The Bioenergetics of Mammalian Sperm Motility
Claude Gagnon in Controls of Sperm Motility, 2020
The immediate source of energy for motility is the hydrolysis of ATP catalyzed by dynein ATPase which drives the sliding of adjacent microtubules.3 ATP is regenerated by the breakdown of sugars (principally glucose or fructose) to lactate by the glycolytic pathway and the mitochondrial oxidation of substrates through the citric acid cycle (Figure 1).3-5 ATP in the sperm cell turns over rapidly e.g., in boar sperm, the concentration of ATP is about 20 nmol/108 spermatozoa and about half of this is metabolized each minute because the rate of the mitochondrial respiration of endogenous substrates is equivalent to 8 nmol ATP/min/108 sperm.6 The close integration of energy metabolism with energy consumption is therefore essential and one way this might be achieved is by a negative feedback of ATP and related factors to inhibit ATP production countered by a positive feedback of ADP or related factors to stimulate it. We shall examine how far this simple hypothesis can account for published experimental observations. Other effectors (E) can act on the flagellar apparatus or directly on the metabolic pathways (Figure 1).
IRT for Growth and Change
Steven P. Reise, Dennis A. Revicki in Handbook of Item Response Theory Modeling, 2014
The second model for the same data is a Linear Change Score (LCS) model where a linear slope is added. This allows concepts of growth or decline, or just changes, and the level of the individual. To do this, we allow the slope to have a mean (μ1) and variance of growth (ϕ12), and we usually allow a correlation with the initial level score (ρ0,1). This growth is defined by a set of basis coefficients that demand an equal (ω[t] = 1) weighting of all the changes. Of course, equality of the changes is not necessary because changes can be different at different times. This is a latent growth component because the growth is independent of the uniqueness. This is also called constant change because mean slope represents the average amount of change per unit of time. In the present study, the constant change would identify the average increase or decline in depression per grade. We basically test the linear model as a simple hypothesis.
Concluding Remarks
Song S. Qian, Mark R. DuFour, Ibrahim Alameddine in Bayesian Applications in Environmental and Ecological Studies with R and Stan, 2023
We use a one sample t-test problem to illustrate the severe testing concept. In a t-test contrasting the null hypothesis of against the alternative hypothesis , we decide which one is supported by the data by first assuming that H0 is true. Under H0, the t-test assumption is that the observed data follows a normal distribution with mean μ0 (i.e., ), which implies that the test statistic , where is the sample average and s is the sample standard deviation. The t-distribution has a range of . Although any value of the test statistic is possible under the null hypothesis, the likelihood of observing a value of the t-statistic decreases under H0 and increases under Ha as the observed value increases. Statistical hypothesis testing is, then, a means to weigh the evidence for and against H0. The evidence is in the form of the p-value.
Antioxidant and Anti-Diabetic Functions of a Polyphenol-Rich Sugarcane Extract
Published in Journal of the American College of Nutrition, 2019
Jin Ji, Xin Yang, Matthew Flavel, Zenaida P.-I. Shields, Barry Kitchen
A statistical analysis was performed for all the study results. First, a correlation analysis was carried out to determine whether there is a relationship between the two variable (x, y) pairs of the study results. Then, a statistical hypothesis testing was performed. Because of the small sample sizes, the Kruskal–Wallis test, a nonparametric test, was used. The Kruskal–Wallis test is the nonparametric alternative to a one-way analysis of variance and does not require normal distributions. The null hypothesis of this test is that all the medians are equal. The alternative hypothesis is that the medians are different. If the Kruskal–Wallis test is significant, it indicates that at least two concentrations have significantly different medians. The statistical analysis was performed using the SAS® software, version 9 (SAS Institute, Inc.).
The role of the p-value in the multitesting problem
Published in Journal of Applied Statistics, 2020
P. Martínez-Camblor, S. Pérez-Fernández, S. Díaz-Coto
Statistical procedures are frequently performed routinely and without the previous checking of the required assumptions. The derived conclusions are sometimes misunderstood and are not taken with the appropriate caution. Those actions contribute to the reproducibility crisis and to the deterioration of the sciences credibility [2]. The problem gets worse when the study involves thousands of statistical hypotheses which cannot be handled individually nor carefully. Such is the case of most of the studies in the so-called -omic sciences in which, commonly, thousands or even hundreds of thousands of null hypotheses are simultaneously tested and, once a threshold is computed, a subset of them are considered as effects. But, of course, statistical analysis cannot replace the rational thinking and the derived conclusions should be carefully considered [35]. Knowing the real implications of the selected threshold and the risks (limitations) of the decisions based on statistical hypothesis testing is crucial to get a good understanding of the observed results.
A Nordic registry-based study of drug treatment patterns in overactive bladder patients
Published in Scandinavian Journal of Urology, 2019
Ian Milsom, Hjalmar A. Schiotz, Maja Svensson, Suzanne Kilany, Fredrik Hansson
All analyses were based on the full analysis set; all patients who picked up index medication during the inclusion period. The data set was extracted in SAS format and analyzed by the study statistician. No statistical hypothesis testing was performed on any outcome variable. All variables were presented by country and then by meta-analyses, which were performed on the primary endpoint and selected secondary endpoints using the inverse variance weighted approach; no sub-groups were used in the meta-analyses. Continuous variables were expressed with standard statistical measures (number of observations, mean and/or median, standard deviation, minimum and maximum values); categorical variables were expressed by absolute numbers, frequencies and percentages. The primary endpoint was described using frequency tables (with count and percent) with a 95% confidence interval (CI) for each index medication in each country. For the secondary endpoint of time to discontinuation, Kaplan-Meier estimates were applied. All other secondary endpoints were presented using descriptive statistics and frequency tables, for all patients and for each sub-group. Pooled data from all three countries were calculated using SAS PROC GLIMMIX, with total n per proportion and with country as random variable.
Related Knowledge Centers
- P-Value
- Pearson'S Chi-Squared Test
- Null Hypothesis
- Analysis of Variance
- Statistical Significance
- Type I & Type II Errors
- Fiducial Inference
- Inductive Reasoning
- Detection Theory
- Test Statistic