Influence of Sex and Maximum Strength on Reactive Strength Index-Modified.

Reactive strength index-modified (RSImod) is a measure of lower body explosiveness calculated by dividing jump height by time to takeoff. RSImod is different between stronger and weaker athletes and between males and females. The purpose of this study was to evaluate differences in RSImod between males and females while controlling for maximal strength and lower body explosiveness. Forty-three female and fifty-eight male Division-I athletes performed countermovement jumps on a force plate during unloaded (0kg) and loaded (20kg) conditions. We used an ANCOVA to test whether RSImod is different between sexes conditioning on relative maximum strength (PFa) and average RFD 0-200ms (RFD200) measured during the isometric mid- thigh pull (IMTP). Differences of 0.087 (95% CI: 0.040-0.134; p = 0.0005) and 0.075 (95% CI: 0.040-0.109, p < 0.0001) were observed for RSImod between sexes in unloaded and loaded conditions, respectively. A male with PFa of 186 (grand mean of the sample) and RFD200 of 6602 N/s (grand mean of the sample) is predicted to have 28% greater RSImod than a female of similar PFa and RFD200. Maximum strength development should be a primary aim of training in female athletes, in addition to other trainable factors, such as stiffness and RFD.

Relationship Between Maximum Pull-up Repetitions and First Repetition Mean Concentric Velocity.

Beckham, GK, Olmeda, JJ, Flores, AJ, Echeverry, JA, Campos, AF, and Kim, SB. Relationship between maximum pull-up repetitions and first repetition mean concentric velocity. J Strength Cond Res 32(7): 1831-1837, 2018-Mean concentric velocity (MCV) of exercise execution has been used by strength and conditioning professionals to improve exercise technique, provide accurate feedback, and predict exercise 1 repetition maximum. There is still limited research on velocity-based training and currently only one research study on the pull-up exercise. The primary purpose of this research was to determine whether the maximum number of pull-ups an individual can perform can be predicted by the MCV of a single pull-up repetition. Forty-nine healthy men and women were recruited who reported they could do at least 2 pull-ups. Each subject performed a standardized warm-up, then a single pull-up repetition, followed by one set of pull-up repetitions to failure. The GymAware PowerTool, a linear position transducer, was used to measure the MCV of each pull-up repetition. Both the MCV of the single repetition and first repetition of the set to failure were recorded, and the greater of the 2 was used in later analysis. Weighted least squares linear regression was used to estimate the relationship between the single-repetition MCV and maximum amount of pull-up repetitions. We observed a statistically significant linear relationship between the maximum number of pull-ups and the MCV of a single pull-up repetition (y = -6.661 + 25.556x, R = 0.841). Prediction of the maximum pull-up number by a single repetition rather than testing the maximal pull-up number may improve efficiency and effectiveness of exercise testing batteries for military, police, and other populations.

Inference for the existence of hormetic dose-response relationships in toxicology studies.

In toxicology studies hormesis refers to a dose-response relationship with a stimulatory response at low doses and an inhibitory response at high doses. In this manuscript, we particularly focus on a J-shaped dose-response relationship for binary cancer responses. We propose and examine two new flexible models for testing the hypothesis of hormesis in a Bayesian framework. The first model is parametric and enhances the flexibility of modeling a hormetic zone by using a non-linear predictor in a multistage model. The second model is non-parametric and allows multiple model specifications, weighting the contribution of each model via Bayesian model averaging (BMA). Simulation studies show that the non-parametric modeling approach with BMA provides robust sensitivity and specificity for detecting hormesis relative to the parametric approach, regardless of the shape of a hormetic zone.

Estimation of a benchmark dose in the presence or absence of hormesis using posterior averaging.

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose-response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose-response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8-tetrachlorodibenzo-p-dioxin.

A diversity index for model space selection in the estimation of benchmark and infectious doses via model averaging.

In chemical and microbial risk assessments, risk assessors fit dose-response models to high-dose data and extrapolate downward to risk levels in the range of 1-10%. Although multiple dose-response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose-response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.

Model uncertainty and model averaging in the estimation of infectious doses for microbial pathogens.

Food-borne infection is caused by intake of foods or beverages contaminated with microbial pathogens. Dose-response modeling is used to estimate exposure levels of pathogens associated with specific risks of infection or illness. When a single dose-response model is used and confidence limits on infectious doses are calculated, only data uncertainty is captured. We propose a method to estimate the lower confidence limit on an infectious dose by including model uncertainty and separating it from data uncertainty. The infectious dose is estimated by a weighted average of effective dose estimates from a set of dose-response models via a Kullback information criterion. The confidence interval for the infectious dose is constructed by the delta method, where data uncertainty is addressed by a bootstrap method. To evaluate the actual coverage probabilities of the lower confidence limit, a Monte Carlo simulation study is conducted under sublinear, linear, and superlinear dose-response shapes that can be commonly found in real data sets. Our model-averaging method achieves coverage close to nominal in almost all cases, thus providing a useful and efficient tool for accurate calculation of lower confidence limits on infectious doses.

Prediction of strawberry yield based on receptacle detection and Bayesian inference.

The receptacle of strawberry is a more direct part than the flower for predicting yield as they eventually become fruits. Thus, we tried to predict the yield by combining an AI technique for receptacle detection in images and statistical analysis on the relationship between the number of receptacles detected and the strawberry yield over a period of time. Five major cultivars were cultivated to consider the cultivar characteristics and environmental factors for two years were collected to consider the climate difference. Faster R-CNN based object detector was used to estimate the number of receptacles per strawberry plant in given two-dimensional images, which achieved a mAP of 0.6587 for our dataset. However, not all receptacles appear on the two-dimensional images, and Bayesian analysis was used to model the uncertainty associated with the number of receptacles missed by the AI. After estimating the probability of fruiting per receptacle, prediction models for the total strawberry yield at the end of harvest season were evaluated. Even though the detection accuracy was not perfect, the results indicated that counting the receptacles by object detection and estimating the probability of fruiting per receptacle by Bayesian modeling are more useful for predicting the total yield per plant than knowing its cumulative yield during the first month.

A potential relationship between soil disinfestation efficacy and leaf green reflectance.

Soil disinfestation with steam was evaluated as an alternative to fumigation. Following soil disinfestation, plant health has traditionally been measured using plant size and yield. Plant health can be measured in a timely manner more efficiently, more easily and non-destructively using image analysis. We hypothesized that plant health could be quantified and treatments can be differentiated using an RGB (Red, Green, Blue) image analysis program, particularly by observing the greenness of plant leaves. However, plant size or the proportion of green area could be unreliable due to plant loss and camera's position and angle. To this end, we decided to evaluate plant health by analyzing the RGB codes associated with the green color only, which detects the chlorophyll reflectance and nutrient status, noting that the degree of greenness within the green-leaf-area was not affected by the plant size. We identified five RGB codes that are commonly observed in the plant leaves and ordered them from dark green to light green. Among the five RGB codes, the relative percentage covered by the darkest green to the lightest green was significantly different between the steam and chloropicrin treatments and the control, and it was not significantly different between the steam and chloropicrin treatments. Furthermore, the result was correlated with the total yield, and the trend observed in the first year was replicated in the second year of this experiment. In this study, we demonstrate that the RGB image analysis can be used as an early marker of the treatment effect on the plant health and productivity.

An image segmentation technique with statistical strategies for pesticide efficacy assessment.

Image analysis is a useful technique to evaluate the efficacy of a treatment for weed control. In this study, we address two practical challenges in the image analysis. First, it is challenging to accurately quantify the efficacy of a treatment when an entire experimental unit is not affected by the treatment. Second, RGB codes, which can be used to identify weed growth in the image analysis, may not be stable due to various surrounding factors, human errors, and unknown reasons. To address the former challenge, the technique of image segmentation is considered. To address the latter challenge, the proportion of weed area is adjusted under a beta regression model. The beta regression is a useful statistical method when the outcome variable (proportion) ranges between zero and one. In this study, we attempt to accurately evaluate the efficacy of a 35% hydrogen peroxide (HP). The image segmentation was applied to separate two zones, where the HP was directly applied (gray zone) and its surroundings (nongray zone). The weed growth was monitored for five days after the treatment, and the beta regression was implemented to compare the weed growth between the gray zone and the control group and between the nongray zone and the control group. The estimated treatment effect was substantially different after the implementation of image segmentation and the adjustment of green area.

Applications of statistical experimental designs to improve statistical inference in weed management.

In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently.

A Tutorial of Bland Altman Analysis in A Bayesian Framework.

There are two schools of thought in statistical analysis, frequentist, and Bayesian. Though the two approaches produce similar estimations and predictions in large-sample studies, their interpretations are different. Bland Altman analysis is a statistical method that is widely used for comparing two methods of measurement. It was originally proposed under a frequentist framework, and it has not been used under a Bayesian framework despite the growing popularity of Bayesian analysis. It seems that the mathematical and computational complexity narrows access to Bayesian Bland Altman analysis. In this article, we provide a tutorial of Bayesian Bland Altman analysis. One approach we suggest is to address the objective of Bland Altman analysis via the posterior predictive distribution. We can estimate the probability of an acceptable degree of disagreement (fixed a priori) for the difference between two future measurements. To ease mathematical and computational complexity, an interface applet is provided with a guideline.

Incorporating statistical strategy into image analysis to estimate effects of steam and allyl isocyanate on weed control.

Weeds are the major limitation to efficient crop production, and effective weed management is necessary to prevent yield losses due to crop-weed competition. Assessments of the relative efficacy of weed control treatments by traditional counting methods is labor intensive and expensive. More efficient methods are needed for weed control assessments. There is extensive literature on advanced techniques of image analysis for weed recognition, identification, classification, and leaf area, but there is limited information on statistical methods for hypothesis testing when data are obtained by image analysis (RGB decimal code). A traditional multiple comparison test, such as the Dunnett-Tukey-Kramer (DTK) test, is not an optimal statistical strategy for the image analysis because it does not fully utilize information contained in RGB decimal code. In this article, a bootstrap method and a Poisson model are considered to incorporate RGB decimal codes and pixels for comparing multiple treatments on weed control. These statistical methods can also estimate interpretable parameters such as the relative proportion of weed coverage and weed densities. The simulation studies showed that the bootstrap method and the Poisson model are more powerful than the DTK test for a fixed significance level. Using these statistical methods, three soil disinfestation treatments, steam, allyl-isothiocyanate (AITC), and control, were compared. Steam was found to be significantly more effective than AITC, a difference which could not be detected by the DTK test. Our study demonstrates that an appropriate statistical method can leverage statistical power even with a simple RGB index.

Reliability and Criterion Validity of the Assess2Perform Bar Sensei.

The Assess2Perform Bar Sensei is a device used to measure barbell velocity for velocity-based training that has not yet been validated. The purpose of this study was to determine criterion validity and reliability of the Assess2Perform Bar Sensei in barbell back squats by comparing it against the GymAware PowerTool, a previously validated instrument. Sixteen injury-free, resistance-trained subjects (eleven males and five females) were recruited. Subjects were tested for their back squat one repetition maximum (1RM). Then, on two separate days, subjects performed two sets of three repetitions at loads of 45%, 60% and 75% 1RM. The GymAware PowerTool and Bar Sensei were attached to the barbell in similar locations for concurrent collection of mean concentric velocity (MCV) and peak concentric velocity (PCV). The Bar Sensei and PowerTool showed generally fair to poor agreement for MCV and PCV when subjects lifted 45% of 1RM (intraclass correlation;ICC 0.4-0.59), and they showed poor agreement when subjects lifted 60% and 75% of 1RM (ICC 0.3-0.4). Inter-repetition/within-set reliability for the Bar Sensei ranged between ICC = 0.273-0.451 for MCV and PCV compared to the far more reliable PowerTool (ICC = 0.651-0.793). Currently, the Bar Sensei is not a reliable or valid tool for measuring barbell velocity in back squats.

Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses.

For many dose-response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified. In dose-response analyses, to account for model uncertainty and to reduce the impact of model misspecification, averaging multiple models have been widely discussed in the literature. In this article, we propose to average semiparametric models when we test for hormesis at low doses. We show the different characteristics of averaging parametric models and averaging semiparametric models by simulation. We apply the proposed method to real data, and we show that P values from averaged semiparametric models are more credible than P values from averaged parametric methods. When the true dose-response relationship does not follow a parametric assumption, the proposed method can be an alternative robust approach.