bluuebunny/crossref_metadata_2025_split huggingface dataset:
Creativity Enhancement: A Primer
Authors: Ding Ke , Kenett Yoed N. 
Published: September 2024

Abstract
Creativity is a complex high-order cognitive capacity, involving multiple cognitive processes interacting together in complex dynamics. It holds significant value at both the individual and societal levels, serving as a driving force for innovation and development. Given its significance, can creativity be enhanced? And if so, in which ways and which cognitive and neural mechanisms are impacted by such creativity enhancement? Creativity enhancement seeks to unlock and cultivate individuals’ creative potential through a variety of methods. This involves training on various aspects, such as associative thinking, cognitive flexibility, and problem-solving strategies, as well as various manipulations, such as priming, meditation, improvision, and brain stimulation, with the aim of fostering more creative thinking. Training programs incorporate various practice sessions to facilitate the transfer of acquired creativity-related skills, while manipulation techniques focus on altering momentary cognitive states to enhance or activate a creative mindset. This chapter provides a general overview of the effective methods and potential challenges of creativity enhancement. Future developments may include conducting more in-depth research into the neural mechanisms of creativity, integrating creativity more broadly into education systems, and leveraging technological innovations to provide more personalized and effective enhancement tools.
Perspectives, Positionalities, and Paradigms in Media and Social Justice Scholarship
Authors: Banjo Omotayo O. , Ramasubramanian Srividya 
Published: September 2024

Abstract
This chapter highlights the necessity for media researchers to engage in social justice scholarship and makes a case for an interdisciplinary theoretical approach to examining the relationship between social inequities, media, and social justice outcomes throughout the world. It also makes a case for media scholars to consider creative, innovative multi- and mixed-methods approaches to unpacking social justice-oriented research questions. The chapter reviews 30 contributions from scholars from the Americas to South Asia, and to the African continent, laying a foundation for scholars to critically consider what it looks like to be a scholar–activist within media and communication. The coverage of the work is vast, with much opportunity for scholars to engage from many perspectives, paradigms, and positionalities. This chapter contends that the work of scholar activism is self-reflexive, community-centered, and, most important, collaborative. As such, this volume would be impossible without the collaborative efforts of global scholars who value this work.
Critical Discourse Analysis
Authors: Doshi Marissa Joanna 
Published: September 2024

Abstract
Critical discourse analysis is an inter- and transdisciplinary methodology that aims to expose how power relations operate. Importantly, this analytic approach can not only be used to identify social injustice but also as a framework for understanding how these injustices came to be and how they are being/can be challenged. Critical discourse analysis accomplishes these goals by being a deeply contextual method that takes into account the role of history, politics, economics, etc. in shaping media texts—often the objects of analysis—with attention paid to how form, content, context, and medium work together to create meaning. This chapter examines critical discourse analysis as a methodology for advancing social justice, particularly in the context of analyzing contemporary digital culture such as social media.
Mediated Socioeconomic Injustice
Authors: Corsbie-Massay Charisse L’Pree 
Published: September 2024

Abstract
Socioeconomic class is the complex position of an individual in an economic social hierarchy and is foundational to all forms of justice through equitable distribution of resources. Classism is the individual and institutional belief systems and practices that assign value to people according to their socioeconomic ranking. In the United States, the ideology of the American Dream and the assumption that hard work is always rewarded with socioeconomic advancement have cultivated a series of stereotypes about poverty and poor people. Through media content, these stereotypes are fleshed into archetypes, which are then used to rationalize ongoing socioeconomic injustices. Drawing on examples from advertising, journalism, entertainment, and digital content, this chapter describes three media archetypes of people in different socioeconomic classes—lower classes as scary, working classes as lazy, and upper classes as saviors—as well as the implications of these representations for social justice in the 21st century.
Heroes of the Border
Authors: Ramirez Anthony R. 
Published: September 2024

Abstract
In the United States, Mexican immigration is constantly a hot topic in the news and within politics, and it is also found in various forms of media and popular culture. This chapter focuses on one of those forms of popular culture, comic books and graphic novels. Using Latina/o critical communication theory as a theoretical approach, the chapter analyzes two series of comic books and graphic novels (El Peso Hero: Border Stories [2016]; El Peso Hero: Borderland [2019]; and Home [2021]) that focus centrally on immigration and Mexican immigration into the United States. The chapter discusses a variety of themes of Latine identity, representation, and border immigration found within these comic books using textual analysis, but it specifically discusses the use of counternarratives. Creators of these comic books use counternarratives to break negative racialized stereotypical tropes found within border related media.
Authoritarianism’s Historical Entanglements
Authors: Slater Dan 
Published: September 2024

Abstract
Authoritarianism, defined residually as the absence of democracy, has been around throughout human history and has always been the form of rule under which most humans live. But defined positively, as the active suppression of political pluralism and opposition, authoritarianism arose in tandem with the modern state as an apparatus for containing mass politics: no modern state and no mass politics, no modern authoritarianism. This chapter distinguishes modern authoritarianism from earlier forms of nondemocratic rule, tracing the evolution of modern authoritarianism primarily as a response to the historical rise of popular sovereignty and the territorial state during the age of revolutions. Adequately understanding authoritarian regimes both past and present requires appreciation of their triple historical entanglement in world-historical time, in historical sequences, and in their own historical origins.
People judge others more harshly after talking to bots
Authors: Tey Kian Siong , Mazar Asaf , Tomaino Geoff , Duckworth Angela L , Ungar Lyle H 
Published: September 2024

Abstract
People now commonly interact with Artificial Intelligence (AI) agents. How do these interactions shape how humans perceive each other? In two preregistered studies (total N = 1,261), we show that people evaluate other humans more harshly after interacting with an AI (compared with an unrelated purported human). In Study 1, participants who worked on a creative task with AIs (versus purported humans) subsequently rated another purported human's work more negatively. Study 2 replicated this effect and demonstrated that the results hold even when participants believed their evaluation would not be shared with the purported human. Exploratory analyses of participants’ conversations show that prior to their human evaluations they were more demanding, more instrumental and displayed less positive affect towards AIs (versus purported humans). These findings point to a potentially worrisome side effect of the exponential rise in human–AI interactions.
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms
Authors: Fyon Arthur , Franci Alessio , Sacré Pierre , Drion Guillaume 
Published: September 2024

Abstract
Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which raises questions regarding how specific ion channel subsets reliably shape intrinsic properties of neurons. Here, we use detailed conductance-based modeling to explore how stable neuronal function is achieved despite variability in channel composition among neurons. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. These two dimensions correspond to two sources of variability that originate from distinct physiologically relevant mechanisms underlying the regulation of neuronal activity, providing quantitative insights into how channel composition is linked to the electrophysiological activity of neurons. These insights allow us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.
Large language models and humans converge in judging public figures’ personalities
Authors: Cao Xubo , Kosinski Michal 
Published: September 2024

Abstract
ChatGPT-4 and 600 human raters evaluated 226 public figures’ personalities using the Ten-Item Personality Inventory. The correlation between ChatGPT-4 and aggregate human ratings ranged from r = 0.76 to 0.87, outperforming the models specifically trained to make such predictions. Notably, the model was not provided with any training data or feedback on its performance. We discuss the potential explanations and practical implications of ChatGPT-4's ability to mimic human responses accurately.
DEFB119 stratifies dysbiosis with distorted networks in the seminal microbiome associated with male infertility
Authors: Jin Jing , Yim Howard Chi Ho , Chang Hsiao Mei Ellie , Wang Yiwei , Choy Kathleen Hoi Kei , Chan Sze Yan , Alqawasmeh Odai A M , Liao Jinyue , Jiang Xiao-Tao , Chan David Yiu Leung , Fok Ellis Kin Lam 
Published: September 2024

Abstract
Infertility is associated with the alteration of the seminal microbiome. However, the onset of dysbiosis remains controversial and the involvement of host factors remains elusive. This study investigates the alterations of the seminal microbiome in male infertility and examines the association and function of DEFB119, a reproductive-tract-specific host antimicrobial peptide, on the seminal microbiome and male fertility. While we observed comparable genera, diversity and evenness of bacterial communities, a marked decrease in the modularity of the metacommunities was observed in patients with abnormal spermiogram (n = 57) as compared to the control (n = 30). A marked elevation of DEFB119 was observed in a subpopulation of male infertile patients (n = 5). Elevated seminal DEFB119 was associated with a decrease in the observed genera, diversity and evenness of bacterial communities, and further distortion of the metacommunities. Mediation analysis suggests the involvement of elevated DEFB119 and dysbiosis of the seminal microbiome in mediating the abnormalities in the spermiogram. Functional experiments showed that recombinant DEFB119 significantly decrease the progressive motility of sperm in patients with abnormal spermiogram. Moreover, DEFB119 demonstrated species-specific antimicrobial activity against common seminal and nonseminal species. Our work identifies an important host factor that mediates the host–microbiome interaction and stratifies the seminal microbiome associated with male infertility. These results may lead to a new diagnostic method for male infertility and regimens for formulating the microbiome in the reproductive tract and other organ systems.
Choice architecture promotes sustainable choices in online food-delivery apps
Authors: Lohmann Paul M , Gsottbauer Elisabeth , Farrington James , Human Steve , Reisch Lucia A 
Published: September 2024

Abstract
Greenhouse gas emissions from the food system constitute about one-third of the global total, hence mitigation in this sphere of human activity is a vital goal for research and policy. This study empirically tests the effectiveness of different interventions to reduce the carbon footprint of food choices made on food-delivery apps, using an incentive-compatible online randomized controlled trial with 4,008 participants. The experiment utilized an interactive web platform that mimics popular online food-delivery platforms (such as Just Eat) and included three treatment conditions: a sign-posted meat tax, a carbon-footprint label, and a choice-architecture intervention that changed the order of the menu so that the lowest carbon-impact restaurants and dishes were presented first. Results show that only the choice-architecture nudge significantly reduced the average meal carbon footprint—by 0.3 kg/CO2e per order (12%), driven by a 5.6 percentage point (13%) reduction in high-carbon meal choices. Moreover, we find evidence of significant health and well-being co-benefits. Menu repositioning resulted in the average meal order having greater nutritional value and fewer calories, whilst significantly increasing self-reported satisfaction with the meal choice. Simple back-of-the-envelope calculations suggest that menu repositioning would be a highly cost-effective policy instrument if implemented at scale, with the return on investment expected to be in the range of £1.28 to £3.85 per metric ton of avoided CO2 emissions, depending on implementation costs.
Mapping science in artificial intelligence policy development: formulation, trends, and influences
Authors: Cabral Bernardo , Salles-Filho Sergio 
Published: September 2024

Abstract
This research maps the evolution of artificial intelligence (AI) policy and its scientific underpinnings. First, we analyzed the global AI policy landscape using the Overton policy documents database, which comprises millions of policy documents. Findings reveal a substantial increase in AI policy documents since 2018, with the USA, European Union (EU), and intergovernmental organizations leading policy development efforts. We also analyzed the scientific articles referenced within these policies. The USA stood out as a central hub in the production and funding of AI research, with other Global North countries playing a notable role alongside China. The research cited in AI policy documents predominantly features journals with a high-impact factor, such as Nature and Science. This analysis aims to deepen the understanding of the AI policy landscape, offering insights for academics and policymakers and contributing to managing AI’s global governance.
The development and evaluation of the Designing for Dissemination and Implementation Learning Hub
Authors: McCarthy Michaela S , McCreight Marina , Lujan Deisy Hernandez , Sjoberg Heidi , Battaglia Catherine 
Published: September 2024

Abstract
There is a growing emphasis on reducing the gap between research and routine practice. Dissemination and Implementation (D&I) science offers theories, models, and frameworks to enhance the implementation, impact, and sustainment of new programs and interventions. Few training opportunities are available that help leaders, researchers, clinicians, and staff (implementers) translate original research into practice settings in a more timely and effective manner without requiring significant time away from their primary clinical duties. To address these needs, we designed a virtual Designing for Dissemination and Implementation (D4D&I) Learning Hub that offered foundational D&I knowledge and opportunities for skill building. We developed the D4D&I Learning Hub curricula to train novice participants in the multicomponent D4D&I implementation strategy bundle when implementing new programs or innovations. The components of the D4D&I strategy bundle include (i) Pre-implementation assessment for proactive planning and multilevel contextual assessment, (ii) Multilevel partner engagement to learn what is important to end-users and obtain buy-in, (iii) Implementation and adaptations guided by a pre-implementation assessment while retaining program fidelity, and (iv) Program evaluation. We utilized a virtual e-learning platform, expert trainers, mentorship, and a Virtual Learning Collaborative to deliver the six-module curricula to support participants’ growth and success. We used quantitative and qualitative methods informed by the Kirkpatrick Evaluation Model to evaluate the D4D&I Learning Hub. Thirty-one participants completed the D4D&I Learning Hub across four cohorts. Participants found the D4D&I curricula relevant and favorable, indicating they acquired the intended knowledge and skills. In presentations of their key takeaways, participants cited a greater understanding of how to apply various D&I theories, models, and frameworks to their research, engage multilevel partners during all phases of implementation and evaluation, and assess fidelity and adaptations. Participants planned to incorporate the acquired D&I knowledge and skills in future publications, grant applications, and when implementing new programs and projects in clinical settings. The D4D&I Learning Hub provides foundational education for novice participants of D&I science. It promotes designing, disseminating, implementing, and evaluating effective programs in clinical settings. This article discusses the development and implementation of the D4D&I Learning Hub. In addition, we evaluated the first four cohorts using the Kirkpatrick Evaluation Model.
Scalable and successful patient portal lifestyle coaching training for primary care clinical staff
Authors: Cedillo Maribel , Zepeda Jesell , Kiraly Bernadette , Flynn Michael , Larios Elizalde Paulina , Zheutlin Emily , Rudd Elizabeth A , Kukhareva Polina V , Butler Jorie M , Hess Rachel , Kawamoto Kensaku , Estabrooks Paul , Conroy Molly B 
Published: September 2024

Abstract
Maintaining a healthy weight postintentional weight loss is crucial for preventing chronic health conditions, yet many regain weight postintervention. Electronic health record (EHR) portals offer a promising avenue for weight management interventions, leveraging patient-primary care relationships. Our previous research demonstrated that coaching alongside self-monitoring improves weight maintenance compared to monitoring alone. Integrating weight management into routine clinical practice by training existing staff could enhance scalability and sustainability. However, challenges such as inconsistent staff qualifications and high coach turnover rates could affect intervention effectiveness. Standardizing services, training, and coaching continuity seem crucial for success. To report on developing, testing, and evaluating an EHR-based coaching training program for clinical staff, guided by an implementation tool for the MAINTAIN PRIME study. Conducted across 14 University of Utah primary care sites, we developed, tested, and evaluated a coaching training for clinical staff. Guided by a planning model and the Predisposing, Enabling, and Reinforcing (PER) tool, stakeholders actively participated in planning, ensuring alignment with clinic priorities. All clinical staff were invited to participate voluntarily. Evaluation measures included staff interest, training effectiveness, confidence, and readiness. Data collection utilized REDCap, with survey results analyzed using descriptive statistics. Despite increased clinical workload and reassignments posed by coronavirus disease 2019, we were able to train 39 clinical staff, with 34 successfully coaching patients. Feedback indicated high readiness and positive perceptions of coaching feasibility. Coaches reported satisfaction with training, support, and enjoyed establishing connections with patients. The PER strategies allowed us to implement a well-received training program found effective by primary care coaches.
An industry perspective on the FDA Modernization Act 2.0/3.0: potential next steps for sponsors to reduce animal use in drug development
Authors: Carratt Sarah A , Zuch de Zafra Christina L , Oziolor Elias , Rana Payal , Vansell Nichole R , Mangipudy Raja , Vaidya Vishal S 
Published: September 2024

Abstract
Pharmaceutical developers are encouraged to adopt the best practices of being purposefully thoughtful about the use of animals, seeking alternatives wherever possible. They should engage with health authorities to increase their familiarity with the methods, study designs, data outputs, and the context of use for new approach methodologies (NAMs). Although current state of technology does not yet provide adequate models to fully replace in vivo studies, many models are sufficiently good for an augmented approach that will enhance our understanding of in vitro to in vivo correlations and advance the long-term goal of reducing animal use through innovative NAMs. The goal of future nonclinical safety packages is to advance the utilization of such enabling technologies toward appropriate human risk characterization. Establishing confidence in NAMs is a critical first step. For example, sponsors may include both “traditional” and NAM-based nonclinical safety data in regulatory submissions to establish confidence with health authorities. In addition, regulators should create a “safe harbor” for hybrid nonclinical data packages to facilitate iterative learning, refinement, and implementation of NAM-based safety assessment strategies. Sponsors are urged to contribute to NAMs evolution through consortia participation, peer-reviewed publications, and documenting animal reduction in studies/programs, accelerating the eventual elimination of animal use in pharmaceutical development, as envisioned in the FDA Modernization Act 3.0.
An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances
Authors: Starnes Hannah M , Green Adrian J , Reif David M , Belcher Scott M 
Published: September 2024

Abstract
Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemical determinants of bioaccumulation and toxicity for most PFAS are not well understood, largely due to experimental data deficiencies. As most PFAS are proteinophilic, protein binding is a critical parameter for predicting PFAS bioaccumulation and toxicity. Among these proteins, human serum albumin (HSA) is the predominant blood transport protein for many PFAS. We previously demonstrated the utility of an in vitro differential scanning fluorimetry assay for determining relative HSA binding affinities for 24 PFAS. Here, we report HSA affinities for 65 structurally diverse PFAS from 20 chemical classes. We leverage these experimental data, and chemical/molecular descriptors of PFAS, to build 7 machine learning classifier algorithms and 9 regression algorithms, and evaluate their performance to identify the best predictive binding models. Evaluation of model accuracy revealed that the top-performing classifier model, logistic regression, had an AUROC (area under the receiver operating characteristic curve) statistic of 0.936. The top-performing regression model, support vector regression, had an R2 of 0.854. These top-performing models were then used to predict HSA–PFAS binding for chemicals in the EPAPFASINV list of 430 PFAS. These developed in vitro and in silico methodologies represent a high-throughput framework for predicting protein–PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints.