Оценка экологической опасности рассеивания газопылевого облака при массовых взрывах в карьерах

Heteroanion (HA) moieties have a key role in templating of heteropolyoxometalate (HPA) architectures, but clusters templated by two different templates are rarely reported. Herein, we show how a cross-shaped HPA-based architecture can self-sort the HA templates by pairing two different guests into a divacant {XYW₁₅O₅₄} building block, with four of these building block units being linked together to complete the cross-shaped architecture. We exploited this observation to incorporate HA templates into well-defined positions within the clusters, leading to the isolation of a collection of mixed-HA templated cross-shaped polyanions [(XYW₁₅O₅₄)₄(WO₂)₄]^32–/36– (X = H–P, Y = Se, Te, As). The template positions have been unambiguously determined by single crystal X-ray diffraction, NMR spectroscopy, and high-resolution electrospray ionization mass spectrometry; these studies demonstrated that the mixed template containing HPA clusters are the preferred products which crystallize from the solution. Theoretical studies using DFT calculations suggest that the selective self-sorting originates from the coordination of the template in solution. The cross-shaped polyoxometalate clusters are redox-active, and the ability of molecules to accept electrons is slightly modulated by the HA incorporated as shown by differential pulse voltammetry experiments. These results indicate that the cross-shaped HPAs can be used to select templates from solution, and themselves have interesting geometries, which will be useful in developing functional molecular architectures based upon HPAs with well-defined structures and electronic properties

Gene expression differences between AlignerBoost filtered or “default “best alignments for two replicate Capture-seq datasets from human brain tissues.

<p>Gene expression is represented by RPKM values. Coding gene (red) and pseudogene (blue) annotations are from GENCODE project (v19). Values in parentheses show the mean gene expression changes of the two replicates. A: Single-end (SE) mapping; B: Paired-end (PE) mapping.</p

AlignerBoost: A Generalized Software Toolkit for Boosting Next-Gen Sequencing Mapping Accuracy Using a Bayesian-Based Mapping Quality Framework

<div><p>Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or "best" hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present. Here we developed a generalized software toolkit "AlignerBoost", which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads. We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds. AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known SNPs. AlignerBoost’s algorithm is computationally efficient, and can process one million alignments within 30 seconds on a typical desktop computer. AlignerBoost is implemented as a uniform Java application and is freely available at <a href="https://github.com/Grice-Lab/AlignerBoost" target="_blank">https://github.com/Grice-Lab/AlignerBoost</a>.</p></div

Mapping sensitivity and precision of simulated RNA-seq datasets by picking “best” hits using AlignerBoost filtering procedures (AlignerBoost) or the aligner’s default best mode (Default).

<p>A: Single-end (SE) mapping; B: Paired-end (PE) mapping.</p

The estimated mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the real capture-seq datasets.

<p>All mappings were performed using STAR. Mapping sensitivity is approximated by the read depth in capture regions. The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. “Default” indicates aligners’ default best hits; “AlignerBoost” indicates best hits via AlignerBoost mapping and filtering procedures. A: Single-end (SE) mapping; B: Paired-end (PE) mapping. Replicate samples have same point types but different line types.</p

The mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the simulated DNA-seq datasets using AlignerBoost and similar tools.

<p>The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. Different tools are labelled with different line points. AlignerBoost is used with Bowtie2 aligner. A-D: Single-end (SE) mapping; E-H: Paired-end (PE) mapping; A/E: Genome, B/F: RefExome, C/G: Pseudogene, D/H: RMSK.</p

The mapping sensitivity vs. False Discovery Rate (FDR) curves under different mapping quality (mapQ) cutoffs for the simulated DNA-seq datasets.

<p>The mapQ varies from 0, 3, 6, 10, 13, 20, then in increments of 10 up to the maximum allowed values of the indicated aligner. “Default” indicates aligners’ default best hits; “AlignerBoost” indicates best hits via AlignerBoost mapping and filtering procedures. A-D: Single-end (SE) mapping; E-H: Paired-end (PE) mapping; A/E: Genome, B/F: RefExome, C/G: Pseudogene, D/H: RMSK.</p

Expression of viral structural proteins in infected HUVEC.

<div><p>(<b>A</b>) Kinetics of viral protein synthesis. HUVEC were mock infected (M) or infected with RV-Dz at an MOI=5. Proteins were separated by 4-12% NuPage gel, either nonreducing (E1, C, β-actin) or reducing (E2), and then the blots were probed with rubella E1, E2 and C-specific MAb and β-actin MAb.</p> <p>(<b>B</b>) Spatial distribution of E1, E2 and C proteins in infected cells. HUVECs were infected with RV-Dz at an MOI=5 on chamber slides and processed for indirect immunofluorescence at 2 dpi using E1, E2 and capsid-specific MAb. Nuclei were counterstained with DAPI.</p></div

Effects of RV infection on cell proliferation and mitosis.

<p>(<b>A</b>) Growth curves of mock-infected and RV-infected HUVECs. HUVEC were mock infected or infected with RV-Dz at MOI=10 and then counted daily. The data are results of 2 independent experiments each performed in duplicate. (<b>B</b>) Mitosis in infected HUVEC. At 2 dpi the mock infected and infected HUVEC (RV-Dz, MOI=10) were immunostained by IFA with capsid MAb and DAPI to quantitate infected cells and mitotic figures. Mitotic indexes (MI) were calculated as % cells with mitotic figures in two duplicate wells. Note RV-antigen positive mitotic cell in red circle. (<b>C</b>) Cell cycle analysis of infected HUVEC. Serum-starved HUVECs were mock-infected or infected with RV-Dz at MOI=10. Histograms of cell cycle analysis at 1 dpi show DNA content of propidium iodide-stained cells by flow cytometry and % of cells in each phase of the cell cycle. The representative results of two independent experiments are shown.</p

Productive infection of HUVEC with low passage wtRV.

<p>(<b>A</b>–<b>B</b>) Kinetics of RV replication in HUVEC, Vero and A549 cells. Cells were infected with RV-Dz at an MOI of 0.05 or 5. Cell culture supernatants (A) or cell lysates (B) were titered in duplicate on Vero cells. Data are presented as a mean value +/- standard deviation of two independent experiments each performed in duplicate. The data were analyzed by two-way ANOVA with the Bonferroni posttests for correcting for multiple comparisons (*, P<0.05; **, P<0.01; ***, P<0.001). (<b>C</b>) Quantitation of intracellular rubella genomic RNA. HUVEC, Vero and A549 cultures were infected with RV-Dz at an MOI of 5. Genomic RNA was quantitated by RT-qPCR. GAPDH mRNA was used for normalization in the comparative threshold cycle method. Data are presented relative to the genomic RNA amount at 4 hpi. The results represent the mean of at least two independent experiments each done in duplicate. (<b>D</b>) Phase contrast pictures of cells at 5 dpi either mock infected or RV-Dz infected at MOI=5. Note cytopathic effect of wtRV in A549. (<b>E</b>) Representative images of rubella virions observed by TEM in HUVEC infected with RV-Dz at MOI=50 at 24 hpi. (<b>F</b>) Representative images of rubella virions and (<b>G</b>) replicative complexes observed by TEMin Vero cells infected with RV-Dz at MOI=50 at 24 hpi. Inserts represent enlarged images from the replicative complex and virions that are marked with the red arrows. Bars, 100 nm.</p