SAG (sensitive to apoptosis gene) was cloned as an inducible gene by 1,10-phenanthroline (OP), a redoxsensitive compound and an apoptosis inducer. SAG encodes a novel zinc RING finger protein that consists of 113 amino acids with a calculated molecular mass of 12.6 kDa. SAG is highly conserved during evolution, with identities of 70% between human and Caenorhabditis elegans sequences and 55% between human and yeast sequences. In human tissues, SAG is ubiquitously expressed at high levels in skeletal muscles, heart, and testis. SAG is localized in both the cytoplasm and the nucleus of cells, and its gene was mapped to chromosome 3q22-24. Bacterially expressed and purified human SAG binds to zinc and copper metal ions and prevents lipid peroxidation induced by copper or a free radical generator. When overexpressed in several human cell lines, SAG protects cells from apoptosis induced by redox agents (the metal chelator OP and zinc or copper metal ions). Mechanistically, SAG appears to inhibit and/or delay metal ion-induced cytochrome c release and caspase activation. Thus, SAG is a cellular protective molecule that appears to act as an antioxidant to inhibit apoptosis induced by metal ions and reactive oxygen species.

The selection of a spatial interpolation methods will impact the quality of site‐specific soil fertility maps. The objective of this study was to describe and predict the relative performance of inverse distance weighted (IDW) and ordinary kriging. Soil samples were collected on 30.5‐m grids for fields in five Kentucky counties and analyzed for pH, buffer pH, P, K, Ca, and Mg. From these data sets, 61‐m grid subsets were extracted. Data were interpolated with IDW and kriging procedures. Prediction efficiency (PE) was determined using an independent dataset (PEvalidation) and with cross‐validation (PEcross‐validation). Multiple stepwise regression was used to develop models that described the relative performance of ordinary kriging and IDW with statistical properties of the data. At the 30.5‐m grid scale, the performance of ordinary kriging relative to IDW improved as the range of spatial correlation increased and fit of the semivariogram model improved. However, at the 61.0‐m grid scale, the performance of ordinary kriging relative to IDW diminished as the degree of spatial structure increased and the fit of the semivariogram model improved. Alone, PEcross‐validation poorly describes the performance of PEvalidation across locations, soil properties, and sampling intervals (r2 = 0.18). However, in combination with the range of spatial correlation, substantial variability at the 30.5‐m grid scale was described for variables with sample semivariograms that reached plateaus (R2 = 0.61). In some situations, better decisions will be made regarding the use of these methods by considering the range of spatial correlation and cross‐validation statistics.

The quality of soil fertility maps affects the efficacy of site‐specific soil fertility management (SSFM). The purpose of this study was to evaluate how different soil sampling approaches and grid interpolation schemes affect map quality. A field in south central Michigan was soil sampled using several strategies including grid‐point (30‐ and 100‐m regular grids), grid cell (100‐m cells), and a simulated soil map unit sampling. Soil fertility [pH, P, K, Ca, Mg, and cation‐exchange capacity (CEC)] data were predicted using ordinary kriging, inverse distance weighted (IDW), and nearest neighbor (NN) interpolations for the various data sets. Each resulting map was validated against an independent data (n = 62) set to evaluate map quality. While soil properties were spatially structured, kriging predictions were marginal (prediction efficiencies ≤48%) at high sample densities and poor at lower densities (i.e., 61‐ and 100‐m grids; prediction efficiencies <21%). The average optimal distance exponent at each scale of measurement was 1.5. The performance of kriging relative to IDW methods (with a distance exponent of 1.5) improved with increasing sampling intensity (i.e., IDW was superior to kriging for 100% of cases with the 100‐m grid, 79% of the cases with the 61.5‐m grid scale, and 67% of the cases with the 30‐m grid). Practically, there was little difference between these interpolation methods. Grid sampling with a 100‐m grid, grid cell sampling, and simulated soil map unit sampling yielded similar prediction efficiencies to those for the field average approach, all of which were generally poor.

Sensors exist that allow rapid mapping of bulk soil electrical confurther research. Variation in bulk soil EC may occur ductivity (EC); however, the utility of these sensors for Kentucky producers is unknown. The purpose of this study was to assess the at spatial and temporal scales including the microscale nature and the causes of soil EC variability and to make a first assess-(variation in EC at distances or times less than the ment of its potential utility in Kentucky, particularly for fields consampling interval) and macroscale (variation in EC at taining soils derived from limestone residuum overlain by loess. Varidistances or times equal to or greater than the sampling ous geostatistical, correlation, and regression analyses were conducted interval). Errors associated with mapping procedures, at seven locations to examine EC map variability. Sensor drift and such as interpolation, are a function of measurement errors associated with changes in coulter depth were minimal. Bulk intensity and have been given considerable attention in soil EC related fairly well with clay content across locations and the literature for grid soil sampling (e.g., Mueller et al., sample dates (r 2 ϭ 0.40); however, many site-and time-specific correla-2001; Mueller and Pierce, 2003) but not EC mapping. tions were better. Clay (maximum r 2 ϭ 0.75), moisture content (maxi-It would be ideal if measurement error, microscale varimum r 2 ϭ 0.76), Ca (maximum r 2 ϭ 0.67), and Mg (maximum r 2 ϭ 0.64) were positively correlated with EC, and depth to argillic or ability, temporal variability, and errors associated with cambic horizon (maximum r 2 ϭ 0.62), depth to fragipan (maximum mapping were small and if macroscale spatial variability r 2 ϭ 0.81), and depth to bedrock (maximum r 2 ϭ 0.32) were negatively accounted for the greatest proportion of the EC variabilcorrelated with EC. A multiple-regression model (R 2 ϭ 0.70) was ity observed. developed to predict EC that included nine factors: clay, sand, soil Many of the factors governing soil EC variability are moisture, buffer pH, base saturation, Ca, soil temperature, depth to understood. The spatial and temporal variability of bulk cambic and argillic horizon, and slope. Soil EC variability was spatially soil EC is affected by the complex movement of elecstructured, and spatial patterns were stable over time; however, the trons through soil. Electrons may travel through the degree to which these patterns could be observed depended on the water in soil macropores, along the surfaces of soil minmapping procedures used. Our research suggested that EC mapping erals (i.e., via exchangeable ions), or through alternating may have utility for Kentucky farmers.