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   https://doi.org/10.1002/ejic.201801076  

   Xue, Zi-Ling; Ramirez-Cuesta, Anibal J.; Brown, Craig M.; Calder, Stuart; Cao, Huibo; Chakoumakos, Bryan C.; Daemen, Luke L.; Huq, Ashfia; Kolesnikov, Alexander I.; Mamontov, Eugene; et al (February 2019, European Journal of Inorganic Chemistry)
2. A Framework for Autonomic Computing for In Situ Imageomics

   https://doi.org/10.1109/ACSOS58161.2023.00018  

   Kline, Jenna; Stewart, Christopher; Berger-Wolf, Tanya; Ramirez, Michelle; Stevens, Samuel; Babu, Reshma Ramesh; Banerji, Namrata; Sheets, Alec; Balasubramaniam, Sowbaranika; Campolongo, Elizabeth; et al (September 2023, IEEE)

   In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal social groups, species, and even whole ecosystems. Monitoring biological traits over large spaces and long periods of time could enable new, data-driven approaches to wildlife conservation, biodiversity, and sustainable ecosystem management. However, to accurately infer biological traits, machine learning methods for images require voluminous and high quality data. Adaptive, data-driven approaches are hamstrung by the speed at which data can be captured and processed. Camera traps and unmanned aerial vehicles (UAVs) produce voluminous data, but lose track of individuals over large areas, fail to capture social dynamics, and waste time and storage on images with poor lighting and view angles. In this vision paper, we make the case for a research agenda for in situ imageomics that depends on significant advances in autonomic and self-aware computing. Precisely, we seek autonomous data collection that manages camera angles, aircraft positioning, conflicting actions for multiple traits of interest, energy availability, and cost factors. Given the tools to detect object and identify individuals, we propose a research challenge: Which optimization model should the data collection system employ to accurately identify, characterize, and draw inferences from biological traits while respecting a budget? Using zebra and giraffe behavioral data collected over three weeks at the Mpala Research Centre in Laikipia County, Kenya, we quantify the volume and quality of data collected using existing approaches. Our proposed autonomic navigation policy for in situ imageomics collection has an F1 score of 82% compared to an expert pilot, and provides greater safety and consistency, suggesting great potential for state-of-the-art autonomic approaches if they can be scaled up to fully address the problem. 

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3. Subgoals for CS1 in Python

   https://doi.org/10.1145/3501709.3544283  

   Morrison, Briana B. (August 2022, Proceedings of the 2022 ACM Conference on International Computing Education Research V.2 (ICER 2022))

   In our previous research we found that teaching novice programmers introductory programming in Java using subgoal labels led to deeper knowledge [2] and increased persistence for students potentially at risk of dropping out or failing their first undergraduate course in CS [3]. Given the increasing number of universities using Python in introductory programming classes, we have begun the process of defining the subgoals to use in Python-based introductory courses. We have repeated the Task Analysis by Problem Solving (TAPS) development process [1] that we used to create Java-based instructional materials. As with the Java-based instructional materials, we have created subgoals for both evaluating programs written in Python and writing programs in Python. The poster will present an overview of the TAPS development process for identifying the Python subgoals and the proposed experimental design for assessing the effectiveness of the instructional materials. 

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4. Determining Growth Models for Human Lymphoblastic Leukemia Cells for Expansion in a Centrifugal Bioreactor for Utilization in Cancer Immunotherapy

   Brenden Fraser-Hevlin, Kitana Kaiphanliam (March 2021, Washington State University Showcase for Undergraduate Research and Creative Activities)
5. Advances in Electrochemical Sensors for Detecting Analytes in Biofluids

   https://doi.org/10.1002/adsr.202200088  

   Lee, Jimin; Kim, Myung Chul; Soltis, Ira; Lee, Sung Hoon; Yeo, Woon‐Hong (August 2023, Advanced Sensor Research)

   Abstract More than half of all Americans suffer from chronic diseases, the leading causes of death and disability. However, prompt treatment of chronic diseases can lead to better patient outcomes and a reduced burden on the healthcare system. This highlights the urgent need for electrochemical (EC) sensors that provide non‐invasive, real‐time monitoring of disease‐indicating biomarkers. Due to their high sensitivity, high selectivity, and cost‐effectiveness, EC biosensors have recently shown tremendous promise for individualized health monitoring. This review explains the working principles of EC biosensors. It summarizes the recent advances and improvements of EC biosensors for detecting biomarkers in different biofluids, including tears, saliva, breath, urine, and sweat. Through a comprehensive overview of EC biosensor technologies, this article is expected to aid the development of flexible and wearable EC biosensing systems that have the potential to provide continuous, long‐term health monitoring for both clinical and at‐home use. 

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