Variable importance in latent variable regression models

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable selection. Thus, these graphs provide visualization of the explanatory variables’ content of response related as well as systematic orthogonal variation at a quantitative level. Furthermore, these graphs are able to reveal and partition the explanatory variables into those that are crucial for both interpretation and predictive performance of the model, and those that are crucial for prediction performance but confounded by large contributions of orthogonal variation. Tools for assessment of explanatory variables may not only aid interpretation and understanding of the model but also be crucial for performing variable selection with the purpose of obtaining parsimonious models with high explanatory information content aswell as predictive performance. We show by example that by just using prediction performance as criterion for variable selection, it is possible to end up with a reducedmodel where the most selective variables are lost in the selection process

Near-field photothermal microspectroscopy for adult stem-cell identification and characterization.

The identification of stem cells in adult tissue is a challenging problem in biomedicine. Currently, stem cells are identified by individual epitopes, which are generally tissue-specific. The discovery of a stem cell marker common to other adult tissue types could open avenues in the development of therapeutic stem cell strategies. We report the use of the novel technique of Fourier transform infrared near-field photothermal micro-spectroscopy (FTIR-PTMS) for the characterization of stem cells, transit amplifying (TA) cells and terminally differentiated (TD) cells in the corneal epithelium. Principal component analysis (PCA) data demonstrates excellent discrimination of cell type by spectra. PCA in combination with linear discriminant analysis (PCA-LDA) shows that FTIR-PTMS very effectively discriminates between the three cell populations. Statistically significant differences above the 99% confidence level between infrared (IR) spectra from stem cells and TA cells suggest that nucleic acid conformational changes are an important component of the differences between spectral data from the two cell types. FTIR-PTMS is a new addition to existing spectroscopy methods based on the concept of interfacing a conventional FTIR spectrometer with an atomic force microscope equipped with a near-field thermal sensing probe. FTIR spectroscopy currently has a spatial resolution which is similar to that of diffraction-limited optical detection FTIR spectroscopy techniques, but as a near-field technique has considerable potential for further improvement. Our work also suggests that FTIR-PTMS is potentially more sensitive than synchrotron radiation FTIR spectroscopy for some applications. Micro-spectroscopy techniques like FTIR-PTMS provide information about the entire molecular composition of cells, in contrast to epitope recognition which only considers the presence or absence of individual molecules. Our results with FTIR-PTMS on corneal stem cells are promising for the potential development of an IR spectral fingerprint for stem cells

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale*

To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R 2), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R 2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948)