Effects of milking, over-milking and vacuum levels on front and rear quarter teats in dairy cows

Mechanical forces to the teat and vacuum during milking negatively affect teat condition and may result in increased mastitis risk. We compared vacuum levels during milking and over-milking as well as teat condition before and after milking between front and rear teats. We expected that the lower milk yield of the front quarters would result in a longer over-milking and higher vacuum levels in front teats, resulting in morphological differences. The study comprised 540 dairy cows in 41 Austrian dairy farms with conventional milking systems. Before and after milking teats were visually assessed (colour, swelling, rings, hyperkeratosis) and teat dimensions (length, diameter, wall thickness, teat canal length) were measured manually and ultrasonographically. Vacuum measurements were taken using a vacuum measurement device attached to the cluster (short milk tube, pulsation tube and mouth-piece chamber). These various measurements of front and rear teats were compared and a multivariable analysis with backward stepwise procedure was used for inclusion or exclusion from the model. Front teats showed a poorer teat condition and were over-milked for longer in comparison to the rear teats. However, during milking and over-milking the vacuum levels in the mouthpiece chamber were significantly higher at the rear teats. The changes in front teat morphology were only partially caused by milking, over-milking and vacuum levels, with approximately 70% of the variation due to other, undetermined variables. Milking, over-milking and vacuum levels had no or very limited impact on the morphological changes of the rear teats.

Stepwise Procedure for Toxicological Assessment Based on Ratio of Mean Differences for a Normally Distributed Data

We propose a new stepwise confidence set procedure for toxicity study based on ratio of mean difference. Statistical approaches for evaluating toxicity studies that properly control familywise error rate (FWER) for difference of means between treatments and a control already exist. However, in some therapeutic areas, ratio of mean differences is desirable. Therefore, we construct stepwise confidence procedure based on Fieller's confidence intervals for multiple ratio of mean difference without multiplicity adjustment for toxicological evaluation. Simulation study revealed that the FWER is well controlled at prespecified nominal level α. Also, the power of our approach increases with increasing sample size and ratio of mean differences.

University Computer Network Vulnerability Management using Nmap and Nexpose

Over the past few years, the advancement of technology in universities have led to rise in the number of vulnerabilities in University computer Network (UCN). To ensure robustness and hardness of UCN, an efficient Vulnerability Management System is required. The focus of current work is on the importance of vulnerability management in a UCN. A plethora of tools are used for vulnerability scanning and assessment. This paper also focuses on the implementation of vulnerability scanning tools on UCN. Assessment of scan results is done to identify vulnerabilities in the network that need to be resolved on priority basis. Based on the scan results obtained after scanning the network using scanning tools, the decision can be taken to mitigate the vulnerabilities on priority basis. Vulnerability Management in a UCN is a stepwise procedure that needs to be implemented to keep the network secure. An effective VM framework is important and inevitable to prevent cyber security breaches in a UCN as it regularly checks for new vulnerabilities on and also provide solutions to remediate or resolve the vulnerabilities. The scanning tools used for the current work were Nmap and Nexpose. Nmap was used for information gathering of network and Nexpose was used for scanning the network for vulnerability detection.

Farmers’ Perception of Entrepreneurial Success: Evidence from the Greek Reality

The purpose of this paper is to evaluate the perceptions of Greeks-farmers regarding success and to investigate the factors that are considered as indicators to explain, predict, and determine perceptional entrepreneurial success. This research focuses on existing agricultural enterprises as more than 400 randomly selected agricultural enterprises compose the survey’s sample. The main research method is through structured questionnaires. A series of multivariate analyses were conducted to examine the data. A stepwise procedure was used to identify the relevant variable and the significant ones were identified based on the ‘F’ test. The results of a discriminant analysis indicate that seven predictors (internal LOC, pull motivation, push motivation, internal funding, innovativeness, entrepreneurial capacity, and educational background) have a significant impact on the dependent variable “perceived entrepreneurial success”. Pull motivation is the most important variable to discriminate the groups. The value of this study lies in the fact that it is an original attempt to assess the parameters that could explain the perceived entrepreneurial success of agripreneurs; a focus that is lacking in previous studies.

An optimized stepwise algorithm combining rapid antigen and RT-qPCR for screening of COVID-19 patients

Background & aim We investigated the combination of rapid antigen detection (RAD) and RT-qPCR assays in a stepwise procedure to optimize the detection of COVID-19. Methods From August 2020 to November 2020, 43,399 patients were screened in our laboratory for COVID-19 diagnostic by RT-qPCR using nasopharyngeal swab. Overall, 4,691 of the 43,399 were found to be positive, and 200 were retrieved for RAD testing allowing comparison of diagnostic accuracy between RAD and RT-qPCR. Cycle threshold (Ct) and time from symptoms onset (TSO) were included as covariates. Results The overall sensitivity, specificity, PPV, NPV, LR-, and LR+ of RAD compared with RT-qPCR were 72% (95%CI 62%–81%), 99% (95% CI95%–100%), 99% (95%CI 93%–100%), and 78% (95%CI 70%–85%), 0.28 (95%CI 0.21–0.39), and 72 (95%CI 10–208) respectively. Sensitivity was higher for patients with Ct ≤ 25 regardless of TSO: TSO ≤ 4 days 92% (95%CI 75%–99%), TSO > 4 days 100% (95%CI 54%–100%), and asymptomatic 100% (95%CI 78–100%). Overall, combining RAD and RT-qPCR would allow reducing from only 4% the number of RT-qPCR needed. Conclusions This study highlights the risk of misdiagnosing COVID-19 in 28% of patients if RAD is used alone. A stepwise analysis that combines RAD and RT-qPCR would be an efficient screening procedure for COVID-19 detection and may facilitate the control of the outbreak.

A Taxonomy for Psycho-Oncological Intervention Techniques in an Acute Care Hospital in Germany

<b><i>Background:</i></b> Due to the establishment of a nationwide certification system for cancer centers in Germany, the availability of psycho-oncological services for cancer patients has increased substantially. However, little is known about the specific intervention techniques that are applied during sessions in an acute care hospital, since a standardized taxonomy is lacking. With this study, we aimed at the investigation of psycho-oncological intervention techniques and the development of a comprehensive and structured taxonomy thereof. <b><i>Methods:</i></b> In a stepwise procedure, a team of psycho-oncologists generated a data pool of interventions and definitions that were tested in clinical practice during a pilot phase. After an adaptation of intervention techniques, interrater reliability (IRR) was attained by rating 10 previously recorded psycho-oncological sessions. A classification of interventions into superordinate categories was performed, supported by cluster analysis. <b><i>Results:</i></b> Between April and June 2017, 980 psycho-oncological sessions took place. The experts agreed on a total number of 22 intervention techniques. An IRR of 89% for 2 independent psycho-oncological raters was reached. The 22 techniques were classified into 5 superordinate categories. <b><i>Discussion/Conclusion:</i></b> We developed a comprehensive and structured taxonomy of psycho-oncological intervention techniques in an acute care hospital that provides a standardized basis for systematic research and applied care. We expect our work to be continuously subjected to further development: future research should evaluate and expand our taxonomy to other contexts and care settings.

A Stepwise Procedure for Toxicity Studies Based on Ratio of two means

This paper proposes a stepwise confidence set procedure for identifying equivalence or safety of compounds in a toxicity study under heteroscedasticity of variances for a normally distributed data. The problem of statistical methodology for drug safety is the control of the familywise error rate (FWER). Hence, we construct a confidence set procedure for toxicological evaluation and incorporating the partitioning principle with a case of heteroscedascity of variances under normal assumption. Our simulation studies demonstrated that the power of the procedures for heterogeneity of variances increases with increasing in ratio of means.

Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances

Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen receptor activity is an important area of research. The aim of the current study was to find a method or combination of methods to predict compounds that can bind to and/or disrupt the androgen receptor, and thereby guide decision making and further analysis. A stepwise procedure proceeded from analysis of protein structures from human, chimp, and rat, followed by docking and subsequent ligand, and statistics based techniques that improved classification gradually. The best methods used multivariate logistic regression of combinations of chimpanzee protein structural docking scores, extended connectivity fingerprints, and naïve Bayesians of known binders and non-binders. Combination or consensus methods included data from a variety of procedures to improve the final model accuracy.