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Results for “root hair pattern”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Biological monitoring of iodine, a water disinfectant for long-term space missions

In order to establish guidelines for exposure of astronauts to iodine, used as a water disinfectant in space, we studied the usefulness of hair, saliva, and urine for biological monitoring in humans and in the human hair/nude mouse model. The monitoring of iodine in patients that received 150 mCi of Na131I (carrier-free) showed similar patterns of elimination for blood, saliva, and urine. The mean correlation coefficient (r) between iodine elimination for blood/saliva was 0.99, for blood/urine, 0.95, and for saliva/urine, 0.97. The absolute value of iodine concentrations in urine revealed marked variability, which was corrected by adjusting for creatinine levels. The autoradiographic studies of human hair demonstrated that iodine is rapidly incorporated into external layers of the hair root and can be removed easily during washing. These data were confirmed after iodine exposure using the human hair/nude mouse model. Hair does not provide satisfactory information about exposure due to unstable incorporation of iodine. The most useful medium for biological monitoring of astronauts exposed to high doses of iodine in drinking water is urine, when adjusted for creatinine, and saliva, if quantitative evaluation of flow rate is provided.

NASA Discipline Environmental Health↗
NASA NTRS1985

Correlations between polyamine ratios and growth patterns in seedling roots

The levels of putrescine, cadaverine, spermidine and spermine were determined in seedling roots of pea, tomato, millet and corn, as well as in corn coleoptiles and pea internodes. In all roots, putrescine content increased as elongation progressed, and the putrescine/spermine ratio closely paralleled the sigmoid growth curve up until the time of lateral root initiation. Spermidine and spermine were most abundant near the apices and declined progressively with increasing age of the cells. In the zone of differentiation of root hairs in pea roots, putrescine rose progressively with increasing age, while cadaverine declined. In both pea internodes and corn coleoptiles, the putrescine/spermidine ratio rises with increasing age and elongation. Thus, a block in the conversion of the diamine putrescine to the triamine spermidine may be an important step in the change from cell division to cell elongation.

Non-NASA Center↗
NASA NTRS2002

The growing world of expansins

Expansins are cell wall proteins that induce pH-dependent wall extension and stress relaxation in a characteristic and unique manner. Two families of expansins are known, named alpha- and beta-expansins, and they comprise large multigene families whose members show diverse organ-, tissue- and cell-specific expression patterns. Other genes that bear distant sequence similarity to expansins are also represented in the sequence databases, but their biological and biochemical functions have not yet been uncovered. Expansin appears to weaken glucan-glucan binding, but its detailed mechanism of action is not well established. The biological roles of expansins are diverse, but can be related to the action of expansins to loosen cell walls, for example during cell enlargement, fruit softening, pollen tube and root hair growth, and abscission. Expansin-like proteins have also been identified in bacteria and fungi, where they may aid microbial invasion of the plant body.

NASA Discipline Plant Biology↗
NASA NTRS1995

A novel root gravitropism mutant of Arabidopsis thaliana exhibiting altered auxin physiology

A root gravitropism mutant was isolated from the DuPont Arabidopsis thaliana T-DNA insertional mutagenesis collection. This mutant has reduced root gravitropism, hence the name rgr1. Roots of rgr1 are shorter than those of wild-type, and they have reduced lateral root formation. In addition, roots of rgr1 coil clockwise on inclined agar plates, unlike wild-type roots which grow in a wavy pattern. The rgr1 mutant has increased resistance, as measured by root elongation, to exogenously applied auxins (6-fold to indole-3-acetic acid, 3-fold to 2,4-dichlorophenoxyacetic acid, and 2-fold to napthyleneacetic acid). It is also resistant to polar auxin transport inhibitors (2-fold to triiodobenzoic acid and 3- to 5-fold to napthylphthalamic acid). The rgr1 mutant does not appear to be resistant to other plant hormone classes. When grown in the presence of 10(-7) M 2,4-dichlorophenoxyacetic acid, rgr1 roots have fewer root hairs than wild type. All these rgr1 phenotypes are Mendelian recessives. Complementation tests indicate that rgr1 is not allelic to previously characterized agravitropic or auxin-resistant mutants. The rgr1 locus was mapped using visible markers to 1.4 +/- 0.6 map units from the CH1 locus at 1-65.4. The rgr1 mutation and the T-DNA cosegregate, suggesting that rgr1 was caused by insertional gene inactivation.

NASA Discipline Plant Biology↗
NASA NTRSDate not supplied

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗