Microbial diversity and metabolic pathways linked to benzene degradation in petrochemical-polluted groundwater.

The rapid advance in shotgun metagenome sequencing has enabled us to identify uncultivated functional microorganisms in polluted environments. While aerobic petrochemical-degrading pathways have been extensively studied, the anaerobic mechanisms remain less explored. Here, we conducted a study at a petrochemical-polluted groundwater site in Henan Province, Central China. A total of twelve groundwater monitoring wells were installed to collect groundwater samples. Benzene appeared to be the predominant pollutant, detected in 10 out of 12 samples, with concentrations ranging from 1.4 µg/L to 5,280 µg/L. Due to the low aquifer permeability, pollutant migration occurred slowly, resulting in relatively low benzene concentrations downstream within the heavily polluted area. Deep metagenome sequencing revealed Proteobacteria as the dominant phylum, accounting for over 63 % of total abundances. Microbial α-diversity was low in heavily polluted samples, with community compositions substantially differing from those in lightly polluted samples. dmpK encoding the phenol/toluene 2-monooxygenase was detected across all samples, while the dioxygenase bedC1 was not detected, suggesting that aerobic benzene degradation might occur through monooxygenation. Sequence assembly and binning yielded 350 high-quality metagenome-assembled genomes (MAGs), with 30 MAGs harboring functional genes associated with aerobic or anaerobic benzene degradation. About 80 % of MAGs harboring functional genes associated with anaerobic benzene degradation remained taxonomically unclassified at the genus level, suggesting that our current database coverage of anaerobic benzene-degrading microorganisms is very limited. Furthermore, two genes integral to anaerobic benzene metabolism, i.e, benzoyl-CoA reductase (bamB) and glutaryl-CoA dehydrogenase (acd), were not annotated by metagenome functional analyses but were identified within the MAGs, signifying the importance of integrating both contig-based and MAG-based approaches. Together, our efforts of functional annotation and metagenome binning generate a robust blueprint of microbial functional potentials in petrochemical-polluted groundwater, which is crucial for designing proficient bioremediation strategies.

[The application of molecular biology-based microbial remediation technologies in petroleum polluted environments].

Owing to human activities and industrial production, petroleum pollution has become a serious environmental issue. Microbial remediation technology, characterized by its eco-friendly characteristics, has drawn significant attention in petroleum pollution remediation. The application of molecular biology technology has led to a drastic revolution in microbial remediation technology, providing resources for the development of highly efficient degrading agents. However, limitations such as the lack of precision in species annotation and the limited detection sensitivity still exist. Other microbial remediation technologies also have substantial potential in enhancing the degradation efficiency of petroleum pollutants and reducing their environmental harm, especially biosurfactants and bio-stimulants, which offer relatively shorter remediation periods and lower costs, promising large-scale application in the future. Moreover, the combination of molecular biology and other microbial remediation technologies may become an effective tool for petroleum pollutant degradation. This review summarized the application of molecular biology methods in petroleum polluted environments, reviewed the recent research progress on microbial remediation techniques for petroleum-contaminated sites, discussed the remediation effects of these microbial remediation techniques, and proposed the future development direction of microbial remediation technology.

Microbial functional heterogeneity induced in a petroleum-polluted soil profile.

Microbial taxonomic diversity declines with increasing stress caused by petroleum pollution. However, few studies have tested whether functional diversities vary similarly to taxonomic diversity along the stress gradient. Here, we investigated soil microbial communities in a petrochemically polluted site in China. Total petroleum hydrocarbon (TPH) concentrations were higher in the middle (2-3 m) and deep soil layer (3-5 m) than in the surface soil layer (0-2 m). Accordingly, microbial taxonomic α-diversity was decreased by 44% (p < 0.001) in the middle and deep soil layers, compared to the surface soil layer. In contrast, functional α-diversity decreased by 3% (p < 0.001), showing a much better buffering capacity to environmental stress. Differences in microbial taxonomic and functional ß-diversities were enlarged in the middle and deep soil layers, extending the Anna Karenina Principle (AKP) that a community adapts to stressful environments in its own way. Consistent with the stress gradient hypothesis, we revealed a higher degree of network connectivity among microbial species and genes in the middle and deep soil layers compared to the surface soil layer. Together, we demonstrate that microbial functionality is more tolerant to stress than taxonomy, both of which were amenable to AKP and the stress gradient hypothesis.

Intermediate irrigation with low fertilization promotes soil nutrient cycling and reduces CO2 and CH4 emissions via regulating fungal communities in arid agroecosystems.

The field practices, including irrigation and fertilization, strongly affect greenhouse gas emissions and soil nutrient cycling from agriculture. Understanding the underlying mechanism of greenhouse gas emissions, soil nutrient cycling, and their impact factors (fungal diversity, network characteristics, soil pH, salt, and moisture) is essential for efficiently managing global greenhouse gas mitigation and agricultural production. By considering abundant and rare taxa, we determine the identities and relative importance of ecological processes that modulate the fungal communities and identify whether they are crucial contributors to soil nutrient cycling and greenhouse gas emissions. The research is based on a 4-year field fertigation experiment with low (300 kg/ha P2O5 with 150 kg/ha urea) and high (600 kg/ha P2O5 with 300 kg/ha urea) fertilization level and three irrigation levels, that is, low (200 mm), medium (300 mm), and high (400 mm). The α-diversity (richness and Shannon index) of fungal subcommunities was significantly higher under medium irrigation (300 mm) and low fertilization (300 kg/ha P2O5 with 150 kg/ha urea) than under other treatments. Intermediate irrigation with low fertilization treatment yielded the most significant higher multinutrient cycling index and the lowest CO2 and CH4 emissions. The null model indicated that abundant taxa are mainly regulated by stochastic processes (dispersal limitation), and rare taxa are mainly regulated by environmental selection, especially by soil salinity. The co-occurrence network of rare taxa explained the changes in the entire fungal network stability. The abundant taxa played vital roles in regulating soil nutrient status, owing to the stronger association between their network and multinutrient cycling index. Furthermore, we have confirmed that soil moisture and fungal network stability are crucial factors affecting greenhouse gas emissions. Together, these results provide a deep understanding of the mechanisms that reveal fungal community assembly and soil fungal-driven variations in nutrient status and network stability, link fungal network characteristics to ecosystem functions, and reveal the factors that influence greenhouse gas emissions.

Petroleum pollution changes microbial diversity and network complexity of soil profile in an oil refinery.

Introduction: Petroleum pollution resulting from spills and leakages in oil refinery areas has been a significant environmental concern for decades. Despite this, the effects of petroleum pollutants on soil microbial communities and their potential for pollutant biodegradation still required further investigation. Methods: In this study, we collected 75 soil samples from 0 to 5 m depths of 15 soil profiles in an abandoned refinery to analyze the effect of petroleum pollution on soil microbial diversity, community structure, and network co-occurrence patterns. Results: Our results suggested soil microbial a-diversity decreased under high C10-C40 levels, coupled with significant changes in the community structure of soil profiles. However, soil microbial network complexity increased with petroleum pollution levels, suggesting more complex microbial potential interactions. A module specific for methane and methyl oxidation was also found under high C10-C40 levels of the soil profile, indicating stronger methanotrophic and methylotrophic metabolic activities at the heavily polluted soil profile. Discussion: The increased network complexity observed may be due to more metabolic pathways and processes, as well as increased microbial interactions during these processes. These findings highlight the importance of considering both microbial diversity and network complexity in assessing the effects of petroleum pollution on soil ecosystems.

Multitask GANs for Semantic Segmentation and Depth Completion With Cycle Consistency.

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.

Relationship between Renying pulse augmentation index and Cunkou pulse condition in different blood pressure groups.

OBJECTIVE: To explore the relationship between Renying pulse (carotid) augmentation index (AI) and Cunkou pulse condition in different blood pressure groups, and the clinical significance of Renying and Cunkou pulse parameters to reflect vascular function. METHODS: Eighty-six patients with essential hypertension (EH) and 52 individuals with normal blood pressure (control group) between September 2010 and January 2012 were included in this study. Renying pulse AI was examined by a new diagnostic tool (ALOKA ProSound Alpha 10)--wave intensity (WI) that is calculated as the product of the derivatives of the simultaneously recorded blood pressure changes (dP/dt) and blood-flow-velocity changes (dU/dt), while Cunkou pulse condition was detected by DDMX-100 Pulse Apparatus in both EH and control groups. A multifactorial correlation analysis was performed for data analysis. RESULTS: After adjusting for potential confounding variables, in the EH group, AI was positively correlated with t5, w2/t (r(t5) = 0.225, P < 0.05; r(w2/t) = 0.230, P < 0.05) and negatively correlated with h5, h5/h1 and w2 (r(h5) = -0.393, P < 0.01; r(h5)/h1) = -0.444, P < 0.01; r(w2) = -0.389, P < 0.01). In the control group, AI was positively correlated with t3, t4, t5 and w1 (r(t3) = 0.595, P < 0.01; r(t4) = 0.292, P < 0.05; r(t5) = 0.318, P < 0.05; r(w1) = 0.541, P < 0.01) and negatively correlated with h1, h2, h3, Ad and A (r(h1) = -0.368, P < 0.05; r(h2) = -0.330, P < 0.05; r(h3) = -0.327, P < 0.05; rAd = -0.322, P < 0.05; rA = -0.410, P < 0.01). In the total sample group (EH plus control group, n = 138), AI was positively correlated with t, t5, w1 and w2t (r(t) = 0.257, P < 0.01; r(t5) = 0.266, P < 0.01; r(w1) = 0.184, P < 0.05; r(w2/t) = 0.210, P < 0.05) and negatively correlated with h5, h5/h1, w2 and Ad (r(h5) = -0.230, P < 0.01; r(h5/h1) = -0.218, P < 0.05; r(w2) = -0.267, P < 0.01; rAd = -0.246, P < 0.01). Multiple linear regression analysis was carried out to model the relationship (F = 7.887, P < 0.001). CONCLUSION: Renying pulse AI can effectively predict arterial stiffness in synchrony with the manifestations of Cunkou pulse in elderly patients with hypertension. Cunkou pulse apparatus is a valuable tool for evaluating AI in clinical practice. The close correlations reported above reflect the holistic concept of Traditional Chinese Medicine.

Competitive adsorption and assembly of block copolymer blends on nanopatterned surfaces.

By employing off-lattice Monte Carlo simulations, the competitive adsorption and assembly of block copolymer blends on a nanopatterned surface were investigated. The segment distributions and polymer configurations are examined by varying the chemical structures of polymers, the interactions between segments and adsorbing stripe domains of the nanopatterned surface, and the width of stripe domains in the nanopatterned surface. The simulation results show that by modulating the affinities between a copolymer and the adsorbing stripe domain, one can adjust the density distributions and adsorption properties of block copolymer blends. With decorating the chemical structure of a surface, the targeted molecules would be actively recognized and separated. This offers a versatile way for novel separation materials and for the fabrication of nanomaterials.

Adsorption of copolymers in a selective nanoslit: a hybrid density functional theory.

A hybrid density functional theory (DFT) is developed for adsorption of copolymers in a selective nanoslit. The DFT incorporates a single-chain simulation for the ideal-gas free energy functional with two weighted density approximations for the residual free energy functional. The theory is found to be insensitive to the width parameter used in the weighted density. Theoretical predictions are in excellent agreement with simulation results in the segment density profiles and the adsorption configurations including tail, loop, and train for copolymers with various sequences over a wide range of surface affinity. The bridge conformation is also observed in multiblock copolymers. Ordered assembly is facilitated in copolymers with longer chain/block and at stronger attraction between segment B and the slit wall. While diblock copolymer shows the longest tail, alternating copolymer has the shortest. As the attraction between segment B and the slit wall increases, the average size and fraction decrease for tail, but increase for loop and train.

Hybrid density functional theory for homopolymer mixtures confined in a selective nanoslit.

By integrating polymer density function theory (DFT) and single-chain molecular simulation, a hybrid DFT is developed for homopolymer mixtures confined in a selective nanoslit. Two weighting functions are adopted separately in the polymer DFT for repulsive and attractive contributions to the excess free energy functional. The theoretical results agree well with simulation data for the density profiles, configurations (tail, loop and train), adsorption amounts, layer thicknesses, and partition coefficients. The polymer-slit interaction is found to have a large effect on the density profiles and partition coefficients but is found to have a small effect on the average sizes and percentages of the configurations. Nearly half of the polymer segments form tails, and the other half form trains. In addition, bridges are observed to form for sufficiently long polymer chains. As the length difference between two polymers increases, the effect of chain connectivity becomes increasingly important.

Density functional theory for copolymers confined in a nanoslit.

A density functional theory is developed for copolymers confined in a nanoslit on the basis of our previous work for homopolymers. The theory accurately captures the structural characteristics for diblock and alternating copolymers composed of hard-sphere or square-well segments. Satisfactory agreement is obtained between the theoretical predictions and simulation results in segment density profiles, segment fractions, and partition coefficients. Structures under confinement strongly depend on the substituent segment sizes for the hard-sphere copolymers and also on the segment-wall attractions for the square-well copolymers. Alternating copolymers are found to behave as homopolymers with effective segment size, and effective segment-segment and segment-wall interactions.

Recognition of multiblock copolymers on nanopatterned surfaces: insight from molecular simulations.

The recognition of multiblock copolymers on nanopatterned surfaces has been investigated by molecular simulations. All the copolymers (AnB12-n)5 are composed of 60 square-well segments, but with various architectures by changing n. Segment density profiles, radii of gyration, pattern transfer parameters, and three adsorption conformations (tail, loop, and train) are examined quantitatively. It is found that the copolymer can recognize the adsorbing stripes on surface and the surface vicinity. The recognition affinity becomes stronger with increasing the stripe width, the adsorption strength, and the number of adsorbing segments in copolymer chain. From surface to bulk phase, the shape of copolymer changes from elongated to elliptical, and finally to globular. Among the three adsorption conformations, tail has the greatest average size while train has the smallest. With the increased number of nonadsorbing segments, the average size shows an increase in tail but a decrease in train.

Density functional theory for the recognition of polymer at nanopatterned surface.

The recognition of homopolymer at nanopatterned surface has been investigated by density functional theory (DFT). Chain conformation and pattern transfer parameter predicted from the DFT are in good agreement with Monte Carlo simulation results. The theory describes satisfactorily the transition from depletion at low packing fractions to adsorption and double-layer adsorption at high packing fractions and also accounts for the crucial effect of the segment-wall interaction. It is found that homopolymer is better recognized at a low bulk density and a stronger interaction with the surface. The polymer can not only recognize the surface but also invert the surface at high bulk densities. The chain in the solution-wall interface exhibits a typical "brush" conformation with a length approximated by half the length of polymer chain.

Density functional theory of homopolymer mixtures confined in a slit.

A density functional theory (DFT) is developed for polymer mixtures with shorted-ranged attractive interparticle interactions confined in a slit. Different weighting functions are used separately for the repulsive part and the attractive part of the excess free energy functional by applying the weighted density approximation. The predicted results by DFT are in good agreement with the corresponding simulation data indicating the reliability of the theory. Furthermore, the center-of-mass profiles and the end-to-end distance distributions are obtained by the single chain simulation; the predictions also agree well with simulation data. The results reveal that both the attraction of the slit wall and the temperature has stronger effect on longer chains than on shorter ones because the intrasegment correlation of chains increases with increasing chain length.

Density and chain conformation profiles of square-well chains confined in a slit by density-functional theory.

Density and chain conformation profiles of square-well chains between two parallel walls were studied by using density-functional theory. The free energy of square-well chains is separated into two contributions: the hard-sphere repulsion and the attraction. The Heaviside function is used as the weighting function for both of the two parts. The equation of state of Hu et al. is used to calculate the excess free energy of the repulsive part. The equation of state of statistical associating fluid theory for chain molecules with attractive potentials of variable range [A. Gil-Villegas et al. J. Chem. Phys. 106, 4168 (1997)] is used to calculate the excess free energy of the attractive part. Because the wall is inaccessible to a mass center of a longer chain, there exists a sharp fall in the distribution of end-to-end distance near the wall as the chain length increases. When the average density of the system is not too low, the prediction of this work is in good agreement with computer simulation results for the density profiles and the chain conformation over a wide range of chain length, temperature, and attraction strength of the walls. However, when the average density and the temperature are very low, the prediction deviates to a certain degree from the computer simulation results for molecules with long chain length. A more accurate functional approximation is needed.