Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Identification and QTL Analysis of Flavonoids and Carotenoids in Tetraploid Roses Based on an Ultra-High-Density Genetic Map

Roses are highly valuable within the flower industry. The metabolites of anthocyanins, flavonols, and carotenoids in rose petals are not only responsible for the various visible petal colors but also important bioactive compounds that are important for human health. In this study, we performed a QTL analysis on pigment contents to locate major loci that determine the flower color traits. An F1 population of tetraploid roses segregating for flower color was used to construct an ultra-high-density genetic linkage map using whole-genome resequencing technology to detect genome-wide SNPs. Previously developed SSR and SNP markers were also utilized to increase the marker density. Thus, a total of 9,259 markers were mapped onto seven linkage groups (LGs). The final length of the integrated map was 1285.11 cM, with an average distance of 0.14 cM between adjacent markers. The contents of anthocyanins, flavonols and carotenoids of the population were assayed to enable QTL analysis. Across the 33 components, 46 QTLs were detected, explaining 11.85–47.72% of the phenotypic variation. The mapped QTLs were physically clustered and primarily distributed on four linkage groups, namely LG2, LG4, LG6, and LG7. These results improve the basis for flower color marker-assisted breeding of tetraploid roses and guide the development of rose products

Associations between microstructural tissue changes, white matter hyperintensity severity, and cognitive impairment: an intravoxel incoherent motion imaging study

IntroductionWhite matter hyperintensities (WMHs) are a common age- and vascular risk factor-related disease and have been recognized to play an important role in cognitive impairment. However, it is still unclear what the mechanism of this effect is. In this study, intravoxel incoherent motion (IVIM) was employed to assess the microvasculature and parenchymal microstructure changes of WMHs and explore their relationship with cognitive function.MethodsForty-nine WMH patients and thirty-one healthy controls underwent IVIM imaging, a diffusion technique that provides parenchymal diffusivity D, intravascular diffusivity D*, and perfusion fraction f . The IVIM dual exponential model parameters were obtained in specific regions of interest, including deep white matter hyperintensities (DWMHs), periventricular white matter hyperintensities (PWMHs), and normal-appearing white matter (NAWM). The independent-sample t-test or Mann–Whitney U-test was utilized to compare IVIM parameters between patients and controls. The Kruskal–Wallis test or one-way analysis of variance was used to compare IVIM parameters among DWMH, PWMH, and NAWM for patients. The Wilcoxon two-sample test or independent-sample t-test was used to assess the differences in IVIM parameters based on the severity of WMH. The multivariate linear regression analysis was conducted to explore the factors influencing cognitive scores.ResultsWMH patients exhibited significantly higher parenchymal diffusivity D than controls in DWMH, PWMH, and NAWM (all p < 0.05). IVIM parameters in the three groups (DWMH, PWMH, and NAWM) were significantly different for patients (all p < 0.001). The severe WMH group had a significantly higher parenchymal diffusivity D (DWMH and PWMH) than mild WMH (both p < 0.05). The multiple linear regression analysis identified D in DWMH and PWMH as influencing cognitive function scores (all p < 0.05).ConclusionIVIM has the potential to provide a quantitative marker of parenchymal diffusivity for assessing the severity of WMH and may serve as a quantitative marker of cognitive dysfunction in WMH patients

Poor-prognosis disclosure preference in cancer patient-caregiver dyads and its association with their quality of life and perceived stress: a cross-sectional survey in mainland China

Background This study attempted to examine the discordance between family caregivers and cancer patients in their poor-prognosis disclosure preferences in mainland China and then ascertained the associations between quality of life (QoL), perceived stress, and poor-prognosis disclosure preferences. Methods Six hundred fifty-one pairs of inpatients and their matched caregivers (participation rate = 92.2%) were recruited in this cross-sectional survey. A set of paired self-administered questionnaires were completed independently by patient–caregiver dyads. Results Fewer family caregivers than cancer patients felt that poor prognosis should be disclosed to patients (61.2% vs. 90.0%, p < 0.001). Patients' positive poor-prognosis disclosure preference was associated with patients' better QoL (p < 0.05) and caregivers' reduced perceived stress levels (p = 0.013). However, caregivers' poor-prognosis disclosure preference correlated only with their own physical state (p = 0.028). Moreover, the caregivers who concurred with patients in positive poor-prognosis disclosure preference were more likely to experience a better QoL (p < 0.05) and lower perceived stress levels (p = 0.048) in the III–IV stage subgroup. Conclusions There was a significant discrepancy in poor-prognosis disclosure preference between cancer patients and caregivers in China. The caregivers' preference of concealing poor prognosis from patients was not related to cancer patients' QoL or perceived stress. In addition, caregivers had better QoL and lower stress levels when they held the same positive poor-prognosis disclosure preference as the patients

PRIMA-1Met suppresses colorectal cancer independent of p53 by targeting MEK

This work was supported by Grant No. 81201779 (Hua Xiong) from the National Natural Science Youth Foundation; Grant No. 81502118 (Yanmei Zou) from the National Natural Science Youth Foundation; Grant No. 2014CFB250 (Yanmei Zou) from the Natural Science Foundation of Hubei Province; Grant No. 81372434 (Huihua Xiong) from the National Natural Science Foundation.PRIMA-1Met is the methylated PRIMA-1 (p53 reactivation and induction of massive apoptosis) and could restore tumor suppressor function of mutant p53 and induce p53 dependent apoptosis in cancer cells harboring mutant p53. However, p53 independent activity of PRIMA-1Met remains elusive. Here we reported that PRIMA-1Met attenuated colorectal cancer cell growth irrespective of p53 status. Kinase profiling revealed that mitogen-activated or extracellular signal-related protein kinase (MEK) might be a potential target of PRIMA-1Met. Pull-down binding and ATP competitive assay showed that PRIMA-1Met directly bound MEK in vitro and in cells. Furthermore, the direct binding sites of PRIMA-1Met were explored by using a computational docking model. Treatment of colorectal cancer cells with PRIMA-1Met inhibited p53-independent phosphorylation of MEK, which in turn impaired anchorage-independent cell growth in vitro. Moreover, PRIMA-1Met suppressed colorectal cancer growth in xenograft mouse model by inhibiting MEK1 activity. Taken together, our findings demonstrate a novel p53-independent activity of PRIMA-1Met to inhibit MEK and suppress colorectal cancer growth.Publisher PDFPeer reviewe

Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease

Coronary heart disease (CHD) is top risk factor for health in modern society, causing high mortality rate each year. However, there is no reliable way for early diagnosis and prevention of CHD so far. So study the mechanism of CHD and development of novel biomarkers is urgently needed. In this study, metabolomics and metagenomics technology are applied to discover new biomarkers from plasma and urine of 59 CHD patients and 43 healthy controls and trace their origin. We identify GlcNAc-6-P which has good diagnostic capability and can be used as potential biomarkers for CHD, together with mannitol and 15 plasma cholines. These identified metabolites show significant correlations with clinical biochemical indexes. Meanwhile, GlcNAc-6-P and mannitol are potential metabolites originated from intestinal microbiota. Association analysis on species and function levels between intestinal microbes and metabolites suggest a close correlation between Clostridium sp. HGF2 and GlcNAc-6-P, Clostridium sp. HGF2, Streptococcus sp. M143, Streptococcus sp. M334 and mannitol. These suggest the metabolic abnormality is significant and gut microbiota dysbiosis happens in CHD patients