In recent years, much of the emphasis for transformation of introductory STEM courses has focused on \"active learning\", and while this approach has been shown to produce more equitable outcomes for students, the construct of \"active learning\" is somewhat ill-defined, and can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy\'s \"A Framework for K-12 Science Education\", which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions as active, 3D, neither, or both 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline. less

Cancer advocates and researchers share the same goal of driving science forward to create new therapies to cure more patients. The power of combining cancer researchers and advocates has become of increased importance due to their complementary expertise. Therefore, advocacy is a critical component of grant structures and has become embedded into the Stand Up 2 Cancer (SU2C) applications. To date, the optimal way to combine these skillsets and experiences to benefit the cancer community is currently unknown. The Saint Baldrick\'s Foundation (SBF)-SU2C now called St. Baldrick\'s Empowering Pediatric Immunotherapies for Childhood Cancer (EPICC) Team is comprised of a collaborative network across nine institutions in the United States and Canada. Since SU2C encourages incorporating advocacy into the team structure, we have assembled a diverse team of advocates and scientists by nominating a young investigator (YI) and advocate from each site. In order to further bridge this interaction beyond virtual monthly and yearly in person meetings, we have developed a questionnaire and conducted interviews. The questionnaire is focused on understanding each member\'s experience at the intersection between science/advocacy, comparing to previous experiences, providing advice on incorporating advocacy into team science and discussing how we can build on our work. Through creating a YI and advocate infrastructure, we have cultivated a supportive environment for meaningful conversation that impacts the entire research team. We see this as a model for team science by combining expertise to drive innovation forward and positively impact pediatric cancer patients, and perhaps those with adult malignancies. less

Plastic traits, capable of taking multiple forms, often correlate with one another or with features of the environment when measured over time. These patterns of correlated change are sometimes assumed to reflect adaptive plasticity, such as coevolved \'integrated phenotypes\' within individuals, synchronisation between social or mating partners, or responses to environmental gradients. Such plasticity is ecologically and evolutionarily important, so there is considerable interest in understanding how it varies between individuals and groups. However, \'regression to the mean\', the statistical tendency for traits to revert to the average value, may create the illusion of strong bivariate correlations in timeseries data, including substantial but meaningless variation between individuals. We demonstrate this using simulated and real data, revealing how regression to the mean can create bias both within and between samples. We then show, however, that its effects can often be eliminated using autoregressive models. We also offer a detailed discussion of how and why regression to the mean arises, introducing the idea that it is both a statistical and ecological phenomenon. less

Climate change is a scientifically proven phenomenon caused by anthropic activities, which requires urgent and significant reductions in greenhouse gas emissions. Despite the increasing vocalization of scientists advocating for political action, the issue of the environmental impact of academic research has been neglected for some time. Now, field-dependent initiatives have emerged, such as the non-profit organization My Green Lab, which delivers green certifications to biology and chemistry labs, and institute-dependent programs, such as the Max Planck Sustainability Network. In France, an independent collective was founded in 2019 to address the environmental footprint of academic research following the COP 15 Paris Agreement: Labos 1Point5. Building on their resources and methodology, we have quantified the overall carbon footprint of our biophysics laboratory, considering energy consumption, purchases and travel, for the years 2021. We investigate how this footprint would decrease by 2030 following systemic changes (change in the energy mix, improvements from suppliers), and we propose scenarios based on additional voluntary initiatives to reach a final reduction of -50% compared to the 2021 baseline, following IPCC targets. We have now formed a group of more than 20 colleagues to achieve this goal, emphasizing the importance of collective action. Finally, we provide advices based on our own experience to assist others in addressing the environmental impact of academic research in their respective laboratories. less

Since being founded in 2001, Wikipedia has grown into a trusted source of knowledge online, feeding Google search results and serving as training data for ChatGPT. Understanding the accuracy of its information, the sources behind its articles and their role in the transference of knowledge to the public are becoming increasingly important questions. Meanwhile, climate change has moved to the forefront of scientific and public discourse after years of warnings from the scientific community. Therefore, to understand how it was represented on English Wikipedia, we deployed a mixed-method approach on the article for \"Effects of climate change\" (ECC), its edit history and references, as well as hundreds of associated articles dealing with climate change in different ways. Using automated tools to scrape data from Wikipedia, we saw new articles were created as climatology-related knowledge grew and permeated into other fields, reflecting a growing body of climate research and growing public interest. Our qualitative textual analysis shows how specific descriptions of climatic phenomena became less hypothetical, reflecting the real-world public debate. The Intergovernmental Panel on Climate Change (IPCC) had a big impact on content and structure, we found using a bibliometric analysis, and what made this possible, we also discovered through a historical analysis, was the impactful work of just a few editors. This research suggests Wikipedia\'s articles documented the real-world events around climate change and its wider acceptance - initially a hypothesis that soon became a regretful reality. Overall, our findings highlight the unique role IPCC reports play in making scientific knowledge about climate change actionable to the public, and underscore Wikipedia\'s ability to facilitate access to research. This work demonstrates Wikipedia can be researched using both computational and qualitative methods to better understand transference of scientific information to the public and the history of contemporary science. less

Light sheet fluorescence microscopy (LSFM) provides the benefit of optical sectioning coupled with rapid acquisition times for imaging of tissue-cleared specimen. This allows for high-resolution 3D imaging of large tissue volumes. Inherently to LSFM, the quality of the imaging heavily relies on the characteristics of the illumination beam, with the notion that the illumination beam only illuminates a thin section that is being imaged. Therefore, substantial efforts are dedicated to identifying slender, non-diffracting beam profiles that can yield uniform and high-contrast images. An ongoing debate concerns the employment of the most optimal illumination beam; Gaussian, Bessel, Airy patterns and/or others. Comparisons among different beam profiles is challenging as their optimization objective is often different. Given that our large imaging datasets (~0.5TB images per sample) is already analyzed using deep learning models, we envisioned a different approach to this problem by hypothesizing that we can tailor the illumination beam to boost the deep learning models performance. We achieve this by integrating the physical LSFM illumination model after passing through a variable phase mask into the training of a cell detection network. Here we report that the joint optimization continuously updates the phase mask, improving the image quality for better cell detection. Our method\'s efficacy is demonstrated through both simulations and experiments, revealing substantial enhancements in imaging quality compared to traditional Gaussian light sheet. We offer valuable insights for designing microscopy systems through a computational approach that exhibits significant potential for advancing optics design that relies on deep learning models for analysis of imaging datasets. less

Scientists must play a significant role in enacting societal change by educating and advising policymakers on relevant policy topics. To fill the identified gap in science policy and advocacy training, during the pandemic years, we piloted and offered the online Science Policy & Advocacy for STEM Scientists Certificate Program starting in 2020. Over three cohorts, the program was focused on practical skills and concepts, networking, and career development opportunities, and providing pathways for PhD students and postdoctoral researchers to learn about and in many cases fully transition into science policy and advocacy roles and careers. Program participants were exposed to many important aspects of what it means to be involved in science policy and advocacy, and many chose to enter the field through subsequent opportunities facilitated by the program training. We sought to reduce a number of barriers to program participation, and additionally provided resources for others to develop similar programs at their university. We believe this training model is innovative and can be recapitulated, and sincerely hope to see more universities create similar programs in the US and internationally in order to serve trainees interested in science policy and advocacy and facilitate their building impactful careers in the field for years to come. less

Purpose: To develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and Methods: LLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. Results: For both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. Conclusion: A transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom. less

We aimed to evaluate how many authors are extremely productive and how their presence across countries and scientific fields has changed during 2000-2022. Extremely productive (EP) authors were defined as those with >60 full papers (articles, reviews, conference papers) published in a single calendar year and indexed in Scopus. We identified 3,191 EP authors across science excluding Physics and 12,624 EP authors in Physics. While Physics had much higher numbers of EP authors in the past, in 2022 the number of EP authors was almost similar in non-Physics and Physics disciplines (1,226 vs. 1,480). Excluding Physics, China had the largest number of EP authors, followed by the USA. However, the largest fold-wise increases between 2016 and 2022 were seen in Thailand (19-fold), Saudi Arabia (11.5-fold), Spain (11.5-fold), India (10.2-fold), Italy (6.9-fold), Russia (6.5-fold), Pakistan (5.7-fold), and South Korea (5.2-fold). Excluding Physics, most EP authors were in Clinical Medicine, but from 2016 to 2022 the largest relative increases were seen in Agriculture, Fisheries & Forestry (14.6-fold), Biology (13-fold), and Mathematics and Statistics (6.1-fold). EP authors accounted for 4,360 of the 10,000 most-cited authors (based on raw citation count) across science. While most EP Physics authors had modest citation impact in a composite citation indicator that adjusts for co-authorship and author positions, 67% of EP authors in non-Physics fields remained within the top-2% according to that indicator among all authors with >=5 full papers. Extreme productivity has become worryingly common across scientific fields with rapidly increasing rates in some countries and settings. less

The Covid-19 crisis unprecedentedly required researchers to adapt to significant changes in their work and personal lives. Our study aims to fill this gap analysing the Covid-19 emotional impact and confinement potential disruptions on researchers activity (specifically, those related to working conditions, caring responsibilities, health, balance, and social support) considering the modulating role played by age, gender, and job position. An online survey was distributed during the first lockdown period of the Covid-19 pandemic, and answers from 1301 researchers (ECR %, senior researchers %) working in Sciences (28.1%), Social Sciences (25.9%), Humanities (16.2%), Health (16.2%) and in Engineering and Architecture (13.5%) were collected. The study highlights that the initial lockdown during the Covid-19 pandemic had a significant emotional impact on researchers, exacerbating pre-existing emotional distress and burnout within this group. Factors such as age, health, gender, and difficulties in balancing work and family life were associated with an increased risk of burnout and emotional distress. Lack of social support was identified as a significant risk factor, while the academic culture prioritizing productivity over well-being contributed to the issue. These findings underscore the need for greater support and cultural changes in academia to preserve researchers\' mental health and prevent the chronicization of mental health issues in young academics. less