Reflectance of vegetation, soil, and water

The author has identified the following significant results. Bands 4, 5, and 7 and 5, 6, and 7 were best for distinguishing among crop and soil categories in ERTS-1 SCENES 1182-16322 (1-21-73) and 1308-16323 (5-21-73) respectively. Chlorotic sorghum areas 2.8 acres or larger in size were identified on a computer printout of band 5 data. Reflectance of crop residues was more often different from bare soil in band 4 than in bands 5, 6, and 7. Simultaneously acquired aircraft and spacecraft MSS data indicated that spacecraft surveys are as reliable as aircraft surveys. ERTS-1 data were successfully used to estimate acreage of citrus, cotton, and sorghum as well as idle crop land

The APOL1 Gene and Allograft Survival after Kidney Transplantation

Coding variants in the apolipoprotein L1 gene (APOL1) are strongly associated with nephropathy in African Americans (AAs). The effect of transplanting kidneys from AA donors with two APOL1 nephropathy risk variants is unknown. APOL1 risk variants were genotyped in 106 AA deceased organ donors and graft survival assessed in 136 resultant kidney transplants. Cox-proportional hazard models tested for association between time to graft failure and donor APOL1 genotypes. The mean follow-up was 26.4 ± 21.8 months. Twenty-two of 136 transplanted kidneys (16%) were from donors with two APOL1 nephropathy risk variants. Twenty-five grafts failed; eight (32%) had two APOL1 risk variants. A multivariate model accounting for donor APOL1 genotype, overall African ancestry, expanded criteria donation, recipient age and gender, HLA mismatch, CIT and PRA revealed that graft survival was significantly shorter in donor kidneys with two APOL1 risk variants (hazard ratio [HR] 3.84; p = 0.008) and higher HLA mismatch (HR 1.52; p = 0.03), but not for overall African ancestry excluding APOL1. Kidneys from AA deceased donors harboring two APOL1 risk variants failed more rapidly after renal transplantation than those with zero or one risk variants. If replicated, APOL1 genotyping could improve the donor selection process and maximize long-term renal allograft survival

Th17 Cytokines and the Gut Mucosal Barrier

Local immune responses serve to contain infections by pathogens to the gut while preventing pathogen dissemination to systemic sites. Several subsets of T cells in the gut (T-helper 17 cells, γδ T cells, natural killer (NK), and NK-T cells) contribute to the mucosal response to pathogens by secreting a subset of cytokines including interleukin (IL)-17A, IL-17F, IL-22, and IL-26. These cytokines induce the secretion of chemokines and antimicrobial proteins, thereby orchestrating the mucosal barrier against gastrointestinal pathogens. While the mucosal barrier prevents bacterial dissemination from the gut, it also promotes colonization by pathogens that are resistant to some of the inducible antimicrobial responses. In this review, we describe the contribution of Th17 cytokines to the gut mucosal barrier during bacterial infections

ERTS-1 Aircraft Support, 24-Channel MSS CCT Experiences and Land Use Classification Results

Aircraft multispectral scanner (MSS) data collected by the NASA 24-channel MSS on July 26, 1972 (Mission 207) over the USDA, Research Farm at Weslaco, Texas, were evaluated for quality and for crop, soil, and water discrimination. The standard deviations for each of the 24 channels for a uniform surface, a water reservoir, were used as an indicator of system noise. By this criterion, channels 22, 20, 15, qnd 21 were of low quality. Based on the ratio of odd to even numbers in all channels, the conclusion was reached that the data are 7-bit precision. An optimum channel selection program selected channels 7, 8, 3, and 18 as the best 4 channels for distinguishing among seven vegetal categories: Stoneville 213 cotton, Anton SP-21 cotton, Valencia orange, Red Blush grapefruit, sugarcane, Coast-Cross 1 bermuda-grass and African stargrass. These same channels also distinguished the nonvegetal categories (water, highway, rooftops, and bare soil) satisfactorily. Among the vegetal categories, sugarcane and cotton had distinctive signatures that allowed them to be distinguished from grass and citrus. Classification accuracies improved to about 81% when the intra plant genera categories (such as the two cotton varieties) were combined into one