No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America.

Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Differential cross-section measurements of the production of four charged leptons in association with two jets using the ATLAS detector

Differential cross-sections are measured for the production of four charged leptons in association with two jets. These measurements are sensitive to final states in which the jets are produced via the strong interaction as well as to the purely-electroweak vector boson scattering process. The analysis is performed using proton-proton collision data collected by ATLAS at √s = 13 TeV and with an integrated luminosity of 140 fb−1. The data are corrected for the effects of detector inefficiency and resolution and are compared to state-of-the-art Monte Carlo event generator predictions. The differential cross-sections are used to search for anomalous weak-boson self-interactions that are induced by dimension-six and dimension-eight operators in Standard Model effective field theory

Flood monitoring in urban areas: Statistical vs. neurofuzzy approach

The paper aims at investigating different classification and segmentation tools for flood monitoring using satellite SAR images. To this aim, two different approaches, namely statistical segmentation and neurofuzzy classification and compared and discussed. The methods show, in general, the possibility to provide to a good extent accurate maps of the flooded areas using simple processing schemes. This stresses the effectiveness of satellite SAR images for real-time flood monitoring

Non-invasive imaging of transplanted human neural stem cells and ECM scaffold remodeling in the stroke-damaged rat brain by F-19- and diffusion-MRI

Transplantation of human neural stem cells (hNSCs) is emerging as a viable treatment for stroke related brain injury. However, intraparenchymal grafts do not regenerate lost tissue, but rather integrate into the host parenchyma without significantly affecting the lesion cavity. Providing a structural support for the delivered cells appears important for cell based therapeutic approaches. The non-invasive monitoring of therapeutic methods would provide valuable information regarding therapeutic strategies but remains a challenge. Labeling transplanted cells with metal-based (1)H-magnetic resonance imaging (MRI) contrast agents affects the visualization of the lesion cavity. Herein, we demonstrate that a (19)F-MRI contrast agent can adequately monitor the distribution of transplanted cells, whilst allowing an evaluation of the lesion cavity and the formation of new tissue on (1)H-MRI scans. Twenty percent of cells labeled with the (19)F-agent were of host origin, potentially reflecting the re-uptake of label from dead transplanted cells. Both T(2)- and diffusion-weighted MRI scans indicated that transplantation of hNSCs suspended in a gel form of a xenogeneic extracellular matrix (ECM) bioscaffold resulted in uniformly distributed cells throughout the lesion cavity. However, diffusion MRI indicated that the injected materials did not yet establish diffusion barriers (i.e. cellular network, fiber tracts) normally found within striatal tissue. The ECM bioscaffold therefore provides an important support to hNSCs for the creation of de novo tissue and multi-nuclei MRI represents an adept method for the visualization of some aspects of this process. However, significant developments of both the transplantation paradigm, as well as regenerative imaging, are required to successfully create new tissue in the lesion cavity and to monitor this process non-invasively