ANN‐based pedotransfer and soil spatial prediction functions for predicting Atterberg consistency limits and indices from easily available properties at the watershed scale in western Iran

Atterberg consistency limits (liquid limit, LL; plastic limit, PL; shrinkage limit, SL) and indices (plasticity index, PI; friability index, FI) are useful indicators of soil mechanical behaviour. This study was conducted to evaluate the use of soil and environmental data for predicting Atterberg limits or indices using artificial neural network (ANN) models at the watershed scale in western Iran. The LL, PL, SL, PI, FI, particle size distribution, organic matter (OM) and calcium carbonate equivalent (CCE) were measured in soil samples collected from 113 locations. Three sets of readily available properties were employed as inputs. The first of these data sets or models consisted of soil properties. The second included topographic attributes and normalized difference vegetation index (NDVI), and the third was a combination of soil, topographic attributes and NDVI. Developed ANN models could explain a majority of the variability (62–94%) in Atterberg limits and indices. Greatest and poorest performances were attributed to the third and first models, respectively. No significant efficacy difference was observed between the second and third models. Therefore, the second data set with its readily available environmental variables is suggested for use in predicting Atterberg limits and indices at the regional scale. Sensitivity analysis showed that NDVI, OM, clay content, CCE and topographic attributes (wetness index, elevation, plan curvature and slope) could explain much of the variance associated with Atterberg limits and indices at the watershed scale in western Iran.     

Lithogenic and anthropogenic impacts on soil surface magnetic susceptibility in an arid region of Central Iran

In this study, natural and anthropogenic factors affecting the spatial distribution of topsoil mass magnetic susceptibility (χlf) were investigated in an arid region in Isfahan province, central Iran. A total of 100 surface (0–5 cm) soil samples were collected, and the χlf value of the soil samples was measured. High values of χlf in the east and northeast parts of the area indicated the occurrence of hilly igneous rocks and pediment geomorphic surface, enriched by ferrimagnetic minerals. In order to investigate the effects of human activities, multivariate geostatistics was applied to the results on the concentrations of eight heavy metals in surface soil samples. The results indicated that the spatial variability of second factor extracted by factor analysis, with high contribution of Zn, Pb and Cu, was well in agreement with the distribution of urban and industrial sites. This implied that magnetic particles accompanying the heavy metal emissions have increased χlf in the topsoil of the west of the area studied. Multiple linear regression analysis of χlf with studied factors revealed that the developed model explained about 64% of total variability in χlf in the studied area; and geology was identified as the most important controlling factor.     

Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in northern Iran

In this study artificial neural network (ANN) models were designed to predict the biomass and grain yield of barley from soil properties; and the performance of ANN models was compared with earlier tested statistical models based on multivariate regression. Barley yield data and surface soil samples (0–30 cm depth) were collected from 1 m² plots at 112 selected points in the arid region of northern Iran. ANN yield models gave higher coefficient of determination and lower root mean square error compared to the multivariate regression, indicating that ANN is a more powerful tool than multivariate regression. Sensitivity analysis showed that soil electrical conductivity, sodium absorption ratio, pH, total nitrogen, available phosphorus, and organic matter consistently influenced barley biomass and grain yield. A comparison of the two methods to identify the most important factors indicated that while in the ANN analysis, soil organic matter (SOM) was included among the most important factors; SOM was excluded from the most important factors in the multivariate analysis. This significant discrepancy between the two methods was apparently a consequence ofthe non-linear relationships of SOM with other soil properties. Overall, our results indicated that the ANN models could explain 93 and 89% of the total variability in barley biomass and grain yield, respectively. The performance of the ANN models as compared to multivariate regression has better chance for predicting yield, especially when complex non-linear relationships exist among thefactors. We suggest that for further potential improvement in predicting thebarley yield, factors other than the soil properties considered such as soil micronutrient status and soil and crop management practices followed during the growing season, need to be included in the models.     

Characterization of selected genes upregulated in non-tuberculous European wild boar as possible correlates of resistance to Mycobacterium bovis infection

Bovine tuberculosis (bTB), caused by Mycobacterium bovis (Mycobacterium tuberculosis complex), is a zoonotic disease that affects cattle and wildlife worldwide. These animal hosts can serve as reservoirs of infection, thus increasing the risk of human exposure and infection. In this study we quantified by RNA macroarray fluorescent hybridization and real-time RT-PCR the mRNA levels of genes differentially expressed in oropharyngeal tonsils and mandibular lymph nodes of three and seven individual non-tuberculous and tuberculous wild boars naturally exposed to M. bovis, respectively. These results demonstrated upregulation of two genes, complement component 3 (C3) and methylmalonyl-CoA mutase (MUT), in the non-tuberculous wild boars. These upregulated genes may contribute to resistance of wild boars to bTB by modifying the innate immunity, which limits the ability of the mycobacterium to infect and persist within macrophages. The C3 and MUT genes, therefore, are likely to be good candidates to study as markers of bTB resistance using functional genomics in animal model systems. Identification of genes upregulated in wild animals resistant to bTB contributes to our understanding of the mechanisms of protective immunity and resistance to mycobacterial organisms.     

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0–20 cm and 20–40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included the land use type, normalized difference vegetation index (NDVI) > normalized difference water index (NDWI) > silt > clay > elevation in the 0–20 cm soil layer. On the other hand, for the 20–40 cm soil layer, the land use type following NDVI > NDWI > clay > silt were identified as the most powerful predictive factors. In the Deylaman region, in both soil layers, sand had a considerable negative effect on SOCD and most of the terrain attributes had no significant impact on the SOCD prediction. Therefore, these results provide valuable information for sustainable management and decision-making on a landscape scale for governors and other users.     

Evolution of anaphylatoxins, their diversity and novel roles in innate immunity: insights from the study of fish complement

Anaphylatoxins are small molecules ( approximately 9 kDa) that are generated as a result of the activation of the complement system. These molecules play an important role in inflammation, and they are responsible for the activation of various innate and adaptive immune processes. The study of these important inflammatory molecules has been restricted to mammalian species so far. Recent studies have shown that teleost fish, unlike any other known animal species, contain multiple forms of the C3a anaphylatoxin, all of which are functionally active and play a prominent role in inducing superoxide production in fish leukocytes. The C5a anaphylatoxin has also been characterized in these animals, and like in mammals, it plays an important role in leukocyte chemotaxis and in triggering the respiratory burst of leukocytes. Interestingly, it has been shown that rainbow trout anaphylatoxins play an unexpected role in enhancing phagocytosis of particles. C5a and C3a receptors have recently been cloned and characterized in rainbow trout, suggesting that the duplication of these receptors from a common ancestor occurred before the emergence of teleosts. The studies derived from these molecules in teleost fish indicate that the basic structure and function of anaphylatoxins and their receptors, have been conserved for more than 300 million years.     

Differential expression of inflammatory and immune response genes in sheep infected with Anaplasma phagocytophilum

Anaplasma phagocytophilum infects a wide variety of host species and causes the diseases tick-borne fever (TBF) in ruminants and granulocytic anaplasmosis in humans, horses and dogs. TBF in sheep has become one of the more prevalent tick-borne diseases in some regions of Europe. A. phagocytophilum infection modifies host gene expression and immune response. The objective of this research was to characterize differential gene expression in sheep experimentally and naturally infected with A. phagocytophilum by microarray hybridization and real-time RT-PCR. The results of these studies demonstrated in sheep the activation of inflammatory and innate immune pathways and the impairment of adaptive immunity during A. phagocytophilum infection. The characterization of the genes and their expression profiles in sheep in response to A. phagocytophilum infection advances our understanding of the molecular mechanisms of pathogen infection and the pathogenesis of TBF. Collectively, these results expand current information on the mammalian host response to A. phagocytophilum infection.