Individualized Growth Hormone Treatment - Using Two Different Prediction Models in a Clinical Setting

BackgroundDiagnosing growth hormone deficiency (GHD) can be challenging; hence, prediction models on growth outcome from growth hormone (GH) treatment have shown to be useful. We aim to compare the accuracy of the more readily available KIGS (Pfizer International Growth Study) prediction model to the previously clinically validated Gothenburg model.MethodsPrepubertal children with GHD who started GH treatment at Queen Silvia Children’s Hospital between 2004 and 2016 were considered for the study. Exclusion criteria were short stature due to syndrome, chronic disease, oncology disease, or known bad adherence. Growth predictions were made according to the Gothenburg model and the KIGS model. Growth data from birth until one year after start of GH treatment were collected from medical charts. Predicted height and observed height were then compared. ResultsA total of 123 children, 47 girls (38%) and 76 boys (62%) were included, with a mean age of 5.71 (±1.81 SD) years at start of GH treatment. The Pearson correlation of predicted first-year growth versus growth outcome were r = 0.990 for the Gothenburg model and r = 0.991 for the KIGS model. Studentized residuals were 0.10 ± 0.81 SD and 0.03 ± 0.96 SD, respectively, for the models. The comparison between the two models showed r = 0.995.ConclusionThe Gothenburg model and the KIGS model are equally accurate at predicting height outcome from GH treatment for our study cohort. We therefore promote the use of either model in clinical settings.

Evaluation of height as a disease risk factor through a phenome-wide association study of genetically-predicted height

Background: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. Methods and Findings: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n=222,300) and non-Hispanic Black (AA, n=58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy. As a number of traits associated with genetically-predicted height frequently co-occur with diabetes mellitus and/or CHD, we evaluated effect modification by diabetes and CHD status of genetically-predicted height associations with risk factors for and complications of diabetes and CHD. We found modification of effects of MR associations by diabetes for skin and bone infections and by CHD status for atrial fibrillation/flutter. Conclusions: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Honeycomb wet scrubber for acidic gas control: modeling and long-term test results

A laboratory scale, 1.0 CMM (m3 min− 1) wet scrubber packed with water-absorbing honeycomb material (HWS) with a very large geometric surface area of 480 m2 m− 3 and a low pressure drop developed in our previous study was shown to achieve a very high removal efficiency for acidic gases but there were no long-term test data. In this study, the HWS scaled up to operate at a 100 CMM flow rate was tested for removing mixed acidic gases at a semiconductor fab for a very long period of 3.5 yr. Results showed that the removal efficiency for the mixed gases emitted from the fab always maintained as high as > 95% for HF, CH3COOH, HCl, HNO3, HNO2, and H2SO4 with the inlet concentrations ranging from supper-ppmv to sub-ppmv, during a 3.5-yr period. With water jet cleaning of the honeycomb modules once per year, the pressure drop of the HWS remained to be low at 0.5–0.8 cm H2O, indicating minimal scaling in the HWS. Additionally, the predicted height and removal efficiencies of the HWS were very close to the experimental data. The excellent long-term performance of the HWS warrants its potential applications in many areas in which liquid absorption is the preferred treatment method and the theoretical equations can facilitate the design of the HWS.

Honeycomb Wet Scrubber For Acidic Gas Control: Modeling And Long-term Test Results

A laboratory scale, 1.0 CMM wet scrubber packed with water-absorbing honeycomb material (HWS) with a very large geometric surface area of 480 m2 m-3 and a low pressure drop developed in our previous study was shown to achieve a very high removal efficiency for gaseous acidic gases but there were no long-term test data. In this study, the HWS scaled up to operate at 100 CMM flow rate was tested for removing mixed acidic gases at a semiconductor fab for a very long period of 3.5 years. Results showed that the removal efficiency for the mixed gases emitted from the fab always maintained as high as > 95% for HF, CH3COOH, HCl, HNO3, HNO2, and H2SO4 with the inlet concentrations ranging from supper-ppmv to sub-ppmv, respectively, during 3.5-yr period. With water jet cleaning of the honeycomb modules once per year, the pressure drop of the HWS remained to be very low at 0.5-0.8 cmH2O, indicating minimal scaling in the HWS existed. Additionally, the predicted height and removal efficiencies of the HWS were very close to the experimental data. The excellent long-term performance of the HWS warrants its potential applications in many areas in which liquid absorption is the preferred treatment method and the theoretical equations can facilitate the design of the HWS.

Y-SHAPED CONVOLUTIONAL NEURAL NETWORK FOR 3D ROOF ELEMENTS EXTRACTION TO RECONSTRUCT BUILDING MODELS FROM A SINGLE AERIAL IMAGE

Fast and efficient detection and reconstruction of buildings have become essential in real-time applications such as navigation, 3D rendering, augmented reality, and 3D smart cities. In this study, a modern Deep Learning (DL)-based framework is proposed for automatic detection, localization, and height estimation of buildings, simultaneously, from a single aerial image. The proposed framework is based on a Y-shaped Convolutional Neural Network (Y-Net) which includes one encoder and two decoders. The input of the network is a single RGB image, while the outputs are predicted height information of buildings as well as the rooflines in three classes of eave, ridge, and hip lines. The extracted knowledge by the Y-Net (i.e. buildings’ heights and rooflines) is utilized for 3D reconstruction of buildings based on the third Level of Detail (LoD2). The main steps of the proposed approach are data preparation, CNNs training, and 3D reconstruction. For the experimental investigations airborne data from Potsdam are used, which were provided by ISPRS. For the predicted heights, the results show an average Root Mean Square Error (RMSE) and a Normalized Median Absolute Deviation (NMAD) of about 3.8 m and 1.3 m, respectively. Moreover, the overall accuracy of the extracted rooflines is about 86%.

Maturation, morphological, motor and technical characteristics of under 16 female track and field athletes

There is growing interest in identifying morphological, motor, maturation characteristics, as well as their changes, of children and adolescents in systematized training in various sports. Knowledge of these characteristics is important for coaches and researchers because they provide parameters for assessing youth development during training. For track and field, studies on the category under 16 are scarce. Thus, the objectives of this study are to describe the profile and to design a percentile table of morphological, motor, maturation and event-specific variables of under 16 athletes. 105 young athletes were evaluated on two consecutive days. On the first day, an anamnesis of athletes and coaches was performed. Anthropometric and anaerobic measurements were analyzed in shot put, long jump, 800 m run. On the second day, flexibility, vertical impulse, upper limb strength, speed and maximal aerobic speed were evaluated. The biological maturation was evaluated by the percentage of the predicted height. A table with percentiles was prepared with the data of all athletes. Another table with the results of the whole group plus mean and standard deviation was prepared. A last table was prepared containing data divided by group of sports events. The morphological variables presented differences between the groups as to body mass, BMI and sum of skinfolds. Significant differences were observed only for the motor variable VMA. No significant differences were observed only for long jump. When divided by groups, it was evident that throwers are different from the other athletes in some morphologic variables and in specific sports events.

Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study

ABSTRACTObjectiveTo determine whether height has a causal effect on risk of atrial fibrillationDesignMendelian randomization studySettingGenome-wide association studies of height and atrial fibrillation; Penn Medicine BiobankParticipantsMultiethnic (predominantly European ancestry) participants in genome-wide association studies of height (693,529 individuals) and atrial fibrillation (65,446 cases and 522,744 controls); 7,023 Penn Medicine Biobank participants of European ancestryExposuresHeight, cardiometabolic risk factors for atrial fibrillation, and randomly allocated genetic variants strongly associated with these traitsMain outcome measureRisk of atrial fibrillation (measured in odds ratio)ResultsAt the population level, a 1 standard deviation increase in genetically-predicted height was associated with increased odds of AF (Odds ratio [OR] 1.34; 95% Confidence Interval [CI] 1.29 to 1.40; p = 5×10−42). These findings remained consistent in sensitivity analyses that were robust to the presence of pleiotropic variants. Results from analyses considering individual-participant data were similar, even after adjustment for clinical covariates, including left atrial size.ConclusionGenetically predicted height is a positive causal risk factor for AF. This finding raises the possibility of investigating height/growth-related pathways as a means for gaining novel mechanistic insights to atrial fibrillation, as well as incorporating height into population screening strategies for atrial fibrillation.

Allometric Models for Estimating Site Index of Teak (Tectona grandis Linn F.) in Kanya Forest Plantation, Kebbi State, Nigeria

This study aims to develop site index for Teak (Tectona grandis) in Kanya Forest Plantation, Nigeria. Site index is defined as the total height of the dominant or co-dominant trees at an arbitrary index age, it is a method used for quantifying site quality for pure even-aged stands which is essential in growth and yield modelling. The data used in this study were obtained from six different age classes. Five sample plots each were selected across all age classes in which a total of 712 trees were measured, variables measured include total height, diameter at the base, middle, top, and diameter at the breast height were taken from 30 temporary sampled plots of 25x25m approximately from the centre, 180 dominant trees were selected from 712 trees. Basal area and volume of sampled trees were computed. Yield values obtained from the dominant trees are (B = 249.312 m3/ha, D = 196.128 m3/ha, F = 134.976 m3/ha, C = 119.328 m3/ha, E = 100.320 m3/ ha and A = 86.976 m3/ha). The results showed that B was the best and A was the poorest. Seventeen models were generated and paired sampled t-test was used for model validation, comparing the actual and predicted height. Two out of 17 were rejected (significant P<0.05). The first model Hd=12075.346-354.809(Age)+3.448(Age)2-135193.126(1/Age) is the recommended height estimation of Teak in Kanya Forest plantation for its best performance.