Memes and Poetry: A Descriptive Analysis on Creative Arts Therapy to Reduce Health Care Worker Burnout
Background: Health care workers (HCWs) face high levels of burnout, which can lead to workforce turnover and poor patient outcomes. Health care leaders should identify strategies to improve staff resilience.
Purpose: The purpose of this study was to describe HCWs’ perspective on using creative arts therapy to reduce burnout and improve resiliency.
Methods: During Infection Prevention week, staff were encouraged to submit and vote on educational memes and haikus. Staff were asked their perspectives on how this activity could be used to reduce burnout and improve resiliency using a 4-point Likert scale.
Results: Twenty-two staff members submitted 26 memes and 27 haikus. Staff felt this activity could be an effective strategy to help reduce burnout and improve resiliency.
Conclusions: Further research is warranted to better understand the correlation between this form of art therapy and burnout and resiliency; however, health care leaders may consider using this as a tool for staff well-being.
Preliminary evidence that brief exposure to vaccination-related internet memes may influence intentions to vaccinate against COVID-19
Despite global efforts to rapidly distribute COVID-19 vaccines, early estimates suggested that 29-35% of the population were hesitant/unwilling to receive them. Countering such vaccine hesitancy is thus an important priority. Across two sets of online studies (total n = 1584) conducted in the UK before (August-October 2020) and immediately after the first effective vaccine was publicly announced (November 10-19, 2020), brief exposure (<1 min) to vaccination memes boosted the potentially life-saving intention to vaccinate against COVID-19. These intention-boosting effects, however, weakened once a COVID-19 vaccine became a reality (i.e., after the announcement of a safe/effective vaccine), suggesting meme-based persuasion may be context-dependent. These findings thus represent preliminary evidence that naturally circulating memes may-under certain circumstances-influence public intentions to vaccinate, although more research regarding this context-specificity, as well as the potential psychological mechanisms through which memes act, is needed.
MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation
In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it.
Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.
A meme-based approach for enhancing student engagement and learning in renal physiology
As educators around the world are exploring new approaches to keep students involved in remote learning during the pandemic, we investigated the utility of memes in promoting engagement in the online environment. Medical students enrolled in human physiology course at the College of Medicine and Health Sciences, Sohar, Oman were provided with an option to create memes related to the learning outcomes in renal physiology. 146 out of 280 students chose to create memes (52%) and the remaining students chose to submit either a labelled diagram or a concept map. Students uploaded their work in the discussion forum of the learning management system.
All students enrolled in the course were given an opportunity for interaction with the uploaded content by commenting and upvoting thereafter. Students were requested to give anonymous feedback on their experience specifically on the activity related to memes. Feedback received from 142 out of 280 students through anonymous comments were subjected to thematic analysis. Based on the analysis of the data, we found that memes elicited interest in the topic, facilitated peer interaction, simplified complex ideas, enhanced retention of associated concepts and fostered a positive learning environment.
MEMES: Machine learning framework for Enhanced MolEcular Screening
In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible.
In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.
MEM |
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| PM150411-500mL | Elabscience Biotech | 500 mL | 10 EUR |
MEM |
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| PM150411 | Elabscience Biotech | 500mL | 10 EUR |
MEM |
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| MBS2567535-500mL | MyBiosource | 500mL | 80 EUR |
MEM |
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| MBS2567535-5x500mL | MyBiosource | 5x500mL | 360 EUR |
MEM/F12 |
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| PM151220-500mL | Elabscience Biotech | 500 mL | 10 EUR |
MEM/F12 |
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| PM151220 | Elabscience Biotech | 500mL | 10 EUR |
MEM/F12 |
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| MBS2570194-500mL | MyBiosource | 500mL | 80 EUR |
MEM/F12 |
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| MBS2570194-5x500mL | MyBiosource | 5x500mL | 360 EUR |
SILAC - MEM |
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| 0424 | AthenaES | 500 ml | 46.2 EUR |
MEM Alpha |
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| CM054-050 | GenDepot | 500ml | 105.6 EUR |
MEM Alpha |
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| CM054-300 | GenDepot | 6x500ml | 216 EUR |
MEM Alpha |
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| CM054-310 | GenDepot | 10x500ml | 295.2 EUR |
MEM Alpha |
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| CM054-320 | GenDepot | 20x500ml | 408 EUR |
MEM Alpha |
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| CM054-350 | GenDepot | 50x500ml | 764.4 EUR |
Entropy and complexity unveil the landscape of memes evolution
On the Internet, information circulates fast and widely, and the form of content adapts to comply with users’ cognitive abilities. Memes are an emerging aspect of the internet system of signification, and their visual schemes evolve by adapting to a heterogeneous context. A fundamental question is whether they present culturally and temporally transcendent characteristics in their organizing principles. In this work, we study the evolution of 2 million visual memes published on Reddit over ten years, from 2011 to 2020, in terms of their statistical complexity and entropy. A combination of a deep neural network and a clustering algorithm is used to group memes according to the underlying templates.
The grouping of memes is the cornerstone to trace the growth curve of these objects. We observe an exponential growth of the number of new created templates with a doubling time of approximately 6 months, and find that long-lasting templates are associated with strong early adoption. Notably, the creation of new memes is accompanied with an increased visual complexity of memes content, in a continuous effort to represent social trends and attitudes, that parallels a trend observed also in painting art.