosis risk model using three AGs (CSNK1E, C1QA, and SOD-2) in the GSE65904 dataset. The aging signature was positively associated with clinical and molecular characteristics and can be used as a prognostic factor for malignant melanoma. The low aging risk score was associated with a poor prognosis and indicated an immunosuppressive microenvironment. Conclusions: To summarize, we established and validated a model of aging risk based on three aging-related genes that acted as an independent prognostic predictor of overall survival. Besides, it also characterized the immune response in the malignant melanoma microenvironment and could provide a potential indicator of individualized immunotherapy in malignant melanoma.

Computational detection of a genome instability-derived lncRNA signature for predicting the clinical outcome of lung adenocarcinoma.

Evidence has been emerging of the importance of long non-coding RNAs (lncRNAs) in genome instability. However, no study has established how to classify such lncRNAs linked to genomic instability, and whether that connection poses a therapeutic significance. Here, we established a computational frame derived from mutator hypothesis by combining profiles of lncRNA expression and those of somatic mutations in a tumor genome, and identified 185 candidate lncRNAs associated with genomic instability in lung adenocarcinoma (LUAD). Through further studies, we established a six lncRNA-based signature, which assigned patients to the high- and low-risk groups with different prognosis. Further validation of this signature was performed in a number of separate cohorts of LUAD patients. In addition, the signature was found closely linked to genomic mutation rates in patients, indicating it could be a useful way to quantify genomic instability. In summary, this research offered a novel method by through which more studies may explore the function of lncRNAs and presented a possible new way for detecting biomarkers associated with genomic instability in cancers.

Medical Image Fusion Based on Low-Level Features.

Medical image fusion is an important technique to address the limited depth of the optical lens for a completely informative focused image. It can well improve the accuracy of diagnosis and assessment of medical problems. However, the difficulty of many traditional fusion methods in preserving all the significant features of the source images compromises the clinical accuracy of medical problems. Thus, we propose a novel medical image fusion method with a low-level feature to deal with the problem. We decompose the source images into base layers and detail layers with local binary pattern operators for obtaining low-level features. The low-level features of the base and detail layers are applied to construct weight maps by using saliency detection. The weight map optimized by fast guided filtering guides the fusion of base and detail layers to maintain the spatial consistency between the source images and their corresponding layers. The recombination of the fused base and detail layers constructs the final fused image. The experimental results demonstrated that the proposed method achieved a state-of-the-art performance for multifocus images.