Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α < 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe.

Identification of effective prognostic biomarkers and targets are of crucial importance to the management of estrogen receptor positive (ER+) breast cancer. CCNA2 (also known as CyclinA2) belongs to the highly conserved cyclin family and is significantly overexpressed in various cancer types. In this study, we demonstrated that CCNA2 had significant predictive power in distant metastasis free survival, disease free survival, recurrence free survival and overall survival of ER+ breast cancer patients. We also found that CCNA2 was closely associated with tamoxifen resistance. In addition, gene set enrichment analysis (GSEA) revealed that its expression was positively associated with genes overexpressed in endocrine therapy resistant samples. Finally, though CCNA2-Drug interaction network, we demonstrated the interactions between CCNA2 and several available cancer drugs. Overall, we suggest that CCNA2 is a biomarker for the prognosis of ER+ breast cancer and monitoring of tamoxifen efficacy. It's also a promising target for developing new strategies to prevent or even reverse tamoxifen resistance. Moreover, CCNA2 expression may help monitoring tamoxifen efficacy and directing personalized therapies. Nevertheless, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed before its application in clinical settings.

Comprising more than 25 000 species, the Sunflower Family (Compositae or Asteraceae) is the largest family of flowering plants. Many of its lineages have experienced recent and rapid radiations, and the family has a deep and widespread history of large-scale gene duplications and polyploidy. Many of the most important evolutionary questions about the family's diversity remain unanswered due to poor resolution and lack of support for major nodes of the phylogeny. Our group has employed a phylogenomics approach using Hyb-Seq that includes sequencing 1000 low-copy number nuclear markers, plus partial plastomes for large numbers of species. Here we discuss our progress to date and present two phylogenies comprising nine subfamilies and 25 tribes using concatenated and coalescence-based analyses. We discuss future plans for incorporating high-quality reference genomes and transcriptomes to advance systematic and evolutionary studies in the Compositae. While we have made great strides toward developing tools for employing phylogenomics and resolving relationships within Compositae, much work remains. Recently formed global partnerships will work to solve the unanswered evolutionary questions for this megafamily.

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Cervical cancer is one of the most common types of female malignant tumor. It is well established that radiotherapy (RT) is the first‑line treatment of cervical cancer; however, radioresistance is a substantial obstacle to cervical cancer RT. At present, the mechanism underlying radioresistance remains unclear. Emerging evidence has demonstrated that long non‑coding RNAs (lncRNAs) function as crucial regulators of diverse cancers. Aerobic glycolysis, which is a common phenomenon in cancer cells, is associated with various biological functions, including radioresistance. To the best of our knowledge, the present study is the first to explore the role of the lncRNA urothelial cancer associated 1 (UCA1) in cervical cancer radioresistance. In the present study, irradiation was used to establish irradiation‑resistant (IRR) cells, after which a clonogenic survival assay was used to validate radioresistance, reverse transcription‑quantitative polymerase chain reaction was used to evaluate the expression levels of UCA1 and western blotting was conducted to detect the expression levels of glycolysis‑related proteins. In addition, a glucose/lactate assay kit was used to evaluate glucose/lactate concentrations and cells were transfected with small interfering RNA/pcDNA to regulate the expression of UCA1. Following the establishment of IRR cell lines (SiHa‑IRR and HeLa‑IRR), it was demonstrated that SiHa‑IRR and HeLa‑IRR cells exhibited increased expression levels of UCA1 and enhanced glycolysis. Dysregulation of UCA1 and inhibition of glycolysis affected radioresistance of cervical cancer cells. In addition, the results indicated that UCA1 promoted radioresistance‑associated glycolysis in SiHa‑IRR and HeLa‑IRR cells, with the enzyme hexokinase 2 (HK2) acting as a significant regulator in this process. Inhibiting glycolysis by 2‑DG reversed the effects of UCA1 overexpression on HK2 protein expression and radioresistance in SiHa and HeLa cells. Taken together, these findings suggested that UCA1 may have an important role in regulating radioresistance through the HK2/glycolytic pathway, providing novel potential targets to improve cervical cancer RT.

Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classes-Pinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved trees-using a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blue-RGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications.

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar's home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.