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Background: This study was a prospective cadaver-derived skeletal study looking at the skeletal remains of a modern human population. The complete arcuate foramen (AF) is an anatomical variant of the atlas vertebra with a complete osseous bridge over the groove for the vertebral artery (VA). Awareness of the anatomic variations of the atlas related to the course of the VA, such as the AF, is important because the course and variations of VAs are critical to spine surgeons. We aimed to detect the prevalence of AF in sub-Saharan African subjects. Materials and methods: We analyzed the prevalence of AF in 120 atlas vertebrae from the Raymond A Dart Collection of the University of the Witwatersrand, Johannesburg, South Africa. Results: Twelve (13.3 %) atlases of the 90 that were from sub-Saharan African ancestry subjects presented at least one AF: the presence of AF frequency was 6.7 % in the Sotho sample, 23.3 % in the Xhosa sample and 10.0 % in the Zulu sample. However, no significant difference was found in their distribution regarding the presence of AF ratios (p = 0.221). The AF frequency was 3.3 % in the South African Caucasian subjects. No significant differences were found in their distribution regarding the presence of AF ratios between the Caucasian and the Sotho (p = 1.000), Zulu (p = 0.612) and Xhosa (p = 0.052) samples. Conclusions: Our research shows a tendency for a higher AF presence in Xhosa subjects. It has increased the knowledge of the AF prevalence in sub-Saharan Africa, drastically increasing the population.

Objective:The aim of this study is to evaluate the theses of residency in medicine in terms of statistical errors made and thus to contribute to the production of quality scientific publications by ensuring that scientific publishers in the field of medicine are sensitive and careful about statistics when doing their work.Methods:In this study, we investigated 321 thesis theses which are defended from 6 different universities are obtained from the database of the Turkish Higher Education Council. The investigation is was conducted in terms of “Errors Related to p-values”, “Errors Related to Tests”, “Mathematical Notation Errors”, “Statistical Symbol Errors”, “Inappropriate Interpretation”, “Presentation of The the Statistical Method Analysis and Results in The the Incorrect Section of The the Manuscript”, “Errors in Summarizing Data”,“Incomprehensible Statistical Terms” and “Errors in Statistical Terminology”.Results:There was at least one statistical error in all 321 medicine residency theses examined. The most common error was “errors in summarizing data” with a ratio of 70.1% (n=225), while the least common error was “incomprehensible statistical expressions” with a ratio of 14.3% (n=46).Conclusion:As a result, both researchers and consultants who undertake scientific studies have a responsibility to minimize these errors. To prevent statistical errors, students who are doing residency in medicine are required to receive the necessary training in statistical literacy, to have basic statistical knowledge, and to receive consultancy from a biostatistics expert for statistical evaluations. Students who residency in medicine in preventing statistical errors are required to receive the necessary training in statistical literacy, to have basic statistical knowledge, and to receive consultancy from a biostatistics expert for statistical evaluations.

Objective: To explore the possible relationship between vitamin D and coronavirus disease-2019 in an urban population known to have relatively low vitamin D levels. Method: The retrospective study was conducted in Bursa, Turkey, and comprised data of 30 family health centers from March 1 to December 15, 2020, related to diagnosed inpatients of coronovairus disease-2019. The diagnosis was based on polymerase chain reaction test for severe acute respiratory syndrome coronavirus-2 infection. Vitamin D levels of these patients were obtained from previous records and compared with those without polymerase chain reaction positivity. Vitamin D levels of positive patients who survived were compared with those who died of coronavirus disease-2019. Data was analysed using SPSS 21. Result: Of the 2,105 patients whose data was retrived, 212(10%) were positive for coronavirus disease-2019, while 1,893(90%) were negative. Among the positive patients, 89(42%) were men and 123(58%) were women. The overall median age was 45 years (Q1-Q3(Q1: first quartile, Q3: third quartile): 35-56 years). Vitamin D level was not significantly different between the positive and negative patients (p>0.05). Among the positive patients, 2(0.9%) patients did not have new admissions to the designated centers and their data was excluded, 206(97.2%) survived and 4(1.9%) died. There was no significant difference in vitamin D levels between those who survived and those who died (p>0.05). Conclusion: There was no significant relationship found between vitamin D levels and coronavirus disease-2019 infection and related deaths. Key Words: COVID-19, PCR positivity, SARS-CoV-2 infection, Vitamin D.

Background Vitamin D, along with parathyroid hormone (PTH) and calcitonin, is an important hormone that affects bone and calcium metabolism. Objectives To evaluate the vitamin D status and its seasonal variation in the adult population of Bursa. Methods Since there is not enough information about vitamin D levels, 25 OH vitamin D levels were analysed retrospectively from the records of 11,734 adult subjects (9142 women, 2592 men) admitted to 24 family health centres located in different districts of Bursa between 1 December 2017 and 30 November 2018. Some tests that can affect vitamin D levels, demographic features, and accompanying comorbidities were also evaluated. A face-to-face questionnaire was administered to subjects who were willing to answer (n = 2965). Results The mean serum vitamin D level was 16.6 ± 11.5 ng/mL in the entire population, 15.8 ± 11.7 ng/mL in women and 19.5 ± 9.9 ng/mL in men. The percentage of subjects with a vitamin D level

PURPOSEWe aimed to evaluate articles in radiology journals indexed in the Science Citation Index or Science Citation Index Expanded in terms of statistical errors. By this means, we aim to contribute to the production of high quality scientific publications.METHODSIn this study, a total of 157 articles published in 2016–2017 in 20 radiology journals were reviewed randomly. Selected articles were evaluated for statistical errors regarding P values and statistical tests, and for errors in terminology and other errors related to interpretation. In addition, in order to examine whether the error rates of the articles published in the radiology journals differed according to the impact factor, the statistical errors were compared according to the impact factors of the journals.RESULTSOf the 157 articles published in radiology journals, 10 had no statistical errors, while 147 had at least one statistical error. The most frequently encountered error was “errors in summarizing data” with a rate of 66%. This was followed by “incorrect representation of P values” with a rate of 42%. The least frequently encountered error was “statistical symbol errors” with a rate of 3%. There was no statistically significant difference according to impact factors.CONCLUSIONIn conclusion, radiology journals, as do journals in different fields, include articles containing statistical errors. Even when the quality of the journal increases, there is no difference in these statistical error rates. In order to prevent statistical errors in manuscripts, there are responsibilities for both the researchers who conduct scientific studies and the editors who publish these studies in their journals. Researchers should have a basic statistical knowledge, and the editor must submit all manuscripts for a statistical review.

Objective: Our aim is to perform an analysis, using big data, of cases diagnosed with primary hypothyroidism and aged 18 and over who presented to our hospital, by evaluating the laboratory and socio-demographic data of the patients. Clustering analysis was performed in the big dataset for the purpose of structure-search study on the subject. Methods: According to ICD-10 diagnoses of hypothyroidism between 2005-2018 in our hospital 130159 patients aged 18 and over with E03 and E06 diagnosis codes were included in the study. Since drugs containing levothyroxine used in primary hypothyroidism treatment have an effect on the measured hormone levels, in our study, TSH, fT3 and fT4 laboratory values in the first diagnosis of cases who had not received any treatment as part of the diagnosis according to demographics were analysed. Patients with one or more missing laboratory values were excluded, and data of 2680 patients with complete data and TSH values above 4.94 mU/L were retained. Analysis was made with the k-means clustering technique, with the data separated into two sets. k-means clustering was performed by including age, TSH, fT3 and fT4 variables. Cliff’s Delta effect size coefficients and confidence intervals were calculated to perform size of the difference. Results: The higher prevalence of primary hypothyroidism in female and the peak in hypothyroidism at 4-5 decades in both genders were observed. In which ages were low, fT3 and fT4 values were higher, whereas TSH values were lower in male. In which ages were low, TSH values were higher, whereas fT4 values were lower in female. Conclusion: This study is the first big data analysis study carried out about primary hypothyroidism in our country. Despite the difficulties in implementation, it should not be forgotten that studies like these are important methods for enabling data to be created in our country.

Aim: In this study, research was conducted to determine the meat consumption preferences of academician veterinarians by conjoint analysis. Materials and Methods: A total of 69 veterinarians, who are working in a veterinary faculty of a public university in Bursa Turkey, participated in this study. Survey method was used as a data collection tool in the research. In the survey conducted in March-April 2014, two levels were determined for each of the health, taste, and hygiene qualities, and orthogonal experimental design was used. With the help of orthogonal experimental design, seven plancards were created and presented to the veterinarians who participated in the survey form. Participants were asked to give sequence numbers to the plancards that were created for four types of meat (poultry meat, beef, mutton, and turkey meat). Importance values were calculated for all meat types studied. Results were evaluated separately according to gender and history of cardiovascular disease presence. Results: The results of the questionnaire applied to the participants showed that health is the most important attribute for all meat types whereas related to the variety of meat, hygiene and taste ranking varied according to gender and family history of cardiovascular disease presence. Conclusion: In this study, it is planned to present a comprehensive study for four meat types by examining how the health, taste and hygiene factors of the veterinarians who have high-level knowledge in terms of animal production and animal health evaluate the meat consumption preferences.

Aim of study: To assess the capabilities of some infrequently used probability density functions (PDFs) in modeling stand diameter distributions and compare their performance to that of typical PDFs. Area of study: The research was conducted in pure and mixed stands located in the OF Planning Unit of the Trabzon Forest Regional Directorate in Northern Türkiye. Material and methods: A set of 17,324 DBH measurements, originating from 608 sample plots located in stands of even-aged and pure and mixed stands, were obtained to represent various stand conditions such as site quality, age, and stand density in OF planning unit forests. In order to ensure a minimum of 30-40 trees in each sample plot, the plot sizes ranged from 0.04 to 0.08 hectares, depending on stand density. The parameters of PDFs include Weibull with 3P and 2P, Rice, Rayleigh, Normal, Nagakami, Lognormal with 2P and 3P, Lévy with 1p and 2P, Laplace, Kumaraswamy, Johnson’s SB, and Gamma were estimated using the maximum likelihood estimation (MLE) prediction procedure. Additionally, the goodness of fit test was combined with the Kolmogorov-Smirnov test (statistically at a 95% confidence interval). Main results: The Rayleigh distribution was the model that best explained the diameter distributions of pure and mixed forests in the OF Planning Unit (as Fit Index (FI) = 0.6743 and acceptance rate 96.4% based on the result of one sample Kolmogorov-Smirnov test). Research highlights: Less commonly used PDFs such as Rice, Nakagami, and Kumaraswamy-4P demonstrated superior predictive performance compared to some traditional distributions widely used in forestry, including Weibull-2P and -3P, Johnson’s SB, Normal, Gamma-3P, and Lognormal-3P.

Aim of study: This paper introduces comparative evaluations of artificial neural network models and regression modeling techniques based on some fitting statistics and desirable characteristics for predicting dominant height. Area of study: The data of this study were obtained from Oriental spruce (Picea orientalis L.) felled trees in even-aged and mixed Oriental spruce and Scotch pine (Pinus sylvestris L.) stands in the northeast of Türkiye. Material and methods: A total of 873 height-age pairs were obtained from Oriental spruce trees in a mixed forest stand. Nonlinear mixed-effects models (NLMEs), autoregressive models (ARM), dummy variable method (DVM), and artificial neural networks (ANNs) were compared to predict dominant height growth. Main results: The best predictive model was NLME with a single random parameter (root mean square error, RMSE: 0.68 m). The results showed that NLMEs outperformed ARM (RMSE: 1.09 m), DVM in conjunction with ARM (RMSE: 1.09 m), and ANNs (RMSE: from 1.11 to 2.40 m) in the majority of the cases. Whereas considering variations among observations by random parameter(s) significantly improved predictions of dominant height, considering correlated error terms by autoregressive correlation parameter(s) enhanced slightly the predictions. ANNs generally underperformed compared to NLMEs, ARM, and DVM with ARM. Research highlights: All regression techniques fulfilled the desirable characteristics such as sigmoidal pattern, polymorphism, multiple asymptotes, base-age invariance, and inflection point. However, ANNs could not replicate most of these features, excluding the sigmoidal pattern. Accordingly, ANNs seem insufficient to assure biological growth assumptions regarding dominant height growth.

Abstract Background Deep Learning Algorithms (DLA) have become prominent as an application of Artificial Intelligence (AI) Techniques since 2010. This paper introduces the DLA to predict the relationships between individual tree height (ITH) and the diameter at breast height (DBH). Methods A set of 2024 pairs of individual height and diameter at breast height measurements, originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) in Konya Forest Enterprise. The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures. The 80 different DLA models, which involve different the alternatives for the numbers of hidden layers and neuron, have been trained and compared to determine optimum and best predictive DLAs network structure. Results It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA, Artificial Neural Network, Nonlinear Regression and Nonlinear Mixed Effect models. The alternative of 100 # neurons and 9 # hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error (RMSE, 0.5575), percent of the root mean squared error (RMSE%, 4.9504%), Akaike information criterion (AIC, − 998.9540), Bayesian information criterion (BIC, 884.6591), fit index (FI, 0.9436), average absolute error (AAE, 0.4077), maximum absolute error (max. AE, 2.5106), Bias (0.0057) and percent Bias (Bias%, 0.0502%). In addition, these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset. Conclusion This study has emphasized the capability of the DLA models, novel artificial intelligence technique, for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models. Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey. Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do). Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset. Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique. Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives. Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method).

Abstract Background Leaf Area Index (LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network (ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands. Methods One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands. Results The correlation coefficients between LAI and stand parameters (stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters (R 2 adj. = 0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4–19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI (SSE (12.1040), MSE (0.1223), RMSE (0.3497), AIC (0.1040), BIC (− 77.7310) and R 2 (0.6392)) compared to the other studied techniques. Conclusion The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Most of the studies in medicine are releated with the examination of geometrical properties of an organ or organism. Commonly, quantitative or qualitative data sets in the statistical analysis consisted of me a suring values, today's an organ or organism's appearance or shape began to be used as input data by the development of imaging techniques. In recent years, it is observed that there has been considerable interest in the statistical analysis of shape. Statistical shape analysis is a geometrical analysis of the statistics me a sured from sets of shapes that determines the features of similar shapes or of different groups comprising similar shapes. Distance between shapes, mean shape and shape variation can be predicted and obtained using statistical shape analysis. Two groups comparison, asymmetry, growth and allometry studies can be given as example for the most used statistical shape analysis applications. Thin Plate Spline analysis, Procrustes analysis and Euclidean Distance Matrix Analysis can be given as examples of frequently used methods in statistical shape analysis. Examining shape differences between groups, shape deformation, examining asymmetry, classification, growth and allometry studies can be given as examples of statistical shape analysis applications in medicine. The last two decades, a growing interest in the use of statistical shape analysis has been observed in the field of medicine. The main reasons for statistical shape analysis is used widely in medicine are advances in imaging technology, and tendency to investigate the effects of diseases and the environmental factors effects on the structure of the organ or organism. [ABSTRACT FROM AUTHOR]

Diameter at breast height (DBH) is the simplest, most common and most important tree dimension in forest inventory and is closely correlated with wood volume, height and biomass. In this study, a number of linear and nonlinear models predicting diameter at breast height from stump diameter were developed and evaluated for Oriental beech (Fagus orientalisLipsky) stands located in the forest region of Ayancık, in the northeast of Turkey. A set of 1,501 pairs of diameter at breast height-stump measurements, originating from 70 sample plots of even-aged Oriental beech stands, were used in this study. About 80 % of the otal data (1,160 trees in 55 sample plots) was used to fit a number of linear and nonlinear model parameters; the remaining 341 trees in 15 sample plots were randomly reserved for model validation and calibration response. The power model data set was found to produce the most satisfactory fits with the Adjusted Coefficient of Determination, R2adj (0.990), Root Mean Square Error, RMSE (1.25), Akaike’s Information Criterion, AIC (3820.5), Schwarz’s Bayesian Information Criterion, BIC (3837.2), and Absolute Bias (1.25). The nonlinear mixed-effect modeling approach for power model with R2adj(0.993), AIC (3598), BIC (3610.1), Absolute Bias (0.73) and RMSE (1.04) provided much better fitting and precise predictions for DBH from stump diameter than the conventional nonlinear fixed effect model structures for this model. The calibration response including tree DBH and stump diameter measurements of the four largest trees in a calibrated sample plot in calibration produced the highest Bias, -5.31 %, and RMSE, -6.30 %, the greatest reduction percentage.

Modelos estadísticos no lineales de efectos mixtos se utilizaron para predecir las relaciones entre la altura total y el diámetro a la altura del pecho (DAP) en rodales de árboles de haya oriental (Fagus orientalis Lipsky) en Kestel, Bursa, al noroeste de Turquía. Un total de 124 parcelas de muestreo se seleccionaron para representar la calidad de sitio, edad y densidad de rodal. Nueve modelos no lineales generalizados de altura-diámetro se ajustaron y evaluaron con base en el criterio de información de Akaike, el criterio de información bayesiana de Schwarz, la raíz del cuadrado medio del error (RMSE por sus siglas en inglés), el sesgo absoluto y el coeficiente de determinación ajustado (R2adj). El modelo no lineal de Schnute se seleccionó como el mejor modelo predictivo. El modelo de altura-diámetro basado en el enfoque del modelo no lineal de efectos mixtos representó 90.6 % de la varianza total en las relaciones de altura-diámetro y los valores de RMSE de 1.48 m. Varios escenarios que difieren en el diseño de muestreo y el tamaño de los árboles submuestra, seleccionados del conjunto de datos de validación, revelaron que cuatro árboles submuestra seleccionados al azar produjeron los mejores resultados predictivos (reducción de 43.3 % de la suma de errores cuadrados, 98.4 % del sesgo absoluto y 36.9 % de la RMSE) en relación con las predicciones de los efectos fijos.

The aim of this study was to conduct a statistical shape analysis of the heart of dogs and to compare this data between German Shepherd and Mongrel dogs. An effective way to examine these shapes is to record the locations of certain points on the object. In this study, 10 hearts were collected from each breed. EDMA and TPS techniques were used to examine genus-based changes in the shape of the heart. The shape deformations were expressed using expansion and compression grids. There was no statistically significant difference with respect to the general shape of the heart between the genera. However, there were local shape differences between the genera in some of the inter-landmark distances: 6% of the inter-landmark distances were greater in German Shepherd dogs, and 11% were greater in Mongrels. There are no heart shape differences between genera, although significant differences were found between the upper part of the left ventricle and the lower part of the right ventricle. The upper part of the left ventricle in Mongrels showed more enlargement than in German Shepherds. The lower part of the right ventricle in Mongrels had more enlargement than in German Shepherds; however, the middle part of the right ventricle of German Shepherds had more enlargement than in Mongrels. Although there were some local significant shape differences between the upper part of the left ventricle and the lower part of the right ventricle, however, there were no general heart shape differences between German Shepherd and Mongrel dogs.