Standard Deviation Of The Difference Research Articles

Model for Calculating the Viscosity of Aqueous Solutions

A new model for calculating the viscosity of aqueous solutions has been developed. Parameters for 74 solutes were established based on a critical review of the literature for solutions of one solute in water, with over 9000 points included. The average difference between the calculated and experimental viscosities is less than 0.1 %, and the standard deviation of this difference is 3.7 % of the average experimental viscosity. The model was validated by estimating published viscosity for systems of more than one solute in water. The average difference between experimental and calculated values for 1700 points is −2.7 %, and the standard deviation of this difference is 16 % of the average experimental viscosity. The median standard deviation of the difference between experimental and calculated values is 3.5 % of the experimental viscosity. The solutes studied are (NH4)2SO4, AlCl3, BaCl2, Ca(NO3)2, CaCl2, Cd(NO3)2, CdCl2, CdSO4, CoCl2, CoSO4, Cr2(SO4)3, CrCl3, Cu(NO3)2, CuCl2, CuSO4, Fe2(SO4)3, FeCl2, FeSO4...

Repeatability of corneal parameters with Pentacam after laser in situ keratomileusis

To investigate the coefficient of repeatability (CR) for corneal parameters evaluated with Pentacam after laser in situ keratomileusis (LASIK) in myopic eyes. Prospective, non-interventional, non-comparative study in an institutional setup. Forty eyes of 40 consecutive subjects who had undergone LASIK for myopia were assessed with the Scheimpflug system (Pentacam 70700: Oculus, Wetzlar Germany). The mean of five consecutive measurements of all the corneal parameters was recorded and CR was calculated as standard deviation of the difference from the mean of these repeat measurements divided by the mean response. The statistical significance of the CR was calculated for these parameters at 5% significance level. The best CR was observed for the periphery of the anterior corneal curvature (0.18%) and the least for the horizontal meridian of the posterior corneal curvature (1.29%). Despite being significantly different ( P < 0.001), both the measurements were highly repeatable in post-LASIK eyes. The central, apical and minimal corneal thickness had a CR of 1%, 0.78% and 0.77% respectively. These were equally repeatable ( P> 0.323). The CR of the mean radius of curvature of the anterior cornea (0.29%) was significantly better ( P < 0.001) than the posterior corneal curvature (0.57%). The CR for the post-LASIK cornea with Pentacam was the best for the anterior corneal curvature. Significantly, Pentacam has a high degree of repeatability for the posterior corneal curvature, which has a potential for early detection of keratectasia in these eyes. Post-LASIK pachymetry with Pentacam also showed excellent repeatability.

Accuracy and Precision of the Transit Tracker System

The accuracy and the precision of Transit Tracker, the Internet- and telephone-based real-time arrival information system of the Tri-County Metropolitan Transportation District of Oregon (TriMet), are analyzed. Although Transit Tracker estimates the arrival times of both bus and rail vehicles, this analysis is limited to bus vehicles. Archived data from TriMet's automatic vehicle location (AVL) and automatic passenger counter systems were used to compare actual bus arrival times with Transit Tracker estimates drawn from logs created in the estimation process. Accuracy was evaluated primarily by calculating the mean of the difference between the actual bus arrival times and the arrival times estimated by Transit Tracker. Precision was determined largely by the degree of variation in the estimates and measured by the standard deviation of the difference between the actual bus arrival times and the arrival times estimated by Transit Tracker. Finally, an arrival estimation error model was developed to test the effects of a series of factors on the degree of estimation error.

Relations between different objective milking speed recording systems

This study aimed to analyse the sources of variation of milking speed assessed through automatic computerised devices included in milking machines, to study the relationships between this trait and milking speed assessed through stopwatch and to develop statistical procedures useful for converting automatic device milking time into stopwatch milking time in order to obtain a fast, simple and cheap collection of milking time records for genetic evaluation purposes.A total of 571 records of stopwatch milking time (SMT), device milking time (DMT) and milk yield at milking were collected in 23 herds of the Trentino Alto Adige region in Italy equipped with two types of automatic milking devices. After log-transformation of SMT (lnSMT) and DMT (lnDMT) and a preliminary analysis of sources of variation of lnDMT, dataset was partitioned into two mutually exclusive subsets: a calibration one, used for statistical analysis, and a validation one, used as test set to validate the prediction models. This procedure was replicated 6 times in order to repeat the cross validation accordingly. Three conversion models have been compared, based on different combinations of the effects of lnDMT, milking device and herd within milking device on lnSMT. Solutions of the models have been applied for each replicate to the validation dataset for estimating lnSMT and the soundness of conversion equations have been evaluated considering the correlation between estimated and actual lnSMT and bias and precision of estimates. Milking time assessed through different procedures resulted in differences between methods for both mean and distribution, and these suggested the need of developing statistical procedures aimed to the conversion of DMT into SMT before their use in sire evaulation. The soundness of the models tended to slightly increase with the increase in the number of effects considered. The correlation between estimated and actual SMT was in the range of 0.80 to 0.86, the estimated bias was close to 0 for all models and the precision, i.e. the average standard deviation of the difference between estimated and actual SMT, in the range of 8-9% of the mean of actual SMT. In conclusion, conversion equations proposed for joining the two sources of information performed satisfactorily, giving rise to SMT accurate estimates, which were not distorted and fairly precise. The use of such equations can support the integration of automatically acquired milking time records into breeding schemes, which is advisable for increasing the number of sires progeny tested and the accuracy of breeding values estimated.

Refined analysis of radar altimetry data applied to the region of the subglacial Lake Vostok/Antarctica

Satellite altimetry is a powerful tool to map the ice sheet elevation as well as a number of other parameters linked to geometrical and geophysical properties of ice sheets. Irrespective of new instrumental developments like the laser altimeter on ICESat (Ice, Cloud and land Elevation Satellite) the well-established radar altimeter (RA) missions ERS-1,2 (European Remote Sensing satellites) and ENVISAT (ENVIronment SATellite) are unique in their temporal coverage over more than a decade and in their temporal and spatial sampling. Therefore, the full exploitation of these RA data by improved methods is imperative. Here we develop improved techniques to correct for topographically induced errors by a refined consideration of the relevant topographic conditions. Furthermore we improve the gridding procedure by adapting it to local conditions and thus preserving smaller-scale features. We apply our methods to the region of the subglacial Lake Vostok/Antarctica and derive digital elevation models (DEMs) for this region with the aim of improving/resolving smaller scales. The effect of our improvements is demonstrated in detail by comparing our DEMs and previously published DEMs to the ICESat laser measurements which are taken as a reference here. Due to our improvements, the standard deviation of the difference between RA-based DEMs of the Lake Vostok region and ICESat data decreases from more than 1.1 m to 0.5 m. This remaining error is chiefly the error inherent in the RA observations. Our RA-ICESat comparisons, supported by Fourier analyses, also reveal the presence and importance of small-scale features that can be detected by laser but not by the RA measurements.

A Non-Invasive Evaluation of Hemoglobin with a New Optical Sensor.

Background: The performance of the NBM-100, a new non-invasive Hematocrit (Hct) measurement device, was tested in primary and secondary medical care environments. The device utilizes a finger sensor with Occlusion Red/Near-Infrared Spectroscopy (O-RNIRS) technology to non-invasively measure hemoglobin and Hct levels (Figure). A non-invasive Hct measurement has many potential advantages including the prevention of pain and potential transmission of infectious diseases, and the reduced need for trained technicians.Methods: In order to investigate the use of the device in both primary and secondary medical environments, clinical trials were conducted in a blood donation center (USA), an oncology clinic (USA) and a hematology clinic (Israel). The studies were carried out on a group of 304 subjects (155 Female, 149 Male), ages 18–96 (average age 54.9).After signing a written consent, all study volunteers were tested non-invasively with the NBM-100 device, and invasively, using a venous sample evaluated on a Cell-Dyn (Abbott) blood analyzer. At the blood donation center the NBM-100 measurements were performed before donation, and the venous blood sample was taken after donation. To neutralize the decrease in Hct occurring during blood donation, as described in published clinical trials, a value of 2.2% was added to the reference venous values obtained from the blood donation center.Results: The operating staff at all centers found the NBM-100 easy to use and appreciated by subjects. The venous Hct measurements ranged from 15% to 51%. The mean NBM-100 Hct was 36%, the same as the mean venous result. The mean absolute error (MAE) was 2.66% and the relative absolute error (RAE) was 7.9%. The standard deviation of the difference between the venous reference and noninvasive Hct readings was found to be 3.34% and the correlation between the two devices was r=0.9. The bias between the two methods was a negligible 0.40%.Conclusions: This study supports the use of the NBM-100 device as a useful, safe and accurate non-invasive method for measuring a wide range of Hct in both a hospital (e.g. surgery and blood transfusions) and ambulatory setting (e.g. blood banks). Good agreement was found between the non-invasive NBM-100 and the invasive Cell-Dyn Hct measurements. The non-invasive system was found to be environmentally - and user-friendly by the operating staff. These findings support the high clinical utility of a commercial application of the NBM-100 device.

Predicting the frequency of storm surges and extreme sea levels in the northwest Atlantic

A 40 year hindcast of storm surges in the northwest Atlantic and adjacent shelf seas is performed using a 2‐D nonlinear barotropic ocean model forced by realistic 6 hourly winds and air pressures. This hindcast is used to generate spatial maps of the return level of storm surges and also to estimate the return period of extreme total sea levels. The accuracy of the hindcast is assessed in two ways. First, the standard deviation of the difference between the observed residuals (total sea level minus tide) and the hindcast is calculated at 24 tide gauge locations. A typical error standard deviation is 8 cm. Second, the 40 year return level of observed residuals is compared to that of the hindcast surges. The predicted 40 year return levels are typically within 10 cm of observed return levels at the 24 observation locations. A spatial map of the 40 year return level of surges is presented for the northwest Atlantic. It identifies the regions exposed to the largest surges. Total sea levels are reconstructed using (1) the hindcast surges and (2) tides and higher‐frequency variability predicted from short, observed sea level records. An extremal analysis of the reconstructed total sea levels shows that their 40 year return levels are in good agreement (within about 10 cm) with the levels calculated from multidecadal observed sea level records. This means that given a short record anywhere within the model domain, or results from a good tidal model, 40 year return levels can be estimated.

Arterial versus capillary blood gases: A meta-analysis

A meta-analysis determined whether capillary blood gases accurately reflect arterial blood samples. A mixed effects model was used on 29 relevant studies obtained from a PubMed/Medline search. From 664 and 222 paired samples obtained from the earlobe and fingertip, respectively, earlobe compared to fingertip sampling shows that the standard deviation of the difference is about 2.5x less (or the precision is 2.5x better) in resembling arterial PO(2) over a wide range of arterial PO(2)'s (21-155 mm Hg ). The lower the arterial PO(2), the more accurate it is when predicting arterial PO(2) from any capillary sample (p<0.05). However, while earlobe sampling predicts arterial PO(2) (adjusted r(2)=0.88, mean bias=3.8 mm Hg compared to arterial), fingertip sampling does not (adjusted r(2)=0.48, mean bias=11.5 mm Hg compared to arterial). Earlobe sampling is slightly more accurate compared to fingertip sampling in resembling arterial PCO(2) (arterial versus earlobe, adjusted r(2)=0.94, mean bias=1.9 mm Hg ; arterial versus fingertip, adjusted r(2)=0.95, mean bias=2.2 mm Hg compared to arterial) but both sites can closely reflect arterial PCO(2) (880 total paired samples, range 10-114 mm Hg ). No real difference between sampling from the earlobe or fingertip were found for pH as both sites accurately reflect arterial pH over a wide range of pH (587 total paired samples, range 6.77-7.74, adjusted r(2)=0.90-0.94, mean bias=0.02). In conclusion, sampling blood from the fingertip or earlobe (preferably) accurately reflects arterial PCO(2) and pH over a wide range of values. Sampling blood, too, from earlobe (but never the fingertip) may be appropriate as a replacement for arterial PO(2), unless precision is required as the residual standard error is 6 mm Hg when predicting arterial PO(2) from an earlobe capillary sample.

Calibration of Infrared Milk Analyzers: Modified Milk Versus Producer Milk

Mid-infrared (MIR) milk analyzers are traditionally calibrated using sets of preserved raw individual producer milk samples. The goal of this study was to determine if the use of sets of preserved pasteurized modified milks improved calibration performance of MIR milk analyzers compared with calibration sets of producer milks. The preserved pasteurized modified milk sets exhibited more consistent day-to-day and set-to-set calibration slope and intercept values for all components compared with the preserved raw producer milk calibration sets. Pasteurized modified milk calibration samples achieved smaller confidence interval (CI) around the regression line (i.e., calibration uncertainty). Use of modified milk calibration sets with a larger component range, more even distribution of component concentrations within the ranges, and the lower correlation of fat and protein concentrations than producer milk calibration sets produced a smaller 95% CI for the regression line due to the elimination of moderate and high leverage samples. The CI for the producer calibration sets were about 2 to 12 times greater than the CI for the modified milk calibration sets, depending on the component. Modified milk calibration samples have the potential to produce MIR milk analyzer calibrations that will perform better in validation checks than producer milk-based calibrations by reducing the mean difference and standard deviation of the difference between instrument values and reference chemistry.

Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms

The accuracy of the sea ice concentration estimates in polar regions is reduced by the effects of atmospheric emission and absorption. A method is presented where a fast atmospheric radiative transfer model and Numerical Weather Prediction (NWP) model fields are used to correct brightness temperatures before they enter the sea ice concentration algorithm. The skill of the method is a function of the errors in the NWP model fields modulated by the sensitivities of the sea ice concentration algorithm used. The NWP model fields representing the most significant atmospheric parameters, i.e. water vapour, cloud liquid water, surface temperature and wind speed over open water are evaluated using remote sensing data. For wind speed and total water vapour, it is found that the standard deviation of the difference is less than the RMS error quoted for the remote sensing algorithms. The best consistency is found for water vapour followed by wind speed. The NWP model cloud liquid water displays standard deviations much higher than the RMS error of the remote sensing algorithm and close to the total average content. Nine sea ice concentration algorithms are further evaluated in a sensitivity study to the above-mentioned atmospheric constituents using a detailed atmospheric radiative transfer model. The result shows that the class of algorithms based solely on the 19 and 37 GHz vertically polarised channels display the smallest sensitivity to all three atmospheric parameters: total water vapour, wind speed and cloud liquid water. Finally, it is demonstrated that this method overcomes many problems associated with conventional weather filtering over mixed ice-water and new-ice pixels and allows the retrieval of sea ice concentrations below 10%.

Precision and Bias of a Normal Finite Mixture Distribution Model to Analyze Twin Data When Zygosity is Unknown: Simulations and Application to IQ Phenotypes on a Large Sample of Twin Pairs

The classification of twin pairs based on zygosity into monozygotic (MZ) or dizygotic (DZ) twins is the basis of most twin analyses. When zygosity information is unavailable, a normal finite mixture distribution (mixture distribution) model can be used to estimate components of variation for continuous traits. The main assumption of this model is that the observed phenotypes on a twin pair are bivariately normally distributed. Any deviation from normality, in particular kurtosis, could produce biased estimates. Using computer simulations and analyses of a wide range of phenotypes from the U.K. Twins' Early Developments Study (TEDS), where zygosity is known, properties of the mixture distribution model were assessed. Simulation results showed that, if normality assumptions were satisfied and the sample size was large (e.g., 2,000 pairs), then the variance component estimates from the mixture distribution model were unbiased and the standard deviation of the difference between heritability estimates from known and unknown zygosity in the range of 0.02-0.20. Unexpectedly, the estimates of heritability of 10 variables from TEDS using the mixture distribution model were consistently larger than those from the conventional (known zygosity) model. This discrepancy was due to violation of the bivariate normality assumption. A leptokurtic distribution of pair difference was observed for all traits (except non-verbal ability scores of MZ twins), even when the univariate distribution of the trait was close to normality. From an independent sample of Australian twins, the heritability estimates for IQ variables were also larger for the mixture distribution model in six out of eight traits, consistent with the observed kurtosis of pair difference. While the known zygosity model is quite robust to the violation of the bivariate normality assumption, this novel finding of widespread kurtosis of the pair difference may suggest that this assumption for analysis of quantitative trait in twin studies may be incorrect and needs revisiting. A possible explanation of widespread kurtosis within zygosity groups is heterogeneity of variance, which could be caused by genetic or environmental factors. For the mixture distribution model, violation of the bivariate normality assumption will produce biased estimates.

Variability of Knee Adduction Moment During Gait

Gait analysis is often performed in order to determine the effect of an injury or a treatment. For example, Munderman et al. (2004) showed a 23% increase in knee adduction moment in subjects with severe osteoarthritis relative to those with less severe disease. However, it is not known if this increased moment is normalized following total knee arthroplasty (TKA). In order to determine this effectively it is necessary to know the measurement variability between repeated tests. Not only will this assist in developing a study, but this type of statistical power analysis is increasingly required for publication. PURPOSE: The purpose of this study was to determine the variability of knee adduction moment. METHODS: Kinetic and kinematic data were recorded from 10 healthy subjects (age range 25–45 years) on two separate days as they walked across a force plate at their preferred walking speed. Motion data were recorded at 60 Hz using a 5-camera system (Qualisys). Ground reaction forces (GRF) were recorded from a force plate (Kistler) at 1000 Hz. Using an inverse dynamics model, we calculated knee adduction moment; the two peaks in knee adduction moment (normalized and expressed as percent body mass times height, BM*ht) were analyzed. Paired t-tests and Pearson product-moment correlations were used to compare the adductor moment peaks from the two different days. RESULTS: The mean of the first adduction moment peak (over both days) was 4.33 ± 1.17 with a mean difference of −0.16 ± 0.65 between days. The mean second peak was 3.66 ± 1.03 with a mean difference of-0.22 ±0.79 between days. Paired t-tests showed that the two adduction moment peaks were not different between days (P = 0.448 and P = 0.399). Additionally, the two moment peaks were significantly correlated from one day to the next (R = 0.856, P= 0.002 and R= 0.770, P= 0.009). CONCLUSION: Based on the mean and standard deviation of the difference in knee adduction moment between days, a 10% change in adduction moment could be detected with a sample of 18 patients, while a 15% change could be detected with a sample of 9 patients (P <0.05, 80% power). This information may be useful for designing studies to examine changes in knee adduction moment with progression of osteoarthritis or treatment with total knee arthroplasty.

Technical note: analytical estimation of the optimal parameters for the EOF retrievals of the IASI Level 2 Product Processing Facility and its application using AIRS and ECMWF data

Abstract. The Empirical Orthogonal Function (EOF) retrieval technique consists of calculating the eigenvectors of the spectra to later perform a linear regression between these and the atmospheric states, this first step is known as training. At a later stage, known as performing the retrievals, atmospheric profiles are derived from measured atmospheric radiances. When EOF retrievals are trained with a statistically different data set than the one used for retrievals two basic problems arise: significant biases appear in the retrievals and differences between the covariances of the training data set and the measured data set degrade them. The retrieved profiles will show a bias with respect to the real profiles which comes from the combined effect of the mean difference between the training and the real spectra projected into the atmospheric state space and the mean difference between the training and the atmospheric profiles. The standard deviations of the difference between the retrieved profiles and the real ones show different behavior depending on whether the covariance of the training spectra is bigger, equal or smaller than the covariance of the measured spectra with which the retrievals are performed. The procedure to correct for these effects is shown both analytically and with a measured example. It consists of first calculating the average and standard deviation of the difference between real observed spectra and the calculated spectra obtained from the real atmospheric state and the radiative transfer model used to create the training spectra. In a later step, measured spectra must be bias corrected with this average before performing the retrievals and the linear regression of the training must be performed adding noise to the spectra corresponding to the aforementioned calculated standard deviation. This procedure is optimal in the sense that to improve the retrievals one must resort to using a different training data set or a different algorithm.

How Complementary are SRTM-X and -C Band Digital Elevation Models?

Two different digital elevation models (DEM) were derived during the 2000 Shuttle Radar Topography Mission from C- and X-band interferometric radar data. While these two DEMs share several of their properties, they were processed independently. Here, we investigate what can be gained by merging the two DEMs into a single composite DEM for four different test areas. Based on an analysis of the relative differences and the deviations from an absolute reference in one test area, we propose an algorithm for combining the two DEMs optimally. We then compare the composite DEM with both individual DEMs and with a reference of a large number of precise GPS profiles in one test area in southern Germany. We find that in our test areas, the area of missing values is reduced significantly in the composite DEM. Even compared with the more complete C-band DEM, the number of void pixels can be reduced by 22 percent to 53 percent. Also, outlier values resulting from errors in the interferometric phase unwrapping can often be identified and removed in the merging. The deviations of both C- and X-band DEMs from the GPS reference are very similar and well within the accuracy specifications of the global data set. The standard deviation of the difference between the composite DEM and the reference is about 14 percent below that of the original values. Depending on the requirements for completeness and accuracy, merging the two SRTM elevation data sets may provide an important improvement above either of the original DEMs.

Evaluation of cuff-wrapping methods for the determination of ankle blood pressure

The ankle-brachial index, which is calculated by dividing ankle systolic blood pressure by brachial systolic blood pressure, is useful in diagnosing peripheral arterial disease. Consensus has not been reached, however, on a standard method for measuring ankle systolic blood pressure. This study evaluated two cuff-wrapping methods for measuring ankle systolic blood pressure, compared with intra-arterial pressure as a gold standard. Study participants were 24 consecutive adult patients who underwent surgery under general anesthesia at Kyoto Prefectural University Hospital in Japan between January and March 2002. Indirect ankle systolic blood pressure was measured in the posterior tibial artery using a Doppler device and two cuff-wrapping methods: spiral, and straight. Direct ankle systolic blood pressure was measured in the dorsalis pedis artery. Mean difference in ankle systolic blood pressure between indirect and direct measurements (indirect minus direct) was 1.4 mmHg [standard deviation of the difference (SDd), 17.6 mmHg] with the straight method and -1.4 mmHg (SDd, 22.2 mmHg) with the spiral method. The limit of agreements (mean difference +/-2 SDd) and intraclass correlation coefficient between two observers were -17.6 to 20.1 mmHg and 0.94, respectively, for the straight method and -39.4 to 40.0 mmHg and 0.78, respectively, for the spiral method. The straight method, the same cuff-wrapping method used for measuring brachial blood pressure, appears to represent a more suitable wrapping method because of better interobserver reproducibility. Accuracy and reproducibility of indirect ankle systolic blood pressure measurement, however, were not adequately improved by either of the wrapping methods.

The accuracy of grid digital elevation models linearly constructed from scattered sample data

In this paper, a theoretical‐empirical model is developed for modelling the accuracy of a grid digital elevation model (DEM) linearly constructed from scattered sample data. The theoretical component integrates sample data accuracy in the model by means of the error‐propagation theory. The empirical component seeks to model what is known as information loss, i.e. the sum of the error due purely to sampling the continuous terrain surface with a finite grid interval and the interpolation error. For this purpose, randomly spaced data points, supposed to be free of error, were converted into regularly gridded data points using triangulation with linear interpolation. Original sample data were collected with a 2×2 m sampling interval from eight different morphologies, from flat terrain to highly rugged terrain, applying digital photogrammetric methods to large‐scale aerial stereo imagery (1 : 5000). The DEM root mean square error was calculated by the true validation method over several sets of check points, obtaining the different sampling densities tested in this work. Several empirical models are calibrated and validated with the experimental data set by modelling the DEM accuracy by combining two variables such as sampling density and a descriptive attribute of terrain morphology. These empirical models presented a morphology based on the product of two potential functions, one related to the terrain roughness and another related to the sampling density. The terrain descriptors tested were average terrain slope, standard deviation of terrain slope, standard deviation of unitary vectors perpendicular to the topographic surface (SDUV), standard deviation of the difference in height between adjacent samples in the grid DEM (SDHD), and roughness estimation by first‐, second‐, or third‐degree surface fitting error. The values obtained for those terrain descriptors were reasonably independent from the number and spatial distribution of the sample data. The models based on descriptors SDHD, SDUV, and standard deviation of slope provided a good fitting to the data observed (R 2>0.94) in the calibration phase, model SDHD being the one that yielded the best results in validation. Therefore, it would be possible to establish a priori the optimum grid size required to generate or store a DEM of a particular accuracy, with the saving in computing time and file size that this would mean for the digital flow of the mapping information in GIS.

Simplified Methods for Estimation of Plasma Clearance of Iohexol in Dogs and Cats

The purpose of this study was to evaluate simplified methods for iohexol plasma clearance estimation in dogs and cats. Serial blood samples were taken before and 5, 20, 40, 60, 80, 100, 120, 150, 180, and 240 minutes after a bolus injection of iohexol in 51 dogs and 25 cats. Iohexol plasma concentration was determined with X‐ray fluorescence. Clearance was calculated by dividing the injected dose by the area under the plasma tracer elimination curve estimated with a 2‐compartment pharmacologic model. Clearance was normalized to body surface area (BSA). The 10‐point clearance was used as a reference for the evaluation of simplified methods. A 2‐sample method based on a single exponential fit and a single‐sample method based on a linear quadratic model were investigated. Simplified methods were evaluated by calculating the standard deviation of the difference (SDD) between the clearances obtained with the simplified methods and the 10‐point reference method. All combinations of sampling times were evaluated. The best sampling times were chosen for dogs and cats as the ones yielding the lowest SDD. Linear regression analysis was performed between the reference method and the optimized simplified methods. The best combination of time for the 2‐sample method was 5 and 120 minutes in dogs and 20 and 180 minutes in cats. The best time for sampling in the single‐sample method was 120 minutes in dogs and 80 minutes in cats. Plasma clearance of iohexol can be estimated in dogs and cats from 1 or 2 blood samples with a reasonable margin of error.

Simplified Methods for Estimation of Plasma Clearance of Iohexol in Dogs and Cats

The purpose of this study was to evaluate simplified methods for iohexol plasma clearance estimation in dogs and cats. Serial blood samples were taken before and 5, 20, 40, 60, 80, 100, 120, 150, 180, and 240 minutes after a bolus injection of iohexol in 51 dogs and 25 cats. Iohexol plasma concentration was determined with X-ray fluorescence. Clearance was calculated by dividing the injected dose by the area under the plasma tracer elimination curve estimated with a 2-compartment pharmacologic model. Clearance was normalized to body surface area (BSA). The 10-point clearance was used as a reference for the evaluation of simplified methods. A 2-sample method based on a single exponential fit and a single-sample method based on a linear quadratic model were investigated. Simplified methods were evaluated by calculating the standard deviation of the difference (SDD) between the clearances obtained with the simplified methods and the 10-point reference method. All combinations of sampling times were evaluated. The best sampling times were chosen for dogs and cats as the ones yielding the lowest SDD. Linear regression analysis was performed between the reference method and the optimized simplified methods. The best combination of time for the 2-sample method was 5 and 120 minutes in dogs and 20 and 180 minutes in cats. The best time for sampling in the single-sample method was 120 minutes in dogs and 80 minutes in cats. Plasma clearance of iohexol can be estimated in dogs and cats from 1 or 2 blood samples with a reasonable margin of error.

Estimation of thin Sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica

AbstractAntarctic coastal polynyas are major areas of intense ocean–atmosphere heat and moisture flux, and associated high Sea-ice production and dense-water formation. Their accurate detection, including an estimate of thin ice thickness, is therefore very important. In this paper, we apply a technique originally developed in the Arctic to an estimation of Sea-ice thickness using Us National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data and meteorological data in the Vincennes Bay polynya off Wilkes Land, East Antarctica. The method is based upon the heat-flux calculation using Sea-ice Surface temperature estimates from the Satellite thermal-infrared data combined with global objective analysis (European Centre for Medium-Range Weather Forecasts (ECMWF)) data. The validity of this method is assessed by comparing results with independent ice-surface temperature and ice-thickness data obtained during an Australian-led research cruise to the region in 2003. In thin-ice (polynya) regions, ice thicknesses estimated by the heat-flux calculation using AVHRR and ECMWF data Show reasonable agreement with those estimated by (a) applying the heat-flux calculation to in Situ radiation thermometer and meteorological data and (b) in Situ observations. The Standard deviation of the difference between the AVHRR-derived and in Situ data is ∽0.02 m. Comparison of the AVHRR ice-thickness retrievals with coincident Satellite passive-microwave polarization ratio data confirms the potential of the latter as a means of deriving maps of thin Sea-ice thickness on the wider Scale, uninterrupted by darkness and cloud cover.

Validation of the TM-2564G device for in-hospital blood pressure measurement

To perform clinical validation of the TM-2564G device for in-hospital blood pressure measurement according to the British Hypertension Society protocol. The tests were carried out on 85 patients aged 19-82 years (41 male patients, mean age 50+/-19 years) with systolic blood pressure 75-219 mmHg and diastolic blood pressure 43-117 mmHg (mean 142+/-33/85+/-20 mmHg). For each patient, three readings of the TM-2564G device were compared with simultaneous auscultatory measurements by two trained independent observers. The observers used a mercury-calibrated sphygmomanometer and dual stethoscope. The results were graded according to the British Hypertension Society protocol 1993. The average difference was -1.85 mmHg for systolic blood pressure and -1.20 mmHg for diastolic blood pressure. The standard deviation of the difference between TM-2564G and the observers' results was 4.85 and 4.16 mmHg for systolic and diastolic blood pressures, respectively. Systolic blood pressure percentage of readings agreeing to within 5 mmHg was 75.7%, 10 mmHg 95.36% and 15 mmHg 99.6%, and for DBP 82.7, 97.6 and 99.8%, respectively. The TM-2564G device achieved A/A British Hypertension Society grade and can be recommended for in-hospital measurement of blood pressure.