Women\u27s Leadership in Higher Education: Status, Barriers, and Motivators

Advancing more women into institutional leadership roles in higher education matters. Although numerous studies have documented the value of involving diverse perspectives in decision-making processes (Donovan & Caplan, 2019; Gero & Garrity, 2018; Williams, 2013; Woolley & Malone, 2011), many individuals and organizations—ranging from the corporate sector and the political realm to postsecondary education—have voiced commitments to increasing the representation of women in higher ranks, yet they have been stymied in achieving measurable results. A variety of examples in the research-based literature reflect the glacial pace of progress for women into leadership roles across a variety of fields. Examining the field of higher education, noted leadership scholars Kellerman and Rhode (2017) have debunked the myth that the oft-touted pipeline theory, which argues that over time, a larger number of women on lower rungs of organizational hierarchies will yield a larger number of women on higher ones (p.11). Yet these authors note that even after more than 30 years in which this theory has held currency, the number of women in positions of leadership and management has remained dauntingly and depressingly low (p. 11)

Using Genetic Algorithms on Groundwater Modeling Problems in a Consulting Setting

This paper presents a practical application for writing and applying simple genetic algorithms (GAs) for the common groundwater flow model, MODFLOW. The method employed by GAs is derived from the driving forces of evolution in the natural world. They employ functions that mimic natural evolutionary processes including selection, mutation, and genetic crossover. A GA solves mathematical problems where a desired outcome to the problem is defined (for example, calibration targets or remediation goals), but the inputs needed to arrive at this outcome are unknown. Our paper includes an introduction to genetic algorithms, the pseudocode of our genetic algorithm for MODFLOW, and the results of an experiential application. Due to the lack of commercially available GAs for MODFLOW, we coded a simple algorithm in Visual Basic Script and applied it to an example model. In the example model, the GA was used to conduct parameter estimation on a MODFLOW model of a river basin in New England that we had previously developed and calibrated in our practice. The calibration target used was net groundwater flow into the river. Four model input parameters were selected as chromosomes for the GA to act on: recharge, river conductance, and two general head boundaries. An initial population of 100 models was developed by varying the value of the gene parameters. The GA ran a MODFLOW simulation for each member of the population, extracted each output file, and established the error of each model from the calibration target. It then evolved the entire population of models towards the calibration target. The GA converged on a single set of input parameter that established best-fit values for all of the chromosome parameters. Genetic algorithms provide a practical alternative to trial-and-error and automated statistical calibration procedures, and can also be used for optimization

Normal RNAi response in human fragile × fibroblasts

<p>Abstract</p> <p>Background</p> <p>Fragile × syndrome is caused by loss of expression of the FMRP protein involved in the control of a large number of mRNA targets. The Drosophila ortholog dFXR interacts with a protein complex that includes Argonaute2, an essential component of the RNA-induced silencing complex (RISC). Furthermore dFXR associates with Dicer, another essential processing enzyme of the RNAi pathway. Both microRNA and microRNA precursors can co-immunoprecipitate with dFXR. Consequently it has been suggested that the Fragile × syndrome may be due to a defect in an RNAi-related apparatus.</p> <p>Findings</p> <p>We have investigated the RNAi response in Fragile × patient cells lacking FMRP compared with normal controls. RNAi responses were successfully detected, but no statistically significant difference between the response in normal cells compared to patients cells was found - neither one nor two days after transfection.</p> <p>Conclusion</p> <p>Our data show that in human fibroblasts from Fragile × patients lacking FMRP the RNAi response is not significantly impaired.</p