Identification of critical ecological areas using the ecosystem multifunctionality-stability-integrity framework: A case study in the Yellow River basin, China.

Critical ecological areas (CEAs), as important regions for biodiversity and ecosystem functions, are crucial for ecological conservation and environmental management at regional and global scales. However, the methodology and framework of CEA identification have not been well established. In this study, a comprehensive CEA identification method was developed based on the ecosystem multifunctionality-stability-integrity framework by using K-means clustering, critical slowing down theory and possible connectivity. Taking the Yellow River basin (YRB) as a case study, our results showed that ecosystem multifunctionality gradually decreased from the southeast to northwest. A decrease in ecosystem stability was observed since 2017 and was mainly due to the increased impacts of human activities and urbanization within the 10-20 km distance threshold from the ecosystem. Based on the proposed framework, 15.13% of the YRB was identified as CEAs with reliable estimates, and most areas were distributed in the Three-River Headwaters, Qinling and Taihang Mountains. Moreover, urbanization and precipitation were found to be the dominant environmental factors affecting the CEA distribution in the YRB. Our results indicated that the proposed framework could provide a comprehensive approach for CEA identification and useful implications for ecological conservation and environmental management.

Identifying ecological security patterns based on the supply, demand and sensitivity of ecosystem service: A case study in the Yellow River Basin, China.

Ecological security is the basis for ecosystems to provide various ecosystem services (ESs) to humans. Identifying ecological security patterns (ESPs) is an effective approach to determine the priority conservation areas and ensure regional ecological security. However, most previous studies on ESPs were based mainly on the supply of ESs, while the demand and sensitivity of ESs were not fully considered. In this study, a comprehensive ESP identification framework was developed by integrating the supply, demand and sensitivity of ESs with the fuzzy multicriteria decision-making and circuit theory. Taking the Yellow River Basin (YRB) as a case study, our results show that the ecological sources (139,633 km2 or 17.3%) of the YRB were located mainly in the transition area between the Qinghai-Tibet Plateau and Loess Plateau, and in the Qinling Mountains and eastern plains; these areas reliably exhibited high conservation efficiency and low decision-making risk and tradeoff levels. However, the northern and western YRB had few ecological sources due to mismatches among the supply, demand and sensitivity of ESs. Based on circuit theory, ecological corridors (36,905 m and 76,878 km2) effectively connected the western, southern and eastern parts of the YRB. These ecological sources and corridors were both dominated by grassland, forest and cropland. However, ten pinch points, primarily covered by cropland, were also recognized in the eastern YRB and should be considered as priority areas for ecological conservation. Moreover, our results indicate that this comprehensive ESP identification framework could provide useful guidance to decision-makers for maintaining ESs and ecological conservation.

A novel long non-coding RNA FGF14-AS2 is correlated with progression and prognosis in breast cancer.

Breast cancer is diverse in their natural history and in their responsiveness to treatments. It is urgent to generate candidate biomarkers for the stratification of patients and personalization of therapy to avoid overtreatment or inadequate treatment. Long noncoding RNAs (lncRNAs) have been found to be pervasively transcribed in the genome and played critical roles in cancer progression. A lot of lncRNAs have been reported as potential prognostic biomarkers and therapeutic targets in multiple cancers. In this study, we demonstrated that FGF14 antisense RNA 2 (FGF14-AS2), a novel long non-coding RNA, was significantly down-regulated in breast cancer tissue compared with adjacent normal tissue both in validated cohort and TCGA cohort. Reduced expression of FGF14-AS2 was correlated with larger tumor size, more lymph node metastasis and advanced clinical stage in both cohorts. Kaplan-Meier analysis indicated that patients with lower FGF14-AS2 expression had a worse overall survival. Moreover, multivariate analysis revealed that decreased expression of FGF14-AS2 was an independent predictor of overall survival. Together, these results suggested that FGF14-AS2 involved in the progress of breast cancer and might act as a tumor suppressor gene. To the best of our knowledge, it was firstly reported that FGF14-AS2 was involved in cancer. This study provided a potential new marker and a target for gene therapy in breast cancer treatment.