A new allele for aluminium tolerance gene in barley (Hordeum vulgare L.)

Background Aluminium (Al) toxicity is the main factor limiting the crop production in acid soils and barley (Hordeum vulgare L.) is one of the most Al-sensitive of the small-grained cereals. The major gene for Al tolerance in barley is HvAACT1 (HvMATE) on chromosome 4H which encodes a multidrug and toxic compound extrusion (MATE) protein. The HvAACT1 protein facilitates the Al-activated release of citrate from root apices which protects the growing cells and enables root elongation to continue. A 1 kb transposable element-like insert in the 5’ untranslated region (UTR) of HvAACT1 is associated with increased gene expression and tolerance and a PCR-based marker is available to score for this insertion. Results We screened a wide range of barley genotypes for Al tolerance and identified a moderately tolerant Chinese genotype named CXHKSL which did not show the typical allele in the 5’ UTR of HvAACT1 associated with tolerance. We investigated the mechanism of Al tolerance in CXHKSL and concluded it also relies on the Al-activated release of citrate from roots. Quantitative trait loci (QTL) analysis of double haploid lines generated with CXHKSL and the Al-sensitive variety Gairdner mapped the tolerance locus to the same region as HvAACT1 on chromosome 4H. Conclusions Our results show that the Chinese barley genotype CXHKSL possesses a novel allele of the major Al tolerance gene HvAACT1

Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data

Introduction US claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies. Objective The objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data. Methods We used a claims dataset with full death records, Optum© De-Identifed Clinformatics® Data-Mart-Database—Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifes the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets. Results Approximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defning death as a predicted risk of>0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases. Conclusions US claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specifcity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel

A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases

Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.Development and application of statistical models for medical scientific researc

Expression of the wheat multipathogen resistance hexose transporter Lr67res is associated with anion fluxes

OnlinePublMany disease resistance genes in wheat (Triticum aestivum L.) confer strong resistance to specific pathogen races or strains, and only a small number of genes confer multipathogen resistance. The Leaf rust resistance 67 (Lr67) gene fits into the latter category as it confers partial resistance to multiple biotrophic fungal pathogens in wheat and encodes a Sugar Transport Protein 13 (STP13) family hexose-proton symporter variant. Two mutations (G144R, V387L) in the resistant variant, Lr67res, differentiate it from the susceptible Lr67sus variant. The molecular function of the Lr67res protein is not understood, and this study aimed to broaden our knowledge on this topic. Biophysical analysis of the wheat Lr67sus and Lr67res protein variants was performed using Xenopus laevis oocytes as a heterologous expression system. Oocytes injected with Lr67sus displayed properties typically associated with proton-coupled sugar transport proteins—glucose-dependent inward currents, a Km of 110 ± 10 µM glucose, and a substrate selectivity permitting the transport of pentoses and hexoses. By contrast, Lr67res induced much larger sugar-independent inward currents in oocytes, implicating an alternative function. Since Lr67res is a mutated hexose-proton symporter, the possibility of protons underlying these currents was investigated but rejected. Instead, currents in Lr67res oocytes appeared to be dominated by anions. This conclusion was supported by electrophysiology and 36Cl− uptake studies and the similarities with oocytes expressing the known chloride channel from Torpedo marmorata, TmClC-0. This study provides insights into the function of an important disease resistance gene in wheat, which can be used to determine how this gene variant underpins disease resistance in planta.Ricky J. Milne, Katherine E. Dibley, Jayakumar Bose, Anthony R. Ashton, Peter R. Ryan, Stephen D. Tyerman, and Evans S. Laguda

Wildland fire in ecosystems: Effects of fire on soil and water

This state-of-knowledge review about the effects of fire on soils and water can assist land and fire managers with information on the physical, chemical, and biological effects of fire needed to successfully conduct ecosystem management, and effectively inform others about the role and impacts of wildland fire. Chapter topics include the soil resource, soil physical properties and fire, soil chemistry effects, soil biology responses, the hydrologic cycle and water resources, water quality, aquatic biology, fire effectson wetland and riparian systems, fire effects models, and watershed rehabilitation

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

Objective: To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility. Methods: Based on existing best practices we propose a 5 step standardized framework for: (1) transparently defining the problem; (2) selecting suitable datasets; (3) constructing variables from the observational data; (4) learning the predictive model; and (5) validating the model performance. We implemented this framework as open-source software utilizing the Observational Medical Outcomes Partnership Common Data Model to enable convenient sharing of models and reproduction of model evaluation across multiple observational datasets. The software implementation contains default covariates and classifiers but the framework enables customization and extension. Results: As a proof-of-concept, demonstrating the transparency and ease of model dissemination using the software, we developed prediction models for 21 different outcomes within a target population of people suffering from depression across 4 observational databases. All 84 models are available in an accessible online repository to be implemented by anyone with access to an observational database in the Common DataModel format. Conclusions: The proof-of-concept study illustrates the framework's ability to develop reproducible models that can be readily shared and offers the potential to perform extensive external validation of models, and improve their likelihood of clinical uptake. In future work the framework will be applied to perform an "all-by-all" prediction analysis to assess the observational data prediction domain across numerous target populations, outcomes and time, and risk settings

Polygenic Multiple Sclerosis Risk and Population-Based Childhood Brain Imaging

Objective: Multiple sclerosis (MS) is a neurological disease with a substantial genetic component and immune-mediated neurodegeneration. Patients with MS show structural brain differences relative to individuals without MS, including smaller regional volumes and alterations in white matter (WM) microstructure. Whether genetic risk for MS is associated with brain structure during early neurodevelopment remains unclear. In this study, we explore the association between MS polygenic risk scores (PRS) and brain imaging outcomes from a large, population-based pediatric sample to gain insight into the underlying neurobiology of MS. Methods: We included 8- to 12-year-old genotyped participants from the Generation R Study in whom T1-weighted volumetric (n = 1,136) and/or diffusion tensor imaging (n = 1,088) had been collected. PRS for MS were calculated based on a large genome-wide association study of MS (n = 41,505) and were regressed on regional volumes, global and tract-specific fractional anisotropy (FA), and global mean diffusivity using linear regression. Results: No associations were observed for the regional volumes. We observed a positive association between the MS PRS and global FA (β = 0.098, standard error [SE] = 0.030, p = 1.08 × 10−3). Tract-specific analyses showed higher FA and lower radial diffusivity in several tracts. We replicated our findings in an independent sample of children (n = 186) who were scanned in an earlier phase (global FA; β = 0.189, SE = 0.072, p = 9.40 × 10−3). Interpretation: This is the first study to show that greater genetic predisposition for MS is associated with higher global brain WM FA at an early age in the general population. Our results suggest a preadolescent time window within neurodevelopment in which MS risk variants act upon the brain. ANN NEUROL 2020

Dust Devil Tracks

Dust devils that leave dark- or light-toned tracks are common on Mars and they can also be found on the Earth’s surface. Dust devil tracks (hereinafter DDTs) are ephemeral surface features with mostly sub-annual lifetimes. Regarding their size, DDT widths can range between ∼1 m and ∼1 km, depending on the diameter of dust devil that created the track, and DDT lengths range from a few tens of meters to several kilometers, limited by the duration and horizontal ground speed of dust devils. DDTs can be classified into three main types based on their morphology and albedo in contrast to their surroundings; all are found on both planets: (a) dark continuous DDTs, (b) dark cycloidal DDTs, and (c) bright DDTs. Dark continuous DDTs are the most common type on Mars. They are characterized by their relatively homogenous and continuous low albedo surface tracks. Based on terrestrial and martian in situ studies, these DDTs most likely form when surficial dust layers are removed to expose larger-grained substrate material (coarse sands of ≥500 μm in diameter). The exposure of larger-grained materials changes the photometric properties of the surface; hence leading to lower albedo tracks because grain size is photometrically inversely proportional to the surface reflectance. However, although not observed so far, compositional differences (i.e., color differences) might also lead to albedo contrasts when dust is removed to expose substrate materials with mineralogical differences. For dark continuous DDTs, albedo drop measurements are around 2.5 % in the wavelength range of 550–850 nm on Mars and around 0.5 % in the wavelength range from 300–1100 nm on Earth. The removal of an equivalent layer thickness around 1 μm is sufficient for the formation of visible dark continuous DDTs on Mars and Earth. The next type of DDTs, dark cycloidal DDTs, are characterized by their low albedo pattern of overlapping scallops. Terrestrial in situ studies imply that they are formed when sand-sized material that is eroded from the outer vortex area of a dust devil is redeposited in annular patterns in the central vortex region. This type of DDT can also be found in on Mars in orbital image data, and although in situ studies are lacking, terrestrial analog studies, laboratory work, and numerical modeling suggest they have the same formation mechanism as those on Earth. Finally, bright DDTs are characterized by their continuous track pattern and high albedo compared to their undisturbed surroundings. They are found on both planets, but to date they have only been analyzed in situ on Earth. Here, the destruction of aggregates of dust, silt and sand by dust devils leads to smooth surfaces in contrast to the undisturbed rough surfaces surrounding the track. The resulting change in photometric properties occurs because the smoother surfaces have a higher reflectance compared to the surrounding rough surface, leading to bright DDTs. On Mars, the destruction of surficial dust-aggregates may also lead to bright DDTs. However, higher reflective surfaces may be produced by other formation mechanisms, such as dust compaction by passing dust devils, as this may also cause changes in photometric properties. On Mars, DDTs in general are found at all elevations and on a global scale, except on the permanent polar caps. DDT maximum areal densities occur during spring and summer in both hemispheres produced by an increase in dust devil activity caused by maximum insolation. Regionally, dust devil densities vary spatially likely controlled by changes in dust cover thicknesses and substrate materials. This variability makes it difficult to infer dust devil activity from DDT frequencies. Furthermore, only a fraction of dust devils leave tracks. However, DDTs can be used as proxies for dust devil lifetimes and wind directions and speeds, and they can also be used to predict lander or rover solar panel clearing events. Overall, the high DDT frequency in many areas on Mars leads to drastic albedo changes that affect large-scale weather patterns