The wind driven by the intense neutrino emission from a protoneutron star (PNS) is an important site for producing nuclei heavier than the Fe group. Because of certain features in the neutrino ...angular distributions, the so-called fast flavor oscillations may occur very close to the PNS surface, effectively resetting the neutrino luminosities and energy spectra that drive the wind. Using the unoscillated neutrino emission characteristics from two core-collapse supernova simulations representative of relevant progenitors at the lower and higher mass end, we study the potential effects of fast flavor oscillations on neutrino-driven winds and their nucleosynthesis. We find that such oscillations can increase the total mass loss by factors up to ∼1.5-1.7 and lead to significantly more proton-rich conditions. The latter effect can greatly enhance the production of 64Zn and the so-called light p-nuclei 74Se, 78Kr, and 84Sr. Implications for abundances in metal-poor stars, Galactic chemical evolution in general, and isotopic anomalies in meteorites are discussed.
We propose a new neutron-capture site in early metal-poor and metal-free stars of ∼20-30 M that results from proton ingestion in the He shell during late stages of the stars' lives. Most of the ...neutron capture occurs in the first 106 s following proton ingestion when 13C( , n)16O produces neutron densities typical of the intermediate neutron-capture process. This phase may be followed by another lasting 107 s with 17O( , n)20Ne producing much lower neutron densities typical of the slow neutron-capture process. We explore the dependence of the proposed neutron-capture nucleosynthesis on the amount and time of proton ingestion, the initial metallicity, and the ensuing supernova shock. We obtain a range of heavy-element abundance patterns, including those attributed to the slow neutron-capture process or a combination of the slow and rapid neutron-capture processes. Our results can account for the observed ubiquity of heavy elements such as Sr and Ba in the early Galaxy and explain puzzling abundance patterns of these elements in at least some very metal-poor (VMP) stars, including those of the carbon-enhanced varieties. In the latter case, the explanation by the single site proposed here differs from the existing paradigm that attributes various classes of VMP stars to enrichment by multiple different sites.
We present a method to find the stationary solutions for fast flavor oscillations of a homogeneous dense neutrino gas. These solutions correspond to collective precession of all neutrino polarization ...vectors around a fixed axis in the flavor space on average, and are conveniently studied in the co-rotating frame. We show that these solutions can account for the numerical results of explicit evolution calculations, and that even with the simplest assumption of adiabatic evolution, they can provide the average survival probabilities to good approximation. We also discuss improvement of these solutions and their use as estimates of the effects of fast oscillations in astrophysical environments.
In order to effectively solve the problems of high error rate, long time consuming, and low accuracy of feature extraction in current painting image classification methods, a painting image ...classification method based on transfer learning and feature fusion was proposed. The global characteristics of the painting picture, such as color, texture, and form, are extracted. The SIFT method is used to extract the painting’s local features, and the global and local characteristics are normalized and merged. The painting images are preliminarily classified using the result of feature fusion, the deterministic and nondeterministic samples are divided, and the estimated Gaussian model parameters are transferred to the target domain via a transfer learning algorithm to alter the distribution of nondeterministic samples, completing the painting image classification. Experimental results show that the proposed method has a low error rate and low feature extraction time and a high accuracy rate of painting image classification.
Polydimethylsiloxanes (PDMS) foam as one of next‐generation polymer foam materials shows poor surface adhesion and limited functionality, which greatly restricts its potential applications. ...Fabrication of advanced PDMS foam materials with multiple functionalities remains a critical challenge. In this study, unprecedented self‐adhesive PDMS foam materials are reported with worm‐like rough structure and reactive groups for fabricating multifunctional PDMS foam nanocomposites decorated with MXene/cellulose nanofiber (MXene/CNF) interconnected network by a facile silicone foaming and dip‐coating strategy followed by silane surface modification. Interestingly, such self‐adhesive PDMS foam produces strong interfacial adhesion with the hybrid MXene/CNF nano‐coatings. Consequently, the optimized PDMS foam nanocomposites have excellent surface super‐hydrophobicity (water contact angle of ≈159o), tunable electrical conductivity (from 10−8 to 10 S m−1), stable compressive cyclic reliability in both wide‐temperature range (from −20 to 200 oC) and complex environments (acid, sodium, and alkali conditions), outstanding flame resistance (LOI value of >27% and low smoke production rate), good thermal insulating performance and reliable strain sensing in various stress modes and complex environmental conditions. It provides a new route for the rational design and development of advanced PDMS foam nanocomposites with versatile multifunctionalities for various promising applications such as intelligent healthcare monitoring and fire‐safe thermal insulation.
Polydimethylsiloxanes (PDMS) foam usually exhibits poor surface adhesion and limited functionality, restricting the potential applications. Here, self‐adhesive PDMS foams with worm‐like rough structure and reactive groups are fabricated by a facile silicone foaming approach. Decorating with MXene/cellulose nanofiber interconnected network and using silane modification, exceptional multifunctionalities PDMS nanocomposites are prepared, showing versatile applications in thermal insulating and smart sensing fields.
The gut microbiota is found to be strongly associated with atherosclerosis (AS). Resveratrol (RSV) is a natural phytoalexin with anti-AS effects; however, its mechanisms of action remain unclear. ...Therefore, we sought to determine whether the anti-AS effects of RSV were related to changes in the gut microbiota. We found that RSV attenuated trimethylamine-N-oxide (TMAO)-induced AS in ApoE(-/-) mice. Meanwhile, RSV decreased TMAO levels by inhibiting commensal microbial trimethylamine (TMA) production via gut microbiota remodeling in mice. Moreover, RSV increased levels of the genera Lactobacillus and Bifidobacterium, which increased the bile salt hydrolase activity, thereby enhancing bile acid (BA) deconjugation and fecal excretion in C57BL/6J and ApoE(-/-) mice. This was associated with a decrease in ileal BA content, repression of the enterohepatic farnesoid X receptor (FXR)-fibroblast growth factor 15 (FGF15) axis, and increased cholesterol 7a-hydroxylase (CYP7A1) expression and hepatic BA neosynthesis. An FXR antagonist had the same effect on FGF15 and CYP7A1 expression as RSV, while an FXR agonist abolished RSV-induced alterations in FGF15 and CYP7A1 expression. In mice treated with antibiotics, RSV neither decreased TMAO levels nor increased hepatic BA synthesis. Additionally, RSV-induced inhibition of TMAO-caused AS was also markedly abolished by antibiotics. In conclusion, RSV attenuated TMAO-induced AS by decreasing TMAO levels and increasing hepatic BA neosynthesis via gut microbiota remodeling, and the BA neosynthesis was partially mediated through the enterohepatic FXR-FGF15 axis.
Recently, trimethylamine-N-oxide (TMAO) has been identified as a novel and independent risk factor for promoting atherosclerosis (AS) partially through inhibiting hepatic bile acid (BA) synthesis. The gut microbiota plays a key role in the pathophysiology of TMAO-induced AS. Resveratrol (RSV) is a natural phytoalexin with prebiotic benefits. A growing body of evidence supports the hypothesis that phenolic phytochemicals with poor bioavailability are possibly acting primarily through remodeling of the gut microbiota. The current study showed that RSV attenuated TMAO-induced AS by decreasing TMAO levels and increasing hepatic BA neosynthesis via gut microbiota remodeling. And RSV-induced hepatic BA neosynthesis was partially mediated through downregulating the enterohepatic farnesoid X receptor-fibroblast growth factor 15 axis. These results offer new insights into the mechanisms responsible for RSV's anti-AS effects and indicate that the gut microbiota may become an interesting target for pharmacological or dietary interventions to decrease the risk of developing cardiovascular diseases.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and the biology of this cancer remains poorly understood. Recent evidence indicates that long non-coding ...RNAs (lncRNAs) are found to be dysregulated in a variety of cancers, including HCC. Taurine Up-regulated Gene 1 (TUG1), a 7.1-kb lncRNA, recruiting and binding to polycomb repressive complex 2 (PRC2), is found to be disregulated in non-small cell lung carcinoma (NSCLC) and esophageal squamous cell carcinoma (ESCC). However, its clinical significance and potential role in HCC remain unclear.
In this study, expression of TUG1 was analyzed in 77 HCC tissues and matched normal tissues by using quantitative polymerase chain reaction (qPCR). TUG1 expression was up-regulated in HCC tissues and the higher expression of TUG1 was significantly correlated with tumor size and Barcelona Clinic Liver Cancer (BCLC) stage. Moreover, silencing of TUG1 expression inhibited HCC cell proliferation, colony formation, tumorigenicity and induced apoptosis in HCC cell lines. We also found that TUG1 overexpression was induced by nuclear transcription factor SP1 and TUG1 could epigeneticly repress Kruppel-like factor 2 (KLF2) transcription in HCC cells by binding with PRC2 and recruiting it to KLF2 promoter region.
Our results suggest that lncRNA TUG1, as a growth regulator, may serve as a new diagnostic biomarker and therapy target for HCC.
•Mid-to-long term wind and solar joint generation prediction model established.•Copula for extracting nonlinear and non-Gaussian distributed key weather factors.•Long short term memory network to ...construct the prediction model with fewer data.•The proposed method is able to improve prediction accuracy validated by real cases.
The accurate estimation of mid-to-long term wind and photovoltaic power generation is important to the power grid's plan improvement, dispatching optimization, management development, and consumption enhancement. These constitute key factors for the realization of power mutual assistance and complementary dispatch of power generation in the broad area of renewable energy. However, owing to the large time scale of mid-to-long term prediction, the low accuracy of weather prediction, the limited data samples of historical power generation, and the significant difference between power generation prediction and short-term power prediction, short-term power prediction technology cannot be directly copied. Thus, the industry has not established yet an effective approach for mid-to-long term wind and photovoltaic power generation predictions. To solve these problems, this study proposed a method for the mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network to achieve an effective extraction of the key meteorological factors that affect power generation owing to nonlinear effects and tendencies, and to deeply exploit the long-term dependencies and tendencies from the limited available data samples. Therefore, the proposed approach is suitable for mid-to-long term wind and photovoltaic power generation prediction using limited data samples. Firstly, the non-linear effects and tendency correlation measurements of the copula function were used to extract the key meteorological factors that influence wind and photovoltaic power generation. Secondly, independent wind/photovoltaic prediction models were established based on long short term memory network using the best input condition obtained by comparing these models to the persistence model. Additionally, the independent wind/photovoltaic models were further compared to support vector machine model with the optimal input condition. Thirdly, the joint prediction models of wind and photovoltaic power generation based on long short term memory network were established using different inputs. The persistence model and the support vector machine model were used as benchmarks to compare the elicited performances. Finally, the validity and applicability of the proposed approach were extensively evaluated using actual data from wind farms and photovoltaic power stations in China and the United States. The independent and joint power generation prediction results demonstrated that the proposed approach outperforms both the persistence model and the support vector machine model, and can have widespread applicability in limited data sample cases.
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