scholarly journals Validity and Absolute Reliability of Axial Vertebral Rotation Measurements in Thoracic and Lumbar Vertebrae

2021 ◽  
Vol 11 (23) ◽  
pp. 11084
Author(s):  
José Hurtado-Avilés ◽  
Vicente J. León-Muñoz ◽  
Pilar Andújar-Ortuño ◽  
Fernando Santonja-Renedo ◽  
Mónica Collazo-Diéguez ◽  
...  
Keyword(s):  
Lumbar Vertebrae ◽  
Vertebral Rotation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Thoracic Vertebrae ◽  
X Rays ◽  
Validity And Reliability ◽  
Axial Vertebral Rotation ◽  
Manual Methods ◽  
Absolute Reliability

Axial vertebral rotation (AVR) and Cobb angles are the essential parameters to analyse different types of scoliosis, including adolescent idiopathic scoliosis. The literature shows significant discrepancies in the validity and reliability of AVR measurements taken in radiographic examinations, according to the type of vertebra. This study’s scope evaluated the validity and absolute reliability of thoracic and lumbar vertebrae AVR measurements, using a validated software based on Raimondi’s method in digital X-rays that allowed measurement with minor error when compared with other traditional, manual methods. Twelve independent evaluators measured AVR on the 74 most rotated vertebrae in 42 X-rays with the software on three separate occasions, with one-month intervals. We have obtained a gold standard for the AVR of vertebrae. The validity and reliability of the measurements of the thoracic and lumbar vertebrae were studied separately. Measurements that were performed on lumbar vertebrae were shown to be 3.6 times more valid than those performed on thoracic, and with almost an equal reliability (1.38° ± 1.88° compared to −0.38° ± 1.83°). We can conclude that AVR measurements of the thoracic vertebrae show a more significant Mean Bias Error and a very similar reliability than those of the lumbar vertebrae.

scholarly journals A Generative Adversarial Inpainting Network to Enhance Prediction of Periodontal Clinical Attachment Level

2021 ◽  
Author(s):  
Vasant Kearney ◽  
Alfa-Ibrahim M. Yansane ◽  
Ryan G. Brandon ◽  
Ram Vaderhobli ◽  
Guo-Hao Lin ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Absolute Error ◽  
Generative Adversarial Networks ◽  
Mean Bias Error ◽  
Bias Error ◽  
X Rays ◽  
Clinical Attachment Level ◽  
Adversarial Networks ◽  
Attachment Level ◽  
Pairwise Test

Abstract Deep learning algorithms has recently been used to determine clinical attachment levels (CAL) which aid in the diagnosis of periodontal disease. However, the limited field-of-view of dental bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were conducted using mean bias error (MBE), mean absolute error (MAE) and Dunn’s pairwise test comparing CAL at p=0.05. Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with a MAE of 1.04mm and 1.50mm respectively. The Dunn’s pairwise test indicated a statistically significant improvement in CAL prediction accuracy between both inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn’s pairwise value of -63.89. This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing images.

scholarly journals Validity and reliability of a computer-assisted system method to measure axial vertebral rotation

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
José Hurtado-Avilés ◽  
Vicente J. León-Muñoz ◽  
Jose Manuel Sanz-Mengibar ◽  
Fernando Santonja-Renedo ◽  
Pilar Andújar-Ortuño ◽  
...  
Keyword(s):  
Vertebral Rotation ◽  
Computer Assisted ◽  
Validity And Reliability ◽  
System Method ◽  
Axial Vertebral Rotation

Validity and reliability of Polar Team Pro GPS units for assessing maximum sprint speed in soccer players

2021 ◽  
pp. 175433712110472
Author(s):  
İsa Sagiroglu ◽  
Zeki Akyildiz ◽  
Mehmet Yildiz ◽  
Filipe Manuel Clemente
Keyword(s):  
Soccer Players ◽  
Mean Bias Error ◽  
Maximum Speed ◽  
Bias Error ◽  
Validity And Reliability ◽  
Good Reliability ◽  
Positioning Systems ◽  
Sprint Speed ◽  
Coefficients Of Variation ◽  
Starting Point

Previous research has reported inconsistencies in the validity and reliability of different brands of global positioning systems (GPS). Therefore, it should be questioned whether GPS units measure the maximum speed measurements validly and reliably. The current study aimed to analyze the validity and reliability of Polar Team Pro GPS units (10 Hz) when used to measure maximum sprint speed. Sixteen amateur soccer players (age: 27.22 ± 4.70 years; height: 177 ± 6.05 cm; body mass: 73.66 ± 5.63 kg) were tested in the 40 m sprint. Two Polar Team Pro GPS units were positioned on each player, while the radar was placed on a 1 m high tripod placed 10 m behind the starting point. The data obtained from the Polar Team Pro GPS units were compared to determine inter-unit reliability. The data obtained from one of the Polar Team Pro GPS units and radar gun (gold standard) were compared to determine validity. Good inter-unit reliability between the Polar Team Pro GPS units was reported for maximum sprint speed, with low coefficients of variation (5%–6%) and low smallest worthwhile changes (0.4 for all systems). Regarding validity, the coefficient of correlation was extremely high for maximum sprint speed ( r = 0.938, p < 0.001). Moreover, measurement differences between both systems were statistically insignificant (Mean Bias error = 0.144, R2 = 0.879, MAPE = 1.6%, MAE = 0.688, and RMSE = 0.697). Consequently, good reliability and perfect validity were observed, indicating that the Polar Team Pro GPS unit is suitable for maximum sprint speed measurements.

scholarly journals Correction of Eddy Covariance Based Crop ET Considering the Heat Flux Source Area

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 281
Author(s):  
Stuart L. Joy ◽  
José L. Chávez
Keyword(s):  
Heat Flux ◽  
Eddy Covariance ◽  
Agricultural Land ◽  
Flux Measurement ◽  
Source Area ◽  
Mean Bias Error ◽  
Bias Error ◽  
Crop Water ◽  
The Past ◽  
Measurement Results

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h−1 (−4.4%) and root mean square error (RMSE) of 0.09 mm h−1 (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Differences in Radiation Exposure of CT-Guided Percutaneous Manual and Powered Drill Bone Biopsy

2021 ◽  
Author(s):  
Sebastian Zensen ◽  
Sumitha Selvaretnam ◽  
Marcel Opitz ◽  
Denise Bos ◽  
Johannes Haubold ◽  
...  
Keyword(s):  
Radiation Exposure ◽  
Diagnostic Yield ◽  
Bone Biopsy ◽  
Lumbar Vertebrae ◽  
Bone Lesions ◽  
Anatomical Location ◽  
Thoracic Vertebrae ◽  
Level Of Evidence ◽  
Ct Guided ◽  
Bone Biopsies

Abstract Purpose Apart from the commonly applied manual needle biopsy, CT-guided percutaneous biopsies of bone lesions can be performed with battery-powered drill biopsy systems. Due to assumably different radiation doses and procedural durations, the aim of this study is to examine radiation exposure and establish local diagnostic reference levels (DRLs) of CT-guided bone biopsies of different anatomical regions. Methods In this retrospective study, dose data of 187 patients who underwent CT-guided bone biopsy with a manual or powered drill biopsy system performed at one of three different multi-slice CT were analyzed. Between January 2012 and November 2019, a total of 27 femur (A), 74 ilium (B), 27 sacrum (C), 28 thoracic vertebrae (D) and 31 lumbar vertebrae (E) biopsies were included. Radiation exposure was reported for volume-weighted CT dose index (CTDIvol) and dose–length product (DLP). Results CTDIvol and DLP of manual versus powered drill biopsy were (median, IQR): A: 56.9(41.4–128.5)/66.7(37.6–76.2)mGy, 410(203–683)/303(128–403)mGy·cm, B: 83.5(62.1–128.5)/59.4(46.2–79.8)mGy, 489(322–472)/400(329–695)mGy·cm, C: 97.5(71.6–149.2)/63.1(49.1–83.7)mGy, 627(496–740)/404(316–515)mGy·cm, D: 67.0(40.3–86.6)/39.7(29.9–89.0)mGy, 392(267–596)/207(166–402)mGy·cm and E: 100.1(66.5–162.6)/62.5(48.0–90.0)mGy, 521(385–619)/315(240–452)mGy·cm. Radiation exposure with powered drill was significantly lower for ilium and sacrum, while procedural duration was not increased for any anatomical location. Local DRLs could be depicted as follows (CTDIvol/DLP): A: 91 mGy/522 mGy·cm, B: 90 mGy/530 mGy·cm, C: 116 mGy/740 mGy·cm, D: 87 mGy/578 mGy·cm and E: 115 mGy/546 mGy·cm. The diagnostic yield was 82.4% for manual and 89.4% for powered drill biopsies. Conclusion Use of powered drill bone biopsy systems for CT-guided percutaneous bone biopsies can significantly reduce the radiation burden compared to manual biopsy for specific anatomical locations such as ilium and sacrum and does not increase radiation dose or procedural duration for any of the investigated locations. Level of Evidence Level 3.

Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

2021 ◽  
pp. 1420326X2110130
Author(s):  
Manta Marcelinus Dakyen ◽  
Mustafa Dagbasi ◽  
Murat Özdenefe
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Mean Squared Error ◽  
Residential Buildings ◽  
Mean Bias Error ◽  
Bias Error ◽  
Sampled Data ◽  
Low Carbon ◽  
Building Stock ◽  
Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

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