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1.
Appl Opt ; 58(29): 8069-8074, 2019 Oct 10.
Artigo em Inglês | MEDLINE | ID: mdl-31674362

RESUMO

The optical properties of symmetric split-ring/ring dimer (SRRD) nanostructures composed of a small nanoring surrounded by an Ag splitting nanoring with a larger diameter are calculated theoretically. The apparent asymmetric Fano line shape in the spectra is related to fast switching of the bonding modes between the split-ring plasmon and ring dipole. The influence of the dimensions of the SRRD nanostructures on the spectral positions and intensity of Fano resonance is studied, and the asymmetric Fano line shape can be flexibly adjusted by varying the geometric parameters. In addition, relatively simple SRRD nanostructures have the same overall sensing figures of merit as conventional nanoparticles, thus rendering them suitable for high-performance optical sensors.

2.
Bioinformatics ; 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31593229

RESUMO

MOTIVATION: Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction. RESULTS: A new end-to-end deep learning framework, named DeepPPISP, through combining local contextual and global sequence features, is proposed for PPI site prediction. For local contextual features, we use a sliding window to capture features of neighbors of a target amino acid as in previous studies. For global sequence features, a text convolutional neural network is applied to extract features from the whole protein sequence. Then the local contextual and global sequence features are combined to predict PPI sites. By integrating local contextual and global sequence features, DeepPPISP achieves the state-of-the-art performance, which is better than the other competing methods. In order to investigate if global sequence features are helpful in our deep learning model, we remove or change some components in DeepPPISP. Detailed analyses show that global sequence features play important roles in DeepPPISP. AVAILABILITY AND IMPLEMENTATION: The DeepPPISP web server is available at http://bioinformatics.csu.edu.cn/PPISP/. The source code can be obtained from https://github.com/CSUBioGroup/DeepPPISP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

3.
Artigo em Inglês | MEDLINE | ID: mdl-31659853

RESUMO

RATIONALE: To establish an absolute quantification method for neutrophil gelatinase-associated lipocalin (NGAL) by ultra-high-performance liquid chromatography tandem positive ion electrospray ionization mass spectrometry (UHPLC/MS/MS) and evaluate its diagnostic efficacy for acute kidney injury (AKI). METHODS: Three target peptides of NGA were prescreened by Skyline software, and two of them could be detected in tryptic peptides of NGAL recombinant protein and human urinary NGAL (uNGAL). Peptide (WYVVGLAGNAILR) was then selected as surrogate peptide. We next synthesized the corresponding isotope-labelled peptide as the internal standard. Quantification of uNGAL was based on equations of linear regression, and method validation was then conducted for the method. We also evaluated the diagnostic efficacy of uNGAL for AKI by receiver operating characteristic curve (ROC) analysis. Lastly, we conducted a comparison between the UHPLC/MS/MS and the PETIA methods for uNGAL quantification. RESULTS: For the y9 and y10 product ions, the linear regression equations were y=2.5519x-4.6955 (R2 =0.994, p<0.01) and y=2.4619x-4.3 (R2 =0.993, p<0.01), respectively, and both of the linear ranges were from 0.5 to 15 mg/L. The limits of detection and quantification were 0.037 mg/L and 0.081 mg/L, respectively. The recoveries were from 97.32% to 107.28% at different uNGAL levels, and the CVs within and between days for uNGAL quantification were from 0.22% to 7.65% and from 0.66% to 5.97%, respectively. The carryover rates of uNGAL were in the range of 0.70% to 0.99%. The area under the ROC curve (AUC) of uNGAL was 0.96 (p<0.01), and the sensitivity and specificity of uNGAL for AKI diagnosis were 90.0% and 92.5%, respectively. In addition, The UHPLC/MS/MS and PETIA methods show good agreement for uNGAL quantification (y=0.7112x-0.0139, p=0.34). CONCLUSION: The UHPLC/MS/MS method for uNGAL quantification has a wide linear range, high sensitivity, precision and recoveries, and low carryover rates, and uNGAL detected by the method had high sensitivity and specificity for AKI diagnosis.

4.
Artigo em Inglês | MEDLINE | ID: mdl-31603794

RESUMO

Assembling genomes from single-cell sequencing data is essential for single-cell studies. However, single-cell assemblies are challenging due to (i) the highly non-uniform read coverage and (ii) the elevated levels of sequencing errors and chimeric reads. In this study, we present a new framework called EPGA-SC for de novo assembly of single-cell sequencing reads. The EPGA assembler has designed strategies to solve the problems caused by sequencing errors, sequencing biases and repetitive regions. However, the extremely unbalanced and richer error types prevent EPGA to achieve high performance in single-cell sequencing data. In this study, we designed EPGA-SC based on EPGA. The main innovations of EPGA-SC are as follows: (i) classifying reads to reduce the proportion of false reads; (ii) using multiple sets of high precision paired-end reads generated from the high precision assemblies produced by other assembler such as SPAdes to overcome the impact of sequencing biases and repetitive regions; (iii) developing novel algorithms for removing chimeric errors and extending contigs. We test EPGA-SC with seven datasets. The experimental results show that EPGA-SC can generate better assemblies than most current tools in most time in term of MAX contig, N50, NG50, NA50 and NGA50.

5.
Br J Cancer ; 121(9): 786-795, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31588122

RESUMO

BACKGROUND: The progression and metastasis of pancreatic ductal adenocarcinoma (PDAC) is highly dependent on the tumour microenvironment. Most tumour-associated macrophages (TAMs) are M2 phenotype macrophages, which normally show anti-inflammatory functions in numerous disorders. Previously, we found that alternatively activated macrophages showed pro-inflammatory characteristics upon stimulation with hepatoma cell-derived debris; however, the molecular mechanism was unclear. METHODS: In vitro and in vivo experiments were employed to investigate the molecular mechanism. Using pancreatic cancer cell lines, mouse models and human tissues, we obtained a general picture of tumour cell-derived debris promoting metastasis of pancreatic cancer by inducing inflammation via TAMs. RESULTS: We showed that M2 macrophage-derived inflammation also exists in PDAC. Debris from PDAC cells induced potent IL-1ß release by M2 macrophages via TLR4/TRIF/NF-κB signalling, and this effect was further boosted by IgG that was also derived from PDAC cells. Increased IL-1ß promoted epithelial-mesenchymal transition and consequent metastasis of PDAC cells. A selective COX-2 inhibitor, celecoxib, enhanced the anti-tumoural efficacy of gemcitabine. CONCLUSIONS: These data revealed a pro-inflammatory mechanism in PDAC, which indicated that IL-1ß and COX-2 could be therapeutic targets of an anti-inflammatory strategy to treat PDAC.

6.
Bioinformatics ; 35(14): i455-i463, 2019 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-31510658

RESUMO

MOTIVATION: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations. RESULTS: In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug-drug and disease-disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug-disease network, which integrates the drug-drug, drug-disease and disease-disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug-disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. AVAILABILITY AND IMPLEMENTATION: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

7.
Sci Adv ; 5(7): eaau8301, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31531392

RESUMO

Cerebral ischemia (CI) results from inadequate blood flow to the brain. The difficulty of delivering therapeutic molecules to lesions resulting from CI hinders the effective treatment of this disease. The inflammatory response following CI offers a unique opportunity for drug delivery to the ischemic brain and targeted cells because of the recruitment of leukocytes to the stroke core and penumbra. In the present study, neutrophils and monocytes were explored as cell carriers after selectively carrying cRGD liposomes, which effectively transmigrated the blood-brain barrier, infiltrated the cerebral parenchyma, and delivered therapeutic molecules to the injured sites and target cells. Our results showed the successful comigration of liposomes with neutrophils/monocytes and that both monocytes and neutrophils were important for successful delivery. Enhanced protection against ischemic injury was achieved in the CI/reperfusion model. The strategy presented here shows potential in the treatment of CI and other diseases related to inflammation.

8.
Anal Biochem ; 587: 113451, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31562851

RESUMO

Cystatin C is considered as an alternative to the evaluation of glomerular filtration rate. In this study, we highlighted an LC-MS/MS approach for the absolute quantitation of serum cystatin C based on label-free internal standards. A tryptic peptide (ALDFAVGEYNK) was selected as the surrogate whilst analogue (ALDFAVGEYQK) served as an internal standard. After denaturation, reduction, alkylation, digestion and concentration, the target peptides were separated on an LC column and monitored under MRM. The calibration range was from 0.25 mg/L to 15 mg/L with LLOQ of 0.05 mg/L and LOD of 0.03 mg/L, respectively. The certified reference material (ERM-DA471) was determined at 5.12 mg/L with bias of 6.57%. The recovery was between 89.68% and 92.43%. The RSD of intra- and inter-assay imprecision were both <10%. Good stability was also observed. The assay also demonstrated that the quantification of native cystatin C in human serum could be achieved using label-free internal standards. The assay was robust, cheap and sensitive.

9.
Artigo em Inglês | MEDLINE | ID: mdl-31478867

RESUMO

Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. In this paper, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we propose to apply a representation learning method on the networks that can generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against the previous multiple alignment methods.

10.
Biomed Pharmacother ; 118: 109270, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31401394

RESUMO

The landscape of cellular plasticity and sources with relevant niche signals in hepatocellular carcinoma is still obscure. Transient receptor potential vanilloid 1 (TRPV1), a non-selective cation channel, is involved in a variety of malignancies and overexpressed in hepatocellular carcinoma (HCC). We have investigated the role of TRPV1 in HCC from different angles by various experimental techniques, such as in vivo and in vitro experiments, and by bioinformatics analysis of data from genetic models induced by diethylnitrosamine (DEN), mice samples and human HCC samples. We find that TRPV1 knockout promotes to hepatocarcinogenesis and deconstructs the portal triad adjacent to tumor border that is contributed by originations of tumor initiating cells and biliary cells. Epithelial to mesenchymal transition (EMT) is involved and transcription factors Ovol2 and Zeb1 coordinated with Sox 10 drive gene expression in the event which is also confirmed by the expression of these proteins in human HCC samples. Treatment with TRPV1 agonist Capsaicin inhibits the growth of HCC cells in xenograft models. Our findings demonstrate that TRPV1 is a potential therapeutic target in human HCC and exerts effects on cellular plasticity with modulation of Ovol2, Zeb1 and Sox10.

11.
J Theor Biol ; 480: 141-149, 2019 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-31398315

RESUMO

Essential proteins have vital functions, when they are destroyed in cells, the cells will die or stop reproducing. Therefore, it is very important to identify essential proteins from a large number of other proteins. Due to the time-consuming, expensive, and inefficient process in biological experimental methods, computational methods become more and more popular to recognize them. In the early stages, these methods mainly rely on protein-protein interaction (PPI) information, which limits their discovery capacities. Researchers find novel methods by fusing multi-information to improve prediction accuracy. According to these features, essential proteins are more important and conservative in the evolution process, their neighbors in PPI networks are usually likely to be essential, there are many false positives in PPI data, whether a protein is essential can be assessed by the importance of a protein itself, the relevance of neighbors and the reliability of PPIs. The importance of neighbors and the reliability of PPIs can be further integrated into neighborhood feature. In the study, orthologous information, edge-clustering coefficient and gene expression information are used to measure the importance of a protein itself, the importance of the neighbors and the reliability of PPIs, respectively. Then, a novel expanded POC model, E_POC, is proposed to fuse the above information to discover essential proteins, a weighted PPI network is constructed. The proteins ranked high according to their weights are treated as candidate essential proteins. This novel method is named as E_POC. E_POC outperforms the existing classical methods on S. cerevisiae and E. coli data.

12.
Bioinformatics ; 2019 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-31386102

RESUMO

SUMMARY: The recent advance in genome engineering technologies based on CRISPR/Cas9 system is enabling people to systematically understand genomic functions. A short RNA string (the CRISPR guide RNA) can guide the Cas9 endonuclease to specific locations in complex genomes to cut DNA double-strands. The CRISPR guide RNA is essential for gene editing systems. Recently, the GuideScan software is developed to design CRISPR guide RNA libraries, which can be used for genome editing of coding and noncoding genomic regions effectively. However, GuideScan is a serial program and computationally expensive for designing CRISPR guide RNA libraries from large genomes. Here, we present an efficient guide RNA library designing tool (MultiGuideScan) by implementing multiple processes of GuideScan. MultiGuideScan speeds up the guide RNA library designing about 9-12 times on a 32-process mode comparing to GuideScan. MultiGuideScan makes it possible to design guide RNA libraries from large genomes. AVAILABILITY AND IMPLEMENTATION: MultiGuideScan is available at GitHub: https://github.com/bioinfomaticsCSU/MultiGuideScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

13.
Artigo em Inglês | MEDLINE | ID: mdl-31403441

RESUMO

Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this paper, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improves the accuracy of deletion calls; (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5' and 3' ends between two sequences) is adopted in Sprites2 to identifying breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. According to the experimental results, we can find that Sprites2 can improve deletion detection performance. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2.

14.
Artigo em Inglês | MEDLINE | ID: mdl-31443046

RESUMO

The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have been proposed to predict the potential lncRNA-disease relationships. However, most of them may give rise to incorrect results due to relying on single similarity measure. This article proposes a novel framework (ILDMSF) by fusing the lncRNA similarities and disease similarities, which are measured by lncRNA-related gene and known lncRNA-disease interaction and disease semantic interaction, and known lncRNA-disease interaction, respectively. Further, the support vector machine is employed to identify the potential lncRNA-disease associations based on the integrated similarity. The leave-one-out cross validation is performed to compare ILDMSF with other state of the art methods. The experimental results demonstrate our method is prospective in exploring potential correlations between lncRNA and disease.

15.
Artigo em Inglês | MEDLINE | ID: mdl-31425047

RESUMO

High-risk prediction of cardiovascular disease is of great significance and impendency in medical fields with the increasing phenomenon of sub-health these years. Most existing pathological methods for the prognosis prediction are either costly or prone to misjudgement. Therefore, plenty of automated models based on machine learning have been proposed to predict the onset of cardiovascular disease with the premorbid information of patients extracted from their historical Electronic Health Records (EHRs). However, it is a tough job to select proper features from longitudinal and heterogeneous EHRs, and also a great challenge to obtain accurate and robust representations for patients. In this paper, we propose an entirely end-to-end model called DeepRisk based on attention mechanism and deep neural networks, which can not only learn high-quality features automatically from EHRs, but also efficiently integrate heterogeneous and time-ordered medical data, and finally predict patients' risk of cardiovascular diseases. Experiments are carried out on a real medical dataset and results show that DeepRisk can significantly improve the high-risk prediction accuracy for cardiovascular disease compared with state-of-the-art approaches.

16.
Artigo em Inglês | MEDLINE | ID: mdl-31369384

RESUMO

One of the current research directions for single-cell RNA sequencing data is to accurately identify different cell types through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the true between-cell difference has become the key to the analysis of scRNA-seq data. For these reasons, unsupervised learning for cell populations discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method is the key to accurately identify cell types. Here, we present BioRank, a new cell similarity assessment method that using annotated gene sets and gene rank. In order to evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to twelve published scRNA-seq datasets, the results show that our method is better than or at least as well as several popular similarity assessment methods and single cell clustering methods.

17.
Int J Nanomedicine ; 14: 4461-4474, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31296986

RESUMO

Background: Vincristine is a potent therapeutic agent with well-defined activity against hematologic malignancies and solid tumors. It is a cell-cycle specific drug with concentration and exposure duration dependent activity. When used by liposomal delivery, it exhibits enhanced anti-tumor activity. However, vincristine liposome formulation in the clinic is supplied as a 3-vial-kit due to lacking sufficient stability. So it has to be prepared in situ prior to use through a multi-step process. Purpose: The purpose here is to develop a more stable and ready-to-use liposomal formulation for vincritstine in one vial. Patients and methods: A series of preparations were investigated based on sphingomyelin/cholesterol/PEG2000-DSPE lipid composition, with different drug/lipid (D/L) ratios (1/10, 1/5, 1/2), using an active sucrose octasulfate triethylamine salt gradient loading method. In this work, compared to generic vincristine sulfate liposome injection (GVM), the stability both in vivo and in vitro and efficacy in vivo of novel vincristine liposomes were investigated. Results: It was shown that the degradation of vincristine during 2-8°C storage was significantly decreased from 8.2% in 1 month (GVM) to 2.9% in 12 months (D/L ratio 1/5). The half-time for sphingomyelin/cholesterol/PEG2000-DSPE liposomes in vivo could be adjusted from 17.4 h (D/L ratio 1/10) to 22.7 h (D/L ratio 1/2) in rats, while the half-time for GVM was only 11.1 h. The increase in drug retention contributed to the lower in vivo toxicity. The antitumor efficacy was evaluated using a human melanoma tumor model and showed remarkable improvement compared to GVM. Conclusion: The study demonstrates that the new formulation with the drug/lipid ratio of 1/5 owns a higher encapsulation efficiency, better stability, lower toxicity and superior antitumor efficacy, which is screened out for further development.


Assuntos
Antineoplásicos Fitogênicos/farmacologia , Sistemas de Liberação de Medicamentos/métodos , Lipossomos/química , Vincristina/química , Vincristina/farmacologia , Animais , Antineoplásicos Fitogênicos/administração & dosagem , Antineoplásicos Fitogênicos/química , Colesterol/química , Estabilidade de Medicamentos , Armazenamento de Medicamentos , Humanos , Masculino , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos ICR , Fosfatidiletanolaminas/química , Polietilenoglicóis/química , Ratos Wistar , Esfingomielinas/química , Vincristina/administração & dosagem , Ensaios Antitumorais Modelo de Xenoenxerto
18.
Spectrochim Acta A Mol Biomol Spectrosc ; 223: 117333, 2019 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-31280125

RESUMO

Ag+ and SCN- play extremely important roles in the fields of the physiology and environment. In this work, on the basis of phenanthro[9,10-d]imidazole derivative (DIPIP) which can exhibit the aggregation-induced emission (AIE) properties in aqueous solution, we achieved a sequential on-off-on switch for Ag+ and SCN- with high selectivity and sensitivity. A remarkable fluorescence quenching effect of Ag+ on the probe DIPIP was observed with 1:2 stoichiometry, Subsequently, the fluorescence intensity of in situ generated DIPIP-Ag+ ensemble was easily switched on after the interaction between Ag+ and SCN-, which was attributed to the stronger affinity of SCN- to capture Ag+. In particular, the extreme limits of detection (LOD) for Ag+ and SCN- in standard solutions were as low as to be 74.5 nM and 7.8 nM, respectively. Furthermore, the probe DIPIP and the DIPIP-Ag+ ensemble could be used to detect Ag+ in the real water and SCN- in smoker saliva samples, respectively. In addition, the sequential "on-off-on" fluorescence mode of DIPIP to Ag+ and SCN- were also successfully applied in living HeLa cells.

19.
Artigo em Inglês | MEDLINE | ID: mdl-31295117

RESUMO

In the study, we propose a low-rank matrix completion method (called MCHMDA) to predict microbe-disease associations by integrating similarities of microbes and diseases and known microbe-disease associations into a heterogeneous network. The microbe similarity is computed from Gaussian Interaction Profile (GIP) kernel similarity based on the known microbe-disease associations. Then we further improve the microbe similarity by taking into account the inhabiting organs of these microbes in human body. The disease similarity is computed by the average of disease GIP similarity, disease symptom-based similarity and disease functional similarity. Then we construct a heterogeneous microbe-disease association network by integrating the microbe similarity network, disease similarity network and known microbe-disease association network. Finally, a matrix completion method is used to calculate the association scores of unknown microbe-disease pairs by the fast Singular Value Thresholding (SVT) algorithm. Via 5-fold Cross Validation (5CV) and Leave-One-Out Cross Validation (LOOCV), we evaluate the prediction performance of MCHMDA and other state-of-the-art methods. The experimental results show that MCHMDA outperforms other methods in terms of area under the receiver operating characteristic curve (AUC). MCHMDA achieves the AUC values of 0.9251 and 0.9495 in 5CV and LOOCV, respectively, which are the highest values among the competing methods.

20.
Sci Rep ; 9(1): 9690, 2019 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-31273293

RESUMO

A ppb-level CO sensor based on multi-comb optical-feedback cavity enhanced absorption spectroscopy with a 2.3 µm diode laser was developed for SF6 decomposition analysis in electric power system. The effective optical path reached to 4.5 km within 35 cm length cavity. Besides, through modulating the cavity length five times automatically, the spectral resolution was improved to 0.0015 cm-1 from 0.0071 cm-1. Targeting the R(6) line of CO first overtone band at 4285.01 cm-1, which is interference free from absorption spectra of SF6 mixtures (SF6, SO2, H2S, SO2F2, HF, CF4, CO2, COS, O2 and H2O), the minimum detection limit and detection precision under different gas pressures were performed. At optimum integration time of 30 s determined by Allan deviation analysis and gas pressure of 40 torr, the minimum detection limit and detection precision of CO were better than 18 ppb and 150 ppt, respectively.

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