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Field experiences provide opportunities for early childhood and early childhood special education (EC/ECSE) educators to implement effective practices in learning settings, and are, therefore, a vital part of EC/ECSE teacher preparation. In this article, we describe field placement models from four universities in the United States: The Bridge Project, Getting Started Early, Peer Coaching to Increase Naturalistic Developmental Behavioral Interventions, and University Supervisors Coaching Teacher Candidates: Supporting Young Bi/Multilingual Children with Disabilities. Although there is variety in the settings and effective practices supported through these field placements, performance feedback and collaboration are clear themes across models.

We explored how teachers perceived challenging behavior in contrast to behaviors that exemplify social-emotional competence, and how descriptions of behavior varied based on child demographics such as gender, race/ethnicity, and disability status. Using a DisCrit lens, we conducted this study under the assumption that perceptions of behavior may be informed by teachers' implicit biases. Fourteen preschool teachers shared their perspectives on behaviors they saw in the classroom, as exemplified by descriptions of the two children they considered the most socially-emotionally successful, A/Z, and the two they considered the most challenging, C/E. Children in the A/Z group were 54% female, 46% male, 54% White, 29% Black, and 82% did not receive special education services or need screening. Children in the C/E group were 86% male, 14% female, 46% Black, 36% White, and 75% were either receiving special education services or identified by teachers as needing a referral for screening. Positive A/Z behaviors included being a model, leader, or helper; having good play skills, manners, and verbal skills; and loving to learn. The challenging behaviors for C/E students included being hard to connect with; engaging in physical aggression and defiance; and having communication difficulties and poor self-regulation skills. These findings help us understand the role of implicit bias, systems issues, and the importance of culturally responsive practices in early childhood settings.

Abstract Background Understanding factors associated with antimicrobial resistance (AMR) distribution across populations is a necessary step in planning mitigation measures. While associations between AMR and socioeconomic-status (SES), including employment and education have been increasingly recognized in low- and middle-income settings, connections are less clear in high-income countries where SES remains an important influence on other health outcomes. Methods We explored the relationship between SES and AMR in Calgary, Canada using spatially-resolved wastewater-based surveillance of resistomes detected by metagenomics across eight socio-economically diverse urban neighborhoods. Resistomes were established by shotgun-sequencing of wastewater pellets, and qPCR of targeted-AMR genes. SES status was established using 2021 Canadian census data. Conducting this comparison during the height of COVID-related international travel restrictions (Dec. 2020–Oct. 2021) allowed the hypotheses linking SES and AMR to be assessed with limited confounding. These were compared with sewage metagenomes from 244 cities around the world, linked with Human Development Index (HDI). Results Wastewater metagenomes from Calgary’s socioeconomically diverse neighborhoods exhibit highly similar resistomes, with no quantitative differences (p > 0.05), low Bray-Curtis dissimilarity, and no significant correlations with SES. By comparison, dissimilarity is observed between globally-sourced resistomes (p

The purpose of this exploratory qualitative study was to examine how the perceptions and experiences of teachers relate to the suspension and expulsion of preschoolers in Catholic schools. Results indicated that teachers have a range of experiences with suspension, from children being removed from the classroom temporarily to out-of-school suspensions lasting up to a week. Most participants also had experienced expelling a student because of behavior. Patterns that emerged from the data included a frequency of extreme behavior from some children, the application of various strategies in response to challenging behavior, the use of exclusionary discipline when other strategies did not work, and a resistance to change practices without additional supports. Implications for research and practice are discussed.

Language testing is witnessing increasing interest in the potential for AI to support test development and validation. To date, published research involving AI in language testing has typically been conducted in the context of high-stakes proficiency tests and the potential for this technology to support local language testing is under-researched. The current study seeks to address this gap by reporting on the piloting of an AI generated language test in the context of a university in the UK. The focus of the paper was selected to explore two key areas in the work of J.D. Brown, namely pragmatics and connected speech. In the study, international students with English as a second language completed an AI generated test of pragmatics created from a transcript of spontaneous interaction, and a test-taking strategy questionnaire. Results demonstrated that the test did not provide a sufficiently reliable measurement of test takers' pragmatic competence, although reliability estimates did improve with the removal of problematic items. Implications for language test development involving AI are discussed.

ABSTRACT: One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated, and each herd was randomly allocated, at a ratio of 80:20, to either training or testing datasets. Following data cleaning, there were a total of 78 herds in the training dataset and 20 herds in the testing dataset. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving, and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59, and 0.62 in the nulliparous, primiparous, and multiparous subsets, respectively. The models were generally well calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012), and 0.020 (0.008) in the nulliparous, primiparous, and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

Background: The purpose of this study was to assess impingement-free internal rotation (IR) in a virtual reverse shoulder arthroplasty simulation using a Statistical Shape Model based on scapula size. Methods: A database of over 10,000 scapulae utilized for preoperative planning for shoulder arthroplasty was analyzed with a Statistical Shape Model to obtain 5 scapula sizes including the mean and 2 standard deviations. For each scapula model, one glenosphere size (33-42 mm) was selected as the best fit based on consensus among 3 shoulder surgeons. Virtual implantation variables included 1) lateral offset (0-12 mm in 2-mm increments), 2) inferior eccentricity (0, 2.5, 5, and 7.5 mm), and 3) posterior eccentricity (0, 2.5, and 5 mm). The neck shaft angle was fixed at 135° with an inlay design humeral prosthesis. IR at the side (IR0) and in abduction (IRABD) were then simulated. Results: Maximum impingement-free IR0 was reached with increasing inferior offset in combination with increasing lateralization. Lateralization was the most important variable in increasing impingement-free IRABD. Maximum IRABD was reached at 4-6 mm of lateralization with smaller scapula (−2 to 0 standard deviation). Increasing lateralization up to 12 mm continues to increase IRABD for larger-sized scapula (+1 to +2 standard deviation). Optimal inferior offset and lateralization to maximize IR did have a small loss of external rotation in neutral abduction. There was no loss of external rotation in 60° of abduction. Conclusion: In a virtual model, the glenosphere position required to maximize IR varied by scapula size. For smaller scapulae, maximum IR0 was reached with a combination of 2.5-mm inferior offset and 0- 4 mm of lateralization. For larger scapulae, maximum IR0 was reached with a combination of 2.5 mm of inferior offset and 4 mm of lateralization. The amount of lateralization required to maximize IRABD also varies by scapula size. Maximum IRABD was reached in smaller scapula with 4-6 mm of lateralization and at least 12 mm of lateralization in larger scapula. These findings may be applied in the clinical decision-making process knowing that impingement-free IR and IRABD can be maximized with combinations of inferior offset and lateralization based on scapula size with minimal effect on external rotation and external rotation in 60° of abduction.

Long-read sequencing is driving rapid progress in genome assembly across all major groups of life, including species of the family Drosophilidae, a longtime model system for genetics, genomics, and evolution. We previously developed a cost-effective hybrid Oxford Nanopore (ONT) long-read and Illumina short-read sequencing approach and used it to assemble 101 drosophilid genomes from laboratory cultures, greatly increasing the number of genome assemblies for this taxonomic group. The next major challenge is to address the laboratory culture bias in taxon sampling by sequencing genomes of species that cannot easily be reared in the lab. Here, we build upon our previous methods to perform amplification-free ONT sequencing of single wild flies obtained either directly from the field or from ethanol-preserved specimens in museum collections, greatly improving the representation of lesser studied drosophilid taxa in whole-genome data. Using Illumina Novaseq X Plus and ONT P2 sequencers with R10.4.1 chemistry, we set a new benchmark for inexpensive hybrid genome assembly at US $150 per genome while assembling genomes from as little as 35 ng of genomic DNA from a single fly. We present 183 new genome assemblies for 179 species as a resource for drosophilid systematics, phylogenetics, and comparative genomics. Of these genomes, 62 are from pooled lab strains and 121 from single adult flies. Despite the sample limitations of working with small insects, most single-fly diploid assemblies are comparable in contiguity (>1 Mb contig N50), completeness (>98% complete dipteran BUSCOs), and accuracy (>QV40 genome-wide with ONT R10.4.1) to assemblies from inbred lines. We present a well-resolved multi-locus phylogeny for 360 drosophilid and 4 outgroup species encompassing all publicly available (as of August 2023) genomes for this group. Finally, we present a Progressive Cactus whole-genome, reference-free alignment built from a subset of 298 suitably high-quality drosophilid genomes. The new assemblies and alignment, along with updated laboratory protocols and computational pipelines, are released as an open resource and as a tool for studying evolution at the scale of an entire insect family.

Molecular subtyping of breast cancer is based mostly on HR/HER2 and gene expression-based immune, DNA repair deficiency, and luminal signatures. We extend this description via functional protein pathway activation mapping using pre-treatment, quantitative expression data from 139 proteins/phosphoproteins from 736 patients across 8 treatment arms of the I-SPY 2 Trial (ClinicalTrials.gov: NCT01042379). We identify predictive fit-for-purpose, mechanism-of-action-based signatures and individual predictive protein biomarker candidates by evaluating associations with pathologic complete response. Elevated levels of cyclin D1, estrogen receptor alpha, and androgen receptor S650 associate with non-response and are biomarkers for global resistance. We uncover protein/phosphoprotein-based signatures that can be utilized both for molecularly rationalized therapeutic selection and for response prediction. We introduce a dichotomous HER2 activation response predictive signature for stratifying triple-negative breast cancer patients to either HER2 or immune checkpoint therapy response as a model for how protein activation signatures provide a different lens to view the molecular landscape of breast cancer and synergize with transcriptomic-defined signatures.

ABSTRACT: Targeted reproductive management (TRM) aims to improve the fertility efficiency of the dairy herd by applying group-level management strategies based on expected reproductive performance. Key to the utility of TRM is the accuracy with which an animal's reproductive performance can be predicted. Automatic milking systems (AMS) allow for the collection of data relating to milk quantity, quality, and robot visit behavior throughout the transition period. In addition to this, auxiliary data sources, such as rumination and activity monitors, as well as historical cow-level data, are often readily available. The utility of this data for the prediction of fertility has not been previously explored. The first objective of this study was to assess the accuracy with which the likelihood of expression of estrus between 22 and 65 DIM and conception to first insemination between 22 and 80 DIM could be predicted using data collected by AMS from 1 to 21 DIM. Our second objective was to assess the change in model performance following the addition of 2 auxiliary data sources. Using data derived solely from the AMS (RBT dataset) a binary random forest classification model was constructed for both outcomes of interest. The performance of these models was compared with models constructed using AMS data in conjunction with 2 auxiliary sources (RBT+ dataset). Expression of estrus was classified with an area under the receiver operator curve (AUC-ROC) of 0.6 and 0.65, conception to first insemination with an AUC-ROC of 0.56 and 0.62 for the RBT and RBT+ datasets, respectively. No statistically significant improvement in classification accuracy was achieved by the addition of auxiliary data sources. This is the first study to report the utility of data collected by AMS for the prediction of reproductive performance. Though the performance described is comparable with previously reported models, their utility for the implementation of TRM is limited by poor classification accuracy within key subgroups. Of note within this study is the failure of the addition of auxiliary data sources to increase the accuracy of prediction over models built using AMS data alone. We discuss the advantages and limitations the integration of additional data sources imposes on model training and deployment and suggest alternative methods to improve performance while preserving model parsimony.