Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Maternal-driven immune education in offspring.

Maternal environmental exposures, particularly during gestation and lactation, significantly influence the immunological development and long-term immunity of offspring. Mammalian immune systems develop through crucial inputs from the environment, beginning in utero and continuing after birth. These critical developmental windows are essential for proper immune system development and, once closed, may not be reopened. This review focuses on the mechanisms by which maternal exposures, particularly to pathogens, diet, and microbiota, impact offspring immunity. Mechanisms driving maternal-offspring immune crosstalk include transfer of maternal antibodies, changes in the maternal microbiome and microbiota-derived metabolites, and transfer of immune cells and cytokines via the placenta and breastfeeding. We further discuss the role of transient maternal infections, which are common during pregnancy, in providing tissue-specific immune education to offspring. We propose a "maternal-driven immune education" hypothesis, which suggests that offspring can use maternal encounters that occur during a critical developmental window to develop optimal immune fitness against infection and inflammation.

Ensuring best practice in genomics education: A scoping review of genomics education needs assessments and evaluations.

A health workforce capable of implementing genomic medicine requires effective genomics education. Genomics education interventions developed for health professions over the last two decades, and their impact, are variably described in the literature. To inform an evaluation framework for genomics education, we undertook an exploratory scoping review of published needs assessments for, and/or evaluations of, genomics education interventions for health professionals from 2000 to 2023. We retrieved and screened 4,659 records across the two searches with 363 being selected for full-text review and consideration by an interdisciplinary working group. 104 articles were selected for inclusion in the review-60 needs assessments, 52 genomics education evaluations, and eight describing both. Included articles spanned all years and described education interventions in over 30 countries. Target audiences included medical specialists, nurses/midwives, and/or allied health professionals. Evaluation questions, outcomes, and measures were extracted, categorized, and tabulated to iteratively compare measures across stages of genomics education evaluation: planning (pre-implementation), development and delivery (implementation), and impact (immediate, intermediate, or long-term outcomes). They are presented here along with descriptions of study designs. We document the wide variability in evaluation approaches and terminology used to define measures and note that few articles considered downstream (long-term) outcomes of genomics education interventions. Alongside the evaluation framework for genomics education, results from this scoping review form part of a toolkit to help educators to undertake rigorous genomics evaluation that is fit for purpose and can contribute to the growing evidence base of the contribution of genomics education in implementation strategies for genomic medicine.

Ensuring best practice in genomics education: A theory- and empirically informed evaluation framework.

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences.

Differences in cognitive function at 18 y of age explain the association between low education and early dementia risk.

Major initiatives attempt to prevent dementia by targeting modifiable risk factors. Low education is frequently pointed to, due to its relationship with dementia. Impact of education is difficult to assess, however, because of associations with multiple other factors, requiring large population-representative samples to tease the relationships apart. We studied 207,814 Norwegian men born between 1950 and 1959 who underwent compulsory cognitive testing during military conscription as young adults, to systematically test associations of education, cognition, and other important factors. Participants were grouped into five education levels and seven cognitive levels. A total of 1,521 were diagnosed with dementia between ages 60 and 69 y. While having compulsory education only was associated with increased risk (Hazard ratio [HR] = 1.37, CI: 1.17 to 1.60), this association was markedly attenuated when controlling for cognitive test scores (HR = 1.08, CI: 0.91 to 1.28). In contrast, low cognitive score was associated with double risk of later diagnosis, even when controlling for education (HR = 2.00, CI: 1.65 to 2.42). This relationship survived controlling for early-life socioeconomic status and replicated within pairs of brothers. This suggests that genetic and environmental factors shared within families, e.g., common genetics, parental education, socioeconomic status, or other shared experiences, cannot account for the association. Rather, independent, nonfamilial factors are more important. In contrast, within-family factors accounted for the relationship between low education and diagnosis risk. In conclusion, implementing measures to increase cognitive function in childhood and adolescence appears to be a more promising strategy for reducing dementia burden.

Higher chronic absenteeism threatens academic recovery from the COVID-19 pandemic.

The broad and substantial educational harm caused by the COVID-19 pandemic has motivated large federal, state, and local investments in academic recovery. However, the success of these efforts depends in part on students' regular school attendance. Using state-level data, I show that the rate of chronic absenteeism among US public-school students grew substantially as students returned to in-person instruction. Specifically, between the 2018-2019 and 2021-2022 school years, the share of students chronically absent grew by 13.5 percentage points-a 91-percent increase that implies an additional 6.5 million students are now chronically absent. State-level increases in chronic absenteeism are positively associated with the prevalence of school closures during the 2020-2021 school year. However, these increases do not appear to be associated with enrollment loss, COVID-19 case rates, school masking policies, or declines in youth mental health. This evidence indicates that the barriers to learning implied by the sharp increase in chronic absenteeism merit further scrutiny and policy responses.

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.

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.

Forecasting Africa's fertility decline by female education groups.

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

Democratizing social scientists' impact on federal policy: Using the evidence act to help government and ourselves.

It is common for social scientists to discuss the implications of our research for policy. However, what actions can we take to inform policy in more immediate and impactful ways, regardless of our existing institutional affiliations or personal connections? Focusing on federal policy, I suggest that the answer requires understanding a basic coordination problem. On the government side, the Foundations of Evidence-based Policymaking Act (2018) requires that large federal agencies pose, communicate, and answer research questions related to their effects on people and communities. This advancement has opened the black box of federal agency policy priorities, but it has not addressed capacity challenges: These agencies often do not have the financial resources or staff to answer the research questions they pose. On the higher education side, we have more than 150,000 academic social scientists who are knowledge producers and educators by training and vocation. However, especially among those in disciplinary departments, or those without existing institutional or personal connections to federal agencies, we often feel locked out of federal policymaking processes. In this article, I define the coordination problem and offer concrete actions that the academic and federal government communities can take to address it. I also offer leading examples of how academics and universities are making public policy impact possible in multiple governmental spheres. I conclude by arguing that both higher education institutions and all levels of government can do more to help academic social scientists put our knowledge to work in service of the public good.

Broadening access to cryoEM through centralized facilities.

Cryogenic electron microscopy (cryoEM) uses images of frozen hydrated biological specimens to produce macromolecular structures, opening up previously inaccessible levels of biological organization to high-resolution structural analysis. CryoEM has the potential for broad impact in biomedical research, including basic cell, molecular, and structural biology, and increasingly in drug discovery and vaccine development. Recent advances have led to the expansion of molecular and cellular structure determination at an exponential rate. National and regional centers have emerged to support this growth by increasing the accessibility of cryoEM throughout the biomedical research community. Through cooperation and synergy, these centers form a network of resources that accelerate the adoption of best practices for access and training and establish sustainable workflows to build future research capacity.

Discovering genes that affect cognitive ability.

Twin and genomic studies indicate that genes play an important role in the development of cognitive ability. However, data limitations have made it difficult to pinpoint specific genes with a large impact. By examining the full gene sequences of >300 000 individuals, Chen et al. find eight such genes.

Cloud-based introduction to BASH programming for biologists.

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 authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. 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.

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.

A global initiative on addressing bioinformatics' grand challenges.

The Bioinformatics Grand Challenges Consortium (BGCC) is a collaborative effort to address the most pressing challenges in bioinformatics. Initially focusing on education and training, the consortium successfully defined seven key grand challenges and is actively developing actionable solutions for these challenges. Building on this foundation, the BGCC plans to broaden its focus to include additional grand challenges in emerging areas.

Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform.

This study 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 at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. 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.

Diverse mathematical knowledge among indigenous Amazonians.

We investigate number and arithmetic learning among a Bolivian indigenous people, the Tsimane', for whom formal schooling is comparatively recent in history and variable in both extent and consistency. We first present a large-scale meta-analysis on child number development involving over 800 Tsimane' children. The results emphasize the impact of formal schooling: Children are only found to be full counters when they have attended school, suggesting the importance of cultural support for early mathematics. We then test especially remote Tsimane' communities and document the development of specialized arithmetical knowledge in the absence of direct formal education. Specifically, we describe individuals who succeed on arithmetic problems involving the number five-which has a distinct role in the local economy-even though they do not succeed on some lower numbers. Some of these participants can perform multiplication with fives at greater accuracy than addition by one. These results highlight the importance of cultural factors in early mathematics and suggest that psychological theories of number where quantities are derived from lower numbers via repeated addition (e.g., a successor function) are unlikely to explain the diversity of human mathematical ability.

Showing high-achieving college applicants past admissions outcomes increases undermatching.

More than 40% of US high school students have access to Naviance, a proprietary tool designed to guide college search and application decisions. The tool displays, for individual colleges, the standardized test scores, grade-point averages, and admissions outcomes of past applicants from a student's high school, so long as a sufficient number of students from previous cohorts applied to a given college. This information is intended to help students focus their efforts on applying to the most suitable colleges, but it may also influence application decisions in undesirable ways. Using data on 70,000 college applicants across 220 public high schools, we assess the effects of access to Naviance on application undermatch, or applying only to schools for which a candidate is academically overqualified. By leveraging variation in the year that high schools adopted the tool, we estimate that Naviance increased application undermatching by more than 50% among 17,000 high-achieving students in our dataset. This phenomenon may be due to increased conservatism: Students may be less likely to apply to colleges when they know their academic qualifications fall below the average of admitted students from their high school. These results illustrate how information on college competitiveness, when not appropriately presented and contextualized, can lead to unintended consequences.

Reimagining the machine learning life cycle to improve educational outcomes of students.

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout to assisting in university admissions and facilitating the rise of massive open online courses (MOOCs). Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand? We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

System-, teacher-, and student-level interventions for improving participation in online learning at scale in high schools.

Many school systems across the globe turned to online education during the COVID-19 pandemic. This context differs significantly from the prepandemic situation in which massive open online courses attracted large numbers of voluntary learners who struggled with completion. Students who are provided online courses by their high schools also have their behavior determined by actions of their teachers and school system. We conducted experiments to improve participation in online learning before, during, and right after the COVID-19 outbreak, with 1,151 schools covering more than 45,000 students in their final years of high school in Ecuador. These experiments tested light-touch interventions at scale, motivated by behavioral science, and were carried out at three levels: that of the system, teacher, and student. We find the largest impacts come from intervening at the system level. A cheap, online learning management system for centralized monitoring increased participation by 0.21 SD and subject knowledge by 0.13 SD relative to decentralized management. Centralized management is particularly effective for underperforming schools. Teacher-level nudges in the form of benchmarking emails, encouragement messages, and administrative reminders did not improve student participation. There was no significant impact of encouragement messages to students, or in having them plan and team-up with peers. Small financial incentives in the form of lottery prizes for finishing lessons did increase study time, but was less cost-effective, and had no significant impact on knowledge. The results show the difficulty in incentivizing online learning at scale, and a key role for central monitoring.