Sex contextualism in laboratory research: Enhancing rigor and precision in the study of sex-related variables.

Understanding sex-related variation in health and illness requires rigorous and precise approaches to revealing underlying mechanisms. A first step is to recognize that sex is not in and of itself a causal mechanism; rather, it is a classification system comprising a set of categories, usually assigned according to a range of varying traits. Moving beyond sex as a system of classification to working with concrete and measurable sex-related variables is necessary for precision. Whether and how these sex-related variables matter-and what patterns of difference they contribute to-will vary in context-specific ways. Second, when researchers incorporate these sex-related variables into research designs, rigorous analytical methods are needed to allow strongly supported conclusions. Third, the interpretation and reporting of sex-related variation require care to ensure that basic and preclinical research advance health equity for all.

Guidelines for organizing accessible and inclusive research retreats.

Research retreats are elements of scientific graduate training programs. Although expected to provide strong educational value, some students are reluctant to attend. Here, we identify participation barriers and provide guidelines for retreat design that minimize obstacles and establish an inclusive environment to improve attendance and enrichment for all attendees.

Toolkit for mapping the clonal landscape of tumor-infiltrating B cells.

Our current understanding of whether B cell involvement in the tumor microenvironment benefits the patient or the tumor - in distinct cancers, subcohorts and individual patients - is quite limited. Both statements are probably true in most cases: certain clonal B cell populations contribute to the antitumor response, while others steer the immune response away from the desired mechanics. To step up to a new level of understanding and managing B cell behaviors in the tumor microenvironment, we need to rationally discern these roles, which are cumulatively defined by B cell clonal functional programs, specificities of their B cell receptors, specificities and isotypes of the antibodies they produce, and their spatial interactions within the tumor environment. Comprehensive analysis of these characteristics of clonal B cell populations is now becoming feasible with the development of a whole arsenal of advanced technical approaches, which include (1) methods of single-cell and spatial transcriptomics, genomics, and proteomics; (2) methods of massive identification of B cell specificities; (3) methods of deep error-free profiling of B cell receptor repertoires. Here we overview existing techniques, summarize their current application for B cells studies and propose promising future directions in advancing B cells exploration.

Charting a landmark-driven path forward for population genetics and ancient DNA research in Africa.

Population history-focused DNA and ancient DNA (aDNA) research in Africa has dramatically increased in the past decade, enabling increasingly fine-scale investigations into the continent's past. However, while international interest in human genomics research in Africa grows, major structural barriers limit the ability of African scholars to lead and engage in such research and impede local communities from partnering with researchers and benefitting from research outcomes. Because conversations about research on African people and their past are often held outside Africa and exclude African voices, an important step for African DNA and aDNA research is moving these conversations to the continent. In May 2023 we held the DNAirobi workshop in Nairobi, Kenya and here we synthesize what emerged most prominently in our discussions. We propose an ideal vision for population history-focused DNA and aDNA research in Africa in ten years' time and acknowledge that to realize this future, we need to chart a path connecting a series of "landmarks" that represent points of consensus in our discussions. These include effective communication across multiple audiences, reframed relationships and capacity building, and action toward structural changes that support science and beyond. We concluded there is no single path to creating an equitable and self-sustaining research ecosystem, but rather many possible routes linking these landmarks. Here we share our diverse perspectives as geneticists, anthropologists, archaeologists, museum curators, and educators to articulate challenges and opportunities for African DNA and aDNA research and share an initial map toward a more inclusive and equitable future.

Expanding the genetic and phenotypic landscape of replication factor C complex-related disorders: RFC4 deficiency is linked to a multisystemic disorder.

The precise regulation of DNA replication is vital for cellular division and genomic integrity. Central to this process is the replication factor C (RFC) complex, encompassing five subunits, which loads proliferating cell nuclear antigen onto DNA to facilitate the recruitment of replication and repair proteins and enhance DNA polymerase processivity. While RFC1's role in cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is known, the contributions of RFC2-5 subunits on human Mendelian disorders is largely unexplored. Our research links bi-allelic variants in RFC4, encoding a core RFC complex subunit, to an undiagnosed disorder characterized by incoordination and muscle weakness, hearing impairment, and decreased body weight. We discovered across nine affected individuals rare, conserved, predicted pathogenic variants in RFC4, all likely to disrupt the C-terminal domain indispensable for RFC complex formation. Analysis of a previously determined cryo-EM structure of RFC bound to proliferating cell nuclear antigen suggested that the variants disrupt interactions within RFC4 and/or destabilize the RFC complex. Cellular studies using RFC4-deficient HeLa cells and primary fibroblasts demonstrated decreased RFC4 protein, compromised stability of the other RFC complex subunits, and perturbed RFC complex formation. Additionally, functional studies of the RFC4 variants affirmed diminished RFC complex formation, and cell cycle studies suggested perturbation of DNA replication and cell cycle progression. Our integrated approach of combining in silico, structural, cellular, and functional analyses establishes compelling evidence that bi-allelic loss-of-function RFC4 variants contribute to the pathogenesis of this multisystemic disorder. These insights broaden our understanding of the RFC complex and its role in human health and disease.

Slow convergence: Career impediments to interdisciplinary biomedical research.

Despite the long-standing calls for increased levels of interdisciplinary research as a way to address society's grand challenges, most science is still disciplinary. To understand the slow rate of convergence to more interdisciplinary research, we examine 154,021 researchers who received a PhD in a biomedical field between 1970 and 2013, measuring the interdisciplinarity of their articles using the disciplinary composition of references. We provide a range of evidence that interdisciplinary research is impactful, but that those who conduct it face early career impediments. The researchers who are initially the most interdisciplinary tend to stop publishing earlier in their careers-it takes about 8 y for half of the researchers in the top percentile in terms of initial interdisciplinarity to stop publishing, compared to more than 20 y for moderately interdisciplinary researchers (10th to 75th percentiles). Moreover, perhaps in response to career challenges, initially interdisciplinary researchers on average decrease their interdisciplinarity over time. These forces reduce the stock of interdisciplinary researchers who can train future cohorts. Indeed, new graduates tend to be less interdisciplinary than the stock of active researchers. We show that interdisciplinarity does increase over time despite these dampening forces because initially disciplinary researchers become more interdisciplinary as their careers progress.

Centering relationships to place for more meaningful research and engagement.

Research has the potential to simultaneously generate new knowledge and contribute meaningful social-ecological benefits; however, research processes and outcomes can also perpetuate extractive patterns that have manifested the climate, biodiversity, and social justice crises. One approach to enhance the societal value of research processes is to strengthen relationships with places of study and the peoples of those places. Deepening relational engagement with the social-ecological context and history of a place can lead to more accurate results and improved public trust in the scientific process and is particularly important for natural scientists who work at the interface of nature and society. We provide three actionable pathways that range from individual to systemic change to enhance place-based relationships within research systems: 1) deepen reflection and communication about relationships with places and peoples; 2) strengthen collaboration among research teams and partners; and 3) transform systems of knowledge creation to foster place-based roots. Action on any of these proposed pathways, but especially action taken across all three, can build empathy and connections to place and people, strengthening the meaningful impact of research both locally and globally.

Elements of successful NIH grant applications.

Is there a formula for a competitive NIH grant application? The Serenity Prayer may provide one: "Grant me the serenity to accept the things I cannot change, the ability to change the things I can, and the wisdom to know the difference." But how to tell the difference? In this Perspective, we provide an inclusive roadmap-elements of NIH funding. Collectively, we have over 30 y of peer review experience as NIH Scientific Review Officers in addition to over 30 y of program experience as NIH Program Officers. This article distills our NIH experience. We use Euclid's 13-book landmark, The Elements, as our template to humbly share what we learned. We have three specific aims: inform, guide, and motivate prospective applicants. We also address ways that support diversity and inclusion among applicants and young investigators in biomedical research. The elements we describe come from a wide range of sources. Some themes will be general. Some will be specific. All will be candid. The ultimate goal is a competitive application, serenity, and hopefully both.

A leadership-level culture cycle intervention changes teachers' culturally inclusive beliefs and practices.

Despite an abundance of support for culturally inclusive learning environments, there is little consensus regarding how to change educational contexts to effectively and sustainably foster cultural inclusion. To address this gap, we report findings from a research-practice partnership that leveraged the Culture Cycle Framework (CCF) to expand educators' praxis to include both independent and interdependent models of self. Most U.S. schools validate independent cultural models (i.e., those that prioritize individuality, uniqueness, and personal agency) and overlook interdependent models (i.e., those that prioritize connectedness, relationality, and collective well-being), which are more common among students from marginalized racial and socioeconomic backgrounds. Using a quasi-experimental longitudinal design, we trained school leadership to integrate ideas about cultural inclusion (i.e., validating the importance of both independent and interdependent cultural models) into school-wide flagship practices. We assessed downstream indicators of culture change by surveying teachers and students across the district and found that a) leadership-level training enhanced school-wide beliefs about cultural inclusion, b) teachers' endorsement of culturally inclusive beliefs predicted their use of culturally inclusive practices, and c) teachers' use of culturally inclusive practices predicted enhanced psychosocial and academic outcomes among students. This research represents a comprehensive culture change effort using the CCF and illustrates a means of fostering inclusion-focused educational culture change and assessing downstream consequences of culture change initiatives.

Screening p-hackers: Dissemination noise as bait.

We show that adding noise before publishing data effectively screens [Formula: see text]-hacked findings: spurious explanations produced by fitting many statistical models (data mining). Noise creates "baits" that affect two types of researchers differently. Uninformed [Formula: see text]-hackers, who are fully ignorant of the true mechanism and engage in data mining, often fall for baits. Informed researchers, who start with an ex ante hypothesis, are minimally affected. We show that as the number of observations grows large, dissemination noise asymptotically achieves optimal screening. In a tractable special case where the informed researchers' theory can identify the true causal mechanism with very few data, we characterize the optimal level of dissemination noise and highlight the relevant trade-offs. Dissemination noise is a tool that statistical agencies currently use to protect privacy. We argue this existing practice can be repurposed to screen [Formula: see text]-hackers and thus improve research credibility.

Social mobility in the Tang Dynasty as the Imperial Examination rose and aristocratic family pedigree declined, 618-907 CE.

Data from the distant past are fertile ground for testing social science theories of education and social mobility. In this study, we construct a dataset from 3,640 tomb epitaphs of males in China's Tang Dynasty (618-907 CE), which contain granular and extensive information about the ancestral origins, family background, and career histories of the deceased elites. Our statistical analysis of the complete profiles yields evidence of the transition away from an aristocratic society in three key trends: 1) family pedigree (i.e., aristocracy) mattered less for career achievement over time, 2) passing the Imperial Examination (Keju) became an increasingly important predictor of one's career achievement, and 3) father's position always mattered throughout the Tang, especially for men who did not pass the Keju. The twilight of medieval Chinese aristocracy, according to the data, began in as early as the mid-seventh century CE.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Acceleration of infectious disease drug discovery and development using a humanized model of drug metabolism.

A key step in drug discovery, common to many disease areas, is preclinical demonstration of efficacy in a mouse model of disease. However, this demonstration and its translation to the clinic can be impeded by mouse-specific pathways of drug metabolism. Here, we show that a mouse line extensively humanized for the cytochrome P450 gene superfamily ("8HUM") can circumvent these problems. The pharmacokinetics, metabolite profiles, and magnitude of drug-drug interactions of a test set of approved medicines were in much closer alignment with clinical observations than in wild-type mice. Infection with Mycobacterium tuberculosis, Leishmania donovani, and Trypanosoma cruzi was well tolerated in 8HUM, permitting efficacy assessment. During such assessments, mouse-specific metabolic liabilities were bypassed while the impact of clinically relevant active metabolites and DDI on efficacy were well captured. Removal of species differences in metabolism by replacement of wild-type mice with 8HUM therefore reduces compound attrition while improving clinical translation, accelerating drug discovery.

Promoting sustainable smallholder farming via multistakeholder collaboration.

Transforming smallholder farms is critical to global food security and environmental sustainability. The science and technology backyard (STB) platform has proved to be a viable approach in China. However, STB has traditionally focused on empowering smallholder farmers by transferring knowledge, and wide-scale adoption of more sustainable practices and technologies remains a challenge. Here, we report on a long-term project focused on technology scale-up for smallholder farmers by expanding and upgrading the original STB platform (STB 2.0). We created a formalized and standardized process by which to engage and collaborate with farmers, including integrating their feedback via equal dialogues in the process of designing and promoting technologies. Based on 288 site-year of field trials in three regions in the North China Plain over 5 y, we find that technologies cocreated through this process were more easily accepted by farmers and increased their crop yields and nitrogen factor productivity by 7.2% and 28.1% in wheat production and by 11.4% and 27.0% in maize production, respectively. In promoting these technologies more broadly, we created a "one-stop" multistakeholder program involving local government agencies, enterprises, universities, and farmers. The program was shown to be much more effective than the traditional extension methods applied at the STB, yielding substantial environmental and economic benefits. Our study contributes an important case study for technology scale-up for smallholder agriculture. The STB 2.0 platform being explored emphasizes equal dialogue with farmers, multistakeholder collaboration, and long-term investment. These lessons may provide value for the global smallholder research and practitioners.

The economic case for scaling up health research and development: Lessons from the COVID-19 pandemic.

In response to the COVID-19 pandemic, governments directly funded vaccine research and development (R&D), quickly leading to multiple effective vaccines and resulting in enormous health and economic benefits to society. We develop a simple economic model showing this feat could potentially be repeated for other health challenges. Based on inputs from the economic and medical literatures, the model yields estimates of optimal R&D spending on treatments and vaccines for known diseases. Taking a global and societal perspective, we estimate the social benefits of such spending and a corresponding rate of return. Applications to Streptococcus A vaccines and Alzheimer's disease treatments demonstrate the potential of enhanced research and development funding to unlock massive global health and health-related benefits. We estimate that these benefits range from 2 to 60 trillion (2020 US$) and that the corresponding rates of return on R&D spending range from 12% to 23% per year for 30 y. We discuss the current shortfall in R&D spending and public policies that can move current funding closer to the optimal level.

Challenges in distinguishing functional proteins from polyproteins in databases: implications for drug discovery.

This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research. Furthermore, it emphasizes the need of learning from the SARS-CoV-2 pandemic in order to improve global preparedness for future health crises.

Computational model for drug research.

This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Identifying and training deep learning neural networks on biomedical-related datasets.

This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Antiarrhythmic Mechanisms of Epidural Blockade After Myocardial Infarction.

BACKGROUND: Thoracic epidural anesthesia (TEA) has been shown to reduce the burden of ventricular tachycardia in small case series of patients with refractory ventricular tachyarrhythmias and cardiomyopathy. However, its electrophysiological and autonomic effects in diseased hearts remain unclear, and its use after myocardial infarction is limited by concerns for potential right ventricular dysfunction. METHODS: Myocardial infarction was created in Yorkshire pigs (N=22) by left anterior descending coronary artery occlusion. Approximately, six weeks after myocardial infarction, an epidural catheter was placed at the C7-T1 vertebral level for injection of 2% lidocaine. Right and left ventricular hemodynamics were recorded using Millar pressure-conductance catheters, and ventricular activation recovery intervals (ARIs), a surrogate of action potential durations, by a 56-electrode sock and 64-electrode basket catheter. Hemodynamics and ARIs, baroreflex sensitivity and intrinsic cardiac neural activity, and ventricular effective refractory periods and slope of restitution (Smax) were assessed before and after TEA. Ventricular tachyarrhythmia inducibility was assessed by programmed electrical stimulation. RESULTS: TEA reduced inducibility of ventricular tachyarrhythmias by 70%. TEA did not affect right ventricular-systolic pressure or contractility, although left ventricular-systolic pressure and contractility decreased modestly. Global and regional ventricular ARIs increased, including in scar and border zone regions post-TEA. TEA reduced ARI dispersion specifically in border zone regions. Ventricular effective refractory periods prolonged significantly at critical sites of arrhythmogenesis, and Smax was reduced. Interestingly, TEA significantly improved cardiac vagal function, as measured by both baroreflex sensitivity and intrinsic cardiac neural activity. CONCLUSIONS: TEA does not compromise right ventricular function in infarcted hearts. Its antiarrhythmic mechanisms are mediated by increases in ventricular effective refractory period and ARIs, decreases in Smax, and reductions in border zone electrophysiological heterogeneities. TEA improves parasympathetic function, which may independently underlie some of its observed antiarrhythmic mechanisms. This study provides novel insights into the antiarrhythmic mechanisms of TEA while highlighting its applicability to the clinical setting.