Prognostic Significance of miR-181b and miR-21 in Gastric Cancer Patients Treated with S-1/Oxaliplatin or Doxifluridine/Oxaliplatin

Background: The goal of this study is to evaluate the effectiveness of S-1/Oxaliplatin vs. Doxifluridine/Oxaliplatin regimen and to identify miRNAs as potential prognostic biomarkers in gastric cancer patients. The expression of candidate miRNAs was quantified from fifty-five late stage gastric cancer FFPE specimens. Experimental Design: Gastric cancer patients with KPS>70 were recruited for the trial. The control group was treated with 400 mg/twice/day Doxifluridine plus i.v. with Oxaliplatin at 130 mg/m 2/first day/4 week cycle. The testing group was treated with S-1 at 40 mg/twice/day/4 week cycle plus i.v. with Oxaliplatin at 130 mg/m 2/first day/4 week cycle. Total RNAs were extracted from normal and gastric tumor specimens. The levels of miRNAs were quantified using real time qRT-PCR expression analysis. Results: The overall objective response rate (CR+PR) of patients treated with S-1/Oxaliplatin was 33.3% (CR+PR) vs. 17.6% (CR+PR) with Doxifluridine/Oxaliplatin for advanced stage gastric cancer patients. The average overall survival for patients treated with S-1/Oxaliplatin was 7.80 month vs. 7.30 month with patients treated with Doxifluridine/Oxaliplatin. The expression of miR-181b (P = 0.022) and miR-21 (P = 0.0029) was significantly overexpressed in gastric tumors compared to normal gastric tissues. Kaplan-Meier survival analysis revealed that low levels of miR-21 expression (Log rank test, hazard ratio: 0.17, CI = 0.06-0.45; P = 0.0004) and miR-181b (Log rank test, hazard ratio: 0.37, CI = 0.16-0.87; P = 0.018) are closely associated with better patient's overall survival for both S-1 and Doxifluridine based regimens. Conclusion: Patients treated with S-1/Oxaliplatin had a better response than those treated with Doxifluridine/Oxaliplatin. miR-21 and miR-181b hold great potential as prognostic biomarkers in late stage gastric cancer. © 2011 Jiang et al

Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas

Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models

Enhanced coal biomethanation by microbial electrolysis and graphene in the anaerobic digestion

The combination of microbial electrolytic cells and conductive materials can effectively promote direct interspecies electron transfer (DIET) to increase methane production, which has great potential for enhanced anaerobic degradation of organic matter. A single-chamber microbial electrolytic cell containing graphene was constructed using long-flame coal as a substrate. The results showed that the external electric field and graphene increased the abundance of hydrolytic bacteria (Paraclostridium, Sedimentibacter) and hydrogen-producing acetogenic bacteria (Anaerovorax) in the AD system. The consumption rate of alkanes, volatile fatty acids and alcohols was accelerated, which provided sufficient nutrients for methanogens and increased biomethane production by 53.1 %. The abundance of related genes involved in the carbon dioxide reduction pathway was significantly increased. The abundance of pilA gene involved in electron transport in the AD system increased by 153.7 %, and the abundance of electroactive microorganisms Geobacter and Methanosarcina capable of DIET increased significantly, which further promoted coal biomethanation

Enhanced CO2 Electroreduction to Multi‐Carbon Products on Copper via Plasma Fluorination

Abstract The electroreduction of carbon dioxide (CO2) to multi‐carbon (C2+) compounds offers a viable approach for the up‐conversion of greenhouse gases into valuable fuels and feedstocks. Nevertheless, current industrial applications face limitations due to unsatisfactory conversion efficiency and high overpotential. Herein, a facile and scalable plasma fluorination method is reported. Concurrently, self‐evolution during CO2 electroreduction is employed to control the active sites of Cu catalysts. The copper catalyst modified with fluorine exhibits an impressive C2+ Faradaic efficiency (FE) of 81.8% at a low potential of −0.56 V (vs a reversible hydrogen electrode) in an alkaline flow cell. The presence of modified fluorine leads to the exposure and stabilization of high‐activity Cu+ species, enhancing the adsorption of *CO intermediates and the generation of *CHO, facilitating the subsequent dimerization. This results in a notably improved conversion efficiency of 13.1% and a significant reduction in the overpotential (≈100 mV) for the C2+ products. Furthermore, a superior C2+ FE of 81.6% at 250 mA cm−2, coupled with an energy efficiency of 31.0%, can be achieved in a two‐electrode membrane electrode assembly electrolyzer utilizing the fluorine‐modified copper catalyst. The strategy provides novel insights into the controllable electronic modification and surface reconstruction of electrocatalysts with practical potential

A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China

Accurate streamflow prediction is crucial for effective water resource management. However, reliable prediction remains a considerable challenge because of the highly complex, non-stationary, and non-linear processes that contribute to streamflow at various spatial and temporal scales. In this study, we utilized a convolutional neural network (CNN)–Transformer–long short-term memory (LSTM) (CTL) model for streamflow prediction, which replaced the embedding layer with a CNN layer to extract partial hidden features, and added an LSTM layer to extract correlations on a temporal scale. The CTL model incorporated Transformer's ability to extract global information, CNN's ability to extract hidden features, and LSTM's ability to capture temporal correlations. To validate its effectiveness, we applied it for streamflow prediction in the Shule River basin in northwest China across 1-, 3-, and 6-month horizons and compared its performance with Transformer, CNN, LSTM, CNN–Transformer, and Transformer–LSTM. The results demonstrated that CTL outperformed all other models in terms of predictive accuracy with Nash–Sutcliffe coefficient (NSE) values of 0.964, 0.912, and 0.856 for 1-, 3-, 6-month ahead prediction. The best results among the five comparative models were 0.908, 0.824, and 0.778, respectively. This indicated that CTL is an outstanding alternative technique for streamflow prediction where surface data are limited. HIGHLIGHTS CTL integrated and absorbed the respective merits of Transformer, CNN, and LSTM.; CTL achieved exceptional accuracy, surpassing that of the benchmarked models.; CTL effectively predicted multi-time-ahead streamflow in arid northwest China.

Comparison of water use efficiency of sand-binding species along revegetation chronosequence in an alpine desert

Afforestation is an effective measure for ecological restoration in the desert ecosystem. The long-term water use efficiency (WUE) of leaves is an important indicator for evaluating the water adaptation strategy of sand-fixing species. However, the WUE of typical sand-fixing plants in the alpine desert and its responses to local climatic, micro-geomorphology, environmental conditions and nutrient limitations are still unclear. In this study, sand-fixing vegetation community along a revegetation chronosequence (Hippophae rhammoides planted on dunes in 1987, 2008, and 2015, respectively. H. rhammoides with the longest recovery period were defined as mature plantations, and the shortest recovery period were defined as juveniles.) at the alpine desert at the eastern shore of the Qinghai Lake were used as the research plots. Stable carbon isotope was used to evaluate species WUE. The results showed that: (1) the δ13C values of H. rhammoides showed a decreasing trend as plants grew. The seasonal δ13C variation was mainly affected by average relative humidity (MRH). (2) The δ13C in the dunes revegetated in 2008 and 2015 were significantly higher than that in 1987 at the windward slope (P < 0.05). The values of δ13C had various responses to the available soil water under the influence of topography, and soil water content was the key factor for the WUE of individuals in communities. There was a significant negative correlation between the ratio of carbon and nitrogen content in H. rhammoides leaves and WUE. The research indicated that mature plantations had adopted a more stable water use pattern than the juveniles, which provide an effective insight for ecological restoration in fragile ecological regions

Allelic variation within the S-adenosyl-L-homocysteine hydrolase gene family is associated with wood properties in Chinese white poplar (Populus tomentosa)

BACKGROUND: S-adenosyl-l-homocysteine hydrolase (SAHH) is the only eukaryotic enzyme capable of S-adenosyl-l-homocysteine (SAH) catabolism for the maintenance of cellular transmethylation potential. Recently, biochemical and genetic studies in herbaceous species have obtained important discoveries in the function of SAHH, and an extensive characterization of SAHH family in even one tree species is essential, but currently lacking. RESULTS: Here, we first identified the SAHH family from Populus tomentosa using molecular cloning method. Phylogenetic analyses of 28 SAHH proteins from dicotyledons, monocotyledons, and lower plants revealed that the sequences formed two monophyletic groups: the PtrSAHHA with PtoSAHHA and PtrSAHHB with PtoSAHHB. Examination of tissue-specific expression profiles of the PtoSAHH family revealed similar expression patterns; high levels of expression in xylem were found. Nucleotide diversity and linkage disequilibrium (LD) in the PtoSAHH family, sampled from P. tomentosa natural distribution, revealed that PtoSAHH harbors high single-nucleotide polymorphism (SNP) diversity [Formula: see text] and low LD (r(2 )> 0.1, within 800 bp and 2,200 bp, respectively). Using an LD-linkage analysis approach, two noncoding SNPs (PtoSAHHB_1065 and PtoSAHHA_2203) and the corresponding haplotypes were found to significantly associate with α-cellulose content, and a nonsynonymous SNP (PtoSAHHB_410) within the SAHH signature motifs showed significant association with fiber length, with an average of 3.14% of the phenotypic variance explained. CONCLUSIONS: The present study demonstrates that PtoSAHHs were split off prior to the divergence of interspecies in Populus, and SAHHs may play a key role promoting transmethylation reactions in the secondary cell walls biosynthesis in trees. Hence, our findings provide insights into SAHH function and evolution in woody species and also offer a theoretical basis for marker-aided selection breeding to improve the wood quality of Populus

The Ecosystem Effects of Sand-Binding Shrub Hippophae rhamnoides in Alpine Semi-Arid Desert in the Northeastern Qinghai–Tibet Plateau

The planting of sand-binding vegetation in the Qinghai Lake watershed at the northeastern edge of the Qinghai&ndash;Tibet Plateau began in 1980. For this paper, we took the desert on the eastern shore of Qinghai Lake as the study area. We analyzed a variety of aged Hippophae rhamnoides communities and aeolian activities, and we discuss the relationship between them. The main conclusions are as follows: (1) With an increasing number of binding years, the species composition became more abundant, natural vegetation began to recover, and biodiversity increased year by year. At the same time, plant height, canopy width, and community coverage increased, but H. rhamnoides coverage was reduced to 36.70% as coverage of Artemisia desertorum increased to 25.67% after 10 years of fixing. The biomass of H. rhamnoides increased significantly, especially the underground biomass. For example, the biomass of area 15a was about 10 to 30 times that of area 1a. (2) Plants are a useful obstacle to aeolian activity. The presence of plants reduced the wind flow in the upper parts of the plants, but it did not have obvious regular characteristics. The longer the fixation term, the lower the surface sediment transport. It is significant that the sediment transport amount in winter was four times that in the summer. After 15 years of binding, H. rhamnoides grows well, and the community is still stable in the study area