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The processes involved in soil domestication have altered the soil microbial ecology. We examined the question of whether animal manure application affects the soil microbial ecology of farmlands. The effects of global animal manure application on soil microorganisms were subjected to a meta-analysis based on randomized controlled treatments. A total of 2303 studies conducted in the last 30 years were incorporated into the analysis, and an additional 45 soil samples were collected and sequenced to obtain 16S rRNA and 18S rRNA data. The results revealed that manure application increased soil microbial biomass. Manure application alone increased bacterial diversity (M-Z: 7.546 and M-I: 8.68) and inhibited and reduced fungal diversity (M-Z: −1.15 and M-I: −1.03). Inorganic fertilizer replaced cattle and swine manure and provided nutrients to soil microorganisms. The soil samples of the experimental base were analyzed, and the relative abundances of bacteria and fungi were altered compared with no manure application. Manure increased bacterial diversity and reduced fungal diversity. Mrakia frigida and Betaproteobacteriales, which inhibit other microorganisms, increased significantly in the domesticated soil. Moreover, farm sewage treatments resulted in a bottleneck in the manure recovery rate that should be the focus of future research. Our results suggest that the potential risks of restructuring the microbial ecology of cultivated land must be considered.

Soil microorganisms are important components of agricultural ecosystems; they are important in agricultural soil nutrient cycle and are easily affected by soil tillage. The response of soil microbial community to tillage is very complex, and the effect of the no tillage and residue mulching method on soil microbial diversity remains unclear. In 2019, the soil was collected from an experimental field after 10 years of continuous cultivation in the black soil area of the Sanjiang Plain in Northeastern China. In this study, the diversity and composition of the soil bacterial community and their relationship with soil properties were explored via high-throughput sequencing under no tillage with four residue mulching treatments. No tillage with 60% residue mulching (NTR3) significantly increased the alpha diversity of the rhizosphere soil bacteria and changed the composition of the bacterial community-consistent with changes in soil physicochemical properties. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla in the sample soil. Soil physicochemical properties explained 80.6% of the changes in soil diversity and composition, of which soil organic carbon, soil pH, and soil temperature were the principal contributors. Our results suggest that no tillage and residue mulching is conducive to increasing soil organic carbon and soil nutrient content, which is a beneficial conservation tillage measure for black soil protection in Sanjiang Plain of Northeast China. The no tillage with residue mulching, especially 60% residue mulching, alters soil bacterial community and highlights the importance of soil physicochemical properties in shaping the diversity and composition of the soil bacterial community. Our findings contribute to a broad understanding of the effects of no tillage and residue mulching on bacterial community differences and provide a scientific basis for the optimization of no tillage measures and sustainable utilization of the black soil of the Sanjiang Plain in Northeastern China.

Hirschsprung's disease (HSCR), the most common congenital malformation of the gut, is regulated by multiple signal transduction pathways. Several components of these pathways are important targets for microRNAs (miRNAs). Multiple miRNAs have been associated with the pathophysiology of HSCR, and serum miRNAs profiles of HSCR patients have been reported, but miRNA expression in HSCR colon tissue is almost completely unexplored. Using microarray technology, we screened colon tissue to detect miRNAs whose expression profiles were altered in HSCR and identify targets of differentially expressed miRNAs. Following filtering of low-intensity signals, data normalization, and volcano plot filtering, we identified 168 differentially expressed miRNAs (104 up-regulated and 64 down-regulated). Fifty of these mRNAs represent major targets of dysegulated miRNAs and may thus important roles in the pathophysiology of HSCR. Pathway analysis revealed that 7 of the miRNA targets encode proteins involved in regulation of cell proliferation and migration via RET and related signaling pathways (MAPK and PI3K/AKT). Our results identify miRNAs that play key roles in the pathophysiology of the complex multi-factorial disease HSCR.

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.

The C-terminal domain (CTD) of the largest subunit in DNA-dependent RNA polymerase II (RNAP II) is essential for mRNA synthesis and processing, through coordination of an astounding array of protein-protein interactions. Not surprisingly, CTD mutations can have complex, pleiotropic impacts on phenotype. For example, insertions of five alanine residues between CTD diheptads in yeast, which alter the CTD's overall tandem structure and physically separate core functional units, dramatically reduce growth rate and result in abnormally large cells that accumulate increased DNA content over time. Patterns by which specific CTD-protein interactions are disrupted by changes in CTD structure, as well as how downstream metabolic pathways are impacted, are difficult to target for direct experimental analyses. In an effort to connect an altered CTD to complex but quantifiable phenotypic changes, we applied network analyses of genes that are differentially expressed in our five alanine CTD mutant, combined with established genetic interactions from the Saccharomyces cerevisiae Genome Database (SGD). We were able to identify candidate genetic pathways, and several key genes, that could explain how this change in CTD structure leads to the specific phenotypic changes observed. These hypothetical networks identify links between CTD-associated proteins and mitotic function, control of cell cycle checkpoint mechanisms, and expression of cell wall and membrane components. Such results can help to direct future genetic and biochemical investigations that tie together the complex impacts of the CTD on global cellular metabolism.

The C-terminal domain (CTD) of the largest subunit in DNA-dependent RNA polymerase II (RNAP II) is essential for mRNA synthesis and processing, through coordination of an astounding array of protein-protein interactions. Not surprisingly, CTD mutations can have complex, pleiotropic impacts on phenotype. For example, insertions of five alanine residues between CTD diheptads in yeast, which alter the CTD's overall tandem structure and physically separate core functional units, dramatically reduce growth rate and result in abnormally large cells that accumulate increased DNA content over time. Patterns by which specific CTD-protein interactions are disrupted by changes in CTD structure, as well as how downstream metabolic pathways are impacted, are difficult to target for direct experimental analyses. In an effort to connect an altered CTD to complex but quantifiable phenotypic changes, we applied network analyses of genes that are differentially expressed in our five alanine CTD mutant, combined with established genetic interactions from the Saccharomyces cerevisiae Genome Database (SGD). We were able to identify candidate genetic pathways, and several key genes, that could explain how this change in CTD structure leads to the specific phenotypic changes observed. These hypothetical networks identify links between CTD-associated proteins and mitotic function, control of cell cycle checkpoint mechanisms, and expression of cell wall and membrane components. Such results can help to direct future genetic and biochemical investigations that tie together the complex impacts of the CTD on global cellular metabolism. [ABSTRACT FROM AUTHOR]