Dissolving the Dichotomies Between Online and Campus-Based Teaching: a Collective Response to The Manifesto for Teaching Online (Bayne et al. 2020)

This article is a collective response to the 2020 iteration of The Manifesto for Teaching Online. Originally published in 2011 as 20 simple but provocative statements, the aim was, and continues to be, to critically challenge the normalization of education as techno-corporate enterprise and the failure to properly account for digital methods in teaching in Higher Education. The 2020 Manifesto continues in the same critically provocative fashion, and, as the response collected here demonstrates, its publication could not be timelier. Though the Manifesto was written before the Covid-19 pandemic, many of the responses gathered here inevitably reflect on the experiences of moving to digital, distant, online teaching under unprecedented conditions. As these contributions reveal, the challenges were many and varied, ranging from the positive, breakthrough opportunities that digital learning offered to many students, including the disabled, to the problematic, such as poor digital networks and access, and simple digital poverty. Regardless of the nature of each response, taken together, what they show is that The Manifesto for Teaching Online offers welcome insights into and practical advice on how to teach online, and creatively confront the supremacy of face-to-face teaching

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Revising working models across time: Relationship situations that enhance attachment security

We propose the Attachment Security Enhancement Model (ASEM) to suggest how romantic relationships can promote chronic attachment security. One part of the ASEM examines partner responses that protect relationships from the erosive effects of immediate insecurity, but such responses may not necessarily address underlying insecurities in a person’s mental models. Therefore, a second part of the ASEM examines relationship situations that foster more secure mental models. Both parts may work in tandem. We posit that attachment anxiety should decline most in situations that foster greater personal confidence and more secure mental models of the self. In contrast, attachment avoidance should decline most in situations that involve positive dependence and foster more secure models of close others. The ASEM integrates research and theory, suggests novel directions for future research, and has practical implications, all of which center on the idea that adult attachment orientations are an emergent property of close relationships

In vitro prevention by ACE inhibitors of cataract induced by glucose

Objectives: To study, the anticataract activity of lisinopril and enalapril on cataract induced by glucose, in goat lenses. Materials and Methods: Goat lenses were incubated in artificial aqueous humor containing 55 mM glucose (cataractogenesis) with lisinopril or enalapril in different concentrations at room temperature for 72 h. Biochemical parameters studied in the lens were electrolytes (Na+, K+), Na+-K+-ATPase activity, malondialdehyde (MDA) and proteins. Results: Glucose induced opacification of goat lens began 8-10 hrs after incubation and was complete in 72-80 hrs. Cataractous lenses showed higher Na+, MDA (P< 0.001), lower Na+-K+-ATPase activity, and water-soluble protein content. Lenses treated with lisinopril or enalapril in concentrations of 1, 5, and 10 ng/ml showed higher protein (total and water soluble proteins) content and prevented formation and progress of cataract by glucose, as evidenced by biochemical parameters. Conclusion: The anticataract activity of lisinopril and enalapril may be because of the antioxidant and free radical scavenging activity, as evidenced by a decrease in MDA in treated lenses. Further in-vitro and in-vivo studies in various experimental models and long term clinical trials are required to validate the anticataract activity of ACE-inhibitors

Partners’ attachment insecurity predicts greater physiological threat in anticipation of attachment-relevant interactions

© The Author(s) 2017. This study examined whether anticipating interacting with a partner higher in attachment insecurity predicted greater physiological threat in an emotion regulation context. Eighty-eight couples watched an emotionally negative film clip, prepared to discuss the video with their partner, and then engaged in a conversation. One dyad member (regulator) was randomly assigned to express versus suppress affective displays while his/ her partner (target) was given no additional instructions. Greater partner avoidance was associated with stronger physiological responses consistent with the experience of threat—sympathetic arousal coupled with increased vascular resistance—when regulators anticipated suppressing versus expressing affective displays. Greater partner anxiety was associated with greater physiological threat responses regardless of the emotion regulation context. Threat responses also manifested during the conversation: Regulators and targets with highly avoidant partners exhibited greater threat responses when suppressing versus expressing affective displays. Additionally, more insecure partners found the conversation more difficult. These data are the first to show that anticipating attachment-relevant interactions with more insecure partners elicit cardiovascular responses diagnostic of threat

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships