Regeneration of injured tubular cells occurs after acute tubular necrosis primarily from intrinsic renal cells. This may occur from a pre-existing intratubular stem/progenitor cell population or from any surviving proximal tubular cell. In this study, we characterize a CD24-, CD133-, and vimentin-positive subpopulation of cells scattered throughout the proximal tubule in normal human kidney. Compared to adjacent ‘normal’ proximal tubular cells, these CD24-positive cells contained less cytoplasm, fewer mitochondria, and no brush border. In addition, 49 marker proteins are described that are expressed within the proximal tubules in a similar scattered pattern. For eight of these markers, we confirmed co-localization with CD24. In human biopsies of patients with acute tubular necrosis (ATN), the number of CD24-positive tubular cells was increased. In both normal human kidneys and the ATN biopsies, around 85% of proliferating cells were CD24-positive – indicating that this cell population participates in tubular regeneration. In healthy rat kidneys, the novel cell subpopulation was absent. However, upon unilateral ureteral obstruction (UUO), the novel cell population was detected in significant amounts in the injured kidney. In summary, in human renal biopsies, the CD24-positive cells represent tubular cells with a deviant phenotype, characterized by a distinct morphology and marker expression. After acute tubular injury, these cells become more numerous. In healthy rat kidneys, these cells are not detectable, whereas after UUO, they appeared de novo – arguing against the notion that these cells represent a pre-existing progenitor cell population. Our data indicate rather that these cells represent transiently dedifferentiated tubular cells involved in regeneration.

Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

Significance Acute kidney injury (AKI) is a common and significant clinical problem for which no specific therapy has been developed. There is controversy about the origin of the regenerating tubular cells after AKI. Attention has recently focused on “scattered tubular cells” (STCs), which are by far the best candidate cells for the postulated fixed progenitor population of kidney tubular cells. In the present study, we clarify this question by genetic cell fate labeling using a unique transgenic mouse. We show that STCs may arise from any tubular cell and that these cells do not represent fixed progenitor cells. Rather, upon different injuries, proximal tubular cells transiently acquire the STC phenotype, which we show to have reparative characteristics.

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Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.