No abstract

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.

Background: The relationship between triglyceride-glucose index (TyG index) and the prevalence and prognosis of cardiovascular disease has been confirmed by former studies. However, it remains uncertain whether TyG index has a prognostic impact in patients with type 2 diabetes mellitus (T2DM) and non-ST-segment elevation acute coronary syndrome (NSTE-ACS) undergoing percutaneous coronary intervention (PCI). Methods: The study retrospectively enrolled 798 patients (mean age: 60.9 ± 8.3 years; 68.3% men) with T2DM and NSTE-ACS who underwent PCI at Beijing Anzhen Hospital from January to December 2015. TyG index was calculated as previously reported: ln [fasting TGs (mg/dL) * FBG (mg/dL)/2]. The primary endpoint was a composite of adverse events as follows: all-cause death, non-fatal myocardial infarction (MI) and ischemia-driven revascularization. Results: TyG index was significantly higher in patients with a primary endpoint event compared with those without. Multivariate Cox proportional hazards analysis showed that 1-unit increase of TyG index was independently associated with higher risk of primary endpoint, independent of other risk factors [hazard ratio (HR) 3.208 per 1-unit increase, 95% confidence interval (CI) 2.400-4.289, P < 0.001]. The addition of TyG index to a baseline risk model had an incremental effect on the predictive value for adverse prognosis [AUC: baseline risk model, 0.800 vs. baseline risk model + TyG index, 0.856, P for comparison < 0.001; category-free net reclassification improvement (NRI) 0.346, P < 0.001; integrated discrimination improvement (IDI) 0.087, P < 0.001]. Conclusions: Increased TyG index is a significant predictor of adverse prognosis in patients with T2DM and NSTE-ACS undergoing PCI. Further studies need to be performed to determine whether interventions for TyG index have a positive impact on improving clinical prognosis.

Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A “gedanken” (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment usingin silicogenome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities ofEscherichia colistrains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor.IMPORTANCEUnderstanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia.

Aim: The triglyceride-glucose index (TyG index) is proposed as a surrogate parameter for insulin resistance (IR) and, when elevated, is related to increased cardiovascular risks. Whether the TyG index is of great value in predicting adverse prognosis for individuals diagnosed with non-ST-segment elevation acute coronary syndrome (NSTE-ACS), who received elective percutaneous coronary intervention (PCI), and without recognized diabetes remains unclear. Methods: Overall, 1,510 subjects diagnosed with NSTE-ACS, who received elective PCI, and without recognized diabetes were enrolled in the current study. All participants received a routine follow-up after discharge. The TyG index was obtained from the following equation: napierian logarithmic (ln) [fasting triglyceride (TG, mg/dL)×fasting blood glucose (FBG, mg/dL)/2]. Adverse cardiovascular events included all-cause death, nonfatal myocardial infarction (MI), nonfatal ischemic stroke, and ischemia-driven revascularization, composite of which was defined as the primary endpoint. Results: Overall, 316 (20.9%) endpoint events were documented during a 48-month follow-up. Despite adjusting for confounding variates, the TyG index remains to be a significant risk predictor for the primary endpoint, with a hazard ratio (HR) [95% confidence interval (CI)] of 2.433 (1.853-3.196) ( P ＜0.001). A significant enhancement on the predictive performance for the primary endpoint emerged when adding the TyG index into a baseline model [area under the receiver-operating characteristic (ROC) curve (AUC), 0.835 for baseline model vs. 0.853 for baseline model＋TyG index, P ＜0.001; net reclassification improvement (NRI), 0.194, P ＜0.001; integrated discrimination improvement (IDI), 0.023, P =0.007]. Conclusions: The TyG index is an independent risk predictor for adverse cardiovascular events in nondiabetic subjects diagnosed with NSTE-ACS and who received elective PCI. Further prospective studies are needed to verify these findings.