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This study aims to illustrate the potential of sequential experimentation for statistically scientific based optimization of Tazarotene (TAZA) cubosomes. Hot melt emulsification method was used for cubosomes preparation. A preliminary (3.2) mixed factorial design (MFD) was conducted to choose suitable types of stabilizer and surfactant that maximize entrapment efficiency (EE) and minimize particle size (PS). These chosen stabilizer and surfactant were to be used in the statistical design proposed for optimization of TAZA cubosomes (I-optimal mixture design) (IOMD). Glyceryl monooleate (GMO), stabilizer and surfactant amounts were the three mixture components (MixCs) studied in that design. Responses (EE, PS and drug percent released after 24 hours (Q24h)) were statistically analyzed. Numerical optimization using desirability function based on different responses' importance was used to find an IOMD-optimized formulation (IOMD-OF) with the predetermined characters. Then, a novel statistical methodology of design space expansion was adopted to enhance Q24h. Suitable models to express EE, PS and Q24h were elucidated over the expanded mixture design (EMD) space. Validity of derived models was verified via prediction intervals and percent deviations of actual values from predicted ones for all the EMD design points. EMD was then navigated to find EMD-OF. Analysis of MFD showed that Pluronic-F68 and polyvinyl alcohol were the best stabilizer and surfactant to be used. First stage optimization after IOMD analysis led to a formulation with unsatisfactory Q24h of 58.8%. After design space expansion adoption, re-analysis and re-optimization, a satisfactory EMD-OF having EE of 82.1%, PS of 273.0 nm and Q24h of 68.8% was found. Statistical sequential experimentation with the novel design space expansion approach proved to be a successful paradigm for enhancing TAZA cubosomes optimization. Thus, this paradigm is expected to have promising future applications in various pharmaceutical formulations optimization.

Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is combined with data using the weighted averaging derived from probability modeling. In a scientific research project, inferences from data analysis are interpreted in light of mechanistic models and substantive theories. Within a scholarly or applied research community, data and conclusions from separate laboratories are amalgamated through a series of steps, including peer review, meta-analysis, review articles, and replication studies. These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the renewed popularity of Bayesian inference and then with concerns about the replication crisis in science. In this article, we review the amalgamation of statistical evidence from different perspectives, connecting the foundations of statistics to the social processes of validation, criticism, and consensus building.

This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase).

Masculinity, Militarism and Eighteenth-Century Culture, 1689–1815 (Cambridge: Cambridge University Press, 2018). The Well-Ordered Universe: The Philosophy of Margaret Cavendish (Oxford: Oxford University Press, 2017). Trading in War: London's Maritime World in the Age of Cook and Nelson (New Haven: Yale University Press, 2018). Masculinity, Militarism and Eighteenth-Century Culture, 1689–1815 (Cambridge: Cambridge University Press, 2018). The Well-Ordered Universe: The Philosophy of Margaret Cavendish (Oxford: Oxford University Press, 2017). Trading in War: London's Maritime World in the Age of Cook and Nelson (New Haven: Yale University Press, 2018).

Launched in 2014, the RARE Compassion Program is the first international educational program to pair medical students with rare disease patients in order to enhance exposure to and comfort with rare diseases. As part of ongoing quality improvement, this study retrospectively reviewed four years of participant registration data to conduct a program evaluation of the RARE Compassion Program between 2014–2018. During the study period, there were 334 student participants, representing 67.3% of Association of American Medical Colleges (AAMC) member medical schools, and 5389 rare disease volunteers. Despite not requiring in-person interaction, 90.64% of student–volunteer interactions were in-person, while only 5.89% and 3.46% were by video messaging or email correspondence, respectively (p = 0.0002). In a limited post participation survey, 91.7% of students, who matched to 19 out of 27 residency specialities, indicated they would recommend the program to their peers. These findings suggest that the RARE Compassion Program, designed to increase medical student engagement with rare disease patients, has broad appeal. It serves as a novel case study of how extracurricular initiatives supported by non-profit organizations can augment the medical training experience and improve understanding of important and often neglected perspectives.

Abstract This paper explores the determinants of ministerial duration within the German Länder between 1990 and 2010. In arguing that different terminal events ceasing ministerial tenures should be analyzed separately, it distinguishes four exit types: voluntary, forced, collective (ministers leaving office because their whole party does so) and exits that are neither volitional acts of the minister nor politically induced. Depending on the exit type, competing-risks Cox-models show different effects for one and the same variable on the hazard for ministerial turnover. Seniority in high-level politics for example helps not to be forced out of office while it has no effect on voluntary or collective exits. Heading an important ministry on the other hand increases the chances to rise to other positions in high politics or private business, but does not impact the other two hazards. The analysis furthermore shows that the principal-agent-logic known from Westminster systems with the prime minister being largely sovereign in hiring and firing cabinet members must be adapted to the German context of frequent coalition governments. In coalition governments, only ministers from the same party as the prime minister exhibit higher hazards for forced exits, while ministers from other coalition partners are much safer in that regard.

Dave served on the National Bio ethics Advisory Commission, the Health Sciences Policy Board of the Institute of Medicine of the National Academy of Sciences, the Board of the American Society for Human Genetics and the Council ofthe Human Genome Organization. A near-evangelical belief in the awesome power of genetics is what motivated Dave to develop and apply a method called radiation hybrid mapping, which provided a critical stepping stone to integrate physical and genetic measures of the human genome and which has also gone on to be applied productively to dozens of organisms in addition to humans.