Category Archives: Research

Paper published: Rates of Biotic Interactions Scale Predictably with Temperature Despite Variation

Rates of Biotic Interactions Scale Predictably with Temperature Despite Variation Burnside, W. R., Erhardt, E. B., Hammond, S. T. and Brown, J. H. Oikos, 123 pp. 1449–1456 Online: May 27, 2014 http://onlinelibrary.wiley.com/doi/10.1111/oik.01199/abstract DOI: 10.1111/oik.01199 Abstract Most biological processes are temperature dependent. To quantify the temperature dependence of biotic interactions and evaluate predictions of metabolic theory, we: 1) compiled a database of 81 studies that provided 112 measures of rates of herbivory, predation, parasitism, parasitoidy, or competition between two species at two or more temperatures; and 2) analyzed the temperature dependence of these rates in the framework of metabolic ecology to test our prediction that the “activation energy,” E, centers around 0.65 eV. We focused on studies that assessed rates or associated times of entire biotic interactions, such as time to consumption of all prey, rather than rates of components of these interactions, such as prey encounter rate. Results were: 1) the frequency distribution of E for each interaction type was typically peaked and right skewed; 2) the overall mean is E= 0.96 eV and median E= 0.78 eV; 3) there was significant variation in E within but not across interaction types; but 4) average values of E were not significantly different from 0.65 eV by interaction type and 5) studies with measurements at more temperatures were more consistent with E= 0.65 eV. These synthetic findings suggest that, despite the many complicating factors, the temperature-dependence of rates of biotic interactions broadly reflect of rates of metabolism, a relationship with important implications for a warming world.
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Paper published: Stable Isotope Sourcing using Sampling

Stable Isotope Sourcing using Sampling Erhardt, EB, BO Wolf, M Ben-David, and EJ Bedrick Open Journal of Ecology 4 (6) pp. 289–298 Online: May 2014 http://www.scirp.org/journal/PaperInformation.aspx?PaperID=46187 DOI: 10.4236/oje.2014.46027 Abstract Stable isotope mixing models are used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets, plant nutrient use, geochemistry, pollution, and forensics. We describe an algorithm implemented as SISUS software for providing a user-specified number of probabilistic exact solutions derived quickly from the extended mixing model. Our method outperforms IsoSource, a deterministic algorithm for providing approximate solutions to represent the solution polytope. Our method is an approximate Bayesian large sample procedure. SISUS software is freely available at StatAcumen.com/sisus and as an R package at cran.r-project.org/web/packages/sisus.
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Paper published: Infectious, autoimmune, and allergic diseases and risk of Hodgkin lymphoma in children and adolescents: A Children’s Oncology Group (COG) study

Infectious, autoimmune, and allergic diseases and risk of Hodgkin lymphoma in children and adolescents: A Children’s Oncology Group (COG) study Linabery, A. M., Erhardt, E. B., Fonstad, R. K., Ambinder, R. F., Bunin, G. R., Ross, J. A., Spector, L. G. and Grufferman, S. International Journal of Cancer, 135: 1454–1469 Online: March 18, 2014 http://onlinelibrary.wiley.com/doi/10.1002/ijc.28785/abstract DOI: 10.1002/ijc.28785 Abstract An infectious origin for pediatric Hodgkin lymphoma (HL) has long been suspected and Epstein-Barr virus (EBV) has been implicated in a subset of cases. Increased HL incidence in children with congenital and acquired immunodeficiencies, consistent associations between autoimmune diseases and adult HL and genome-wide association and other genetic studies together suggest immune dysregulation is involved in lymphomagenesis. Here, healthy control children identified by random digit dialing were matched on sex, race/ethnicity and age to HL diagnosed in 1989-2003 at 0-14 years at Children’s Oncology Group institutions. Parents of 517 cases and 784 controls completed telephone interviews, including items regarding medical histories. Tumor EBV status was determined for 355 cases. Using conditional logistic regression, we calculated odds ratios (ORs) and 95% confidence intervals (CIs) for risk of HL. Cases were more likely to have had an infection>1 year prior to HL diagnosis (OR=1.69, 95% CI: 0.98-2.91); case siblings were also more likely to have had a prior infection (OR=2.04, 95% CI: 1.01-4.14). Parental history of autoimmunity associated with increased EBV+ HL risk (OR=2.97, 95% CI: 1.34-6.58), while having a parent (OR=1.47, 95% CI: 1.01-2.14) or sibling (OR=1.62, 95% CI: 1.11-2.36) with an allergy was associated with EBV - HL. These results may indicate true increased risk for infections and increased risk with family history of autoimmune and allergic conditions that varies by tumor EBV status, or they may be attributable to inaccurate recall. In addition to employing biomarkers to confirm the role of immune-modulating conditions in pediatric HL, future studies should focus on family based designs.
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Paper published: Inference for stable isotope mixing models: a study of the diet of dunlin

Inference for stable isotope mixing models: a study of the diet of dunlin Erhardt, EB and EJ Bedrick Journal of the Royal Statistical Society: Series C. pp. 579–593 Online: February 4, 2014 http://onlinelibrary.wiley.com/doi/10.1111/rssc.12047/abstract doi: 10.1111/rssc.12047 Abstract Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets and plant nutrient use. Statistical methods for inference on the diet proportions by using stable isotopes have focused on the linear mixing model. Existing frequentist methods assume that the diet proportion vector can be uniquely solved for in terms of one or two isotope ratios. We develop large sample methods that apply to an arbitrary number of isotope ratios, assuming that the linear mixing model has a unique solution or is overconstrained. We generalize these methods to allow temporal modelling of the population mean diet, assuming that isotope ratio response data are collected over time. The methodology is motivated by a study of the diet of dunlin, a small migratory seabird.
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Paper published: Targeted 13C enrichment of lipid and protein pools in the body reveals circadian changes in oxidative fuel mixture during prolonged fasting: a case study using Japanese quail

Targeted 13C enrichment of lipid and protein pools in the body reveals circadian changes in oxidative fuel mixture during prolonged fasting: a case study using Japanese quail McCue, MD, JA Amaya, AS Yang, EB Erhardt, BO Wolf, and DT Hanson Comparative Biochemistry and Physiology – Part A: Molecular & Integrative Physiology 166 (4), pdf, pp. 546–554 Online: August 27, 2013 http://www.sciencedirect.com/science/article/pii/S1095643313002237 DOI: 10.1016/j.cbpa.2013.08.009 Abstract Many animals undergo extended periods of fasting. During these fasts, animals oxidize a ratio of macronutrients dependent on the nutritional, energetic, and hydric requirements of the fasting period. In this study, we use Japanese quail (Coturnix coturnix japonica), a bird with natural intermediate fasting periods, to examine macronutrient use during a 6 d fast. We raised groups of quail on isotopically labeled materials (13C-1-leucine, 13C-U-glucose, or 13C-1-palmitic acid) with the intent of labeling specific macronutrient/tissue pools in each treatment, and then traced their use as fuels by measuring the δ13C values of breath CO2. Based on changes in δ13C values during the fast, it appears that the carbohydrate label,13C-U-glucose, was largely incorporated into the lipid pool and thus breath samples ultimately reflected lipid use rather than carbohydrate use. In the lipid treatment, the 13C-1-palmitic acid faithfully labeled the lipid pool and was reflected in the kinetics δ13C values in breath CO2 during the fast. Endogenous lipid oxidation peaked after 24 h of fasting and remained constantly elevated thereafter. The protein label,13C-1-leucine, showed clear diurnal periods of protein sparing and degradation, with maximal rates of protein oxidation occurring at night and the lowest rates occurring during the day time. This stable isotope tracer method provides a noninvasive approach to study the nutrient dynamics of fasting animals and should provide new insights into how different types of animals use specific nutrient pools during fasting and possibly other non-steady physiological states.
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Paper published: Allergies, atopy, immune-related factors and childhood rhabdomyosarcoma: A report from the Children’s Oncology Group

Allergies, atopy, immune-related factors and childhood rhabdomyosarcoma: A report from the Children’s Oncology Group Lupo, PJ, R Zhou, SX Skapek, DS Hawkins, LG Specor, ME Scheurer, B Melin, K Papworth, EB Erhardt, and S Grufferman International Journal of Cancer 134 (2). pdf, pp. 431–436 Online: August 1, 2013 http://onlinelibrary.wiley.com/doi/10.1002/ijc.28363/abstract DOI: 10.1002/ijc.28363 Abstract Rhabdomyosarcoma (RMS) is a highly malignant tumor of developing muscle that can occur anywhere in the body. Due to its rarity, relatively little is known about the epidemiology of RMS. Atopic disease is hypothesized to be protective against several malignancies; however, to our knowledge, there have been no assessments of atopy and childhood RMS. Therefore, we explored this association in a case-control study of 322 childhood RMS cases and 322 pair-matched controls. Cases were enrolled in a trial run by the Intergroup Rhabdomyosarcoma Study Group. Controls were matched to cases on race, sex and age. The following atopic conditions were assessed: allergies, asthma, eczema and hives; in addition, we examined other immune-related factors: birth order, day-care attendance and breastfeeding. Conditional logistic-regression models were used to calculate an odds ratio (OR) and 95% confidence interval (CI) for each exposure, adjusted for age, race, sex, household income and parental education. As the two most common histologic types of RMS are embryonal (n = 215) and alveolar (n = 66), we evaluated effect heterogeneity of these exposures. Allergies (OR = 0.60, 95% CI: 0.41–0.87), hives (OR = 0.61, 95% CI: 0.38–0.97), day-care attendance (OR = 0.48, 95% CI: 0.32–0.71) and breastfeeding for ≥ 12 months (OR = 0.36, 95% CI: 0.18–0.70) were inversely associated with childhood RMS. These exposures did not display significant effect heterogeneity between histologic types (p > 0.52 for all exposures). This is the first study indicating that atopic exposures may be protective against childhood RMS, suggesting additional studies are needed to evaluate the immune system’s role in the development of this tumor.
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Paper published: Negotiating for improved reimbursement for Dialectical Behavior Therapy: A successful project

Negotiating for improved reimbursement for Dialectical Behavior Therapy: A successful project Cedar R. Koons, Beth O’Rourke, Barbara Carter, Erik B. Erhardt Cognitive and Behavioral Practice Received: 25 May 2012 Accepted: 18 January 2013 Online: 1 March 2013 DOI: 10.1016/j.cbpra.2013.01.003 Abstract Dialectical Behavior Therapy (DBT) is an evidence-based treatment for borderline personality disorder that has been widely disseminated to many outpatient treatment settings. Many practitioners depend on third-party payers to fund treatment delivery. DBT requires additional clinical services not often included in outpatient therapy, including a weekly skills group led by 2 clinicians, and the requirement for clinicians to attend weekly consultation team and provide intersession contact for coaching. Standard outpatient insurance rates for individual and group sessions do not provide adequate reimbursement for the additional services of DBT. This paper describes how two DBT team leaders collaborated to obtain improved reimbursement for their programs. The two teams met with insurers, educated them about DBT, and showed outcomes from their programs to achieve large increases in reimbursement rates. The paper includes client outcome data from both programs.
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TV: KOB4, Police cadet test scores under investigation

Tonight KOB-TV4 aired the NM Law Enforcement Academy “Police cadet test scores under investigation” story on the Eyewitness News 4 at 10 P.M., for which I gave a short interview to Gadi Schwartz using a plot I created from the test score data. I gave the information and interview out of a personal desire to be helpful and was not acting on the University’s behalf. I did not speculate on the cause for the outlying class’s scores. I value the men and women who risk their lives daily serving our communities.
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Plot improved: NM Registered voters 2008

Showing party affiliation by age group can be made more informative by representing voter power with census data.  Note that in the “before” plot, years 60+ appear to be almost half the plot width while in the “after” plot we see that 60+ only represent 25% of the voting pool. Before After R code to create the “after” plot follows.
# Erik B. Erhardt
# 4/28/2012

# Recreating this plot as a Marimekko mosaic chart
# NM Registered Voters - Party by Age Line Chart (Oct 2008)
# http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

# Census population sizes
# NM population numbers
# http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_QTP1&prodType=table

ages <- c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
          "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-99")
pop.ages <- c(149861,142370,139678,127567,123303,125220,144839,147170,
              136799,120137, 87890, 65904, 50230, 36238, 21622, 10371)

age <- seq(18,99)
pop <- c(rep(pop.ages/5,each=5)[c(4:75)], rep(pop.ages[length(pop.ages)]/10,10))
pop.prop <- pop/sum(pop)

# datathief http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

dem <- c(0.46,0.46,0.45,0.44,0.40,0.41,0.42,0.43,0.43,0.44,0.46
        ,0.46,0.46,0.46,0.47,0.48,0.48,0.48,0.48,0.48,0.48,0.48
        ,0.48,0.48,0.49,0.49,0.49,0.49,0.49,0.49,0.49,0.50,0.50
        ,0.51,0.52,0.53,0.52,0.53,0.54,0.55,0.55,0.55,0.55,0.54
        ,0.54,0.55,0.54,0.54,0.53,0.55,0.55,0.55,0.55,0.55,0.55
        ,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.58,0.58,0.57,0.57
        ,0.57,0.57,0.56,0.58,0.56,0.58,0.58,0.58,0.59,0.59,0.61
        ,0.59,0.62,0.61,0.62,0.60)

rep <- c(0.24,0.25,0.26,0.28,0.28,0.27,0.27,0.27,0.27,0.27,0.26
        ,0.27,0.27,0.28,0.28,0.28,0.29,0.30,0.31,0.31,0.32,0.32
        ,0.33,0.33,0.33,0.33,0.34,0.34,0.34,0.35,0.35,0.35,0.34
        ,0.34,0.34,0.33,0.33,0.33,0.32,0.31,0.31,0.31,0.31,0.32
        ,0.33,0.32,0.34,0.34,0.35,0.34,0.35,0.35,0.35
        ,0.35,0.35,0.35,0.35,0.36,0.36,0.36,0.36,0.35,0.34,0.34
        ,0.35,0.35,0.34,0.34,0.36,0.35,0.36,0.35,0.34,0.35,0.34
        ,0.34,0.33,0.34,0.33,0.32,0.30,0.31)

dts  <- c(0.26,0.25,0.25,0.24,0.30,0.29,0.28
        ,0.28,0.26,0.26,0.24,0.24,0.23,0.23,0.21,0.20,0.19,0.19
        ,0.18,0.18,0.17,0.17,0.16,0.16,0.15,0.15,0.14,0.15,0.14
        ,0.14,0.13,0.13,0.13,0.12,0.12,0.12,0.12,0.12,0.12,0.12
        ,0.12,0.12,0.12,0.12,0.11,0.11,0.10,0.10,0.11,0.10,0.09
        ,0.09,0.09,0.08,0.08,0.08,0.08,0.07,0.07,0.07,0.07,0.07
        ,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.06,0.06
        ,0.07,0.07,0.07,0.06,0.06,0.04,0.06,0.05,0.06)

other <- c(0.05,0.05,0.05,0.05,0.03,0.03,0.04,0.04,0.04,0.04,0.04
          ,0.04,0.04,0.04,0.04,0.04,0.04,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.01
          ,0.02,0.02,0.01,0.01,0.02,0.01,0.01,0.02,0.01,0.02,0.01
          ,0.01,0.01,0.00,0.01,0.02)

all <- data.frame(dem, rep, dts, other)
rowSums(all)

# correct rounding errors from datathief
for (i in 1:length(age)) {
  all[i,] <- all[i,]/sum(all[i,]);
}
rowSums(all)

## getting data list above
# x <- scan()
# [datathief numbers]
#
# round(matrix(x,ncol=2,byrow=TRUE)[,2],2)
# plot(round(matrix(x,ncol=2,byrow=TRUE)[,1],0))

# following example from http://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/

################################################################################
df <- data.frame(
          segment = age
        , segpct = pop.prop * 100
        , Other = all$other * 100
        , DTS  = all$dts    * 100
        , Rep = all$rep     * 100
        , Dem = all$dem     * 100
      )

df$xmax <- cumsum(df$segpct)
df$xmin <- df$xmax - df$segpct
df$segpct <- NULL

library(ggplot2)
library(reshape)

dfm <- melt(df, id = c("segment", "xmin", "xmax"))

dfm1 <- ddply(dfm , .(segment), transform, ymax = cumsum(value))
dfm1 <- ddply(dfm1, .(segment), transform, ymin = ymax - value)

dfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)
dfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)

dfm1$segmentlabel <- rep("",length(dfm1$segment))
ss <- ((dfm1$segment %% 5)==0); # every 5 years, display age
dfm1$segmentlabel[ss] <- dfm1$segment[ss]
dfm1$segmentlabel[(dfm1$segment==18)] <- "age"

p <- ggplot(dfm1, aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable))

p <- p + geom_rect(colour = I("grey"), alpha=0.75, size=.01) +
      xlab("Percentage age distribution") +
      ylab("Percent registered voter for party by age") +
      labs(title="NM Registered Voters - Party by Age (Oct 2008)")

p <- p + geom_text(aes(x = xtext, y = ytext,
     label = ifelse(segment == 20, paste(variable), " ")), size = 3.5)

p <- p + geom_text(aes(x = xtext, y = -3, label = paste(dfm1$segmentlabel)), size = 3)
p

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Paper Published: Tracking whole-brain connectivity dynamics in the resting-state

Tracking Whole-Brain Connectivity Dynamics in the Resting State Elena A. Allen, Eswar Damaraju, Sergey M. Plis, Erik B. Erhardt, Tom Eichele, and Vince D. Calhoun Cerebral Cortex Received: July 24, 2012 Accepted: October 15, 2012 Online: November 11, 2012 http://cercor.oxfordjournals.org/content/early/2012/11/09/cercor.bhs352.abstract doi: 10.1093/cercor/bhs352 Abstract Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.
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