Open Resources for Biology Education (ORBE): a resource collection

In undergraduate life sciences education, open educational resources (OERs) increase accessibility and retention for students, reduce costs, and save instructors time and effort. Despite increasing awareness and utilization of these resources, OERs are not centrally located, and many undergraduate instructors describe challenges in locating relevant materials for use in their classes. To address this challenge, we have designed a resource collection (referred to as Open Resources for Biology Education, ORBE) with 89 unique resources that are primarily relevant to undergraduate life sciences education. To identify the resources in ORBE, we asked undergraduate life sciences instructors to list what OERs they use in their teaching and curated their responses. Here, we summarize the contents of the ORBE and describe how educators can use this resource as a tool to identify suitable materials to use in their classroom context. By highlighting the breadth of unique resources openly available for undergraduate biology education, we intend for the ORBE to increase instructors' awareness and use of OERs.

Illegal interlocks among life science company boards of directors

Competition between life science companies is critical to ensure innovative therapies are efficiently developed. Anticompetitive behavior may harm scientific progress and, ultimately, patients. One well-established category of anticompetitive behavior is the 'interlocking directorate'. It is illegal for companies' directors to 'interlock' by also serving on the boards of competitors. We evaluated overlaps in the board membership of 2,241 public life science companies since 2000. We show that a robust network of interlocking companies is present among these firms. At any given time, 10-20 percent of board members are interlocked; the number of interlocks has more than doubled in the last two decades. Over half of these interlocked firms report over $5 million in historical revenue, exceeding a legal threshold that makes an interlocking directorate a violation of antitrust law. Those interlocks are only illegal if the companies compete, even in part. Using the disease categories for which companies have sponsored clinical trials, we discover that a few markets are responsible for a large fraction of interlocks. We show that in dozens of cases, companies share directors with the very firms they identify as their biggest competitive threats. We provide a data-driven roadmap for policymakers, regulators, and companies to further investigate the contribution of anticompetitive behavior to increased healthcare costs and to enforce the law against illegal interlocks between firms.

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.

Biotechnology in India: An Analysis of 'Biotechnology Industry Research Assistance Council' (BIRAC)-Supported Projects

A comprehensive analysis of 2165 projects funded by India's Department of Biotechnology since 2005 through private-public partnerships, and as of 2012 through the 'Biotechnology Industry Research Assistance Council (BIRAC)' until BIRAC's tenth anniversary at the end of March 2022 reveals details of the science and technology underpinning past and current biotechnology research and development projects in the country. They are led by human healthcare projects (74.9 % overall), of which medical technology (58.7 %) and therapeutics (24.5 %) are the main drivers, ahead of vaccines (4.3 %), regenerative medicine (3.9 %), public health (3.5 %) and others (5.1 %). Agricultural projects (15.2 % overall) have mainly been driven by plant breeding and cloning (24.6 %), animal biotechnology (20.4 %), agri-informatics (13.4 %), aquaculture (6.1 %), and (bio)fertilizers (4.3 %). The key components of industrial biotechnology (9.9 % overall) have been fine chemicals (44.7 %), environmental projects (23.3 %), clean energy (18.1 %) and industrial enzymes (12.1 %). Analysis of the projects funded pre- versus post-2017, compared to the distribution of equity funding as of early 2022 identifies trends in terms of growth areas and locations of industrial biotechnology projects and activities in India.

Life Sciences undergraduate students' preferences on online learning

Online learning is implemented in response to emergency remote teaching during global pandemic. We conducted a survey on Life Sciences undergraduate students on their preferences on mode of lesson delivery, mode of learning and learning activities. Students across different study years responded in a similar order ranking blended learning delivery as highly favorable. The survey gathered insightful understandings on Life Sciences undergraduates' learning preferences on online learning which inform future purposeful learning design taking student's preferences into consideration.

Scientists as spies?: Assessing U.S. claims about the security threat posed by China's Thousand Talents Program for the U.S. life sciences

In 2008, the Chinese government created the Thousand Talents Program (TTP) to recruit overseas expertise to build up China's science and technology knowledge and innovation base. Ten years later, in 2018, the Federal Bureau of Investigation (FBI) announced a new "China Initiative" that aimed to counter the transfer by U.S.-based scientists involved in the TTP of knowledge and intellectual property that could support China's military and economic might and pose threats to U.S. national security. This initiative launched a number of investigations into major U.S. federal funding agencies and universities and charged several scientists, many of them life scientists, with failing to accurately report their work and affiliations with Chinese entities and illegally transferring scientific information to China. Although the FBI cases demonstrate a clear problem with disclosure of foreign contracts and research integrity among some TTP recipients, they have failed to demonstrate any harm to U.S. national security interests. At the heart of this controversy are core questions that remain unresolved and need more attention: What is required to transfer and develop knowledge to further a country's science and technology ambitions? And can the knowledge acquired by a visiting scientist be easily used to further a country's ambitions? Drawing on literature from the field of science and technology studies, this article discusses the key issues that should be considered in evaluating this question in the Chinese context and the potential scientific, intelligence, and policy implications of knowledge transfer as it relates to the TTP.

Reflexive expectations in innovation financing: An analysis of venture capital as a mode of valuation

Social studies of expectations are premised on the notion that the future is brought into the present, and thereby expectations about the future come to shape our actions, decisions, and practices in ways that performatively bring about the imagined future. In this article, I examine how social actors themselves understand, construct, and deploy future expectations in innovation financing, focusing specifically on the venture capital industry financing of the life sciences sector. I do so to analyse how these reflexive efforts configure the valuation and investment decisions of these social actors and others. I build on analytical perspectives in STS and adjacent fields such as organization studies and economic sociology that analyse the role of expectations - manifested as stories, narratives, and accounts - in social action. To do so, I unpack how reflexivity comes to configure valuation and investment decisions, and the goals (e.g. exits) they rationalize.

Transformer-based deep learning for predicting protein properties in the life sciences

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.

How to teach life sciences students about dual-use research-a view from the field

To reduce biological risks, raising awareness for dual-use issues already at the level of university education is essential. Currently, most life sciences education programs do not incorporate biosecurity and dual-use in their regular curricula. Consequently, the responsibility rests with individual lecturers and depends on their initiative to incorporate dual-use topics into teaching activities. Students interested in biosecurity and dual-use topics often only have the option to educate themselves in external or online courses. Here, we provide practical guidance on how to initiate and integrate a dual-use education program within the curriculum and provide a selection of existing teaching materials. In addition, we suggest key learning objectives to guide the planning of dual-use courses. Different course formats like lectures, seminars, or stand-alone events are discussed regarding their advantages, disadvantages, and suitability for conveying the learning objectives to different educational stages and audiences. As a minimum, we recommend the incorporation of dual-use issues into at least one mandatory course. Ideally, students should additionally participate in in-depth seminars, which can be voluntary and offered in cooperation with external organisations.

Challenges related to data protection in clinical research before and during the COVID-19 pandemic: An exploratory study

Background

The COVID-19 pandemic brought global disruption to health, society and economy, including to the conduct of clinical research. In the European Union (EU), the legal and ethical framework for research is complex and divergent. Many challenges exist in relation to the interplay of the various applicable rules, particularly with respect to compliance with the General Data Protection Regulation (GDPR). This study aimed to gain insights into the experience of key clinical research stakeholders [investigators, ethics committees (ECs), and data protection officers (DPOs)/legal experts working with clinical research sponsors] across the EU and the UK on the main challenges related to data protection in clinical research before and during the pandemic.

Materials and methods

The study consisted of an online survey and follow-up semi-structured interviews. Data collection occurred between April and December 2021. Survey data was analyzed descriptively, and the interviews underwent a framework analysis.

Results and conclusion

In total, 191 respondents filled in the survey, of whom fourteen participated in the follow-up interviews. Out of the targeted 28 countries (EU and UK), 25 were represented in the survey. The majority of stakeholders were based in Western Europe. This study empirically elucidated numerous key legal and ethical issues related to GDPR compliance in the context of (cross-border) clinical research. It showed that the lack of legal harmonization remains the biggest challenge in the field, and that it is present not only at the level of the interplay of key EU legislative acts and national implementation of the GDPR, but also when it comes to interpretation at local, regional and institutional levels. Moreover, the role of ECs in data protection was further explored and possible ways forward for its normative delineation were discussed. According to the participants, the pandemic did not bring additional legal challenges. Although practical challenges (for instance, mainly related to the provision of information to patients) were high due to the globally enacted crisis measures, the key problematic issues on (cross-border) health research, interpretations of the legal texts and compliance strategies remained largely the same.

Genetic epidemiology of human neutrophil antigen variants suggests significant global variability

Human neutrophil antigens possess significant clinical implications especially in the fields of transfusion and transplantation medicine. Efforts to estimate the prevalence of genetic variations underpinning the antigenic expression are emerging. However, there lacks a precise capture of the global frequency profiles. Our article emphasizes the potential utility of maintaining an organized online repository of evidence on neutrophil antigen-associated genetic variants from published literature and reports. This, in our opinion, is an emerging area and would significantly benefit from the awareness and understanding of population-level diversities.

Three decades of ethical, legal, and social implications research: Looking back to chart a path forward

More than thirty years ago in the United States, the National Center for Human Genome Research (NCHGR) at the National Institutes of Health (NIH) and its partner in the Human Genome Project (HGP), the Department of Energy (DOE), called for proposals from social scientists, ethicists, lawyers, and others to explore the ethical, legal, and social implications (ELSI) of mapping and sequencing the human genome. Today, nearly twenty years after the completion of the HGP, the ELSI Research Program of the National Human Genome Research Institute (NHGRI) continues this support. It has fostered the growth of ELSI research into a global field of study, uniquely positioned at the nexus of many academic disciplines and in proximity to basic and applied scientific research. We examine the formation of the first ELSI program and consider whether science policy in the public interest can exist within the confines of a set-aside from the NHGRI budget.

Innovative Sensor Technology for Emergency Detection in Life Science Laboratories

Chemical, analytical and biological laboratories use a variety of different solvents and gases. Many of these compounds are harmful or even toxic to laboratory personnel. Permanent monitoring of the air quality is therefore of great importance regarding the greatest possible occupational safety and the detection of dangerous situations in the work process. An increasing need exists for the development and application of small and portable sensor solutions that enable personal monitoring and that can be flexibly adapted to different environments and situations. Different sensor principles are available for the detection of gases and solvent vapors, which differ in terms of their selectivity and sensitivity. Besides simple sensing elements, integrated sensors and smart sensors are increasingly available, which, depending on their scope of functions, require a distinct effort in integration. This chapter gives an overview of available sensors and their integration options, and describes ready-to-use sensor systems for personal monitoring in life science laboratories.

Human-centered explainability for life sciences, healthcare, and medical informatics

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and healthcare data have enabled the development of a wide variety of models. Significant success has been achieved in a wide range of fields, such as genomics, protein folding, disease diagnosis, imaging, and clinical tasks. Although widely used, the inherent opacity of deep AI models has brought criticism from the research field and little adoption in clinical practice. Concurrently, there has been a significant amount of research focused on making such methods more interpretable, reviewed here, but inherent critiques of such explainability in AI (XAI), its requirements, and concerns with fairness/robustness have hampered their real-world adoption. We here discuss how user-driven XAI can be made more useful for different healthcare stakeholders through the definition of three key personas-data scientists, clinical researchers, and clinicians-and present an overview of how different XAI approaches can address their needs. For illustration, we also walk through several research and clinical examples that take advantage of XAI open-source tools, including those that help enhance the explanation of the results through visualization. This perspective thus aims to provide a guidance tool for developing explainability solutions for healthcare by empowering both subject matter experts, providing them with a survey of available tools, and explainability developers, by providing examples of how such methods can influence in practice adoption of solutions.

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.

Life sciences and mass spectrometry: some personal reflections

Molecular analysis of biological systems by mass spectrometry was in focus of technological developments in the second half of the 20th century, in which the issues of chemical identification of high molecular diversity by biophysical instrumental methods appeared as a mission impossible. By developing dialogs between researchers dealing with life sciences and medicine on one side and technology developers on the other, new horizons toward deciphering, identifying and quantifying of complex systems became a reality. Contributions toward this goal can be today considered as pioneering efforts delivered by a number of researchers, including generations of motivated students and associates.

Academic Teams and Commercialization in the Life Sciences

We review the literature on entrepreneurial team formation with a focus on data to study academic teams and summarize our empirical work on the life sciences industry. We consider how academics form teams to start new companies and the implications of various configurations on firm behavior with regards to patenting, survival and firm growth. We present several empirical challenges facing research on academic teams and conclude with suggestions for future research.

Perspectives on automated composition of workflows in the life sciences

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

[Development of practical liberal courses for students with non-biology majors under the "Double First-class" background]

Life sciences are the disciplines most closely related with human beings. As experimental disciplines, life sciences develop rapidly and highly intersect in many scientific fields. Under the "double first-class" initiative, the comprehensive development-oriented talent training system has put forward an urgent need for life sciences literacy and comprehensive ability training of college students. Taking the reform of liberal education curriculum system as an opportunity, we developed a series of eight life sciences practical liberal courses for students with non-biology majors. The courses cover all sub-disciplines or directions of life sciences, and aim to foster interdisciplinary talents with life sciences knowledge and literacy, as well as practical and innovative abilities. These courses could serve as references for experimental teaching centers in colleges and universities to set up practical liberal and experimental courses.

[Application of micro-teaching in life sciences courses based on the "National Universities Micro-teaching Competition of Life Sciences" analysis]

With improvements in information technology and expansion in education reforms, more innovative teaching reform programs have also been launched. Information technology has increased interest in the use of the flipped classroom innovative teaching model. In order to explore new ideas for the improvement of teaching, this paper focuses on the flipped classroom teaching approach with the integration of information technology. Micro-teaching is an important innovative flipped classroom teaching approach with a number of advantages as it is short, concise, and interesting, which therefore helps improve students' self-learning ability. Designing and preparing micro-teaching would become a prerequisite skill for college teachers. Based on the analysis of the entries in the "National Universities Micro-teaching Competition of Life Science", this paper explores the application of micro-teaching in life sciences teaching from the perspective of curriculum introduction, mode of presentation, teaching design, and other aspects of teaching. This information could serve as a guide to frontline college teachers to help them understand and master the skills of designing micro-teaching, so as to generate interest and improve learning efficiency among college students.

The Potential Role of School Citizen Science Programs in Infectious Disease Surveillance: A Critical Review

Public involvement in science has allowed researchers to collect large-scale and real-time data and also engage citizens, so researchers are adopting citizen science (CS) in many areas. One promising appeal is student participation in CS school programs. In this literature review, we aimed to investigate which school CS programs exist in the areas of (applied) life sciences and if any projects target infectious disease surveillance. This review’s objectives are to determine success factors in terms of data quality and student engagement. After a comprehensive search in biomedical and social databases, we found 23 projects. None of the projects found focused on infectious disease surveillance, and the majority centered around species biodiversity. While a few projects had issues with data quality, simplifying the protocol or allowing students to resubmit data made the data collected more usable. Overall, students at different educational levels and disciplines were able to collect usable data that was comparable to expert data and had positive learning experiences. In this review, we have identified limitations and gaps in reported CS school projects and provided recommendations for establishing future programs. This review shows the value of using CS in collaboration with traditional research techniques to advance future science and increasingly engage communities.

De novo genome assemblies of butterflies

BACKGROUND

The availability of thousands of genomes has enabled new advancements in biology. However, many genomes have not been investigated for their quality. Here we examine quality trends in a taxonomically diverse and well-known group, butterflies (Papilionoidea), and provide draft, de novo assemblies for all available butterfly genomes. Owing to massive genome sequencing investment and taxonomic curation, this is an excellent group to explore genome quality.

FINDINGS

We provide de novo assemblies for all 822 available butterfly genomes and interpret their quality in terms of completeness and continuity. We identify the 50 highest quality genomes across butterflies and conclude that the ringlet, Aphantopus hyperantus, has the highest quality genome. Our post-processing of draft genome assemblies identified 118 butterfly genomes that should not be reused owing to contamination or extremely low quality. However, many draft genomes are of high utility, especially because permissibility of low-quality genomes is dependent on the objective of the study. Our assemblies will serve as a key resource for papilionid genomics, especially for researchers without computational resources.

CONCLUSIONS

Quality metrics and assemblies are typically presented with annotated genome accessions but rarely with de novo genomes. We recommend that studies presenting genome sequences provide the assembly and some metrics of quality because quality will significantly affect downstream results. Transparency in quality metrics is needed to improve the field of genome science and encourage data reuse.

Where Do We Stand in Regularization for Life Science Studies?

More and more biologists and bioinformaticians turn to machine learning to analyze large amounts of data. In this context, it is crucial to understand which is the most suitable data analysis pipeline for achieving reliable results. This process may be challenging, due to a variety of factors, the most crucial ones being the data type and the general goal of the analysis (e.g., explorative or predictive). Life science data sets require further consideration as they often contain measures with a low signal-to-noise ratio, high-dimensional observations, and relatively few samples. In this complex setting, regularization, which can be defined as the introduction of additional information to solve an ill-posed problem, is the tool of choice to obtain robust models. Different regularization practices may be used depending both on characteristics of the data and of the question asked, and different choices may lead to different results. In this article, we provide a comprehensive description of the impact and importance of regularization techniques in life science studies. In particular, we provide an intuition of what regularization is and of the different ways it can be implemented and exploited. We propose four general life sciences problems in which regularization is fundamental and should be exploited for robustness. For each of these large families of problems, we enumerate different techniques as well as examples and case studies. Lastly, we provide a unified view of how to approach each data type with various regularization techniques.

[Exploration and practice of blended teaching in "Introduction to Life Sciences" for non-biology students]

In the era of Internet +, teaching models in universities are undergoing changes due to the rapid development of information technology. Blended teaching, combining online with offline teaching, is being implemented and developed in universities. In order to reform teaching mode and improve teaching effect, the curriculum team carried out the exploration of blended teaching reform for the "Introduction to Life Sciences" for non-biology students. The course combined high-level MOOC (Massive Open Online Course), small class teaching, diversified platform and multi-dimensional teaching mode, built a multi-disciplinary collaborative teaching team, formed a multi-dimensional evaluation system focusing on process and ability, practiced the education concept of combining knowledge teaching and value leading, gained valuable practical experience, and achieved the expected teaching results. It can provide reference for the reform and construction of similar courses in other colleges and universities. The development of blended teaching expands the breadth and depth of teaching, stimulates students' interest and potential for learning, opens up students' thinking and perspective, cultivates students' scientific literacy and comprehensive ability, and plays a positive role in the cultivation of innovative and inter-disciplinary talents.