Functionalised B-Cu@F-Ag@Au trimetallic nanomaterials with long term stability for rapid and highly sensitive in situ SERS detection of furfural in transformer oil

The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer oil.However,detection substrates with long-term stability are still extremely challenging.In this work,we anchored the thiol-containing coupling agents 2,5-dimercapto-1,3,4-thiadiazole(DMTD)and 1,4-benzenedithiol(BDT)on the surface of bubble copper(B-Cu)and flower-like silver nanoparticles(FAg),respectively.The three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were constructed.The substrate has a minimum detection limit of 10^(−9) M for rhodamine B in oil with an enhancement factor of up to 2.23×10^(7).Importantly,the three-crystal BCu@F-Ag_(1)@Au_(5) substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%.After 60 days of a simulated operation,the detection signal of furfural in the transformer oil samples at 75℃ and still reached the initial value of 77.53%,indicating that the substrate has a good long-term stability.This triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems.

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).

Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis

Clinical manifestations of rheumatoid arthritis(RA)are diversified,and based on the manifestations,the patients with RA could be classified into different patterns under traditional Chinese medicine.These patterns decide the selection of herbal prescription,and thus they can help find a subset of rheumatoid arthritis patients for a type of therapy.In the present study,we combine genome-wide expression analysis with methods of systems biology to identify the functional gene networks for the sets of clinical symptoms that comprise the major information for pattern classification.Clinical manifestations in rheumatoid arthritis were clustered with factor analysis,and two factors(similar to cold and hot patterns in traditional Chinese medicine)were found.Microarray technology was used to reveal gene expression profiles in CD4^(+)T cells from 21 rheumatoid arthritis patients.Protein-protein interaction information for these genes from databases and literature data was searched.The highly-connected regions were detected to infer significant complexes or pathways in this protein-protein interaction network.The significant pathways and function were extracted from these subnetworks using the Biological Network Gene Ontology tool.The genes significantly related to hot and cold patterns were identified by correlations analysis.MAPK signalling pathway,Wnt signaling pathway,and insulin signaling pathway were found to be related to hot pattern.Purine metabolism was related to both hot and cold patterns.Alanine,aspartate,and tyrosine metabolism were related to cold pattern,and histindine metabolism and lysine degradation were related to hot pattern.The results suggest that cold and hot patterns in traditional Chinese medicine were related to different pathways,and the network analysis might be used for identifying the pattern classification in other diseases.

Hyperspectral diagnosis of nitrogen status in arbuscular mycorrhizal inoculated soybean leaves under three drought conditions

Precision diagnosis of leaf nitrogen(N)content in arbuscular mycorrhizal inoculated crops under drought stress,using hyperspectral remote sensing technology,would be significant to evaluate the mycorrhizal effect on crop growth condition in the arid and semi-arid region.In this study,soybean plants with inoculation and non-inoculation treatments were grown under severe drought,moderate drought and normal irrigation conditions.Leaf spectral reflectance and several biochemical parameters were measured at 30 d,45 d and 64 d after inoculation.Correlation analyses were conducted between leaf N content and the original and first derivative spectral reflectance.A series of first-order differential area indices and differential area ratio indices were proposed and explored.Results indicated that arbuscular mycorrhizal fungi improved leaf N content under drought stresses,the spectral reflectance in visible to red edge regions of inoculated plants was lower than that of non-inoculated plants.The first-order differential area index at bands of 638-648 nm achieved the best estimation and prediction accuracies in leaf N content inversion,with the determination coefficient of calibration of 0.72,root mean square error of prediction and relative error of prediction of 0.46 and 11.60%,respectively.This study provides a new insight for the evaluation of mycorrhizal effect under drought stress and opens up a new field of application for hyperspectral remote sensing.

Magnetic motive, ordered mesoporous carbons with partially graphitized framework and controllable surface wettability： preparation, characterization and their selective adsorption of organic pollutants in water

有磁力地活跃，订并且稳定的 mesoporous 碳与部分， graphitized 网络和可控制的表面 wettability (PR-Fe-P123-800 和 PR-Ni-P123-800 ) 通过 Fe 或 Ni functionalized 的直接碳化被综合了，并且在 800 点订了 mesoporous

Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

Leaf water content(LWC)of crops is a suitable parameter for evaluation of plant water status and arbuscular mycorrhizal effect on the host plant under drought stress.Remote sensing technology provides an effective avenue to estimate LWC in crops.However,few LWC retrieval models have been developed specifically for the arbuscular mycorrhizal inoculated crops.In this study,soybean with inoculation and non-inoculation treatments were planted under the severe drought,moderate drought and normal irrigation levels.The LWC changes under different treatments at the 30 th,45 th and 64 th day after the inoculation were investigated,and the spectral response characteristics of inoculated and non-inoculated soybean leaves under the three drought stresses were analyzed.Five types of spectral variables/indices including:raw spectral reflectance(R),continuum-removed spectral reflectance(R C),difference vegetation index(DVI),normalized difference vegetation index(NDVI)and ratio vegetation index(RVI)were applied to determine the best estimator of LWC.The results indicate that LWC decreased as the aggravating of drought stress levels.However,LWC in inoculated leaves was higher than that in the counterparts under the same drought stress level,and the values of raw reflectance measured at inoculated leaves were lower than the non-inoculated leaves,especially around 1900 nm and 1410 nm.These water spectral features were more evident in the corresponding continuum-removed spectral reflectance.The newly proposed DVI C(2280,1900)index,derived from the continuum-removed spectral reflectance at 2280 nm and the raw spectral reflectance at 1900 nm in DVI type of index,was the most robust for soybean LWC assessment,with R 2 value of 0.72(p<0.01)and root mean square error(RMSE)and mean absolute error(MAE)of 2.12%and 1.75%,respectively.This study provides a means to monitor the mycorrhizal effect on drought-induced crops indirectly and non-destructively.

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

Leaf area index(LAI)and canopy chlorophyll density(CCD)are key indicators of crop growth status.In this study,we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages.The indices were calculated with Sentinel-2 MSI data and hyperspectral data.Their performances were validated against ground measurements using R2,RMSE,and bias.The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling stage.Furthermore,rededge modified indices outperformed the traditional indices for both data genres.Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early joint-ing and milk development stage,while indices with long red-edge wavelength estimate the sought variables better at the middle three stages.The results were consistent with the red-edge inflec-tion point shift at different growth stages.The best indices for Sentinel-2 LAI retrieval,Sentinel-2 CCD retrieval,hyperspectral LAI retrieval,and hyperspectral CCD retrieval at five growth stages were determined in the research.These results are beneficial to crop trait monitoring by providing references for crop biophysical and bio-chemical parameters retrieval.