Standard Error Of Estimate Research Articles

Long-term trends of pH and inorganic carbon in the Eastern North Atlantic: the ESTOC site

Using 25 years of data from the North-East Atlantic Ocean at the ESTOC site, we confirm the surface ocean is actively absorbing carbon emissions caused by human activities and undergoing ocean acidification. The carbon dioxide is also increasing in the subsurface and deepest waters. Seawater salinity normalized inorganic carbon (NCT), fugacity of CO2 (fCO2) and anthropogenic CO2 increase at a rate of 1.17 ± 0.07 µmol kg−1, 2.1 ± 0.1 µatm yr−1 and 1.06 ± 0.11 μmol kg−1 yr−1, respectively, while the ocean pHT fixed to the average temperature of 21°C, declines at a rate of 0.002 ± 0.0001 pH yr−1 in the first 100 m. These rates are 20% higher than values determined for the period 1995–2010. Over the 25 years, the average surface fCO2 increased by 52.5 µatm while the pHT declined by 0.051 pH units (~13% increase in acidity), like the observed seasonal signal. After 2020, seawater conditions are outside the range of surface fCO2 and pHT seasonal amplitude observed in the 1990s. It was also predicted by the year 2040, fCO2 seawater data will be smaller than atmospheric one and the area will be acting as a sink the full year around. Parameterizations of AT, CT, pHT and fCO2 using observations of water temperature, salinity and dissolved oxygen were determined for the ESTOC site with standard error of estimation of 6.5 µmol kg−1, 6.8 µmol kg−1, 0.010 pH and 9.6 µatm, respectively, and were applied to the North-East Atlantic Ocean. The observations and the parameterizations showed that the trends of the carbonate variables along the water column in the eastern subtropical ESTOC region are dominated by anthropogenically induced changes, observed in the whole water profile.

Formulating Equations for Estimating Forest Stand Carbon Stock for Various Tree Species Groups in Northern Thailand

Through this study, we established equations for estimating the standing tree carbon stock based on 24 tree species in multiple size classes in a case study at the Ngao Demonstration Forest (NDF) in northern Thailand. Four hundred thirty-nine wood samples from trees in mixed deciduous forest (MDF), dry dipterocarp forest (DDF), and dry evergreen forest (DEF) were collected using non-destructive methods to estimate aboveground carbon equations through statistical regression. The equations were established based on four criteria: (1) the coefficient of determination (R2), (2) standard error of estimate (SE), (3) F-value, and (4) significant value (p-value, α ≤ 0.05). The aboveground carbon stock (C) equations for standing trees in the MDF was C = 0.0199DBH2.1887H0.5825, for DDF was C = 0.0145DBH2.1435H0.748, for DEF was C = 0.0167DBH2.1423H0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH2.1625H0.6614, where DBH is tree diameter at breast height, and H is tree total height. The aboveground carbon stock in the DDF, MDF, and DEF was 142, 53.02, and 12 tons/ha, respectively, and the estimated aboveground carbon stock in the Mae Huad sector at the NDF was 61 tons/ha.

A Maximal Rowing-Ergometer Protocol to Predict Maximal Oxygen Uptake in Female Rowers.

Laboratory assessment of maximal oxygen uptake (V˙O2max) is physically and mentally draining for the athlete and requires expensive laboratory equipment. Indirect measurement of V˙O2max could provide a practical alternative to laboratory testing. To examine the relationship between the maximal power output (MPO) in an individualized 7 × 2-minute incremental test (INCR-test) and V˙O2max and to develop a regression equation to predict V˙O2max from MPO in female rowers. Twenty female club and Olympic rowers (development group) performed the INCR-test on a Concept2 rowing ergometer to determine V˙O2max and MPO. A linear regression analysis was used to develop a prediction of V˙O2max from MPO. Cross-validation analysis of the prediction equation was performed using an independent sample of 10 female rowers (validation group). A high correlation coefficient (r = .94) was found between MPO and V˙O2max. The following prediction equation was developed: V˙O2max (mL·min-1) = 9.58 × MPO (W) + 958. No difference was found between the mean predicted V˙O2max in the INCR-test (3480mL·min-1) and the measured V˙O2max (3530mL·min-1). The standard error of estimate was 162mL·min-1, and the percentage standard error of estimate was 4.6%. The prediction model only including MPO, determined during the INCR-test, explained 89% of the variability in V˙O2max. The INCR-test is a practical and accessible alternative to laboratory testing of V˙O2max.

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R2 (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm3, MAE of 67.94 mg/cm3, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm3, 460.52 mg/cm3, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R2 of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm3, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm3. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.

Lumber yield of Sucupira-Vermelha in central Amazon sawmill

The sustainable use of forestry resources has an intrinsic relationship with log yield in sawmills, because the knowledge of this variable can optimize the processed wood production. Thus, the objective of this work was to determine the percentage yield in the sawing of Sucupira-Vermelha (Andira parviflora Ducke.) logs in different diameter classes and to adjust mathematical models to estimate processed wood. It was sampled 56 logs with diameters between 31 and 69 cm grouped into 4 diameter classes. Afterwards the sawing was performed obtaining various pieces of wood which were cubed to determine the yielding percentage. The increase in diameter classes provided an increase in sawn wood yield, which showed a significant difference only between the diameter class 1 and the others studied. Twelve mathematical models, 6 linear and 6 nonlinear, were evaluated. The models quality adjustment and selection was based on the highest adjusted determination coefficient (R²adj), lowest estimation standard error (Syx) and variation coefficient (VC%) and, most homogeneous residues graphic distribution. The nonlinear models of single entry (diameter) and double entry (diameter and length) were the most accurate to estimate the sawn wood yield of Sucupira-Vermelha species.

Vegetation Stands Biomass and Carbon Stock Estimation using NDVI - Landsat 8 Imagery in Mixed Garden of Rancakalong, Sumedang, Indonesia

Human activities in modifying land use and land cover increasingly put pressure to many regulatory ecosystem services, one of which is carbon sequestration. If forests, the area with the most vegetation cover are decrease, the amount of carbon sequestered will decrease significantly. Currently, agroforestry systems or Talun (in West Java) in Sumedang was eleven times larger than secondary forest. Carbon stocks in this agricultural area need to be estimated so that their carbon sequestration capacity can be known in order to improve the quality of regulatory ecosystem services. NDVI value of the Landsat 8 OLI was obtained by conducting raster calculation in ArcMap. Field inventory was conducted by measuring stem DBH and height of all vegetation stands in 31 plots measuring 30 x 30 m, a similar plot size to the resolution of the Landsat imagery. Biomass of vegetation stands was calculated using allometric equations and then converted into the carbon content of the biomass. In order to analyze the correlation of NDVI and carbon inventory data, a Pearson product-moment correlation analysis in the form of simple linear regression, non-linear exponential, and polynomial order 2 and order 3 model were carried out. Standard error of estimate (SEE) was performed to identify the best equation to model the aboveground carbon stocks in the area. The results show that the four regression models give a positive correlation between NDVI and carbon stocks. The strongest category was the polynomial order 2 and order 3 regression model with 0.795 coefficient of determination. Yet, the linear simple regression model obtained the highest accuracy with estimated error 0.445 tons/pixel. The estimated carbon stock obtained by linear regression model was 16150.40 tons with an average of 104.95 tons/ha. Visually, according to carbon distribution map, the carbon content of vegetation stands in the mixed garden of Rancakalong was mainly distributed in the north of Rancakalong District which is located in Cibungur Village.

Model-assisted domain estimation of postfire tree regeneration in the western US using nearest neighbor techniques

Many nations administer national forest inventory programs for unbiased estimation of forest attributes over broad spatial and temporal extents. However, management and conservation decisions often demand reliable estimates for finer spatiotemporal domains. In the western US, wildfire activity is expanding and postfire regeneration must contend with a warmer, drier climate. We evaluate the potential of K nearest neighbor (KNN) strategies for estimation of stocking across postfire measurements of Forest Inventory & Analysis plots in 11 western US states, and subsequently for model-assisted (MA) estimation of stocking over domains defined by aggregations of burned areas within individual states and 4-year periods. In particular, we develop and evaluate a form of constrained KNN that allows for unbiased MA domain estimation under simple random sampling by drawing only on measurements external to a domain of interest. KNN strategies based on geographically, radiometrically, and climatically proximate measurements are found to provide more accurate estimates of stocking at the plot level than domain means. Applying the selected external KNN strategy also reduced standard errors of MA domain estimates by 16% over direct domain estimators, but bias correction introduces substantial variability over synthetic estimates. Further applications of the external constraint imposed on KNN are discussed.

3D-QSAR and Molecular Docking Studies of Novel GPR52 Agonists

To study the three-dimensional quantitative structure–activity relationship (3D-QSAR) of a series of GPR52 agonists, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to establish 3D-QSAR models. The cross-validation coefficient ([Formula: see text]), non-cross validation coefficient ([Formula: see text]) and standard error of estimate (SEE) of the constructed CoMFA model were 0.679, 0.981 and 0.058, respectively. While those for the CoMSIA model were 0.661, 0.846 and 0.110, respectively. The internal and external validation parameters showed that the constructed 3D-QSAR models had good prediction ability and significant statistical reliability. According to the information provided by 3D-QSAR analysis, new compounds were designed, and the molecular docking together with absorption, distribution, metabolism, exclusion, toxicity (ADMET) prediction results showed that the new compounds had good GPR52 agonistic activity.

Normative database of response surface method for human trunk extension in isometric mode.

Recent studies show that asymmetric movements are important Low Back Disorders risk factors. Measuring the trunk strength and identifying the coupling effects in different postures can provide an objective tool to assess one's task capacity. This paper estimates the maximum performance capacity for isometric trunk extension and accompanying torques. Thirty males performed maximum voluntary isometric extension in 33 trunk postures on Sharif Lumbar Isometric Strength Tester device. Corresponding moments and angular positions were collected. Second-order full response surface models (RSM) were exploited to formulate the relationship between strengths and three trunk angles. The results of correlation coefficient, percent of standard estimation error and lack of fit reflected the adequacy of models. In conclusion, the main torque was the extension, but at the same time lateral bending and rotation torques were observed. For predicting these three torques in a specific posture and injury prevention, the second order RSM is a useful tool. The presented models can be used in the fields of ergonomics, occupational biomechanics and sport.

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography.

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

QSAR Study on the Antitumor Activity of Novel 1,2, 3-Triazole Compounds based on Topomer Comfa Method

Background: The high mortality rate of cancer is endangering human health, and the research and development of new anticancer drugs have the attention of scientists worldwide. Sulfonamides have become the focus of anticancer drug research. 1,2,3-triazole compounds can inhibit the formation of a variety of tumor cells. Based on the excellent antitumor activity exhibited by the 1,2,3-triazole compound skeleton, the sulfonamide moiety in the sulfonamide structure can be introduced into the triazole compound skeleton to obtain highly active anticancer drugs. Methods: The Topomer CoMFA method was used to study the three-dimensional quantitative structureactivity relationship of 58 new 1,2,3-triazole compounds with sulfa groups, and a 3D-QSAR model was obtained. Results: The cross-validation coefficient q2 is 0.545, the non-cross-correlation coefficient r2 is 0. 754, r_pred2 is 0.930, the optimal number of principal components N is 4, and the standard estimation error SEE is 0.319. These results show that the model has good internal and external forecasting capabilities. By searching for the R group in the Topomer search module and combining with the more active groups in the existing molecules, 6 new compounds with theoretically higher anti-HL-60 (leukemia cell line) activity are obtained. Conclusion: The prediction result of the Topomer CoMFA model is good, and the statistical verification is effective. The prediction results of ADMET show that the 6 new compounds meet the drug requirements and are expected to become potential anti-HL-60 inhibitors, providing guidance for the synthesis of anti-tumor drugs.

Application of Kvaal’s Age Estimation Method in Maxillary Central Incisor: A CBCT Study

Background: Radiographic dental age estimation methods are viable both for living and deceased people. One such method is the indirect assessment of quantified secondary dentinal deposition through measurements of tooth and pulp. Kvaal, et al., developed a method for chronological age estimation based on pulp size using periapical dental radiographs. There is a need to test this method of age estimation in the Indian population on living individuals not requiring tooth extraction. The current study aimed to assess the applicability of Kvaal’s method in maxillary permanent central incisor using CBCT. Materials and Methods: The study included 185 CBCT images of the individuals, ranging in age from 14 to 64 years. CBCT images were evaluated for the maxillary central incisor and metric measurements were taken from which ratios were derived. Using the ratios, a linear regression equation was derived, from which the age of an individual was predicted. Result and Conclusion: The correlation between the individual ratio and the chronological age was calculated using the Pearson correlation coefficient. The age of the individual was predicted using a linear regression equation with a SEE ranging from 10.05 to 12.78 years. When the samples were divided into various age groups, the Standard Error of Estimate has drastically reduced. The radiographic pulpal morphometric analysis used in present study can be recommended to assess the age of an adult for forensic purposes.

ANN-based PCA to predict evapotranspiration: a case study in India

Abstract The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman–Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively.

Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.

Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study

Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution, . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.

Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach

Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999–2002 and 2003–2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL). Three models included BMI, BMI2, age, gender, education, income, and interaction terms and produced R-squared values of 0.87 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was −0.005. Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.

Automatic Spine Segmentation and Parameter Measurement for Radiological Analysis of Whole-Spine Lateral Radiographs Using Deep Learning and Computer Vision.

Radiographic examination is essential for diagnosing spinal disorders, and the measurement of spino-pelvic parameters provides important information for the diagnosis and treatment planning of spinal sagittal deformities. While manual measurement methods are the golden standard for measuring parameters, they can be time consuming, inefficient, and rater dependent. Previous studies that have used automatic measurement methods to alleviate the downsides of manual measurements showed low accuracy or could not be applied to general films. We propose a pipeline for automated measurement of spinal parameters by combining a Mask R-CNN model for spine segmentation with computer vision algorithms. This pipeline can be incorporated into clinical workflows to provide clinical utility in diagnosis and treatment planning. A total of 1807 lateral radiographs were used for the training (n = 1607) and validation (n = 200) of the spine segmentation model. An additional 200 radiographs, which were also used for validation, were examined by three surgeons to evaluate the performance of the pipeline. Parameters automatically measured by the algorithm in the test set were statistically compared to parameters measured manually by the three surgeons. The Mask R-CNN model achieved an average precision at 50% intersection over union (AP50) of 96.2% and a Dice score of 92.6% for the spine segmentation task in the test set. The mean absolute error values of the spino-pelvic parameters measurement results were within the range of 0.4° (pelvic tilt) to 3.0° (lumbar lordosis, pelvic incidence), and the standard error of estimate was within the range of 0.5° (pelvic tilt) to 4.0° (pelvic incidence). The intraclass correlation coefficient values ranged from 0.86 (sacral slope) to 0.99 (pelvic tilt, sagittal vertical axis).

Differences in survivorship care experiences among older breast cancer survivors by clinical cancer characteristics, race/ethnicity, and socioeconomic factors: A SEER-CAHPS study.

To determine if disparities exist in survivorship care experiences among older breast cancer survivors by breast cancer characteristics, race/ethnicity, and socioeconomic factors. A total of 19,017 female breast cancer survivors (≥ 65 at post-diagnosis survey) contributed data via SEER-CAHPS data linkage (2000-2019). Analyses included overall and stratified multivariable linear regression to estimate beta (β) coefficients and standard errors (SE) to identify relationships between clinical cancer characteristics and survivorship care experiences. Minority survivors were mostly non-Hispanic (NH)-Black (8.1%) or NH-Asian (6.5%). Survivors were 76.3years (SD = 7.14) at CAHPS survey and were 6.10years (SD = 3.51) post-diagnosis on average. Survivors with regional breast cancer vs. localized at diagnosis (β = 1.00, SE = 0.46, p = 0.03) or treated with chemotherapy vs. no chemotherapy/unknown (β = 1.05, SE = 0.48, p = 0.03) reported higher mean scores for Getting Needed Care. Results were similar for Overall Care Ratings (β = 0.87, SE = 0.38, p = 0.02) among women treated with chemotherapy. Conversely, women diagnosed with distant breast cancer vs. localized reported lower mean scores for Physician Communication (β = -1.94, SE = 0.92, p = 0.03). Race/ethnicity, education, and area-level poverty significantly modified several associations between stage, estrogen receptor status, treatments, and various CAHPS outcomes. These study findings can be used to inform survivorship care providers treating women diagnosed with more advanced stage and aggressive disease. The disparities we observed among minority groups and by socioeconomic status should be further evaluated in future research as these interactions could impact long-term outcomes, including survival.

Age estimation by palatal suture using modified Kamijo’s method

The present study aimed to investigate the correlation between palatal suture obliteration and age in modern Japanese and to develop an age estimation equation by modifying Kamijo's (1949) method. The subjects were 195 Japanese skeletal remains (155 males and 40 females) whose age and sex were known. First, obliteration score (OS) was obtained by measuring palatal suture obliteration from photographic images taken at the time of forensic autopsy, and the correlation with age was examined; no significant correlation was found in females. Second, the palatal sutures were divided into 14 sections, and each section was scored from 0 to 4 points according to the degree of the suture obliteration. Suture scores (SS) were then calculated for each of the four sutures, and the sum of the 14 scores (TSS: total suture score) was used to perform regression analysis for age. For male and all subjects (male and female), age significantly increased (p < 0.001) according to increment of SSs for all sutures. TSS has the highest regression coefficient (r = 0.540), and the lowest standard error of estimation (13.54 years) for all of the patients. The intra- and inter-observer agreement scoring showed high reliability. Validation study using the formulae showed a high percentage of correct responses (80 %). In conclusion, age estimation regression formula by palatal suture using modified Kamijo’s method was established for Japanese population, and the study showed the formula might be valid for age estimation.

Age estimation by evaluating median palatine suture closure using postmortem CT.

This study sought to develop an age-estimation formula to evaluate the extent of median palatine suture (MP) closure using postmortem computed tomographic (PMCT) images. The PMCT images of 634 Japanese subjects (mean age, 54.5 years; standard deviation [SD], 23.2 years) with known age and sex were examined. The degree of suture closure of the MP, anterior median palatine suture (AMP), and posterior median palatine suture (PMP) was measured and scored (suture closure score, SCS), and a single linear regression analysis was conducted with age at death. On the analysis, SCS of MP, AMP, and PMP showed a significant correlation with age (p < 0.001). The correlation coefficient of MP was higher (0.760, male; 0.803, female; and 0.779, total) than that of AMP (0.726, male; 0.745, female; and 0.735, total) or PMP (0.457, male; 0.630, female; and 0.549, total). The regression formula and standard error of estimation (SEE) of MP were calculated as Age = 100.95 × SCS + 20.51 (SEE 14.87 years) for male subjects, Age = 91.93 × SCS + 26.65 (SEE 14.12 years) for female subjects, and Age = 95.17 × SCS + 24.09 (SEE 14.59 years) for the total, respectively. In addition, another 50 Japanese subjects were randomly selected to validate the age-estimation formula. In this validation, the actual age of 36 subjects (72%) was within the estimated age ± SEE. This study showed that the age estimation formula using PMCT images of MPs was potentially useful for estimating the age of unidentified corpses.