Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.
Measurements such as flow rates from a chemical process violate conservation laws and other process constraints because they are contaminated by random errors and possibly gross errors such as process disturbances, leaks, departures from steady state, and biased instrumentation. Data reconcilation is aimed at estimating the true values of measured variables that are consistent with the constraints, at detecting gross errors, and at solving for unmeasured variables. An approach to constructing sequential principal‐component tests for detecting and identifying persistent gross errors during data reconciliation by combining principal‐component analysis and sequential analysis is presented. The tests detect gross errors as early as possible with fewer measuremennts. They were sharper in detecting and have a substantially greater power in correctly identifying gross errors than the currently used statistical tests in data reconciliation.
The siphon outlet is widely used in pumping stations due to its reliable and convenient cut-off performance. Long siphoning time or high hydraulic loss caused by the inappropriate design of the siphon outlet can significantly affect the safety of stations. The air compressibility volume-of-fluid (VOF) method is conducted to simulate the two-phase flow in the siphoning formation process at the design points selected by the optimal Latin hypercube design (OLHD), the results of which show good agreement with the experimental data. In this work, the siphoning time and hydraulic loss coefficient are selected as the objective functions, and a multi-objective shape optimization strategy is proposed for the siphon outlet in conjunction with the response surface method (RSM). This optimization strategy can not only reconcile conflicting objective functions but also obtain the effect and interaction of design variables. Sensitivity analysis on the constructed response surface models indicates that among three design variables, the aspect ratio has the greatest effect on the objective functions, the descending angle has the second greatest effect, and the ascending angle has almost no effect. Compared with the original design, the hydraulic loss coefficient and siphoning time of the optimized design are reduced by 2.95% and 26.76%, respectively. A higher vorticity magnitude and more uniform outflow are created in the optimized design, which results in the improvement of hydraulic performance.
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