Bias Standard Deviation Research Articles

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Feasibility of using KardiaMobile-L6 for QT interval monitoring during the early phase of the COVID-19 pandemic in critical care patients

The electrocardiogram (ECG) represents an essential tool to determine cardiac electrical abnormalities in COVID-19 patients, the effects of anti-SARS-CoV-2 drugs, and potential drug interactions. Smartphone-based heart monitors have increased the spectrum of ECG monitoring however, we are not aware of its reliability in critically ill COVID-19 patients. We aim to evaluate the feasibility and reliability of nurse-performed smartphone electrocardiography for QT interval monitoring in critically ill COVID-19 patients using KardiaMobile-6L compared with the standard 12-lead ECG. An observational comparative study was conducted comparing consecutive KardiaMobile-6L and 12-lead ECG recordings obtained from 20 patients admitted to the intensive care unit with SARS-CoV-2 infection and on invasive mechanical ventilation. The heart rate-corrected QT (QTc) intervals measured by KardiaMobile-6L and 12-lead ECG were compared. In 60 percent of the recordings, QTc intervals measured by KardiaMobile-6L matched those by 12-lead ECG. The QTc intervals measured by KardiaMobile-6 and 12-lead ECG were 428 ± 45 ms and 425 ± 35 ms (p = 0.82), respectively. The former demonstrated good agreement (bias = 2.9 ms; standard deviation of bias = 29.6 ms) with the latter, using the Bland–Altman method of measurement agreement. In all but one recording, KardiaMobile-6L demonstrated QTc prolongation. QTc interval monitoring with KardiaMobile-6L in critically ill COVID-19 patients was feasible and demonstrated reliability comparable to the standard 12-lead ECG.

The Influence of Methods for Cardiac Output Determination on the Diagnosis of Precapillary Pulmonary Hypertension: A Mathematical Model.

precapillary pulmonary hypertension (PH, PcPH) is now defined as a mean pulmonary artery pressure (mPAP) > 20 mmHg, a pulmonary artery wedge pressure (PAWP) ≤ 15 mmHg and a pulmonary vascular resistance (PVR) > 2 WU. For PVR calculation, the measurement of cardiac output (CO) is necessary. It is generally measured using thermodilution. However, recent data showed that the agreement with direct Fick method, historically the gold standard, is less than previously reported. We aimed to create a mathematical model that calculated the probability of being classified differently (PcPH or unclassified PH) if CO measured by direct Fick was used instead of thermodilution for any individual patients with a mPAP > 20 mmHg and a PAWP ≤ 15 mmHg. The model is based on Bland and Altman analysis with a normally distributed difference of cardiac output, fixed 1.96 standard deviation of bias, bias and physiological cardiac output limits. Following a literature review of the studies comparing CO measured with direct Fick and thermodilution, we fixed the 1.96 standard deviation of bias at 2 L/min, bias at 0 L/min and physiological resting CO limits between 1.3 L/min and 10.2 L/min. This model can help the clinician to evaluate the potential benefit of measuring CO using direct Fick during the diagnostic work-up and its utility in confirming or ruling out a diagnosis of PcPH in any given patient with a mPAP > 20 mmHg and a PAWP ≤ 15 mmHg.

Comparison of contraction intensity and perceived intensity between dominant and non-dominant leg in sedentary adults

IntroductionAccurate measurement of isometric contraction intensity is a prerequisite for establishing an exercise program and developing a rehabilitation plan. Despite numerous studies performed previously, there is no research on sedentary populations susceptible to injury during exercise. Additionally, although dominant and non-dominant legs showed differences in muscle properties, the legs were not separately investigated. The purposes of this study were to compare the contraction intensity with target intensity and with perceived intensity across 3 different target intensities in sedentary adults and examine the difference between the dominant and non-dominant leg.MethodsVoluntary isometric contractions were performed with resistance provided by a sling rope and measured with a strain gauge during hip extension. First, maximum voluntary contraction (MVC) was executed; subsequently, submaximal contractions at target intensities (75%, 50%, and 25% of MVC) were randomly performed. Perceived intensity was measured at each target intensity.ResultsHip extension forces were significantly different across the 3 intensities (<i>p</i> < 0.001), with no significant difference between target intensity and contraction intensity or between perceived intensity and contraction intensity. There was no significant difference between the dominant and non-dominant leg. Bland-Altman analysis of the agreement between perceived and contraction intensities revealed a proportional bias for the values within each target intensity (mean bias: –1.15%, standard deviation of bias: 20.96).ConclusionsSedentary subjects were able to distinguish 3 different contraction intensities. Perceived intensity could statistically significantly predict contraction intensity. However, clinicians should note that under- or overestimation can occur regardless of the level of target intensity.

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner

BackgroundAccurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling.MethodsDynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35–65, 40–65, 45–65, 50–65, and 55–65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed.ResultsThere was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55–65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55–65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB.ConclusionWe demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

A non-parametric evaluation method in staircase test for improving fatigue limit assessment

A new evaluation method based on Kernel Density Estimation (KDE) was proposed to estimate the fatigue limit distribution from staircase tests without prior knowledge. The proposed method notably reduced the standard deviation bias of the fatigue limit using small sample tests. A simulation-based investigation was performed to compare the effectiveness of the proposed method with other evaluation methods. The obtained results reveal that the proposed technique offers a better estimation of the median, the standard deviation, and the probability distribution of the fatigue limit. Moreover, this technique is insensitive to the test parameters of the staircase method.

Advancement of Generalized Method of Moment Estimation (GMM) For Spatial Dynamic Panel Simultaneous Equations Models with Fixed Time Effect

This research paper aims to form and estimate the spatial dynamic panel simultaneous equations models (SDPS) with fixed time effect that potentially have heteroscedasticity cases. The model formed with the individual effect is not eliminated but placed in the error model to accommodate cases of heteroscedasticity in the model. GMM with two stages least square (2SLS) method for the single equation is deliberately chosen as the estimation method for the SDPS model because it can eliminate heterogeneity cases in the model. The effectiveness of the estimate is seen based on the value of RMSE (Root Mean Square Error), mean and standard deviation (SD) of bias estimate by simulating Monte Carlo 100 times with different parameter pairs and different pairs N and T can also be concluded that parameter scenario changes do not give much effect on the mean bias value and SD bias. The SDPS model shows that the consistency of the estimated parameter values can be achieved easily if the number of T is added. Changes in the number of N and T indicate that the greater the N and T, the smaller RMSE value tends to be.

The Performance of Equations That Estimate Glomerular Filtration Rate against Measured Urinary Creatinine Clearance in Critically Ill Patients.

The performance of glomerular filtration rate- (GFR-) estimating equations was studied against creatinine clearance measured by 24-hour urine collection (CrCl24h-urine) in critically ill patients. Methods. In this substudy of the PermiT trial (https://clinicaltrials.gov/ct2/show/ISRCTN68144998), patients from King Abdulaziz Medical City-Riyadh who had CrCl24h-urine were included. We estimated GFR using Cockroft–Gault (CG), modification of diet in renal disease study (MDRD), chronic kidney disease epidemiology collaboration (CKD-EPI), and Jelliffe equations. For the CG equation, we entered the actual weight in one calculation (CGactual-wt), and if BMI ≥30 kg/m2, we entered the ideal body weight (CGideal-wt) and the adjusted body weight (CGadjusted-wt) in two calculations. We calculated the MDRD equation based on 4 (MDRD-4) and 6 variables (MDRD-6). The performance of these equations was assessed by different ways including Spearman correlation, bias (difference between estimated GFR and CrCl24h-urine), precision (standard deviation of bias), and Bland–Altman plot analysis. Results. The cohort consisted of 237 patients (age 45 ± 20 years, males 75%, mechanically ventilated 99% with serum creatinine 101 ± 94 µmol/L and CrCl24h-urine 108 ± 69 ml/min/1.73 m2). The correlations between the different equations and CrCl24h-urine were modest (r: 0.62 to 0.79; p < 0.0001). Bias was statistically significant for CGactual-wt (21 ml/min), CGadjusted-wt (12 ml/min), and MDRD-6 (-10 ml/min) equations. Precision ranged from 46 to 54 ml/min. The sensitivity of equations to correctly classify CrCl24h-urine 30–59.9 ml/min/1.73 m2 was 17.2% for CGactual-wt, 30.0% for CGideal-wt, 31.0% for CGadjusted-wt, 31.0% for MDRD-4, 39.1% for MDRD-6, 13.8% for CKD-EPI, and 34.5% for Jelliffe equation. Conclusions. Commonly used GFR-estimating equations had limited ability to properly estimate CrCl24h-urine and to correctly classify GFR into clinically relevant ranges that usually determine dosing of medications.

Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?

Droughts pose significant economic and ecological concerns, and considering climate change projections, timely monitoring and early warning based on satellite observations must be realized at regional to global scales. Nevertheless, whether data reconstruction is necessary to produce high-quality satellite-based time series data for drought monitoring and the data reconstruction approaches to be applied, if necessary, remain unclear. We attempted to fill this knowledge gap by investigating three widely used data reconstruction approaches, i.e., the Savitzky–Golay filter, harmonic analysis of time series (HANTS) and Whittaker Smoother, across the Lancang–Mekong river basin through the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MOD11A2 products for 2001–2018 and Google Earth Engine cloud platform. Several remote sensing drought indices based on the unreconstructed and reconstructed vegetation indices (VIs) and Land Surface Temperature (LST) were retrieved for basin-wide drought detection. The performance of the examined reconstruction approaches was evaluated using three statistical indices (coefficient of determination (R2), standard deviation of bias (BIAS(std)) and mean squared error (MSE)), spatial consistency with the reference dataset and capability to characterize the drought events. Data reconstruction considerably enhanced the drought index performances for drought detection; however, reconstruction was not necessary in all situations. The reconstructed drought indices exhibited higher R2 values (by 10.6–10.8%), lower BIAS(std) values (by −1.7– −12.5%), and smaller MSE values (by −5.8– −13.4%) compared to those of unreconstructed indices. For most evaluation indicators, HANTS outperformed the other methods, and Vegetation Condition Index (VCI) and Vegetation Health Index (VHI) outperformed the other drought indices. The findings highlight the importance of data reconstruction to detect and characterize drought events and the dependency of the performance of reconstruction methods on drought indices and evaluation metrics when using MODIS time series data.

A comparison of the baseflow recession constant (K) between a Japanese cypress and mixed-broadleaf forest via six estimation methods

The baseflow recession constant (K) is known to be a versatile parameter used in forecasting low flows, flow-frequency analyses, improving regional models, and other various hydrological analyses. This study took place in Inuyama city, Aichi prefecture, central Japan, where K for two catchments of differing vegetation [Japanese cypress (CF), 2.42 ha; mixed-broadleaf (MB), 2.08 ha] were produced via six estimation methods and compared. Water level was recorded by dataloggers (converted to daily discharge) and rainfall, by raingauges. Over 5 years (May 2013–April 2018), 18 baseflow recession events were qualified for analysis from which K were calculated. Overall, K in CF (0.969) was marginally higher than in MB (0.955), which translates to lower water depletion in CF possibly due to lower transpiration. In each catchment, K did not differ significantly between the growth and dormant period. Root mean square error (RMSE) is lower and less variable in CF than in MB. Reproducing baseflow using season-specific K values did not show any clear reduction in RMSE; hence, the complexity is not justified. In general, all K-estimators performed well (low RMSE) and can be used. Out of six K-estimators, two have relatively higher standard deviation (SD) and will underestimate baseflow especially when the K was produced from short (< 6 days) baseflow recession limbs. Others reproduce unbiased baseflow but have relatively higher SD and RMSE. None of the estimators were unusable, but two estimators that have the lowest SD and least bias were recommended.

A stiffness-tunable MEMS accelerometer

This paper reports a novel MEMS accelerometer whose effective stiffness can be adjusted flexibly and even tuned to almost zero by using the electrostatic softening effect of two parallel plate capacitors: the comb-finger capacitor and the triangular capacitor. When applied to a biased voltage, the comb-finger capacitor provides a relatively large and nonlinear negative stiffness in the gap-varying direction, while the triangular capacitor produces a small yet linear negative stiffness in the area-varying direction. The proposed accelerometer is fabricated by using a standard silicon-on-glass manufacturing process and controlled by a customized digital circuit. An analytical model based on the pull-in dynamics as a result of the stiffness tuning is developed and simulated to investigate the dynamic behavior of the nonlinear accelerometer system. The proportion integral differential controller is utilized to improve the performance of the accelerometer while maintaining the stability of the closed-loop system operating with extremely low effective stiffness. The experimental results indicate that a two-fold increase of the sensitivity, a 30% reduction of Allan deviation bias instability, and a 20% reduction of Allan velocity random walk are achieved for the open-loop accelerometer system. A 75% decrease of the standard deviation bias instability and nearly two orders of magnitude improvement of the noise floor at 1 Hz for the closed-loop accelerometer system are realized when the effective stiffness is reduced to almost zero.

Correlation of Measured and Estimated Creatinine Clearance in Hospitalized Elderly Patients: A Retrospective Cohort Study.

Background: Accurate assessment of renal function is essential in hospitalized elderly patients. Few studies have examined the accuracy of Cockcroft-Gault (C-G) estimates of creatinine clearance (CrCl) compared with measured clearance in these patients. Objective: The objective of this study was to determine the correlation between C-G estimates of CrCl and measured CrCl in hospitalized elderly patients. Methods: This Institutional Review Board-approved, single-center retrospective observational cohort study included all patients who were 65 years and older admitted to our medical center in January to September 2018 with either an 8- or 24-hour urine collected during admission. The primary outcome was correlation, bias, and precision of C-G estimates of CrCl versus measured CrCl using Pearson correlation, Spearman linear regression, and Bland-Altman analysis. Outliers were determined using a cut-off of ±20%. Data are presented as median (interquartile range) or percentages. Results: A total of 108 urine collections from 90 unique patients were included in the study. The patients were 51% female, median age was 71 (68-77) years, and median body mass index was 26.6 (22.8-31) kg/m2. Most collections were over 24 hours (66.7%), and 38% were performed while patients were in an intensive care unit. Median blood urea nitrogen (BUN) was 24.5 (17-36) mg/dL and median serum creatinine was 0.71 (0.55-1.09) mg/dL. The median C-G estimation was 75.4 (48.2-110.6) mL/min, and the median measured CrCl was 79.1 (38.1-99.5) mL/min, r 2 = .56 (P < .001). Bland-Altman analysis showed large limits of agreement (-75.5-57.7 mL/min), with a bias of -8.9 and precision (standard deviation of bias) of 34 mL/min. Outliers were common, with 38% of C-G estimation values >120% of measured CrCl, and 18% of C-G estimates <80% of measured CrCl. Conclusions: Measured CrCl varied significantly from C-G estimates in hospitalized elderly patients. It is important to recognize characteristics of patients who may benefit from measurement of CrCl. Future studies should examine the impact of this variance on clinical outcomes.

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

Evaluation of the Accuracy of the Delta Life DL 1000 Oscillometric Monitor for Blood Pressure Measurement in Anesthetized Dogs of Different Weight Ranges

Background: Arterial blood pressure is one of the most commonly variables monitored during anesthetic procedures in veterinary patients. The most reliable method for measuring arterial blood pressure in dogs and cats is the direct (invasive) method. However, the oscillometric method is less complex and more practical for clinical routine in small animals. Nevertheless, oscillometric monitors present great variability in accuracy. The present study aimed to determine the accuracy of the Delta Life DL 1000 oscillometric monitor for measurement of systolic, mean and diastolic blood pressures (SAP, MAP and DAP, respectively) in anesthetized dogs of different weight ranges.Materials, Methods &amp; Results: This study was approved by the Institutional Ethics Committee of Animal Use. Fifteen female dogs of different breeds, weighing 11.6 ± 10.0 kg and with a mean age of 48 ± 51 months were used. All animals were scheduled for elective surgery under general anesthesia in the Institution Veterinary Hospital. Dogs were anesthetized with morphine, propofol and isoflurane and had one 20 or 22 gauge catheter introduced into the dorsal pedal artery for continuous, invasive monitoring of SAP, MAP and DAP. A blood pressure cuff was positioned over the middle third of the radius and connected to Delta Life DL 1000 monitor. Oscillometric readings of SAP, MAP and DAP were registered every 5 minutes, and invasive values were simultaneously recorded. Values obtained with both methods were compared (invasive versus oscillometric) by use of the Bland Altman method to determine the bias, standard deviation of bias and 95% limits of agreement. The percentages of errors between the methods within 10 mmHg and within 20 mmHg were calculated. The results obtained were compared with the criteria from the American College of Veterinary Internal Medicine (ACVIM) for validation of indirect methods of arterial blood pressure measurement. Data were stratified into two groups according to the weight: &lt; 10 kg (Group 1; n = 9); and ≥ 10 kg (Group 2; n = 6). In Group 1, 119 paired measurements were obtained, four of which classified as hypotension (SAP &lt; 90 mmHg), 98 as normotension (SAP from 90 to 140mmHg) and 17 as hypertension (SAP &gt; 140 mmHg). Bias (± SD) values in Group 1 were as follows: SAP, 5.2 ± 18.1 mmHg; MAP, -3.4 ± 17.2 mmHg; and DAP, 12.0 ± 17.5 mmHg. The percentages of errors within 10 mmHg were 40.3% for SAP; 45.4% for MAP and 28.6% for DAP. The percentages of errors within 20 mmHg were 72.3% for SAP, 84.0% for MAP and 68.1% for DAP. In Group 2, 66 paired measurements were obtained, nine of which classified as hypotension, 56 as normotension and one as hypertension. Bias (± SD) in Group 2 were as follows: SAP, 13.6 ± 14.3 mmHg; MAP, -1.1 ± 13.5 mmHg; and DAP, 8.2 ± 16.0 mmHg. The percentages of errors within 10 mmHg were 33.3% for SAP, 77.3% for MAP and 33.3% for DAP. The percentages of errors within 20 mmHg were 65.1% for SAP, 92.4% for MAP and 83.4% for DAP.Discussion: Based on the results of this study and reference criteria from the ACVIM, the Delta Life DL 1000 monitor had a poor accuracy for SAP, MAP and DAP and did not meet the criteria from the ACVIM in anesthetized dogs under 10 kg. Measurements of MAP in dogs ≥ 10 kg met the ACVIM criteria, but measurements of SAP and DAP did not. Based on the findings in this study, the DL 1000 oscillometric monitor is not recommended for blood pressure measurement in anesthetized dogs &lt; 10 kg. In dogs ≥ 10 kg, measurements of MAP yielded acceptable values, but SAP and DAP measurements did not.

Agreement and Precision Analyses of Various Estimated Glomerular Filtration Rate Formulae in Cancer Patients

The accuracy of the estimated glomerular filtration rate (eGFR) in cancer patients is very important for dose adjustments of anti-malignancy drugs to reduce toxicities and enhance therapeutic outcomes. Therefore, the performance of eGFR equations, including their bias, precision, and accuracy, was explored in patients with varying stages of chronic kidney disease (CKD) who needed anti-cancer drugs. The reference glomerular filtration rate (GFR) was assessed by the 99mTc-diethylene triamine penta-acetic acid (99mTc-DTPA) plasma clearance method in 320 patients and compared with the GFRs estimated by i) the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, ii) the unadjusted for body surface area (BSA) CKD-EPI equation, iii) the re-expressed Modification of Diet in Renal Disease (MDRD) study equation with the Thai racial factor, iv) the Thai eGFR equation, developed in CKD patients, v) the 2012 CKD-EPI creatinine-cystatin C, vi) the Cockcroft-Gault formula, and vii) the Janowitz and Williams equations for cancer patients. The mean reference GFR was 60.5 ± 33.4 mL/min/1.73 m2. The bias (mean error) values for the estimated GFR from the CKD-EPI equation, BSA-unadjusted CKD-EPI equation, re-expressed MDRD study equation with the Thai racial factor, and Thai eGFR, 2012 CKD-EPI creatinine-cystatin-C, Cockcroft-Gault, and Janowitz and Williams equations were −2.68, 1.06, −7.70, −8.73, 13.37, 1.43, and 2.03 mL/min, respectively, the precision (standard deviation of bias) values were 6.89, 6.07, 14.02, 11.54, 20.85, 10.58, and 8.74 mL/min, respectively, and the accuracy (root-mean square error) values were 7.38, 6.15, 15.97, 14.16, 24.74, 10.66, and 8.96 mL/min, respectively. In conclusion, the estimated GFR from the BSA-unadjusted CKD-EPI equation demonstrated the least bias along with the highest precision and accuracy. Further studies on the outcomes of anti-cancer drug dose adjustments using this equation versus the current standard equation will be valuable.

Solcast: Validation of a satellite-derived solar irradiance dataset

Solcast (https://solcast.com/) is a global solar forecasting and historical solar irradiance data company. Their datasets are important to the scientific community as substantial amounts of historical data is made freely available to researchers. Solcast’s hourly averages and sub-hourly instantaneous (10/15-min) global horizontal irradiance (GHI) estimates are validated at 48 BSRN ground stations that contain 1,014,792 valid hourly average measurements and 2,932,976 valid instantaneous measurements. The data are validated globally and across four climates under clear-sky, clouded-sky and all-sky periods, repeated for ‘all zenith angles’ (θ_z<85°) and high-sun zenith angles (θ_z<50°). A site-by-site validation is also performed for both hourly and instantaneous datasets. All validations and recommendations are impartial and unbiased.The hourly dataset validated similarly for both zenith angle scenarios, which is remarkable due to the added complexities at low-sun angles. In terms of rRMSE, the global performances of hourly GHI under clear-sky, clouded-sky and all-sky were 3.4%, 25.6% and 16.9%, respectively. With each ground station equally weighted, the overall bias was 0.33%, and standard deviation of biases at 1.7%. Solcast’s instantaneous GHI product validations were at times similar to the hourly validations though is naturally harder with increasing temporal resolution.The historical datasets from Solcast are found to be of good quality, thus, they are recommended for use by researchers. Identified areas for improvement herein will guide future developments of Solcast’s methodology.

Simulation Study The Implementation of Quantile Bootstrap Method on Autocorrelated Error

Quantile regression is a regression method with the approach of separating or dividing data into certain quantiles by minimizing the number of absolute values from asymmetrical errors to overcome unfulfilled assumptions, including the presence of autocorrelation. The resulting model parameters are tested for accuracy using the bootstrap method. The bootstrap method is a parameter estimation method by re-sampling from the original sample as much as R replication. The bootstrap trust interval was then used as a test consistency test algorithm constructed on the estimator by the quantile regression method. And test the uncommon quantile regression method with bootstrap method. The data obtained in this test is data replication 10 times. The biasness is calculated from the difference between the quantile estimate and bootstrap estimation. Quantile estimation methods are said to be unbiased if the standard deviation bias is less than the standard bootstrap deviation. This study proves that the estimated value with quantile regression is within the bootstrap percentile confidence interval and proves that 10 times replication produces a better estimation value compared to other replication measures. Quantile regression method in this study is also able to produce unbiased parameter estimation values.

Actigraphy of Wrist and Ankle for Measuring Sleep Duration in Altitude Travelers.

Fifty-one healthy men, aged mean ± standard deviation (SD) 27 ± 9 years, were studied during one night at Zurich (490 m), two nights at Davos Wolfgang (1630 m), and two nights at Jakobshorn (2590 m), in randomized order. Sleep duration measured by actigraphy, using a one-axis device at the wrist (n = 51), a three-axis device at the other wrist, and a three-axis device at the ankle (n = 22), was compared with corresponding total sleep time (TST) measured by polysomnography. During 255 polysomnographic overnight studies, 449 paired actigraphic recordings were obtained. The median polysomnographic-derived TST ranged from 397 to 408 minutes. Actigraphic mean TST from wrists with one-axis and three-axis devices, and from ankle agreed well with polysomnographic values with a bias of +1, -7, +6 minutes, respectively. Corresponding limits of agreement (±2 SD of bias) were ±51, ±60, and ±59 minutes. Limits of agreement of mean TST over five nights by actigraphy and polysomnography were similar to the coefficient of repeatability (2 SD of mean) of polysomnographic TST, that is, ±31, ±38, and ±36 minutes versus ±34 minutes. Actigraphy of the wrist or ankle by a one-axis or a three-axis device accurately estimates mean TST in groups of subjects and mean TST over several nights in individuals traveling to altitude. Therefore, actigraphy is valuable for assessing effects of altitude and other environmental influences on sleep duration during field studies over extended periods.

Quality control methods for KOOS operational sea surface temperature products

Sea surface temperature SST obtained from the initial version of the Korea Operational Oceanographic System (KOOS) SST satellite have low accuracy during summer and daytime. This is attributed to the diurnal warming effect. Error estimation of SST data must be carried out to use the real-time forecasting numerical model of the KOOS. This study suggests two quality control methods for the KOOS SST system. To minimize the diurnal warming effect, SSTs of areas where wind speed is higher than 5 m/s were used. Depending on the wind threshold value, KOOS SST data for August 2014 were reduced by 0.15°C. Errors in SST data are considered to be a combination of random, sampling, and bias errors. To estimate bias error, the standard deviation of bias between KOOS SSTs and climatology SSTs were used. KOOS SST data yielded an analysis error standard deviation value similar to OSTIA and NOAA NCDC (OISST) data. The KOOS SST shows lower random and sampling errors with increasing number of observations using six satellite datasets. In further studies, the proposed quality control methods for the KOOS SST system will be applied through more long-term case studies and comparisons with other SST systems.

The nature of GPS differential receiver bias variability: An examination in the polar cap region

AbstractWhile modern GPS receiver differential code bias estimation techniques have become highly refined, they still demonstrate unphysical behavior, namely, notable solar cycle variability. This study investigates the nature of these seasonal and solar cycle bias variabilities in the polar cap region using single‐station bias estimation methods. It is shown that the minimization of standard deviation bias estimation technique is linearly dependent on the user's choice of shell height, where the sensitivity of this dependence varies significantly from 1 total electron content unit (1 TECU = 1016 el m−2) per 4000 km in solar minimum winter to in excess of 1 TECU per 90 km during solar maximum summer. Using an ionosonde, we find appreciable shell height variability resulting in bias variabilities of up to 2 TECU. Comparing northward face Resolute Incoherent Scatter Radar (RISR‐N) measurements to a collocated GPS station, we find that RISR‐derived GPS receiver biases vary seasonally but not with solar cycle. RMS differences between bias estimation methods and observation between 2009 and 2013 were found to range from 2.7 TECU to 3.4 TECU, depending on method. To account for the erroneous solar cycle variability of standard bias estimation approaches, we linearly fit these biases to sunspot number, removing the trend. RMS errors after sunspot detrending these biases are reduced to 1.91 TECU. Also, these ISR‐derived and sunspot‐detrended biases are fit to ambient temperature, where a significant correlation is found. By using these temperature‐fitted biases we further reduce RMS errors to 1.66 TECU. These results can be taken as further evidence of temperature‐dependent dispersion in the GPS cabling and antenna hardware.