A Glyptosaurine Lizard from the Eocene (late Uintan) of San Diego, California, and Implications for Glyptosaurine Evolution and Biogeography

Glyptosaurinae is an extinct subfamily of lizards of the family Anguidae. Glyptosaurine lizards are known exclusively from the Paleogene of North America and Eurasia, reaching their peak of diversity and distribution in the Eocene. In North America these lizards are largely restricted to the intermontane basins along the Rocky Mountain range, with only sparse, indeterminately-identified skeletal elements known from outside of this region. Glyptosaurine lizards are split into two tribes: the monophyletic Glyptosaurini and paraphyletic “Melanosaurini”. Within Glyptosaurini, the most common and widespread genus is Glyptosaurus. In this study I describe a new specimen assignable to G. sylvestris, notable for being recovered from the late Uintan of the Santiago Formation in southern California, significantly outside the typical known geographic range of well-preserved glyptosaurine fossils. The presence of Glyptosaurus in southern California at a time of widespread tectonic and climatic change and increasing regional endemism in mammalian faunas, when also considering the results other studies of Eocene lizards, indicates a pattern of evolution for lizards of the time that is quite different from the dramatic turnovers and regional restrictions observed in Eocene mammals. The specimen described here also shows features consistent with ontogenetic variation and may help to provide insight into the life history of glyptosaurine lizards

Comparison of measured and modeled ambient hydrogen sulfide concentrations near a 4000 head swine facility

Air dispersion models are currently being used to regulate agriculture facilities and/or assess their environmental impact. As such, it is critical that these models accurately reflect these impacts. Meteorological conditions, hydrogen sulfide emissions, and downwind hydrogen sulfide concentrations at a four barn, 4000-head, swine finishing facility in Northeast Iowa were measured for a three week period in October 2003. Meteorological conditions and hydrogen sulfide emissions from the barns were used as inputs into two air dispersion models, INPUFF-2 and AERMOD. Model results were compared to measured results at eighteen receptor locations. Results indicate the models did not accurately predict spatial and temporal ambient concentrations. However, a rank order comparison of data (not matched in space and time) shows the models may be useful in predicting maximum concentrations over a period of time

1973

Skunks Shoot Back by William Stewart (page 1) The Daily Plan: A Management Factor by Rob McIntyre (2) Teed Off by Samuel Pavadore (3) A Responsible Neighbor by Gerald Moscato (4) Drainage by David Clement (A-1) Fairway Renovation at Baltusrol Golf Club by Joseph R. Flaherty (A-4) Converting to Kentucky Bluegrass Fairways by Thomas Rewinski (A-10) Grooming the Golf Course by Melvin B. Lucas, Jr. (A-12) Turf Diseases of 1972, Controls and Prevention for 1973 by Stanley J. Zontek (A-13) Noncropland Weed Control by John E. Gallagher (A-16) Soil Factors Affecting Arsenic Toxicity by Robert N. Carrow (A-24) The Necessity of Cart Paths and Traffic Regulations by William G. Buchanan (A-27) Herbicides for Turfgrass Areas by John A. Jagschitz (A-29) Perspectives on Lawn Making and Keeping by Robert Schery (A-33) Stadium Turf Maintenance by George P. Toma (A-39) Highway Turfgrass by Robert W. Duell (A-44) Cemetery Maintenance by Martin Stolpe (A-50) Keeping Records by Al Barauskas (A-54) Planning Capital Expenditures by Sherwood A. Moore (A-59) British Golf Course Architecture -Historical Influences- Current Trends by F.W. Hawtree (A-64) Golf Course Architecture in North America By Geoffrey S. Cornish (A-70

Unitary 4-point correlators from classical geometries

SCOAP

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

Background: Several lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate the possibility that this network could be mapped using microarray technologies and contemporary bioinformatics methods on a dataset derived from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls. Methodology/Principal Findings: We have used two different analytical methodologies: a non-standard differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that are statistically overrepresented in genes that are either differentially expressed (or differentially co-expressed) in cases and controls (e.g., V$KROX_Q6, p-value ,3.31E-6; V$CREBP1_Q2, p-value ,9.93E-6, V$YY1_02, p-value ,1.65E-5). Conclusions/Significance: Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. The most significant transcription factor motifs were for the Early Growth Response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families. These transcription factors are involved in early T-lymphocyte specification and commitment as well as in oligodendrocyte dedifferentiation and development, both pathways that have significant biological plausibility in MS causation

Correlators at large c without information loss

SCOAP

Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips

The Brassica napus 60K Illumina Infinium™ SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications

An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks