Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

The endemic occurrence of obesity and the associated risk factors that constitute the metabolic syndrome have been predicted to lead to a dramatic increase in chronic liver disease. Non-alcoholic steatohepatitis (NASH) has become the most frequent liver disease in countries with a high prevalence of obesity. In addition, hepatic steatosis and insulin resistance have been implicated in disease progression of other liver diseases, including chronic viral hepatitis and hepatocellular carcinoma. The molecular mechanisms underlying the link between insulin signaling and hepatocellular injury are only partly understood. We have explored the role of the antiapoptotic caspase-8 homolog cellular FLICE-inhibitory protein (cFLIP) on liver cell survival in a diabetic model with hypoinsulinemic diabetes in order to delineate the role of insulin signaling on hepatocellular survival. cFLIP regulates cellular injury from apoptosis signaling pathways, and loss of cFLIP was previously shown to promote injury from activated TNF and CD95/Apo-1 receptors. In mice lacking cFLIP in hepatocytes (flip−/−), loss of insulin following streptozotocin treatment resulted in caspase- and c-Jun N-terminal kinase (JNK)-dependent liver injury after 21 days. Substitution of insulin, inhibition of JNK using the SP600125 compound in vivo or genetic deletion of the mitogen-activated protein kinase (MAPK)9 (JNK2) in all tissues abolished the injurious effect. Strikingly, the difference in injury between wild-type and cFLIP-deficient mice occurred only in vivo and was accompanied by liver-infiltrating inflammatory cells with a trend toward increased amounts of NK1.1-positive cells and secretion of proinflammatory cytokines. Transfer of bone marrow from rag-1-deficient mice that are depleted from B and T lymphocytes prevented liver injury in flip−/− mice. These findings support a direct role of insulin on cellular survival by alternating the activation of injurious MAPK, caspases and the recruitment of inflammatory cells to the liver. Thus, increasing resistance to insulin signaling pathways in hepatocytes appears to be an important factor in the initiation and progression of chronic liver disease.

In recent years, the rapid development of industrialization and urbanization has caused serious environmental pollution, especially particulate pollution. As the “Earth’s kidneys,” wetland plays a significant role in improving the environmental quality and adjusting the climate. To study how wetlands work in this aspect, from the early autumn of 2014 to 2015, we implemented a study to measure the PM concentration and chemical composition at three heights (1.5, 6, and 10 m) during different periods (dry, normal water, and wet periods) in the Cuihu wetland park in Beijing for analyzing the dry deposition flux and the effect of meteorological factors on the concentration. Results indicated that (1) the diurnal variations of the PM2.5 and PM10 concentrations at the three heights were similar in that the highest concentration occurred at night and the lowest occurred at noon, and the daytime concentration was lower than that at night; (2) the PM2.5 and PM10 concentrations also varied between different periods that wet period > normal water period > wet period, and the concentration at different heights during different periods varied. In general, the lowest concentration occurred at 10 m during the dry and normal water periods, and the highest concentration occurred at 1.5 m during the wet period. (3) SO42−, NO3−, and Cl− are the dominant constituents of PM2.5, accounting for 42.22, 12.6, and 21.56%, respectively; (4) the dry depositions of PM2.5 and PM10 at 10 m were higher than those at 6 m, and the deposition during the dry period was higher than those during the wet and normal water periods. In addition, the deposition during the night-time was higher than that during the daytime. Moreover, meteorological factors affected the deposition, the temperature and wind speed being negatively correlated with the deposition flux and the humidity being positively correlated. (5) The PM10 and PM2.5 concentrations were influenced by meteorological factors. The PM2.5 and PM10 concentrations were negatively correlated with temperature and wind speed but positively correlated with relative humidity.

The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short-(5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area (B) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR+Landsat+inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat+inventory), respectively. In the long term, predicted AGB using LiDAR+Landsat+inventory and Land-sat+inventory variables had similar AGB patterns (differences within 7.2 Mg ha −1 ) to those predicted by matrix models with only inventory variables from 2015-2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha −1 ) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation (E), plot slope (S) and aspect (A), and site productivity (C) could produce suitable estimation of AGB dynamics (ranging from 67.1-105.5 Mg ha −1 in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series.

Abstract:Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson's correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R 2 ) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management.

Converting secondary natural forests (SFs) to Chinese fir plantations (CFPs) represents one of the most important (8.9 million ha) land use changes in subtropical China. This study estimated both biomass and soil C stocks in a SF and a CFP that was converted from a SF, to quantify the effects of land use change on ecosystem C stock. After the forest conversion, biomass C in the CFP (73 Mg¨ha´1) was significantly lower than that of the SF (114 Mg¨ha´1). Soil organic C content and stock decreased with increasing soil depth, and the soil C stock in the 0-10 cm layer accounted for more than one third of the total soil C stock over 0-50 cm, emphasizing the importance of management of the top soil to reduce the soil C loss. Total ecosystem C stock of the SF and the CFP was 318 and 200 Mg¨ha´1, respectively, 64% of which was soil C for both stands (205 Mg¨ha´1 for the SF and 127 Mg¨ha´1 for the CFP). This indicates that land use change from the SF to the CFP significantly decreased ecosystem C stock and highlights the importance of managing soil C.