Increasing Sampling Interval Research Articles

Transcriptome Analysis Reveals the Stress Tolerance to and Accumulation Mechanisms of Cadmium in Paspalum vaginatum Swartz.

Cadmium (Cd) is a non-essential heavy metal and high concentrations in plants causes toxicity of their edible parts and acts as a carcinogen to humans and animals. Paspalum vaginatum is widely cultivating as turfgrass due to its higher abiotic stress tolerance ability. However, there is no clear evidence to elucidate the mechanism for heavy metal tolerance, including Cd. In this study, an RNA sequencing technique was employed to investigate the key genes associated with Cd stress tolerance and accumulation in P. vaginatum. The results revealed that antioxidant enzyme activities catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and glutathione S-transferase GST) were significantly higher at 24 h than in other treatments. A total of 6820 (4457/2363, up-/down-regulated), 14,038 (9894/4144, up-/down-regulated) and 17,327 (7956/9371, up-/down-regulated) differentially expressed genes (DEGs) between the Cd1 vs. Cd0, Cd4 vs. Cd0, and Cd24 vs. Cd0, respectively, were identified. The GO analysis and the KEGG pathway enrichment analysis showed that DEGs participated in many significant pathways in response to Cd stress. The response to abiotic stimulus, the metal transport mechanism, glutathione metabolism, and the consistency of transcription factor activity were among the most enriched pathways. The validation of gene expression by qRT-PCR results showed that heavy metal transporters and signaling response genes were significantly enriched with increasing sampling intervals, presenting consistency to the transcriptome data. Furthermore, over-expression of PvSnRK2.7 can positively regulate Cd-tolerance in Arabidopsis. In conclusion, our results provided a novel molecular mechanism of the Cd stress tolerance of P. vaginatum and will lay the foundation for target breeding of Cd tolerance in turfgrass.

The Advantage Analysis of 3-D Inversion of Airborne Gravity Gradiometry Data With Larger Sampling Interval

Airborne gravity gradiometry is an effective tool for its high efficiency and sensitivity to the interesting buried targets, which has been increasingly introduced in mineral and petroleum exploration. When compared to gravimetry, full tensor gradient (FTG) measurements provide higher resolution and signal-to-noise ratio. In this article, we build and test models with different sampling intervals and compare the performances of individual components or their combinations in minimum structure inversion to evaluate the benefits of FTG combinations. As part of a theoretical study, the singular value spectrum (SVS) and depth-resolution plot (DRP) are examined to determine how much information and resolution different datasets provide in the underdetermined inverse problem. The synthetic examples show that integrating more components in inversion improves the resolution of the recovered model, and the inversion result of gravity or individual component cannot accurately restore the distribution of anomalous bodies. With the increasing sampling interval, joint inversion of FTG data still provides enough information corresponding to the source density distribution, and the limit survey interval is tested through models. In addition, we use a real survey over the Vinton dome as a case study and discover that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {zz}}$ </tex-math></inline-formula> component fails to reconstruct the caprock model at coarse line spacing, whereas FTG combinations recover the acceptable geometry flawlessly. It also reflects the benefit of FTG combinations for inversion processing, which reduces the interference of measured noise on inversion results and allows for longer sampling intervals during the survey, effectively lowering the cost of an airborne survey and improving exploration efficiency

On the approximating criteria of parabolic asperities for measured surface profiles

Surface description is fundamental in mechanical contact analysis. In this paper, some of the most common approaches for roughness description are first briefly described. Some models for approximating a real rough profile with parabolas that guarantee the preservation of some specific characteristics are presented. The specimens of aluminum alloy with different surface roughnesses are prepared and measured. A data analysis program is developed to identify the measured profile with quadratic functions for different approximating criteria. Based on this, the effect of the approximating criterion and the sampling interval on the surface roughness parameters and the mechanical parameters is then presented and compared. The results show that the surface roughness Ra, asperity height H, and peak radii R increase with increasing surface roughnesses for different approximating criteria. The same root mean square approximating criterion is more suitable for calculating the surface roughness Ra. The asperity height H and peak radii R increase with increasing sampling intervals for all roughnesses, while the trend is opposite for the surface roughness Ra. The sampling interval has little effect on these parameters, especially for smoother surfaces.

Oxygen uptake plateau: calculation artifact or physiological reality?

To test whether the oxygen uptake ([Formula: see text]) plateau at [Formula: see text] is simply a calculation artifact caused by the variability of [Formula: see text] or a clearly identifiable physiological event. Forty-six male participants performed an incremental ramp and a [Formula: see text] verification test. Variability of the difference between adjacent sampling intervals (difference) and of the slope of the [Formula: see text]-workload relationship (slope) in the submaximal intensity domain were calculated. Workload defined sampling intervals used for the calculation of the difference and slope were systematically increased from 20 to 100W until the expected risk of false plateau diagnoses based on the Gaussian distribution function was lower than 5%. Overall, more than 1500 differences and slopes were analyzed. Subsequently, frequencies of plateau diagnoses in the submaximal and maximal intensity domains were compared. Variability of the difference and slope decreased with increasing sampling interval (p < 0.001). At a sampling interval of 50W, the predefined acceptable risk of false plateau diagnoses (≤ 5%) was achieved. At this sampling interval, the actual frequency (1.4%) of false-positive plateau diagnoses did not differ from the expected frequency in the submaximal intensity domain (1.6%; p = 0.491). In contrast, the actual frequency at maximal intensity (35.7%) was significantly higher compared to the submaximal intensity domain (p < 0.001) and even higher than the expected frequency of false-positive diagnoses (p < 0.001). The [Formula: see text] plateau at [Formula: see text] represents a physiological event and no calculation artifact caused by [Formula: see text] variability. However, detecting a [Formula: see text] plateau with sufficient certainty requires large sampling intervals.

The role of sampling strategy on apparent temporal stability of soil moisture under subtropical hydroclimatic conditions

Abstract Subtropical regions have clay-rich, weathered soils, and long dry periods followed by intense rainfall that produces large fluctuations in soil water content (SWC) and hydrological behavior. This complicates predictions of spatio-temporal dynamics, as datasets are typically collected at too coarse resolution and observations often represent a duration that is too short to capture temporal stability. The aim of the present study was to gain further insights into the role of temporal sampling scale on the observed temporal stability features of SWC order to aid the design of optimal SWC sampling strategies. This focused on both sampling frequency and total monitoring duration, as previous analyses have not considered both of these sampling aspects simultaneously. We collected relatively high resolution data of SWC (fortnightly over 3.5 years) for various soil depths and under contrasting crops (peanuts and citrus) at the red soil region of southeast China. The dataset was split into a three-year training period and a six-month evaluation period. Altogether 13 sampling frequencies (intervals ranging from 15 to 240 days) and eight monitoring duration periods (between three and 36 months) were derived from the training period to identify temporal stability features and the most time stable location (MTSL). The prediction accuracies of these MTSLs were tested using the independent evaluation data. Results showed that vegetation type did affect the spatio-temporal patterns of SWC, whereby the citrus site exhibited a stronger temporal variation and weaker temporal stability than the peanut site. However, the effects of both sampling frequency and observation duration were more pronounced, irrespective of the role of vegetation type or soil depth. With increasing sampling interval or decreasing monitoring duration, temporal stability of SWC was generally overestimated; by less than 10% when sampling interval increased from every 15 to 240 days and by up to 40% with duration decreasing from 36 to 3 months. Our results suggest that sampling strategies and trade-offs between sampling interval and duration should focus on capturing the main variability in hydro-climatological conditions. For subtropical conditions, we found that sampling once every 45 days over 24 months to be the minimum sampling strategy to ensure errors in SWC temporal stability of less than 10%.

Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval.

Responses of the citrus nematode population densities post-application of Nemarioc-AL phytonematicide and aldicarb synthetic nematicide

Density-dependent growth patterns suggested that under phytonematicides population densities of plant-parasitic nematodes could either be stimulated, neutral or inhibited, thereby resulting in ‘inconsistent results' concept. The objective of this study was to determine whether post-application sampling of the citrus nematode (Tylenchulus semipenetrans Cobb) population densities under untreated control, Nemarioc-AG phytonematicide and aldicarb on rough lemon (Citrus jambhiri Lush. ) seedlings could provide quantitative evidence of ‘inconsistent results'. Uniform three-month old C. jambhiri seedlings in plastic pots containing 2700 ml growing mixture were each inoculated with 10000 T. semipenetrans eggs and second-stage juveniles (J2). A week after inoculation, seedlings were split into three groups of seventy each and subjected to once-off treatment of untreated control, 2 g Nemarioc-AG phytonematicide and 2 g aldicarb. Five seedlings from each group were randomly placed on greenhouse benches to serve as a sampling block, with a total of 14 blocks. The entire block was sampled on a weekly basis and assessed for final nematode population density (Pf). After the final assessment, Pf under untreated control over increasing sampling intervals exhibited positive quadratic relations, with the model explaining 90% associations, with optimum Pf of 13804 eggs and J2 at six weeks post-application. In contrast, Pf over increasing sampling intervals under phytonematicide and aldicarb exhibited negative quadratic relations, with the model explaining 95 and 92% associations, respectively. The two had minimum Pf at 974 eggs and J2 and 2205 eggs and J2, respectively, each at approximately six weeks. In conclusion, opposite temporal cyclic nematode population growth patterns post-application of nematicidal products and untreated control provided empirically-based evidence of ‘inconsistent results' in nematode management under phytonematicides.

Uncertainty in nutrient loads from tile-drained landscapes: Effect of sampling frequency, calculation algorithm, and compositing strategy

Summary Accurate estimates of annual nutrient loads are required to evaluate trends in water quality following changes in land use or management and to calibrate and validate water quality models. While much emphasis has been placed on understanding the uncertainty of nutrient load estimates in large, naturally drained watersheds, few studies have focused on tile-drained fields and small tile-drained headwater watersheds. The objective of this study was to quantify uncertainty in annual dissolved reactive phosphorus (DRP) and nitrate-nitrogen (NO 3 -N) load estimates from four tile-drained fields and two small tile-drained headwater watersheds in Ohio, USA and Ontario, Canada. High temporal resolution datasets of discharge (10–30 min) and nutrient concentration (2 h to 1 d) were collected over a 1–2 year period at each site and used to calculate a reference nutrient load. Monte Carlo simulations were used to subsample the measured data to assess the effects of sample frequency, calculation algorithm, and compositing strategy on the uncertainty of load estimates. Results showed that uncertainty in annual DRP and NO 3 -N load estimates was influenced by both the sampling interval and the load estimation algorithm. Uncertainty in annual nutrient load estimates increased with increasing sampling interval for all of the load estimation algorithms tested. Continuous discharge measurements and linear interpolation of nutrient concentrations yielded the least amount of uncertainty, but still tended to underestimate the reference load. Compositing strategies generally improved the precision of load estimates compared to discrete grab samples; however, they often reduced the accuracy. Based on the results of this study, we recommended that nutrient concentration be measured every 13–26 h for DRP and every 2.7–17.5 d for NO 3 -N in tile-drained fields and small tile-drained headwater watersheds to accurately (±10%) estimate annual loads.

Determination of Joint Roughness Coefficients Using Roughness Parameters

This study used precisely digitized standard roughness profiles to determine roughness parameters such as statistical and 2D discontinuity roughness, and fractal dimensions. Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval.

Effect of Sampling Frequency on Perfusion Values in Perfusion CT of Lung Tumors

The purpose of this study was to assess as a potential means of limiting radiation exposure the effect on perfusion CT values of increasing sampling intervals in lung perfusion CT acquisition. Lung perfusion CT datasets in patients with lung tumors (> 2.5 cm diameter) were analyzed by distributed parameter modeling to yield tumor blood flow, blood volume, mean transit time, and permeability values. Scans were obtained 2-7 days apart with a 16-MDCT scanner without intervening therapy. Linear mixed-model analyses were used to compare perfusion CT values for the reference standard sampling interval of 0.5 second with those of datasets obtained at sampling intervals of 1, 2, and 3 seconds, which included relative shifts to account for uncertainty in preenhancement set points. Scan-rescan reproducibility was assessed by between-visit coefficient of variation. Twenty-four lung perfusion CT datasets in 12 patients were analyzed. With increasing sampling interval, mean and 95% CI blood flow and blood volume values were increasingly overestimated by up to 14% (95% CI, 11-18%) and 8% (95% CI, 5-11%) at the 3-second sampling interval, and mean transit time and permeability values were underestimated by up to 11% (95% CI, 9-13%) and 3% (95% CI, 1-6%) compared with the results in the standard sampling interval of 0.5 second. The differences were significant for blood flow, blood volume, and mean transit time for sampling intervals of 2 and 3 seconds (p ≤ 0.0002) but not for the 1-second sampling interval. The between-visit coefficient of variation increased with subsampling for blood flow (32.9-34.2%), blood volume (27.1-33.5%), and permeability (39.0-42.4%) compared with the values in the 0.5-second sampling interval (21.3%, 23.6%, and 32.2%). Increasing sampling intervals beyond 1 second yields significantly different perfusion CT parameter values compared with the reference standard (up to 18% for 3 seconds of sampling). Scan-rescan reproducibility is also adversely affected.

Impacts of Interpolation Formula, Correlation Coefficient and Sampling Interval on the Accuracy of Grace Follow‐on Intersatellite Range‐Acceleration

AbstractThis paper presents our studies of the impacts of the interpolation formula, correlation coefficient and sampling interval on the accuracies of the GRACE Follow‐On intersatellite range‐acceleration using the Intersatellite Range‐Acceleration Method (IRAM). The results of numerical simulation show that the interpolation accuracy can be efficiently improved with appropriately increasing the number of the interpolation points from the numerical differential formula. The interpolation error of intersatellite range‐acceleration is 4.401×10–13 m·s–2 using the nine‐point Newton's interpolation formula, and errors are enhanced 1.192 times, 6.912 times and 274.029 times based on the seven‐point, five‐point, three‐point interpolation formulas, respectively. The error of intersatellite range‐acceleration is observably decreased with the proper increase of the correlation coefficients. The variance of intersatellite range‐acceleration is 3.777×10–24 m2·s–4 using a correlation coefficient of 0.99, and the variances are respectively increased 9.780 times, 22.404 times, 26.217 times and 26.820 times based on the correlation coefficients 0.90, 0.70, 0.50 and 0.00, respectively. With increasing sampling intervals, the variances of intersatellite range‐acceleration gradually decrease, while the spatial resolution of satellite observations will be simultaneously reduced. Therefore, the accuracy of the Earth's gravitational field can be improved by a preferred selection of sampling intervals. Finally, the Earth's gravitational field from GRACE Follow‐On complete up to degree and order 120 is accurately and rapidly recovered using the nine‐point Newton's interpolation formula, correlation coefficients (0.85 in intersatellite range and intersatellite range‐rate, 0.95 in orbital position and orbital velocity, and 0.90 in non‐conservative force) and a sampling interval of 10 s based on the Pre‐Conditioned Conjugate‐Gradient (PCCG) approach, and the cumulative geoid height error is 4.602×10–4 m at degree 120.

Protocol modifications for CT perfusion (CTp) examinations of abdomen-pelvic tumors: Impact on radiation dose and data processing time

To evaluate the effect of CT perfusion (CTp) protocol modifications on quantitative perfusion parameters, radiation dose and data processing time. CTp datasets of 30 patients (21M:9F) with rectal (n = 24) or retroperitoneal (n = 6) tumours were studied. Standard CTp protocol included 50 sec cine-phase (0.5 sec/rotation) and delayed-phase after 70 ml contrast bolus at 5-7 ml/sec. CTp-data was sub-sampled to generate modified datasets (n = 105) with cine-phase(n = 15) alone, varying cine-phase duration (20-40 sec, n = 45) and varying temporal sampling-interval (1-3 sec, n = 45). The estimated CTp parameters (BF,BV,MTT&PS) and radiation dose of standard CTp served as reference for comparison. CTp with 50 sec cine-phase showed moderate to high correlation with standard CTp for BF&MTT (r = 0.96&0.85) and low correlation for BV (0.75, p = 0.04). Limiting cine-phase duration to 30 sec demonstrated comparable results for BF&MTT, while considerable variation in CTp values existed at 20 sec. There was moderate-to-high correlation of CTp parameters with sampling interval of 1&2 sec (r = 0.83-0.97, p > 0.05), while at 3 sec only BF showed high correlation (r = 0.96, p = 0.05). Increasing sampling interval (47-60%) and reducing cine-phase duration substantially reduced dose(30.8-65%) which paralleled reduced data processing time (3-10 min). Limiting CTp cine-phase to 30 sec results in comparable BF&MTT values and increasing cine-phase sampling interval to 2 sec provides good correlation for all CTp parameters with substantial dose reduction and improved computational efficiency.

Development of a GPS Herd Activity and Well-Being Kit (GPS HAWK) to Monitor Cattle Behavior and the Effect of Sample Interval on Travel Distance

As an alternative to commercial GPS tracking collars, a low-cost GPS Herd Activity and Well-being Kit (GPS HAWK) was developed to monitor locomotion behavior of cattle at a high sampling frequency (20 s). The operational goal of the GPS HAWK was to collect GPS location data at a user-specified frequency and store the data in a secure format (compact flash media) for retrieval and optimize power consumption to extend the sampling period. The GPS HAWK uses a Garmin 12-channel low-power GPS receiver powered by a sealed-lead acid battery housed in an aluminum enclosure on a shoulder-mounted harness. Data gathered by the GPS HAWKs were used to determine individual daily travel distance (DTD) and the effect of sample interval (SI) on this measurement. Differences (P < 0.0001) were shown in DTD across four days using animals with available data. The Angus cows (Bos Tarus L.) averaged 4.05 0.14 km/d (2.52 0.09 mi/d) during the four days. Sample interval (SI) had an effect on DTD. Differences in DTD were detected for all SIs (P < 0.0001) except between the 60- and 120-s intervals. By changing the SI from 20 s to 20 min, the mean DTD decreased by 1.68 km or 44%. Significant errors in estimates of cattle energetics from GPS monitoring can be introduced by increasing sampling interval. Therefore, researchers must account for increasing error in DTD due to undersampling as SI is increased to save battery power and to increase the interval between animal handling periods. Holding the GPS system in place consistently with a shoulder mounted harness proved to be somewhat challenging.

Whole-Brain Perfusion Imaging With 320-MDCT Scanner: Reducing Radiation Dose by Increasing Sampling Interval

The purpose of this study was to investigate whether perfusion CT values obtained with a reduced-dose imaging protocol on a 320-MDCT scanner are similar to those obtained with a standard protocol. Similar perfusion values at one-half the radiation dose can be obtained with the alternative algorithm used in this study.

Similar basis function algorithm for numerical estimation of Fourier integrals

The methodological difficulties of estimating Fourier integrals using the fast Fourier transform (FFT) algorithm have intensified the interest in an alternative approach based on the Filon’s method of computing the trigonometric integrals. Following this approach, we introduce in this paper a similar basis function (SBF) algorithm that decomposes the function to be transformed into the sum of finite elements termed “similar basis functions”. Due to a simple analytical form of SBF, the reassignment of the SBFs’ similarity relationships into the transformation domain reduces the estimation of the Fourier integrals to a number of standard computational procedures. The SBF algorithm is capable to deal with both uniform and non-uniform samples of the function under analysis. Using this opportunity, we extend a general SBF algorithm by a fast SBF algorithm which deals with exponentially increasing sampling intervals. The efficiency and the accuracy of the method are illustrated by computer experiments with frequency characteristics and transient responses of a typical dynamic system.

Effects of Sampling Interval on Spatial Patterns and Statistics of Watershed Nitrogen Concentration

This study investigates how spatial patterns and statistics of a 30 m resolution, model-simulated, watershed nitrogen concentration surface change with sampling intervals from 30 m to 600 m for every 30 m increase for the Little River Watershed (Georgia, USA). The results indicate that the mean, standard deviation, and variogram sills do not have consistent trends with increasing sampling intervals, whereas the variogram ranges remain constant. A sampling interval smaller than or equal to 90 m is necessary to build a representative variogram. The interpolation accuracy, clustering level, and total hot spot areas show decreasing trends approximating a logarithmic function. The trends correspond to the nitrogen variogram and start to level at a sampling interval of 360 m, which is therefore regarded as a critical spatial scale of the Little River Watershed.

Determining Vertical Root and Microbial Biomass Distributions from Soil Samples

When vertical density distributions of root or microbial biomass are calculated using each sampling interval's midpoint as the depth coordinate, the calculated distribution is biased if it is a nonlinear function with depth. In the root biomass literature, distributions are often described by a power function R ∝ βz, where β is a decay coefficient and z is depth. A common alternative formulation is an exponential function, R ∝ , where Zr is a characteristic length scale. These functions are equivalent when Zr = −1/ln β, so the data according to either function may be unified. The bias can be eliminated by representing the vertical distribution with a continuous function, integrating it over the sampling interval, and using a least squares method to determine the function's parameters. The bias increased by nearly threefold when the sampling interval increased from 0.01 to 1 m. As the sampling interval increases, the bias shifts the function down the z axis. This results in the intercept increasing with increasing sampling interval. When a single profile was sampled at different intervals, the function's intercept and Zr changed. The parameter Zr changed fivefold when the sampling interval increased from 0.1 to 0.5 m, while the calculated fraction of roots above a depth of 0.1 m decreased threefold for the same change in sampling interval. Beneath a tropical forest where root biomass and microbial respiration were sampled throughout the same soil profile, the corresponding microbial and root biomass length scales averaged 0.17 m and differed by only 11%.

Salivary and plasma cortisol response to adrenocorticotropin administration in pigs.

Two experiments were conducted to determine the responsiveness of salivary and plasma cortisol to acute (i.v.), depot (i.m.) and chronic (repeated i.m.) adrenocorticotropin (ACTH) administration in swine. In Experiment 1, barrows (castrated pigs) were assigned to one of three injection treatments: (1) saline i.m. (SHAM1, n=2); (2) 0.75 IU/kg BW ACTH in saline i.v. (ACUTE, n=2); (3) 2.25 IU/kg BW ACTH in gel i.m. (DEPOT, n=3). Total cortisol concentrations were determined for concurrent saliva and blood samples. Correlations between salivary and plasma cortisol within treatments were: SHAM1, r=0.60; ACUTE, r=0.58; DEPOT, r=0.79. In Experiment 2, barrows were assigned to one of two injection treatments: (1) gel i.m. (SHAM2, n=3); (2) 2.25 IU/kg BW ACTH in gel i.m. (CHRONIC, n=4). The injections occurred every 6 h for a total of eight injections. Concurrent saliva and blood samples were obtained every 3 h for 72 h followed by an increasing sampling interval until day 6. Overall correlations between salivary and plasma cortisol were: SHAM2, r=0.30 and CHRONIC, r=0.61. Experiment 1 found that the relationship between salivary and plasma cortisol was stronger during longer (DEPOT) than shorter (ACUTE) ACTH stimulation. Experiment 2 found a strong relationship between the two measurements during chronic ACTH stimulation, but that relationship weakened after ACTH stimulation ceased.

The influence of sampling interval on the accuracy of trail impact assessment

Trail impact assessment and monitoring (IA&M) programs have been growing in importance and application in recreation resource management at protected areas. This paper addresses a fundamental issue in designing trail IA&M surveys: the choice of sampling interval. Specifically, the influence of sampling interval on the accuracy of estimates for selected trail impact problems was examined using a resampling simulation method. A complete census of four impact-types on 70 backcountry trails in the Great Smoky Mountains National Park was utilized as the base dataset for the analyses. The census data were resampled at increasing intervals to create a series of simulated point datasets. At each sampling interval level, the accuracy of simulated datasets was evaluated by comparing the estimates of frequency of occurrence and lineal extent for each impact-type with actual census values. Simulation results indicate that increasing sampling intervals are associated with an overall increase in accuracy loss for all four impact-types. The direction of accuracy loss for lineal extent estimates is mixed, while frequency of occurrence estimates are consistently and substantially lower than the actual values. Responses of accuracy loss to increasing sampling intervals vary across impact-types on extent estimates, but are consistent on the frequency estimates. These findings suggest that systematic point sampling can be an appropriate method for estimating lineal extent but not the frequency of trail impacts. Sample intervals of less than 100 m appear to yield an excellent level of estimate accuracy for the four impact-types evaluated. The census-based trail survey and the resampling simulation method developed in this study can be a valuable first step in establishing long-term trail IA&M programs, in which an optimal sampling interval range with acceptable accuracy is determined before investing efforts in data collection.

Experimental Investigation of Thermal Contact Conductance: Variations of Surface Microhardness and Roughness

Surface roughness parameters were measured using nine different sampling intervals. The results showed that the roughness of profile was nearly a constant at all sampling intervals; the slope, peak curvature and density of the profile decreased with increasing sampling interval; and the distribution of the peak height was different from the profile height distribution and was not Gaussian. It was found that the surface microhardness value, under small loads, varied with the indentation size and the surface preparation procedure. A model was developed which used a discretization method to obtain the contact radius and area in each section of the surface summit height distribution curve. This model eliminated some of the assumptions made by previous models and can be used to predict the thermal contact conductance without assuming the asperity deformation mode and surface summit height distribution. The predicted thermal contact conductance values were in good agreement with experimental results.