Overcoming the Barriers to Teaching Teamwork to Undergraduates in STEM

There is widespread recognition that undergraduate students in the life sciences must learn how to work in teams. However, instructors who wish to incorporate teamwork into their classrooms rarely have formal training in how to teach teamwork. This is further complicated by the application of synonymous and often ambiguous terminology regarding teamwork that is found in literature spread among many different disciplines. There are significant barriers for instructors wishing to identify and implement best practices. We synthesize key concepts in teamwork by considering the knowledge, skills, and attitudes (KSAs) necessary for success, the pedagogies and curricula for teaching those KSAs, and the instruments available for evaluating and assessing success. There are only a limited number of studies on teamwork in higher education that present an intervention with a control group and a formal evaluation or assessment. Moreover, these studies are almost exclusively outside STEM disciplines, raising questions about their extensibility. We conclude by considering how to build an evidence base for instruction that will empower students with the KSAs necessary for participating in a lifetime of equitable and inclusive teamwork.

Perspectives from Undergraduate Life Sciences Faculty: Are We Equipped to Effectively Accommodate Students With Disabilities in Our Classrooms?

Higher education has evolved in ways that may increase the challenges life science faculty face in providing accommodations for students with disabilities. Guided by Expectancy-Value Theory, we interviewed 34 life sciences faculty instructors from institutions nationwide to explore faculty motivation to create disability-inclusive educational experiences. We found that faculty in our sample perceive that providing most standard accommodations is a manageable but often challenging task. Further, faculty in our sample feel that improving accommodations necessitates additional support from their institutions. Most faculty had high attainment value for providing accommodations, in that they strongly believed that supporting students with disabilities is the fair and right thing to do. However, faculty did not perceive much utility value or intrinsic value in their task of providing accommodations, and most reported that providing accommodations can be a substantial burden on faculty. These findings imply that current approaches to providing inclusive educational experiences for students with disabilities rely primarily on the personal belief that providing accommodations is the right thing to do, which likely results in a flawed and inequitable system given that not all faculty equally share this conviction.

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Advice to a Young Mathematical Biologist

This paper offers advice to early-mid career researchers in Mathematical Biology from ten past and current Presidents of the Society for Mathematical Biology. The topics covered include deciding if a career in academia is right for you; finding and working with a mentor; building collaborations and working with those from other disciplines; formulating a research question; writing a paper; reviewing papers; networking; writing fellowship or grant proposals; applying for faculty positions; and preparing and giving lectures. While written with mathematical biologists in mind, it is hoped that this paper will be of use to early and mid career researchers across the mathematical, physical and life sciences, as they embark on careers in these disciplines.

EU-LIFE charter of independent life science research institutes

The diverse range of organizations contributing to the global research ecosystem is believed to enhance the overall quality and resilience of its output. Mid-sized autonomous research institutes, distinct from universities, play a crucial role in this landscape. They often lead the way in new research fields and experimental methods, including those in social and organizational domains, which are vital for driving innovation. The EU-LIFE alliance was established with the goal of fostering excellence by developing and disseminating best practices among European biomedical research institutes. As directors of the 15 EU-LIFE institutes, we have spent a decade comparing and refining our processes. Now, we are eager to share the insights we've gained. To this end, we have crafted this Charter, outlining 10 principles we deem essential for research institutes to flourish and achieve ground-breaking discoveries. These principles, detailed in the Charter, encompass excellence, independence, training, internationality and inclusivity, mission focus, technological advancement, administrative innovation, cooperation, societal impact, and public engagement. Our aim is to inspire the establishment of new institutes that adhere to these principles and to raise awareness about their significance. We are convinced that they should be viewed a crucial component of any national and international innovation strategies.

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

Controlled experiment finds no detectable citation bump from Twitter promotion

Multiple studies across a variety of scientific disciplines have shown that the number of times that a paper is shared on Twitter (now called X) is correlated with the number of citations that paper receives. However, these studies were not designed to answer whether tweeting about scientific papers causes an increase in citations, or whether they were simply highlighting that some papers have higher relevance, importance or quality and are therefore both tweeted about more and cited more. The authors of this study are leading science communicators on Twitter from several life science disciplines, with substantially higher follower counts than the average scientist, making us uniquely placed to address this question. We conducted a three-year-long controlled experiment, randomly selecting five articles published in the same month and journal, and randomly tweeting one while retaining the others as controls. This process was repeated for 10 articles from each of 11 journals, recording Altmetric scores, number of tweets, and citation counts before and after tweeting. Randomization tests revealed that tweeted articles were downloaded 2.6-3.9 times more often than controls immediately after tweeting, and retained significantly higher Altmetric scores (+81%) and number of tweets (+105%) three years after tweeting. However, while some tweeted papers were cited more than their respective control papers published in the same journal and month, the overall increase in citation counts after three years (+7% for Web of Science and +12% for Google Scholar) was not statistically significant (p > 0.15). Therefore while discussing science on social media has many professional and societal benefits (and has been a lot of fun), increasing the citation rate of a scientist's papers is likely not among them.

Investigation of the dynamical structures of double-chain deoxyribonucleic acid model in biological sciences

The present research investigates the double-chain deoxyribonucleic acid model, which is important for the transfer and retention of genetic material in biological domains. This model is composed of two lengthy uniformly elastic filaments, that stand in for a pair of polynucleotide chains of the deoxyribonucleic acid molecule joined by hydrogen bonds among the bottom combination, demonstrating the hydrogen bonds formed within the chain's base pairs. The modified extended Fan sub equation method effectively used to explain the exact travelling wave solutions for the double-chain deoxyribonucleic acid model. Compared to the earlier, now in use methods, the previously described modified extended Fan sub equation method provide more innovative, comprehensive solutions and are relatively straightforward to implement. This method transforms a non-linear partial differential equation into an ODE by using a travelling wave transformation. Additionally, the study yields both single and mixed non-degenerate Jacobi elliptic function type solutions. The complexiton, kink wave, dark or anti-bell, V, anti-Z and singular wave shapes soliton solutions are a few of the creative solutions that have been constructed utilizing modified extended Fan sub equation method that can offer details on the transversal and longitudinal moves inside the DNA helix by freely chosen parameters. Solitons propagate at a consistent rate and retain their original shape. They are widely used in nonlinear models and can be found everywhere in nature. To help in understanding the physical significance of the double-chain deoxyribonucleic acid model, several solutions are shown with graphics in the form of contour, 2D and 3D graphs using computer software Mathematica 13.2. All of the requisite constraint factors that are required for the completed solutions to exist appear to be met. Therefore, our method of strengthening symbolic computations offers a powerful and effective mathematical tool for resolving various moderate nonlinear wave problems. The findings demonstrate the system's potentially very rich precise wave forms with biological significance. The fundamentals of double-chain deoxyribonucleic acid model diffusion and processing are demonstrated by this work, which marks a substantial development in our knowledge of double-chain deoxyribonucleic acid model movements.

An open-source, high-resolution, automated fluorescence microscope

Fluorescence microscopy is a fundamental tool in the life sciences, but the availability of sophisticated equipment required to yield high-quality, quantitative data is a major bottleneck in data production in many laboratories worldwide. This problem has long been recognized and the abundancy of low-cost electronics and the simplification of fabrication through 3D-printing have led to the emergence of open-source scientific hardware as a research field. Cost effective fluorescence microscopes can be assembled from cheaply mass-produced components, but lag behind commercial solutions in image quality. On the other hand, blueprints of sophisticated microscopes such as light-sheet or super-resolution systems, custom-assembled from high quality parts, are available, but require a high level of expertise from the user. Here, we combine the UC2 microscopy toolbox with high-quality components and integrated electronics and software to assemble an automated high-resolution fluorescence microscope. Using this microscope, we demonstrate high resolution fluorescence imaging for fixed and live samples. When operated inside an incubator, long-term live-cell imaging over several days was possible. Our microscope reaches single molecule sensitivity, and we performed single particle tracking and SMLM super-resolution microscopy experiments in cells. Our setup costs a fraction of its commercially available counterparts but still provides a maximum of capabilities and image quality. We thus provide a proof of concept that high quality scientific data can be generated by lay users with a low-budget system and open-source software. Our system can be used for routine imaging in laboratories that do not have the means to acquire commercial systems and through its affordability can serve as teaching material to students.

Demystifying dimensionality reduction techniques in the 'omics' era: A practical approach for biological science students

Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.

Flow cytometry: Aspects and application in plant and biological science

Flow cytometry is a potent method that enables the quick and concurrent investigation of several characteristics of single cells in solution. Photodiodes or photomultiplier tubes are employed to detect the dispersed and fluorescent light signals that are produced by the laser beam as it passes through the cells. Photodetectors transform the light signals produced by the laser into electrical impulses. A computer then analyses these electrical impulses to identify and measure the various cell populations depending on their fluorescence or light scattering characteristics. Based on their fluorescence or light scattering properties, cell populations can be examined and/or isolated. This review covers the basic principle, components, working and specific biological applications of flow cytometry, including studies on plant, cell and molecular biology and methods employed for data processing and interpretation as well as the potential future relevance of this methodology in light of retrospective analysis and recent advancements in flow cytometry.

Past, present, and future of CRISPR genome editing technologies

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.

Ionic Liquid-Mediated Dynamic Polymerization for Facile Aqueous-Phase Synthesis of Enzyme-Covalent Organic Framework Biocatalysts

Utilizing covalent organic framework (COF) as a hypotoxic and porous scaffold to encapsulate enzyme (enzyme@COF) has inspired numerous interests at the intersection of chemistry, materials, and biological science. In this study, we report a convenient scheme for one-step, aqueous-phase synthesis of highly crystalline enzyme@COF biocatalysts. This facile approach relies on an ionic liquid (2 μL of imidazolium ionic liquid)-mediated dynamic polymerization mechanism, which can facilitate the in situ assembly of enzyme@COF under mild conditions. This green strategy is adaptive to synthesize different biocatalysts with highly crystalline COF "exoskeleton", as well evidenced by the low-dose cryo-EM and other characterizations. Attributing to the rigorous sieving effect of crystalline COF pore, the hosted lipase shows non-native selectivity for aliphatic acid hydrolysis. In addition, the highly crystalline linkage affords COF "exoskeleton" with higher photocatalytic activity for in situ production of H2 O2 , enabling us to construct a self-cascading photo-enzyme coupled reactor for pollutants degradation, with a 2.63-fold degradation rate as the poorly crystalline photo-enzyme reactor. This work showcases the great potentials of employing green and trace amounts of ionic liquid for one-step synthesis of crystalline enzyme@COF biocatalysts, and emphasizes the feasibility of diversifying enzyme functions by integrating the reticular chemistry of a COF.

A Research Operations, Management, and Strategy Fellowship for Life Sciences PhD Graduates

PROBLEM

With less than 25% of PhD-trained scientists in the United States securing a tenure-track faculty position following training, nonacademic careers have become common. As the academic research enterprise has increased, business-oriented careers have emerged. The Research Operations, Management, and Strategy (ROMS) Fellowship was developed to increase awareness of and prepare life sciences PhD graduates for business-focused careers.

APPROACH

The ROMS Fellowship was developed from March through December 2018 by the University of Michigan Medical School. Launched in 2019 and based on real-world experiences, the 2-year ROMS Fellowship combines immersion rotations and project work to develop an understanding of foundational infrastructure across the full spectrum of research.

OUTCOMES

From 2019 to 2022, there were 4 ROMS Fellowship recruitment cycles, with a mean of 7 applicants per cycle and 2 fellows selected each year. Of the 8 fellows recruited, 5 (62.5%) joined directly from PhD training, whereas 3 (37.5%) had 2 to 6 years of postdoctoral training. Fellows have worked with 26 departments on 44 rotation projects and 30 impact projects and self-reported significant skill development in communicating with diverse stakeholders, strategic thinking, using new tools and resources, developing and scoping a project plan, and managing and leading a project. To date, 4 fellows have completed the program and were hired immediately into full-time positions at the University of Michigan Medical School.

NEXT STEPS

Early feedback indicates that the program has been well received and effective. Previously, program refinement was directed by qualitative input from fellows and unit directors. However, for future cohorts, assessment tools will be implemented to capture qualitative and quantitative data to measure acquired skills and how program components contribute to professional development and career placement. A longitudinal follow-up will also be conducted with program alumni to track longer-term outcomes and career pathways.

From garages to ecosystems: the coevolution of life science incubators and accelerators

Incubators and accelerators catalyze the launch of life science startups and have evolved from simple facilities to vibrant ecosystems offering research infrastructure, programs, and funding. Analysis of financing activities indicates the outperformance of incubator companies relative to accelerators in fundraising, mergers and acquisitions (M&As), and initial public offerings (IPOs), attributed to extended interactions with investors and peers.

Transitions in development - an interview with Peng Du

Peng Du is Associate Professor at Peking University College of Life Sciences, where he started his own lab in 2018. Peng's research focusses on post-transcriptional RNA regulatory pathways in early mammalian embryonic development and disease. We spoke to Peng over Zoom to find out more about his career path, his transition from plant to mammalian research and his experience becoming a group leader.

[Exploring the innovative talents training mode in new era]

Innovation is an important way to promote economic development and social progress. Recent years have seen rapid development of biological sciences. In response to social demands and the needs for developing an innovative country, fostering innovative talents in the field of biosciences has become a significant initiative supported by national policies and the needs from talent market. Taking the innovative talent training mode implemented by Zhejiang Normal University in the field of biological sciences as an example, this paper comprehensively introduces several key aspects of the mode. This includes establishing a mentorship system as the foundation, carrying out curriculum reform through project competitions and practical platforms, and promoting synergy among industry, academia, and research in talent training. This training mode has achieved positive results in practice, promoting the training of outstanding innovative talents in biological science majors, and may facilitate the reform of talent training in similar majors.

Health Digital Twins in Life Science and Health Care Innovation

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Mentoring across difference and distance: building effective virtual research opportunities for underrepresented minority undergraduate students in biological sciences

IMPORTANCE

Summer Research Experiences for Undergraduates (REUs) are established to provide platforms for interest in scientific research and as tools for eventual matriculation to scientific graduate programs. Unfortunately, the COVID-19 pandemic forced the cancellation of in-person programs for 2020 and 2021, creating the need for alternative programming. The National Summer Undergraduate Research Project (NSURP) was created to provide a virtual option to REUs in microbiology to compensate for the pandemic-initiated loss of research opportunities. Although in-person REUs have since been restored, NSURP currently remains an option for those unable to travel to in-person programs in the first place due to familial, community, and/or monetary obligations. This study examines the effects of the program's first 3 years, documenting the students' experiences, and suggests future directions and areas of study related to the impact of virtual research experiences on expanding and diversifying science, technology, engineering, and mathematics.

When remediating one artifact results in another: control, confounders, and correction

Scientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not satisfy the criterion of causal modularity. Thus, remediation can affect more than just the factor responsible for the artifact. However, if researchers can determine the consequences of their remediation, they can make adjustments that control for the present artifact as well as for previously controlled ones. Current philosophical accounts of artifacts and the factors responsible for them cannot adequately address this issue, as they do not account for what is needed for artifact remediation (and specifically correction). I support my argument by paralleling it with ongoing concerns regarding the transparency of complex computational systems, as near future remediation across the experimental life sciences will likely make greater use of AI tools to correct for artifacts.

Triple-Bond Vibrations: Emerging Applications in Energy and Biological Sciences

Triple bonds, such as that formed between two carbon atoms (i.e., C≡C) or that formed between one carbon atom and one nitrogen atom (i.e., C≡N), afford unique chemical bonding and hence vibrational characteristics. As such, they are not only frequently used to construct molecules with tailored chemical and/or physical properties but also employed as vibrational probes to provide site-specific chemical and/or physical information at the molecular level. Herein, we offer our perspective on the emerging applications of various triple-bond vibrations in energy and biological sciences with a focus on C≡C and C≡N triple bonds.

The CRISPR Cas patent files, part 1: Cas9 - Where to we stand at the 10 year halftime?

CRISPR Cas9 has turned out to be one of the most influential technologies in the life sciences. However, ferocious patent debates and an unclear licensing situation makes access to this technology difficult for Small and medium enterprises. This article gives an overview of the status quo 10 years after the seminal patents were filed.

Europe PMC in 2023

Europe PMC (https://europepmc.org/) is an open access database of life science journal articles and preprints, which contains over 42 million abstracts and over 9 million full text articles accessible via the website, APIs and bulk download. This publication outlines new developments to the Europe PMC platform since the last database update in 2020 (1) and focuses on five main areas. (i) Improving discoverability, reproducibility and trust in preprints by indexing new preprint content, enriching preprint metadata and identifying withdrawn and removed preprints. (ii) Enhancing support for text and data mining by expanding the types of annotations provided and developing the Europe PMC Annotations Corpus, which can be used to train machine learning models to increase their accuracy and precision. (iii) Developing the Article Status Monitor tool and email alerts, to notify users about new articles and updates to existing records. (iv) Positioning Europe PMC as an open scholarly infrastructure through increasing the portion of open source core software, improving sustainability and accessibility of the service.

Design automation of microfluidic single and double emulsion droplets with machine learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.

Elements and development processes for test methods in toxicology and human health-relevant life science research

Many laboratory procedures generate data on properties of chemicals, but they cannot be equated with toxicological "test methods". This apparent discrepancy is not limited to in vitro testing, using animal-free new approach methods (NAM), but also applies to animal-based testing approaches. Here, we give a brief overview of the differences between data generation and the setup or use of a complete test method. While there is excellent literature available on this topic for specialists (GIVIMP guidance; ToxTemp overview), a brief overview and easily-accessible entry point may be useful for a broader community. We provide a single figure to summarize all test method elements and processes required in the development (setup and adaptation) of a test method. The exposure scheme, the endpoint, and the test system are briefly outlined as fundamental elements of any test method. A rationale is provided, why they are not sufficient. We then explain the importance and role of purpose definition (including some information on what is modelled) and the prediction model, aka data interpretation procedure, which depends on the purpose definition, as further essential elements. This connection exemplifies that all fundamental elements are interdependent, and none can be omitted. Finally, discussion is provided on validation as a measure to provide confidence in the reliability, performance, and relevance of a test method. In this sense, validation may be considered a sixth fundamental element for practical use of test methods.

Biological thermodynamics: Ervin Bauer and the unification of life sciences and physics

Biological systems operate toward the maximization of their self-maintenance and adaptability. This is achieved through the establishment of robust self-maintaining configurations acting as attractors resistant to external and internal perturbations. Ervin Bauer (1890-1938) was the first who formulated this essential thermodynamic constraint in the operation of biological systems, which he defined as the stable non-equilibrium state. The latter appears as the basic attractor relative to which biological organization is established. The stable non-equilibrium state represents a generalized cell energy status corresponding to efficient spatiotemporal organization of the fluxes of matter and energy and constantly reproducing the conditions of self-maintenance of metabolism and controlling the rates of major metabolic fluxes that follow thermodynamically and kinetically defined computational principles. This state is realized in the autopoietic structures having closed loops of causation based on the operation of biological codes. The principle of thermodynamic buffering determines the conditions for optimization of the fluxes of load and consumption in metabolism establishing the conditions of metabolic stable non-equilibrium. In developing and evolving biological systems, the principle of stable non-equilibrium is transformed into the principle of increasing external work, which is grounded in the hyper-restorative non-equilibrium dynamics. Bauer's concept of the stable non-equilibrium state puts thermodynamics into the frames of the internal biological causality governing self-maintenance and development of living systems. It can be defined as a relational theory of biological thermodynamics since the standard to which it refers represents the actual biological function rather than the abstract state of thermodynamic equilibrium.

The FEBS Journal in 2024: maintaining standards and taking the road less travelled

The FEBS Journal publishes research on diverse topics in the molecular life sciences relating to the molecules and mechanisms underpinning biological processes. Editor-in-Chief Seamus Martin discusses the rewards of pursuing a career in basic research, some highlights of the past year at the journal, and what's in store for 2024.

Modeling in molecular genetics allows students to make connections between biological scales

Students often see college courses as the presentation of disconnected facts, especially in the life sciences. Student-created Structure Mechanism/Relationship Function (SMRF) models were analyzed to understand students' abilities to make connections between genotype, phenotype, and evolution. Students were divided into two sections; one section received instructions that included a specific gene as an example related to larger issues like human disease or the environment. The other section was only given generic examples, like gene X and phenotype Y. Coding of exam models and a comprehensive (extensive) model reveled students were able to make links and work within and between biological scales of organization. Modeling provided a way to show and allow students to practice and demonstrate the ability to build step-by-step causal relationships that link ideas together. We also observed a small differing with students receiving the specific prompt performing better than students receiving generic prompt at the point in the semester where linking across many biological scales was required to be successful.

Atomic Force Microscopy: An Introduction

Imaging of nano-sized particles and sample features is crucial in a variety of research fields, for instance, in biological sciences, where it is paramount to investigate structures at the single particle level. Often, two-dimensional images are not sufficient, and further information such as topography and mechanical properties are required. Furthermore, to increase the biological relevance, it is desired to perform the imaging in close to physiological environments. Atomic force microscopy (AFM) meets these demands in an all-in-one instrument. It provides high-resolution images including surface height information leading to three-dimensional information on sample morphology. AFM can be operated both in air and in buffer solutions. Moreover, it has the capacity to determine protein and membrane material properties via the force spectroscopy mode. Here we discuss the principles of AFM operation and provide examples of how biomolecules can be studied. New developments in AFM are discussed, and by including approaches such as bimodal AFM and high-speed AFM (HS-AFM), we show how AFM can be used to study a variety of static and dynamic single biomolecules and biomolecular assemblies.

Phage DNA Extraction, Genome Assembly, and Genome Closure

Bacteriophages, or more simply phages, are currently experiencing a renaissance in life science research for their roles in natural microbial communities, their potential use as antimicrobials, and biotechnological applications. In the modern era, one of the primary steps in phage characterization is obtaining the sequence of the complete genome; this information can be used to determine the relationship of the phage to known phages, predict phage lifestyle, and is a prerequisite for many downstream applications. This protocol describes methods for determining the complete sequence of a double-stranded DNA bacteriophage genome, including DNA extraction from a phage lysate, sending the DNA out to a sequencing service, assembly of the sequence raw reads, and completion of the genome sequence.

The Future of (Radio)-Labeled Compounds in Research and Development within the Life Science Industry

In this review the applications of isotopically labeled compounds are discussed and put into the context of their future impact in the life sciences. Especially discussing their use in the pharma and crop science industries to follow their fate in the environment, in vivo or in complex matrices to understand the potential harm of new chemical structures and to increase the safety of human society.

Gender Identity and Student Perceptions of Peer Research Aptitude in CUREs and Traditional Laboratory Courses in the Biological Sciences

While several studies have investigated gender inequities in the social learning environment of biology lecture courses, that same phenomenon remains largely unexplored in biology laboratory contexts. We conducted a mixed methods study to understand the influence of gender on student perceptions of their peers' research aptitude in introductory biology CUREs and traditional laboratory courses. Specifically, students (N = 125) were asked to complete a name generator survey at three time points across the semester. This survey asked students to list the names of peers whom they viewed as "most proficient" in the course investigations and to justify their choice via an open-ended response prompt. Using social network analysis, exponential random graph modeling (ERGM), and thematic analysis, we demonstrate that student gender identity did not influence nomination behaviors in CURE or traditional laboratory courses. However, the ERGMs reveal the presence of a popularity effect in CUREs and demonstrate that mutual nominations were more prevalent in traditional laboratory courses. Our qualitative data further provide insights into the reasons students nominated peers as proficient in CURE and traditional courses.

Re-Envisioning the Culture of Undergraduate Biology Education to Foster Black Student Success: A Clarion Call

The purpose of this paper is to present an argument for why there is a need to re-envision the underlying culture of undergraduate biology education to ensure the success, retention, and matriculation of Black students. The basis of this argument is the continued noted challenges with retaining Black students in the biological sciences coupled with existing research that implicates science contexts (i.e., the cultural norms, values, and beliefs manifesting through policies and practices) as being the primary source of the challenges experienced by Black students that lead to their attrition. In presenting this argument, we introduce the Re-Envisioning Culture Network, a multigenerational, interdisciplinary network comprised of higher education administrators, faculty, staff, Black undergraduate students majoring in biology, Black cultural artists, community leaders, and STEM professionals to work together to curate and generate resources and tools that will facilitate change. In introducing the REC Network and disseminating its mission and ongoing endeavors, we generate a clarion call for educators, researchers, STEM professionals, students, and the broader community to join us in this endeavor in fostering transformative change.

2023 at PLOS Biology

2023 saw many important advances in the life sciences. In this editorial, we highlight research from across the breadth of PLOS Biology's scope.

Preparing Teaching Assistants to Facilitate Course-based Undergraduate Research Experiences (CUREs) in the Biological Sciences: A Call to Action

Course-based undergraduate research experiences (CUREs) offer an expanding avenue to engage students in real-world scientific practices. Increasingly, CUREs are instructed by graduate teaching assistants (TAs), yet TAs may be underprepared to facilitate and face unique barriers when teaching CUREs. Consequently, unless TAs are provided professional development (PD) and resources to teach CUREs effectively, they and their students may not reap the assumed benefits of CURE instruction. Here, we describe three perspectives - that of the CURE TA, the CURE designer/facilitator, and the CURE student - that are collectively intended to inform the development of tentative components of CURE TA PD. We compare these perspectives to previous studies in the literature in an effort to identify commonalities across all sources and offer potential insights for advancing CURE TA PD efforts across a diversity of institutional environments. We propose that the most effective CURE TA PD programs will promote the use of CURE-specific instructional strategies as benchmarks for guiding change in teaching practices and should focus on three major elements: 1) enhancement of research and teaching acumen, 2) development of effective and inclusive mentoring practices, and 3) identification and understanding of the factors that make CUREs a unique learning experience.

"I don't Know what I Would do Without it" How Life Science Graduate Students Describe Resource Value

Graduate students often face choices about which resources to use to help them succeed in their programs. These choices likely differ among students, in part, due to different perceptions of resource value. However, little is known about why particular resources might be considered highly valuable to students, thus driving choice. Utilizing expectancy-value theory for help sources as our theoretical framework, this qualitative study explored life science (LS) graduate students' top three resource choices, their explanations about why they made those choices, and whether students' perceptions of value differed among resources and across demographic groups. We addressed two research questions: 1) What resources do LS graduate students consider to be the most important? 2) What drives LS graduate students' perceptions of resource value? Many participants indicated that 'advisor' and 'academic stipend' were most important. Student perceptions of value were driven by their perceptions of which needs resources fulfilled, such as basic needs, academic help, or support. Participants' top resource choices and underlying values of those resources did not differ among demographic groups. We propose a model for understanding graduate student resource choice that may inform future work on student outcomes.

"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

A machine learning-enabled open biodata resource inventory from the scientific literature

Modern biological research depends on data resources. These resources archive difficult-to-reproduce data and provide added-value aggregation, curation, and analyses. Collectively, they constitute a global infrastructure of biodata resources. While the organic proliferation of biodata resources has enabled incredible research, sustained support for the individual resources that make up this distributed infrastructure is a challenge. The Global Biodata Coalition (GBC) was established by research funders in part to aid in developing sustainable funding strategies for biodata resources. An important component of this work is understanding the scope of the resource infrastructure; how many biodata resources there are, where they are, and how they are supported. Existing registries require self-registration and/or extensive curation, and we sought to develop a method for assembling a global inventory of biodata resources that could be periodically updated with minimal human intervention. The approach we developed identifies biodata resources using open data from the scientific literature. Specifically, we used a machine learning-enabled natural language processing approach to identify biodata resources from titles and abstracts of life sciences publications contained in Europe PMC. Pretrained BERT (Bidirectional Encoder Representations from Transformers) models were fine-tuned to classify publications as describing a biodata resource or not and to predict the resource name using named entity recognition. To improve the quality of the resulting inventory, low-confidence predictions and potential duplicates were manually reviewed. Further information about the resources were then obtained using article metadata, such as funder and geolocation information. These efforts yielded an inventory of 3112 unique biodata resources based on articles published from 2011-2021. The code was developed to facilitate reuse and includes automated pipelines. All products of this effort are released under permissive licensing, including the biodata resource inventory itself (CC0) and all associated code (BSD/MIT).

Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data

Biological science produces "big data" in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call "subjective data models". We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants' computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

Donald Trump's contribution to the study of politics and the life sciences

If the life sciences are to have much to say about politics, there needs to be a universal element to political orientations. In this essay, I argue that the recent prominence of nativist, law-and-order, populist politicians reveals the nature of this universal element. All social units have to address bedrock dilemmas about how to deal with norm violators and how welcoming to be to outsiders as well as to proponents of new lifestyles. Might differences on these core dilemmas be the universal element of political life? Using the followers of one of the most prominent examples of a nativist political leader-Donald Trump-as an example, I present data showing that Trump's most earnest followers are different from others-even those who share their general ideological leanings-not on traditional economic or social issues, but rather on the group-based security issues that grow out of the bedrock dilemmas of social life.

Understanding biochemistry: basic aspects of statistics for life sciences

If the biological world is one thing it is variable. As scientists we seek to measure, quantify and explain the causes of this variation. The approach we take to this is remarkably similar whether our research is exploring global temperature, blood pressure, cancer incidence or enzyme kinetics. This approach involves defining clear research questions and applying statistical methods to answer them robustly. This article will introduce a practical example that will be used throughout, specifically whether genetic variation can explain variation in coffee consumption. We assume little experience with statistics and walk through the statistical approach that biologists can use, firstly by describing our data with summary statistics and then by using statistical tests to help arrive at answers to our research question. A General Linear Model (GLM) approach will be used as this is what many common statistical tests are. We explore how to visualise and report results, while checking the assumptions of our analysis. The better we can understand and apply statistics to biological problems, the better we can communicate results and future research to others. The popular statistical programming language R will be used throughout.

Open Hardware for Microfluidics: Exploiting Raspberry Pi Singleboard Computer and Camera Systems for Customisable Laboratory Instrumentation

The integration of Raspberry Pi miniature computer systems with microfluidics has revolutionised the development of low-cost and customizable analytical systems in life science laboratories. This review explores the applications of Raspberry Pi in microfluidics, with a focus on imaging, including microscopy and automated image capture. By leveraging the low cost, flexibility and accessibility of Raspberry Pi components, high-resolution imaging and analysis have been achieved in direct mammalian and bacterial cellular imaging and a plethora of image-based biochemical and molecular assays, from immunoassays, through microbial growth, to nucleic acid methods such as real-time-qPCR. The control of image capture permitted by Raspberry Pi hardware can also be combined with onboard image analysis. Open-source hardware offers an opportunity to develop complex laboratory instrumentation systems at a fraction of the cost of commercial equipment and, importantly, offers an opportunity for complete customisation to meet the users' needs. However, these benefits come with a trade-off: challenges remain for those wishing to incorporate open-source hardware equipment in their own work, including requirements for construction and operator skill, the need for good documentation and the availability of rapid prototyping such as 3D printing plus other components. These advances in open-source hardware have the potential to improve the efficiency, accessibility, and cost-effectiveness of microfluidic-based experiments and applications.