The Effects of Plant-Soil-Enzyme Interactions on Plant Composition, Biomass and Diversity of Alpine Meadows in the Qinghai-Tibetan Plateau

Four different alpine meadow communities were studied to examine the effects of plant-soil-enzyme interactions on plant composition and diversity. Enzyme activities differed by meadow type, and in general were higher in the upper soil layers (0–10 and 10–20 cm) than in the 20–40 cm layer. Community differences in plant composition or functional group composition were reflected in plant biomass distribution. The identity of a species (or a functional group) was a greater determinant of ecosystem function than the number of plant species. A significant correlation was found between the coverage per functional group and the aboveground biomass of functional groups in four alpine meadows. Soil microbial biomass carbon (Cmic) and enzyme activity were each affected by both functional group biomass and CAB in the different meadow types. The negative correlation between diversity and CAB in the KTS may be influenced by a high soil nutrients input as a result of a higher litter input because of high aboveground biomass. Soil enzyme activities have been related to soil physio-chemical characters and plant primary production to change in vegetation. The original soil conditions, plant community composition, and community productivity are very important in regulating plant community productivity and microbial biomass and activity

Boosting the efficiency of inverted quantum dot light-emitting diodes by balancing charge densities and suppressing exciton quenching through band alignment.

We report an inverted and multilayer quantum dot light emitting diode (QLED) which boosts high efficiency by tuning the energy band alignment between charge transport and light emitting layers. The electron transport layer (ETL) was ZnO nanoparticles (NPs) with an optimized doping concentration of cesium azide (CsN3) to effectively reduce electron flow and balance charge injection. This is by virtue of a 0.27 eV upshift of the ETL's conduction band edge, which inhibits the quenching of excitons and preserves the superior emissive properties of the quantum dots due to the insulating characteristics of CsN3. The demonstrated QLED exhibits a peak current efficiency, power efficiency and external quantum efficiency of up to 13.5 cd A-1, 10.6 lm W-1 and 13.4% for the red QLED, and correspondingly 43.1 cd A-1, 33.6 lm W-1 and 9.1% for green, and 4.1 cd A-1, 2.0 lm W-1 and 6.6% for the blue counterparts. Compared with QLEDs without optimization, the performance of these modified devices shows drastic improvement by 95.6%, 39.4% and 36.7%, respectively. This novel device architecture with heterogeneous energy levels reported here offers a new design strategy for next-generation high efficiency QLED displays and solid-state lighting technologies

Tumor-preventing activity of aspirin in multiple cancers based on bioinformatic analyses

Background Acetylsalicylic acid was renamed aspirin in 1899, and it has been widely used for its multiple biological actions. Because of the diversity of the cellular processes and diseases that aspirin reportedly affects and benefits, uncertainty remains regarding its mechanism in different biological systems. Methods The Drugbank and STITCH databases were used to find direct protein targets (DPTs) of aspirin. The Mentha database was used to analyze protein–protein interactions (PPIs) to find DPT-associated genes. DAVID was used for the GO and KEGG enrichment analyses. The cBio Cancer Genomics Portal database was used to mine genetic alterations and networks of aspirin-associated genes in cancer. Results Eighteen direct protein targets (DPT) and 961 DPT-associated genes were identified for aspirin. This enrichment analysis resulted in eight identified KEGG pathways that were associated with cancers. Analysis using the cBio portal indicated that aspirin might have effects on multiple tumor suppressors, such as TP53, PTEN, and RB1 and that TP53 might play a central role in aspirin-associated genes. Discussion The results not only suggest that aspirin might have anti-tumor actions against multiple cancers but could also provide new directions for further research on aspirin using a bioinformatics analysis approach

Greening China naturally

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in AMBIO: A Journal of the Human Environment 40 (2011): 828-831, doi:10.1007/s13280-011-0150-8.China leads the world in afforestation, and is one of the few countries whose forested area is increasing. However, this massive ‘‘greening’’ effort has been less effective than expected; afforestation has sometimes produced unintended environmental, ecological, and socioeconomic consequences, and has failed to achieve the desired ecological benefits. Where afforestation has succeeded, the approach was tailored to local environmental conditions. Using the right plant species or species composition for the site and considering alternatives such as grassland restoration have been important success factors. To expand this success, government policy should shift from a forest-based approach to a results-based approach. In addition, long-term monitoring must be implemented to provide the data needed to develop a cost-effective, scientifically informed restoration policy.This work was supported by the Fundamental Research Funds for the Central Universities (HJ2010-3) and the CAS/ SAFEA International Partnership Program for Creative Research Teams of ‘‘Ecosystem Processes and Services’’

Embedded Sensing for On-line Bearing Condition Monitoring and Diagnosis

The object of this project is to develop a new methodology to improve bearing condition monitoring and diagnosis, based on embedded sensing approach. The presented study involves work in four areas. First is investigation of the embedded sensing approach. Studies of the bearing structure dynamics and defect-related vibrations reveal the important characteristics of defect signals. The sensor placement strategy of embedded sensing is verified through theoretical analysis, numerical simulation, and experiment test. The embedded sensing enables the acquisition of bearing signals with a high signal-to-noise ratio, which is critical to the reliable defect detection. Second is the feasibility investigation of the embedded sensing approach through a wireless sensing module design. The miniaturized sensing module, configured as an accelerometer or dynamic load sensor, have an on-board signal conditioner and a wireless transmitter. It has been designed, prototyped, bench-scale tested, and structurally integrated into a customized bearing test bed for on-line bearing monitoring. A combination of surface mount (SMT) and thick-film hybrid technologies has been used to reduce the size of the sensing module. Third is the defect signal extraction for the embedded sensing approach. A new signal processing technique has been developed in order to improve the effectiveness of the defect feature extraction. It combines the wavelet analysis on the time-scale domain and the analysis on the frequency domain. Experimental studies have shown that it is more effective in detecting the existence of a small bearing defect of 0.25mm in diameter, as compared to the frequency-domain analysis or the wavelet analysis alone. Fourth is the defect evaluation. To enable automated bearing defect evaluation, a bearing health index is introduced from the quantization of defect severity. Under different bearing operation conditions (e.g. speed, radial and axial loads), bearing defects are evaluated through a backpropagation neural network. Its input features are constructed based on the defect signatures, which are extracted by the combined wavelet and frequency-domain analysis. In addition to on-line bearing condition monitoring, the techniques developed for the embedded sensing can be also adapted to the condition monitoring of many other types of manufacturing equipment

Smart design of hollow AuPt nanospheres with a porous shell as superior electrocatalysts for ethylene glycol oxidation

Hollow bimetallic electrocatalysts have recently stimulated increasing attention due to their accessible interior surface and bimetallic synergetic effect. Herein, for the first time, we successfully anchor a porous AuPt shell on modified silica spheres with the assistance of Pluronic F127. The whole process consists of simple ultrasonic mixing for 30 min at room temperature without any organic solvent. After metal deposition and silica etching, well-defined hollow structures are obtained in high yield. The hollow spheres are further characterized by scanning electron microscopy and transmission electron microscopy. The electrochemical analysis reveals that the as-prepared samples demonstrate superior electrocatalytic activity and durability relative to the commercial Pt/C catalyst, showcasing the potential of these AuPt hollow spheres as electrocatalysts for ethylene glycol oxidation in an alkaline medium