Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study.

BACKGROUND: Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed. METHODS: In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways. RESULTS: A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72-16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10-6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34-6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15-3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent. CONCLUSIONS: MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.

Ratio of carbon and nitrogen in fertilizer treatment drives distinct rhizosphere microbial community composition and co-occurrence networks.

Fertilization is the main strategy to accelerate vegetation restoration and improve the rhizosphere microbial community in the northeast China. However, the responses of rhizosphere microbial community structure, specific microbial community and symbiotic pattern to manure fertilization in grassland (alfalfa only) are not well clear. In this study, the variation of bacterial community structures in R_Manure (extracted liquid of fermented cow manure), E_Manure (extracted residue of fermented cow manure), F_Manure (full fermented cow manure), and Control (without fermented cow manure) collected from the rhizosphere microbial community of alfalfa were analyzed by the application of an Illumina HiSeq high-throughput sequencing technique. A total of 62,862 microbial operational taxonomic units (OTUs) were detected and derived from 21 phyla of known bacteria. The dominant bacteria in the rhizosphere include Proteobacteria (70.20%), Acidobacteria (1.24%), Actinobacteria (2.11%), Bacteroidetes (6.15%), Firmicutes (4.21%), and Chlorofexi (2.13%) accounting for 86% of the dominant phyla in all treatments. At the genus level, the dominant genus include NB1-j, Lysobacter, Alphaproteobacteria, Subgroup_6, Actinomarinales, Saccharimonadales, Aneurinibacillus, MO-CFX2, SBR1031, Caldilineaceae, and so on with the average relative abundance (RA) of 1.76%, 1.52%, 1.30%, 1.24%, 1.61%, 2.39%, 1.36%, 1.42%, 1.27%, and 1.03%, respectively. Bacterial diversities and community structures were significantly differentiated by different treatments of fertilization. The results of community structure composition showed that R_Manure treatment significantly increased the population abundance of Firmicutes, Chlorofexi, and Patescibacteria by 34.32%, 6.85%, and 2.70%, and decreased the population abundance of Proteobacteria and Actinobacteria by 16.83% and 1.04%, respectively. In addition, it showed that all treatments significantly resulted in an increase or decrease at the genus level. R_Manure had the higher richness and diversity of the bacterial community, with the greatest topology attributes of the co-occurrence networks. Through the analysis of the molecular ecological network (MENA), the co-occurrence networks had a shorter average path distance and diameter in R_Manure than in others, implying more stability to environmental changes. Redundancy analysis (RDA) showed that the ratio of carbon and nitrogen (C/N) was the main factor affecting rhizosphere microbial community composition while driving distinct rhizosphere bacterial community and its co-occurrence networks. The R_Manure associated with more C/N had relatively complex microbial co-occurrence network with a large number of nodes and edges, while the microbial network of others associated with less C/N had fewer taxa with loose mutual interactions. These results suggested that organic fertilizer with high C/N can regulate the rhizosphere microorganism, while high C/N can determine bacterial community structures, specific bacterial taxa, and their relationships with the nodule size of alfalfa. These significant changes can be used to evaluate soil fertility and fertilizer management in the artificial grassland system, while the potential biological indicators of the rhizosphere microbial community will play an important role in future eco-agriculture.

Single-Stage Extraction Technique for Ce Stable Isotopes and Measurement by MC-ICP-MS.

The separation of Ce from other rare earth elements has not been well established because of their similar geochemical properties. In this study, we report a single-stage extraction technique to purify Ce from natural samples with Eichrom DGA resin. This method separates Ce effectively from matrices and interfering elements, such as Ba, La, and Nd. The Ce elution curve would not drift with different Ce loading masses and rock types. The Ce isotope compositions were measured using a Thermo Scientific Neptune Plus multicollector (MC)-inductively coupled plasma (ICP)-mass spectrometry (MS) instrument. The instrumental mass bias of Ce isotopes was corrected with a sample-standard bracketing combined with a Sm-doping method. The δ142Ce values of standard solutions (CDUT-Ce and JMC304) relative to National Institute of Standards and Technology SRM 3110 measured were +0.128 ± 0.028‰ (2SD, N = 30) and 0.005 ± 0.038‰ (2SD, N = 30), respectively. The reproducibility for δ142Ce was better than 0.040‰. The Ce isotopic compositions of nine United States Geological Survey standard rocks, including carbonatite, basalt, andesite, quartz latite, dolerite, rhyolite, and granodiorite, were measured in this study. Our result showed that δ142Ce values of these rocks varied slightly, indicating that insignificant fractionation occurred during igneous processes. The technique proposed in this study is simple and time-efficient, which is beneficial for further studies on Ce isotope geochemistry.