A case study showing highly traceable sources of bacteria on surfaces of university buildings.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin.

Effect of on Soil Microbial Assembly Process and Co-Occurrence Patterns in the Alpine Meadow Ecosystem.

Burrowing animals are a critical driver of terrestrial ecosystem functioning, but we know little about their effects on soil microbiomes. Here, we evaluated the effect of burrowing animals on microbial assembly processes and co-occurrence patterns using soil microbiota from a group of habitats disturbed by s (). Pika disturbance had different impacts on bacterial and fungal communities.

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.

[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.

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.

Intermediate irrigation with low fertilization promotes soil nutrient cycling and reduces CO and CH 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.