Cadmium (Cd) is highly toxic to most organisms, but some rare plant species can hyperaccumulate Cd in aboveground tissues without suffering from toxicity. The mechanism underlying Cd detoxification ...by hyperaccumulators is interesting but unclear.
Here, the heavy metal ATPase 3 (SpHMA3) gene responsible for Cd detoxification was isolated from the Cd/zinc (Zn) hyperaccumulator Sedum plumbizincicola. RNA interference (RNAi)-mediated silencing and overexpression of SpHMA3 were induced to investigate its physiological functions in S. plumbizincicola and a nonhyperaccumulating ecotype of Sedum alfredii.
Heterologous expression of SpHMA3 in Saccharomyces cerevisiae showed Cd-specific transport activity. SpHMA3 was highly expressed in the shoots and the protein was localized to the tonoplast. The SpHMA3-RNAi lines were hypersensitive to Cd but not to Zn, with the growth of shoots and young leaves being severely inhibited by Cd. Overexpressing SpHMA3 in the nonhyperaccumulating ecotype of S. alfredii greatly increased its tolerance to and accumulation of Cd, but not Zn.
These results indicate that elevated expression of the tonoplast-localized SpHMA3 in the shoots plays an essential role in Cd detoxification, which contributes to the maintenance of the normal growth of young leaves of S. plumbizincicola in Cd-contaminated soils.
Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic wavelet, ...observed data band limitation and noise, but it also requires a forward operator that characterizes physical relation between measured data and model parameters. Deep learning methods have been successfully applied to solve geophysical inversion problems recently. It can obtain results with higher resolution compared to traditional inversion methods, but its performance often not fully explored for the lack of adequate labeled data (i.e., well logs) in training process. To alleviate this problem, we propose a semi-supervised learning workflow based on generative adversarial network (GAN) for acoustic impedance inversion. The workflow contains three networks: a generator, a discriminator and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. The benchmark models Marmousi2, SEAM and a field data are used to demonstrate the performance of our method. Results show that impedance predicted by the presented method, due to making use of both labeled and unlabeled data, are better consistent with ground truth than that of conventional deep learning methods.
① and ② denoted utilizing sequence of the two nitrogen source.
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•Vibrio sp. Y1-5 exhibited efficient nitrate and ammonium removal in the single aerobic heterotrophic ...condition.•Nitrogen removal was assimilatory nitrate reduction and ammonium assimilation.•Neither N lost nor greenhouse gas NxO producted in the process.•The strain could tolerate 1600mg/L ammonium with 844.6mg/L ammonium removal.•As a common pathogenic genus, Vibrio sp. Y1-5 was safe in aquatic ecosystems.
An aerobic marine bacterium Vibrio sp. Y1-5 was screened to achieve efficient nitrate and ammonium removal simultaneously and fix nitrogen in cells without N loss. Approximately 98.0% of nitrate (100mg/L) was removed in 48h through assimilatory nitrate reduction and nitrate reductase was detected in the cytoplasm. Instead of nitrification, the strain assimilated ammonium directly, and it could tolerate as high as 1600mg/L ammonium concentration while removing 844.6mg/L. In addition, ammonium assimilation occurred preferentially in the medium containing nitrate and ammonium with a total nitrogen (TN) removal efficiency of 80.4%. The results of nitrogen balance and Fourier infrared spectra illustrated that the removed nitrogen was all transformed to protein or stored as organic nitrogen substances in cells and no N was lost in the process. Toxicological studies with the brine shrimp species Artemia naupliia indicated that Vibrio sp. Y1-5 can be applied in aquatic ecosystems safely.
Manufacturing industrial transfer is an important approach to promote the optimization and upgrading of industry, as well as leads to the spatial response of environmental pollution. In this paper, ...we applied the gravity centre model to measure the changes in pollutant discharge patterns from 2005 to 2015 in the Pan-Yangtze River Delta, and then investigated the drivers of pollutant discharge patterns changes with an improved econometric model which integrated the push–pull theory and STIRPAT random regression model. The results revealed great changes in pollutant discharge patterns due to manufacturing industrial transfer, with the pollution gravity centre and total discharge transferring from east to west. We also found indications of a delayed response between the movement of industry and a corresponding reduction in pollutant emissions. The modelling results suggested that differences in regional economy, technological innovation, industrial upgrading, and environmental regulation influenced observed changes in pollutant discharge patterns. Specifically, regional economic conditions and more stringent environmental regulations can push pollution-intensive industry out of developed areas into developing areas. Meanwhile improved innovation milieu and industrial optimization provided resistance for pollution-intensive industries to stay in developed areas as well as encouraging movement into developing areas.
•We study change, mechanism, and response of pollutant discharge pattern in PYRD.•Pollutant discharge patterns transfer from developed to developing areas.•The pollutant discharge pattern shows spatial correlation with industrial transfer.•Interregional differences drive pollutant discharge transfer.
Aiming at the deep desulfurization of the diesel oil, a comparison of the catalytic effects of several Keggin type POMs, including H
3PW
x
Mo
12−
x
O
40 (
x
=
1, 3, 6), Cs
2.5H
0.5PW
12O
40, and H
...3PW
12O
40, was made, using the solution of DBT in normal octane as simulated diesel oil, H
2O
2 as oxidant, and acetonitrile as extractant. H
3PW
6Mo
6O
40 was found to be the best catalyst, with a desulfurization efficiency of 99.79% or higher. Hence, it is promising for the deep desulfurization of actual ODS process. The role of the main factors affecting the process including temperature, O/S molar ratio, initial sulfur concentration, and catalyst dosage, was investigated, whereby the favourable operating conditions were recommended as
T
=
60
°C, O/S
=
15, and a catalyst dosage of 6.93
g (H
3PW
6Mo
6O
40)/L (simulated diesel). With the aid of GC–MS analysis, sulfone species was confirmed to be the only product after reaction for 150
min. Furthermore, macro-kinetics of the process catalyzed by H
3PW
6Mo
6O
40 was studied, from which the reaction orders were found to be 1.02 to DBT and 0.38 to H
2O
2, and the activation energy of the reaction was found to be 43.3
kJ/mol. Moreover, the catalyst recovered demonstrated almost the same activity as the fresh.
Mitogen-activated protein kinases (MAPKs) plays essential roles in the development, hormone regulation and abiotic stress response of plants. Nevertheless, a comprehensive study on MAPK family ...members has thus far not been performed in Tartary buckwheat.
Here, we identified 16 FtMAPKs in the Fagopyrum tataricum genome. Phylogenetic analysis showed that the FtMAPK family members could be classified into Groups A, B, C and D, in which A, B and C members contain a Thr-Glu-Tyr (TEY) signature motif and Group D members contain a Thr-Asp-Tyr (TDY) signature motif. Promoter cis-acting elements showed that most Pro
contain light response elements, hormone response elements and abiotic stress response elements, and several Pro
have MYB-binding sites, which may be involved in the regulation of flavonoid biosynthesis-related enzyme gene expression. Synteny analysis indicated that FtMAPKs have a variety of biological functions. Protein interaction prediction suggested that MAPKs can interact with proteins involved in development and stress resistance. Correlation analysis further confirmed that most of the FtMAPK genes and transcription factors involved in the stress response have the same expression pattern. The transient transformation of FtMAPK1 significantly increased the antioxidant enzymes activity in Tartary buckwheat leaves. In addition, we also found that FtMAPK1 can respond to salt stress by up-regulating the transcription abundance of downstream genes.
A total of 16 MAPKs were identified in Tartary buckwheat, and the members of the MAPK family containing the TDY motif were found to have expanded. The same subfamily members have relatively conserved gene structures and similar protein motifs. Tissue-specific expression indicated that the expression of all FtMAPK genes varied widely in the roots, stems, leaves and flowers. Most FtMAPKs can regulate the expression of other transcription factors and participate in the abiotic stress response. Our findings comprehensively revealed the FtMAPK gene family and laid a theoretical foundation for the functional characterization of FtMAPKs.
Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a ...large number of repetitive planar structures, the point-only based methods struggle to extract distinctive features from the similar structures, which greatly limits the accuracy of registration. Moreover, the deep learning-based approaches achieve commendable results on public datasets, but they face challenges in generalizing to unseen few-shot datasets with significant domain differences from the training data, and that is especially common in industrial applications where samples are generally scarce. Moreover, existing registration methods can achieve high accuracy on complete point clouds. However, for partial point cloud registration, many methods are incapable of accurately identifying correspondences, making it challenging to estimate precise rigid transformations. This paper introduces a domain-adaptive multimodal feature fusion method for partial point cloud registration in an unsupervised manner, named DAMF-Net, that significantly addresses registration challenges in scenes dominated by repetitive planar structures, and it can generalize well-trained networks on public datasets to unseen few-shot datasets. Specifically, we first introduce a point-guided two-stage multimodal feature fusion module that utilizes the geometric information contained in point clouds to guide the texture information in images for preliminary and supplementary feature fusion. Secondly, we incorporate a gradient-inverse domain-aware module to construct a domain classifier in a generative adversarial manner, weakening the feature extractor’s ability to distinguish between source and target domain samples, thereby achieving generalization across different domains. Experiments on a public dataset and our industrial components dataset demonstrate that our method improves the registration accuracy in specific scenarios with numerous repetitive planar structures and achieves high accuracy on unseen few-shot datasets, compared with the results of state-of-the-art traditional and deep learning-based point cloud registration methods.
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