The SkillsCenter: Creating scalable research opportunities for STEM students.

Undergraduate students generally need laboratory skills and experience to be accepted into a position within an academic lab or a company. However, those settings are traditionally where students would develop that necessary expertise. We developed a laboratory course paradigm to equip students with the skills they need to access future opportunities.

Insights and strategies for improving equity in graduate school admissions.

Applying to graduate school can be particularly challenging for students from historically minoritized backgrounds due to a hidden curriculum in the graduate admissions process. To address this issue, a team of volunteer STEM trainees established the Científico Latino Graduate Student Mentorship Initiative (CL-GSMI) in 2019 to support applicants from historically minoritized backgrounds. CL-GSMI is designed to improve access to critical resources, including information, mentorship, and financial support, and has assisted 443 students in applying and matriculating to graduate school. Using program evaluation data from 2020 to 2021, we highlight areas in graduate school admissions that can be improved to promote equity and inclusion.

Unrealized potential from smaller institutions: Four strategies for advancing STEM diversity.

Diversity within science, technology, engineering, and mathematics (STEM) remains disturbingly low. Relative to larger, highly funded universities, smaller schools harbor more diverse student demographics and more limited resources. Here, we propose four strategies leveraging the unique advantages of smaller institutions to advance underrepresented scholars along STEM pathways.

Breaking Out of the Academic Pipeline.

For many graduate students, the academic path may not be the best fit, and with limited faculty positions available, many students are now looking to other career possibilities. University programs are helping students to explore and pursue alternative careers.

Education evolving: teaching biology online.

The rise of massive open online courses (MOOCs) is shaking up education. For science professors, the Internet offers new opportunities and technological tools to develop new materials, rethink curricula, and teach more effectively, benefiting students both on campus and on the web.

Catalyzing curriculum evolution in graduate science education.

Strategies in life science graduate education must evolve in order to train a modern workforce capable of integrative solutions to challenging problems. Our institution has catalyzed such evolution through building a postdoctoral Curriculum Fellows Program that provides a collaborative and scholarly education laboratory for innovation in graduate training.

It is time to authenticate the Microbiome Sciences with accredited educational programs and departments.

The Microbiome Sciences are at a crucial maturation stage. Scientists and educators should now view the Microbiome Sciences as a flourishing and autonomous discipline, creating degree programs and departments that are conducive to cohesive growth.

Organizing graduate life sciences education around nodes and connections.

Biomedical education is currently faced with a number of significant challenges, including the explosion of information and the need to train researchers who can work across traditional disciplinary boundaries. We propose a new integrated model for graduate education in the life sciences that addresses these issues.

Interdisciplinary college curriculum and its labor market implications.

This article sheds light on how to capture knowledge integration dynamics in college course content, improves and enriches the definition and measurement of interdisciplinarity, and expands the scope of research on the benefits of interdisciplinarity to postcollege outcomes. We distinguish between what higher education institutions claim regarding interdisciplinarity and what they appear to actually do. We focus on the core academic element of student experience-the courses they take, develop a text-based semantic measure of interdisciplinarity in college curriculum, and test its relationship to average earnings of graduates from different types of schools of higher education. We observe that greater exposure to interdisciplinarity-especially for science majors-is associated with increased earnings after college graduation.

Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices.

Throughout their education and when reading the scientific literature, students may get the impression that there is a unique and correct analysis strategy for every data analysis task and that this analysis strategy will always yield a significant and noteworthy result. This expectation conflicts with a growing realization that there is a multiplicity of possible analysis strategies in empirical research, which will lead to overoptimism and nonreplicable research findings if it is combined with result-dependent selective reporting. Here, we argue that students are often ill-equipped for real-world data analysis tasks and unprepared for the dangers of selectively reporting the most promising results. We present a seminar course intended for advanced undergraduates and beginning graduate students of data analysis fields such as statistics, data science, or bioinformatics that aims to increase the awareness of uncertain choices in the analysis of empirical data and present ways to deal with these choices through theoretical modules and practical hands-on sessions.

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

Curriculum learning for human compositional generalization.

Generalization (or transfer) is the ability to repurpose knowledge in novel settings. It is often asserted that generalization is an important ingredient of human intelligence, but its extent, nature, and determinants have proved controversial. Here, we examine this ability with a paradigm that formalizes the transfer learning problem as one of recomposing existing functions to solve unseen problems. We find that people can generalize compositionally in ways that are elusive for standard neural networks and that human generalization benefits from training regimes in which items are axis aligned and temporally correlated. We describe a neural network model based around a Hebbian gating process that can capture how human generalization benefits from different training curricula. We additionally find that adult humans tend to learn composable functions asynchronously, exhibiting discontinuities in learning that resemble those seen in child development.

Establishing the short course in transferable skills training program.

While PhD programs prepare graduate students to perform biomedical research, a defined systematic training program for transferable skills is generally lacking. When provided, this training is often informal, unstructured, or inconsistent. Therefore, there is a need to provide critical skills in marketing, relationship building, project management, and budgeting to prepare trainees to navigate into a productive, engaging, and rewarding biomedical research career. To address this gap in training, the School of Graduate Studies at Meharry Medical College has developed the SHort Course In transFerable skills Training (SHIFT) Program, a 1-year professional development program accessible to graduate students in the United States who are enrolled in graduate biomedical research related programs. The SHIFT Program has been launched to equip trainees with skills essential for success in all biomedical science careers. PhD students will be taught the primary constituents of career management through the use of four training modules. In Module I, students complete self-assessments and are assigned to a small peer-mentoring team with mentors. Module II consists of a 5-day workshop that encompasses instruction on the transferable skills identified as essential for career success. Module III entails monthly interactive discussions over a 6-month period involving case study review and mentor-guided discussions to further reinforce skills learned. In Module IV, students compile the information learned from Modules I-III to develop an Individual Development Plan that incorporates 3-5 specific, measurable, attainable, relevant, and time-based career goals. Collectively, the SHIFT Program will allow participants to train, practice, and refresh skills, empowering them to navigate career transitions and obtain success in the career of their choice.

Teaching the way I wish I was taught: Design and implementation of a class on historically excluded and underrepresented scientists.

As the first Inupiaq person to earn a PhD in microbiology, I learned the hard way that groups of people have been excluded from science, technology, engineering and mathematics in the United States since the first University was built by Black and Indigenous slaves. Students from historically excluded and underrepresented (HEU) backgrounds typically do not see themselves in textbooks, conferences, or classrooms, especially in science, technology, engineering, mathematics and medicine (STEMM) fields. Similarly, students from these backgrounds and non-excluded backgrounds typically do not understand the history or consequences of exclusion. Here I describe the development and implementation of a class that teaches undergraduate students about the current state of diversity in STEMM jobs in the US, the history of exclusion that resulted in a deficit of people from various backgrounds, the consequences of excluding these people from research specifically, current leaders in research from HEU backgrounds, and how to implement changes. The students are taught how to communicate their findings in oral and written communication to various audiences. Based on decades of experiences, discussions, readings, and more, I teach students the reasons there are so few people from HEU backgrounds in academia and in STEMM specifically, and what can be done at the University level to ensure that people from all backgrounds are represented in STEMM. In this way, I teach students what I wish I had been taught decades ago.

Running a successful STEMM summer program: A week-by-week guide.

While some established undergraduate summer programs are effective across many institutions, these programs may only be available to some principal investigators or may not fully address the diverse needs of incoming undergraduates. This article outlines a 10-week science, technology, engineering, mathematics, and medicine (STEMM) education program designed to prepare undergraduate students for graduate school through a unique model incorporating mentoring dyads and triads, cultural exchanges, and diverse activities while emphasizing critical thinking, research skills, and cultural sensitivity. Specifically, we offer a straightforward and adaptable guide that we have used for mentoring undergraduate students in a laboratory focused on mitochondria and microscopy, but which may be customized for other disciplines. Key components include self-guided projects, journal clubs, various weekly activities such as mindfulness training and laboratory techniques, and a focus on individual and cultural expression. Beyond this unique format, this 10-week program also seeks to offer an intensive research program that emulates graduate-level experiences, offering an immersive environment for personal and professional development, which has led to numerous achievements for past students, including publications and award-winning posters.

Generating a Prioritized List of Operative Procedures for Simulation-based Assessment of General Surgery Trainees Through Consensus.

OBJECTIVE: To develop appropriate content for high-stakes simulation-based assessments of operative competence in general surgery training through consensus. BACKGROUND: Valid methods of summative operative competence assessment are required by competency-based training programs in surgery. METHOD: An online Delphi consensus study was conducted. Procedures were derived from the competency expectations outlined by the Joint Committee on Surgical Training Curriculum 2021, and subsequent brainstorming. Procedures were rated according to their perceived importance, perceived procedural risk, how frequently they are performed, and simualtion feasibility by a purposive sample of 30 surgical trainers and a 5-person steering group. A modified Copenhagen Academy for Medical Education and Simulation Needs Assessment Formula was applied to the generated data to produce ranked procedural lists, which were returned to participants for re-prioritization. RESULTS: Prioritized lists were generated for simulation-based operative competence assessments at 2 key stages of training; the end of 'phase 2' prior to the development of a sub-specialty interest, and the end of 'phase 3', that is, end-of-training certification. A total of 21 and 16 procedures were deemed suitable for assessments at each of these stages, respectively. CONCLUSIONS: This study describes a national needs assessment approach to content generation for simulation-based assessments of operative competence in general surgery using Delphi consensus methodology. The prioritized procedural lists generated by this study can be used to further develop operative skill assessments for use in high-stakes scenarios, such as trainee progression, entrustment, and end-of-training certification, before subsequent validity testing.

Obesity preclinical elective: a qualitative thematic analysis of student feedback.

BACKGROUND: Education about the prevalent chronic disease of obesity is still minimal and variable in medical school curricula. In a student-led effort with faculty support, the authors designed and implemented an obesity medicine elective at Case Western Reserve University School of Medicine (CWRU). The 10-week elective, taught by seven physicians and one dietitian, was offered in January 2023 to medical students and included: weekly lectures, an interactive session with a patient, shadowing in obesity medicine practices, attendance at a distance-learning intensive behavioral lifestyle program, student presentations, and a final written reflection. The purpose of this study was to analyze the elective reflections and identify themes about the elective's value and areas to improve. METHODS: The authors analyzed reflections from the 20 medical students that completed the elective via qualitative thematic analysis. The analysis was performed using the Braun and Clarke six-phase framework: (1) become familiar with the data, (2) generate initial codes, (3) search for themes, (4) review themes, (5) define themes, and (6) write-up. RESULTS: The themes identified were improved: (1) understanding of obesity as a chronic disease, (2) knowledge about treatment options for obesity (3) confidence in compassionate obesity counseling skills, and (4) skills to confront weight bias. Theme (5) consisted of highlights (hearing from experts, practicing evidence-based medicine, and interacting with patients), and areas to improve (session length, presentation format, more peer-to-peer interaction, and more diverse patient interactions). CONCLUSIONS: Medical student assessments of a new obesity medicine elective described improved attitudes, knowledge, and skills to address obesity and obesity bias. Students were very satisfied and contributed ideas for improvements. This elective structure and evaluation method is a feasible model to provide medical students with meaningful experiences related to obesity.

An efficient curriculum learning-based strategy for molecular graph learning.

Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG's encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning. Availability: The source code is available in https://github.com/gu-yaowen/CurrMG.

Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction.

MOTIVATION: Proteins play crucial roles in biological processes, with their functions being closely tied to thermodynamic stability. However, measuring stability changes upon point mutations of amino acid residues using physical methods can be time-consuming. In recent years, several computational methods for protein thermodynamic stability prediction (PTSP) based on deep learning have emerged. Nevertheless, these approaches either overlook the natural topology of protein structures or neglect the inherent noisy samples resulting from theoretical calculation or experimental errors. RESULTS: We propose a novel Global-Local Graph Neural Network powered by Unbiased Curriculum Learning for the PTSP task. Our method first builds a Siamese graph neural network to extract protein features before and after mutation. Since the graph's topological changes stem from local node mutations, we design a local feature transformation module to make the model focus on the mutated site. To address model bias caused by noisy samples, which represent unavoidable errors from physical experiments, we introduce an unbiased curriculum learning method. This approach effectively identifies and re-weights noisy samples during the training process. Extensive experiments demonstrate that our proposed method outperforms advanced protein stability prediction methods, and surpasses state-of-the-art learning methods for regression prediction tasks. AVAILABILITY AND IMPLEMENTATION: All code and data is available at https://github.com/haifangong/UCL-GLGNN.

Reprohackathons: promoting reproducibility in bioinformatics through training.

MOTIVATION: The reproducibility crisis has highlighted the importance of improving the way bioinformatics data analyses are implemented, executed, and shared. To address this, various tools such as content versioning systems, workflow management systems, and software environment management systems have been developed. While these tools are becoming more widely used, there is still much work to be done to increase their adoption. The most effective way to ensure reproducibility becomes a standard part of most bioinformatics data analysis projects is to integrate it into the curriculum of bioinformatics Master's programs. RESULTS: In this article, we present the Reprohackathon, a Master's course that we have been running for the last 3 years at Université Paris-Saclay (France), and that has been attended by a total of 123 students. The course is divided into two parts. The first part includes lessons on the challenges related to reproducibility, content versioning systems, container management, and workflow systems. In the second part, students work on a data analysis project for 3-4 months, reanalyzing data from a previously published study. The Reprohackaton has taught us many valuable lessons, such as the fact that implementing reproducible analyses is a complex and challenging task that requires significant effort. However, providing in-depth teaching of the concepts and the tools during a Master's degree program greatly improves students' understanding and abilities in this area.