Efficient Mining of Partial Periodic Patterns in Time Series Database

Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. Previous studies on periodicity search mainly consider finding full periodic patterns, where every point in time contributes (precisely or approximately) to the periodicity. However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns

Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines

Discovery of differential metabolites is the focus of metabonomics study. It has very important applications in pathogenesis and disease classification. The aim of this work is to identify differential metabolites for classifying the patients with hepatocellular carcinoma, cirrhosis and hepatitis based on metabolic profiling data analyzed by gas chromatography-time of flight mass spectrometry. A two-stage feature selection algorithm, F-SVM, combining F-score in analysis of variance and support vector machine (SVM), was applied in discovering discriminative metabolites for three different types of liver diseases. The results show that the accuracy rate of the double cross-validation was 73.68 +/- 2.98%. 22 important differential metabolites selected by F-SVM were identified and related pathophysiological process of liver diseases was set forth. We conclude that F-SVM is quite feasible to be applied in the selection of biologically relevant features in metabonomics

Noralashinol A, a new norlignan from stem barks of <i>Syringa pinnatifolia</i>

<p>One new norlignan, namely noralashinol A (<b>1</b>), one known analogue (<b>2</b>), together with seven known lignans (<b>3</b>–<b>9</b>) were isolated from the stem barks of <i>Syringa pinnatifolia</i>. Their structures were elucidated extensively by spectroscopic methods, including mass spectrometry and 1D and 2D NMR spectroscopies. Compound <b>8</b> significantly inhibited NO production in LPS-induced BV-2 murine microglia cells with its IC₅₀ value of 20.7 μM, compared to a positive control quercetin with its IC₅₀ value of 15.3 μM.</p

Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer

In-depth delineation of lipid metabolism in prostate cancer (PCa) is significant to open new insights into prostate tumorigenesis and progression, and provide potential biomarkers with greater accuracy for improved diagnosis. Here, we performed lipidomics and transcriptomics in paired prostate cancer tumor (PCT) and adjacent nontumor (ANT) tissues, followed by external validation of biomarker candidates. We identified major dysregulated pathways involving lipogenesis, lipid uptake and phospholipids remodeling, correlated with widespread lipid accumulation and lipid compositional reprogramming in PCa. Specifically, cholesteryl esters (CEs) were most prominently accumulated in PCa, and significantly associated with cancer progression and metastasis. We showed that overexpressed scavenger receptor class B type I (SR-BI) may contribute to CEs accumulation. In discovery set, CEs robustly differentiated PCa from nontumor (area under curve (AUC) of receiver operating characteristics (ROC), 0.90-0.94). In validation set, CEs potently distinguished PCa and non-malignance (AUC, 0.84-0.91), and discriminated PCa and benign prostatic hyperplasia (BPH) (AUC, 0.90-0.96), superior to serum prostate-specific antigen (PSA) (AUC = 0.83). Cholesteryl oleate showed highest AUCs in distinguishing PCa from non-malignance or BPH (AUC = 0.91 and 0.96). Collectively, our results unravel the major lipid metabolic aberrations in PCa and imply the potential role of CEs, particularly, cholesteryl oleate, as molecular biomarker for PCa detection

Analysis of Urinary Metabolic Signatures of Early Hepatocellular Carcinoma Recurrence after Surgical Removal Using Gas Chromatography–Mass Spectrometry

The objective of present study was to offer insights into the metabolic responses of hepatocellular carcinoma (HCC) to surgical resection and the metabolic signatures latent in early HCC recurrence (one year after operation). Urinary metabolic profiling employing gas chromatography time-of-flight mass spectrometry (GC-TOF MS) was utilized to investigate the complex physiopathologic regulations in HCC after operational intervention. It was revealed that an intricate series of metabolic regulations including energy metabolism, amino acid metabolism, nucleoside metabolism, tricarboxylic acid (TCA) cycle, gut floral metabolism, etc., principally leading to the direction of biomass synthesis, could be observed after tumor surgical removal. Moreover, metabolic differences between recurrent and nonrecurrent patients had emerged 7 days after initial operation. The metabolic signatures of HCC recurrence principally comprised notable up-regulations of lactate excretion, succinate production, purine and pyrimidine nucleosides turnover, glycine, serine and threonine metabolism, aromatic amino acid turnover, cysteine and methionine metabolism, and glyoxylate metabolism, similar to metabolic behaviors of HCC burden. Sixteen metabolites were found to be significantly increased in the recurrent patients compared with those in nonrecurrent patients and healthy controls. Five metabolites (ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose) were further defined; they were favorable to the prediction of early recurrence