Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation

Abstract The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method

Mapping potential water repellency of Danish topsoil

Soil water repellency (SWR) is a natural process and affects water dynamics from nano to ecosystem scales. However, the spatial distribution of SWR at the ecosystem scale, as well as the underlying drivers across diverse habitats, land uses and soil textures, remain underexplored. This study presents a comprehensive survey of SWR in Denmark and its predicted spatial distribution, using approximately 7,500 samples. We used digital soil mapping methods (Quantile Random Forest model) to map and identify the relationship between SWR and various environmental variables, including vegetation (via satellite imagery), soil properties (texture and soil organic carbon), and landforms (slope and wetness index). The predicted maps at 10 m resolution revealed that SWR varies across different land uses and vegetation types, with higher values in areas of natural vegetation (e.g., heathlands and coniferous forests) compared to grasslands and croplands (mostly hydrophilic). The analysis also identified soil organic carbon, Sentinel band 3 (Green band − Chlorophyll absorption) and soil texture as key drivers of spatial variation in SWR at the national extent. We found that soil texture influences SWR intensity, which generally decreases as clay content increases across most land use types, except for heathlands. While the predicted maps provided valuable insights into SWR distribution and its environmental drivers, further research is needed to explore the spatio-temporal dynamics of SWR within each habitat, particularly in relation to soil moisture changes. This study highlights the potential of combining machine learning and remote sensing to provide crucial spatial information for managing water resources and enhancing ecosystem resilience in the face of climate change

Placental lactogens induce serotonin biosynthesis in a subset of mouse beta cells during pregnancy

AIMS/HYPOTHESIS: Upregulation of the functional beta cell mass is required to match the physiological demands of mother and fetus during pregnancy. This increase is dependent on placental lactogens (PLs) and prolactin receptors, but the mechanisms underlying these events are only partially understood. We studied the mRNA expression profile of mouse islets during pregnancy to gain a better insight into these changes. METHODS: RNA expression was measured ex vivo via microarrays and quantitative RT-PCR. In vivo observations were extended by in vitro models in which ovine PL was added to cultured mouse islets and MIN6 cells. RESULTS: mRNA encoding both isoforms of the rate-limiting enzyme of serotonin biosynthesis, tryptophan hydroxylase (TPH), i.e. Tph1 and Tph2, were strongly induced (fold change 25- to 200-fold) during pregnancy. This induction was mimicked by exposing islets or MIN6 cells to ovine PLs for 24 h and was dependent on janus kinase 2 and signal transducer and activator of transcription 5. Parallel to Tph1 mRNA and protein induction, islet serotonin content increased to a peak level that was 200-fold higher than basal. Interestingly, only a subpopulation of the beta cells was serotonin-positive in vitro and in vivo. The stored serotonin pool in pregnant islets and PL-treated MIN6 cells was rapidly released (turnover once every 2 h). CONCLUSIONS/INTERPRETATION: A very strong lactogen-dependent upregulation of serotonin biosynthesis occurs in a subpopulation of mouse islet beta cells during pregnancy. Since the newly formed serotonin is rapidly released, this lactogen-induced beta cell function may serve local or endocrine tasks, the nature of which remains to be identified

The pancreatic beta cell surface proteome

The pancreatic beta cell is responsible for maintaining normoglycaemia by secreting an appropriate amount of insulin according to blood glucose levels. The accurate sensing of the beta cell extracellular environment is therefore crucial to this endocrine function and is transmitted via its cell surface proteome. Various surface proteins that mediate or affect beta cell endocrine function have been identified, including growth factor and cytokine receptors, transporters, ion channels and proteases, attributing important roles to surface proteins in the adaptive behaviour of beta cells in response to acute and chronic environmental changes. However, the largely unknown composition of the beta cell surface proteome is likely to harbour yet more information about these mechanisms and provide novel points of therapeutic intervention and diagnostic tools. This article will provide an overview of the functional complexity of the beta cell surface proteome and selected surface proteins, outline the mechanisms by which their activity may be modulated, discuss the methods and challenges of comprehensively mapping and studying the beta cell surface proteome, and address the potential of this interesting subproteome for diagnostic and therapeutic applications in human disease