Dr. Daza is the principal investigator of the Causes and Associations in Single-Individual Analysis (CASIA) Project. He leads a Stanford Center for Clinical and Translational Research and Education (Spectrum) pilot study to improve personalized discovery of health-related causes and effects using machine learning---in particular, utilizing data from wearable or implanted devices, sensors, or mobile apps.

Eric J. joined the Stanford Prevention Research Center in 2015 as a postdoctoral research fellow under the mentorship of Mike Baiocchi. He earned his DrPH (Doctor of Public Health) in Biostatistics at the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill, under advisors Michael Hudgens and Amy Herring. He had earlier completed his MPS in 2002 and BA in 2000 at Cornell University. He has worked in both the pharmaceutical industry and academia (i.e., research projects involving clinical trials, survey sampling, global nutrition and maternal/child health, and health promotion + disease prevention).

His main interests include causal inference, longitudinal missing data methods, mobile health, self-experimentation, n-of-1 (i.e., single-subject) studies, public health data science, minority health (focusing on Asian Americans, in particular Filipinos), microbiome research, and research on gun violence and use-of-force training.

Honors & Awards

  • Young Investigator Award: 2018 Sage Assembly: Algorithms and the Role of the Individual, Sage Bionetworks (19 – 21 April 2018)
  • Pilot Award: Improving Personalized Medicine through N-of-1 Causal Inference and Predictive Modeling, Stanford Center for Clinical and Translational Research and Education (Spectrum) (May 2017 – June 2018)

Professional Education

  • DrPH, The University of North Carolina at Chapel Hill, Biostatistics (2015)
  • MPS, Cornell University, Applied Statistics (2002)
  • BA, Cornell University, Neurobiology & Behavior and Cognitive Studies (2000)

Stanford Advisors

Research & Scholarship

Current Research and Scholarly Interests

Practical causal-inference methods. Personalized health interventions, self-experimentation, n-of-1 studies / single-case experiments, and precision medicine. Public health data science, minority health (focusing on Asian Americans, in particular Filipinos), microbiome research, and research on gun violence and use-of-force training. Longitudinal missing-data methods. Reproducible or replicable study designs. Areas of particular interest include iterative causal discovery/induction (e.g., mobile health apps, wearable devices, just-in-time adaptive interventions, micro-randomized trials, ecological momentary assessment, quantified-self data), GEE, inverse-probability weighting, Bayesian methods, and meta-analysis.


  • Causes and Associations in Single-Individual Analysis (CASIA; pronounced, Stanford University (September 13, 2016 - Present)

    The Situation: You have a lot of data from your wearable or implantable device, sensor, or mobile health app. You have a recurring outcome you’d like to change (e.g., weight, irritable bowel syndrome, migraine headaches, asthma attacks, chronic pain, blood glucose levels). You’ve identified possible triggers, but their effects may take some time to appear---and it may be expensive or painful to test all or even just a few of them.

    The Challenge: Design experiments to conduct on yourself to characterize the effects of only the most likely triggers.

    I am the principal investigator (PI) of the Causes and Associations in Single-Individual Analyses (CASIA) Project. My goals are to establish the feasibility of applying causal inference methods to improve causal discovery for personalized/precision health, and to develop analysis and analytic methods based on n-of-1 randomized trials (N1RTs), for both observational and experimental studies. (An N1RT is often a randomized single-subject crossover trial. These studies are also called "single-case designs".)

    I am supported by a 2017-2018 Stanford Center for Clinical and Translational Research and Education (Spectrum) Pilot Grant for Population Health Sciences, titled: Improving personalized medicine through n-of-1 causal inference and predictive modeling (N1CPM). I lead the N1CPM Study alongside Professor Lorene Nelson (co-PI) and graduate student Katherine Holsteen. To meet The Challenge above, our general approach is to conduct longitudinal analyses on time series partitioned by transforming exposure levels into treatment periods akin to those of an N1RT. The resulting analyses rely on the potential-outcomes (i.e., counterfactual) framework to draw causal inference---strengthened using machine learning---from these observational data, in what I call an n-of-1 observational study, with the target estimand being what I call an average period treatment effect (Daza, 2017).

    General applications (i.e., not specific to health) of the N1CPM methods include situations described by the following criteria (taken in part from Karkar et al, 2015):

    1.) Recurrence (Outcome): The outcome you want to change occurs regularly.
    2.) Precedence (Exposure): The exposure that might change the outcome precedes said outcome.
    3.) Classifiability (Exposure): The exposure can be categorized into generally non-overlapping treatment or intervention levels (e.g., treatment vs. control, A vs. B).
    4.) Manipulability (Intervention): You must be able to manipulate or otherwise change the intervention levels.
    5.) Dynamism (Effect): The effect of the intervention on the outcome may vary over time, and may take time to stabilize (if at all).

    Dr. Nelson and Ms. Holsteen lead a larger study called Studying TRiggers in Everyday Activity for Migraine (STREAM), where we hope to apply these N1CPM methods to help each participant discover what triggers their migraine headaches, and how they can change or avoid them. We foresee the application of these methods to many other recurring chronic conditions, such as asthma, functional gastrointestinal disorders (e.g., irritable bowel syndrome), and chronic pain.

    --- Daza EJ. Causal analysis of self-tracked time series data using a counterfactual framework for n-of-1 trials. Methods of Information in Medicine. (in press).
    --- Karkar R, Zia J, Vilardaga R, Mishra SR, Fogarty J, Munson SA, Kientz JA. A framework for self-experimentation in personalized health. Journal of the American Medical Informatics Association. 2015 Dec 7;23(3):440-8.


    Stanford, CA


    • Eric J. Daza, Postdoctoral Research Fellow, Stanford Prevention Research Center
    • Lorene Nelson, Associate Professor, Health Research and Policy (Epidemiology)
    • Michael Hittle, Human Biology
    • Mike Baiocchi, Assistant Professor, Stanford Prevention Research Center
  • DISCOVeR Lab, Stanford University School of Medicine (October 1, 2015 - Present)


    Stanford, CA


    • Latha Palaniappan, Professor, Stanford University School of Medicine
    • Nadejda R Marques, School of Medicine


All Publications

  • Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials. Methods of information in medicine Daza, E. J. 2018; 57 (1): e10–e21


    Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

    View details for DOI 10.3414/ME16-02-0044

    View details for PubMedID 29621835

  • Thyroid cancer mortality is higher in Filipinos in the United States: An analysis using national mortality records from 2003 through 2012 CANCER Nguyen, M. T., Hu, J., Hastings, K. G., Daza, E. J., Cullen, M. R., Orloff, L. A., Palaniappan, L. P. 2017; 123 (24): 4860–67

    View details for DOI 10.1002/cncr.30925

    View details for Web of Science ID 000417078600017

  • A Bayesian approach to the g-formula Statistical Methods in Medical Research Keil, A. P., Daza, E., Engel, S. M., Buckley, J. P., Edwards, J. K. 2017

    View details for DOI 10.1177/0962280217694665

  • Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command STATA JOURNAL Daza, E. J., Hudgens, M. G., Herring, A. H. 2017; 17 (2): 253–78


    Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.

    View details for Web of Science ID 000406436600002

    View details for PubMedID 29755297

    View details for PubMedCentralID PMC5947963

  • Online Patient-Provider E-cigarette Consultations: Perceptions of Safety and Harm. American journal of preventive medicine Brown-Johnson, C. G., Burbank, A., Daza, E. J., Wassmann, A., Chieng, A., Rutledge, G. W., Prochaska, J. J. 2016


    E-cigarettes are popular and unregulated. Patient-provider communications concerning e-cigarettes were characterized to identify patient concerns, provider advice and attitudes, and research needs.An observational study of online patient-provider communications was conducted January 2011-June 2015 from a network providing free medical advice, and analyzed July 2014-May 2016. Patient and provider themes, and provider attitudes toward e-cigarettes (positive, negative, or neutral) were coded qualitatively. Provider attitudes were analyzed with cumulative logit modeling to account for clustering. Patient satisfaction with provider responses was expressed via a Thank function.An increase in e-cigarette-related questions was observed over time. Patient questions (N=512) primarily concerned specific side effects and harms (34%); general safety (27%); e-cigarettes as quit aids (19%); comparison of e-cigarette harms relative to combusted tobacco (18%); use with pre-existing medical conditions (18%); and nicotine-free e-cigarettes (14%). Half of provider responses discussed e-cigarettes as a harm reduction option (48%); 26% discussed them as quit aids. Overall, 47% of providers' responses represented a negative attitude toward e-cigarettes; 33% were neutral (contradictory or non-committal); and 20% were positive. Attitudes did not differ statistically by medical specialty; provider responses positive toward e-cigarettes received significantly more Thanks.Examination of online patient-provider communications provides insight into consumer health experience with emerging alternative tobacco products. Patient concerns largely related to harms and safety, and patients preferred provider responses positively inclined toward e-cigarettes. Lacking conclusive evidence of e-cigarette safety or efficacy, healthcare providers encouraged smoking cessation and recommended first-line cessation treatment approaches.

    View details for DOI 10.1016/j.amepre.2016.06.018

    View details for PubMedID 27576005

    View details for PubMedCentralID PMC5118131

  • Likelihood of Unemployed Smokers vs Nonsmokers Attaining Reemployment in a One-Year Observational Study JAMA INTERNAL MEDICINE Prochaska, J. J., Michalek, A. K., Brown-Johnson, C., Daza, E. J., Baiocchi, M., Anzai, N., Rogers, A., Grigg, M., Chieng, A. 2016; 176 (5): 662-670


    Studies in the United States and Europe have found higher smoking prevalence among unemployed job seekers relative to employed workers. While consistent, the extant epidemiologic investigations of smoking and work status have been cross-sectional, leaving it underdetermined whether tobacco use is a cause or effect of unemployment.To examine differences in reemployment by smoking status in a 12-month period.An observational 2-group study was conducted from September 10, 2013, to August 15, 2015, in employment service settings in the San Francisco Bay Area (California). Participants were 131 daily smokers and 120 nonsmokers, all of whom were unemployed job seekers. Owing to the study's observational design, a propensity score analysis was conducted using inverse probability weighting with trimmed observations. Including covariates of time out of work, age, education, race/ethnicity, and perceived health status as predictors of smoking status.Reemployment at 12-month follow-up.Of the 251 study participants, 165 (65.7) were men, with a mean (SD) age of 48 (11) years; 96 participants were white (38.2%), 90 were black (35.9%), 24 were Hispanic (9.6%), 18 were Asian (7.2%), and 23 were multiracial or other race (9.2%); 78 had a college degree (31.1%), 99 were unstably housed (39.4%), 70 lacked reliable transportation (27.9%), 52 had a criminal history (20.7%), and 72 had received prior treatment for alcohol or drug use (28.7%). Smokers consumed a mean (SD) of 13.5 (8.2) cigarettes per day at baseline. At 12-month follow-up (217 participants retained [86.5%]), 60 of 108 nonsmokers (55.6%) were reemployed compared with 29 of 109 smokers (26.6%) (unadjusted risk difference, 0.29; 95% CI, 0.15-0.42). With 6% of analysis sample observations trimmed, the estimated risk difference indicated that nonsmokers were 30% (95% CI, 12%-48%) more likely on average to be reemployed at 1 year relative to smokers. Results of a sensitivity analysis with additional covariates of sex, stable housing, reliable transportation, criminal history, and prior treatment for alcohol or drug use (25.3% of observations trimmed) reduced the difference in employment attributed to smoking status to 24% (95% CI, 7%-39%), which was still a significant difference. Among those reemployed at 1 year, the average hourly wage for smokers was significantly lower (mean [SD], $15.10 [$4.68]) than for nonsmokers (mean [SD], $20.27 [$10.54]; F(1,86) = 6.50, P = .01).To our knowledge, this is the first study to prospectively track reemployment success by smoking status. Smokers had a lower likelihood of reemployment at 1 year and were paid significantly less than nonsmokers when reemployed. Treatment of tobacco use in unemployment service settings is worth testing for increasing reemployment success and financial well-being.

    View details for DOI 10.1001/jamainternmed.2016.0772

    View details for Web of Science ID 000375292500023

    View details for PubMedID 27065044

  • Plasma Micronutrient Concentrations Are Altered by Antiretroviral Therapy and Lipid-Based Nutrient Supplements in Lactating HIV-Infected Malawian Women JOURNAL OF NUTRITION Flax, V. L., Adair, L. S., Allen, L. H., Shahab-Ferdows, S., Hampel, D., Chasela, C. S., Tegha, G., Daza, E. J., Corbett, A., Davis, N. L., Kamwendo, D., Kourtis, A. P., van der Horst, C. M., Jamieson, D. J., Bentley, M. E. 2015; 145 (8): 1950-1957


    Little is known about the influence of antiretroviral therapy with or without micronutrient supplementation on the micronutrient concentrations of HIV-infected lactating women in resource-constrained settings.We examined associations of highly active antiretroviral therapy (HAART) and lipid-based nutrient supplements (LNS) with concentrations of selected micronutrients in HIV-infected Malawian women at 24 wk postpartum.Plasma micronutrient concentrations were measured in a subsample (n = 690) of Breastfeeding, Antiretrovirals, and Nutrition (BAN) study participants who were randomly assigned at delivery to receive HAART, LNS, HAART+LNS, or no HAART/no LNS (control). HAART consisted of protease inhibitor-based triple therapy. LNS (140 g/d) met energy and micronutrient requirements of lactation. Multivariable linear regression tested the association of HAART and LNS, plus their interaction, with micronutrient concentrations, controlling for season, baseline viral load, and baseline CD4 count.We found significant HAART by LNS interactions for folate (P = 0.051), vitamin B-12 (P < 0.001), and transferrin receptors (TfRs) (P = 0.085). HAART was associated with lower folate (with LNS: -27%, P < 0.001; without LNS: -12%, P = 0.040) and higher TfR concentrations (with LNS: +14%, P = 0.004; without LNS: +28%, P < 0.001), indicating iron deficiency. LNS increased folate (with HAART: +17%, P = 0.037; without HAART: +39%, P < 0.001) and decreased TfR concentrations (with HAART only: -12%, P = 0.023). HAART was associated with lower vitamin B-12 concentrations only when LNS was present (-18%, P = 0.001), whereas LNS increased vitamin B-12 only when no HAART was present (+27%, P < 0.001). HAART, but not LNS, was associated with higher retinol-binding protein (RBP; +10%, P = 0.007). We detected no association of HAART or LNS with selenium, ferritin, or hemoglobin.The association of HAART with lower folate, iron deficiency, and higher RBP plus the attenuation of LNS effects on folate and vitamin B-12 when combined with HAART has implications for the health of lactating HIV-infected women taking HAART in prevention of mother-to-child transmission programs. This trial was registered at as NCT00164736.

    View details for DOI 10.3945/jn.115.212290

    View details for Web of Science ID 000359037500035

    View details for PubMedID 26156797

  • Integrating Group Counseling, Cell Phone Messaging, and Participant-Generated Songs and Dramas into a Microcredit Program Increases Nigerian Women's Adherence to International Breastfeeding Recommendations JOURNAL OF NUTRITION Flax, V. L., Negerie, M., Ibrahim, A. U., Leatherman, S., Daza, E. J., Bentley, M. E. 2014; 144 (7): 1120-1124


    In northern Nigeria, interventions are urgently needed to narrow the large gap between international breastfeeding recommendations and actual breastfeeding practices. Studies of integrated microcredit and community health interventions documented success in modifying health behaviors but typically had uncontrolled designs. We conducted a cluster-randomized controlled trial in Bauchi State, Nigeria, with the aim of increasing early breastfeeding initiation and exclusive breastfeeding among female microcredit clients. The intervention had 3 components. Trained credit officers led monthly breastfeeding learning sessions during regularly scheduled microcredit meetings for 10 mo. Text and voice messages were sent out weekly to a cell phone provided to small groups of microcredit clients (5-7 women). The small groups prepared songs or dramas about the messages and presented them at the monthly microcredit meetings. The control arm continued with the regular microcredit program. Randomization occurred at the level of the monthly meeting groups. Pregnant clients were recruited at baseline and interviewed again when their infants were aged ≥6 mo. Logistic regression models accounting for clustering were used to estimate the odds of performing recommended behaviors. Among the clients who completed the final survey (n = 390), the odds of exclusive breastfeeding to 6 mo (OR: 2.4; 95% CI: 1.4, 4.0) and timely breastfeeding initiation (OR: 2.6; 95% CI: 1.6, 4.1) were increased in the intervention vs. control arm. Delayed introduction of water explained most of the increase in exclusive breastfeeding among clients receiving the intervention. In conclusion, a breastfeeding promotion intervention integrated into microcredit increased the likelihood that women adopted recommended breastfeeding practices. This intervention could be scaled up in Nigeria, where local organizations provide microcredit to >500,000 clients. Furthermore, the intervention could be adopted more widely given that >150 million women, many of childbearing age, are involved in microfinance globally.

    View details for DOI 10.3945/jn.113.190124

    View details for Web of Science ID 000337984200018

    View details for PubMedID 24812071

  • Plasma and breast-milk selenium in HIV-infected Malawian mothers are positively associated with infant selenium status but are not associated with maternal supplementation: results of the Breastfeeding, Antiretrovirals, and Nutrition study AMERICAN JOURNAL OF CLINICAL NUTRITION Flax, V. L., Bentley, M. E., Combs, G. F., Chasela, C. S., Kayira, D., Tegha, G., Kamwendo, D., Daza, E. J., Fokar, A., Kourtis, A. P., Jamieson, D. J., van der Horst, C. M., Adair, L. S. 2014; 99 (4): 950-956


    Selenium is found in soils and is essential for human antioxidant defense and immune function. In Malawi, low soil selenium and dietary intakes coupled with low plasma selenium concentrations in HIV infection could have negative consequences for the health of HIV-infected mothers and their exclusively breastfed infants.We tested the effects of lipid-based nutrient supplements (LNS) that contained 1.3 times the Recommended Dietary Allowance of sodium selenite and antiretroviral drugs (ARV) on maternal plasma and breast-milk selenium concentrations.HIV-infected Malawian mothers in the Breastfeeding, Antiretrovirals, and Nutrition study were randomly assigned at delivery to receive: LNS, ARV, LNS and ARV, or a control. In a subsample of 526 mothers and their uninfected infants, we measured plasma and breast-milk selenium concentrations at 2 or 6 (depending on the availability of infant samples) and 24 wk postpartum.Overall, mean (± SD) maternal (range: 81.2 ± 20.4 to 86.2 ± 19.9 μg/L) and infant (55.6 ± 16.3 to 61.0 ± 15.4 μg/L) plasma selenium concentrations increased, whereas breast-milk selenium concentrations declined (14.3 ± 11.5 to 9.8 ± 7.3 μg/L) from 2 or 6 to 24 wk postpartum (all P < 0.001). Compared with the highest baseline selenium tertile, low and middle tertiles were positively associated with a change in maternal plasma or breast-milk selenium from 2 or 6 to 24 wk postpartum (both P < 0.001). With the use of linear regression, we showed that LNS that contained selenium and ARV were not associated with changes in maternal plasma and breast-milk selenium, but maternal selenium concentrations were positively associated with infant plasma selenium at 2 or 6 and 24 wk postpartum (P < 0.001) regardless of the study arm.Selenite supplementation of HIV-infected Malawian women was not associated with a change in their plasma or breast-milk selenium concentrations. Future research should examine effects of more readily incorporated forms of selenium (ie, selenomethionine) in HIV-infected breastfeeding women.

    View details for DOI 10.3945/ajcn.113.073833

    View details for Web of Science ID 000333173100023

    View details for PubMedID 24500152

  • Changes in Soluble Transferrin Receptor and Hemoglobin Concentrations in Malawian Mothers Are Associated with Those Values in their Exclusively Breastfed, HIV-Exposed Infants JOURNAL OF NUTRITION Widen, E. M., Bentley, M. E., Kayira, D., Chasela, C. S., Daza, E. J., Kacheche, Z. K., Tegha, G., Jamieson, D. J., Kourtis, A. P., van der Horst, C. M., Allen, L. H., Shahab-Ferdows, S., Adair, L. S. 2014; 144 (3): 367-374


    Infant iron status at birth is influenced by maternal iron status during pregnancy; however, there are limited data on the extent to which maternal iron status is associated with infant iron status during exclusive breastfeeding. We evaluated how maternal and infant hemoglobin and iron status [soluble transferrin receptors (TfR) and ferritin] were related during exclusive breastfeeding in HIV-infected women and their infants. The Breastfeeding, Antiretrovirals, and Nutrition Study was a randomized controlled trial in Lilongwe, Malawi, in which HIV-infected women were assigned with a 2 × 3 factorial design to a lipid-based nutrient supplement (LNS), or no LNS, and maternal, infant, or no antiretroviral drug, and followed for 24 wk. Longitudinal models were used to relate postpartum maternal hemoglobin (n = 1926) to concurrently measured infant hemoglobin, adjusting for initial infant hemoglobin values. In a subsample, change in infant iron status (hemoglobin, log ferritin, log TfR) between 2 (n = 352) or 6 wk (n = 167) and 24 wk (n = 519) was regressed on corresponding change in the maternal indicator, adjusting for 2 or 6 wk values. A 1 g/L higher maternal hemoglobin at 12, 18, and 24 wk was associated with a 0.06 g/L (P = 0.01), 0.10 g/L (P < 0.001), and 0.06 g/L (P = 0.01), respectively, higher infant hemoglobin. In the subsample, a reduction in maternal log TfR and an increase in hemoglobin from initial measurement to 24 wk were associated with the same pattern in infant values (log TfR β = -0.18 mg/L, P < 0.001; hemoglobin β = 0.13 g/L, P = 0.01). Given the observed influence of maternal and initial infant values, optimizing maternal iron status in pregnancy and postpartum is important to protect infant iron status. This trial was registered at as NCT00164736.

    View details for DOI 10.3945/jn.113.177915

    View details for Web of Science ID 000332054300018

    View details for PubMedID 24381222

  • Weight status in HIV-positive women after termination of a drug and lipid-based nutrient supplement intervention is predicted by food availability and health Experimental Biology Meeting 2012 Jordan-Bell, E., Adair, L., Flax, V., Chasela, C., Kayira, D., Daza, E., Tembo, M., Chitsulo, P., Jamieson, D., van der Horst, C., Bentley, M. FEDERATION AMER SOC EXP BIOL. 2012