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The objective of this study was to analyze the chemical composition and evaluate the biological capabilities of the essential oils (EOs) extracted from leaves and stems of wild Aeschynomene indica L. plants by the hydrodistillation method. By using GC-FID/MS, fifty-six and fifty-five compounds, representing 95.1 and 97.6% of the essential oils in the leaves and stems, respectively, were characterized. The predominant constituents of A. indica EOs were (E)-caryophyllene, linalool, viridiflorol, phytol, hexadecanoic acid, trans-verbenol, and α-guaiene. The antibacterial and synergistic activities of the EOs were assessed by microdilution and checkerboard assays. The results revealed a potent inhibition and bactericidal activity against Staphylococcus aureus and Bacillus subtilis with MICs of 0.312-0.625 mg/mL. When combined with traditional antibiotics, the essential oils of A. indica possessed excellent synergistic effects against all tested bacteria. Additionally, the EOs of A. indica leaves showed higher antioxidant activity (IC50 = 0.11 ± 0.01 µg/mL) compared to the stem oil (IC50 = 0.19 ± 0.01 µg/mL) using the ABTS radical scavenging assay. The in vitro cytotoxicity of EOs against human cancer cell lines HepG2, MCF-7, A-549, and HCT-116 was examined, and MTT assays showed that the EOs possessed a significant cytotoxic potential against MCF-7 breast cancer cells, with IC50 values of 10.04 ± 1.82 and 15.89 ± 1.66 µg/mL, and a moderate cytotoxic activity against other tested cells. In conclusion, the A. indica EOs could be considered a potential source of pharmacologically active compounds.

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The accurate prediction of fatigue performance is of great engineering significance for the safe and reliable service of components. However, due to the complexity of influencing factors on fatigue behavior and the incomplete understanding of the fatigue failure mechanism, it is difficult to correlate well the influence of various factors on fatigue performance. Machine learning could be used to deal with the association or influence of complex factors due to its good nonlinear approximation and multi-variable learning ability. In this paper, the gradient boosting regression tree model, the long short-term memory model and the polynomial regression model with ridge regularization in machine learning are used to predict the fatigue strength of a nickel-based superalloy GH4169 under different temperatures, stress ratios and fatigue life in the literature. By dividing different training and testing sets, the influence of the composition of data in the training set on the predictive ability of the machine learning method is investigated. The results indicate that the machine learning method shows great potential in the fatigue strength prediction through learning and training limited data, which could provide a new means for the prediction of fatigue performance incorporating complex influencing factors. However, the predicted results are closely related to the data in the training set. More abundant data in the training set is necessary to achieve a better predictive capability of the machine learning model. For example, it is hard to give good predictions for the anomalous data if the anomalous data are absent in the training set.

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Titanium alloys have been widely used in the structural parts of deep-sea equipment and aviation industries. In this paper, the effects of loading frequency and specimen geometry on the high cycle and very high cycle fatigue life of the high strength titanium alloy Ti-6Al-2Sn-2Zr-3Mo-X is investigated by conventional fatigue test and ultrasonic frequency fatigue test. The results indicate that ultrasonic frequency could enhance the fatigue life of the highstrength titanium alloy compared with that under conventional frequency, and the frequency effect is related to the stress amplitude. This phenomenon is explained by the heat generation in specimens and heat dissipation, in combination with the high strain rate leading to the higher yield strength in the ultrasonic fatigue test. Moreover, it is indicated that the effect of specimen geometry on the fatigue life of the highstrength titanium alloy could be evaluated from the view of control volume.

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High-cycle and very-high-cycle fatigue tests via rotary bending (52.5 Hz), electromagnetic resonance (120 Hz) axial cycling, and ultrasonic (20 kHz) axial cycling were performed for a high-strength steel with three heat treatment conditions, and the effects of loading frequency and loading type on fatigue strength and fatigue life were investigated. The results revealed that the loading frequency effect is caused by the combined response of strain rate increase and induced temperature rise. A parameter η was proposed to judge the occurrence of loading frequency effect, and the calculated results were in agreement with the experimental data. In addition, a statistical method based on the control volume was used to reconcile the effect of loading type, and the predicted data were consistent with the experimental results.

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An important application of expression profiles is to stratify patients into high-risk and low-risk groups using limited but key covariates associated with survival outcomes. Prior to that, variables considered to be associated with survival outcomes are selected. A combination of single variables, each of which is significantly related to survival outcomes, is always regarded to be candidates for posterior patient stratification. Instead of individually significant variables, a combination that contains not only significant but also insignificant variables is supposed to be concentrated on. By means of bottom-up enumeration on each pair of variables, we propose a joint covariate detection strategy to select candidates that not only correspond to close association with survival outcomes but also help to make a clear stratification of patients. Experimental results on a publicly available dataset of glioblastoma multiforme indicate that the selected pair composed of an individually significant and an insignificant miRNA keeps a better performance than the combination of significant single variables. The selected miRNA pair is ultimately regarded to be associated with the prognosis of glioblastoma multiforme by further pathway analysis.

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