Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks

We provide a simple, remote, continuous calibration technique suitable for application in a hierarchical network featuring a few well-maintained, high-quality instruments (“proxies”) and a larger number of low-cost devices. The ideas are grounded in a clear definition of the purpose of a low-cost network, defined here as providing reliable information on air quality at small spatiotemporal scales. The technique assumes linearity of the sensor signal. It derives running slope and offset estimates by matching mean and standard deviations of the sensor data to values derived from proxies over the same time. The idea is extremely simple: choose an appropriate proxy and an averaging-time that is sufficiently long to remove the influence of short-term fluctuations but sufficiently short that it preserves the regular diurnal variations. The use of running statistical measures rather than cross-correlation of sites means that the method is robust against periods of missing data. Ideas are first developed using simulated data and then demonstrated using field data, at hourly and 1 min time-scales, from a real network of low-cost semiconductor-based sensors. Despite the almost naïve simplicity of the method, it was robust for both drift detection and calibration correction applications. We discuss the use of generally available geographic and environmental data as well as microscale land-use regression as means to enhance the proxy estimates and to generalize the ideas to other pollutants with high spatial variability, such as nitrogen dioxide and particulates. These improvements can also be used to minimize the required number of proxy sites

Chemical Transformations of the Fungal Meroterpenoid Dhilirolide A Reveal Skeletal Degradation and Rearrangement Reactions with Biosynthetic Implications

Treatment of the fungal meroterpenoid dhilirolide A (<b>1</b>) with either sodium azide or perchloric acid results in conversion of the dhilirane carbon skeleton of <b>1</b> to the 14,15-dinordhilirane carbon skeleton of the products <b>5</b>–<b>7</b>, with and without concomitant transfer of an acetyl residue to form a C-9 acetate ester. The discovery of these transformations, which are vinylogous retro-Claisen-type condensations, suggests an efficient biogenetic route to 14,15-dinordhiliranes such as dhilirolide K (<b>3</b>)

Marine Sediment-Derived <i>Streptomyces</i> Bacteria from British Columbia, Canada Are a Promising Microbiota Resource for the Discovery of Antimicrobial Natural Products

<div><p>Representatives of the genus <i>Streptomyces</i> from terrestrial sources have been the focus of intensive research for the last four decades because of their prolific production of chemically diverse and biologically important compounds. However, metabolite research from this ecological niche had declined significantly in the past years because of the rediscovery of the same bioactive compounds and redundancy of the sample strains. More recently, a new picture has begun to emerge in which marine-derived <i>Streptomyces</i> bacteria have become the latest hot spot as new source for unique and biologically active compounds. Here, we investigated the marine sediments collected in the temperate cold waters from British Columbia, Canada as a valuable source for new groups of marine-derived <i>Streptomyces</i> with antimicrobial activities. We performed culture dependent isolation from 49 marine sediments samples and obtained 186 <i>Streptomyces</i> isolates, 47 of which exhibited antimicrobial activities. Phylogenetic analyses of the active isolates resulted in the identification of four different clusters of bioactive <i>Streptomyces</i> including a cluster with isolates that appear to represent novel species. Moreover, we explored whether these marine-derived <i>Streptomyces</i> produce new secondary metabolites with antimicrobial properties. Chemical analyses revealed structurally diverse secondary metabolites, including four new antibacterial novobiocin analogues. We conducted structure-activity relationships (SAR) studies of these novobiocin analogues against methicillin-resistant <i>Staphylococcus aureus</i> (MRSA). In this study, we revealed the importance of carbamoyl and OMe moieties at positions 3” and 4” of novobiose as well as the hydrogen substituent at position 5 of hydroxybenzoate ring for the anti-MRSA activity. Changes in the substituents at these positions dramatically impede or completely eliminate the inhibitory activity of novobiocins against MRSA. </p> </div

DIM induces apoptosis in CEM cells as detected by TUNEL.

<p>The <i>In situ</i> cell death detection kit (TUNEL) was applied to fixed CEM cells treated with 0 to 15 µM DIM for 48 hr. (A) Fluorescence images of control (0 µM) and DIM-treated (15 µM) cells were taken at 20× magnification following TUNEL labeling in mounting medium with DAPI. (B) Flow cytometry was used to identify and quantify cells with no, low (open bar) or high (solid bar) intensity staining. **, <i>p</i><0.01 or ***, <i>p</i><0.001 as determined by one-way ANOVA with Dunnett's post-hoc test comparisons for significant effects of DIM treatments within each intensity category as compared to vehicle control (0 µM DIM, 0.1% DMSO).</p

Inhibition of T-ALL cell growth by DIM and I3C.

<p><i>Note</i>: Non-linear regression analyses (four parameters, variable slope) were performed using data generated from each DIM and I3C concentration-response curve generated for each of the four cell lines tested (GraphPad Prism v5.0, San Diego, CA). IC₅₀ values are the concentrations of DIM or I3C required to inhibit cell proliferation or viability by 50% compared to the vehicle control (0.1% DMSO).</p

DIM reduces expression of cell-cycle regulatory proteins.

<p>Following either 12 hr (gray bars) or 24 hr (black bars) treatment with increasing concentrations of DIM, CEM cells were harvested and protein immunoassays were performed for detection of CCND3, CDK4 and CDK6 proteins (three replicate experiments performed). (A) A representative immunoblot is shown for each protein assay. (B) Values shown are average protein expression ± SEM normalized to β-actin, expressed as a percentage difference from time-matched vehicle controls (0.1% DMSO), which were assigned a value of 100%. *, <i>p</i><0.05 or **, <i>p</i><0.01 compared to 0 µM DIM (vehicle control) as determined by one-way ANOVA with Dunnett's post-hoc test for multiple comparisons; overall ANOVA <i>p</i>-values within each time group are indicated in each panel. In some cases where the <i>p</i>-value for the ANOVA was not <0.05, a significant linear trend was evident, as indicated by trend <i>p</i>-values in the figure. Finally, a Student's <i>t-</i>test (###, <i>p</i><0.001) was performed to compare 15 µM DIM to vehicle control for CCND3 expression at 24 hr because high variability observed at the 3.8 µM concentration confounded the ANOVA post-hoc results (overall effect of DIM was significant).</p

DIM-induced changes in expression of select apoptosis-related genes in CEM cells. ^*

*<p>A complete list of DIM-induced changes in gene expression, including all genes on the RT² Profiler Apoptosis array, is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034975#pone.0034975.s002" target="_blank">Table S1</a>.</p>†<p>Log₂ fold change (R) values are highlighted in bold if level of change is >1.5-fold (Log₂ R<−0.58 or >0.58) compared to vehicle (0.1% DMSO) control. <i>p</i>-values were determined by a Student's <i>t</i>-test assuming equal variances.</p>‡<p>nd, not detected by RT² PCR profiler array at this time point (C_t>35).</p

DIM induces apoptosis in human T-ALL cells.

<p>CEM, HSB2, SUP-T1 and Jurkat cells were treated with 3.8 to 15 µM DIM for 48 hr. Values are the proportion of apoptotic cells as determined using the ViaCount assay + SEM (n = 3 to 4 independent experiments). *, <i>p</i><0.05; **, <i>p</i><0.01 or *** <i>p</i><0.001 for compared to the vehicle control (0 µM DIM, 0.1% DMSO) as determined by one-way RM ANOVA (matching by experiment day) with Dunnett's multiple comparisons post-hoc test.</p