Complex Questions Research Articles

ChatGPT as a Source for Patient Information on Patellofemoral Surgery—A Comparative Study Amongst Laymen, Doctors, and Experts

Introduction: In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. Methods: A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann–Whitney U Test. A p-value of less than 0.05 was considered statistically significant. Results: The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. Conclusion: ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of “AI hallucinations” remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Efficacy of Complexity-Based Target Selection for Treating Morphosyntactic Deficits in Children With Developmental Language Disorder and Children With Down Syndrome: A Single-Case Experimental Design.

Selecting targets for morphosyntactic intervention is a critical component of treatment planning. The complexity approach suggests that, by treating a complex morphosyntactic target, improvements will occur for the treated structure and for related, simpler structures. This study evaluated the efficacy of the complexity approach for treating morphosyntactic deficits by targeting a complex BE verb question structure for children with developmental language disorder (DLD) and children with Down syndrome (DS) and observing its impact on treated and untreated BE verb structures. We also explored whether etiology impacted our participants' treatment responses. Three participants with DLD and three with DS received treatment for the BE verb question structure in the context of a single-case multiple-baseline design across participants. Accuracy of production for the treated structure and untreated BE verb structures was measured across baseline, treatment, and posttreatment phases. Treatment of the complex BE verb question structure resulted in change on the treated structure for three participants (i.e., two with DLD and one with DS). Generalization of treatment to untreated, related BE verb structures occurred for all six participants. Outcomes indicated participants from both etiologies benefited from treatment. This study provides evidence supporting the use of a complexity-based approach for selecting morphosyntactic treatment targets for children with DLD and children with DS. Additional research is needed to identify specific characteristics that may influence individual treatment responses. https://doi.org/10.23641/asha.27018124.

An evaluation of a visual interface for supporting query formulation in scholarly searching

Performing an effective literature review is a fundamental academic skill. Whether writing up a final project, preparing a research proposal, or simply finding answers to complex questions, students need to be able to systematically gather and analyse evidence from a variety of sources. However, many students have difficulty formulating effective search queries and understanding how to optimise their search strategy. This paper evaluates a novel approach to query formulation based on an alternative, visual interface. The approach is evaluated alongside a conventional, form-based interface in a comparative, controlled setting. The study finds that overall, participants considered the visual interface to be more complex and difficult to use. However, at the same time they used significantly more terms, facets and reformulations in the visual interface, and gave more attention to creating and refining more complex queries. In addition, the majority considered the visual interface to have greater transparency, with a clearer overview of the search tasks that encouraged them to invest greater effort in completing them successfully. The results demonstrate that visual approaches can offer tangible benefits in developing and improving students’ competence in exploratory search tasks.

The Philosophy, Politics and Economics of Food

The undergraduate degree Philosophy, Politics and Economics (PPE) has attracted a stereotype of developing future political and financial elites with little regard to the subject material covered in the programme. This clashes with student expectations and university messaging on PPE, which, especially at the University of Warwick, highlight the importance of interdisciplinary perspectives for addressing complex global challenges. This introduction to this special section argues that it is imperative to centre students’ voices in defining PPE, and, to this end, showcases a co-creation process aimed at developing an interdisciplinary module. The resulting module on the PPE of food thereby speaks to how students understand their PPE: as a programme that breaks with interdisciplinary silos to answer complex questions relevant to the world we live in.

Maternal Question Use Relates to Syntactic Skills in 5- to 7-Year-Old Children.

This study examined how mothers' question-asking behavior relates to their child's syntactic skills. One important aspect of maternal question-asking behavior is the use of complex questions when speaking with children. These questions can differ based on both their purpose and structure. The purpose may be to seek out information, to teach, or to get a simple yes/no response. Questions may even be rhetorical, with no answer intended at all. Structurally, questions can include a wh-word (who, what, when, where, why, and how) or not; however, these wh-questions are important because they elicit utterances from the child and support vocabulary development. Despite wh-questions eliciting a response from children, it remains unknown how these questions relate to children's syntactic skills. Thirty-four mother-child dyads participated in a 15-min seminaturalistic play session. Children were between the ages of 5 and 7 years (M = 6.26 years, SD = 1.04 years; 20 girls/14 boys). The Diagnostic Evaluation of Language Variation (DELV) assessment was used to measure syntactic skills in children. Using the Systematic Analysis of Language Transcripts, questions were categorized based on structure (wh-questions vs. non-wh-questions) and purpose (information-seeking, pedagogical, or yes/no and rhetorical questions). A repeated-measures analysis of covariance and a linear regression model were implemented to address the frequency of different questions asked by mothers, as well as what types of questions are most related to children's concurrent syntactic skills. When controlling for total maternal utterances, results revealed that non-wh-questions and rhetorical/yes and no questions were the most frequent types of questions produced by mothers, in terms of structure and purpose, respectively. However, wh-questions were predominantly information-seeking questions. This is important, as the use of information-seeking wh-questions was positively associated with children's syntactic skills, as measured by the DELV, and resulted in children producing longer utterances in response to these questions, as determined by child mean length of utterance in words. Taken together, these findings suggest maternal use of wh-questions aids syntactic skills in children ages 5-7 years, likely because they require a more syntactically complex response on the child's behalf. https://doi.org/10.23641/asha.27276891.

A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions

Abstract Background Paediatric cardiology presents unique challenges with its diverse and complex cases, limited evidence base, and the necessity for multi-expert involvement in decision-making processes. In this context, the introduction of generative pre-trained transformer (GPT) based large language models (LLMs) offers a potential avenue for the provision of complex information and clinical decision support. Purpose This study evaluates the quality of three different GPT LLMs in answering complex medical questions, including a state-of-the-art preview model that incorporates the German paediatric cardiology guidelines. Methods Seven paediatric cardiologists and paediatric cardiac surgeons generated 72 questions, including complex questions and medical cases with associated questions. The questions were categorized by difficulty and required knowledge (factual and experience-based or mostly experience-based). We prompted the questions to three LLMs: GPT 3.5, GPT 4 and a GPT 4 turbo preview. The GPT 4 turbo preview was customized by incorporating all guidelines from the German Society for Paediatric Cardiology by a retrieval function. Employing one complex instruction for all questions, we prompted the LLMs to provide precise and detailed expert-level responses. The responses from each model were evaluated by experts based on relevance, factual accuracy, severity of possible harm, completeness, superfluous content, and age-related appropriateness from 0 (very bad) to 7 (very good). Differences were calculated using the Kruskal-Wallis-test in SPSS Version 28. Results Our findings indicated a good performance of all models regarding the dimensions tested. The figures show the average ratings (Figure 1, Figure 2A) and highlight significant differences after Bonferroni correction in bold (Figure 2B). The GPT 4 turbo preview, including the retrieval of guidelines, provided significantly more relevant (average rating [AR] 5.94, meaning mostly relevant), accurate (AR 5.6, meaning between somewhat and mostly accurate) and complete (AR 5, meaning fairly complete) answers compared to GPT 3.5 and GPT 4. In terms of difficulty levels or the type of questions, there was no significant difference in rating. Relevance ratings were slightly better in factual questions (AR 5.7) than in those requiring more experience-based knowledge (AR 5,3). Although GPT4 had higher average scores compared to GPT 3.5 in all dimensions except superfluous content, the differences in rating were not statistically significant. All models had relevant difficulties considering the age-related aspects of the questions (AR 4.06-4.45, p=0.455). Conclusion This study highlights the potential and limitations of AI language models in addressing complex medical questions in fields characterized by highly individualized decision-making scenarios. The findings advocate for the development of more specialized AI tools in medicine, tailored to specific medical fields and patient age groups.Fig 1:Average ratings of LLMsFig 2:Rating differences between LLMs

Generative AI in Higher Education: Educators' Perspectives on Academic Learning and Integrity

Generative Artificial Intelligence (Gen AI), exemplified by models such as ChatGPT, has rapidly advanced, becoming a significant force in various sectors, including higher education. ChatGPT, a leading application of Gen AI, utilizes large language models (LLMs) to generate human-like text, providing capabilities that range from answering complex questions to facilitating software development. As these tools become increasingly integrated into academic environments, their impact on teaching and learning processes has become more under focus. However, there are not many studies focusing on the impact of such systems on students’ performance and the use of such systems. This study therefore examines the impact of GPT tools on higher education, aiming to address the following research questions: How does the use of GPTs influence the teaching and learning process in higher education? What are the perceived impacts of GPTs on educational practices from university educators’ perspective? The research employs a qualitative methodology, incorporating semi-structured interviews with educators from Swedish universities who have integrated GPT into their curricula. Data were transcribed and analysed using OpenAI's Whisper to extract key themes and insights. Data analysis was done through thematic analysis and categorizing the data using codes. The study uncovers a dual impact of GPT on education, while it offers substantial opportunities for enhancing productivity and personalized learning, it also raises significant concerns about academic integrity, over-reliance on AI, and the potential influences on students’ soft skills. These findings contribute to the discourse on digital learning by highlighting the need for instructional and constructive integration of AI technologies such as GenAI, in educational settings. In addition, the rise and integration of GPT technology is irreversible, and we must adapt to it rather than return to old ways. Embracing AI's potential while addressing its challenges is essential for progress and innovation in this new era. The study emphasizes the importance of developing clear policies and guidelines to ensure that the benefits of GPT are realized without compromising the integrity of the educational process. As such, this research provides valuable insights for educators, policymakers, and scholars interested in the ethical and effective implementation of AI in higher education

Revitalizing the heart: cutting-edge nucleic acid therapies in cardiac regeneration

Heart failure (HF) arises from cardiac injury and subsequent loss of contractile cells. While some organisms can regenerate cardiac tissue through cardiomyocyte proliferation, the adult mammalian heart has limited regenerative capacity due to cardiomyocyte replication ceasing shortly after birth. To enhance proliferation in the adult heart, we aim to identify master regulators of proliferation. In Professor Mauro Giacca’s laboratory, human-specific microRNAs (miRNAs) like miR199a-3p or miR-1825 have been identified as potential enhancers of cardiomyocyte proliferation. My PhD will evaluate these miRNAs using different delivery methods, comparing viral vectors with lipid nanoparticle (SNALP) technology. Additionally, I will adapt CycleTrack, a tool for labelling mitotic cells, to study cardiomyocyte replication accurately in response to these miRNAs. By assessing these approaches in human myocardial slices, this work seeks to uncover the molecular mechanisms of pro-proliferative miRNAs and pave the way for future clinical interventions targeting heart regeneration. In this Student Focus article, I also highlight another aspect: the framework used to address complex biological questions is crucial in shaping the answers we seek. What intrigues me most about my research is the interplay between elucidating discrete molecular pathways and employing holistic approaches to understand cardiac proliferation.

Antibiotic prescription, dispensing and use in humans and livestock in East Africa: does morality have a role to play?

Antimicrobial resistance (AMR) is a global threat to human and livestock health. Although AMR is driven by use of antimicrobials, it is often attributed to "misuse" and "overuse", particularly for antibiotics. To curb resistance, there has been a global call to embrace new forms of moral personhood that practice "proper" use, including prescription, dispensing and consumption of antimicrobials, especially antibiotics. This paper seeks to reflect on complex questions about how morality has become embedded /embodied in the AMR discourse as presented in the data collected on antimicrobial prescription, dispensing and use in human and livestock health in Tanzania, primarily focusing on antibiotics. This reflection is anchored on Jarrett Zigon's morality framework that is comprised of three dimensions of discourse; the institutional, public, and embodied dispositions. The data we use within this framework are derived from a qualitative study targeting human and animal health care service providers and community members in northern Tanzania. Data were collected through 28 in-depth interviews and ten focus group discussions and analysed through content analysis after translation and transcription. In addition, a review of the Tanzania's National Action Plans on antimicrobial resistance was conducted. Application of the framework demonstrates points of convergence and divergence in the institutional morality discourse articulated by the Tanzania National Action Plans, the public discourse and the embodied dispositions/ lived experiences of human and animal health care service providers and community members. We demonstrate that AMR is not just associated with "inappropriate" behaviour on the part of drug prescribers, dispensers, and users but also with shortcomings in health systems and service delivery. Antibiotic dispensing and use practices that may be associated with the development of AMR should not be viewed in isolation from the broader health context within which they occur.

RNA Polymerase II is a Polar Roadblock to a Progressing DNA Fork.

DNA replication and transcription occur simultaneously on the same DNA template, leading to inevitable conflicts between the replisome and RNA polymerase. These conflicts can stall the replication fork and threaten genome stability. Although numerous studies show that head-on conflicts are more detrimental and more prone to promoting R-loop formation than co-directional conflicts, the fundamental cause for the RNA polymerase roadblock polarity remains unclear, and the structure of these R-loops is speculative. In this work, we use a simple model system to address this complex question by examining the Pol II roadblock to a DNA fork advanced via mechanical unzipping to mimic the replisome progression. We found that the Pol II binds more stably to resist removal in the head-on configuration, even with minimal transcript size, demonstrating that the Pol II roadblock has an inherent polarity. However, an elongating Pol II with a long RNA transcript becomes an even more potent and persistent roadblock while retaining the polarity, and the formation of an RNA-DNA hybrid mediates this enhancement. Surprisingly, we discovered that when a Pol II collides with the DNA fork head-on and becomes backtracked, an RNA-DNA hybrid can form on the lagging strand in front of Pol II, creating a topological lock that traps Pol II at the fork. TFIIS facilitates RNA-DNA hybrid removal by severing the connection of Pol II with the hybrid. We further demonstrate that this RNA-DNA hybrid can prime lagging strand replication by T7 DNA polymerase while Pol II is still bound to DNA. Our findings capture basal properties of the interactions of Pol II with a DNA fork, revealing significant implications for transcription-replication conflicts.

Navigating personal and disciplinary frontiers: engaging with undergraduate geography students’ reflections on their geographical education

ABSTRACT In this article, we engage with undergraduate geography students’ reflections on their geography education. We begin by examining the position of geography within education across the British Isles. Following this, we critically consider how geography education is shaped by “the gap” between school and university geography and geographies of education, before reporting on the findings from a survey of 333 undergraduate students studying geography across the British Isles conducted in late 2020. We examine the complex question of who is studying geography at undergraduate level, students’ journeys through their geography degree, and their experiences of personal and disciplinary frontiers in education. We conclude by arguing the importance of educators engaging with students’ “everyday” geographies, and for further research into progress across education phases in geography to co-construct a rich and rounded geographic education.

Uncovering malate dehydrogenase: structure, function and role in disease.

Malate dehydrogenases (MDHs) have been extensively studied since the 1960s due to their key roles in carbon metabolism and pathways such as redox balance and lipid synthesis. Recently, there has been renewed interest in these enzymes with the discovery of their role in the metabolic changes that occur during cancer and a widespread community of undergraduate teaching laboratories addressing MDH research questions, the Malate Dehydrogenase CUREs Community (MCC). This special issue describes different facets of MDH, including its physiological role, its structure-function relationships, its regulation through post-translational modifications, and perspectives on its evolutionary history. There are two human isoforms: a cytoplasmic isoform that carries out formation of NAD+ for glycolysis, and a mitochondrial isoform that plays a major role in the citric acid cycle. Although the sequences of these two isoforms vary, the structures of the enzymes are similar, and studies suggest that each isoform may form complexes with other enzymes in common pathways. Experimental and theoretical advances have helped to characterize the post-translational modifications of MDH, allowing us to ask more complex questions involving the regulation of the enzyme and substrate promiscuity in the context of cancer. Additionally, there are many unresolved questions on the role of malate dehydrogenase in other organisms, especially in parasites. The review articles in this issue seek to shed light on the latest advances in our understanding of MDH and highlight areas for future studies.

The Plight of Employment Policies that Exclude Customary Practices in the Workplace

South Africa is called a “rainbow nation”, and with its 11 official languages, the country is a melting pot of diverse cultures, traditions, and beliefs. This diversity is not without challenges. This brings interesting and complex questions from an employment perspective; therefore, striking a balance between the employer’s rights versus the employee’s traditional practices has become a significant challenge. The article analyses these challenges that arise as a result of the failure of section 8(3) of the Constitution to give a direct mandate to develop customary law to put into effect or limit a right that is found in the Bill of Rights and in this instance the right to fair labour practices in terms of section 23. The article will also analyse the cases dealing with employees who wanted to enforce their rights conferred to them by the Constitution to practice their culture but were excluded and discriminated against on some of the grounds listed in section 9(3) of the Constitution. The article will conclude by recommending that labour laws and the Constitution should be reviewed to cater for native persons, in particular, the recognition of customary laws and practices at the workplace.

Shareholder Loans: Fact or Fiction

Shareholder loans are often used as an alternative to traditional third-party loans or equity especially for private companies in various jurisdictions, including South Africa, to finance their business activities. These loans provide companies with greater flexibility to meet their financing needs, i.e., there is no need to seek external financing while offering shareholders a potentially profitable investment opportunity. However, the legal nature of shareholder loans could pose complex legal questions and this form of loans may not necessarily be as straightforward as it first appears. This article explores the legal framework and practical considerations surrounding shareholder loans in South Africa in small private companies, with a focus on developments in case law and their implications for companies and shareholders. Amongst other issues, the application of the principle of arbitrium boni viri to the interpretation and enforcement of these agreements will be discussed. The article aims to provide a critical analysis of the legal questions associated with shareholder loans in South Africa.

Mass Spectrometry-based proteomics for the masses: Peptide and protein identification in the Hunt laboratory during the 2000’s

There has been a rapid increase in the number of individuals utilizing mass spectrometry (MS)-based proteomics to study complex biological systems and questions since the start of the 2000's. Building off the advancements in ionization and liquid chromatography scientists continued to push towards technology that would enable in-depth analysis of biological specimen. Donald F Hunt and the Hunt laboratory were major contributors to this effort with their work on improving upon existing Fourier Transform MS, development of electron transfer dissociation, and continued work on ion-ion reactions to improve intact protein analysis. Collaboration with other instrumentation laboratories and instrument companies led to the sharing of technology and eventual commercialization providing greater access. Additionally, the Hunt laboratory spread the gospel of mass spectrometry-based proteomics through collaborations that lasted decades with other scientists who were experts in immunology, cellular signaling, epigenetics, and other fascinating fields. This article attempts to highlight the many contributions of Don and the Hunt laboratory to peptide and protein identification since the year 2000.

Incorporating sources of correlation between outcomes: An introduction to mixed models.

Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.

Retrieval In Decoder benefits generative models for explainable complex question answering

Large-scale Language Models (LLMs) utilizing the Chain-of-Thought prompting demonstrate exceptional performance in a variety of tasks. However, the persistence of factual hallucinations remains a significant challenge in practical applications. Prevailing retrieval-augmented methods treat the retriever and generator as separate components, which inadvertently restricts the generator’s capabilities to those of the retriever through intensive supervised training. In this work, we propose an unsupervised Retrieval In Decoder framework for multi-granularity decoding called RID, which integrates retrieval directly into the decoding process of generative models. It dynamically adjusts decoding granularity based on retrieval outcomes, and duly corrects the decoding direction through its direct impact on the next token. Moreover, we introduce a reinforcement learning-driven knowledge distillation method for adaptive explanation generation to better apply to Small-scale Language Models (SLMs). The experimental results across six public benchmarks surpass popular LLMs and existing retrieval-augmented methods, which demonstrates the effectiveness of RID in models of different scales and verifies its applicability and scalability.

COMPARATIVE ANALYSIS OF REGULATORY ACTS OF THE EU COUNTRIES ON THE PROTECTION OF INTELLECTUAL PROPERTY IN THE CONDITIONS OF THE USE OF ARTIFICIAL INTELLIGENCE

The rise of artificial intelligence (AI) has fundamentally challenged traditional intellectual property (IP) frameworks, particularly in the European Union (EU), where regulatory efforts are aimed at balancing innovation with legal protections. AI’s ability to autonomously create, modify, and use IP raises complex questions about authorship, inventorship, ownership, and enforcement, which existing laws were not designed to handle. As EU countries attempt to adapt their legal systems to address these challenges, a comparative analysis of their regulatory acts is essential to understand how different member states are responding to the intersection of AI and IP protection. The aim of this article is to provide a comparative analysis of the regulatory frameworks governing IP protection in the context of AI across selected EU countries. By examining national legislation and harmonization efforts, the study seeks to identify common challenges, highlight divergent approaches, and offer insights into the evolving legal landscape of IP protection in the age of AI. The article employs a qualitative, comparative research methodology. It focuses on six EU countries—Germany, France, the Netherlands, Poland, Greece, and Romania—analyzing their IP laws concerning AI-related issues. The study reviews national regulations, EU directives, and case law to evaluate how each country addresses AI-generated IP in terms of ownership, authorship, patentability, trademark issues, and enforcement mechanisms. A thematic coding approach is used to identify key trends and divergences between member states. The analysis reveals that all EU countries maintain the requirement for human authorship and inventorship, which limits the legal recognition of fully autonomous AI-generated content. While countries like Germany, France, and the Netherlands have initiated discussions on potential legal reforms, others, such as Poland, Greece, and Romania, rely more heavily on existing frameworks and await further EU guidance. Additionally, enforcement mechanisms vary significantly, with more technologically advanced countries adopting AI-driven tools to monitor and enforce IP rights. As AI continues to evolve and play a larger role in creative and technical industries, the legal frameworks governing IP in the EU must adapt accordingly. Future regulatory efforts should focus on creating new categories for AI-generated works, investing in AI-powered enforcement tools, and ensuring greater harmonization across member states. By addressing these challenges proactively, the EU can strike a balance between fostering AI innovation and maintaining robust IP protections, positioning itself as a global leader in both technology and intellectual property rights.

Translating and performing China for the English-speaking stage: Royal Shakespeare Company’s re-staging of The Injustice to Dou E

Contemporary China is increasingly open in its interaction with the global cultural community, generating one of the most prestigious projects—the Royal Shakespeare Company’s “Chinese Classics Translation Projects” (2015–2023). This article examines this project’s first production Snow in Midsummer, a performance based on a new translation of the Chinese classic Dou E Yuan (窦娥冤, The Injustice to Dou E That Moved Heaven and Earth). Through exploring the multiple agents involved in the RSC’s re-staging of the play, this article traces the processes of translation and performance characterised by an oft-hidden collaborative agency. It uncovers the collaborative agency at work and argues that the agency in this theatre production aims to set the conditions to make the original Chinese classic new, so as to enable its rejuvenation and transformation. Key finding of this article is that the collaborative agency of the translator, playwright, director, stage designer, and actors/actresses in re-staging has enlivened the 13th-century Chinese classic with enriched narratives and produced a performance that is nuanced and relevant to the contemporary world. This article makes a case for the paradigmatic shift from a product-based approach to theatre translation to a process-based one. It opens new avenues to future research into the question of complexity of collaborative agency in restaging the foreign classic to the contemporary English-speaking audiences for both areas of theatre translation for performance and transnational theatre production.

Why More Biologists Must Embrace Quantitative Modeling.

Biology as a field has transformed since the time of its foundation from an organized enterprise cataloging the diversity of the natural world to a quantitatively rigorous science seeking to answer complex questions about the functions of organisms and their interactions with each other and their environments. As the mathematical rigor of biological analyses has improved, quantitative models have been developed to describe multi-mechanistic systems and to test complex hypotheses. However, applications of quantitative models have been uneven across fields, and many biologists lack the foundational training necessary to apply them in their research or to interpret their results to inform biological problem-solving efforts. This gap in scientific training has created a false dichotomy of "biologists" and "modelers" that only exacerbates the barriers to working biologists seeking additional training in quantitative modeling. Here, we make the argument that all biologists are modelers and are capable of using sophisticated quantitative modeling in their work. We highlight four benefits of conducting biological research within the framework of quantitative models, identify the potential producers and consumers of information produced by such models, and make recommendations for strategies to overcome barriers to their widespread implementation. Improved understanding of quantitative modeling could guide the producers of biological information to better apply biological measurements through analyses that evaluate mechanisms, and allow consumers of biological information to better judge the quality and applications of the information they receive. As our explanations of biological phenomena increase in complexity, so too must we embrace modeling as a foundational skill.