Adaptive learning for event modeling and pattern classification

It is crucial to detect, characterize and model events of interest in a new propulsion system. As technology advances, the amount of data being generated increases significantly with respect to time. This increase substantially strains our ability to interpret the data at an equivalent rate. It demands efficient methodologies and algorithms in the development of automated event modeling and pattern recognition to detect and characterize events of interest and correlate them to the system performance. The fact that the information required to properly evaluate system performance and health is seldom known in advance further exacerbates this issue. Event modeling and detection is essentially a discovery problem and involves the use of techniques in the pattern classification domain, specifically the use of cluster analysis if a prior information is unknown. In this dissertation, a framework of Adaptive Learning for Event Modeling and Characterization (ALEC) system is proposed to deal with this problem. Within this framework, a wavelet-based hierarchical fuzzy clustering approach which integrates several advanced technologies and overcomes the disadvantages of traditional clustering algorithms is developed to make the implementation of the system effective and computationally efficient. In another separate but related research, a generalized multi-dimensional Gaussian membership function is constructed and formulated to make the fuzzy classification of blade engine damage modes among a group of engines containing historical flight data after Principal Component Analysis (PCA) is applied to reduce the excessive dimensionality. This approach can be effectively used to deal with classification of patterns with overlapping structures in which some patterns fall into more than one classes or categories

Impact of Black Carbon on Surface Ozone in the Yangtze River Delta from 2015 to 2018

From MDPI via Jisc Publications RouterHistory: accepted 2021-05-10, pub-electronic 2021-05-13Publication status: PublishedFunder: National Natural Science Foundation of China; Grant(s): 41805096Funder: National Key Research and Development Program of China; Grant(s): 2016YFA0602001Despite the yearly decline in PM2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements during 2015 to 2018 in Nanjing, China to analyze the daily correlation between black carbon and ozone (CBO), and the distribution of the pollutant concentrations under different levels of CBO. Besides, the diurnal variations of pollutant concentrations and meteorological elements under high positive and negative CBO were discussed and compared. The results show that the percentage of positive CBO had been increasing at the average rate of 7.1%/year, and it was 38.7% in summer on average, nearly twice of that in other seasons (19.2%). The average black carbon (BC), PM2.5 and NO2 under positive CBO was lower than those under negative CBO. It is noticeable that the surface ozone began to ascend when CBO was up to 0.2, with PM2.5 and NO2 decreasing and BC remaining steady. Under negative CBO, pollutant concentrations and meteorological elements showed obvious diurnal variations: BC showed a double-peak pattern and surface ozone, PM2.5, SO2 and CO showed single-peak patterns and NO2 showed a trough from 10:00 to 19:00. Wind speed and visibility showed a single-peak pattern with little seasonal difference. Relative humidity rose first, then it lowered and then it rose. Under positive CBO, the patterns of diurnal variations became less obvious, and some of them even showed no patterns, but just fluctuated at a certain level

Proteomics and network pharmacology of Ganshu Nuodan capsules in the prevention of alcoholic liver disease

IntroductionGanshu Nuodan is a liver-protecting dietary supplement composed of Ganoderma lucidum (G. lucidum) spore powder, Pueraria montana (Lour.) Merr. (P. montana), Salvia miltiorrhiza Bunge (S. miltiorrhiza) and Astragalus membranaceus (Fisch.) Bunge. (A. membranaceus). However, its pharmacodynamic material basis and mechanism of action remain unknown.MethodsA mouse model of acute alcohol liver disease (ALD) induced by intragastric administration of 50% alcohol was used to evaluate the hepatoprotective effect of Ganshu Nuodan. The chemical constituents of Ganshu Nuodan were comprehensively identified by UPLC-QTOF/MS, and then its pharmacodynamic material basis and potential mechanism of action were explored by proteomics and network pharmacology.ResultsGanshu Nuodan could ameliorate acute ALD, which is mainly manifested in the significant reduction of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum and malondialdehyde (MDA) content in liver and the remarkably increase of glutathione (GSH) content and superoxide dismutase (SOD) activity in liver. Totally 76 chemical constituents were identified from Ganshu Nuodan by UPLC-QTOF/MS, including 21 quinones, 18 flavonoids, 11 organic acids, 7 terpenoids, 5 ketones, 4 sterols, 3 coumarins and 7 others. Three key signaling pathways were identified via proteomics studies, namely Arachidonic acid metabolism, Retinol metabolism, and HIF-1 signaling pathway respectively. Combined with network pharmacology and molecular docking, six key targets were subsequently obtained, including Ephx2, Lta4h, Map2k1, Stat3, Mtor and Dgat1. Finally, these six key targets and their related components were verified by molecular docking, which could explain the material basis of the hepatoprotective effect of Ganshu Nuodan.ConclusionGanshu Nuodan can protect acute alcohol-induced liver injury in mice by inhibiting oxidative stress, lipid accumulation and apoptosis. Our study provides a scientific basis for the hepatoprotective effect of Ganshu Nuodan in acute ALD mice and supports its traditional application

Seasonal succession of phytoplankton functional groups in Lake Fuxian and its driving factors

The concept of phytoplankton functional groups was proposed based on data from numerous European lakes and has been widely used in lakes, reservoirs, rivers worldwide. However, the application of this concept to subtropical plateau lakes has rarely been reported. In this study, 16 sampling sites were selected across the entirety of Lake Fuxian, Yunnan, China. Eighteen phytoplankton functional groups (F, G, J, X2, X1,T, P, MP, D, C, H1, LO, S1, M, Y, E, W1 and W2) were classified according to the investigation of surface water and gradient depth samples. Nine of these groups, namely LO, H1, C, MP, P, T, X1, J and F, were identified as dominant species (>5% total biomass). Furthermore, LO, H1 and T were considered predominant (accounting for the maximum percentage of biomass in each month). The sampling showed that the seasonal succession of predominant assemblages in surface water was T (October) to H1 (January) to H1 (April) to Lo (July) and T+Lo (October) to T (January) to H1 (April) to Lo (July) in the gradient depth water. Redundancy analysis (RDA) combined with the indicator function of the phytoplankton groups suggested that WT and TN/TP were important factors in driving the succession of predominant assemblages all year around.The concept of phytoplankton functional groups was well applicable in our study of Lake Fuxian and it was suggested that predominant assemblages succession were acting as T (October) to H1 (January) to H1 (April) to Lo (July). Furthermore, according to RDA analysis, it was indicated that WT and TN/TP might be key driving factors in the phytoplankton changes of Lake Fuxian

SCD1 Confers Temozolomide Resistance to Human Glioma Cells via the Akt/GSK3β/β-Catenin Signaling Axis

Resistance to temozolomide (TMZ), the standard chemotherapy agent for glioblastoma (GBM), poses a major clinical challenge to GBM prognosis. Understanding the mechanisms of TMZ resistance can help to identify novel drug targets and more effective therapies. Recent studies suggest that bioenergetic alterations of cancer cells play important roles in drug resistance. In our study, the altered metabolism of cancer cells was observed using a metabolic PCR array. We found that stearoyl-coenzyme A desaturase 1 (SCD1), a key rate-limiting enzyme for synthesis of monounsaturated fatty acids, was significantly upregulated in TMZ-resistant GBM cells compared to their parental counterparts. Overexpression of SCD1 promoted resistance to TMZ in parental GBM cells, whereas SCD1 downregulation by siRNA could re-sensitize TMZ-resistant cells in vitro. Combinational treatment of TMZ and an SCD1-specific inhibitor showed a combined inhibitory effect on TMZ-resistant glioma cells. We also observed that overexpression of SCD1 promoted Akt/GSK3β/β-catenin signaling, while silencing of SCD1 inhibited the signaling. The combination of an Akt activator with exogenous SCD1 or the combined inhibition of Akt and enforced expression of SCD1 resulted in the most significant changes of Akt signaling. Functionally, significantly lower viability and mobility rates were observed in TMZ-resistant cells when treated with Akt inhibitors and an SCD1 inhibitor simultaneously compared to when treated individually. In conclusion, our study identified SCD1 along with its functional pathway as a novel target in the development of TMZ resistance. SCD1 inhibition used alone or in combination with Akt inhibition could effectively overcome TMZ resistance in gliomas

Lanostane Triterpenoids and Ergostane Steroids from <i>Ganoderma luteomarginatum</i> and Their Cytotoxicity

Macrofungus Ganoderma luteomarginatum is one of the main species of Ganoderma fungi distributed in Hainan province of China, the fruiting bodies of which have been widely used in folk as a healthy food to prevent tumors. To explore the potential cytotoxic constituents from G. luteomarginatum, the phytochemical investigation on the ethyl acetate soluble fraction of 95% ethanolic extract from the fruiting bodies of this fungus led to the isolation of twenty-six lanostane triterpenoids (1–26), including three undescribed ones (1–3), together with eight ergostane steroids (27–34). The structures of three new lanostane triterpenoids were elucidated as lanosta-7,9(11)-dien-3β-acetyloxy-24,25-diol (1), lanosta-7,9(11)-dien-3-oxo-24,26-diol-25-methoxy (2), and lanosta-8,20(22)-dien-3,11,23-trioxo-7β,15β-diol-26-oic acid methyl ester (3) by the analysis of 1D, 2D NMR, and HRESIMS spectroscopic data. All isolates were assayed for their cytotoxic activities using three human cancer cell lines (K562, BEL-7402, and SGC-7901) and seven lanostane triterpenoids (1, 2, 7, 13, 18, 22, and 24), and one ergostane steroid (34) showed definite cytotoxicity with IC50 values that ranged from 6.64 to 47.63 μg/mL. Among these cytotoxic lanostane triterpenoids, compounds 2 and 13 showed general cytotoxicity against three human cancer cell lines, while compounds 1 and 18 exhibited significant selective cytotoxicity against K562 cells with IC50 values of 8.59 and 8.82 μg/mL, respectively. Furthermore, the preliminary structure–cytotoxicity relationships was proposed

Clinicopathological characteristics and cancer-specific prognosis of primary pulmonary lymphoepithelioma-like carcinoma: a population study of the US SEER database and a Chinese hospital

IntroductionPrimary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare histological type of non-small cell lung cancer (NSCLC), which accounts for less than 1% of NSCLC. Currently, there is no well-recognized treatment guideline for PPLELC.MethodsWe identified PPLELC patients from the Surveillance, Epidemiology, and End Results (SEER) dataset between 2000 and 2015 (n = 72) as well as from our medical center between 2014 and 2020 (n = 16). All diagnoses were confirmed by pathological testing, and the clinicopathological characteristics of patients were retrieved and summarized. Survival analyses were conducted using the Kaplan–Meier analysis and log-rank tests. Multivariate survival analysis was performed with the Cox regression hazards model.ResultsThe median age at diagnosis of the PPLELC cohort was 64 years, ranging from 15 to 86 years. The percentages of patients with TNM stages I, II, III, and IV were 52.3%, 10.2%, 20.5%, and 17.0%, respectively. Among the 88 cases, lesion resection was performed in 69 cases (78.4%), 16 cases (18.1%) received beam radiation, and 40 cases (45.5%) underwent chemotherapy. In the SEER dataset of lung cancer, the percentage of PPLELC in the Asian race (0.528‰) was almost 10 times higher than that in the white (0.065‰) and black (0.056‰) races. Patients with TNM stage III–IV exhibited a worse prognosis than those with TNM stage I–II (p = 0.008), with a 5-year cancer-specific survival (CSS) rate of 81.8% for TNM stage I–II and 56.2% for TNM stage III–IV. Specifically, the N stage and M stage were the leading prognostic factors, not the T stage and tumor size. Moreover, patients who underwent surgery had significantly better outcomes than those who did not (p = 0.014). Additional multivariate analysis indicated that the TNM stage was an independent prognosis factor for CSS (HR, 3.31; 95% CI, 1.08–10.14).ConclusionPPLELC is a rare tumor with Asian susceptibility. Although the prognosis of PPLELC is better than that of other subtypes of NSCLC, it remains unsatisfactory for advanced-stage disease. The current treatment options for PPLELC include surgical resection, chemotherapy, radiotherapy, and immune therapy. Among these options, patients with surgical resection have better survival rates in this study. However, large-scale clinical research trials will be necessary to develop effective treatment guidelines for PPLELC

Gene set enrichment analysis for non-monotone association and multiple experimental categories

Background Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. Results For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. Conclusion The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets