National Soils Database

End of project reportThe objectives of the National Soils Database project were fourfold. The first was to generate a national database of soil geochemistry to complete the work that commenced with a survey of the South East of Ireland carried out in 1995 and 1996 by Teagasc (McGrath and McCormack, 1999). Secondly, to produce point and interpolated spatial distribution maps of major, minor and trace elements and to interpret these with respect to underlying parent material, glacial geology, land use and possible anthropogenic effects. A third objective was to investigate the microbial community structure in a range of soil types to determine the relationship between soil microbiology and chemistry. The final objective was to establish a National Soils Archive

Spatial distribution patterns of phosphorus in top-soils of Greater London Authority area and their natural and anthropogenic factors

Soil phosphorus (P) has a strong impact on soil and water quality. Soils in urban areas tend to enrich P, however, they have not been adequately investigated. A total of 6467 top-soil samples were collected and analysed by the British Geological Survey, providing basic data for studying the top-soil P distribution patterns and their environmental implications. The hotspots and cool spots were identified using the index of local Moran's I, which is a powerful methodology for discerning spatial clusters and spatial outliers. Combined with the results of one-way analysis of variances (ANOVA), a strong natural control of P was illustrated with elevated concentrations in areas of alluvium and river terrace deposits. P concentration in the lower Thames Estuary was clearly influenced by the tidal effect which has diluted the P-enriched sediments. The high concentration of Si and low pH level were linked to the low value clusters of P in Hyde Park and Richmond Park. Besides the natural control, the high value clusters concentrated in the city centre and built-up area, which indicated soil P content was strongly affected by human activities. The results of a t-test also showed the significant distinction of P concentrations between urban area and non-urbanised area, implying that urbanization and built-up materials accounted mostly for the locations and magnitude of the P pool. To conclude, the spatial patterns of P observed in the study area were controlled by both natural (parent materials (PMs) and geomorphology) and anthropogenic (urbanization) factors

Using Local Moran's I to identify contamination hotspots of rare earth elements in urban soils of London

Rare earth elements (REEs) in urban soils are receiving more attention because of their increased use in modern technology. Up to now, there is little information about the spatial patterns of REEs and their influencing factors in urban soils. In this study, based on the British Geological Survey “London Earth” geochemical survey data containing a total of 6467 soil samples collected from the urban area of London, the spatial distributions of Ce, La, Nd, Sc, Sm, Yb and Y, and their influencing factors were investigated. The element Sc is included in this study as related element. Urban samples were collected at a depth of (0–20 cm), and total concentrations of 48 chemical elements were determined in the <2 mm size fraction of the topsoils using X-ray Fluorescence Spectrometry (XRFS). Spatial clusters and spatial outliers were identified using the index of local Moran's I, showing elevated concentrations of REEs in the northwest, northeast and southern parts of the study areas while all the 7 REEs exhibited generally similar spatial patterns: high values were mainly located in soils over Alluvium, Glacial till and Clay-with-flints deposits, demonstrating the close relationships between REEs and the lithology. Meanwhile, hotspot analysis revealed high values of REEs in public parks, private golf clubs, wetlands and agricultural lands especially in Hillingdon, Enfield, Brent, Harrow, Croydon and Bromley. Soil parent material was identified as the primary source for larger spatial patterns of natural origin, while smaller spatial patterns were associated with anthropogenic sources including agricultural practices, industrial activities, vehicular emission and urbanization. The REE hotspots revealed in urban soils may imply potential contamination requiring further attention

Mid-infrared spectroscopy as an alternative to laboratory extraction for the determination of lime requirement in tillage soils

peer-reviewedLime is a crucial soil conditioner to bring agricultural soils to optimum pH values for nutrient availability. Lime recommendations are typically determined in laboratory extractions, the most common being the “Shoemaker- McLean and Pratt” (SMP) buffer method, that requires carcinogenic reagents soon to be abolished under the EU legislation. As an alternative to wet chemistry, mid-infrared (MIR) spectroscopy has shown to be a cost-and time effective method at predicting soil properties. The capability and feasibility of diffuse reflectance infrared spectroscopy (DRIFTS) to predict lime requirement (LR) in tillage fields is examined. Samples from 41 cereal tillage fields (n = 655) are used to build a calibration for DRIFTS using partial least squares regression (PLSR). The samples were split into calibration set (31 fields, n=495) and validation set (10 fields, n= 160). After preprocessing with trim, smoothing and standard normal variate, a calibration model using 6 latent variables, provided R2 of 0.89 and root mean square error of cross-validation (RMSECV) of 1.56 t/ha. Prediction of all fields from the validation set resulted in R2 of 0.76 and root mean square error of prediction (RMSEP) of 1.68 t/ ha. The predictions of the single fields ranged from R2 values of 0.41 to 0.72, RMSEP of 0.48 to 4.2 t/ha and ratios of performance to inter-quartile distance (RPIQ) of 0.45 to 3.56. It was shown that the signals of soil constituents having an influence on the LR were picked up in the spectra and were identified in the loading weights of the PLSR. While the error is too high to predict the variability of LR within the field, MIR prediction using field averages provided a viable alternative to current laboratory methods for blanket spreading of lime on tillage fields.Teagas

Multi-Effects Coupled Nanogenerators for Simultaneously Harvesting Solar, Thermal, and Mechanical Energies

As a result of the widespread use of small-scale and low-power electronic devices, the demand for micro-energy sources has increased, in particular the potential to harvest the wide variety of energy sources present in their surrounding environment. In this paper, a novel coupled nanogenerator that can realize energy harvesting for multiple energy sources is reported. Based on the unique electrical properties of ferroelectric Bi 0.5Na 0.5TiO 3 (BNT) materials, it is possible to combine a photovoltaic cell, pyroelectric nanogenerator, and triboelectric-piezoelectric nanogenerator in a single element to harvest light, heat, and mechanical energy simultaneously. To evaluate the effectiveness of coupling for different materials, a Yang coupling factor (k C,Q) is defined in terms of transferred charge, where BNT has the largest k C,Q of 1.29 during heating, indicating that BNT has the best coupling enhancement compared to common ferroelectric materials. This new criterion and novel device structure therefore provide a new basis for the future development of coupled nanogenerators which are capable of harvesting multiple sources of energy.</p

Comparative transcriptome analysis reveals resistance-related genes and pathways in Musa acuminata banana 'Guijiao 9' in response to Fusarium wilt.

Fusarium wilt caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most devastating diseases in bananas resulting in significant loss of Cavendish bananas production worldwide. Here we show the agronomic traits and the resistance of 'Guijiao 9' in the field trials from 2012 to 2017. And then we dissect and compare the transcriptome response from these two cultivars (cv. 'Guijiao 9' and cv. Williams) in an attempt to understand the molecular basis that contribute to the enhanced Foc tropical race 4 (Foc-TR4) resistance. 'Guijiao 9' is a Cavendish cultivar with strong resistance to Foc-TR4, which was reflected in a lower disease severity and incidence in glasshouse and field trails, when compared to the susceptible cultivar Williams. Gene expression profiles of 'Guijiao 9' and Williams were captured by performing RNA-Seq analysis on 16 biological samples collected over a six day period post inoculation with Foc-TR4. Transcriptional reprogramming in response to Foc-TR4 was detected in both genotypes but the response was more drastic in 'Guijiao 9' than in Williams. Specific genes involved in plant-pathogen interaction and defense signaling including MAPK, calcium, salicylic acid, jasmonic acid and ethylene pathways were analyzed and compared between 'Guijiao 9' and Williams. Genes associated with defense-related metabolites synthesis such as NB-LRR proteins, calmodulin-binding protein and phenylpropanoids biosynthesis genes were significantly up-regulated in 'Guijiao 9' resistant to Foc-TR4 infection. Taken together, this study highlights the important roles of plant hormone regulation and defense gene activation in mediating resistance in 'Guijiao 9'

Immune-related potential biomarkers and therapeutic targets in coronary artery disease

BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease