Generational Specific Recruitment of Arsenic- and Antimony-Reducing Microorganisms in Plant Root-Associated Niches for Adapting to Metalloid-Metal Pollution.

The recruitment of microorganisms by plants can enhance their adaptability to environmental stressors, but how root-associated niches recruit specific microorganisms for adapting to metalloid-metal contamination is not well-understood. This study investigated the generational recruitment of microorganisms in different root niches of Vetiveria zizanioides (V. zizanioides) under arsenic (As) and antimony (Sb) stress. The V. zizanioides was cultivated in As- and Sb-cocontaminated mine soils (MS) and artificial pollution soils (PS) over two generations in controlled conditions. The root-associated microbial communities were analyzed through 16S rRNA, arsC, and aioA gene amplicon and metagenomics sequencing. V. zizanioides accumulated higher As(III) and Sb(III) in its endosphere in MS in the second generation, while its physiological indices in MS were better than those observed in PS. SourceTracker analysis revealed that V. zizanioides in MS recruited As(V)- and Sb(V)-reducing microorganisms (e.g., Sphingomonales and Rhodospirillaceae) into the rhizoplane and endosphere. Metagenomics analysis further confirmed that these recruited microorganisms carrying genes encoding arsenate reductases with diverse carbohydrate degradation abilities were enriched in the rhizoplane and endosphere, suggesting their potential to reduce As(V) and Sb(V) and to decompose root exudates (e.g., xylan and starch). These findings reveal that V. zizanioides selectively recruits As- and Sb-reducing microorganisms to mitigate As-Sb cocontamination during the generational growth, providing insights into novel strategies for enhancing phytoremediation of metalloid-metal contaminants.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

Methods of privacy-preserving genomic sequencing data alignments.

Genomic data alignment, a fundamental operation in sequencing, can be utilized to map reads into a reference sequence, query on a genomic database and perform genetic tests. However, with the reduction of sequencing cost and the accumulation of genome data, privacy-preserving genomic sequencing data alignment is becoming unprecedentedly important. In this paper, we present a comprehensive review of secure genomic data comparison schemes. We discuss the privacy threats, including adversaries and privacy attacks. The attacks can be categorized into inference, membership, identity tracing and completion attacks and have been applied to obtaining the genomic privacy information. We classify the state-of-the-art genomic privacy-preserving alignment methods into three different scenarios: large-scale reads mapping, encrypted genomic datasets querying and genetic testing to ease privacy threats. A comprehensive analysis of these approaches has been carried out to evaluate the computation and communication complexity as well as the privacy requirements. The survey provides the researchers with the current trends and the insights on the significance and challenges of privacy issues in genomic data alignment.

Two-Dimensional Unsupervised Feature Selection via Sparse Feature Filter.

Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D feature selection methods show good performance on image analysis by utilizing the structure information of image. Current 2-D methods usually adopt a sparse regularization to spotlight the key features. However, such scheme introduces additional hyperparameter needed for pruning, limiting the applicability of unsupervised algorithms. To overcome these challenges, we design a feature filter to estimate the weight of image features for unsupervised feature selection. Theoretical analysis shows that a sparse regularization can be derived from the feature filter by transformation, indicating that the filter plays the same role as the popular sparse regularization does. We deploy two distinct strategies in terms of feature selection, called multiple feature filters and single common feature filter. The former divides the optimization problem into multiple independent subproblems and selects features that meet the respective interests of each subproblem. The latter selects features that are in the interest of the overall optimization problem. Extensive experiments on seven benchmark datasets show that our unsupervised 2-D weight-based feature selection methods achieve superior performance over the state-of-the-art methods.

Fast and Accurate Clustering of Multiple Modality Data via Feature Matching.

Multiple modality clustering seeks to partition objects via leveraging cross-modality relations to provide comprehensive descriptions of the same objects. Current clustering methods rely heavily on accurate affinity measurements among samples. The samplewise affinity is costive to be constructed yet easy to corrupt by the heterogeneous gap. In the era of big data, fast and accurate clustering of multiple modality data remains challenging. To fill the gap, we propose a novel approach to achieve the clustering by focusing on feature matching across different modalities instead of samplewise affinity. First, a feature matching matrix is calculated by measuring the potential featurewise correlations. The obtained matching matrix is decomposed into two bases corresponding to the column and row spaces of feature matching, acting as coded bases within feature spaces of the different modalities. Then, the sample assignment is obtained by jointly reconstructing the samples by the two bases. The feature matching potential and sample assignment are collaboratively learned by an alternating optimization scheme. The proposed method dramatically reduces the computational cost by avoiding the costive samplewise affinity estimation, without sacrificing accuracy. Extensive experiments on the synthetic and real-world datasets demonstrate its superior speed and high accuracy.

Extracellular proteins of Desulfovibrio vulgaris as adsorbents and redox shuttles promote biomineralization of antimony.

Biomineralization is the key process governing the biogeochemical cycling of multivalent metals in the environment. Although some sulfate-reducing bacteria (SRB) are recently recognized to respire metal ions, the role of their extracellular proteins in the immobilization and redox transformation of antimony (Sb) remains elusive. Here, a model strain Desulfovibrio vulgaris Hildenborough (DvH) was used to study microbial extracellular proteins of functions and possible mechanisms in Sb(V) biomineralization. We found that the functional groups (N-H, CO, O-CO, NH2-R and RCOH/RCNH2) of extracellular proteins could adsorb and fix Sb(V) through electrostatic attraction and chelation. DvH could rapidly reduce Sb(V) adsorbed on the cell surface and form amorphous nanometer-sized stibnite and/or antimony trioxide, respectively with sulfur and oxygen. Proteomic analysis indicated that some extracellular proteins involved in electron transfer increased significantly (p < 0.05) at 1.8 mM Sb(V). The upregulated flavoproteins could serve as a redox shuttle to transfer electrons from c-type cytochrome networks to reduce Sb(V). Also, the upregulated extracellular proteins involved in sulfur reduction, amino acid transport and protein synthesis processes, and the downregulated flagellar proteins would contribute to a better adaption under 1.8 mM Sb(V). This study advances our understanding of how microbial extracellular proteins promote Sb biomineralization in DvH.

Synergistic effects of antimony and arsenic contaminations on bacterial, archaeal and fungal communities in the rhizosphere of Miscanthus sinensis: Insights for nitrification and carbon mineralization.

The impacts of metal(loids) on soil microbial communities are research focuses to understand nutrient cycling in heavy metal-contaminated environments. However, how antimony (Sb) and arsenic (As) contaminations synergistically affect microbially-driven ecological processes in the rhizosphere of plants is poorly understood. Here we examined the synergistic effects of Sb and As contaminations on bacterial, archaeal and fungal communities in the rhizosphere of a pioneer plant (Miscanthus sinensis) by focusing on soil carbon and nitrogen cycle. High contamination (HC) soils showed significantly lower levels of soil enzymatic activities, carbon mineralization and nitrification potential than low contamination (LC) environments. Multivariate analysis indicated that Sb and As fractions, pH and available phosphorus (AP) were the main factors affecting the structure and assembly of microbial communities, while Sb and As contaminations reduced the microbial alpha-diversity and interspecific interactions. Random forest analysis showed that microbial keystone taxa provided better predictions for soil carbon mineralization and nitrification under Sb and As contaminations. Partial least squares path modeling indicated that Sb and As contaminations could reduce the carbon mineralization and nitrification by influencing the microbial biomass, alpha-diversity and soil enzyme activities. This study enhances our understanding of microbial carbon and nitrogen cycling affected by Sb and As contaminations.

Stress-strain relationship and seismic performance of cast-in-situ phosphogypsum.

BACKGROUND: Phosphogypsum is a waste by-product during the production of phosphoric acid. It not only occupies landfill, but also pollutes the environment, which becomes an important factor restricting the sustainable development of the phosphate fertilizer industry. Research into cast-in-situ phosphogypsum will greatly promote the comprehensive utilization of stored phosphogypsum. The aim of this study was to clarify the mechanical properties of phosphogypsum. METHODS: Stress-strain relationships of cast-in-situ phosphogypsum were investigated through axial compressive experiments, and seismic performance of cast-in-situ phosphogypsum walls and aerated-concrete masonry walls were simulated based on the experimental results and using finite element analysis. RESULTS: The results showed that the stress-strain relationship fitted into a polynomial equation. Moreover, the displacement ductility index and the energy dissipation index of cast-in-situ phosphogypsum wall were 6.587 and 3.425, respectively. CONCLUSIONS: The stress-strain relationship for earthquake-resistant performance of cast-in-situ phosphogypsum walls is better than that of aerated-concrete masonry walls. The curve of stress-strain relationship and the evaluation of earthquake-resistant performance provide theoretical support for the application of cast-in-situ phosphogypsum in building walls.