SummaryGut microbiota typically occupy habitats with definable limits/borders that are comparable to oceanic islands. The gut therefore can be regarded as an 'island' for the assembly of microbial communities within the 'sea' of surrounding environments. This study aims to reveal the ecological mechanisms that govern microbiota in the fish gut 'island' ecosystem. Taxonomic compositions, phylogenetic diversity, and community turnover across host development were analyzed via the high-throughput sequencing of 16S rRNA gene amplicons. The results indicate that the Shannon diversity of gut microbiota in the three examined freshwater fish species all significantly decreased with host development, and the dominant bacterial taxa also changed significantly during host development. Null model and phylogenetic-based mean nearest taxon distance (MNTD) analyses suggest that host gut environmental filtering led to the assembly of microbial communities in the fish gut 'island'. However, the phylogenetic clustering of local communities and deterministic processes that governed community turnover became less distinct as the fish developed. The observed mechanisms that shaped fish gut microbiota seemed to be mainly shaped by the gut environment and by some other selective changes accompanying the host development process. These findings greatly enhance our understanding of stage-specific community assembly patterns in the fish gut ecosystem.

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

In this work, superlubricity between glass and Si(3)N(4) surfaces lubricated by mixtures of acid solutions and glycerol solutions has been found by using a traditional tribometer. Ultralow friction coefficients of between 0.004 and 0.006 were obtained after a running-in period. Related experiments indicate that the hydrogen ions in the mixtures play an important role in achieving superlubricity. Moreover, the ultralow friction is also closely related to the pH value of the acid and the concentration of glycerol. According to these results, the possible superlubricity mechanism has been revealed, which is attributed to a fluid-hydrated water layer between the hydrogen-bonded networks of glycerol and water molecules on the positively charged surfaces.

Conspectus Molecular crystals are chemists' solids in the sense that their structures and properties can be understood in terms of those of the constituent molecules merely perturbed by a crystalline environment. They form a large and important class of solids including ices of atmospheric species, drugs, explosives, and even some organic optoelectronic materials and supramolecular assemblies. Recently, surprisingly simple yet extremely efficient, versatile, easily implemented, and systematically accurate electronic structure methods for molecular crystals have been developed. The methods, collectively referred to as the embedded-fragment scheme, divide a crystal into monomers and overlapping dimers and apply modern molecular electronic structure methods and software to these fragments of the crystal that are embedded in a self-consistently determined crystalline electrostatic field. They enable facile applications of accurate but otherwise prohibitively expensive ab initio molecular orbital theories such as Møller-Plesset perturbation and coupled-cluster theories to a broad range of properties of solids such as internal energies, enthalpies, structures, equation of state, phonon dispersion curves and density of states, infrared and Raman spectra (including band intensities and sometimes anharmonic effects), inelastic neutron scattering spectra, heat capacities, Gibbs energies, and phase diagrams, while accounting for many-body electrostatic (namely, induction or polarization) effects as well as two-body exchange and dispersion interactions from first principles. They can fundamentally alter the role of computing in the studies of molecular crystals in the same way ab initio molecular orbital theories have transformed research practices in gas-phase physical chemistry and synthetic chemistry in the last half century. In this Account, after a brief summary of formalisms and algorithms, we discuss applications of these methods performed in our group as compelling illustrations of their unprecedented power in addressing some of the outstanding problems of solid-state chemistry, high-pressure chemistry, or geochemistry. They are the structure and spectra of ice Ih, in particular, the origin of two peaks in the hydrogen-bond-stretching region of its inelastic neutron scattering spectra, a solid-solid phase transition from CO2-I to elusive, metastable CO2-III, pressure tuning of Fermi resonance in solid CO2, and the structure and spectra of solid formic acid, all at the level of second-order Møller-Plesset perturbation theory or higher.

Friction behavior of aqueous solution at macroscale is quite different from that at nanoscale. At macroscale, tribochemistry usually occurs between lubricant and friction surfaces in the running-in process due to a high contact pressure, and most such processes can lead to friction reduction. In the present work, we reported that the hydrogen ions in aqueous solution played an important role in tribochemistry in running-in process (friction reducing process), which could result in the friction coefficient reducing from 0.4 to 0.04 between Si(3)N(4) and glass surfaces at macroscale. It is found that the running-in process and low friction state are closely dependent on the concentration of hydrogen ions in the contact region between the two friction surfaces. The lubrication mechanism is attributed to tribochemical reaction occurring between hydrogen ions and surfaces in the running-in process, which forms an electrical double layer and hydration layer to lower friction force. Finally, the running-in process of H(3)PO(4) (pH = 1.5) was investigated, which could realize superlubricity with an ultralow friction coefficient of about 0.004.

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The Three Gorges Dam has significantly altered ecological and environmental conditions within the reservoir region, but how these changes affect bacterioplankton structure and function is unknown. Here, three widely accepted metagenomic tools were employed to study the impact of damming on the bacterioplankton community in the Xiangxi River. Our results indicated that bacterioplankton communities were both taxonomically and functionally different between backwater and riverine sites, which represent communities with and without direct dam effects, respectively. There were many more nitrogen cycling Betaproteobacteria (e.g., Limnohabitans), and a higher abundance of functional genes and KEGG orthology (KO) groups involved in nitrogen cycling in the riverine sites, suggesting a higher level of bacterial activity involved in generating more nitrogenous nutrients for the growth of phytoplankton. Additionally, the KO categories involved in carbon and sulfur metabolism, as well as most of the detected functional genes also showed clear backwater and riverine patterns. As expected, these diversity patterns all significantly correlated with environmental characteristics, confirming that the bacterioplankton communities in the Xiangxi River were really affected by environmental changes from the Three Gorges Dam. This study provides a first comparative metagenomic insight for evaluating the impacts of the large dam on microbial function.