Use of endocrine and immune responses as predictors of bull sperm motility

Research has shown that peripheral blood cell populations change in response to an immune challenge, and hormone concentrations directly affect sperm characteristics. The objective of this study was to utilize immune responses and hormone concentrations as biomarkers for yearling bull sperm motility. Seventeen Brahman-influenced bulls (mean age 1.1 ± 0.1 yr; body weight 478 ± 38 kg) were administered an intraperitoneal injection of lipopolysaccharide (Salmonella typhimirium 0.7 µg/kg of body weight). Blood was collected 0, 3, 6, 9, and 24 h after LPS injection then analyzed for differential cell count and endocrine concentrations of prolactin, insulin-like growth factor-1 (IGF), and cortisol. Semen was collected using electroejaculation every month for five months then analyzed for motility and morphology characteristics. Hormone concentrations and immune response had an effect on sperm characteristics. Number of sperm was correlated (r \u3e 0.65; P \u3c 0.01) with the IGF to prolactin ratio. Using stepwise regression analysis, we predicted that number of sperm = 172.43 + 12.8 (IGF:prolactin), r2 = 0.43, and progressive sperm motility = -1469.6 + 1.63 (IGF:cortisol) + 14.41 (average temperature during immune challenge), r2 = 0.43. This study showed that endocrine response to stress and activation of the immune system was associated with subsequent sperm motility characteristics. Our results suggest that endocrine and immune responses may be used as biomarkers for sperm motility. Those biomarkers may be useful in selecting replacement bulls

Simulation of Tail Weight Distributions in Biological Year 1986–2006 Landings of Brown Shrimp, Farfantepenaeus aztecus, from the Northern Gulf of Mexico Fishery

Size distribution within re- ported landings is an important aspect of northern Gulf of Mexico penaeid shrimp stock assessments. It reflects shrimp population characteristics such as numerical abundance of various sizes, age structure, and vital rates (e.g. recruitment, growth, and mortality), as well as effects of fishing, fishing power, fishing practices, sampling, size-grading, etc. The usual measure of shrimp size in archived landings data is count (C) the number of shrimp tails (abdomen or edible portion) per pound (0.4536 kg). Shrimp are marketed and landings reported in pounds within tail count categories. Statistically, these count categories are count class intervals or bins with upper and lower limits expressed in C. Count categories vary in width, overlap, and frequency of occurrence within the landings. The upper and lower limits of most count class intervals can be transformed to lower and upper limits (respectively) of class intervals expressed in pounds per shrimp tail, w, the reciprocal of C (i.e. w = 1/C). Age based stock assessments have relied on various algorithms to estimate numbers of shrimp from pounds landed within count categories. These algorithms required un- derlying explicit or implicit assumptions about the distribution of C or w. However, no attempts were made to assess the actual distribution of C or w. Therefore, validity of the algorithms and assumptions could not be determined. When different algorithms were applied to landings within the same size categories, they produced different estimates of numbers of shrimp. This paper demonstrates a method of simulating the distribution of w in reported biological year landings of shrimp. We used, as examples, landings of brown shrimp, Farfantepenaeus aztecus, from the northern Gulf of Mexico fishery in biological years 1986–2006. Brown shrimp biological year, Ti, is defined as beginning on 1 May of the same calendar year as Ti and ending on 30 April of the next calendar year, where subscript i is the place marker for biological year. Biological year landings encompass most if not all of the brown shrimp life cycle and life span. Simulated distributions of w reflect all factors influencing sizes of brown shrimp in the landings within a given biological year. Our method does not require a priori assumptions about the parent distributions of w or C, and it takes into account the variability in width, overlap, and frequency of occurrence of count categories within the landings. Simulated biological year distributions of w can be transformed to equivalent distributions of C. Our method may be useful in future testing of previously applied algorithms and development of new estimators based on statistical estimation theory and the underlying distribution of w or C. We also examine some applications of biological year distributions of w, and additional variables derived from them

Comparison of a Traditional and Modified Daniel Fast on Blood Lipids, Lipid Peroxidation, and Inflammation

Dietary modification involving the removal of animal products, protein, and processed foods results in rapid and significant improvements in multiple health markers. We compared the impact of two restriction models on biomarkers of inflammation, oxidative stress, and cardiovascular health over a 21-day intervention. One model was a stringent vegan diet; the other was identical but included ~30g/d of additional protein in the form of meat and dairy. Compared to baseline, both models resulted in similar and significant improvements in blood lipids, as well as a reduction in inflammation. Modification of dietary intake may improve risk factors for cardiovascular disease

Communicating the Value of Ergonomics to Management – Part 2: Ergonomics ROI Case Study Applications

More than ever, human factors engineers and ergonomists need to justify our practice’s value to management. How can we effectively communicate with management? How should we present a Return on Investment (ROI) that leadership will find useful that addresses company profits, cost savings, productivity, first time quality, and turnover? What else does management care about other than ROI? This second panel in a two panel series will specifically highlight case studies in which presenters give examples of situations in which ROI for ergonomics was investigated from a business value. The session will start with four case study lectures followed by a panel discussion led by the moderators. The audience will be encouraged to participate with their own questions and comments

Standardizing disease-specific quality of life measures across multiple chronic conditions: development and initial evaluation of the QOL Disease Impact Scale (QDIS(R))

BACKGROUND: To document the development and evaluation of the Quality of life Disease Impact Scale (QDIS(R)), a measure that standardizes item content and scoring across chronic conditions and provides a summary, norm-based QOL impact score for each disease. METHODS: A bank of 49 disease impact items was constructed from previously-used descriptions of health impact to represent ten frequently-measured quality of life (QOL) content areas and operational definitions successfully utilized in generic QOL surveys. In contrast to health in general, all items were administered with attribution to a specific disease (osteoarthritis, rheumatoid arthritis, angina, myocardial infarction, congestive heart failure, chronic kidney disease (CKD), diabetes, asthma, or COPD). Responses from 5418 adults were analyzed as five disease groups: arthritis, cardiovascular, CKD, diabetes, and respiratory. Unidimensionality, item parameter and scale-level invariance, reliability, validity and responsiveness to change during 9-month follow-up were evaluated by disease group and for all groups combined using multi-group confirmatory factor analysis (MGCFA), item response theory (IRT) and analysis of variance methods. QDIS was normed in an independent chronically ill US population sample (N = 4120). RESULTS: MGCFA confirmed a 1-factor model, justifying a summary score estimated using equal parameters for each item across disease groups. In support of standardized IRT-based scoring, correlations were very high between disease-specific and standardized IRT item slopes (r = 0.88-0.96), thresholds (r = 0.93-0.99) and person-level scores (r \u3e /= 0.99). Internal consistency, test-retest and person-level IRT reliability were consistently satisfactory across groups. In support of interpreting QDIS as a disease-specific measure, in comparison with generic measures, QDIS consistently discriminated markedly better across disease severity levels, correlated higher with other disease-specific measures in cross-sectional tests, and was more responsive in comparisons of groups with better, same or worse evaluations of disease-specific outcomes at the 9-month follow-up. CONCLUSIONS: Standardization of content and scoring across diseases was shown to be justified psychometrically and enabled the first summary measure of disease-specific QOL impact normed in the chronically ill population. This disease-specific approach substantially improves discriminant validity and responsiveness over generic measures and provides a basis for better understanding the relative QOL impact of multiple chronic conditions in research and clinical practice

Evaluation of smoking-specific and generic quality of life measures in current and former smokers in Germany and the United States

BACKGROUND: Health-related quality of life (QOL) surveys include generic measures that enable comparisons across conditions and measures that focus more specifically on one disease or condition. We evaluated the psychometric properties of German- and English-language versions of survey scales representing both types of measures in samples of current and former smokers. METHODS: TQOLITv1 integrates new measures of smoking-specific symptoms and QOL impact attributed to smoking with generic SF-36 Health Survey measures. For purposes of evaluation, cross-sectional data were analyzed for two independent samples. Disease-free (otherwise healthy) adults ages 23-55 used a tablet to complete surveys in a clinical trial in Germany (125 current and 54 former smokers). Online general population surveys were completed in the US by otherwise healthy current and former smokers (N = 149 and 110, respectively). Evaluations included psychometric tests of assumptions underlying scale construction and scoring, score distributions, and reliability. Tests of validity included cross-sectional correlations and analyses of variance based on a conceptual framework and hypotheses for groups differing in self-reported smoking behavior (current versus former smoker, cigarettes per day (CPD)) and severity of smoking symptoms in both samples and, in the German trial only, clinical parameters of biomarkers of exposure. RESULTS: Tests of scaling assumptions and internal consistency reliability (alpha = 0.71-0.79) of the smoking-specific measures were satisfactory, although ceiling effects attenuated correlations for former smokers in both samples. Correlational evidence supporting validity of smoking-specific symptom and impact measures included their substantial inter-correlation and higher correlations (than generic measures) with smoking behavior (favoring former over current groups) and CPD in both samples. In the German trial, both smoking-specific measures correlated significantly (p \u3c 0.05) with all four biomarkers. QOL impact attributed to smoking correlated with the SF-36 mental but not physical summary measures in both samples. CONCLUSIONS: German- and English-language TQOLITv1 surveys have comparable and satisfactory psychometric properties. Cross-sectional tests, including correlations with four biomarkers, support the validity of the new smoking-specific measures for use in studies of otherwise healthy smokers. Smoking-specific measures consistently performed better than generic QOL measures in all tests of validity

Mastering DICOM with DVTk

The Digital Imaging and Communications in Medicine (DICOM) Validation Toolkit (DVTk) is an open-source framework with potential value for anyone working with the DICOM standard. DICOM’s flexibility requires hands-on experience in understanding ways in which the standard’s interpretation may vary among vendors. DVTk was developed as a clinical engineering tool to aid and accelerate DICOM integration at clinical sites. DVTk is used to provide an independent measurement of the accuracy of a product’s DICOM interface, according to both the DICOM standard and the product’s conformance statement. DVTk has stand-alone tools and a framework with which developers can create new tools. We provide an overview of the architecture of the toolkit, sample scenarios of its utility, and evidence of its relative ease of use. Our goal is to encourage involvement in this open-source project and attract developers to build off and further enrich this platform for DICOM integration testing