Multiple factors co-limit short-term in situ soil carbon dioxide emissions.

Soil respiration is a major source of atmospheric CO2. If it increases with warming, it will counteract efforts to minimize climate change. To improve understanding of environmental controls over soil CO2 emission, we applied generalized linear modeling to a large dataset of in situ measurements of short-term soil respiration rate, with associated environmental attributes, which was gathered over multiple years from four locations that varied in climate, soil type, and vegetation. Soil respiration includes many CO2-producing processes: we theorized that different environmental factors could limit each process distinctly, thereby diminishing overall CO2 emissions. A baseline model that included soil temperature, soil volumetric water content, and their interaction was effective in estimating soil respiration at all four locations (p < 0.0001). Model fits, based on model log likelihoods, improved continuously as additional covariates were added, including mean daily air temperature, enhanced vegetation index (EVI), and quadratic terms for soil temperature and water content, and their interactions. The addition of land cover and its direct interactions with environmental variables further improved model fits. Significant interactions between covariates were observed at each location and at every stage of analysis, but the interaction terms varied among sites and models, and did not consistently maintain importance in more complex models. A main-effects model was therefore tested, which included soil temperature and water content, their quadratic effects, EVI, and air temperature, but no interactions. In that case all six covariates were significant (p < 0.0001) when applied across sites. We infer that local-scale soil-CO2 emissions are commonly co-limited by EVI and air temperature, in addition to soil temperature and water content. Importantly, the quadratic soil temperature and moisture terms were significantly negative: estimated soil-CO2 emissions declined when soil temperature exceeded 22.5°C, and as soil moisture differed from the optimum of 0.27 m3 m-3.

Time of Soybean Sudden Death Syndrome Foliar Symptom Onset Influences Final Disease Intensity, Yield, and Yield Components.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, causes substantial yield losses in soybean. However, relationships between soybean yield and components of disease progress, including time of disease onset, are poorly understood. Individual soybean plants (2018) and quadrats (2016 to 2018) were monitored in commercial fields and experimental plots in Iowa to quantify the impact of SDS foliar symptom onset on final SDS intensity, soybean yield components, and yield. The date when SDS foliar symptoms were first detected (onset time) and progress of SDS incidence and severity were recorded weekly. Individual soybean plants and quadrats were harvested at the end of each season. Beta-regression showed that date of SDS onset had a consistent and stable effect on final disease intensity both at individual plant and quadrat levels. The slope of the relationship between date of SDS onset and final SDS severity was common across all field sites and years. Weighted linear regression revealed that SDS onset explained 60 to 83% of the variation in number of pods, number of seeds, and total seed weight in individual plants, and 94 to 97% of the variation in seed yield in quadrats. Soybean yield damage functions (slopes) indicated that for each day SDS onset was delayed, soybean yield increased by 30.5 to 31.3 kg/ha. This new quantitative information improves understanding of the impact of SDS on final disease intensity and soybean yield. Further experiments are needed to determine how this relationship is affected by site-specific factors.

Home Blood Pressure Monitoring in Cases of Clinical Uncertainty to Differentiate Appropriate Inaction From Therapeutic Inertia.

PURPOSE: Conventional clinic blood pressure (BP) measurements are routinely used for hypertension management and physician performance measures. We aimed to check home BP measurements after elevated conventional clinic BP measurements for which physicians did not intensify treatment, to differentiate therapeutic inertia from appropriate inaction. METHODS: We conducted a pre and post study of home BP monitoring for patients with uncontrolled hypertension as determined by conventional clinic BP measurements for which physicians did not intensify hypertension management. Physicians were notified of average home BP 2-4 weeks after the initial clinic visit. Outcome measures were the proportion of patients with controlled hypertension using average home BP measurements, changes in hypertension management by physicians, changes in physicians' hypertension metrics, and factors associated with home-clinic BP differences. RESULTS: Of 90 recruited patients who had elevated conventional clinic BP recordings, 65.6% had average home BP measurements that were <140/90 mm Hg. Physicians changed treatment plans for 61% of patients with average home BP readings of ≥140/90 mm Hg, whereas decisions to not change treatment for the remaining patients were based on contextual factors. Substituting average home BP for conventional clinic BP for 4% of patients from 2 physicians' hypertension registries improved the physicians' hypertension control rates by 3% to 5%. Greater body mass index and increased number of BP medications were associated with home BP measurement ≥140/90 mm Hg. Clinic BP levels did not estimate normal home BP levels. CONCLUSIONS: Documented home BP in cases of clinical uncertainty helped differentiate therapeutic inertia from appropriate inaction and improved physicians' hypertension metrics.

A neighborhood statistics model for predicting stream pathogen indicator levels.

Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli, a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale.

Moesin1 and Ve-cadherin are required in endothelial cells during in vivo tubulogenesis.

Endothelial tubulogenesis is a crucial step in the formation of functional blood vessels during angiogenesis and vasculogenesis. Here, we use in vivo imaging of living zebrafish embryos expressing fluorescent fusion proteins of beta-Actin, alpha-Catenin, and the ERM family member Moesin1 (Moesin a), to define a novel cord hollowing process that occurs during the initial stages of tubulogenesis in intersegmental vessels (ISVs) in the embryo. We show that the primary lumen elongates along cell junctions between at least two endothelial cells during embryonic angiogenesis. Moesin1-EGFP is enriched around structures that resemble intracellular vacuoles, which fuse with the luminal membrane during expansion of the primary lumen. Analysis of silent heart mutant embryos shows that initial lumen formation in the ISVs is not dependent on blood flow; however, stabilization of a newly formed lumen is dependent upon blood flow. Zebrafish moesin1 knockdown and cell transplantation experiments demonstrate that Moesin1 is required in the endothelial cells of the ISVs for in vivo lumen formation. Our analyses suggest that Moesin1 contributes to the maintenance of apical/basal cell polarity of the ISVs as defined by adherens junctions. Knockdown of the adherens junction protein Ve-cadherin disrupts formation of the apical membrane and lumen in a cell-autonomous manner. We suggest that Ve-cadherin and Moesin1 function to establish and maintain apical/basal polarity during multicellular lumen formation in the ISVs.

Exploring dependence with data on spatial lattices.

The application of Markov random field models to problems involving spatial data on lattice systems requires decisions regarding a number of important aspects of model structure. Existing exploratory techniques appropriate for spatial data do not provide direct guidance to an investigator about these decisions. We introduce an exploratory quantity that is directly tied to the structure of Markov random field models based on one-parameter exponential family conditional distributions. This exploratory diagnostic is shown to be a meaningful statistic that can inform decisions involved in modeling spatial structure with statistical dependence terms. In this article, we develop the diagnostic, illustrate its use in guiding modeling decisions with simulated examples, and reexamine a previously published application.

Transgenic alfalfa that accumulates piceid (trans-resveratrol-3-O-beta-D-glucopyranoside) requires the presence of beta-glucosidase to inhibit the formation of aberrant crypt foci in the colon of CF-1 mice.

Plants have been genetically enhanced to produce a number of products for agricultural, industrial and pharmaceutical purposes. This technology could potentially be applied to providing chemoprevention strategies to the general population. Resveratrol (3,5,4'-trihydroxystilbene) is a compound that has been shown to have protective activity against a number of cancers and could be an ideal candidate for such an application. Alfalfa that was genetically modified to express resveratrol-synthase was used as a model in applying biotechnological approaches to cancer prevention. The transgenic alfalfa, which accumulates resveratrol as a glucoside (piceid = trans-resveratrol-3-O-Beta-D-glucopyranoside) (152 +/- 17.5 microg piceid/g dry weight), was incorporated into a standard mouse diet at 20% of the diet by weight and fed for 5 wk to 6-wk-old, female CF-1 mice (N = 17-30) that were injected with a single dose of azoxymethane (5 mg/kg body weight). While the addition of resveratrol-aglycone (20 mg/kg diet) to the basal diet reduced the number of aberrant crypt foci/mouse, the transgenic alfalfa did not inhibit the number, size, or multiplicity of aberrant crypt foci in the colon of the CF-1 mice relative to control alfalfa which does not accumulate resveratrol-glucoside. However, diets containing transgenic alfalfa with an exogenous Beta-glucosidase (860 U/kg diet) did significantly inhibit the number of aberrant crypt foci in the distal 2 cm of the colon of the mice relative to mice fed diets containing the transgenic alfalfa without the enzyme (P < 0.05; Fisher's Combination of p-values). The Beta-glucosidase alone appeared to have no effect on the inhibition of aberrant crypt foci. These results suggest that piceid in transgenic piceid-accumulating alfalfa was not bioavailable.

Changes in immunomodulatory properties of Echinacea spp. root infusions and tinctures stored at 4 degrees C for four days.

BACKGROUND: Phytomedicinal preparations from members of the genus Echinacea are popular worldwide and frequently used to treat upper respiratory infections. With the increasing popularity of herbal medicines, many people are making their own Echinacea extracts at home and storing them at refrigerator (4 degrees C) temperatures. We tested the hypothesis that Echinacea extracts made using homemade methods change in immunomodulatory efficacy with storage at 4 degrees C over a 4-day period. METHODS: Three extract types (50% ethanol tincture, cold water infusion, hot water infusion) from 5 different species (Echinacea angustifolia, E. pallida, E. purpurea, E. sanguinea, E. tennesseensis) were prepared. Four in vitro immune assays (monocyte secretion of TNF-alpha, IL-10, and IL-12; and peripheral blood mononuclear cell proliferation) using human blood were used to test extract efficacy at Days 1 and 4 post-extraction. Two statistical analyses, traditional ANOVA and several statistical models that account for endotoxin effects, were used. RESULTS: Endotoxin was found to significantly impact immune outcomes only in 4-day old cold water infusions and not in all assays. Extracts showed the greatest stimulation in TNF-alpha assays. By extract type, 50% ethanol tinctures produced the most immune stimulation. By species, extracts from E. angustifolia extracts were the most efficacious in our assays; extracts from E. sanguinea showed the least activity overall. CONCLUSIONS: Taken together, these results suggest that: (1) homemade Echinacea extracts are efficacious in modulating immune cell activity in vitro but that their properties change with time during storage at 4 degrees C; and (2) endotoxin effects from extracts may be important considerations in the analysis of immunobiological data.

Modeling the dependence between number of trials and success probability in beta-binomial-Poisson mixture distributions.

Beta-binomial models are widely used for overdispersed binomial data, with the binomial success probability modeled as following a beta distribution. The number of binary trials in each binomial is assumed to be nonrandom and unrelated to the success probability. In many behavioral studies, however, binomial observations demonstrate more complex structures. In this article, a general beta-binomial-Poisson mixture model is developed, to allow for a relation between the number of trials and the success probability for overdispersed binomial data. An EM algorithm is implemented to compute both the maximum likelihood estimates of the model parameters and the corresponding standard errors. For illustration, the methodology is applied to study the feeding behavior of green-backed herons in two southeastern Missouri streams.