Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LA) is one of the most common malignancies and is responsible for the greatest number of tumor-related deaths. Our research aimed to explore the molecular subtype signatures of LA to clarify the correlation among the immune microenvironment, clinical outcomes, and therapeutic response. METHODS: The LA immune cell marker genes (LICMGs) identified by single-cell RNA sequencing (scRNA-seq) analysis were used to discriminate the molecular subtypes and homologous immune and metabolic traits of GSE72094 LA cases. In addition, the model-building genes were identified from 1441 LICMGs by Cox-regression analysis, and a LA immune difference score (LIDscore) was developed to quantify individual differences in each patient, thereby predicting prognosis and susceptibility to immunotherapy and chemotherapy of LA patients. RESULTS: Patients of the GSE72094 cohort were divided into two distinct molecular subtypes based on LICMGs: immune activating subtype (Cluster-C1) and metabolically activating subtype (cluster-C2). The two molecular subtypes have distinct characteristics regarding prognosis, clinicopathology, genomics, immune microenvironment, and response to immunotherapy. Among the LICMGs, LGR4, GOLM1, CYP24A1, SFTPB, COL1A1, HLA-DQA1, MS4A7, PPARG, and IL7R were enrolled to construct a LIDscore model. Low-LIDscore patients had a higher survival rate due to abundant immune cell infiltration, activated immunity, and lower genetic variation, but probably the higher levels of Treg cells in the immune microenvironment lead to immune cell dysfunction and promote tumor immune escape, thus decreasing the responsiveness to immunotherapy compared with that of the high-LIDscore patients. Overall, high-LIDscore patients had a higher responsiveness to immunotherapy and a higher sensitivity to chemotherapy than the low-LIDscore group. CONCLUSIONS: Molecular subtypes based on LICMGs provided a promising strategy for predicting patient prognosis, biological characteristics, and immune microenvironment features. In addition, they helped identify the patients most likely to benefit from immunotherapy and chemotherapy.

Inhibiting NLRP3 inflammasome signaling pathway promotes neurological recovery following hypoxic-ischemic brain damage by increasing p97-mediated surface GluA1-containing AMPA receptors.

BACKGROUND: The nucleotide-binding oligomeric domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is believed to be a key mediator of neuroinflammation and subsequent secondary brain injury induced by ischemic stroke. However, the role and underlying mechanism of the NLRP3 inflammasome in neonates with hypoxic-ischemic encephalopathy (HIE) are still unclear. METHODS: The protein expressions of the NLRP3 inflammasome including NLRP3, cysteinyl aspartate specific proteinase-1 (caspase-1) and interleukin-1ß (IL-1ß), the α-amino-3-hydroxy-5-methyl-4-isoxazole-propionicacid receptor (AMPAR) subunit, and the ATPase valosin-containing protein (VCP/p97), were determined by Western blotting. The interaction between p97 and AMPA glutamate receptor 1 (GluA1) was determined by co-immunoprecipitation. The histopathological level of hypoxic-ischemic brain damage (HIBD) was determined by triphenyltetrazolium chloride (TTC) staining. Polymerase chain reaction (PCR) and Western blotting were used to confirm the genotype of the knockout mice. Motor functions, including myodynamia and coordination, were evaluated by using grasping and rotarod tests. Hippocampus-dependent spatial cognitive function was measured by using the Morris-water maze (MWM). RESULTS: We reported that the NLRP3 inflammasome signaling pathway, such as NLRP3, caspase-1 and IL-1ß, was activated in rats with HIBD and oxygen-glucose deprivation (OGD)-treated cultured primary neurons. Further studies showed that the protein level of the AMPAR GluA1 subunit on the hippocampal postsynaptic membrane was significantly decreased in rats with HIBD, and it could be restored to control levels after treatment with the specific caspase-1 inhibitor AC-YVAD-CMK. Similarly, in vitro studies showed that OGD reduced GluA1 protein levels on the plasma membrane in cultured primary neurons, whereas AC-YVAD-CMK treatment restored this reduction. Importantly, we showed that OGD treatment obviously enhanced the interaction between p97 and GluA1, while AC-YVAD-CMK treatment promoted the dissociation of p97 from the GluA1 complex and consequently facilitated the localization of GluA1 on the plasma membrane of cultured primary neurons. Finally, we reported that the deficits in motor function, learning and memory in animals with HIBD, were ameliorated by pharmacological intervention or genetic ablation of caspase-1. CONCLUSION: Inhibiting the NLRP3 inflammasome signaling pathway promotes neurological recovery in animals with HIBD by increasing p97-mediated surface GluA1 expression, thereby providing new insight into HIE therapy.

Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

Aagab acts as a novel regulator of NEDD4-1-mediated Pten nuclear translocation to promote neurological recovery following hypoxic-ischemic brain damage.

Hypoxic-ischemic encephalopathy (HIE) is a main cause of mortality and severe neurologic impairment in the perinatal and neonatal period. However, few satisfactory therapeutic strategies are available. Here, we reported that a rapid nuclear translocation of phosphatase and tensin homolog deleted on chromosome TEN (PTEN) is an essential step in hypoxic-ischemic brain damage (HIBD)- and oxygen-glucose deprivation (OGD)-induced neuronal injures both in vivo and in vitro. In addition, we found that OGD-induced nuclear translocation of PTEN is dependent on PTEN mono-ubiquitination at the lysine 13 residue (K13) that is mediated by neural precursor cell expressed developmentally downregulated protein 4-1 (NEDD4-1). Importantly, we for the first time identified α- and γ-adaptin binding protein (Aagab) as a novel NEDD4-1 regulator to regulate the level of NEDD4-1, subsequently mediating Pten nuclear translocation. Finally, we demonstrated that genetic upregulation of Aagab or application of Tat-K13 peptide (a short interference peptide that flanks K13 residue of PTEN) not only reduced Pten nuclear translocation, but also significantly alleviated the deficits of myodynamia, motor and spatial learning and memory in HIBD model rats. These results suggest that Aagab may serve as a regulator of NEDD4-1-mediated Pten nuclear translocation to promote functional recovery following HIBD in neonatal rats, and provide a new potential therapeutic target to guide the clinical treatment for HIE.

Development of an α-synuclein knockdown peptide and evaluation of its efficacy in Parkinson's disease models.

Convincing evidence supports the premise that reducing α-synuclein levels may be an effective therapy for Parkinson's disease (PD); however, there has been lack of a clinically applicable α-synuclein reducing therapeutic strategy. This study was undertaken to develop a blood-brain barrier and plasma membrane-permeable α-synuclein knockdown peptide, Tat-ßsyn-degron, that may have therapeutic potential. The peptide effectively reduced the level of α-synuclein via proteasomal degradation both in cell cultures and in animals. Tat-ßsyn-degron decreased α-synuclein aggregates and microglial activation in an α-synuclein pre-formed fibril model of spreading synucleinopathy in transgenic mice overexpressing human A53T α-synuclein. Moreover, Tat-ßsyn-degron reduced α-synuclein levels and significantly decreased the parkinsonian toxin-induced neuronal damage and motor impairment in a mouse toxicity model of PD. These results show the promising efficacy of Tat-ßsyn-degron in two different animal models of PD and suggest its potential use as an effective PD therapeutic that directly targets the disease-causing process.

LTD is involved in the formation and maintenance of rat hippocampal CA1 place-cell fields.

Hippocampal synaptic plasticity includes both long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength, and has been implicated in shaping place field representations that form upon initial exposure to a novel environment. However, direct evidence causally linking either LTP or LTD to place fields remains limited. Here, we show that hippocampal LTD regulates the acute formation and maintenance of place fields using electrophysiology and blocking specifically LTD in freely-moving rats. We also show that exploration of a novel environment produces a widespread and pathway specific de novo synaptic depression in the dorsal hippocampus. Furthermore, disruption of this pathway-specific synaptic depression alters both the dynamics of place field formation and the stability of the newly formed place fields, affecting spatial memory in rats. These results suggest that activity-dependent synaptic depression is required for the acquisition and maintenance of novel spatial information.

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra.

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

p97 regulates GluA1 homomeric AMPA receptor formation and plasma membrane expression.

The α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid subtype glutamate receptors (AMPARs) mediate the fast excitatory synaptic transmission in the mammalian brain and are important for synaptic plasticity. In particular, the rapid insertion of the GluA1 homomeric (GluA1-homo) AMPARs into the postsynaptic membrane is considered to be critical in the expression of hippocampal CA1 long-term potentiation (LTP), which is important for certain forms of learning and memory. However, how the formation and trafficking of GluA1-homo AMPARs are regulated remains poorly understood. Here, we report that p97 specifically interacts with and promotes the formation of GluA1-homo AMPARs. The association with p97 retains GluA1-homo AMPARs in the intracellular compartment under basal conditions, and its dissociation allows GluA1-homo AMPARs to be rapidly inserted into the postsynaptic membrane shortly after LTP induction. Thus, our results shed lights into the molecular mechanisms by which p97 regulates GluA1-homo AMPARs formation and trafficking, thereby playing a critical role in mediating synaptic plasticity.

A Random Walk Based Cluster Ensemble Approach for Data Integration and Cancer Subtyping.

Availability of diverse types of high-throughput data increases the opportunities for researchers to develop computational methods to provide a more comprehensive view for the mechanism and therapy of cancer. One fundamental goal for oncology is to divide patients into subtypes with clinical and biological significance. Cluster ensemble fits this task exactly. It can improve the performance and robustness of clustering results by combining multiple basic clustering results. However, many existing cluster ensemble methods use a co-association matrix to summarize the co-occurrence statistics of the instance-cluster, where the relationship in the integration is only encapsulated at a rough level. Moreover, the relationship among clusters is completely ignored. Finding these missing associations could greatly expand the ability of cluster ensemble methods for cancer subtyping. In this paper, we propose the RWCE (Random Walk based Cluster Ensemble) to consider similarity among clusters. We first obtained a refined similarity between clusters by using random walk and a scaled exponential similarity kernel. Then, after being modeled as a bipartite graph, a more informative instance-cluster association matrix filled with the aforementioned cluster similarity was fed into a spectral clustering algorithm to get the final clustering result. We applied our method on six cancer types from The Cancer Genome Atlas (TCGA) and breast cancer from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Experimental results show that our method is competitive against existing methods. Further case study demonstrates that our method has the potential to find subtypes with clinical and biological significance.

MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.

BACKGROUND: Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. METHODS: We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. RESULTS: We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. CONCLUSIONS: We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation's effect on the transcriptional network, but also assesses the differential expression's effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

Neuroprotective Effects of Ginsenoside Rf on Amyloid-ß-Induced Neurotoxicity in vitro and in vivo.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by the deposition of amyloid-ß peptides (Aß). Aß accumulation leads to the formation of neurofibrillary tangles, inflammation, axonal injury, synapse loss, and neuronal apoptosis. Thus, reducing Aß levels should exert a neuroprotective effect against AD. Ginsenoside Rf, an extract from Panax notoginseng, has potent anti-fatigue, anti-nociception, anti-oxidation, and anti-inflammation properties. However, it is unclear whether ginsenoside Rf is effective in the treatment of AD. Here, we reported that ginsenoside Rf could significantly attenuate Aß-induced apoptosis in N2A cells, as reflected by a dramatic increase in mitochondrial membrane potential and decrease in Ca2 + concentration, reactive oxygen species, and active caspase-3 expression. Meanwhile, ginsenoside Rf could alleviate the Aß-induced inflammation reaction, such as the decrease of interferon-gamma (IFN-γ) and active caspase-1 expression and the increase of interleukin-13. Furthermore, we also found that Rf is able to accelerate Aß clearance and subsequently reduces Aß level in N2A cells stably transfected with human Swedish mutant APP695 (N2A-APP). More importantly, daily Rf treatment (20 mg/kg, i.p.) throughout the experiment dramatically improved spatial learning and memory in Aß42-induced mouse model of AD. Taken together, these results indicate that ginsenoside Rf may decrease Aß-induced neurotoxicity and memory decline via anti-inflammatory response during AD development, suggesting that Rf may be a potential therapeutic agent for treating AD.

A Novel MicroRNA-124/PTPN1 Signal Pathway Mediates Synaptic and Memory Deficits in Alzheimer's Disease.

BACKGROUND: Synaptic loss is an early pathological event in Alzheimer's disease (AD), but its underlying molecular mechanisms remain largely unknown. Recently, microRNAs (miRNAs) have emerged as important modulators of synaptic function and memory. METHODS: We used miRNA array and quantitative polymerase chain reaction to examine the alteration of miRNAs in AD mice and patients as well as the Morris water maze to evaluate learning and memory in the mice. We also used adeno-associated virus or lentivirus to introduce tyrosine-protein phosphatase non-receptor type 1 (PTPN1) expression of silencing RNAs. Long-term potentiation and Golgi staining were used to evaluate the synaptic function and structure. We designed a peptide to interrupt miR-124/PTPN1 interaction. RESULTS: Here we report that neuronal miR-124 is dramatically increased in the hippocampus of Tg2576 mice, a recognized AD mouse model. Similar changes were observed in specific brain regions of affected AD individuals. We further identified PTPN1 as a direct target of miR-124. Overexpression of miR-124 or knockdown of PTPN1 recapitulated AD-like phenotypes in mice, including deficits in synaptic transmission and plasticity as well as memory by impairing the glutamate receptor 2 membrane insertion. Most importantly, rebuilding the miR-124/PTPN1 pathway by suppression of miR-124, overexpression of PTPN1, or application of a peptide that disrupts the miR-124/PTPN1 interaction could restore synaptic failure and memory deficits. CONCLUSIONS: Taken together, these results identified the miR-124/PTPN1 pathway as a critical mediator of synaptic dysfunction and memory loss in AD, and the miR-124/PTPN1 pathway could be considered as a promising novel therapeutic target for AD patients.

A detection method of vegetable oils in edible blended oil based on three-dimensional fluorescence spectroscopy technique.

Edible blended vegetable oils are made from two or more refined oils. Blended oils can provide a wider range of essential fatty acids than single vegetable oils, which helps support good nutrition. Nutritional components in blended oils are related to the type and content of vegetable oils used, and a new, more accurate, method is proposed to identify and quantify the vegetable oils present using cluster analysis and a Quasi-Monte Carlo integral. Three-dimensional fluorescence spectra were obtained at 250-400nm (excitation) and 260-750nm (emission). Mixtures of sunflower, soybean and peanut oils were used as typical examples to validate the effectiveness of the method.

Determination of Component Contents of Blend Oil Based on Characteristics Peak Value Integration.

Edible blend oil market is confused at present. It has some problems such as confusing concepts, randomly named, shoddy and especially the fuzzy standard of compositions and ratios in blend oil. The national standard fails to come on time after eight years. The basic reason is the lack of qualitative and quantitative detection of vegetable oils in blend oil. Edible blend oil is mixed by different vegetable oils according to a certain proportion. Its nutrition is rich. Blend oil is eaten frequently in daily life. Different vegetable oil contains a certain components. The mixed vegetable oil can make full use of their nutrients and make the nutrients more balanced in blend oil. It is conducive to people's health. It is an effectively way to monitor blend oil market by the accurate determination of single vegetable oil content in blend oil. The types of blend oil are known, so we only need for accurate determination of its content. Three dimensional fluorescence spectra are used for the contents in blend oil. A new method of data processing is proposed with calculation of characteristics peak value integration in chosen characteristic area based on Quasi-Monte Carlo method, combined with Neural network method to solve nonlinear equations to obtain single vegetable oil content in blend oil. Peanut oil, soybean oil and sunflower oil are used as research object to reconcile into edible blend oil, with single oil regarded whole, not considered each oil's components. Recovery rates of 10 configurations of edible harmonic oil is measured to verify the validity of the method of characteristics peak value integration. An effective method is provided to detect components content of complex mixture in high sensitivity. Accuracy of recovery rats is increased, compared the common method of solution of linear equations used to detect components content of mixture. It can be used in the testing of kinds and content of edible vegetable oil in blend oil for the food quality detection, and provide an effective reference for the creation of national standards.

[Research on Spectroscopy Spectrum De-Noising of Mineral Oil Based on Lifting Scheme Wavelet Transform].

The use of the mineral oil is an important cause of air pollution such as fog. The effectiveness and rapidity of the de-noising processing in mineral oil fluorescence spectroscopy detection system is a hot issue of the online real-time monitoring system. The de-noising method of the lifting wavelet transform (LWT) in the application of mineral oil fluorescence spectrum is proposed. Compared with traditional discrete wavelet transform (DWT), this wavelet transform method decomposes the existing wavelet filter module into the basic construction modules and steps to complete the transform with simplicity and a fast speed. There are characteristics of low computational complexity, in situ operation and the easy implement in the denoising process of mineral oil fluorescence spectra. The LWT can effectively solve the problems in these respects. The three methods of LWT, DWT and EMD are applied to the fluorescence spectra of 0# diesel oil, 97# gasoline and kerosene. The indicators evaluating de-noising effect such as the Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE) and Normalied Correlation Coefficient (NCC) of the three kinds of mineral oil in the fluorescence spectra denoising prove the effectiveness of the lifting scheme wavelet transform in the application of mineral oil fluorescence spectrum. Meanwhile, the lifting scheme transform can improve the flexibility of structure and operation simplicity that makes the de-noising time reduced by 62%, validating the speediness of the de-noising method of the LWT in the application of mineral oil fluorescence spectrum and it is suitable for mineral oil fast de-noising processing system in real time.

[The Fluorescence Detection of Oil Pollutants Based on Self-Weighted Alternating Trilinear Decomposition].

The oil pollutants detector is designed in this paper. The pulse xenon lamp is used as light source; the step type multi-mode pure silica fiber is chosen to transmit the excitation and emission light. The asymmetric Czemy-Turner light path of high precision grating monochromator is adopted. The detector is applied to determine the fluorescence spectrum of diesel, gasoline and kerosene. The optimal excitation /emission wavelengths are: 290/330 nm (diesel)，270/300 nm (gasoline) and 280/330 nm (kerosene). The detection limits are: diesel (0.025 mg·L-1), gasoline (0.042 mg·L-1) and kerosene(0.054 mg·L-1). The relative errors are: diesel(2.55%), gasoline(2.06%) and kerosene(1.71%). Experiment results show that the designed detector has high accuracy of measurement. The different concentration of diesel, gasoline and kerosene mixed solution is configured, and three dimensional fluorescence spectra being measured. The self-weighted alternating trilinear decomposition is adopted to decompose the spectrum data. The predicted concentration and recovery rate show that self-weighted alternating trilinear decomposition has high resolution for mixed oil substance.

[Study on Refined Oil Identification and Measurement Based on the Extension Neural Network Pattern Recognition].

There are four major problems related to fuel consumption, "large consumption", "low quality", "lack of front-end clean" and "lack of end emission control", which needs to address urgently for our country. More than 60 percent of the air pollution is due to the burning of coal and oil in our country, so the haze problem depends on how much we can deal with energy issues. We should achieve the identification and measurement of gasoline, diesel, kerosene and other refined oil products rapidly and accurately, which is important for the implementation of air pollution monitoring and controlling. in order to characterize the type information of the refined oil accurately and to improve the efficiency of the network model identification, it is effective to use principal component analysis method which could achieve the data dimension reductionwhile reducing the complexity of the problem. With principal component analysis of the most commonly used three-dimensional fluorescence spectra based on excitation-emission matrix (Excitation-Emission Matrix, EEM) data, we could obtain finer, deeper characteristic parameters. During the process of classification, it could avoid the "over-fitting" phenomenon because of the application of the cross-validation method, A neural network capable of both qualitative and quantitative analysis is designed. The neural network pattern recognition result becomes feedback to the input of the concentration network, together with the relative slope, the comprehensive background parameters, and the relative fluorescence intensity, we could achieve the measurement of the concentration of the corresponding types, then use the extension neural network pattern recognition technology to achieve identification and measurement of kerosene, diesel, gasoline and other refined oil products. The results of the study show that the average recognition rate reaches 0.99, the average recovery rate of concentration reaches 0.95, the average time of pattern recognition is 2.5 seconds and this time is 48.5% of the time used by PARAFAC model analysis method. The method significantly improves the operation speed with ideal application effect . It should be pointed out that, in order to ensure the accuracy and precision of the analysis, we should make corresponding calibration samples for specific analytes in terms of the analysis of complex mixtures such as refined oil, pesticides, tea, etc.

[A De-Noising Algorithm for Fluorescence Detection Signal of Mineral Oil in Water by SWT].

Fluorescence analysis is an important means of detecting mineral oil in water pollutants because of high sensitivity, selectivity, ease of design, etc. Noise generated from Photo detector will affect the sensitivity of fluorescence detection system, so the elimination of fluorescence signal noise has been a hot issue. For the fluorescence signal, due to the length increase of the branch set, it produces some boundary issues. The dbN wavelet family can flexibly balance the border issues, retain the useful signals and get. rid of noise, the de-noising effects of dbN families are compared, the db7 wavelet is chosen as the optimal wavelet. The noisy fluorescence signal is statically decomposed into 5 levels via db7 wavelet, and the thresholds are chosen adaptively based on the wavelet entropy theory. The pure fluorescence signal is obtained after the approximation coefficients and detail coefficients quantified by thresholds reconstructed. Compared with the DWT, the signal de-noised via SWT has the advantage of information integrity and time translation invariance.