Benchmarking of three-dimensional multicomponent lattice Boltzmann equation

We present a challenging validation of phase field multi-component lattice Boltzmann equation (MCLBE) simulation against the Re = 0 Stokes flow regime Taylor-Einstein theory of dilute suspension viscosity. By applying a number of recent advances in the understanding and the elimination of the interfacial micro-current artefact, extending to 3D a class of stability-enhancing multiple relaxation time collision models (which require no explicit collision matrix, note) and developing new interfacial interpolation schemes, we are able to obtain data which show that MCLBE may be applied in new flow regimes. Our data represent one of the most stringent tests yet attempted on LBE-one which received wisdom would preclude on grounds of overwhelming artefact flow

Applying High Throughput Toxicokinetics (HTTK) to Per- and Polyfluoro Alkyl Substances (PFAS)

Presentation to SOT on March 10-14, 2024 in Salt Lake City, UtahSearch for CCTE records in EPA’s Science Inventory by typing in the title at this link.https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25</p

Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain-1

Olored by expression level (blue-green is low, yellow is medium, and red is high). The images at the bottom left corner show the original image data from which the data for a quadrat in the subiculum were measured along with some of the surrounding tissue. The detected signal, color mapped by expression level, is shown blended with the original image. The tick marks on the ISH image indicate 100 μm intervals, and the markings on the reference atlas Nissl sections indicate 1 cm.<p><b>Copyright information:</b></p><p>Taken from "Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain"</p><p>http://www.biomedcentral.com/1471-2105/9/153</p><p>BMC Bioinformatics 2008;9():153-153.</p><p>Published online 18 Mar 2008</p><p>PMCID:PMC2375125.</p><p></p

Uptake of Perfluoroalkyl Acids into Edible Crops via Land Applied Biosolids: Field and Greenhouse Studies

The presence of perfluoroalkyl acids (PFAAs) in biosolids destined for use in agriculture has raised concerns about their potential to enter the terrestrial food chain via bioaccumulation in edible plants. Uptake of PFAAs by greenhouse lettuce (Lactuca sativa) and tomato (Lycopersicon lycopersicum) grown in an industrially impacted biosolids-amended soil, a municipal biosolids-amended soil, and a control soil was measured. Bioaccumulation factors (BAFs) were calculated for the edible portions of both lettuce and tomato. Dry weight concentrations observed in lettuce grown in a soil amended (biosolids:soil dry weight ratio of 1:10) with PFAA industrially contaminated biosolids were up to 266 and 236 ng/g for perfluorobutanoic acid (PFBA) and perfluoropentanoic acid (PFPeA), respectively, and reached 56 and 211 ng/g for PFBA and PFPeA in tomato, respectively. BAFs for many PFAAs were well above unity, with PFBA having the highest BAF in lettuce (56.8) and PFPeA the highest in tomato (17.1). In addition, the BAFs for PFAAs in greenhouse lettuce decreased approximately 0.3 log units per CF₂ group. A limited-scale field study was conducted to verify greenhouse findings. The greatest accumulation was seen for PFBA and PFPeA in both field-grown lettuce and tomato; BAFs for PFBA were highest in both crops. PFAA levels measured in lettuce and tomato grown in field soil amended with only a single application of biosolids (at an agronomic rate for nitrogen) were predominantly below the limit of quantitation (LOQ). In addition, corn (Zea mays) stover, corn grains, and soil were collected from several full-scale biosolids-amended farm fields. At these fields, all PFAAs were below the LOQ in the corn grains and only trace amounts of PFBA and PFPeA were detected in the corn stover. This study confirms that the bioaccumulation of PFAAs from biosolids-amended soils depends strongly on PFAA concentrations, soil properties, the type of crop, and analyte

Suppression of PPARα by chemical exposure.

<p>A. Suppression of PPARα by acetaminophen. Effects of acetaminophen treatment were examined at either 3 or 6 hrs of exposure in four strains of mice [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref049" target="_blank">49</a>]. B. Suppression of PPARα by LPS and trovafloxacin. Suppression of PPARα by LPS, LPS + lovafloxacin (LVX), trovafloxacin (TVX), and TVX + LPS but not by LVX only [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref051" target="_blank">51</a>]. C. Suppression of PPARα by silicon dioxide nanoparticles. In general, suppression of PPARα was more significant with smaller particle size at equivalent dose levels [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref054" target="_blank">54</a>].</p

Identification of Modulators of the Nuclear Receptor Peroxisome Proliferator-Activated Receptor α (PPARα) in a Mouse Liver Gene Expression Compendium

<div><p>The nuclear receptor family member peroxisome proliferator-activated receptor α (PPARα) is activated by therapeutic hypolipidemic drugs and environmentally-relevant chemicals to regulate genes involved in lipid transport and catabolism. Chronic activation of PPARα in rodents increases liver cancer incidence, whereas suppression of PPARα activity leads to hepatocellular steatosis. Analytical approaches were developed to identify biosets (i.e., gene expression differences between two conditions) in a genomic database in which PPARα activity was altered. A gene expression signature of 131 PPARα-dependent genes was built using microarray profiles from the livers of wild-type and PPARα-null mice after exposure to three structurally diverse PPARα activators (WY-14,643, fenofibrate and perfluorohexane sulfonate). A fold-change rank-based test (Running Fisher’s test (p-value ≤ 10^-4)) was used to evaluate the similarity between the PPARα signature and a test set of 48 and 31 biosets positive or negative, respectively for PPARα activation; the test resulted in a balanced accuracy of 98%. The signature was then used to identify factors that activate or suppress PPARα in an annotated mouse liver/primary hepatocyte gene expression compendium of ~1850 biosets. In addition to the expected activation of PPARα by fibrate drugs, di(2-ethylhexyl) phthalate, and perfluorinated compounds, PPARα was activated by benzofuran, galactosamine, and TCDD and suppressed by hepatotoxins acetaminophen, lipopolysaccharide, silicon dioxide nanoparticles, and trovafloxacin. Additional factors that activate (fasting, caloric restriction) or suppress (infections) PPARα were also identified. This study 1) developed methods useful for future screening of environmental chemicals, 2) identified chemicals that activate or suppress PPARα, and 3) identified factors including diets and infections that modulate PPARα activity and would be hypothesized to affect chemical-induced PPARα activity.</p></div

Perfluoroalkyl Acid Distribution in Various Plant Compartments of Edible Crops Grown in Biosolids-Amended soils

Crop uptake of perfluoroalkyl acids (PFAAs) from biosolids-amended soil has been identified as a potential pathway for PFAA entry into the terrestrial food chain. This study compared the uptake of PFAAs in greenhouse-grown radish (<i>Raphanus sativus</i>), celery (<i>Apium graveolens</i> var. <i>dulce</i>), tomato (<i>Lycopersicon lycopersicum</i>), and sugar snap pea (<i>Pisum sativum </i>var.<i> macrocarpon</i>) from an industrially impacted biosolids-amended soil, a municipal biosolids-amended soil, and a control soil. Individual concentrations of PFAAs, on a dry weight basis, in mature, edible portions of crops grown in soil amended with PFAA industrially impacted biosolids were highest for perfluorooctanoate (PFOA; 67 ng/g) in radish root, perfluorobutanoate (PFBA; 232 ng/g) in celery shoot, and PFBA (150 ng/g) in pea fruit. Comparatively, PFAA concentrations in edible compartments of crops grown in the municipal biosolids-amended soil and in the control soil were less than 25 ng/g. Bioaccumulation factors (BAFs) were calculated for the root, shoot, and fruit compartments (as applicable) of all crops grown in the industrially impacted soil. BAFs were highest for PFBA in the shoots of all crops, as well as in the fruit compartment of pea. Root-soil concentration factors (RCFs) for tomato and pea were independent of PFAA chain length, while radish and celery RCFs showed a slight decrease with increasing chain length. Shoot-soil concentration factors (SCFs) for all crops showed a decrease with increasing chain length (0.11 to 0.36 log decrease per CF₂ group). The biggest decrease (0.54–0.58 log decrease per CF₂ group) was seen in fruit-soil concentration factors (FCFs). Crop anatomy and PFAA properties were utilized to explain data trends. In general, fruit crops were found to accumulate fewer long-chain PFAAs than shoot or root crops presumably due to an increasing number of biological barriers as the contaminant is transported throughout the plant (roots to shoots to fruits). These data were incorporated into a preliminary conceptual framework for PFAA accumulation in edible crops. In addition, these data suggest that edible crops grown in soils conventionally amended for nutrients with biosolids (that are not impacted by PFAA industries) are unlikely a significant source of long-chain PFAA exposure to humans

Activation of PPARα by diverse chemicals.

<p>A. Known activators of PPARα. B. Activation of PPARα by novel chemicals. Novel activators of PPARα identified in the screen are shown. C. Activation of PPARα by TCDD. (Left) Activation of PPARα by TCDD in Balb/c but not C3H or CBA mice at 4 or 40 μg/kg TCDD (from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref047" target="_blank">47</a>]). (Right) Expression behavior of PPARα-regulated genes in wild-type and AhR-null mice after exposure to TCDD. Significantly different from corresponding control: * p < 0.05, **p < 0.01. Significantly different between controls in wild-type and nullizygous mice: ^# p < 0.05, ^## p < 0.01.</p

PPARα signature development/characterization and screening of a mouse liver gene expression compendium.

<p>Left, signature development and characterization. Wild-type and PPARα-null mice were treated with fenofibrate (Feno) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref021" target="_blank">21</a>], perfluorohexanesulfonate (PFHxS) (GSE55756), or WY-14,643 (WY) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref022" target="_blank">22</a>] in separate experiments carried out in three different labs. Differentially expressed genes (DEGs) were identified using Rosetta Resolver as indicated. Signature genes were identified from the DEGs after applying a number of filtering steps. Genes in the signature were evaluated by Ingenuity Pathway Analysis (IPA) for canonical pathway enrichment and potential transcription factor regulators and by the Comparative Toxicogenomics Database (CTD) to evaluate literature evidence for consistent regulation of signature genes by PPARα activators. Right, signature testing and screening. The PPARα signature was imported into the NextBio environment in which internal protocols rank ordered the genes based on their fold-change. Screening was carried out by comparison of the signature to each bioset using a pair-wise rank-based enrichment analysis (the Running Fishers algorithm). The results of the test including the direction of correlation and p-value for each bioset in the compendium were exported and used to populate a master table containing bioset experimental details. A test of the accuracy of the signature predictions was carried out with treatments that are known positives and negatives for PPARα activation. Screening “hits” were characterized, and a number of predictions were tested in independent studies. Additionally, an external gene expression database of experiments using Affymetrix gene chips was used in a machine learning classification analysis by BRB Array Tools, principal components analysis (PCA), and examination of the relationships between p-value and gene behavior. Part of the figure was adapted from a figure in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112655#pone.0112655.ref027" target="_blank">27</a>].</p

Prediction of PPARα activation using the Running Fisher’s algorithm.

<p>A. Heat map showing the expression of genes in the PPARα signature across 332 biosets. Biosets were ordered based on their similarity to the PPARα signature using the p-value from the Running Fisher’s algorithm. Biosets with positive correlation are on the left and biosets with negative correlation are on the right. The red vertical lines denote the position of biosets with a p-value = 10^-4. B. (Left) Correlation of the PPARα signature to contrasts from the three chemicals used to derive the signature in wild-type but not PPARα-null mice. All p-values were converted to –log10 values. Those comparisons which exhibited negative correlation to the signature were converted to a negative value. (Right) Correlation of the PPARα signature to chemical and synthetic triglyceride activators of PPARα from wild-type but not PPARα-null mice. The compounds were WY-14,643 (WY) (GSE8396), PFOA at 3 mg/kg/day (PFOA-3) (GSE9786), PFOS at 3 or 10 mg/kg/day (PFOS-3, -10) (GSE22871) or synthetic triglycerides composed of the indicated fatty acids (GSE8396). C. The PPARα signature correctly identifies PFNA and PFHxS as PPARα activators in wild-type but not PPARα-null mice. Exposure to the indicated chemicals is described in Study 1 (Methods). D. The PPARα signature correctly identifies the two known PPARα activators (WY and ciprofibrate) in male and female mice exposed to 12 diverse treatments. See Study 3 in Methods for details of exposure conditions. E. The signature genes separate known positives and negatives for PPARα activation using principal components analysis. The first three principal components are shown, derived from the unfiltered expression changes of the signature genes. Red and green, the three chemical treatments in wild-type or PPARα-null mice used to derive the signature, respectively. Blue and black, chemicals or synthetic triglycerides in wild-type or PPARα-null mice, respectively. F. Summary of the sensitivity and specificity of the test for PPARα activation. The signature was compared to chemicals in wild-type or PPARα-null mice that were known positives or negatives for PPARα activation.</p