ACHIEVING AN ETHNICALLY AND RACIALLY DIVERSE STUDENT POPULATION IN HIGHER EDUCATION

Leaders in higher education continue to pursue the lofty goal of diversifying their college and university environments. Although improvements are evident in larger percentages of racially and ethnically diverse students and faculty on campuses, college and university leaders have much work left to do. The benefits associated with diversifying higher education environments have yet to be fully achieved. This article provides an overview of the diversity issue and identifies its importance not only to the welfare of those invested in higher education but also to the health and well-being of all people groups, nationally and globally. To illuminate the current condition of diversity, this paper includes an examination of past and present key policies and laws. To address and analyze the diversity problem, this exam presents existing research. Finally, this paper concludes with suggestions for embracing and affirming difference in college populations that leads to achieving the benefits of diversity for higher education institutions and society

Plasticizer degradation by marine bacterial isolates : a proteogenomic and metabolomic characterization

Many commercial plasticizers are toxic endocrine-disrupting chemicals that are added to plastics during manufacturing and may leach out once they reach the environment. Traditional phthalic acid ester plasticizers (PAEs), such as dibutyl phthalate (DBP) and bis(2-ethyl hexyl) phthalate (DEHP), are now increasingly being replaced with more environmentally friendly alternatives, such as acetyl tributyl citrate (ATBC). While the metabolic pathways for PAE degradation have been established in the terrestrial environment, to our knowledge, the mechanisms for ATBC biodegradation have not been identified previously and plasticizer degradation in the marine environment remains underexplored. From marine plastic debris, we enriched and isolated microbes able to grow using a range of plasticizers and, for the first time, identified the pathways used by two phylogenetically distinct bacteria to degrade three different plasticizers (i.e., DBP, DEHP, and ATBC) via a comprehensive proteogenomic and metabolomic approach. This integrated multi-OMIC study also revealed the different mechanisms used for ester side-chain removal from the different plasticizers (esterases and enzymes involved in the β-oxidation pathway) as well as the molecular response to deal with toxic intermediates, that is, phthalate, and the lower biodegrading potential detected for ATBC than for PAE plasticizers. This study highlights the metabolic potential that exists in the biofilms that colonize plastics-the Plastisphere-to effectively biodegrade plastic additives and flags the inherent importance of microbes in reducing plastic toxicity in the environment

Factors Affecting Faculty Use of Video Conferencing in Teaching: A Mixed-method Study

Teaching and learning can now utilize a variety of real-time technologies to build online social presence and learning interactions. However, teachers and students must effectively prepare for this experience; and the identification of contextual and perceptual influences become evolving and necessary (Lehman & Conceição, 2010; Liu & Kaye, 2016). In this paper, the authors explore factors that impact faculty use of synchronous video conferencing (VC) in teaching. The two-phase mixed-method study spanned a year, converging qualitative and quantitative approaches through observations and recordings during a 6-week faculty professional development program, a campus-wide survey, and focus groups. Thematic analysis was used for coding qualitative data (Guest, MacQueen, & Namey, 2012). Descriptive statistics, cross tabulation, logistic regression, and standard multiple regression were used to analyze quantitative data. A model with faculty demographic factors and perceived importance of technology features and quality for teaching was initially developed and tested, which explained 69.1% of the variance in predicting faculty use of VC technologies in teaching. The perceived importance of VC features and quality scale generated Cronbach’s Alpha .866. The study then provides meaningful process and recommendations to define institutional support to the VC adoption in teaching

Waardenburg Syndrome and Left Persistent Superior Vena Cava

Waardenburg syndrome (WS) is a rare genetic disorder secondary to neural crest cell developmental abnormalities. It is predominantly described as an auditory-pigmentary syndrome with diverse patient presentation, typically involving congenital sensorineural hearing loss and pigmentation abnormalities of the skin, hair, and iris. Other developmental abnormalities that may be associated with this syndrome are Hirschsprung\u27s disease and a myriad of cardiovascular congenital defects. We present a case of a young girl with WS who found to have a persistent left superior vena cava (PLSVC) draining into the coronary sinus. The prevalence of PLSVC is increased in patients with chromosomal and genetic abnormalities. However, we are the first to report its presence in association with WS while discussing the challenges that may arise during central venous catheter placement in patients with PLSVC

Speeding up Langevin Dynamics by Mixing

We study an overdamped Langevin equation on the $d$-dimensional torus with stationary distribution proportional to $p = e^{-U / \kappa}$. When $U$ has multiple wells the mixing time of the associated process is exponentially large (of size $e^{O(1/\kappa)}$). We add a drift to the Langevin dynamics (without changing the stationary distribution) and obtain quantitative estimates on the mixing time. We show that an exponentially mixing drift can be rescaled to make the mixing time of the Langevin system arbitrarily small. For numerical purposes, it is useful to keep the size of the imposed drift small, and we show that the smallest allowable rescaling ensures that the mixing time is $O( d/\kappa^3)$, which is an order of magnitude smaller than $e^{O(1/\kappa)}.$ We provide one construction of an exponentially mixing drift, although with rate constants whose $\kappa$-dependence is unknown. Heuristics (from discrete time) suggest that $\kappa$-dependence of the mixing rate is such that the imposed drift is of size $O(d / \kappa^3)$. The large amplitude of the imposed drift increases the numerical complexity, and thus we expect this method will be most useful in the initial phase of Monte Carlo methods to rapidly explore the state space

Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis.

In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of solution quality to an end-user is vital. This paper investigates a novel method of explanatory feature extraction based on analysis of the search trajectory and compares the results to those of sensitivity analysis using “Weighted Ranked Biased Overlap”. We apply these techniques to search trajectories generated by a genetic algorithm as it solves a staff rostering problem. We show that there is a significant overlap between these two explainability methods when identifying subsets of rostered workers whose allocations are responsible for large portions of fitness change in an optimization run. Both methods identify similar patterns in sensitivity, but our method also draws out additional information. As the search progresses, the techniques reveal how individual workers increase or decrease in the influence on the overall rostering solution’s quality. Our method also helps identify workers with a lower impact on overall solution fitness and at what stage in the search these individuals can be considered highly flexible in their roster assignment

Manganese oxide biomineralization is a social trait protecting

Manganese bio-mineralization is a widespread process among bacteria and fungi. To date there is no conclusive experimental evidence for, how and if this process impacts microbial fitness in the environment. Here we show how a model organism for manganese oxidation is growth-inhibited by nitrite, and that this inhibition is mitigated in presence of manganese. We show that such manganese-mediated mitigation of nitrite-inhibition is dependent on the culture inoculum size and that manganese oxide (MnOX) forms granular precipitates in the culture, rather than sheaths around individual cells. We provide evidence that MnOX protection involves both its ability to catalyze nitrite oxidation into (non-toxic) nitrate under physiological conditions, and its potential role in influencing processes involving reactive oxygen species (ROS). Taken together, these results demonstrate improved microbial fitness through MnOX deposition in an ecological setting, i.e. mitigation of nitrite toxicity, and point to a key role of MnOX in handling stresses arising from ROS

Investigating benchmark correlations when comparing algorithms with parameter tuning: detailed experiments and results.

Benchmarks are important to demonstrate the utility of optimisation algorithms, but there is controversy about the practice of benchmarking; we could select instances that present our algorithm favourably, and dismiss those on which our algorithm underperforms. Several papers highlight the pitfalls concerned with benchmarking, some of which concern the context of the automated design of algorithms, where we use a set of problem instances (benchmarks) to train our algorithm. As with machine learning, if the training set does not reflect the test set, the algorithm will not generalize. This raises some open questions concerning the use of test instances to automatically design algorithms. We use differential evolution and sweep the parameter settings to investigate the practice of benchmarking using the BBOB benchmarks. We make three key findings. Firstly, several benchmark functions are highly correlated. This may lead to the false conclusion that an algorithm performs well in general, when it performs poorly on a few key instances, possibly introducing unwanted bias to a resulting automatically designed algorithm. Secondly, the number of evaluations can have a large effect on the conclusion. Finally, a systematic sweep of the parameters shows how performance varies with time across the space of algorithm configurations. The datasets, including all computed features, the evolved policies and their performances, and the visualisations for all feature sets are available from the University of Stirling Data Repository (http://hdl.handle.net/11667/109)

Investigating benchmark correlations when comparing algorithms with parameter tuning.

Benchmarks are important for comparing performance of optimisation algorithms, but we can select instances that present our algorithm favourably, and dismiss those on which our algorithm under-performs. Also related are automated design of algorithms, which use problem instances (benchmarks) to train an algorithm: careful choice of instances is needed for the algorithm to generalise. We sweep parameter settings of differential evolution to applied to the BBOB benchmarks. Several benchmark functions are highly correlated. This may lead to the false conclusion that an algorithm performs well in general, when it performs poorly on a few key instances. These correlations vary with the number of evaluations