Athleta B Corporation Case Study

As a B Corporation, Athleta has the unique challenge of identifying a competitive strategy that will guide the company to success in the highly competitive athleisure market while also remaining consistent with its mission, vision, and values and supporting its parent company, Gap Inc. This case study was developed to evaluate this challenge through internal and external analysis of the company and to encourage readers to consider Athleta’s future priorities as the company undergoes changes in strategic leadership. The case study begins with a brief overview of the competitive landscape of the Athleisure market and a review of the history of Athleta before diving into the company’s positioning in the market as a company that values sustainability and female empowerment. Athleta’s focus on inclusive sizing, sustainability, partnerships with female athletes, and customer engagement provide strong tools for the company to differentiate itself from competitors. These strengths will be critical for Athleta to utilize in the future as it seeks to become a leading Athleisure brand and a strong brand in Gap Inc.’s portfolio

Nebraska School Activities Association: Web 2.0

The Nebraska School Activities Association (NSAA) is the sanctioning body of high school athletics and activities in the state. This non-profit organization facilitates the operations of these five activities and twenty-one sports for more than three-hundred public and non-public schools. The NSAA maintains an extensive amount of data to ensure the compliance of its members in all activities. To aid in this maintenance, NSAA developed a website in the early 2000s to allow the organization to facilitate and regulate interscholastic activities. However, as time progressed, inefficiencies and vulnerabilities were introduced into the system. It became increasingly important to the organization to maintain the integrity and reliability of its members’ data. To address these growing difficulties, NSAA approached Design Studio to collaborate on a multi-year-long project to replace its twenty-year-old system. This revitalized system would utilize cutting-edge technology to construct a system that is modular, adaptive to change, user-friendly, ADA-compliant, and secure. As part of this multi-year project, the team embarked on the second phase of the project, building upon the foundational framework established by the previous team. In particular, the team delivered comprehensive functionality for three activities that the NSAA facilitates: Speech, Play Production, and Music

Mutual of Omaha Mortgage: First Time Homebuyers

The 2021-2022 Mutual of Omaha Mortgage project is focused on innovating across the home search process by providing a video-first home search experience for potential application users. Mutual of Omaha Mortgage’s goal is to provide a seamless process for homebuyers to find homes on their application and direct them to apply for a mortgage with the company. By organizing both processes on the same application, they can compete with home search platforms such as Zillow and Redfin. Additionally, with users having the capacity to list their own homes for sale in-app, this reduces the need to rely on a real estate agent and provides more savings for the homebuyer and home seller. Through development of this application, Mutual of Omaha Mortgage will lay the foundation for the creation of an application ecosystem that will be the all-in-one platform for helping users manage their finances and their home owning experience

Groundwater Sustainability Platform

The Twin Platte Natural Resource District (TPNRD) and the Nebraska Department of Natural Resources (NDNR) are in the process of jointly adopting an Integrated Management Plan (IMP). The TPNRD must show sufficient progress toward meeting the goals and objectives of the IMP within four years. The TPNRD seeks to automate the collection and processing of the required data and to ultimately automate the annual evaluations of progress toward meeting these goals. This will be accomplished with a could-based platform that will bring together various data sources including daily precipitation, irrigation pumping, evapotranspiration, groundwater levels, and other producer inputs. Through data analytics, application program interfaces (API), and advanced visualizations, the TPNRD will have up-to-date information on their level of success in meeting the IMP goals

Groundwater Sustainability Platform

The Twin Platte Natural Resource District (TPNRD) and the Nebraska Department of Natural Resources (NDNR) are in the process of jointly adopting an Integrated Management Plan (IMP). The TPNRD must show sufficient progress toward meeting the goals and objectives of the IMP within four years. The TPNRD seeks to automate the collection and processing of the required data and to ultimately automate the annual evaluations of progress toward meeting these goals. This will be accomplished with a could-based platform that will bring together various data sources including daily precipitation, irrigation pumping, evapotranspiration, groundwater levels, and other producer inputs. Through data analytics, application program interfaces (API), and advanced visualizations, the TPNRD will have up-to-date information on their level of success in meeting the IMP goals

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL