Molecular Defensive Mechanism of Echinacea purpurea (L.) Moench against PAH Contaminations.

The understanding of the molecular defensive mechanism of Echinacea purpurea (L.) Moench against polycyclic aromatic hydrocarbon (PAH) contamination plays a key role in the further improvement of phytoremediation efficiency. Here, the responses of E. purpurea to a defined mixture of phenanthrene (PHE) and pyrene (PYR) at different concentrations or a natural mixture from an oilfield site with a history of several decades were studied based on transcriptomics sequencing and widely targeted metabolomics approaches. The results showed that upon 60-day PAH exposure, the growth of E. purpurea in terms of biomass (p < 0.01) and leaf area per plant (p < 0.05) was negatively correlated with total PAH concentration and significantly reduced at high PAH level. The majority of genes were switched on and metabolites were accumulated after exposure to PHE + PYR, but a larger set of genes (3964) or metabolites (208) showed a response to a natural PAH mixture in E. purpurea. The expression of genes involved in the pathways, such as chlorophyll cycle and degradation, circadian rhythm, jasmonic acid signaling, and starch and sucrose metabolism, was remarkably regulated, enhancing the ability of E. purpurea to adapt to PAH exposure. Tightly associated with transcriptional regulation, metabolites mainly including sugars and secondary metabolites, especially those produced via the phenylpropanoid pathway, such as coumarins, flavonoids, and their derivatives, were increased to fortify the adaptation of E. purpurea to PAH contamination. These results suggest that E. purpurea has a positive defense mechanism against PAHs, which opens new avenues for the research of phytoremediation mechanism and improvement of phytoremediation efficiency via a mechanism-based strategy.

Extensive numerical simulations of surface growth with temporally correlated noise.

Surface growth processes can be significantly affected by long-range temporal correlations. In this work, we perform extensive numerical simulations of a (1+1)- and (2+1)-dimensional ballistic deposition (BD) model driven by temporally correlated noise, which is regarded as the temporal correlated Kardar-Parisi-Zhang universality class. Our results are compared with the existing theoretical predictions and numerical simulations. When the temporal correlation exponent is above a certain threshold, BD surfaces develop gradually faceted patterns. We find that the temporal correlated BD system displays nontrivial dynamic properties, and the characteristic roughness exponents satisfy α≃α_{loc}<α_{s} in (1+1) dimensions, which is beyond the existing dynamic scaling classifications.

IE-IQA: Intelligibility Enriched Generalizable No-Reference Image Quality Assessment.

Image quality assessment (IQA) for authentic distortions in the wild is challenging. Though current IQA metrics have achieved decent performance for synthetic distortions, they still cannot be satisfactorily applied to realistic distortions because of the generalization problem. Improving generalization ability is an urgent task to make IQA algorithms serviceable in real-world applications, while relevant research is still rare. Fundamentally, image quality is determined by both distortion degree and intelligibility. However, current IQA metrics mostly focus on the distortion aspect and do not fully investigate the intelligibility, which is crucial for achieving robust quality estimation. Motivated by this, this paper presents a new framework for building highly generalizable image quality model by integrating the intelligibility. We first analyze the relation between intelligibility and image quality. Then we propose a bilateral network to integrate the above two aspects of image quality. During the fusion process, feature selection strategy is further devised to avoid negative transfer. The framework not only catches the conventional distortion features but also integrates intelligibility features properly, based on which a highly generalizable no-reference image quality model is achieved. Extensive experiments are conducted based on five intelligibility tasks, and the results demonstrate that the proposed approach outperforms the state-of-the-art metrics, and the intelligibility task consistently improves metric performance and generalization ability.