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This collaboration between the Nez Perce Tribe and the University of Idaho aimed to address the unique needs and perspectives required for Tribal Natural Resources Management (TNRM). TNRM involves the governance and caretaking of the land and waters, emphasizing the recognition of cultural significance, sovereignty, self-determination, and traditional knowledge systems. A workforce development program was created, focusing on Fisheries, Forestry, and Fire Management, while being grounded in Indigenous knowledge, Indigenous STEM identities, and culturally sustaining pedagogy. The philosophical foundations of the program emphasized the importance of integrating Indigenous knowledge and learning approaches alongside technical skills. By broadening the conceptions of "what counts" in science, students were encouraged to recognize the value of Indigenous knowledge and develop a sense of responsibility toward caring for the Land and waters. Data collected from the program revealed its success in helping students connect Indigenous ways of knowing to their understanding of STEM. Students found meaning in Indigenous knowledge as a means to perpetuate Nimiipuu lifeways, while also recognizing the utility of Western STEM. The involvement of Elders and Native professionals as teachers in the STEM curriculum highlighted the importance of intergenerational knowledge transmission. By combining Indigenous ways of knowing with technical skills, the program successfully laid the groundwork for students to become future leaders in Tribal Natural Resources Management, equipped with the necessary cultural, environmental, and scientific expertise to caretake Lands and waters effectively.

COVID-19 has changed the daily lives of families, impacted on work, social interactions, and mental health. Since spring 2020, parents have been working from home and children have been home from daycare and school. Parents are experiencing stress in an attempt to satisfy the demands of work, family, and COVID-19 concerns. Due to the fact that children have been home from daycare and school, parents have the sole responsibility of caring for and teaching their children until schools are able to fully and effectively meet the needs of educating students in an adapted format. Research provides a wealth of information documenting the advantages of parents reading to their children. Children benefit from read-alouds with parental interaction, and these benefits include an increase in oral language skills, reading comprehension, vocabulary, and an increase in motivation to read. The purpose of this study is to answer two questions: (1) Since parents were home more often with their children, were parents spending more quality time reading to their two-to four-year-old children? This can be defined as reading developmentally appropriate books to their children with their undivided attention; and (2) Since parents were home more often with their two-to four-year-old children, were parents reading more to their young children? Parents of pre-kindergarten students were surveyed to determine the answers to these questions.

Conjoint Behavioral Consultation (CBC) is an evidence-based, indirect service delivery model that addresses children's behavioral concerns across home and school. However, to date, researchers have not yet examined specific aspects of the intervention that maximize the effects of CBC. The current study examined whether a foundational aspect of consultation--the framing of target behaviors (i.e., positive behaviors to increase versus negative behaviors to decrease)--moderated the effects of CBC on children's problem and adaptive behaviors. Participants were 267 children in Kindergarten through third grade (n[subscript CBC] = 159, n[subscript Control] = 108) and their parents and teachers. Results revealed positively framed target behaviors were associated with fewer behavior concerns at school for children who received CBC relative to other child participants. Children who received CBC and whose target behaviors were framed negatively showed more noncompliant behavior relative to all other participants. Implications of these findings for practice, study limitations, and areas for future research are discussed.

Background: The positive youth development (PYD) framework orients developmental scholarship to focusing on youth's strengths rather than deficits, however, validity evidence on PYD measures among ethnic-minority youth is limited. Objectives: The objectives of the current study were to (a) examine the factor structure of a PYD measure within a Latin American adolescent sample and (b) test associations between PYD constructs and adjustment indicators. Methods: The sample included 288 Latin American students from the Southwest (M[subscript age] = 13.69 years, SD = 0.56; 47.7 female; 86.3% U.S.-born). Participants reported their PYD (i.e., Competence, Confidence, Character, Connection, and Caring) and academic performance. Mothers reported on youth's internalizing and externalizing behaviors. The internal structure of the PYD measure was assessed through confirmatory factor analyses (CFAs) and model comparisons. Criterion evidence was evaluated via regressions between the Five C's, academic performance, internalizing, and externalizing behaviors. Results: The five-correlated factor structure best fit the data with certain modifications. Character was negatively associated with externalizing behavior. All C's were positively related to academic performance. Conclusions: The results provide empirical support for the five-correlated factor model within a sample of Latin American adolescents, but modifications were necessary to maximize model fit. Dropping items within the "values diversity" subscale highlight the need for cultural assets within PYD measurement models for ethnic-minority youth, to best reflect their appraisals of diversity as minoritized individuals. Additionally, our findings suggest that the C's are salient to specific indices of Latin American adolescents' adjustment.

Newcomer immigrant adolescents (NIA) represent a particularly vulnerable group in the United States, facing numerous stressors placing them at risk for social-emotional and academic concerns. Schools play a critical role in supporting NIA in the United States. Despite this, insufficient research addressing the needs of this group within schools has been conducted. Considering the complexity of engaging in community-based research with marginalized populations, such as NIA, in this article, we elaborate on the challenges we faced during our research with NIA and propose solutions to these challenges. We do so with the goal of promoting continued engagement of such efforts within our field as to better address the needs of NIA within the United States.

In this chapter, the authors describe the theory of change behind a regional cross-institutional community of practice (CoP) for educational developers seeking to strengthen their institution's SoTL culture and increase faculty SoTL production by providing institution-level support initiatives and programming.

This chapter describes the development, implementation, and assessment of an intensive Scholarship of Teaching and Learning (SoTL) Scholars program grounded in self-determination theory. These findings provide one of the first integrations of theory into studying the impact of SoTL and can support educational developers with SoTL initiatives at their own institutions.

As centers for teaching and learning increasingly offer support and leadership for the scholarship of teaching and learning (SoTL) at their institutions, educational developers need better tools to plan their SoTL programming. This article shares the work of a regional network of educational developers across six institutions in Virginia, who aimed to enhance SoTL offerings within and across their institutions. While SoTL tools and models for individual instructors proliferate, this community of practice noted a gap in support for developers doing more institution-level planning. Through their collaboration, they developed two tools for planning and launching institution-level SoTL programs: the SoTL Strategic Planning Worksheet and the SoTL Program Taxonomy. This article describes the development of these tools and assesses their implications for educational development practice.

Background: Deep grey matter pathology is a key driver of disability worsening in people with multiple sclerosis. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique which quantifies local magnetic susceptibility from variations in phase produced by changes in the local magnetic field. In the deep grey matter, susceptibility has previously been validated against tissue iron concentration. However, it currently remains unknown whether susceptibility is abnormal in older progressive MS cohorts, and whether it correlates with disability. Objectives: To investigate differences in mean regional susceptibility in deep grey matter between people with secondary progressive multiple sclerosis (SPMS) and healthy controls; to examine in patients the relationships between deep grey matter susceptibility and clinical and imaging measures of disease severity. Methods: Baseline data from a subgroup of the MS-STAT2 trial (simvastatin vs. placebo in SPMS, NCT03387670) were included. The subgroup underwent clinical assessments and an advanced MRI protocol at 3T. A cohort of age-matched healthy controls underwent the same MRI protocol. Susceptibility maps were reconstructed using a robust QSM pipeline from multi-echo 3D gradient-echo sequence. Regions of interest (ROIs) in the thalamus, globus pallidus and putamen were segmented from 3D T1-weighted images, and lesions segmented from 3D fluid-attenuated inversion recovery images. Linear regression was used to compare susceptibility from ROIs between patients and controls, adjusting for age and sex. Where significant differences were found, we further examined the associations between ROI susceptibility and clinical and imaging measures of MS severity. Results: 149 SPMS (77% female; mean age: 53 yrs; median Expanded Disability Status Scale (EDSS): 6.0 [interquartile range 4.5–6.0]) and 33 controls (52% female, mean age: 57) were included.Thalamic susceptibility was significantly lower in SPMS compared to controls: mean (SD) 28.6 (12.8) parts per billion (ppb) in SPMS vs. 39.2 (12.7) ppb in controls; regression coefficient: −12.0 [95% confidence interval: −17.0 to −7.1], p

Burgeoning evidence demonstrates that effects of environmental exposures can be transmitted to subsequent generations through the germline without DNA mutations1,2. This phenomenon remains controversial because underlying mechanisms have not been identified. Therefore, understanding how effects of environmental exposures are transmitted to unexposed generations without DNA mutations is a fundamental unanswered question in biology. Here, we used an established murine model of male-specific transgenerational obesity to show that exposure to the obesogen tributyltin (TBT) elicited heritable changes in chromatin interactions (CIs) in primordial germ cells (PGCs). New CIs were formed within the Ide gene encoding Insulin Degrading Enzyme in the directly exposed PGCs, then stably maintained in PGCs of the subsequent (unexposed) two generations. Concomitantly, Ide mRNA expression was decreased in livers of male descendants from the exposed dams. These males were hyperinsulinemic and hyperglycemic, phenocopying Ide-deficient mice that are predisposed to adult-onset, diet-induced obesity. Creation of new CIs in PGCs, suppression of hepatic Ide mRNA, increased fat mass, hyperinsulinemia and hyperglycemia were male-specific. Our results provide a plausible molecular mechanism underlying transmission of the transgenerational predisposition to obesity caused by gestational exposure to an environmental obesogen. They also provide an entry point for future studies aimed at understanding how environmental exposures alter chromatin structure to influence physiology across multiple generations in mammals.

Neurogenetic diseases affect individuals across the lifespan, but accurate diagnosis remains elusive for many patients. Adults with neurogenetic disorders often undergo a long diagnostic odyssey, with multiple specialist evaluations and countless investigations without a satisfactory diagnostic outcome. Reasons for these diagnostic challenges include: (1) clinical features of neurogenetic syndromes are diverse and under-recognized, particularly those of adult-onset, (2) neurogenetic syndromes may manifest with symptoms that span multiple neurological and medical subspecialties, and (3) a positive family history may not be present or readily apparent. Furthermore, there is a large gap in the understanding of how to apply genetic diagnostic tools in adult patients, as most of the published literature focuses on the pediatric population. Despite these challenges, accurate genetic diagnosis is imperative to provide affected individuals and their families guidance on prognosis, recurrence risk, and, for an increasing number of disorders, offer targeted treatment. Here, we provide a framework for recognizing adult neurogenetic syndromes, describe the current diagnostic approach, and highlight studies using next-generation sequencing in different neurological disease cohorts. We also discuss diagnostic pitfalls, barriers to achieving a definitive diagnosis, and emerging technology that may increase the diagnostic yield of testing.

Analyzing hydrogel microstructure through scanning electron microscopy (SEM) images is crucial in understanding hydrogel properties. However, the analysis of SEM images in hydrogel research heavily relies on the intuition of individual researchers and is constrained by the limited size of the dataset. To address this, we propose SEMPro, a data-driven solution using web-scraping and deep learning (DL) to compile and analyze the structure-property relationships of hydrogels through SEM images. It accurately predicts the elastic modulus from SEM images within the same order of magnitude and displays a learned extraction of modulus-relevant features in SEM images as seen through the nontrivial activation mapping and transfer learning. By employing Explainable AI through activation map exposure, SEMPro validates the model predictions. SEMPro represents a closed-loop data collection and analysis pipeline, providing critical insights into hydrogels and soft materials. This innovative approach has the potential to revolutionize hydrogel research, offering high-dimensional insights for further advancements.

The Sustainable Development Goals (SDGs) represent a central framework for guiding sustainable urban development. However, it is not clear how and where SDG 11 (Sustainable Cities and Communities) is being implemented, and how such implementation might be improved. We investigate this subject by analyzing United Nations (UN) SDG Good Practices data from 2018 to 2021, using content analysis, classification, and descriptive statistics. The resulting analysis and classification is new in this field. We applied five criteria to the 336 SDG 11 responses: (1) geography; (2) actors; (3) progress toward targets; (4) areas of implementation; and (5) scale of action. Overall, 86 of the 193 countries that adopted the 2030 Agenda submitted at least one SDG 11 good practice. Thirty-three countries contributed 73.8% of the total, and most developing countries did not make submissions. A very small number of responses (between 2.6 and 9.1%) addressed equity-related targets. Good practices from developed countries were usually more technology-oriented and system-forming; developing countries typically focused on issues of day-to-day concern. This analysis points to the need for better and more systematic reporting on SDG 11 implementation, a more active public sector role in SDG implementation and reporting, more focus on dimensions related to social equity, and better formulation of urban sustainability targets and indicators.

RNA is integral to the regulatory circuits that control cell identity and behavior. Cis-regulatory elements in mRNAs interact with RNA-binding proteins (RBPs) that can alter RNA sequence, stability, and translation into protein. Similarly, long noncoding RNAs (lncRNAs) scaffold ribonucleoprotein complexes that mediate transcriptional and post-transcriptional regulation of gene expression. Indeed, cell programming is fundamental to multicellular life and, in this era of cellular therapies, it is of particular interest in T cells. Here, we review key concepts and recent advances in our understanding of the RNA circuits and RBPs that govern mammalian T cell differentiation and immune function.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

The premutation of the fragile X messenger ribonucleoprotein 1 (FMR1) gene is characterized by an expansion of the CGG trinucleotide repeats (55 to 200 CGGs) in the 5 untranslated region and increased levels of FMR1 mRNA. Molecular mechanisms leading to fragile X-premutation-associated conditions (FXPAC) include cotranscriptional R-loop formations, FMR1 mRNA toxicity through both RNA gelation into nuclear foci and sequestration of various CGG-repeat-binding proteins, and the repeat-associated non-AUG (RAN)-initiated translation of potentially toxic proteins. Such molecular mechanisms contribute to subsequent consequences, including mitochondrial dysfunction and neuronal death. Clinically, premutation carriers may exhibit a wide range of symptoms and phenotypes. Any of the problems associated with the premutation can appropriately be called FXPAC. Fragile X-associated tremor/ataxia syndrome (FXTAS), fragile X-associated primary ovarian insufficiency (FXPOI), and fragile X-associated neuropsychiatric disorders (FXAND) can fall under FXPAC. Understanding the molecular and clinical aspects of the premutation of the FMR1 gene is crucial for the accurate diagnosis, genetic counseling, and appropriate management of affected individuals and families. This paper summarizes all the known problems associated with the premutation and documents the presentations and discussions that occurred at the International Premutation Conference, which took place in New Zealand in 2023.

BACKGROUND: Dilated cardiomyopathy (DCM) can lead to advanced disease, defined herein as necessitating a durable left ventricular assist device or a heart transplant (LVAD/HT). DCM is known to have a genetic basis, but the association of rare variant genetics with advanced DCM has not been studied. METHODS: We analyzed clinical and genetic sequence data from patients enrolled between 2016 and 2021 in the US multisite DCM Precision Medicine Study, which was a geographically diverse, multiracial, multiethnic cohort. Clinical evaluation included standardized patient interview and medical record query forms. DCM severity was classified into 3 groups: patients with advanced disease with LVAD/HT; patients with an implantable cardioverter defibrillator (ICD) only; or patients with no ICD or LVAD/HT. Rare variants in 36 DCM genes were classified as pathogenic or likely pathogenic or variants of uncertain significance. Confounding factors we considered included demographic characteristics, lifestyle factors, access to care, DCM duration, and comorbidities. Crude and adjusted associations between DCM severity and rare variant genetic findings were assessed using multinomial models with generalized logit link. RESULTS: Patients mean (SD) age was 51.9 (13.6) years; 42% were of African ancestry, 56% were of European ancestry, and 44% were female. Of 1198 patients, 347 had LVAD/HT, 511 had an ICD, and 340 had no LVAD/HT or ICD. The percentage of patients with pathogenic or likely pathogenic variants was 26.2%, 15.9%, and 15.0% for those with LVAD/HT, ICD only, or neither, respectively. After controlling for sociodemographic characteristics and comorbidities, patients with DCM with LVAD/HT were more likely than those without LVAD/HT or ICD to have DCM-related pathogenic or likely pathogenic rare variants (odds ratio, 2.3 [95% CI, 1.5-3.6]). The association did not differ by ancestry. Rare variant genetic findings were similar between patients with DCM with an ICD and those without LVAD/HT or ICD. CONCLUSIONS: Advanced DCM was associated with higher odds of rare variants in DCM genes adjudicated as pathogenic or likely pathogenic, compared with individuals with less severe DCM. This finding may help assess the risk of outcomes in management of patients with DCM and their at-risk family members. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03037632.

Written instructions improve patient comprehension of discharge instructions but are often provided only in English even for patients with a non-English language preference (NELP). We implemented standardized written discharge instructions in English, Spanish, and Chinese for hospital medicine patients at an urban academic medical center. Using an interrupted time series analysis, we assessed the impact on medication-related postdischarge questions for patients with English, Spanish, or Chinese language preferences. Of 4013 patients, ∼15% had NELP. Preintervention, Chinese-preferring patients had a 5.6 percentage point higher probability of questions (adjusted odds ratio [aOR] = 1.55, 95% confidence interval [CI]: 1.08, 2.21) compared to English-preferring patients; Spanish-preferring and English-preferring patients had similar rates of questions. Postintervention, English-preferring and Spanish-preferring patients had no significant change; Chinese-preferring patients had a significant 10.9 percentage point decrease in the probability of questions (aOR = 0.38, 95% CI: 0.21, 0.69) thereby closing the disparity. Language-concordant written discharge instructions may reduce disparities in medication-related postdischarge questions for patients with NELP.

In this paper we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy—a recently developed technique for whole-brain imaging. Most existing statistical methods solve this problem by partitioning the brain regions into two classes: significantly and nonsignificantly activated. However, for the brain imaging problem at the center of our study, such binary grouping may provide overly simplistic discoveries by filtering out weak but important signals that are typically adulterated by the noise present in the data. To overcome this limitation, we introduce a new Bayesian approach that allows classifying the brain regions into several tiers with varying degrees of relevance. Our approach is based on a combination of shrinkage priors, widely used in regression and multiple hypothesis testing problems, and mixture models, commonly used in model-based clustering. In contrast to the existing regularizing prior distributions, which use either the spike-and-slab prior or continuous scale mixtures, our class of priors is based on a discrete mixture of continuous scale mixtures and devises a cluster shrinkage version of the horseshoe prior. As a result, our approach provides a more general setting for Bayesian sparse estimation, drastically reduces the number of shrinkage parameters needed, and creates a framework for sharing information across units of interest. We show that this approach leads to more biologically meaningful and interpretable results in our brain imaging problem, since it allows the discrimination between active and inactive regions, while at the same time ranking the discoveries into clusters representing tiers of similar importance.

Enterovirus D68 (EV-D68) causes epidemics of asthma-like respiratory disease and clusters of cases of the paralytic polio-like disease known as acute flaccid myelitis (AFM) (1). During summer/fall seasonal peaks, EV-D68 [...]