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Due to the marginalization of supervision (Butler, et al., 2023; Nolan, 2022) and few frameworks to conceptualize supervision in teacher preparation, educational supervision of clinical experiences receives less attention and fewer resources, which perpetuates its marginalization. It is imperative that scholars develop additional theoretical models or constructs to improve the understanding and practice of supervision to elevate its status beyond technical helping. In this paper, we draw upon several sources in the instructional supervision literature to re-conceptualize commonly used images of supervisors in teacher education. In addition to traditional conceptions (The Critic, the Popular Parent, the Co-Inquirer), we 'introduce' two new images, The Advocate and The Contemplative, to reflect changes and movements in education. These images can serve as one theoretical model or construct to improve understanding and practice of supervision to elevate its status.

Abstract Background The financial strain fertility issues cause the dairy cattle industry is substantial, with over $7 billion in lost revenue accrued annually due to a relatively low cow conception rate (CCR; 30–43%) for US dairy cows. While CCR has been improving through genomic selection, identification of causal mutations would help improve the rate of genetic progress with genomic selection and provide a better understanding of infertility. The objectives of this study were to: (1) identify genes and gene-sets associated with CCR to the first breeding (CCR1) and the number of breedings required to conceive (TBRD) in Holstein cows and (2) identify putative functional variants associated with CCR1 and TBRD through a custom genotype-by-sequencing array. The study consisted of 1,032 cows (494 pregnant to first breeding, 472 pregnant to subsequent [2–20] services, and 66 that never conceived). Cows were artificially inseminated, and pregnancy was determined 35d later by rectal palpation of uterine contents. Gene-set enrichment analyses with SNP data (GSEA-SNP) were conducted for CCR1 and TBRD with a normalized enrichment score (NES) ≥ 3.0 required for significance. Leading edge genes (LEG) and positional candidate genes from this and 26 additional studies were used to validate 100 loci associated (P

To address the inequities within the educational system, there are calls for teachers to have an intentional focus on equity. Unfortunately, teachers often do not receive enough preparation within their certification programs or during in-service trainings to center equity- driven practices. This study provided insight into the features that one group of teacher leaders relied upon and found essential when working to enact high-quality, equity-centered professional learning for teachers, and researchers utilized these findings to develop a framework for equity-centered professional learning. The context for this research was teacher leader-led professional learning within the context of a job-embedded professional learning experience for teacher leaders. Data collection included professional learning lesson plans, written reflections by teacher leaders after the professional learning sessions, and field notes. Findings point to five features of professional learning focused on equity that teacher leaders utilized. These include: (1), Safe, trusting community grounded in relationships, (2) Dialogue to explore and critically reflect upon biases, beliefs, and experiences, (3) Active learning opportunities that promote dissonance, (4) Authentic connections to equity within the local context through inquiry, and (5) Responsiveness to participants' varied experiences and understanding of equity. The findings have implications for routines of practice that can be utilized to enact these essential features and how an equity lens can permeate the design of professional learning.

Summary: The mechanisms underlying the pathophysiology of endometriosis, characterized by the presence of endometrium-like tissue outside the uterus, remain poorly understood. This study aimed to identify cell type-specific gene expression changes in superficial peritoneal endometriotic lesions and elucidate the crosstalk among the stroma, epithelium, and macrophages compared to patient-matched eutopic endometrium. Surprisingly, comparison between lesions and eutopic endometrium revealed transcriptional similarities, indicating minimal alterations in the sub-epithelial stroma and epithelium of lesions. Spatial transcriptomics highlighted increased signaling between the lesion epithelium and macrophages, emphasizing the role of the epithelium in driving lesion inflammation. We propose that the superficial endometriotic lesion epithelium orchestrates inflammatory signaling and promotes a pro-repair phenotype in macrophages, providing a new role for complement 3 in lesion pathobiology. This study underscores the significance of considering spatial context and cellular interactions in uncovering mechanisms governing disease in endometriotic lesions.

Ecological intensification has been embraced with great interest by the academic sector but is still rarely taken up by farmers because monitoring the state of different ecological functions is not straightforward. Modelling tools can represent a more accessible alternative of measuring ecological functions, which could help promote their use amongst farmers and other decision-makers. In the case of crop pollination, modelling has traditionally followed either a mechanistic or a data-driven approach. Mechanistic models simulate the habitat preferences and foraging behaviour of pollinators, while data-driven models associate georeferenced variables with real observations. Here, we test these two approaches to predict pollination supply and validate these predictions using data from a newly released global dataset on pollinator visitation rates to different crops. We use one of the most extensively used models for the mechanistic approach, while for the data-driven approach, we select from among a comprehensive set of state-of-the-art machine-learning models. Moreover, we explore a mixed approach, where data-derived inputs, rather than expert assessment, inform the mechanistic model. We find that, at a global scale, machine-learning models work best, offering a rank correlation coefficient between predictions and observations of pollinator visitation rates of 0.56. In turn, the mechanistic model works moderately well at a global scale for wild bees other than bumblebees. Biomes characterized by temperate or Mediterranean forests show a better agreement between mechanistic model predictions and observations, probably due to more comprehensive ecological knowledge and therefore better parameterization of input variables for these biomes. This study highlights the challenges of transferring input variables across multiple biomes, as expected given the different composition of species in different biomes. Our results provide clear guidance on which pollination supply models perform best at different spatial scales – the first step towards bridging the stakeholder–academia gap in modelling ecosystem service delivery under ecological intensification.

Abstract. Introduction:. We previously conducted a 3-arm randomized trial (263 adults with chronic low back pain) which compared group-based (1) single-session pain relief skills intervention (Empowered Relief; ER); (2) 8-session cognitive behavioral therapy (CBT) for chronic back pain; and (3) single-session health and back pain education class (HE). Results suggested non-inferiority of ER vs. CBT at 3 months post-treatment on an array of outcomes. Methods:. Here, we tested the durability of treatment effects at 6 months post-treatment. We examined group differences in primary and secondary outcomes at 6 months and the degree to which outcomes eroded or improved from 3-month to 6-month within each treatment group. Results:. Empowered Relief remained non-inferior to CBT on most outcomes, whereas both ER and CBT remained superior to HE on most outcomes. Outcome improvements within ER did not decrease significantly from 3-month to 6-month, and indeed ER showed additional 3- to 6-month improvements on pain catastrophizing, pain bothersomeness, and anxiety. Effects of ER at 6 months post-treatment (moderate term outcomes) kept pace with effects reported by participants who underwent 8-session CBT. Conclusions:. The maintenance of these absolute levels implies strong stability of ER effects. Results extend to 6 months post-treatment previous findings documenting that ER and CBT exhibit similarly potent effects on outcomes.

Abstract The oceans play an important role in mitigating climate change by acting as large carbon sinks, especially at high latitude regions. The Southern Ocean plays a major role in the global carbon dioxide (CO2) budget. This work aims to investigate the behavior of turbulent CO2 fluxes and quantify it under different atmospheric and oceanic conditions in the Drake Passage and Bransfield Strait regions on high spatiotemporal resolutions when compared with traditional CO2 fluxes estimations. The atmospheric stability condition was used to corroborate the description of CO2 fluxes. In situ, satellite, and reanalysis data from 08 to 22 November 2018, were used in this work. The Bransfield Strait uptaked 38.59% more CO2 than the Drake Passage due to the cold and fresh waters, allied to the influence of glacial meltwater dilution. Which increased the CO2 solubility, directing the CO2 fluxes to the ocean. The Bransfield Strait had predominantly stable atmospheric conditions, which contributed to this region acting as a CO2 sink. The Drake Passage, on average, behaved as a CO2 sink, mainly due to physical characteristics. This research contributes to a better understanding of the Southern Ocean’s role in the global carbon balance on scales that are very difficult to monitor.

The development and progression of endometriotic lesions are poorly understood, but immune cell dysfunction and inflammation are closely associated with the pathophysiology of endometriosis. There is a need for 3D in vitro models to permit the study of interactions between cell types and the microenvironment. To address this, we developed endometriotic spheroids (ES) to explore the role of epithelial-stromal interactions and model peritoneal invasion associated with lesion development. Using a nonadherent microwell culture system, spheroids were generated with immortalized endometriotic epithelial cells (12Z) combined with endometriotic stromal (iEc-ESC) or uterine stromal (iHUF) cell lines. Transcriptomic analysis found 4,522 differentially expressed genes in ES compared with spheroids containing uterine stromal cells. The top increased gene sets were inflammation-related pathways, and an overlap with baboon endometriotic lesions was highly significant. Finally, to mimic invasion of endometrial tissue into the peritoneum, a model was developed with human peritoneal mesothelial cells in an extracellular matrix. Invasion was increased in the presence of estradiol or pro-inflammatory macrophages and suppressed by a progestin. Taken together, our results strongly support the concept that ES are an appropriate model for dissecting mechanisms that contribute to endometriotic lesion development.

In June of 2020 in response to the murder of George Floyd and the additional atrocities against historically marginalized people and communities across the United States, the Commission on Education (COE) of the American Occupational Therapy Association (AOTA) sought to act within its scope. A retrospective survey (N= 1,692) was developed, deployed, and analyzed in accordance with the COE’s standard operating procedures relating specifically to monitoring trends that impact the education of occupational therapy (OT) and occupational therapy assistant (OTA) students. This survey aimed to capture perspectives from OT/OTA students regarding the delivery of culturally aware educational content in curricula provided by faculty. Results indicated that faculty are attempting to deliver aspects of culturally aware content with a limited number of delivery methods. Furthermore, findings suggested the need for rigorous and comprehensive improvements to content delivery related to diversity, equity, inclusion (DEI), social determinants of health (SDOH), and intersectionality across the classroom and clinical settings. Implementation of basic instruction, such as lectures/readings, and higher-level processing and application-based materials (i.e., case studies, discussions, role-playing, and simulations) need to be included throughout the entire OT/OTA curriculum experience and are critical to comprehensively educate and equip students for contemporary and culturally informed practice. Faculty would benefit from the development of instructional best practice strategies that expand beyond the delivery of knowledge acquisition. This study aims to lay the groundwork to advance and inform the skills of faculty, fieldwork, and capstone coordinators to deliver culturally aware curricula.

Uterine fibroids (leiomyomas) affect Black women disproportionately compared with women of other races and ethnicities in terms of prevalence, incidence, and severity of symptoms. The causes of this racial disparity are essentially unknown. We hypothesized that myometria of Black women are more susceptible to developing fibroids, and we examined the transcriptomic and DNA methylation profiles of myometria and fibroids from Black and White women for comparison. Myometrial samples cluster by race in both their transcriptome and DNA methylation profiles, whereas fibroid samples only cluster by race in the latter. More differentially expressed genes (DEGs) were detected in the Black and White myometrial sample comparison than in the fibroid comparison. Leiomyoma gene set expression analysis identified 4 clusters of DEGs, including a cluster of 24 genes with higher expression in myometrial samples from Black women. One of the DEGs in this group, von Willibrands factor (VWF), was significantly hypomethylated in both myometrial samples from Black women and in all fibroids at 2 CpG probes that are near a putative enhancer site and that are correlated with VWF expression levels. These results suggest that the molecular basis for the disparity in fibroid disease between Black and White women could be found in the myometria before fibroid development and not in the fibroids themselves.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.