The ability of the following four organic amendments to ameliorate saline soil in coastal northern China was investigated from April 2010 to October 2012 in a field experiment: green waste compost (GWC), sedge peat (SP), furfural residue (FR), and a mixture of GWC, SP and FR (1∶1∶1 by volume) (GSF). Compared to a non-amended control (CK), the amendments, which were applied at 4.5 kg organic matter m−3, dramatically promoted plant growth; improved soil structure; increased the cation exchange capacity (CEC), organic carbon, and available nutrients; and reduced the salt content, electrical conductivity (EC), and exchangeable sodium percentage (ESP). At the end of the experiment in soil amended with GSF, bulk density, EC, and ESP had decreased by 11, 87, and 71%, respectively, and total porosity and organic carbon had increased by 25 and 96% respectively, relative to the CK. The GSF treatment resulted in a significantly lower Na++K+ content than the other treatments. CEC and the contents of available N, P, and K were significantly higher in the GSF-treated soil than in the CK and were the highest in all treatments. The FR treatment resulted in the lowest pH value and Ca2+ concentration, which decreased by 8% and 39%, respectively, relative to the CK. Overall, the results indicate that a combination of green waste compost, sedge peat and furfural residue (GSF treatment) has substantial potential for ameliorating saline soils in the coastal areas of northern China, and it works better than each amendment alone. Utilization of GWC and FR can be an alternative organic amendment to substitute the nonrenewable SP in saline soil amelioration.

Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species’ ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.

Global warming may alter microbially mediated ecosystem functions through reshaping of microbial diversity and modified microbial interactions. Here, we examined the effects of 5-year experimental warming on different microbial hierarchical groups in a coastal nontidal soil ecosystem, including prokaryotes (i.e., bacteria and archaea), fungi, and Cercozoa, which is a widespread phylum of protists. Warming significantly

Background: Accumulating evidence has highlighted the crucial role of long noncoding RNAs (lncRNAs) in the tumorigenesis of gastric cancer (GC), which is the most common gastrointestinal malignancy. The present study aimed to identify the capacity of lncRNA LINC01419 (LINC01419) in GC progression, with the potential mechanism explored. Methods: Highly expressed lncRNAs were identified by in silico analysis, with the LINC01419 expression in GC tissues measured using reverse transcription-quantitative PCR (RT-qPCR). The GC cells were subsequently transfected with siRNA against LINC01419 or Rapamycin (the inhibitor of the mTOR pathway), or both, in order to measure cell migration and invasion in vitro as well as tumor growth and metastasis in vivo. Moreover, the expression of PI3K/Akt1/mTOR pathway-associated factors was determined. Results: LINC01419, highly expressed in GC samples of the Gene Expression Omnibus database, was observed to be markedly upregulated in GC tissues. Moreover, LINC01419 silencing, or PI3K/Akt1/mTOR pathway inhibition, exhibited an inhibitory role in GC cell migration and invasion in vitro, coupled with promoted cell autophagy in vitro, and inhibited tumor growth and metastasis in vivo. It was also revealed that LINC01419 silencing blocked the PI3K/Akt1/mTOR pathway, as proved by decreased extents of Akt1 and mTOR phosphorylation. Conclusions: In conclusion, LINC01419 inhibition may suppress GC cell invasion and migration, and promote autophagy via inhibition of the PI3K/Akt1/mTOR pathway. This provides significant theoretical basis and possibilities for further elucidation of the molecular mechanism of GC and finding new molecular-targeted therapeutic regimens.