Antibiotics and antibiotic resistance genes (ARGs) are prevalent in estuarine and coastal environments due to substantial terrestrial input, aquaculture effluent, and sewage discharge. In this ...article, based on peer-reviewed papers, the sources, spatial patterns, driving factors, and environmental implications of antibiotics and ARGs in global estuarine and coastal environments are discussed. Riverine runoff, WWTPs, sewage discharge, and aquaculture, are responsible for the prevalence of antibiotics and ARGs. Geographically, pollution due to antibiotics in low- and middle-income countries is higher than that in high-income countries, and ARGs show remarkable latitudinal variations. The distribution of antibiotics is driven by antibiotic usage and environmental variables (heavy metals, nutrients, organic pollutants, etc.), while ARGs are affected by antibiotics residues, environmental variables, microbial communities, and mobile genetic elements (MGEs). Antibiotics and ARGs alter microbial communities and biogeochemical cycles, as well as pose threats to marine organisms and human health. Our results provide comprehensive insights into the transport and environmental behaviors of antibiotics and ARGs in global estuarine and coastal environments.
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•Antibiotics distribution is driven by antibiotic usage and environmental variables.•Latitudinal variations of ARGs link with environmental variables and MGEs.•ARGs distribution exhibits distance decay law at continental and global scale.•Antibiotics and ARGs interfere element cycling via inhibiting functional bacteria.•Antibiotics and ARGs pose potential health threats to marine organisms and humans.
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an ...acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
•DEM super-resolution (SR) is conducted in the complex area of High Mountain Asia.•Deep learning method with new terrain loss functions is developed for DEM SR.•The proposed method achieves high ...accuracy of DEM during the SR process.•This DEM SR research can be expanded to other complicated terrain areas.
High Mountain Asia (HMA) is characterized by some of the most complex and rugged terrain conditions in the world. However, high resolution terrain data are not easy to quickly acquire from the area due to difficulties in accessing the region. In this study, we trained a modified super-resolution residual network (MSRResNet) to develop super-resolution (SR) digital elevation models (DEMs) in the HMA areas by using freely available DEM data from the HMA region and limited high resolution (HR) DEMs from other areas to train the model. In this network, a new loss function was constructed that considered the terrain parameters of slope and curvature to constrain the network learning and convergence. The proposed method was applied to and validated by data from the Hengduan Mountains in the southeastern part of HMA, which is a world-famous longitudinal belt of mountains and canyons. A comparative analysis between the current and existing methods (i.e., SRGAN and Bicubic interpolation) was conducted to assess the effectiveness of the proposed approach. The experimental results were also investigated and evaluated by visual inspection and analysis of the terrain parameters. The results demonstrate that the proposed MSRResNet super-resolution process can achieve highly accurate terrain data by downscaling DEMs in HMA. This SR process also outperforms the other comparable methods. Compared to the Bicubic interpolation method, the RMSE and MAE accuracy are improved by 32.17% and 33.97%, and compared to the SRGAN method, the RMSE and MAE accuracy are improved by 39.15% and 32.47%. The HR DEM generated by the new method is more conducive to improving the accuracy of extracted terrain features, such as stream networks. It is promising to apply this model on other areas on Earth or even other planets with terrain similar to that of HMA.
A compound system involving three matrices (water, sediment, and paddy soil) was conceived to determine the potential sources of metal(loid)s (Ti, Fe, Co, Ni, Cu, Zn, As, Cd, Pb, and U) and ...synthetically evaluate their pollution levels in the Le’an River basin. The result indicated that the established background values (BVs) of paddy soil and sediment in the compound system were obviously higher than those of the upper continental crust (UCC) and soils from Jiangxi Province, a difference which was especially marked for sediment. The concentrations of Cu, Zn, As, Cd in the system had high coefficients of variation (CVs), and metal(loid)s in sediment showed higher levels than those in paddy soil, except for Pb. Cd and Cu in the system had the highest Ef levels, which probably pose a high risk to organisms and the health of local residents. There were significantly linear relationships between the site rank index (SRI) for water and that for sediment or paddy soil, revealing that matrices in the system interacted with each other. Principal component analysis (PCA) and absolute principal component scores and multiple linear regression model (APCS-MLR) results demonstrated that Cu, Zn, As, Cu, Pb, and U enrichments in the system were mainly affected by mining activities and were predominately deposited in sediment. Point pollution sources rather than non-point pollution sources such as mining activities, contributed most of the anthropogenic metal(loid)s to sediment. Both SRI and Hierarchical cluster analysis (HCA) results visually showed that S5, S8, S9, S10, S11, and S12 severe pollution grouped together and scattered through areas with extensive mining activities, while other sites with moderate pollution were spread along the main stream of the Le’an River.
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•The compound system involving water, sediment and paddy soil was established.•There were latent interactions of metal(loid) among matrices in the compound system.•Cd and Cu enrichment in the compound system likely posed high risk to the organism and human health.•Sediment was the main sink of anthropogenic metal(loid)s mainly contributed by point pollution sources.•Sampling sites in the area with violent mining activities suffered severe pollution.
Main finding: Establishing the compound system is an effective way to evaluate the pollution level in a specific basin. Efs indicate the migration of anthropogenic pollutants in the system.
The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures ...that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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•Bacterial and fungal succession along a reclamation chronosequence were explored.•Coastal reclamation to paddy soils caused biotic homogenization of microbiomes.•Fungal community was ...more affected by homogeneous selection than that of bacteria.•Coastal reclamation simplified and destabilized bacterial and fungal networks.•Fungal network complexity and α-diversity regulated coastal multifunctionality.
Coastal soil microbiomes play a key role in coastal ecosystem functioning and are intensely threatened by land reclamation. However, the impacts of coastal reclamation on soil microbial communities, particularly on their assembly processes, co-occurrence patterns, and the multiple soil functions they support, remain poorly understood. This impedes our capability to comprehensively evaluate the impacts of coastal reclamation on soil microbiomes and to restore coastal ecosystem functions degraded by reclamation. Here, we investigated the temporal dynamics of bacterial and fungal communities, community assembly processes, co-occurrence patterns, and ecosystem multifunctionality along a 53-year chronosequence of paddy soil following reclamation from tidal flats. Reclamation of tidal flats to paddy soils resulted in decreased β-diversity, increased homogeneous selection, and decreased network complexity and robustness of both bacterial and fungal communities, but caused contrasting α-diversity response patterns of them. Reclamation of tidal flats to paddy soils also decreased the multifunctionality of coastal ecosystems, which was largely associated with the fungal network complexity and α-diversity. Collectively, this work demonstrates that coastal reclamation strongly reshaped the soil microbiomes at the level of assembly mechanisms, interaction patterns, and functionality level, and highlights that soil fungal community complexity should be considered as a key factor in restoring coastal ecosystem functions deteriorated by land reclamation.
Investigating the mechanisms that influence the concentration of heavy metals (Cu, Cr, Ni, Zn, Pb, and Cd) and possible sources of these elements is vital in developing lake management strategies and ...conserving lake ecosystems. This is the first systematic research focusing on the content, distribution, and origin of heavy metals in Poyang Lake area, the largest freshwater lake in China. Samples were collected, and the concentration of trace elements was measured. The distribution, sources, and potential ecological risk, which has undergone rapid economic development and intensive anthropogenic activity, was evaluated. Multivariate statistical analyses were carried out to determine the relationship between these trace elements and to identify the possible pollution sources. Assessment methods were carried out by applying the geoaccumulation index (
I
geo
) along with potential ecological risk indices (PERI) and USEPA guidelines. The results show that: (1) in comparison to Chinese Soil Standard I, the study area was polluted with Cd and Cr, which had an average concentration that was higher than the Class I criteria. However, the results showed that the heavy metal concentration of Cd was lower than that in other areas. (2) The correlation analysis indicates that Pb and Cd may potentially have the same pollution source. (3) The geoaccumulation (Igeo) and potential ecological risk index (PERI) of these metals were evaluated. The average pollution degree of Igeo decreased in the following order: Cd > Cr > Ni > Cu > Zn > Pb > As, which is similar to that observed from the EF values. PERI varied between 48.64 and 453.45 for all metals, and the general average was calculated as 113.71. Both Igeo and ER indicate that the study area was polluted by Cd. The results of FA show that 87.90 % of the variance could be explained by three factors and an independent variable. The research results obtained from this study can provide the scientific impetus to create policies for the economic development and environmental protection in Poyang Lake and other areas of the world.
Cadmium (Cd) contamination in paddy soil threatens rice growth and food safety, enriching manganese (Mn) in rice seedlings is expected to reduce Cd uptake by rice. The effects of 250 μM Mn-treated ...seedlings on reducing Cd uptake of four rice genotypes (WYJ21, ZJY1578, HHZ, and HLYSM) planted in 0.61 mg kg−1 Cd-contaminated soil, were studied through the hydroponic and pot experiments. The results showed that the ZJY1578 seedling had the highest Mn level (459 μg plant–1), followed by WYJ21 (309 μg plant–1), and less Mn accumulated in the other genotypes. The relative expression of OsNramp5 (natural resistance-associated macrophage protein) was reduced by 42.7 % in ZJY1578 but increased by 23.3 % in HLYSM. The expressions of OsIRT1 (iron-regulated transporter-like protein) were reduced by 24.0–56.0 % in the four genotypes, with the highest reduction in ZJY1578. Consequently, a greater reduction of Cd occurred in ZJY1578 than that in the other genotypes, i.e., the root and shoot Cd at the tillering were reduced by 27.8 % and 48.5 %, respectively. At the mature stage, total Cd amount and distribution in the shoot and brown rice were also greatly reduced in ZJY1578, but the inhibitory effects were weakened compared to the tillering stage. This study found various responses of Cd uptake and transporters to Mn-treated seedlings among rice genotypes, thus resulting in various Cd reductions. In the future, the microscopic transport processes of Cd within rice should be explored to deeply explain the genotypic variation.
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•High Mn cultivation resulted in various Mn accumulation in different rice genotypes.•Mn induced various inhibitions on OsNramp5 and OsIRT1 expression among rice genotypes.•Cd uptake and transport within rice were depressed variously by Mn among genotypes.•Mn induced a lower Cd reduction at the mature than that at the tillering stage.
A reduced removal of dysfunctional mitochondria is common to aging and age-related neurodegenerative pathologies such as Alzheimer's disease (AD). Strategies for treating such impaired mitophagy ...would benefit from the identification of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds. From a library of naturally occurring compounds, the workflow allowed us to identify 18 small molecules, and among them two potent mitophagy inducers (Kaempferol and Rhapontigenin). In nematode and rodent models of AD, we show that both mitophagy inducers increased the survival and functionality of glutamatergic and cholinergic neurons, abrogated amyloid-β and tau pathologies, and improved the animals' memory. Our findings suggest the existence of a conserved mechanism of memory loss across the AD models, this mechanism being mediated by defective mitophagy. The computational-experimental screening and validation workflow might help uncover potent mitophagy modulators that stimulate neuronal health and brain homeostasis.
Surface water samples were collected from 20 sampling sites throughout the Ganjiang River during pre-monsoon, monsoon, and post-monsoon seasons, and the concentrations of dissolved trace elements ...were determined by inductively coupled plasma-mass spectrometry (ICP-MS) for the spatial and seasonal variations, risk assessment, source identification, and categorization for risk area. The result demonstrated that concentrations of the elements exhibited significant seasonality. The high total element concentrations were detected at sites close to the intensive mining and urban activities. The concentrations of the elements were under the permissible limits as prescribed by related standards with a few exceptions. The most of heavy metal pollution index (HPI) values were lower than the critical index limit, indicating the basically clean water used as habitat for aquatic life. As was identified as the priority pollutant of non-carcinogenic and carcinogenic concerns, and the inhabitants ingesting the surface water at particular site might be subjected to the integrated health risks for exposure to the mixed trace elements. Multivariate statistical analyses confirmed that Zn, As, Cd, and Tl were derived from mining and urban activities; V, Cd, and Pb exhibited mixed origin; and Co, Ni, and Cu mainly resulted from natural processes. Three categorized risk areas corresponded to high, moderate, and low risks, respectively. As a whole, the upstream of the Ganjiang River was identified as the high-risk area relatively.
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