Data-Enhancement Strategies in Weather-Related Health Studies

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series, (ii) decomposing the different time scales of the data by empirical model decomposition, (iii) disaggregating the exposure series by considering the whole daily temperature curve as a single function, and (iv) considering the whole year of data as a single, continuous function. These four strategies allow studying non-conventional aspects of the mortality-temperature relationship by retrieving non-dominant time scale from data and allow to study the impact of the time of occurrence of particular event. A real-world case study of temperature-related cardiovascular mortality in the city of Montreal, Canada illustrates that these strategies can shed new lights on the relationship and outlines their strengths and weaknesses. A cross-validation comparison shows that the flexibility of functional regression used in strategies (iii) and (iv) allows a good fit of temperature-related mortality. These strategies can help understanding more accurately climate-related health.

Functional Regression

This chapter deals with the main theoretical fundamentals and practical issues of using functional regression in the context of genomic prediction. We explain how to represent data in functions by means of basis functions and considered two basis functions: Fourier for periodic or near-periodic data and B-splines for nonperiodic data. We derived the functional regression with a smoothed coefficient function under a fixed model framework and some examples are also provided under this model. A Bayesian version of functional regression is outlined and explained and all details for its implementation in glmnet and BGLR are given. The examples take into account in the predictor the main effects of environments and genotypes and the genotype × environment interaction term. The examples are done with small data sets so that the user can run them on his/her own computer and can understand the implementation process.

Functional response regression model on correlated longitudinal microbiome sequencing data

Functional regression has been widely used on longitudinal data, but it is not clear how to apply functional regression to microbiome sequencing data. We propose a novel functional response regression model analyzing correlated longitudinal microbiome sequencing data, which extends the classic functional response regression model only working for independent functional responses. We derive the theory of generalized least squares estimators for predictors’ effects when functional responses are correlated, and develop a data transformation technique to solve the computational challenge for analyzing correlated functional response data using existing functional regression method. We show by extensive simulations that our proposed method provides unbiased estimations for predictors’ effect, and our model has accurate type I error and power performance for correlated functional response data, compared with classic functional response regression model. Finally we implement our method to a real infant gut microbiome study to evaluate the relationship of clinical factors to predominant taxa along time.

VE-Cadherin Is Required for Cardiac Lymphatic Maintenance and Signaling

Background: The adherens protein VE-cadherin has diverse roles in organ-specific lymphatic vessels. However, its physiological role in cardiac lymphatics and its interaction with lymphangiogenic factors, has not been fully explored. We sought to determine the spatio-temporal functions of VE-cadherin in cardiac lymphatics and mechanistically elucidate how VE-cadherin loss influences pro-lymphangiogenic signaling pathways, such as adrenomedullin (AM) and VEGF-C/VEGFR3 signaling. Methods: Cdh5 flox/flox ;Prox1CreER T2 mice were used to delete VE-cadherin in lymphatic endothelial cells (LECs) across life stages, including embryonic, postnatal and adult. Lymphatic architecture and function was characterized utilizing immunostaining and functional lymphangiography. To evaluate the impact of temporal and functional regression of cardiac lymphatics in Cdh5 flox/flox ;Prox1CreER T2 mice, left anterior descending artery ligation was performed and cardiac function and repair after myocardial infarction was evaluated by echocardiography and histology. Cellular effects of VE-cadherin deletion on lymphatic signaling pathways were assessed by knock-down of VE-cadherin in cultured LECs. Results: Embryonic deletion of VE-cadherin produced edematous embryos with dilated cardiac lymphatics with significantly altered vessel tip morphology. Postnatal deletion of VE-cadherin caused complete disassembly of cardiac lymphatics. Adult deletion caused a temporal regression of the quiescent epicardial lymphatic network which correlated with significant dermal and cardiac lymphatic dysfunction, as measured by fluorescent and quantum dot lymphangiography, respectively. Surprisingly, despite regression of cardiac lymphatics, Cdh5 flox/flox ;Prox1CreER T2 mice exhibited preserved cardiac function, both at baseline and following myocardial infarction, compared to control mice. Mechanistically, loss of VE-cadherin leads to aberrant cellular internalization of VEGFR3, precluding the ability of VEGFR3 to be either canonically activated by VEGF-C or non-canonically transactivated by AM signaling, impairing downstream processes such as cellular proliferation. Conclusions: VE-cadherin is an essential scaffolding protein to maintain pro-lymphangiogenic signaling nodes at the plasma membrane, which are required for the development and adult maintenance of cardiac lymphatics, but not for cardiac function basally or after injury.

Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes

Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data’s standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality.