SciCombinator

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Concept: Mean absolute percentage error

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Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665nm), band 8 (central band: 681.25nm), and band 9 (central band: 708.75nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

Concepts: Estimator, Optics, Water, Spectrum, Type, Mean absolute percentage error, The Band

28

We investigated intermodality agreements of strains from two-dimensional echocardiography (2DE) and cardiac magnetic resonance (CMR) feature tracking (FT) in the assessment of right (RV) and left ventricular (LV) mechanics in tetralogy of Fallot (TOF). Patients were prospectively studied with 2DE and CMR performed contiguously. LV and RV strains were computed separately using 2DE and CMR-FT. Segmental and global longitudinal strains (GLS) for the LV and RV were measured from four-chamber views; LV radial (global radial strain [GRS]) and circumferential strains (GCS) measured from short-axis views. Intermodality and interobserver agreements were examined. In 40 patients (20 TOF, mean age 23 years and 20 adult controls), LV, GCS showed narrowest intermodality limits of agreement (mean percentage error 9.5%), followed by GLS (16.4%). RV GLS had mean intermodality difference of 25.7%. GLS and GCS had acceptable interobserver agreement for the LV and RV with both 2DE and CMR-FT, whereas GRS had high interobserver and intermodality variability. In conclusion, myocardial strains for the RV and LV derived using currently available 2DE and CMR-FT software are subject to considerable intermodality variability. For both modalities, LV GCS, LV GLS, and RV GLS are reproducible enough to warrant further investigation of incremental clinical merit.

Concepts: Heart, Tetralogy of Fallot, Mean absolute percentage error

4

This study tested the validity of revolutions per minute (RPM) measurements from the Pennington Pedal Desk™. Forty-four participants (73 % female; 39 ± 11.4 years-old; BMI 25.8 ± 5.5 kg/m(2) [mean ± SD]) completed a standardized trial consisting of guided computer tasks while using a pedal desk for approximately 20 min. Measures of RPM were concurrently collected by the pedal desk and the Garmin Vector power meter. After establishing the validity of RPM measurements with the Garmin Vector, we performed equivalence tests, quantified mean absolute percent error (MAPE), and constructed Bland-Altman plots to assess agreement between RPM measures from the pedal desk and the Garmin Vector (criterion) at the minute-by-minute and trial level (i.e., over the approximate 20 min trial period).

Concepts: Measurement, Psychometrics, Criterion validity, Frequency, Approximation, Numerical analysis, Mean absolute percentage error, Revolutions per minute

4

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Concepts: Angular momentum, Measurement, Activity, Velocity, Task, Mean absolute percentage error, Symmetric mean absolute percentage error, Nike, Inc.

1

Early childhood caries (ECC) is the most common chronic disease in young children. A reliable predictive model for ECC prevalence is needed in China as a decision supportive tool for planning health resources. In this study, we first established the autoregressive integrated moving average (ARIMA) model and grey predictive model (GM) based on the estimated national prevalence of ECC with meta-analysis from the published articles. The pooled data from 1988 to 2010 were used to establish the model, while the data from 2011 to 2013 were used to validate the models. The fitting and prediction accuracy of the two models were evaluated by mean absolute error (MAE) and mean absolute percentage error (MAPE). Then, we forecasted the annual prevalence from 2014 to 2018, which was 55.8%, 53.5%, 54.0%, 52.9%, 51.2% by ARIMA model and 52.8%, 52.0%, 51.2%, 50.4%, 49.6% by GM. The declining trend in ECC prevalence may be attributed to the socioeconomic developments and improved public health service in China. In conclusion, both ARIMA and GM models can be well applied to forecast and analyze the trend of ECC; the fitting and testing errors generated by the ARIMA model were lower than those obtained from GM.

Concepts: Regression analysis, Public health, Epidemiology, Prediction, Futurology, Forecasting, Mean absolute percentage error, Autoregressive moving average model

1

The rapid growth of very elderly populations requires accurate population estimates up to the highest ages. However, it is recognised that estimates derived from census counts are often unreliable. Methods that make use of death data have not previously been evaluated for Australia and New Zealand. The aim was to evaluate a number of nearly-extinct cohort methods for producing very elderly population estimates by age and sex for Australia and New Zealand. The accuracy of official estimates was also assessed. Variants of three nearly-extinct cohort methods, the Survivor Ratio method, the Das Gupta method and a new method explicitly allowing for falling mortality over time, were evaluated by retrospective application over the period 1976-1996. Estimates by sex and single years of age were compared against numbers derived from the extinct cohort method. Errors were measured by the Weighted Mean Absolute Percentage Error. It is confirmed that for Australian females the Survivor Ratio method constrained to official estimates for ages 90+ performed well. However, for Australian males and both sexes in New Zealand, more accurate estimates were obtained by constraining the Survivor Ratio method to official estimates for ages 85+. Official estimates in Australia proved reasonably accurate for ages 90+ but at 100+ they varied significantly in accuracy from year to year. Estimates produced by Statistics New Zealand in aggregate for ages 90+ proved very accurate. We recommend the use of the Survivor Ratio method constrained to official estimates for ages 85+ to create very elderly population estimates for Australia and New Zealand.

Concepts: Male, Female, Demography, Population, Sex, Immigration, New Zealand, Mean absolute percentage error

1

BACKGROUND: Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China. METHODS: Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Health, China. A seasonal autoregressive integrated moving average (SARIMA) model and a hybrid model which combined the SARIMA model and a generalized regression neural network model were used to fit the data from 2005 to 2010. Simulation performance parameters of mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the goodness-of-fit between these two models. Data from 2011 TB incidence data was used to validate the chosen model. RESULTS: Although both two models could reasonably forecast the incidence of TB, the hybrid model demonstrated better goodness-of-fit than the SARIMA model. For the hybrid model, the MSE, MAE and MAPE were 38969150, 3406.593 and 0.030, respectively. For the SARIMA model, the corresponding figures were 161835310, 8781.971 and 0.076, respectively. The seasonal trend of TB incidence is predicted to have lower monthly incidence in January and February and higher incidence from March to June. CONCLUSIONS: The hybrid model showed better TB incidence forecasting than the SARIMA model. There is an obvious seasonal trend of TB incidence in China that differed from other countries.

Concepts: Regression analysis, Epidemiology, Prediction, Futurology, Future, Mean squared error, Forecasting, Mean absolute percentage error

0

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Concepts: Regression analysis, Standard deviation, Root mean square, Mean squared error, Optimization, Mean absolute percentage error, Root mean square deviation, Ant colony optimization

0

An improved paper-based analytical device (PAD) using color screening to enhance device performance is described. Current detection methods for PADs relying on the distance-based signalling motif can be slow due to the assay time being limited by capillary flow rates that wick fluid through the detection zone. For traditional distance-based detection motifs, analysis can take up to 45 min for a channel length of 5 cm. By using a color screening method, quantification with a distance-based PAD can be achieved in minutes through a “dip-and-read” approach. A colorimetric indicator line deposited onto a paper substrate using inkjet-printing undergoes a concentration-dependent colorimetric response for a given analyte. This color intensity-based response has been converted to a distance-based signal by overlaying a color filter with a continuous color intensity gradient matching the color of the developed indicator line. As a proof-of-concept, Ni quantification in welding fume was performed as a model assay. The results of multiple independent user testing gave mean absolute percentage error and average relative standard deviations of 10.5% and 11.2% respectively, which were an improvement over analysis based on simple visual color comparison with a read guide (12.2%, 14.9%). In addition to the analytical performance comparison, an interference study and a shelf life investigation were performed to further demonstrate practical utility. The developed system demonstrates an alternative detection approach for distance-based PADs enabling fast (∼10 min), quantitative, and straightforward assays.

Concepts: Assay, Arithmetic mean, Mean, Performance, Standard deviation, Color temperature, Titration, Mean absolute percentage error

0

Informal training in preclinical research may be a contributor to the poor reproducibility of preclinical cardiology research and low rates of translation into clinical research and practice. Mouse echocardiography is a widely used technique to assess cardiac structure and function in drug intervention studies using disease models. The inter-observer variability (IOV) of clinical echocardiographic measurements has been shown to improve with formalized training, but preclinical echocardiography lacks similarly critical standardization of training. The aims of this investigation were to assess the IOV of echocardiographic measurements from studies in mice, and address any technical impediments to reproducibility by implementing standardized guidelines through formalized training. In this prospective, single-site, observational cohort study, 13 scientists performing preclinical echocardiographic image analysis were assessed for measurement of short-axis M-mode-derived dimensions and calculated left ventricular mass (LVMass). Ten M-mode images of mouse hearts acquired and analyzed by an expert researcher with a spectrum of LVMass were selected for assessment, and validated by autopsy weight. Following the initial observation, a structured formal training program was introduced, and accuracy and reproducibility were re-evaluated. Mean absolute percentage error (MAPE) for Expert-calculated LVMass was 6{plus minus}4% compared to autopsy LVMass, and 25{plus minus}21% for participants before training. Standardized formal training improved participant MAPE by approximately 30% relative to expert-calculated LVMass (p<0.001). Participants initially categorized with high-range error (25-45%) improved to low-moderate error ranges (<15-25%). This report reveals an example of technical skill training insufficiency likely endemic to preclinical research and provides validated guidelines for echocardiographic measurement for adaptation to formalized in-training programs.

Concepts: Scientific method, Cohort study, Clinical trial, Cardiology, Observation, Psychometrics, Mean absolute percentage error, Symmetric mean absolute percentage error