Linear Covariance Research Articles

An approach to assess and adjust for the influence of multicollinear covariates on metabolomics association patterns—applied to a study of the associations between a comprehensive lipoprotein profile and the homeostatic model assessment of insulin resistance

IntroductionComprehensive lipoprotein profiling using proton nuclear magnetic resonance (NMR) spectroscopy of serum represents an alternative to the homeostatic model assessment of insulin resistance (HOMA-IR). Both adiposity and physical (in)activity associate to insulin resistance, but quantification of the influence of these two lifestyle related factors on the association pattern of HOMA-IR to lipoproteins suffers from lack of appropriate methods to handle multicollinear covariates.ObjectivesWe aimed at (i) developing an approach for assessment and adjustment of the influence of multicollinear and even linear dependent covariates on regression models, and (ii) to use this approach to examine the influence of adiposity and physical activity on the association pattern between HOMA-IR and the lipoprotein profile.MethodsFor 841 children, lipoprotein profiles were obtained from serum proton NMR and physical activity (PA) intensity profiles from accelerometry. Adiposity was measured as body mass index, the ratio of waist circumference to height, and skinfold thickness. Target projections were used to assess and isolate the influence of adiposity and PA on the association pattern of HOMA-IR to the lipoproteins.ResultsAdiposity explained just over 50% of the association pattern of HOMA-IR to the lipoproteins with strongest influence on high-density lipoprotein features. The influence of PA was mainly attributed to a strong inverse association between adiposity and moderate and high-intensity physical activity.ConclusionThe presented covariate projection approach to obtain net association patterns, made it possible to quantify and interpret the influence of adiposity and physical (in)activity on the association pattern of HOMA-IR to the lipoprotein features.

Performance of Different LDL-C Equations in an Intervention Improving Atherogenic Dyslipidemia in Participants with Type 2 Diabetes

<h3>Lead Author's Financial Disclosures</h3> S.J.A. is an employee of Virta Health and offered stock options from Virta Health. <h3>Study Funding</h3> None. <h3>Background/Synopsis</h3> LDL-C estimated using the Friedewald equation (LDL-CF) is limited in type 2 diabetes (T2D) due to hypertriglyceridemia (HTG). In our recent study of 262 patients with T2D, a low carbohydrate intervention with nutritional ketosis significantly improved atherogenic dyslipidemia (ALP) in participants with T2D, where a majority of the participants transitioned from a predominantly small, dense LDL phenotype (phenotype B) to a large, buoyant LDL phenotype (phenotype A). However, the improvement in ALP was associated with an increase in LDL-CF. <h3>Objective/Purpose</h3> This study assessed differences between LDL-CF versus other recently adopted LDL-C equations to estimate the effect of our intervention on LDL-C, and assess the performance of these equations across a broad range of HTG. <h3>Methods</h3> LDL-C were recalculated using Martin-Hopkins (LDL-CMH) and the new proposed NIH 2 (LDL-CN2) equations. The effect size of the intervention on LDL-C change from baseline to 2 years was assessed using each LDL-C equation with repeated measure ANOVA. Since LDL-CN2 is recommended for use with triglycerides (TG) up to 800mg/dL, we calculated LDL-C with this equation using two cut-offs: TG≤400mg/dL and TG≤800mg/dL. We then assessed the differences between LDL-CMH, LDL-CN2 and LDL-CF at each time point among those with TG≤400mg/dL using a linear mixed effect model (LMM) and covariates that were initially used for the assessment of LDL-CF (Table 1). <h3>Results</h3> There was an increase in LDL-C in all three LDL-C equations, with medium effect sizes in both LDL-CF (ηp2=0.08, +13.5% from 100.9mg/dL) and LDL-CN2 (ηp2 =0.07 for TG≤400mg/dL, +11.6% from 104.8mg/dL or ηp2 =0.06 for TG≤800mg/dL, +10.7% from 104.6mg/dL) and a small effect with LDL-CMH (ηp2=0.04, +9.2% from 106.5mg/dL). At baseline, LDL-CMH estimated significantly greater LDL-C than the other two equations, especially in individuals with HTG and phenotype B (Table 1). At 1 and 2 years when the proportion of individuals with ALP and HTG significantly decreased in the intervention, there were no significant differences in the estimated LDL-C between the equations. <h3>Conclusions</h3> In patients with T2D where HTG is a common lipid abnormality, LDL-CMH may be the most appropriate equation to use in assessing change in LDL-C. For patients with greater than 400mg/dL of TG, LDL-CN2 can be alternatively considered. Future studies should compare these equations with direct LDL-C measurements in patients with T2D.

CORR Insights®: How Should We Measure Social Deprivation in Orthopaedic Patients?

CORR Insights®: How Should We Measure Social Deprivation in Orthopaedic Patients?

Linear Covariance Analysis for Head-On Target Engagements

A linear covariance simulation framework is developed and validated for a two-dimensional target engagement scenario. The scenario comprises a single interceptor equipped with an inertial navigation system aided by absolute position measurements, as well as range, angle, and range rate measurements relative to the incoming target. The interceptor uses a proportional navigation guidance law to engage the target, modeled as nominally constant velocity perturbed by Singer motion accelerations. The linear covariance framework is developed by linearizing the differential and measurement equations about the nominal trajectory, and forming an augmented system comprising truth and navigation state dispersions. In contrast with sample-based methods, the developed linear covariance framework can calculate the truth state dispersion covariance and the estimation error covariance throughout the engagement in a single run. It also provides several advantages over analysis methods such as the adjoint technique or traditional covariance analysis. The linear covariance computational efficiency is exploited to rapidly analyze a head-on target engagement problem.

Estimation and inference for partial linear regression surfaces using monotone warped-plane splines

Methods are proposed for spline estimation of monotone regression surfaces, without additivity assumptions and allowing for linear covariate effects. The surfaces are estimated by a continuous piece-wise warped-plane spline with linear inequality constraints. Surface and covariate effects are estimated simultaneously with cone projection, leading to useful inference methods. For surfaces in two predictors, a cube-root convergence rate is attained, and conditions for square-root convergence of the linear terms are derived. Point-wise confidence intervals for the surfaces incorporate mixtures of covariance matrices, with the mixing parameters estimation by simulations. The methods are implemented in the R package cgam.

Multivariate functional additive mixed models

Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary such as precipitation, temperature and wind speeds over time at a given weather station. We propose a multivariate functional additive mixed model (multiFAMM) and show its application to both data situations using examples from sports science (movement trajectories of snooker players) and phonetic science (acoustic signals and articulation of consonants). The approach includes linear and nonlinear covariate effects and models the dependency structure between the dimensions of the responses using multivariate functional principal component analysis. Multivariate functional random intercepts capture both the auto-correlation within a given function and cross-correlations between the multivariate functional dimensions. They also allow us to model between-function correlations as induced by, for example, repeated measurements or crossed study designs. Modelling the dependency structure between the dimensions can generate additional insight into the properties of the multivariate functional process, improves the estimation of random effects, and yields corrected confidence bands for covariate effects. Extensive simulation studies indicate that a multivariate modelling approach is more parsimonious than fitting independent univariate models to the data while maintaining or improving model fit.

Closed-Loop Linear Covariance Framework for Path Planning in Static Uncertain Obstacle Fields

Path planning in an uncertain environment is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures. Important features of the developed framework include 1) separation of high-level guidance from low-level control; 2) support for output-feedback controllers with internal states, dynamics, and output; and 3) multi-use continuous sensors for navigation state propagation, guidance, and feedback control. The closed-loop nature of the framework preserves the important coupling between the system dynamics; exogenous disturbances; and the guidance, navigation, and control algorithms. The developed framework is applied to a simplified model of an unmanned aerial vehicle and validated by comparison via Monte Carlo analysis. The utility of the CL-LinCov information is illustrated by its application to path planning in a static, uncertain obstacle field via a modified version of the rapidly exploring random tree algorithm.

Fast and efficient algorithms for sparse semiparametric bifunctional regression

SummaryA new sparse semiparametric model is proposed, which incorporates the influence of two functional random variables in a scalar response in a flexible and interpretable manner. One of the functional covariates is included through a single‐index structure, while the other is included linearly through the high‐dimensional vector formed by its discretised observations. For this model, two new algorithms are presented for selecting relevant variables in the linear part and estimating the model. Both procedures utilise the functional origin of linear covariates. Finite sample experiments demonstrated the scope of application of both algorithms: the first method is a fast algorithm that provides a solution (without loss in predictive ability) for the significant computational time required by standard variable selection methods for estimating this model, and the second algorithm completes the set of relevant linear covariates provided by the first, thus improving its predictive efficiency. Some asymptotic results theoretically support both procedures. A real data application demonstrated the applicability of the presented methodology from a predictive perspective in terms of the interpretability of outputs and low computational cost.

Gaussian sum reapproximation applied to the probability of collision calculations

In predicting the collision of space debris, the propagated orbital uncertainty may not follow a Gaussian distribution if the initial orbital uncertainty is large or the propagation time is long. In this paper, a Gaussian mixture uncertainty propagation method developed by (Psiaki et al., 2015) is used to calculate the collision probability. The initial Gaussian distribution is fitted by the weighted Gaussian mixture components. The linear matrix inequality is optimized to prevent the covariance matrix of Gaussian mixture components from being too small, and an appropriate number of Gaussian mixture components is used to approximate the initial orbital covariance. At the same time, this paper provides a method to calculate the collision probability of two objects in which a Gaussian mixture is used to represent the distribution of orbital uncertainty. The linear method and the unscented Kalman filter (UKF) method for propagating the Gaussian covariance are analysed. The results of numerical simulations show that compared with the linear covariance propagation method, UKF method, and high-precision Monte Carlo covariance propagation method for space objects with a large initial orbital uncertainty, the Gaussian mixture method can be effectively applied to capture the non-Gaussian characteristics of the predicted nonlinear orbital dynamic uncertainty. Compared with the univariate splitting method, the advantage of this Gaussian mixture method is that it does not need to search for the most nonlinear direction. The accuracy of the collision probability calculation is improved from 1.460 × 10−3 to 1.663 × 10−3. A comparison of the computational burden between the Gaussian mixture algorithm and Vittaldev’s algorithm to achieve the same results is presented. The calculation burden of the Gaussian mixture method is approximately 3 times that of the univariate Gaussian method.

Worry and rumination predict insomnia in patients with coronary heart disease: a cross-sectional study with long-term follow-up.

Insomnia is highly prevalent and associated with anxiety and depression in patients with coronary heart disease patients. The development of effective psychological interventions is needed. Worry and rumination are potential risk factors for the maintenance of insomnia, anxiety, and depression that may be modified by psychological treatment grounded in the Self-Regulatory Executive Function model. However, the relationships between worry, rumination, anxiety and depression, and insomnia are not known. Therefore, we investigated these relationships both cross-sectionally and longitudinally among patients with coronary heart disease. A cross-sectional study consecutively included 1,082 patients in 2014-2015, and 686 were followed up after mean of 4.7 years. Data were gathered from hospital records and self-report questionnaires comprising assessment of worry (Penn State Worry Questionnaire), rumination (Ruminative Responses Scale), anxiety and depression (Hospital Anxiety and Depression Scale), and insomnia (Bergen Insomnia Scale). Insomnia correlated moderately with all other psychological variables (R 0.18-0.50, all P values < .001). After adjustments for anxiety and depression, odds ratios for insomnia at baseline were 1.27 (95% confidence interval 1.08-1.50) and 1.60 (95% confidence interval 1.31-1.94) per 10 points increase of worry and rumination, respectively. Corresponding odds ratios for insomnia at follow-up were 1.28 (95% confidence interval 1.05-1.55) and 1.38 (95% confidence interval 1.09-1.75). Depression was no longer significantly associated with insomnia after adjustments for worry and rumination, but anxiety remained significant. Worry and rumination predicted insomnia both cross-sectionally and prospectively, even after controlling for anxiety and depression, although anxiety remained significant. Future studies may test psychological interventions targeting these factors in patients with coronary heart disease and insomnia. Frøjd LA, Papageorgiou C, Munkhaugen J, etal. Worry and rumination predict insomnia in patients with coronary heart disease: a cross-sectional study with long-term follow-up. J Clin Sleep Med. 2022;18(3):779-787.

Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling

AbstractLarge‐scale covariations of freshwater fluxes and storages on land can critically regulate the balance of green (evapotranspiration) and blue (runoff) water fluxes, and related land‐atmosphere interactions and hydroclimatic hazards. Such large‐scale covariation patterns are not evident from smaller‐scale hydrological studies that have been most common so far, and remain largely unknown for various regions and climates around the world. To contribute to bridging the large‐scale knowledge gaps, we synthesize and decipher hydroclimatic data time series over the period 1980–2010 for 6,405 catchments around the world. From observation‐based data, we identify dominant large‐scale linear covariation patterns between monthly freshwater fluxes and soil moisture (SM) for different world parts and climates. These covariation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation‐based data sets robustly show the strongest large‐scale hydrological relationship to be that between SM and runoff (R), consistently across the study catchments and their different climate characteristics. This predominantly strongest covariation between monthly SM and R is also the most misrepresented by ESMs and reanalysis products, followed by that between monthly precipitation and R. Comparison of observation‐based and ESM results also shows that an ESM may perform well for individual monthly variables, but fail in representing the patterns of large‐scale linear covariations between them. Observation‐based quantification of these patterns, and ESM and reanalysis improvements for their representation are essential for fundamental understanding, and more accurate and reliable modeling and projection of large‐scale hydrological conditions and changes under ongoing global and regional change.

Landmark database selection for vision-aided inertial navigation in planetary landing missions

Vision-aided inertial navigation (VAIN) system offers a means to enable high-accuracy autonomous navigation for planetary landing. The VAIN system provides precise state estimates for the landing vehicle by combining measurements from an inertial measurement unit (IMU) with visual observations of the mapped landmarks contained in a predetermined terrain database. Due to the constraints of the onboard storage and computation capabilities of the landing vehicle, a terrain database containing the minimum number of landmarks should be found to guarantee the requirements of the navigation task. In this paper, a landmark database selection algorithm based on linear covariance (LinCov) technique is presented. The algorithm selects landmarks into the terrain database by minimizing the uncertainties of the chosen state parameters at each imaging time point along the nominal trajectory. The uncertainties are calculated by LinCov which is an efficient approach to analyze the effect of landmark databases on the navigation performance and allows for considering the visibility constraints of landmarks prior to actual flight. Extensive simulations are performed to evaluate the proposed landmark selection algorithm, and the results show that the proposed method is effective.

Random Forests for Spatially Dependent Data

Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process (GP) distributed spatial random effect, are widely used for analyses of geospatial data. We consider the setting where the covariate effect is nonlinear. Random forests (RF) are popular for estimating nonlinear functions but applications of RF for spatial data have often ignored the spatial correlation. We show that this impacts the performance of RF adversely. We propose RF-GLS, a novel and well-principled extension of RF, for estimating nonlinear covariate effects in spatial mixed models where the spatial correlation is modeled using GP. RF-GLS extends RF in the same way generalized least squares (GLS) fundamentally extends ordinary least squares (OLS) to accommodate for dependence in linear models. RF becomes a special case of RF-GLS, and is substantially outperformed by RF-GLS for both estimation and prediction across extensive numerical experiments with spatially correlated data. RF-GLS can be used for functional estimation in other types of dependent data like time series. We prove consistency of RF-GLS for β-mixing dependent error processes that include the popular spatial Matérn GP. As a byproduct, we also establish, to our knowledge, the first consistency result for RF under dependence. We establish results of independent importance, including a general consistency result of GLS optimizers of data-driven function classes, and a uniform law of large number under β-mixing dependence with weaker assumptions. These new tools can be potentially useful for asymptotic analysis of other GLS-style estimators in nonparametric regression with dependent data.

Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age.

Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

A knowledge-based differential covariance matrix adaptation cooperative algorithm

In this paper, a knowledge-based differential covariance matrix adaptation cooperative algorithm (DCMAC) is proposed for continuous problems. On the basis of combining successful history-based adaptive DE variants with linear population size reduction (LSHADE) and covariance matrix adaptation evolutionary strategy (CMA-ES), DCMAC proposes a strategy based on knowledge reward and punishment to achieve the purpose of collaborative optimization. Afterward, an adaptive learning mechanism is introduced to optimize the parameters to balance the exploitation and exploration of DCMAC. This process enables the algorithm to have global search capability. Finally, the niching-based population size reduction mechanism is introduced to improve the local search ability of the DCMAC. A weighted mutation strategy with dynamic greedy p value and covariance matrix adaptation (CMA) sampled based on differential vector are presented. Meanwhile, the knowledge acquired in the previous iteration process is adopted in the algorithm to select a mutation strategy for generating the new candidate solutions in the next iteration. The niching population size reduction mechanism is introduced to maintain the diversity of the population and compared with the other classical population size reduction methods. The optimal combination of parameters in the DCMAC algorithm is testified by the design of the experiment. Furthermore, the DCMAC is testified on the CEC2017 benchmark functions. The effectiveness and efficiency of the DCMAC are demonstrated by the experimental results in solving complex continuous problems.

Bayesian EWMA control chart with measurement error under different loss functions

AbstractThis paper presents the Bayesian EWMA control chart under two different loss functions (i) squared error loss function (SELF) and (ii) linex loss function (LLF) in the presence of measurement error (ME). We take posterior and posterior predictive distribution under the conjugate prior. We used a linear covariate model in the existence of ME to evaluate the control chart. We also studied the effects of multiple measurements and linear increasing variance methods in the existence of the ME. The average run length and standard deviation of run length are used to measure the performance of the Bayesian EWMA control chart with ME. We conducted the Monte Carlo simulation study to evaluate the performance of Bayesian EWMA control chart with ME. A real‐life data example is also presented to demonstrate the application of the control chart.

The impact of pretreatment PSA on risk stratification in men with Gleason 6 prostate cancer: Implications for active surveillance

BackgroundThere are limited data to support the safety of active surveillance in men with favorable-intermediate risk prostate cancer due only to a prostate specific antigen (PSA) above 10 ng/ml. We therefore evaluated the impact of pretreatment PSA on risk-stratification in men with Gleason 6 prostate cancer. MethodsWe identified men aged 18 to 75 with cT1-2cN0cM0, pre-treatment PSA < 20 ng/ml, Gleason 6 prostate cancer diagnosed from 2010 to 2016 in the National Cancer Database who underwent radical prostatectomy. The associations of patient and disease features with Gleason score upgrading or adverse pathologic features at prostatectomy were evaluated using logistic regression. To evaluate for non linear relationships between PSA and each outcome, we examined predicted marginal event rates standardized for baseline characteristics with PSA modeled using restricted cubic splines ResultsA total of 75,566 patients were included in the cohort. In unadjusted analyses, patients with pretreatment PSA ≥ 10 ng/ml had higher rates of Gleason core upgrading (58.8% vs. 47.9%; P< 0.001) and adverse pathologic features (19.7% vs. 10.0%; P< 0.001) compared to patients with PSA < 10 ng/ml. In multivariable analyses, PSA ≥ 10 ng/ml was associated with statistically significantly increased risks of Gleason score upgrading (OR 1.47;95%CI 1.39 – 1.55) and adverse pathologic features (OR 2.15;95%CI 2.01 – 2.30). When modeled as a non linear continuous covariate, PSA was associated with increased adjusted rates of Gleason score upgrading and adverse pathologic features without a clear dichotomization at a threshold of 10 ng/ml. ConclusionHigher pretreatment PSA was independently associated with increased risks of Gleason score upgrading and adverse pathologic features at prostatectomy. Flexible modeling of the relationship between PSA and each outcome did not support dichotomization at a threshold of 10 ng/ml. These results can be used to improve patient risk-stratification for active surveillance.

Incremental Model Fit Assessment in the Case of Categorical Data: Tucker–Lewis Index for Item Response Theory Modeling

The Tucker–Lewis index (TLI; Tucker & Lewis, 1973), also known as the non-normed fit index (NNFI; Bentler & Bonett, 1980), is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research. It augments information provided by other indices such as the root-mean-square error of approximation (RMSEA). In this paper, we develop and examine an analogous index for categorical item level data modeled with item response theory (IRT). The proposed Tucker–Lewis index for IRT (TLIRT) is based on Maydeu-Olivares and Joe's (2005) M_2 family of limited-information overall model fit statistics. The limited-information fit statistics have significantly better Chi-square approximation and power than traditional full-information Pearson or likelihood ratio statistics under realistic situations. Building on the incremental fit assessment principle, the TLIRT compares the fit of model under consideration along a spectrum of worst to best possible model fit scenarios. We examine the performance of the new index using simulated and empirical data. Results from a simulation study suggest that the new index behaves as theoretically expected, and it can offer additional insights about model fit not available from other sources. In addition, a more stringent cutoff value is perhaps needed than Hu and Bentler's (1999) traditional cutoff criterion with continuous variables. In the empirical data analysis, we use a data set from a measurement development project in support of cigarette smoking cessation research to illustrate the usefulness of the TLIRT. We noticed that had we only utilized the RMSEA index, we could have arrived at qualitatively different conclusions about model fit, depending on the choice of test statistics, an issue to which the TLIRT is relatively more immune.

PSI-6 Genetic Parameters for Food Allergy Responses in Divergently-selected Pig Lines

Abstract Genetic selection for health and welfare-related traits is paramount in livestock breeding. Piglet allergic responses to soybean protein negatively impact animal growth and welfare. The objectives of this study were to estimate the heritability of soybean allergic responses and genetic correlations between soybean and peanut allergies (r1,2) in divergently-selected pig lines. The base population consisted of nine sire lines (primarily Yorkshire and Landrace) and two dam sources (Yorkshire × Chester White-F1 and Landrace × Yorkshire-F1). Soybean and peanut-allergic responses were measured through a skin test (0–6 scale; 0-no allergic responses; 6-severe allergic responses; characterized by wheal and flair) on pigs fed a diet containing soybean meal for 21 d post-weaning. A total of 5,505 animals from nine generations of two contrasting lines (i.e., high and low skin test reaction) were included in the analyses. The statistical model included contemporary group, breeding lines, replication, sex, and weaning weight (linear covariate) as fixed effects (P-value &amp;lt; 0.05), and additive genetic, maternal genetic, and maternal permanent environment as random effects. Threshold and linear Bayesian models were used to estimate genetic parameters, using a pedigree-based relationship matrix containing 9,201 animals. The heritability estimates for the general soybean allergic response were 0.199 ± 0.045 and 0.119 ± 0.025 for the threshold (liability scale) and linear models, respectively, suggesting that soybean allergic responses are heritable and can be improved through selective breeding. A weak negative genetic correlation between allergic responses and birth weight was observed (r1,2 = -0.253 ± 0.192), which shows that the genetic variance of soybean allergy is less dependent on birth weight. However, a high positive genetic correlation was estimated between soybean and peanut-allergic responses (r1,2 = 0.89 ± 0.048), which indicates a potential cross-reactivity of soybean and peanut allergies. Our findings suggest that it is possible to reduce food allergy responses in pigs through selective breeding.

Local Drivers Associated to Temporal Spectral Response of Chlorophyll-a in Mangrove Leaves

The pigment content in leaves has commonly been used to characterize vegetation condition. However, few studies have assessed temporal changes of local biotic and abiotic factors on leaf pigments. Here, we evaluated the effect of local environmental variables and tree structural characteristics, in the chlorophyll-a leaf concentration (Chl-a) associated with temporal change in two mangrove species. Rhizophora mangle (R. mangle) and Avicennia germinans (A. germinans) trees of a fringe mangrove forest (FMF) and lower basin mangrove forest (BMF) were visited over a period of one year, to obtain radiometric readings at leaf level to estimate Chl-a. Measurements on tree characteristics included diameter at breast height (DBH), basal area (BA), and maximum height (H). Environmental variables included soil interstitial water temperature (Ti), salinity (Si), and dissolved oxygen (Oi), flood level (fL), ambient temperature (Tamb), and relative humidity (Hrel). Generalized linear models and covariance analysis showed that the variation of Chl-a is mainly influenced by the species, the interaction between species and mangrove forest type, DBH, seasonality and its influence on the species, soil conditions, and fL. Studies to assess spatial and temporal change on mangrove forests using the spectral characteristics of the trees should also consider the temporal variation of leave chlorophyll-a concentration.