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Cross-coupling reactions for constructing C-N bonds represent a pivotal advancement in chemical science. Traditional methodologies, including nucleophilic aromatic substitution (SNAr) and transition metal-catalyzed cross-couplings, have limitations concerning aryl scope, reliance on toxic and costly transition-metal catalysts, and issues related to atom economy and waste generation from ligands and additives. In this work, we introduce a novel method for aminating neutral, electron-rich, and electron-deficient aryl halides, eliminating the need for transition metals. Our approach involves the activation of aryl halides using solvated electrons generated from granulated lithium and sonication. This serves as a sustainable source of reducing power, facilitating the efficient formation of C-N bonds under near ambient conditions. Competitive selectivity studies between halide and ester functionalities were explored. Reaction scope and conducted mechanistic studies which supported the proposed radical-nucleophilic substitution (SRN 1 ) mechanism for the reaction. Notably, the developed reaction has a highly competitive reductive dehalogenation pathway during the C-N coupling reaction, and this mechanistic divergency was thoroughly explored. This work not only broadens the scope of C-N coupling reactions which typically employs aryl bromides and iodides and rarely aryl fluorides which is also equally abundant, but also introduces a new way to do C-N coupling reactions using solvated electrons., (© 2024 Wiley-VCH GmbH.)

As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions-an ability we define as "collective intelligence." We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.

Role-based frameworks have long been the cornerstone of organizational coordination, providing clarity in role expectations among team members. However, the rise of "fluid participation"-a constant shift in team composition and skill sets-poses new challenges to traditional coordination mechanisms. In particular, with fluid participation, a team's roles can oscillate between disconnected and intersecting, or between lacking and having overlap in the capabilities and expectations of different roles. This study investigates the possibility that a disconnected set of roles creates a structural constraint on the flexible coordination needed to perform in volatile contexts, as well as the mitigating role of cognitive versatility in a team's strategically-central member. Utilizing a sample of 342 teams from a hospital Emergency Department, we find that teams with a disconnected role set are less effective than teams with an intersecting role set as demonstrated by longer patient stays and increased handoffs during shift changes. Importantly, the presence of a cognitively versatile attending physician mitigates these negative outcomes, enhancing overall team effectiveness. Our findings remain robust even after accounting for other variables like team expertise and familiarity. This research extends the Carnegie School's seminal work on fluid participation by integrating insights from psychology and organizational behavior, thereby identifying key individual attributes that can bolster team coordination in dynamic settings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Aggarwal, Mayo, Murase, Zhang, Aven and Woolley.)

Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Community-engaged research (CEnR) is a potent tool for addressing health inequities and fostering equitable relationships among communities, researchers, and institutions. CEnR involves collaboration throughout the research process, demonstrating improvements in study recruitment and retention, intervention efficacy, program sustainability, capacity building among partners, and enhanced cultural relevance. Despite the increasing demand for CEnR, institutional policies, particularly human participation protection training (HPP), lag behind, creating institutional barriers to community partnerships. Here, we highlight challenges encountered in our ongoing study, Fostering Opportunities in Research through Messaging and Education (FOR ME), focused on promoting shared decision-making around clinical trial participation among Black women diagnosed with breast cancer. Grounded in CEnR methods, FOR ME has a partnership with a community-based organization (CBO) that addresses the needs of Black women with breast cancer. Our CBO partner attempted to obtain HPP training, which was administratively burdensome and time-consuming. As CEnR becomes more prevalent, academic and research institutions, along with researchers, are faced with a call to action to become more responsive to community partner needs. Accordingly, we present a guide to HPP training for community partners, addressing institutional barriers to community partner participation in research. This guide outlines multiple HPP training pathways for community partners, aiming to minimize institutional barriers and enhance their engagement in research with academic partners.

Dominant paradigms in science foster integration of research findings, but at what cost? Forcing convergence requires centralizing decision-making authority, and risks reducing the diversity of methods and contributors, both of which are essential for the breakthrough ideas that advance science.

The opioid overdose epidemic continues to disproportionately impact underserved rural areas throughout the nation, with many of these rural areas experiencing greater opioid-related mortality rates than their urban counterparts. With limited treatment infrastructure and resources, two rural communities in Southeast Utah utilized community-based participatory research collaboration principles to develop, implement, and evaluate a series of evidence-based community opioid education events. This practical and quantitative study surveying 123 participants describes the collaborative efforts of two rural communities in addressing the devastating impacts of the opioid overdose epidemic and reflects on the success of the events via descriptive analysis of summary data. These events increased participants' reported perceptions of and knowledge in four main education areas: stigma reduction, prevention and treatment awareness, naloxone education and use, and resource location awareness. Post-event surveys further supported these results, revealing improved learning in each of these four areas, indicating increased knowledge toward opioid use disorder treatments and stigma reduction. In addition, participants identified key takeaways such as local resource awareness and dismantling stigma as effective strategies to reduce the negative effects of the opioid overdose epidemic. This model for rural community education supports previous research and serves as an effective strategy of public health practice to address the opioid overdose epidemic on a local level., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Positive, supportive responses to child maltreatment disclosure are critical for victims to receive appropriate resources and support for healing. Young people often prefer to disclose to their peers, frequently on social media platforms., Objective: We assessed young people's use of TalkLife, an online peer-to-peer support platform, to respond to the disclosure of child maltreatment., Methods: We conducted a qualitative content analysis of 1090 comments on childhood maltreatment-related posts on TalkLife between 2013 and 2020. We used an iterative, team-based qualitative content analysis approach to understand how peers responded to maltreatment disclosure., Findings: Peer responses tended to be supportive, including asking questions about the abuse and offering advice, emotional support, and other positive responses. Most commonly, peers advised the victim to report, focus on their strengths instead of the abuse, reach out to adults for more support, or confront the perpetrator. On occasion, however, peers began an irrelevant discussion, joked about the situation, or even directly attacked the discloser., Conclusions: Learning about child maltreatment disclosures on social media builds the foundation for research to assist in identifying and applying interventions on online platforms. Further, these findings can inform programs that teach how to provide healthy responses to child maltreatment disclosures., (Copyright © 2023. Published by Elsevier Ltd.)

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human-machine interactions, is exhibiting collective intelligence? Research on human-machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain-Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human-AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human-machine intelligence., (© 2023 The Authors. Topics in Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society.)