Intercept Term Research Articles

Psychometric Properties of the Service Leadership Attitude Scale in Hong Kong.

Positive attitude to service leadership is fundamental for the development of successful leadership qualities in the service-dominated economy. The purpose of this study was to examine the psychometric properties of the 46-item Service Leadership Attitude Scale (SLA) in Hong Kong. A total of 2,240 undergraduate students in Hong Kong (mean age = 20.44±1.64 years; 66.9% were female) completed a questionnaire containing the 46-item SLA and other leadership-related scales. The psychometric properties of SLA were assessed using confirmatory factor analyses (CFA), reliability analyses, measurement invariance tests, and convergent validity test. Previous exploratory factor analyses suggested a seven-factor model of SLA which was supported by CFA in this study. By adding an additional dimension (“Implicit leadership theory”) in the scale, the final eight-factor model with 46 items showed superior fit using CFA. Factorial invariance tests showed that the factor structure was invariant in terms of construct dimensionality, factor loadings, intercepts, and factor covariance across gender and subgroups split based on “odd” and “even” case numbers. Convergent validity tests showed that the scale scores were correlated with several leadership-related scales. The scale and subscales also demonstrated good internal consistency reliability. This study is the first scientific work to validate a measure of attitude to service leadership via CFA. It contributes significantly to the development and validation of measures of attitude to service leadership, particularly in the higher education sector.

Towards the characterization of the heat loss coefficient via on-board monitoring: Physical interpretation of ARX model coefficients

Abstract This paper explores the concept of characterizing the as-built Heat Loss Coefficient (HLC) of buildings based on-board monitoring (OBM), via energy consumption and temperature sensors, and time series analysis. It is examined (1) how the coefficients of different Auto-Regressive with eXogenous inputs (ARX) models can be interpreted and (2) whether these conclusions are sensitive to the building envelope assembly or the applied indoor temperature profile. The paper presents a theoretical case study whereby detailed building energy simulations are used to accurately map the impact of physical phenomena on the characterization process. The simulation models and boundary conditions are composed to focus on the link between the estimated ARX-coefficients and the physical driving forces for transmission heat loss to the ground and the exterior environment. The results show how the various ARX model coefficients are linked to specific components of the HLC (e.g. heat transfer through the walls and roof or through the slab-on-ground floor) and to what extent they are affected by the selection of input variables. By monitoring the ground temperature, the transmission heat losses can rather accurately be assigned to either the slab-on-ground or the walls and roof. Without this measurement data, the uncertainty on the estimates increases (ranges of the 95% confidence interval of up to 35% of the mean estimate). Modeling the ground heat losses by a constant intercept term leads to underestimations of the reference HLC of up to 59%, whereas adding heat flux sensors to monitor the transmission heat losses to the ground to the measurement set-up allows to assess the transmission heat transfer coefficient to the exterior environment HLCe within 2%.

Comparison of education, home exercise and supervised exercise in patients with knee and hip osteoarthritis: data from the BOA registry

Comparison of education, home exercise and supervised exercise in patients with knee and hip osteoarthritis: data from the BOA registry

Climate change and vine training systems: the influence different spatial distribution of shoots may have on sugar accumulation in Sangiovese grapevines.

The choice of training system may influence vineyard efficiency in terms of light interception and water consumption, particularly in the current context of climate change. On this basis, during the 2017 season, Sangiovese potted vines were grown outdoors using two different training systems: guyot vertical shoot positioned system (C) and V-shaped open canopy (A). From the end of June until September, vine transpiration was continuously monitored by the gravimetric approach and at different times in the season, the leaf area, light interception, photosynthetic activity and stem water potential were measured. Grape yield and fruit composition were recorded at harvest. C plants did not differ from A in terms of leaf area during the entire season. Light interception was higher in C vines during the early hours of the morning and lower in the central part of the day and the transpiration loss was higher, as was the net photosynthesis detected on some days in August. No differences were detected in terms of yield but a significant increase in soluble solids was found in C compared to A. The open canopy compared to a closed one, in a particularly hot year like 2017, resulted in negative consequences on the accumulation of soluble solids, probably as a result of the radiative stress suffered by the A vines in the middle hours of the day.

Genetic (co)variances between milk mineral concentration and chemical composition in lactating Holstein-Friesian dairy cows

Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 test-day records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31±0.05 (5 DIM) to 0.67±0.04 (181 DIM) for Ca, from 0.18±0.03 (60 DIM) to 0.24±0.05 (305 DIM) for K, from 0.08±0.03 (15 DIM) to 0.37±0.03 (223 DIM) for Mg, from 0.16±0.03 (30 DIM) to 0.37±0.04 (305 DIM) for Na and from 0.21±0.04 (12 DIM) to 0.57±0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38±0.21 (5 DIM) to 0.79±0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration.

The effect of a porcine reproductive and respiratory syndrome outbreak on genetic parameters and reaction norms for reproductive performance in pigs1.

The objective of this study was to estimate genetic parameters of antibody response and reproductive traits after exposure to porcine reproductive and respiratory syndrome virus. Blood samples were taken approximately 60 d after the outbreak. Antibody levels were quantified as the sample-to-positive ratio (S/P ratio) using a fluorescent microsphere assay. Reproductive traits included total number born (TNB), number born alive (NBA), number stillborn (NSB), number mummified (NBM), and number born dead (NBD). Mortality traits were log transformed for genetic analyses. Data were split into prior, during, and after the disease outbreak phases using visual appraisal of the estimates of farm-year-week effects for each reproductive trait. For NBA, data from all phases were combined into a reaction norm analysis with regression on estimates of farm-year-week effects for NBA. Heritability for S/P ratio was estimated at 0.17 ± 0.05. Heritability estimates for reproduction traits were all low and were lower during the outbreak for NBA but greater for mortality traits. TNB was not greatly affected during the outbreak, as many sows that farrowed during the outbreak were mated prior to the outbreak. Heritability for TNB decreased from 0.13 (prior) to 0.08 (after). Genetic correlation estimates between prior to and during the outbreak were high for TNB (0.86 ± 0.23) and NBA (0.98 ± 0.38) but lower for mortality traits: 0.65 ± 0.43, −0.42 ± 0.55, and 0.29 ± 1.39 for LNSB, LNBM, and LNBD, respectively. TNB prior to and after the outbreak had a lower genetic correlation (0.32 ± 0.33). In general, genetic correlation estimates of S/P ratio with reproductive performance during the outbreak were below 0.20 in absolute value, except for LNSB (−0.73 ± 0.29). Based on the reaction norm model, estimates of genetic correlations between the intercept and slope terms ranged from 0.24 ± 0.50 to 0.54 ± 0.35 depending on the parameterization used, indicating that selection for the intercept may result in indirect selection for steeper slopes, and thus, less resilient animals. In general, estimates of genetic correlations between farm-year-week effect classes based on the reaction norm model resembled estimates of genetic correlations from the multivariate analysis. Overall, compared to previous studies, antibody S/P ratios showed a lower heritability (0.17 ± 0.05) and low genetic correlations with reproductive performance during a porcine reproductive and respiratory syndrome outbreak, except for the LNSB.

449: Randomized-controlled trial of ibuprofen on post-partum blood pressure in women with hypertensive disorders of pregnancy

To assess the effect of ibuprofen compared to acetaminophen on post-partum blood pressure in women with gestational hypertension (gHTN) or preeclampsia without severe features (preE). Single center, randomized, double-blind crossover trial among women with gHTN or preE who delivered vaginally. Participants were assigned to encapsulated tablets of ibuprofen 600 mg or acetaminophen 650 mg every 6 hours for 24 hours followed by cross-over to the other agent. Group 1 received ibuprofen first and group 2 received acetaminophen first. Blood pressures were measured per clinical protocol with additional measures from an ambulatory blood pressure monitor (Spacelabs model 90207) that measured every 60 minutes while worn. Primary outcome was the difference in systolic blood pressure from baseline over 24 hours of drug exposure. The baseline was defined as the first 5 blood pressures on admission. A linear mixed model with Gaussian subject-specific intercept term was used to regress the blood pressures on time and the interaction of group and time. Analyses were performed according to intention-to-treat principles. We screened 185 women, of whom 74 enrolled. After further exclusion for severe features or cesarean delivery, 37 women were randomized and received at least 1 dose of study drug (n=19 in group 1, n=18 in group 2). Twenty- seven women crossed-over to study drug 2 (n=17 in group 1, n=10 in group 2). The difference in systolic blood pressure per 24-hour period compared to baseline was -0.62 mm Hg (95% CI -1.77, 0.54) among women in group 1 and -0.51 mm Hg (95% CI -1.85, 0.82) among women in group 2 with no difference in rate of change between groups (p=0.91). When limited to women who crossed-over, results were similar: -0.46 mmHg (95% CI -1.71, 0.79) in group 1 vs. -0.49 mmHg (95% CI -2.13, 1.15) in group 2. The difference in diastolic blood pressure compared to baseline was -1.49 mm Hg (95% CI -2.65, -0.33) in group 1 vs. -1.21 mmHg (95% CI -2.72, 0.30) in group 2 with no difference in rate of change between groups (p=0.76). Five women in each group had at least 1 severe range blood pressure (26.3 vs. 27.8%, p=1.0). Ibuprofen did not worsen postpartum blood pressure when compared with acetaminophen among women with gHTN or preE without severe features.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

A Multivariate Regression Reconstruction of the Quasi-Global Annual Precipitation on 1-Deg Grid From 1900 To 2015

This paper presents a multivariate linear regression reconstruction for the near-global annual precipitation anomalies with 1-deg latitude–longitude resolution from 1900 to 2015. The regression’s explanatory variables are the empirical orthogonal functions (EOFs), computed from the Global Precipitation Climatology Project (GPCP) dataset. The data for the regression’s dependent variable are from the station dataset of the Global Historical Climatology Network (GHCN). The data for the explanatory variables are the EOF data at the GHCN data locations. Compared to the earlier work of reconstruction at [Formula: see text] latitude–longitude resolution, our current reconstruction has two contributions. First, the spatial resolution is reduced to [Formula: see text] latitude–longitude. The finer resolution allows the data to be more useful in applications, such as historical drought assessment for a given region. Second, the multivariate regression is directly computed from linear regression models and hence includes the intercept term, which is not a coefficient of an EOF. The intercept enables a more realistic detection of the long-term trend of the spatial average. The trend of the global average annual precipitation from 1900 to 2015 is 0.133 (mm/day)/100a for the reconstruction with an intercept, and is 0.022 (mm/day)/100a without an intercept. The latter agrees with the trends of other models. The reconstruction error is assessed by a time-varying standard deviation.

English

This study examined and identified the determinants of modern contraceptive use among fertile women in Debark district. In order to meet the objective descriptive, multiple logistic regression and multilevel logistic regression statistical techniques were used for data analysis. The results of multiple logistic regressions showed that health center, place of residence, marital status, access to media, environmental condition, frequency of modern contraceptive use, type of modern contraceptive and HIV test are significant determinant factors of modern contraceptive use in Debark districts. The results of multilevel logistic regression analysis showed that the random intercept and the fixed coefficient model provided the best fit for the data under consideration. The variance of the random component related to the intercept term was found to be statistically significant, implying differences in the distribution of modern contraceptive use in the health center. It also found that place of residence, marital status, environmental conditions, access of media, frequency of modern contraceptive use, type of modern contraceptive and HIV test were significant determinant factors of distribution of modern contraceptive among health center. Key words: Determinants, modern contraceptive use, debark.

Bayesian Linear Size-and-Shape Regression with Applications to Face Data

Regression models for size-and-shape analysis are developed, where the model is specified in the Euclidean space of the landmark coordinates. Statistical models in this space (which is known as the top space or ambient space) are often easier for practitioners to understand than alternative models in the quotient space of size-and-shapes. We consider a Bayesian linear size-and-shape regression model in which the response variable is given by labelled configuration matrix, and the covariates represent quantities such as gender and age. It is important to parameterize the model so that it is identifiable, and we use the LQ decomposition in the intercept term in the model for this purpose. Gamma priors for the inverse variance of the error term, matrix Fisher priors for the random rotation matrix, and flat priors for the regression coefficients are used. Markov chain Monte Carlo algorithms are used for sampling from the posterior distribution, in particular by using combinations of Metropolis-Hastings updates and a Gibbs sampler. The proposed Bayesian methodology is illustrated with an application to forensic facial data in three dimensions, where we investigate the main changes in growth by describing relative movements of landmarks for each gender over time.

Individual participant data meta‐analysis of continuous outcomes: A comparison of approaches for specifying and estimating one‐stage models

One‐stage individual participant data meta‐analysis models should account for within‐trial clustering, but it is currently debated how to do this. For continuous outcomes modeled using a linear regression framework, two competing approaches are a stratified intercept or a random intercept. The stratified approach involves estimating a separate intercept term for each trial, whereas the random intercept approach assumes that trial intercepts are drawn from a normal distribution. Here, through an extensive simulation study for continuous outcomes, we evaluate the impact of using the stratified and random intercept approaches on statistical properties of the summary treatment effect estimate. Further aims are to compare (i) competing estimation options for the one‐stage models, including maximum likelihood and restricted maximum likelihood, and (ii) competing options for deriving confidence intervals (CI) for the summary treatment effect, including the standard normal‐based 95% CI, and more conservative approaches of Kenward‐Roger and Satterthwaite, which inflate CIs to account for uncertainty in variance estimates. The findings reveal that, for an individual participant data meta‐analysis of randomized trials with a 1:1 treatment:control allocation ratio and heterogeneity in the treatment effect, (i) bias and coverage of the summary treatment effect estimate are very similar when using stratified or random intercept models with restricted maximum likelihood, and thus either approach could be taken in practice, (ii) CIs are generally best derived using either a Kenward‐Roger or Satterthwaite correction, although occasionally overly conservative, and (iii) if maximum likelihood is required, a random intercept performs better than a stratified intercept model. An illustrative example is provided.

Research on Landmarks of SLAM Based on Square Root Cubature Kalman Filter

Under the large noise, the system's observability is weak, which leads to the instability of the filtering algorithm and the slow convergence speed. The algorithm of simultaneous localization and mapping based on the square-root Cubature Kalman filter (SRCKF) with Spherical Simplex(SS) was proposed in this paper. It not only abandoned the intercept term of traditional filtering algorithm, but also avoided the large amount of computation in sigma point algorithm. The new algorithm was used in the navigation, and the simulation experiment results and data showed that the number of sampling points and the amount of calculation were reduced because of the hypersphere distribution sampling. At the same time, the square root decomposition guaranteed the non-negative of the matrix, and the algorithm had s a good stability and a certain navigation precision.

Abstract 2201: Prostate-specific antigen velocity is associated with future prostate cancer mortality among men in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

Abstract Higher prostate-specific antigen (PSA) velocity (change in PSA over time) measured after diagnosis or treatment is associated with poorer prognosis for prostate cancer patients. However, less is known about the association between PSA velocity measured prior to a diagnosis of cancer and future prostate cancer mortality. We evaluated whether PSA velocity is associated with the risk of prostate cancer mortality among men without a history of prostate cancer in the screening arm of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. As part of the PLCO trial, men in the screening arm were offered PSA screening at baseline in the trial and annually thereafter for five years. From the screening arm, we included 33,961 men who had at least two prediagnostic trial PSA measurements (to allow for PSA velocity calculation), among whom 167 later died of prostate cancer. We estimated PSA velocity and initial PSA concentration for each participant from the random slope and random intercept terms in a mixed effects model based on up to the first three trial PSA measures per person since participants varied in the number of available measures. We defined our study baseline for survival analysis as the time of the last PSA measurement used to calculate PSA velocity. Participants were 55 to 79 years of age at our study baseline, and we followed them for up to 18 years (through Dec. 31, 2012) for prostate cancer mortality, with a median follow-up time of 12.7 years (interquartile range: 10.7-14.4 years). We dichotomized PSA velocity as <0.4 vs. ≥0.4 ng/mL/year for comparison with previous studies. We analyzed the association between PSA velocity and prostate cancer mortality using Cox proportional hazards regression, adjusting for initial PSA concentration, age, race, body mass index, our study baseline year, and past finasteride use. Higher PSA velocity was associated with an increased hazard of prostate cancer mortality compared to lower PSA velocity [Hazard Ratio (HR)=2.5, 95% Confidence Interval (CI): 1.7-3.7]. Additional adjustment for family history of prostate cancer or comorbidities did not appreciably alter findings. When we stratified by age at our study baseline, we observed a stronger association among men who were less than 70 years of age (HR=3.1, 95% CI: 1.9-4.9) than among those who were 70 years of age or older (p-interaction=0.04). In conclusion, in this large prospective study, higher prediagnostic PSA velocity was associated with an increased risk of prostate cancer mortality. Our findings suggest a potential benefit of using longitudinal PSA measures in predicting future prostate cancer mortality. Citation Format: Erik Barr, Sonja I. Berndt, John D. Sorkin, Wen-Yi Huang, Kathryn Hughes Barry. Prostate-specific antigen velocity is associated with future prostate cancer mortality among men in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2201.

Inherent variation of functional traits in winter and summer leaves of Mediterranean seasonal dimorphic species: evidence of a 'within leaf cohort' spectrum.

The covariation pattern among leaf functional traits involved in resource acquisition has been successfully provided by the leaf economic spectrum (LES). Nevertheless, some aspects such as how the leaf trait variation sources affect LES predictions are still little investigated. Accordingly, the aim of this paper was to test whether leaf trait variations within different leaf cohorts could alter LES. Improving this knowledge can extend the potential of trait-based approaches in simulating future climate effects on ecosystems. A database on leaf morphological and physiological traits from different leaf cohorts of Cistus spp. was built by collecting data from literature. These species are seasonal dimorphic shrubs with two well-defined leaf cohorts during a year: summer leaves (SL) and winter leaves (WL). Traits included: leaf mass area (LMA), leaf thickness (LT), leaf tissue density (LTD), net photosynthetic rate on area (Aa) and mass (Am) base, nitrogen content on area (Na) and mass (Nm) base. The obtained patterns were analysed by standardized major axis regression and then compared with the global spectrum of evergreens and deciduous species. Climatic variable effect on leaf traits was also tested. Winter leaves and SL showed a great inherent variability for all the considered traits. Nevertheless, some relationships differed in terms of slopes or intercepts between SL and WL and between leaf cohorts and the global spectrum of evergreens and deciduous. Moreover, climatic variables differently affected leaf traits in SL and WL. The results show the existence of a ‘within leaf cohort’ spectrum, providing the first evidence on the role of leaf cohorts as LES source of variation. In fact, WL showed a high return strategy as they tended to maximize, in a short time, resource acquisition with a lower dry mass investment, while SL were characterized by a low return strategy.

Abstract P251: Relationship Between Body Size of Social Network Members and Body Size Norms of South Asian Adults

Objective: Perceptions of a healthy body weight may be influenced by social norms within a social network. This study tested the hypothesis that social network body size is associated with body size norms in South Asian adults from the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study. Methods: South Asian adults (n=700) were asked to list their personal social network members, defined as the “people who you regularly talk with about things that are important to you,” at the social network ancillary study examination (2014-2017). Participants’ perceptions of their network members’ body sizes, their own body size (self-body size), and a healthy body size (body size norm) for men and women were assessed using the Stunkard 9-figure scale. The Stunkard scale is a silhouette figure rating scale consisting of 9 male and 9 female figures of increasing body size (range 1-9). Participants’ height and weight were measured, and body mass index (BMI) was calculated. The average body size of network members was the main independent variable. Body size norm was the dependent variable in a regression model that controlled for age, sex, gender of Stunkard figure, cultural identity, self-body size, participants’ measured BMI, and social network size. A random intercept term was included at the participant level to account for clustering of male and female body size norms within participants. Results: Participants’ average age was 59 years (SD+/-9 years) and 43% were female. The average body size norm for male and female Stunkard images was 3.6 (SD+/-1.0) and 3.4 (SD+/-0.8), respectively. Participants’ average self-body size was 4.7 (SD+/-1.5). There were 2,991 social network members identified (mean number of network members=4, SD=1), and the majority (70%) were kin. The average network body size was 3.9 (SD +/-1.1). For every unit increase in their networks’ average body size, participants’ perceptions of body size norms increased 0.31 points on the Stunkard scale (p<0.01; 95% CI: 0.25, 0.36), independent of self-body size and BMI. Perception of a healthy body size norm was 0.25 points lower for female Stunkard figures than for male figures (p<.01; 95% CI: -0.30, -0.20). Conclusions: Having social network members with larger body sizes was associated with a higher body size norm in South Asian adults. Norms for female body images were smaller than for male images. Long-term follow-up of the MASALA cohort will determine if network members’ body size and body size norms are associated with weight change and weight-control behaviors in South Asians.

Degrees‐of‐freedom, errors, and replicates

An important concept required for understanding formal experimental design is that of degrees-of-freedom (DoFs). Degrees-of-freedom are used in different contexts throughout science, for example, we have encountered these when discussing statistical distributions such as the F statistic1 and they have an important role in statistical mechanics. In this article, we will focus on their role in statistical design. A first job in analysing a designed experiment is to determine the number of degrees-of-freedom attached to different sources of error. We can see that for model B, all residual errors appear to be equal to 0. This is because we have not performed enough experiments to see whether the data is truly linear. There are two experiments and two terms in the model. For model A, we can calculate residual errors. The interested reader will note that these errors do not sum to 0: This will only happen if the intercept term b0 is included in a model or the data are centred. In most chemometric models, we do include the intercept and so assume that the sum of residual errors is always 0. Models do not need to be restricted to linear terms, for example, a series of 3 observations can be used to obtain a linear model with no intercept with D = 2 degrees-of-freedom for lack-of-fit, a linear model with an intercept with D = 1, or a model including intercept, linear, and quadratic terms with D = 0. If we really wanted to see whether there was curvature in a univariate relationship, it would be advisable to perform at least 6 or 7 unique experiments, so that there are 3 or 4 degrees-of-freedom for the lack-of-fit error. There are three factors. A common model as discussed previously2 is Sometimes, the degrees-of-freedom can be represented by a “degrees-of-freedom tree.” In our case, it is represented in Figure 2 and is a good way of summarising a design. Sometimes, degrees-of-freedom trees can be more elaborate, for example, if errors are viewed as coming from different sources. The lack-of-fit error tells us only if the overall model is a sensible one, and we may, for example, be interested in whether a specific term or group of terms in the model is significant. However, as a first step for visualising most common designs encountered in chemometrics, the analysis in this article is a common and valuable first step. The residual errors between the estimated and observed values of the response in the design of Table 2 therefore have 10 degrees-of-freedom. Similar lack-of-fit and replicate errors can be calculated, as described in subsequent articles. If there are no replicates, the lack-of-fit error is the same as the residual error. Every type of error has its own degrees-of-freedom associated with it. Sometimes, the mean squared errors are also called variances as discussed in later articles. Note that if we changed the model, for example, by removing the three quadratic terms, we will increase the number of degrees-of-freedom for the lack-of-fit and reduce the number of degrees-of-freedom for regression. If very little is known about whether a model is suitable for a particular set of experiments, a good rule of thumb is to make the number of degrees-of-freedom for the lack-of-fit and replicate errors approximately equal for the desired model. If, however, we are pretty certain that a response behaves in a certain manner, eg, we may be sure that an experiment results in a linear response, we might reduce or even remove replication, and focus on unique experiments to obtain as accurate a numerical relationship as possible. For further reading, Stan Deming and Steve Morgan wrote several articles and books3 about degrees-of-freedom trees in the 1970s to 1990s.

Assessment of Anti-Mullerian Hormone (AMH) levels in a pilot cohort of peripubertal females: Correlation with sex maturity rating (SMR)

Abstract Objective Anti-Mullerian Hormone (AMH) is a quantitative marker for ovarian reserve found to be independent of the menstrual cycle. The objective of this study was to determine whether serum AMH levels correlate with sex maturity rating (SMR) in a group of peripubertal elementary school girls. Study design This cohort study was conducted in a tertiary care facility. Baseline blood sampling was obtained in 71 peripubertal healthy elementary school girls from Upper Egypt. AMH was measured using commercially available kit (DSL Inc., Webster, TX, USA). SMR was determined clinically through observations of pubic hair and breast development rated from 1 (preadolescent) to 5 (mature adult female). A bivariate analysis was conducted between age, weight, SMR, and the AMH level. Data was stratified for further analysis by SMR, and SMR 3 and 4 were combined to ensure suitable sample size. Statistical analysis was done using R software version 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria). Results Seventy-one observations were analyzed with a mean age of 9.31 ± 1.80 years. Mean weight was 28.87 ± 7.45 kg, and the mean AMH level was 2.05 ± 0.52 ng/mL. SMR observations included 15 with SMR of 1 (21%), 26 with SMR of 2 (37%), 26 with SMR of 3 (37%), and 4 with SMR of 4 (6%). There were no significant associations between age, weight, SMR and AMH level. Upon stratifying the analysis by SMR, a significant correlation was found between age and AMH within the SMR of 3 and 4 grouping (rho = −0.4194, p = 0.02106). When considering all variables for regression it was found that the model that best fit the data was comprised of only an intercept term. Age, weight, and SMR were not included in the final regression model. Conclusion The correlation of AMH with an advanced stage of maturation (SMR 3&4) may reflect modest late pubertal AMH peaks in this population of females. The proportionate association of AMH with breast development, reflective of the gonadotropins and steroidogenic functions, supports its role as a marker of ovarian function in young females.

English

Ethiopia is among the African countries with high maternal and child mortality rates, with a fast-growing population. To prevent the fast growing population; availability, the stability of contraceptive method and identifying factors that affects the practice of modern contraceptives should be given a priority. Thus, this study aims to identify the practice of contraceptives focusing on women’s empowerment and other gender-related factors among married women. Primary data were collected from 984 married women out of which 561 (57.01) were users of contraceptives in Amhara region. The collected data were analyzed using descriptive statistics and econometric models such as multiple and multilevel logistic regressions. The results showed that; zone, the educational status of the couple, the age of women, discussing family planning issues with a partner, decisions about how each partner’s income would be used, the income of women, wife beating and sexual response were significant factors in family planning. The multilevel analysis found that the variance of the random component related to the intercept term was found to be statistically significant, implying differences in the prevalence of family planning among the zone. It also found that the educational status of the couple, the age of the women, discussing family planning issues with a partner, decisions making in income earnings, the income of women, wife beating and sexual response were significant determinants of varying proportions of family planning.   Key words: Family planning, multilevel logistic regression model, women empowerment. &nbsp

Mendelian randomization with Egger pleiotropy correction and weakly informative Bayesian priors.

BackgroundThe MR-Egger (MRE) estimator has been proposed to correct for directional pleiotropic effects of genetic instruments in an instrumental variable (IV) analysis. The power of this method is considerably lower than that of conventional estimators, limiting its applicability. Here we propose a novel Bayesian implementation of the MR-Egger estimator (BMRE) and explore the utility of applying weakly informative priors on the intercept term (the pleiotropy estimate) to increase power of the IV (slope) estimate.MethodsThis was a simulation study to compare the performance of different IV estimators. Scenarios differed in the presence of a causal effect, the presence of pleiotropy, the proportion of pleiotropic instruments and degree of ‘Instrument Strength Independent of Direct Effect’ (InSIDE) assumption violation. Based on empirical plasma urate data, we present an approach to elucidate a prior distribution for the amount of pleiotropy.ResultsA weakly informative prior on the intercept term increased power of the slope estimate while maintaining type 1 error rates close to the nominal value of 0.05. Under the InSIDE assumption, performance was unaffected by the presence or absence of pleiotropy. Violation of the InSIDE assumption biased all estimators, affecting the BMRE more than the MRE method.ConclusionsDepending on the prior distribution, the BMRE estimator has more power at the cost of an increased susceptibility to InSIDE assumption violations. As such the BMRE method is a compromise between the MRE and conventional IV estimators, and may be an especially useful approach to account for observed pleiotropy.

3D plant model assessed by terrestrial LiDAR and hemispherical photographs: A useful tool for comparing light interception among oil palm progenies

The paradigm of functional-structural models (FSPM) assumes that studying the detailed organisation of plant structure allows a better understanding of functional processes; in particular the way plants capture light for performing photosynthesis. However, much attention must be paid toward the consistency between virtual plants and plants in the field in terms of size and geometry to accurately evaluate light interception. This paper thus aimed at i) assessing the capacity of a 3D architectural model based on oil palms (Elaeis guineensis) to accurately represent plants structural characteristics at both the scale of the individual plant and the cultivated plot and ii) employing the validated 3D mock-ups to investigate how light interception efficiency varies among progenies that exhibit different architectures. Innovative indicators related to plant geometry and topology were derived from terrestrial LiDAR scanners (TLS) and hemispherical photographs (HP) in order to assess a 3D plant model. Indicators such as plant height, width and volume, gap fractions and solid angle projections were established from field measurements and were compared to equivalent indicators that had been extracted from virtual TLS (VTLS) and virtual HP (VHP) simulated on 3D mock-ups. Indicators were then evaluated for their significance in terms of light interception. Progeny effect on light interception efficiency was finally evaluated for five progenies. The structural indicators estimated from VTLS and VHP were significantly correlated with equivalent indicators estimated from TLS and HP, respectively, and with simulated outputs related to light interception. Light interception efficiencies estimated from validated 3D mock-ups differed significantly among the five progenies under study, most notably along plant development. Our results highlight the relevance of combining TLS- and HP-derived indicators to evaluate the reliability of virtual 3D reconstruction of plants in relation to light capture, at both the plant and plot scales. The study paves the way for further investigations aiming at unravelling the relationships between oil palm architecture and the physiological processes driving its production.(Resume d'auteur)