Unraveling the Complexity to Observe Associations Between Welfare Indicators and Hair Cortisol Concentration in Dairy Calves

Using levels of the stress hormone cortisol as an indicator for welfare is a common, but debated practice. In this observational study, hair cortisol concentration (HCC) of samples from 196 dairy calves from 7 to 302 days of age collected from 12 Swedish farms was determined using a commercially available ELISA. An assessment of animal welfare, assessed using animal-based indicators, was performed on the day of sampling. First, methodological factors with the potential to impact HCC and the effect of age were analyzed using generalized additive models. This revealed a significant peak in hair cortisol in young calves (around 50 days of age) and an association between fecal contamination of hair samples and the level of cortisol extracted. Second, associations between welfare indicators and HCC were explored using cluster analysis and regularized regression. The results show a complex pattern, possibly related to different coping styles of the calves, and indicators of poor welfare were associated with both increased and decreased hair cortisol levels. High cortisol levels were associated with potential indicators of competition, while low cortisol levels were associated with the signs of poor health or a poor environment. When running the regularized regression analysis without the contaminated hair samples and with the contaminated samples (including a contamination score), the results did not change, indicating that it may be possible to use a contamination score to correct for contamination.

Compressive nano-FTIR chemical mapping

Nano-Fourier-transform infrared spectroscopy (nano-FTIR) combines infrared spectroscopy with scanning probe microscopy (SPM) techniques and enables spectroscopic imaging of molecular and electronic properties of matter at nanometer spatial resolution. The spectroscopic imaging can be used to derive chemical mappings, i.e., the spatial distribution of concentrations of the species contained in a given sample. However, due to the sequential scanning principle underlying SPM, recording the complete spectrum over a large spatial area leads to long measurement times. Furthermore, the acquired spectrum often contains additional signals from species and lineshape effects that are not explicitly accounted for. A compressive chemical mapping approach is proposed for undersampled nano-FTIR data that utilizes sparsity of these additional signals in the spectral domain. The approach combines a projection technique with standard compressed sensing, followed by a spatially regularized regression. Using real nano-FTIR measurements superimposed by simulated interferograms representing the chemical mapping of the contained species, it is demonstrated that the proposed procedure performs well even in cases in which the simulated interferograms and the sparse additional signals exhibit a strong spectral overlap.

Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula

Rhizobia can establish associations with legumes to provide plants with nitrogen, in a critical symbiosis for agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet the extent of chromatin changes and impact on gene expression is unknown. We combined gene regulatory features and their chromatin accessibility (ATAC-seq) profile to predict the temporal transcriptome (RNA-seq) dynamics of roots treated with rhizobia lipo-chitooligosaccharides. Using a novel approach, Dynamic Regulatory Module Networks, we predicted gene expression as a function of regulatory feature chromatin accessibility. This approach uses regularized regression and identifies the cis-regulatory elements and associated transcription factors that most significantly contribute to transcriptomic changes triggered by lipo-chitooligosaccharides. Regulators involved in auxin (SHY2), ethylene (EIN3), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics.