Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg-1) for Cu and 23.4 (mg kg-1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.

Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning

Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices.

Gradient boosting machine learning model to predict aflatoxins in Iowa corn

Introduction

Aflatoxin (AFL), a secondary metabolite produced from filamentous fungi, contaminates corn, posing significant health and safety hazards for humans and livestock through toxigenic and carcinogenic effects. Corn is widely used as an essential commodity for food, feed, fuel, and export markets; therefore, AFL mitigation is necessary to ensure food and feed safety within the United States (US) and elsewhere in the world. In this case study, an Iowa-centric model was developed to predict AFL contamination using historical corn contamination, meteorological, satellite, and soil property data in the largest corn-producing state in the US.

Methods

We evaluated the performance of AFL prediction with gradient boosting machine (GBM) learning and feature engineering in Iowa corn for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. A 90%-10% training-to-testing ratio was utilized in 2010, 2011, 2012, and 2021 (n = 630), with independent validation using the year 2020 (n = 376).

Results

The GBM model had an overall accuracy of 96.77% for AFL with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas GBM had an overall accuracy of 90.32% with a balanced accuracy of 64.88% for a 5-ppb threshold. The GBM model had a low power to detect high AFL contamination events, resulting in a low sensitivity rate. Analyses for AFL showed satellite-acquired vegetative index during August significantly improved the prediction of corn contamination at the end of the growing season for both risk thresholds. Prediction of high AFL contamination levels was linked to aflatoxin risk indices (ARI) in May. However, ARI in July was an influential factor for the 5-ppb threshold but not for the 20-ppb threshold. Similarly, latitude was an influential factor for the 20-ppb threshold but not the 5-ppb threshold. Furthermore, soil-saturated hydraulic conductivity (Ksat) influenced both risk thresholds.

Discussion

Developing these AFL prediction models is practical and implementable in commodity grain handling environments to achieve the goal of preventative rather than reactive mitigations. Finding predictors that influence AFL risk annually is an important cost-effective risk tool and, therefore, is a high priority to ensure hazard management and optimal grain utilization to maximize the utility of the nation's corn crop.

Transcriptional analysis of the Caulobacter 4.5 S RNA ffs gene and the physiological basis of an ffs mutant with a Ts phenotype

A temperature-sensitive (ts) mutation in the ffs gene, encoding 4.5 S RNA, gives rise to cell division and DNA replication defects in Caulobacter crescentus. The ffs gene is transcribed throughout the cell-cycle and is transcribed at similar rates in mutant (ffs36) and wild-type strains, but in the mutant the 4.5 S RNA is unstable leading to lower 4.5 S RNA levels. The ffs36 phenotype results from a single base change in one of the non-conserved stems of the mature RNA, and is completely rescued by a compensating mutation in the opposite strand, providing confirmation of the predicted secondary structure of the 4.5 S RNA. The Caulobacter ffs gene was shown to be functionally comparable to the Escherichia coli ffs gene by complementation. Comparison of the ffs36 strain to a ts secA strain of Caulobacter, also having cell-cycle and DNA replication phenotypes, showed that both exhibit a permanent induction of a heat shock response at the restrictive temperature. To explain the phenotype of both the secA and ffs36 strains, we propose that a cell-cycle checkpoint prevents further progression through the cell-cycle in response to increased intracellular levels of heat shock and misfolded proteins.

Translation of the leaderless Caulobacter dnaX mRNA

The expression of the Caulobacter crescentus homolog of dnaX, which in Escherichia coli encodes both the gamma and tau subunits of the DNA polymerase III holoenzyme, is subject to cell cycle control. We present evidence that the first amino acid in the predicted DnaX protein corresponds to the first codon in the mRNA transcribed from the dnaX promoter; thus, the ribosome must recognize the mRNA at a site downstream of the start codon in an unusual but not unprecedented fashion. Inserting four bases in front of the AUG at the 5' end of dnaX mRNA abolishes translation in the correct frame. The sequence upstream of the translational start site shows little homology to the canonical Shine-Dalgarno ribosome recognition sequence, but the region downstream of the start codon is complementary to a region of 16S rRNA implicated in downstream box recognition. The region downstream of the dnaX AUG, which is important for efficient translation, exhibits homology with the corresponding region from the Caulobacter hemE gene adjacent to the replication origin. The hemE gene also appears to be translated from a leaderless mRNA. Additionally, as was found for hemE, an upstream untranslated mRNA also extends into the dnaX coding sequence. We propose that translation of leaderless mRNAs may provide a mechanism by which the ribosome can distinguish between productive and nonproductive templates.

A novel promoter motif for Caulobacter cell cycle-controlled DNA replication genes

Caulobacter crescentus contains a single chromosome that is replicated once during a defined period in the cell cycle. The onset of replication coincides with the stimulation of transcription of several genes involved in the replication process. Analysis of the C. crescentus homolog of dnaX, which in Escherichia coli encodes both the gamma and tau subunits of the DNA polymerase III holoenzyme, identified the dnaX transcription start site and showed that activity from the dnaX promoter is stimulated fourfold at the onset of DNA replication. We have identified a conserved sequence motif that is present in the promoter of dnaX and several other genes involved in the replication of DNA, all of which show an induction of transcription at the onset of chromosome replication. Independent mutations in the conserved sequence that lies between the -10 and -35 regions increased transcription, suggesting that a repressor may bind at this site. We propose that the coincident transcriptional activation of several dna genes at the swarmer to stalked cell transition occurs in response to cell cycle regulatory factors, in a manner analogous to the transient transcriptional regulation of flagellar and DNA methylation genes later in the cell cycle.

Use of flow cytometry to identify a Caulobacter 4.5 S RNA temperature-sensitive mutant defective in the cell cycle

Flow cytometry was used to screen a collection of temperature-sensitive mutants for those blocked at discrete points in the cell cycle with respect to the replicative status of the chromosome. At the non-permissive temperature, one such mutant, LS439, could not initiate new rounds of DNA replication and arrested primarily as cells with two completed chromosomes Extended incubation at the restrictive temperature resulted in filament formation. Following the shift to the restrictive temperature protein synthesis continued, but at a reduced rate. A 0.2 kb fragment of DNA located immediately upstream of the Caulobacter homolog of the Escherichia coli dnaX gene was able to completely rescue the temperature-sensitive phenotype of LS439. The 0.2 kb fragment contained a homolog of the bacterial gene encoding 4.5 S RNA. The original point mutation is predicted to disrupt the stem structure in the 4.5 S RNA thus providing a rationale for the genetic basis of the LS439 phenotype.