Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India

The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India. For this purpose, a canonical correlation analysis (CCA) based statistical downscaling approach and dynamical downscaling approach (at 30 km) with an optimized configuration of the regional climate model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200 hPa, temperature at 200 hPa and sea surface temperatures) over three different domains were selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Furthermore, two bias correction methods namely quantile mapping (QM) and mean bias-remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM-based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA-based statistical downscaling. Overall, the results indicate that the QM-based bias-corrected downscaled RegCM model is a useful tool for wintertime seasonal-scale precipitation prediction over north India.Peer reviewedFinal Accepted Versio

Improving Reproducibility of Phenotypic Assessments in the DyW Mouse Model of Laminin-α2 Related Congenital Muscular Dystrophy

Laminin-α2 related Congenital Muscular Dystrophy (LAMA2-CMD) is a progressive muscle disease caused by partial or complete deficiency of laminin-211, a skeletal muscle extracellular matrix protein. In the last decade, basic science research has queried underlying disease mechanisms in existing LAMA2-CMD murine models and identified possible clinical targets and pharmacological interventions. Experimental rigor in preclinical studies is critical to efficiently and accurately quantify both negative and positive results, degree of efficiency of potential therapeutics and determine whether to move a compound forward for additional preclinical testing. In this review, we compare published available data measured to assess three common parameters in the widely used mouse model DyW, that mimics LAMA2-CMD, we quantify variability and analyse its possible sources. Finally, on the basis of this analysis, we suggest standard set of assessments and the use of available standardized protocols, to reduce variability of outcomes in the future and to improve the value of preclinical research

Soil Salinity and pH Drive Soil Bacterial Community Composition and Diversity Along a Lateritic Slope in the Avon River Critical Zone Observatory, Western Australia

Soils are crucial in regulating ecosystem processes, such as nutrient cycling, and supporting plant growth. To a large extent, these functions are carried out by highly diverse and dynamic soil microbiomes that are in turn governed by numerous environmental factors including weathering profile and vegetation. In this study, we investigate geophysical and vegetation effects on the microbial communities of iron-rich lateritic soils in the highly weathered landscapes of Western Australia (WA). The study site was a lateritic hillslope in southwestern Australia, where gradual erosion of the duricrust has resulted in the exposure of the different weathering zones. High-throughput amplicon sequencing of the 16S rRNA gene was used to investigate soil bacterial community diversity, composition and functioning. We predicted that shifts in the microbial community would reflect variations in certain edaphic properties associated with the different layers of the lateritic profile and vegetation cover. Our results supported this hypothesis, with electrical conductivity, pH and clay content having the strongest correlation with beta diversity, and many of the differentially abundant taxa belonging to the phyla Actinobacteria and Proteobacteria. Soil water repellence, which is associated with Eucalyptus vegetation, also affected beta diversity. This enhanced understanding of the natural system could help to improve future crop management in WA since the physicochemical properties of the agricultural soils in this region are inherited from laterites via the weathering and pedogenesis processes

Sorption of pesticides by a mineral sand mining by-product, neutralised used acid (NUA)

This study investigated the sorption-desorption behaviour of four pesticides by a by-product from mineral sand mining, commonly referred to as neutralised used acid (NUA). In batch studies the average amount of pesticide removed after 6 h was 69% for atrazine, 89% for diuron, 61% for 2,4-D and 83% for chlorpyrifos. The lower sorption of 2,4-D to NUA compared with the other pesticides studied is most likely to be due to the high pH of the solutions (7.8 to 8.8) which would have resulted in 2,4-D being predominantly in an anionic form. The presence of other pesticides only significantly decreased the amount of 2,4-D sorbed from 59% to 34% when present in a mixture. Little (2 to 17%) diuron, chlorpyrifos, atrazine or 2,4-D were found to desorb from the NUA. The presence of nitrate or phosphate had minimal effect on the amount of diuron or atrazine sorbed to the NUA. However, all phosphate and nitrate treatments significantly (