Exploring the effects of extended interval dosing of natalizumab and drug concentrations on brain atrophy in multiple sclerosis

BACKGROUND: Extended interval dosing (EID) of natalizumab treatment is increasingly used in multiple sclerosis. Besides the clear anti-inflammatory effect, natalizumab is considered to have neuroprotective properties as well. OBJECTIVES: This study aimed to study the longitudinal effects of EID compared to standard interval dosing (SID) and natalizumab drug concentrations on brain atrophy. METHODS: Patients receiving EID or SID of natalizumab with a minimum radiological follow-up of 2 years were included. Changes in brain atrophy measures over time were derived from clinical routine 3D-Fluid Attenuated Inversion Recovery (FLAIR)-weighted magnetic resonance imaging (MRI) scans using SynthSeg. RESULTS: We found no differences between EID (n = 32) and SID (n = 50) for whole brain (-0.21% vs -0.16%, p = 0.42), ventricular (1.84% vs 1.13%, p = 0.24), and thalamic (-0.32% vs -0.32%, p = 0.97) annualized volume change over a median follow-up of 3.2 years. No associations between natalizumab drug concentration and brain atrophy rate were found. CONCLUSION: We found no clear evidence that EID compared to SID or lower natalizumab drug concentrations have a negative impact on the development of brain atrophy over time

SoilGrids1km — Global Soil Information Based on Automated Mapping

Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license

Scenarios of land use and land cover change and their multiple impacts on natural capital in Tanzania

REDD+ (reducing emissions from deforestation, and forest degradation, plus the conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, in developing countries) requires information on land use and land cover changes (LULCC) and carbon emissions trends from the past to the present and into the future. Here we use the results of participatory scenario development in Tanzania, to assess the potential interacting impacts on carbon stock, biodiversity and water yield of alternative scenarios where REDD+ is effectively implemented or not by 2025, the green economy (GE) and the business as usual (BAU) respectively. Under the BAU scenario, land use and land cover changes causes 296 MtC national stock loss by 2025, reduces the extent of suitable habitats for endemic and rare species, mainly in encroached protected mountain forests, and produce changes of water yields. In the GE scenario, national stock loss decreases to 133 MtC. In this scenario, consistent LULCC impacts occur within small forest patches with high carbon density, water catchment capacity and biodiversity richness. Opportunities for maximising carbon emissions reductions nationally are largely related to sustainable woodland management but also contain trade-offs with biodiversity conservation and changes in water availability

Twelve-year outcomes of watchful waiting versus surgery of mildly symptomatic or asymptomatic inguinal hernia in men aged 50 years and older:a randomised controlled trial

Background: Inguinal hernia belongs to the most common surgical pathology worldwide. Approximately, one third is asymptomatic. The value of watchful waiting (WW) in patients with asymptomatic or mildly symptomatic inguinal hernia has been established in a few randomised controlled trials (RCTs). The aim of this study was to assess long-term outcomes of a RCT comparing WW and elective surgery.Methods: In the original study, men aged ≥50 years with an asymptomatic or mildly symptomatic inguinal hernia were randomly assigned to WW or elective repair. In the present study, the primary outcome was the 12-year crossover rate to surgery, secondary outcomes were time-to-crossover, patient regret, pain, quality of life and incarceration. Dutch Trial Registry: NTR629. Findings: Out of 496 originally analysed patients, 488 (98.4%) were evaluable for chart review (WW: n = 258, surgery: n = 230), and 200 (41.0%) for telephone contact (WW: n = 106, surgery: n = 94) between November 2021 and March 2022 with a median 12 years follow-up (IQR 9–14). After 12 years, the estimated cumulative crossover rate to surgery was 64.2%, which was higher in mildly symptomatic than in asymptomatic patients (71.7% versus 60.4%, HR 1.451, 95% CI: 1.064–1.979). Time-to-crossover was longer in asymptomatic patients (50% after 6.0 years versus 2.0 years, p = 0.019). Patient regret was higher in the WW group (37.7 versus 18.0%, p = 0.002), as well as pain/discomfort (p = 0.031). Quality of life did not differ (p = 0.737). In the WW group, incarceration occurred in 10/255 patients (3.9%). Interpretation: During 12-year follow-up, most WW patients crossed over to surgery, significantly earlier with mildly symptomatic hernia. Considering the relatively low incarceration rate, WW might still be an option in asymptomatic patients with a clear preference and being well-informed about pros and cons.</p

Twelve-year outcomes of watchful waiting versus surgery of mildly symptomatic or asymptomatic inguinal hernia in men aged 50 years and older:a randomised controlled trial

Background: Inguinal hernia belongs to the most common surgical pathology worldwide. Approximately, one third is asymptomatic. The value of watchful waiting (WW) in patients with asymptomatic or mildly symptomatic inguinal hernia has been established in a few randomised controlled trials (RCTs). The aim of this study was to assess long-term outcomes of a RCT comparing WW and elective surgery. Methods: In the original study, men aged ≥50 years with an asymptomatic or mildly symptomatic inguinal hernia were randomly assigned to WW or elective repair. In the present study, the primary outcome was the 12-year crossover rate to surgery, secondary outcomes were time-to-crossover, patient regret, pain, quality of life and incarceration. Dutch Trial Registry: NTR629. Findings: Out of 496 originally analysed patients, 488 (98.4%) were evaluable for chart review (WW: n = 258, surgery: n = 230), and 200 (41.0%) for telephone contact (WW: n = 106, surgery: n = 94) between November 2021 and March 2022 with a median 12 years follow-up (IQR 9–14). After 12 years, the estimated cumulative crossover rate to surgery was 64.2%, which was higher in mildly symptomatic than in asymptomatic patients (71.7% versus 60.4%, HR 1.451, 95% CI: 1.064–1.979). Time-to-crossover was longer in asymptomatic patients (50% after 6.0 years versus 2.0 years, p = 0.019). Patient regret was higher in the WW group (37.7 versus 18.0%, p = 0.002), as well as pain/discomfort (p = 0.031). Quality of life did not differ (p = 0.737). In the WW group, incarceration occurred in 10/255 patients (3.9%). Interpretation: During 12-year follow-up, most WW patients crossed over to surgery, significantly earlier with mildly symptomatic hernia. Considering the relatively low incarceration rate, WW might still be an option in asymptomatic patients with a clear preference and being well-informed about pros and cons. Funding: The initial trial was funded by the Netherlands Organisation for Health Research and Development (ZonMW). This long-term study did not receive funding.</p

Operationalizing digital soil mapping for nationwide updating of the 1 : 50,000 soil map of the Netherlands

This paper presents a pedometric approach to updating the Dutch 1:50,000 national soil map for the peatlands, and illustrates this approach for a 187,525ha area in the northern peatlands. This is the first time that digital soil mapping replaces conventional soil mapping in a nationwide, government-funded soil survey program in the Netherlands. Soil classes were updated indirectly through mapping two quantitative diagnostic soil properties: the thickness and starting depth of the peat layer. From these, five major soil groups could be constructed. Because the point data were zero-inflated, a two-step simulation approach was implemented. First, peat presence/absence indicators were simulated from probabilities of peat occurrence that were predicted with a generalized linear model. Second, conditional peat thickness values were simulated from kriging with external drift predictions. The indicator and peat thickness simulations were combined to obtain simulations of the unconditional peat thickness. A similar approach was followed for the starting depth. From the simulated soil properties, probability distributions of soil groups were derived. These groups were refined with information on (static) soil properties derived from the 1:50,000 map to obtain soil classes according to the 1:50,000 legend. The updated raster map was then incorporated in the 1:50,000 polygon map. The prediction models were calibrated with legacy point data, that were updated for peat thickness before being used, in addition to a set of newly acquired point data. The uncertainty associated to the updated peat thickness values in the legacy dataset was quantified and accounted for by the prediction models. The peat thickness map and a map with three soil orders were validated with independent probability sample data. The overall purity of the soil order map was 66% for both subareas. For subarea 1 this was a 12% purity improvement compared to the current 1:50,000 map, for subarea 2 this was 3%. For subarea 1, the mean absolute error of the predicted peat thickness was 23.5cm, and the R2 is 0.50. For subarea 2 these accuracy measures were 30.9cm and 0.65. We conclude that nationwide updating the 1:50,000 map with pedometric techniques is feasible. In order to increase the value and usability of the legacy point data as well as the large set of newly acquired field observations and the updated 1:50,000 map, we recommend installation of a soil monitoring network in the Dutch peatlands.</p

Spatial predictions of maize yields using QUEFTS – A comparison of methods

Using fertilisers is indispensable for closing yield gaps in Sub Saharan Africa. Current fertiliser recommendations, however, are often blanket recommendations which do not take spatial variation in soil conditions within a region or country into account. Soil maps can potentially support fertiliser recommendations at a higher spatial resolution. The QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model is a decision support tool that predicts crop yields as an indicator of soil fertility and can be used to evaluate yield responses to fertilisers. It was designed for field level output and runs on field-specific soil information. The aim of this study was to compare two methods for developing maps of QUEFTS output, i.e. maize yield and the yield-limiting nutrient, with Rwanda as a case study. We used a database containing soil analysis results of 999 samples collected across Rwanda. Transfer functions were applied to predict the required P-Olsen and Exchangeable K input for QUEFTS based on the soil data. For the “Calculate-then-Interpolate” (CI) method, transfer functions and QUEFTS were applied to point data, and the final output was then interpolated using random forest modelling. For the “Interpolate-then-Calculate” (IC) method, maps of the soil parameters were developed first, before applying calculations. Implications of the chosen method (i.e. CI or IC) on QUEFTS predictions on a national scale were evaluated using set-aside locations. Results showed low precision and accuracy of QUEFTS maize yield predictions across Rwanda. The CI method performed better in predicting QUEFTS yield and yield-limiting nutrient than the IC method. Correlations between mapped yield predictions and predictions on set-aside evaluation locations were similar for the CI (r = 0.444) and IC (r = 0.439) methods. The poorer performance of the IC method was mostly due to overestimation of yields, which was most likely caused by the effect of smoothing on the soil maps used as input for QUEFTS. We conclude that the CI method is the preferred method for spatial application of QUEFTS

Mapping the soils of an Argentine Pampas region using structural equation modeling

Current digital soil mapping (DSM) methods have limitations. For instance, it is difficult to predict a large number of soil properties simultaneously, while preserving the relationships between them. Another problem is that prevalent prediction models use pedological knowledge in a very crude way only. To tackle these problems, we investigated the use of structural equation modelling (SEM). SEM has its roots in the social sciences and is recently also being used in other scientific disciplines, such as ecology. SEM integrates empirical information with mechanistic knowledge by deriving the model equations from known causal relationships, while estimating the model parameters using the available data. It distinguishes between endogenous and exogenous variables, where, in our application, the first are soil properties and the latter are external soil forming factors (i.e. climate, relief, organisms). We introduce SEM theory and present a case study in which we applied SEM to a 22,900 km2 region in the Argentinian Pampas to map seven key soil properties. In this case study, we started with identifying the main soil forming processes in the study area and assigned for each process the main soil properties affected. Based on this analysis we defined a conceptual soil-landscape model, which was subsequently converted to a SEM graphical model. Finally, we derived the SEM equations and implemented these in the statistical software R using the latent variable analysis (lavaan) package. The model was calibrated using a soil dataset of 320 soil profile data and 12 environmental covariate layers. The outcomes of the model were maps of seven soil properties and a SEM graph that shows the strength of the relationships. Although the accuracy of the maps, based on cross-validation and independent validation, was poor, this paper demonstrates that SEM can be used to explicitly include pedological knowledge in prediction of soil properties and modelling of their interrelationships. It bridges the gap between empirical and mechanistic methods for soil-landscape modelling, and is a tool that can help produce pedologically sound soil maps.Inst.de SuelosFil: Angelini, Marcos Esteban. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC-World Soil Information; Holanda. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Hauvelink, Gerard B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC-World Soil Information; HolandaFil: Morras, Hector. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Kempen, Bas. ISRIC-World Soil Information; Holand

Pilot Project Land Degradation Neutrality (LDN), Namibia: Establishment of a baseline for land degradation in the region of Otjozondjupa

Soil and vegetation data collected to develop LDN baselines in Otjozondjupa region of Namibia. The baselines include: land cover change, land productivity, soil organic carbon, and bush encroachment