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9. Creating and Applying Relational Maintenance in Various Relational Contexts

Author

Hall, Robert D.

Abstract

Relational maintenance is a universal aspect of human relationships. As such, our family and interpersonal communication texts often include relational maintenance as a key point of discussion. Communication scholars also continue to demonstrate how understanding and incorporating positive relational maintenance behaviors is beneficial to our relationships. Given the importance of relational maintenance in family and interpersonal courses and relationships, I developed this activity for instructors to engage their students effectively in not only understanding concepts related to relational maintenance, but also analyzing and applying relational maintenance strategies in their day-to-day lives. Courses: Family communication, interpersonal communication. Objectives: This activity will enable students to (1) identify relational maintenance strategies as communicative behaviors in family and interpersonal relationships, (2) apply relational maintenance strategies in personal experiences, and (3) illustrate personal experiences as exemplars of relational maintenance strategies in family and interpersonal relationships.

Published
2023
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12. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Author

Shrine, Nick, Izquierdo, Abril G., Chen, Jing, Packer, Richard, Hall, Robert J., Guyatt, Anna L., Batini, Chiara, Thompson, Rebecca J., Pavuluri, Chandan, Malik, Vidhi, Hobbs, Brian D., Moll, Matthew, Kim, Wonji, Tal-Singer, Ruth, Bakke, Per, Fawcett, Katherine A., John, Catherine, Coley, Kayesha, Piga, Noemi Nicole, Pozarickij, Alfred, Lin, Kuang, Millwood, Iona Y., Chen, Zhengming, Li, Liming, Wijnant, Sara R. A., Lahousse, Lies, Brusselle, Guy, Uitterlinden, Andre G., Manichaikul, Ani, Oelsner, Elizabeth C., Rich, Stephen S., Barr, R. Graham, Kerr, Shona M., Vitart, Veronique, Brown, Michael R., Wielscher, Matthias, Imboden, Medea, Jeong, Ayoung, Bartz, Traci M., Gharib, Sina A., Flexeder, Claudia, Karrasch, Stefan, Gieger, Christian, Peters, Annette, Stubbe, Beate, Hu, Xiaowei, Ortega, Victor E., Meyers, Deborah A., Bleecker, Eugene R., Gabriel, Stacey B., Gupta, Namrata, Smith, Albert Vernon, Luan, Jian’an, Zhao, Jing-Hua, Hansen, Ailin F., Langhammer, Arnulf, Willer, Cristen, Bhatta, Laxmi, Porteous, David, Smith, Blair H., Campbell, Archie, Sofer, Tamar, Lee, Jiwon, Daviglus, Martha L., Yu, Bing, Lim, Elise, Xu, Hanfei, O’Connor, George T., Thareja, Gaurav, Albagha, Omar M. E., Suhre, Karsten, Granell, Raquel, Faquih, Tariq O., Hiemstra, Pieter S., Slats, Annelies M., Mullin, Benjamin H., Hui, Jennie, James, Alan, Beilby, John, Patasova, Karina, Hysi, Pirro, Koskela, Jukka T., Wyss, Annah B., Jin, Jianping, Sikdar, Sinjini, Lee, Mikyeong, May-Wilson, Sebastian, Pirastu, Nicola, Kentistou, Katherine A., Joshi, Peter K., Timmers, Paul R. H. J., Williams, Alexander T., Free, Robert C., Wang, Xueyang, Morrison, John L., Gilliland, Frank D., Chen, Zhanghua, Wang, Carol A., Foong, Rachel E., Harris, Sarah E., Taylor, Adele, Redmond, Paul, Cook, James P., Mahajan, Anubha, Lind, Lars, Palviainen, Teemu, Lehtimäki, Terho, Raitakari, Olli T., Kaprio, Jaakko, Rantanen, Taina, Pietiläinen, Kirsi H., Cox, Simon R., Pennell, Craig E., Hall, Graham L., Gauderman, W. James, Brightling, Chris, Wilson, James F., Vasankari, Tuula, Laitinen, Tarja, Salomaa, Veikko, Mook-Kanamori, Dennis O., Timpson, Nicholas J., Zeggini, Eleftheria, Dupuis, Josée, Hayward, Caroline, Brumpton, Ben, Langenberg, Claudia, Weiss, Stefan, Homuth, Georg, Schmidt, Carsten Oliver, Probst-Hensch, Nicole, Jarvelin, Marjo-Riitta, Morrison, Alanna C., Polasek, Ozren, Rudan, Igor, Lee, Joo-Hyeon, Sayers, Ian, Rawlins, Emma L., Dudbridge, Frank, Silverman, Edwin K., Strachan, David P., Walters, Robin G., Morris, Andrew P., London, Stephanie J., Cho, Michael H., Wain, Louise V., Hall, Ian P., and Tobin, Martin D.

Published
2023
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17. Light and flow regimes regulate the metabolism of rivers

Author

Bernhardt, Emily S., Savoy, Phil, Vlah, Michael J., Appling, Alison P., Koenig, Lauren E., Hall, Robert O., Arroita, Maite, Blaszczak, Joanna R., Carter, Alice M., Cohen, Matt, Harvey, Judson W., Heffernan, James B., Helton, Ashley M., Hosen, Jacob D., Kirk, Lily, McDowell, William H., Stanley, Emily H., Yackulic, Charles B., and Grimm, Nancy B.

Published
2022

29. Predictors of multi-domain cognitive decline following DBS for treatment of Parkinson's disease

Author

Follett, Kenneth, Stern, Matthew, Weaver, Frances, Ippolito, Dolores, Stoner, Gatana, Barnett, Tammy, Bukowski, Ken, DeNicolo, Rosemarie, Hur, Kwan, Jimenez, Joyce, Luo, Ping, Motyka, Jan, Reda, Domenic, Simon, Theresa, Thakkar, Bharat, Woolson, Robert, Fye, Carol, Gagne, William, Harris, Crystal, Heemskerk, Jill, Moy, Claudia, Sheehy, Paul, O'Leary, Timothy, Huang, Grant D., Fiore, Louis, Hall, Robert, Stroupe, Kevin, Burchiel, Kim, Koller, William, Marks, William J., Jr., Pahwa, Rajesh, Rothlind, Johannes, Sagher, Oren, Bakay, Roy, Chappell, Rick, Hart, Robert, Holloway, Robert, Jr., McCabe, George, Schenkman, Margaret, Taha, Jamal, Buckelew, Julia, Garin, Marilyn, Matzek, Sharon, Smith, Donna, Bronstein, Jeff, Duda, John, Hogarth, Penelope, Holloway, Kathryn, Horn, Stacy, Lai, Eugene C., Samii, Ali, Farah Atassi, Bello, Cecilia, Bunting-Perry, Lisette, Conn, Tina, Cugley, Alice, Eubank, Nanette, Fincher, Linda, Franks, Romay, Harris, Tammy, Haselman, Mariann, Heath, Susan, Hirsch, Miriam, Janovsky, Virginia, Lanier, Elaine, Lloyd, Mary, Loehner, Susan, O'Connor, Susan, Ordonez, Ligaya, Maccarone, Heather, Massey-Makhoul, Kelli, Matthews, Mary, Meyn, Elizabeth, Mimura, Keiko, Morrow, Wes, Searles, Tammy, Valotta, Jamye, Vasthare, Usha, Volz, Monica, Ward, Constance, Warker, Rebecca, Watson, Heidi, Willson, Pamela, Baron, Mark, Brodsky, Matthew, Calabrese, Vincent, Campbell, Gordon, Colcher, Amy, Farag, Emad, Henry, Eva, Hou, Jyh-Gong, Kang, Gail, Kleiner-Fisman, Galit, Kraakevik, Jeff, Nutt, John, Ostrem, Jill, Sarwar, Aliya, Subramanian, Indu, Vanek, Zeba, Baltuch, Gordon, De Salles, Antonio, Eller, Jorge, Larson, Paul, Simpson, Richard, Starr, Philip, Carne, William, Erikson, Tom, Kreutzer, Jeffrey, Mendez, Mario, Moberg, Paul, Ragland, John, Seel, Ronald, Storzbach, Daniel, Troster, Alexander, York, Michele, Jaggi, Jurg, Rothlind, Johannes C., York, Michele K., Carlson, Kim, Weaver, Frances M., Follett, Kenneth A., Duda, John E., and Reda, Domenic J.

Published
2022
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38. We Are Family, or Are We? An Activity on Identifying Differences in Attachment Styles and Family Communication Patterns through Music Media

Author

Hall, Robert D.

Abstract

This piece builds upon literature using media to teach not only college students in economics, social justice, and sociology, but also those college students in family and interpersonal communication as well. I provide a resource aimed to engage and expand students' understanding thoughtfully of two well-known family communication and interpersonal communication concepts: attachment styles and family communication patterns. The activity uses music to provide tangible examples for students to understand theoretical family and interpersonal communication concepts, as previous research stresses the importance of multimedia tools of inquiry for teaching classroom concepts. A detailed process, guidelines for debriefing, and alternatives are considered for various classroom climates or contexts. Courses: Family Communication, Interpersonal Communication. Objectives: (1) Identify conformity and conversation-oriented families. (2) Identify secure, preoccupied, dismissive, and anxious-ambivalent family attachment styles. (3) Demonstrate that attachment styles and family communication patterns are rarely explicitly stated.

Published
2020
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39. Lessons from an Online Teacher Preparation Program: Flexing Work Experience to Meet Student Needs and Regulators' Requirements in the United States

Author

Barnes, Richard, Hall, Robert, Lowe, Verna, Pottinger, Carrie, and Popham, Aaron

Abstract

In the United States, educator preparation is regulated at the state level. Accordingly, each state education system, and even individual districts, responded uniquely to the COVID-19 pandemic. Western Governors University (WGU) compiled a list of each state regulators' response to COVID, many of which included relaxation of student teaching requirements. While most of WGU's teacher preparation programme is delivered online, students complete observations and student teaching (teaching practice) in traditional classrooms. WGU had 1,875 student teacher candidates in the field on 1 March 2020. Through diligent effort all the candidates were able to complete their student teaching. WGU responded by allowing parallelism between the student teachers and their mentor teacher so that the student teacher could engage in teaching in the same manner as their mentor teacher, which varied on a district-by-district and state-by-state basis. WGU was also careful to evaluate whether the host school's virtualisation was still meeting the needs of the student teacher and if not, then to shift the student teacher to a different placement that was more virtual-friendly. Additionally, WGU plans to pilot intentionally virtual student teaching opportunities with online charter schools in autumn 2020.

Published
2020
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40. Evaluating O2 : Ar, N2 : Ar, and 29,30N2 using membrane inlet mass spectrometry configured to minimize oxygen interference.

Author

Boedecker, Ashlynn R., Taylor, Jason M., Tappenbeck, Tyler H., Hall, Robert O., Robbins, Caleb J., and Scott, J. Thad

Subjects
MASS spectrometry, ION sources, ENVIRONMENTAL sampling, FURNACES, NITROGEN
Abstract

Membrane inlet mass spectrometry (MIMS) provides detailed measures of dissolved 28,29,30N2, O2, and argon (Ar) for estimating important gas fluxes and concentrations in aquatic ecosystems. Previous studies demonstrated a large O2 scavenging effect while using a MIMS, where varying concentrations of O2 can affect measured N2 : Ar because O2 interacts with N2 in the ion source to produce NO+ (m/z = 30), potentially decreasing the detected current for 28,29N2 and increasing the detected current for 30N2. A common solution is to use a muffle furnace heated to 600°C with a copper reduction column to reduce the concentration of O2 to minimal levels and accurately measure 28,29,30N2. However, this solution eliminates the detection of O2 in environmental samples, which is a major benefit of using a MIMS. We questioned whether the MIMS was sensitive enough to provide accurate O2 estimates when using the furnace and whether the O2 scavenging effect was real and consistent among MIMS. We conducted four separate experiments on three different MIMS to test the O2 scavenging effect and the potential detection of O2 when using a MIMS with furnace. The furnace removed ~ 99% of O2, and O2 scavenging had little to no detectable effect on N2 : Ar and an unclear effect on 29N2 : 28N2, but increased 30N2 : 28N2. In most cases, accurate O2 data could be retrieved despite using the furnace. The need for O2 reduction may be limited to measuring accurate 30N2 : 28N2 in isotope pairing studies, but without substantial loss of MIMS measurements used to describe O2 dynamics. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Divergent metabolism estimates from dissolved oxygen and inorganic carbon: Implications for river carbon cycling.

Author

Shangguan, Qipei, Payn, Robert A., Hall, Robert O., Young, Fischer L., Valett, H. Maurice, and DeGrandpre, Michael D.

Subjects
CARBON metabolism, RESPIRATORY quotient, METABOLIC models, CIRCADIAN rhythms, CARBON cycle
Abstract

Rivers efficiently collect, process, and transport terrestrial‐derived carbon. River ecosystem metabolism is the primary mechanism for processing carbon. Diel cycles of dissolved oxygen (DO) have been used for decades to infer river ecosystem metabolic rates, which are routinely used to predict metabolism of carbon dioxide (CO2) with uncertainties of the assumed stoichiometry ranging by a factor of 4. Dissolved inorganic carbon (DIC) has been less used to directly infer metabolism because it is more difficult to quantify, involves the complexity of inorganic carbon speciation, and as shown in this study, likely requires a two‐station approach. Here, we developed DIC metabolism models using single‐ and two‐station approaches. We compared metabolism estimates based on simultaneous DO and DIC monitoring in the Upper Clark Fork River (USA), which also allowed us to estimate ecosystem‐level photosynthetic and respiratory quotients (PQE and RQE). We observed that metabolism estimates from DIC varied more between single‐ and two‐station approaches than estimates from DO. Due to carbonate buffering, CO2 is slower to equilibrate with the atmosphere compared to DO, likely incorporating a longer distance of upstream heterogeneity. Reach‐averaged PQE ranged from 1.5 to 2.0, while RQE ranged from 0.8 to 1.5. Gross primary production from DO was larger than that from DIC, as was net ecosystem production by 100mmolm−2d−1. The river was autotrophic based on DO but heterotrophic based on DIC, complicating our understanding of how metabolism regulated CO2 production. We suggest future studies simultaneously model metabolism from DO and DIC to understand carbon processing in rivers. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams.

Author

Sadler, Jeffrey M., Koenig, Lauren E., Gorski, Galen, Carter, Alice M., and Hall, Robert O.

Subjects
MACHINE learning, WATER quality, WATERSHEDS, SPATIAL resolution, AQUATIC habitats
Abstract

Dissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high‐frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored locations. Machine learning (ML) has been a commonly used approach for modelling DO, however, conventional ML models have no representation of the limnological processes governing DO dynamics. Here we implement and evaluate two process‐guided deep learning (PGDL) approaches for predicting daily minimum, mean and maximum DO concentrations in rivers from the Delaware River Basin, USA. In both cases, a multi‐task approach was taken in which the PGDL models predicted stream metabolism and gas exchange rates in addition to the DO concentrations themselves. Our results showed that for these sites, the PGDL approaches did not improve upon baseline predictions in temporal and spatially similar holdout experiments. One of the approaches did, however, improve predictions when applied to spatially dissimilar sites. Although this particular PGDL approach did not improve predictive accuracy in most cases, our results suggest that process guidance, perhaps a more constrained approach, could benefit a data‐driven DO model. [ABSTRACT FROM AUTHOR]

Published
2024
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