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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.

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.

ABSTRACT: The CellCheck dry cow consult (DCC) was developed by the CellCheck Technical Working Group to enable farmers to engage with their nominated veterinarian to develop farm-specific selective dry cow therapy (SDCT) plans, where appropriate. This study evaluated the effect of the DCC on farmer decision-making around dry cow therapy, and the udder health effects of implementing SDCT, in study herds over the 2019 and 2020 dry periods. The DCC was a 3-h consult, delivered and funded as part of the Targeted Advisory Service on Animal Health. Herds that completed a DCC were invited to register for a dry cow review the following year. The combined dataset for analysis across both years comprised 439 herds and 25,357 cows. Herd size ranged from 25 to 800. The median SCC of cows dried off with teat sealant only was 47,000 cells/mL before drying off in 2019 and 48,000 cells/mL at first milk recording in 2020, and 43,000 cells/mL before drying off in 2020 and 39,000 cells/mL at first milking recording in 2021. Following both the 2019 and 2020 dry periods, cows tended to converge toward a similar median SCC early in the following lactation, irrespective of prior dry cow treatment strategy. The uptake of SDCT was greater in review herds, with 21% of cows receiving teat sealant only in 2020, compared with 16.3% of cows in herds participating in a consult for the first time in 2020. At an individual cow level, in both years dry period new infection rate (NIR) was approximately 2.7% higher for cows treated with teat sealant only, than for those treated with both dry cow antibiotic tubes and teat sealant, and 1.2% higher than cows treated with antibiotic only. Regardless of treatment, a significant association was detectable between increasing parity and the risk of a dry period new infection. Increasing herd size had a significant effect on the risk of dry period NIR. At a herd level, no significant increase in NIR occurred when SDCT was used compared with herds where blanket dry cow therapy was used. Though not without risk, SDCT can be successfully implemented in Irish herds; however, constant attention to hygiene and management is essential. Despite the challenges, facilitating continued farmer education and engagement with professional guidance will be important.

Cancer in teenagers and young adults (TYAs) coincides with major life transitions and presents unique psychosocial challenges. Understanding the experiences and needs of TYAs is critical. TYAs want to play an active role in improving cancer for TYAs; however, few opportunities exist for TYAs to do so. Using a tri-partite methodology, an international team collaborated with four TYA co-researchers in this pilot study to explore how performative staging strategies help convey TYA experiences with cancer. Using creative video, TYA co-researchers shared cancer experiences and insights in novel, impactful ways. The process provided intrinsic benefits for co-researchers to connect with other TYAs and creatively share their experiences and perspectives. Furthermore, it provided space for dialogue between TYAs and cancer care allies where TYAs could convey the nuances of their cancer experiences and how cancer care could be improved. This tri-partite methodology can support TYAs to actively engage in a process of connection, reflection, creation, and dissemination to improve cancer experiences for TYAs.

The third decade of the 21st century has seen a focus emerge on the agentic practices of writers, focusing on what, how and why people write. While considerable research has been conducted on school-age writing practices, there is still limited insight into the writing practices of children outside of school or formal education settings. This article adopts a collaborative autoethnographic lens to explore a mother's perception of her children's writing practices and their identity formations as developing writers, in discussion with her doctoral supervisors. The article specifically explores the parental, sibling and environmental supports that are made available to four siblings within the home to support early writing, and how their understandings toward writing are developed and practised. Consideration is given to the relationship between informal home writing practices and implications or opportunities for school and early childhood writing practices.

Background and Aims: Gastric Alimetry™ (Alimetry, New Zealand) is a new clinical test for gastroduodenal disorders involving simultaneous body surface gastric electrical mapping and validated symptom profiling. Studies have demonstrated a range of distinct pathophysiological profiles, and a classification scheme is now required. We used Gastric Alimetry spectral and symptom profiles to develop a mechanism-based test classification scheme, then assessed correlations with symptom severity, psychometrics, and quality of life. Methods: We performed a multicenter prospective cohort study of patients meeting the Rome IV criteria for functional dyspepsia and chronic nausea and vomiting syndromes. Patients underwent Gastric Alimetry profiling, and a standardized digital classification framework was devised and applied to separate patients into those with a) abnormal spectral analyses (ie aberrant gastric frequencies, amplitudes, and rhythms); and normal spectral analyses with b) symptoms correlated to gastric amplitude (subgroups: sensorimotor, postgastric, and activity-relieved), and c) symptoms independent of gastric amplitude (subgroups: continuous, meal-relieved, meal-induced). Results: Two hundred ten patients were included (80% female, median age 37), of whom 169 met the criteria for chronic nausea and vomiting syndromes and 206 met the criteria for functional dyspepsia (79% meeting both criteria). Overall, 83% were phenotyped using the novel scheme, with 79/210 (37.6%) classified as having a spectral abnormality. Of the remainder, the most common phenotypes were “continuous pattern” (37, 17.6%), “meal-induced pattern” (28, 13.3%), and “sensorimotor pattern” (15, 7.1%). Symptom patterns independent of gastric amplitude were more strongly correlated with depression and anxiety (Patient Health Questionnaire 2: exp(β) 2.38, P = .024, State-Trait Anxiety Inventory Short-Form score: exp(β) 1.21, P = .021). Conclusion: A mechanistic classification scheme for assessing gastroduodenal disorders is presented. Classified phenotypes showed independent relationships with symptom severity, quality of life, and psychological measures. The scheme is now being applied clinically and in research studies.

Risk assessments are important tools to identify deficits in biosecurity management practices. A major strength of some existing tools is that they facilitate cross-country comparisons. However, a weakness is their failure to account for unique intra-national farming enterprise structures such as, for example, pasture-based dairying. Currently, there are no suitable biosecurity risk assessment tools applicable to pasture-based dairying as practiced in Ireland. In addition to a need for enterprise-specific biosecurity risk assessment tools, the weighting of risk scores generated by these tools needs to be context-specific to ensure validity in assessing biosecurity risks in the farming sector of interest. Furthermore, existing biosecurity audits rely exclusively on respondent recall to answer questions about management practices. To address each of these limitations of existing biosecurity risk assessment tools we developed and optimised a new biosecurity risk assessment tool (BioscoreDairy) designed to assess the biosecurity status of pasture-based dairy farms in Ireland. It consists of two parts, a biosecurity questionnaire and a cattle movement records audit. A questionnaire was developed on biosecurity management practices appropriate for a pasture-based dairy system. Multiple national expert groups were leveraged to provide weightings for the different management practices in the questionnaire using the best-worst scaling methodology of MaxDiff. The results of this process provided a numerical categorisation that could then be used to assign scores to the individual biosecurity management practices. These practices were grouped into three biosecurity areas; risk of disease entry, speed of disease spread and diagnosis of infection. Within each of these three areas, a traffic light system was used to compare a farm’s biosecurity risks to other similar farms—least risk (green; within the top third of farms), concerning practice (amber; middle third) and worst practice or greatest risk (red; lowest third). In addition to these scores, the cattle introduction profile of a herd over the previous 3 years, based on nationally recorded data, was audited, compared amongst dairy farm enterprise subtypes, and included in the BioscoreDairy report. BioscoreDairy is therefore the first biosecurity risk assessment tool tailored to pasture-based dairy farm systems, both for individual farm reporting and for benchmarking against comparable farms.

ABSTRACTMucinous colorectal cancer (CRC) is a common histological subtype of colorectal adenocarcinoma, associated with a poor response to chemoradiotherapy. The commensal facultative anaerobes fusobacteria, have been associated with poor prognosis specifically in mesenchymal CRC. Interestingly, fusobacterial infection is especially prevalent in mucinous CRC. The objective of this study was therefore to increase our understanding of beneficial and detrimental effects of fusobacterial infection, by contrasting host cell signaling and immune responses in areas of high vs. low infection, using mucinous rectal cancer as a clinically relevant example. We employed spatial transcriptomic profiling of 106 regions of interest from 8 mucinous rectal cancer samples to study gene expression in the epithelial and immune segments across regions of high versus low fusobacterial infection. Fusobacteria high regions were associated with increased oxidative stress, DNA damage, and P53 signaling. Meanwhile regions of low fusobacterial prevalence were characterized by elevated JAK-STAT, Il-17, Il-1, chemokine and TNF signaling. Immune masks within fusobacterial high regions were characterized by elevated proportions of cytotoxic (CD8+) T cells (p = 0.037), natural killer (NK) cells (p

Lung cancer is one of the most commonly diagnosed cancers in Canada and a leading cause of cancer mortality. Lung cancer also affects First Nations, Inuit and Métis peoples significantly in Canada, which deserves further investigation as there is a literature gap on this topic. We sought to develop a deeper understanding of lung cancer diagnosis, incidence, mortality, and survival in First Nations, Inuit, and Métis peoples in Canada. A systematic search was conducted in bibliographic databases to identify relevant studies published between January 2000 and March 2023. Articles were screened and assessed for relevance using the Population/ Concept/ Context (PCC) framework. A total of 22 articles were included in the final analysis, of which 13 were Inuit-specific, 7 were First Nations-specific, and 2 were Métis-specific. The literature suggests that comparative incidence, mortality, and relative risk of lung cancer is higher and survival is poorer in First Nations, Inuit and Métis peoples. Lung cancer also has varying impact on these population depending on sex, age, location and other factors. This review illustrates that more comprehensive quantitative and qualitative lung cancer research is essential to further identify the structural causes for the high incidence of the disease.

Background: Hypertension remains one of the most important modifiable risk factors for stroke and heart disease. Anti-hypertensive medications are effective, but are often not used to maximum benefit. Sub-optimal dosing by prescribers and challenges with medication-taking for patients remain barriers to effective blood pressure control.Objectives: We aimed to systematically develop a theory-based complex intervention to support General Practitioners (GPs) and people with hypertension to maximise medication use to control blood pressure.Methods: We used the three-phase Behaviour Change Wheel (BCW) as the overarching intervention development framework. Collective Intelligence methodology was used to operationalise the stakeholder input to Phases 2 and 3 of the BCW. This took the form of a Collective Intelligence workshop with 19 stakeholders from diverse backgrounds including lived experience, general practice, nursing, pharmacy and health psychology. Techniques such as barrier identification, idea-writing and scenario-based design were used to generate possible intervention options. Intervention options were then selected and refined using the Acceptability, Practicability, Effectiveness, Affordability, Side-effects and Equity (APEASE) criteria and guidance from the MIAMI Public and Patient Involvement Panel.Results: The finalised MIAMI intervention consists of both GP and patient supports. GP supports include a 30-minute online training, information booklet and consultation guide (drop-down menu) embedded within the patient electronic health system. Patient supports include a pre-consultation plan, website, and a structured GP consultation with results from an Ambulatory Blood Pressure Monitor and urine chemical adherence test. The intervention components have been mapped to the intervention functions of the BCW and Behaviour Change Technique Ontology.Conclusion: Collective Intelligence offered a novel method to operationalise stakeholder input to Phases 2 and 3 of the BCW. The MIAMI intervention is now at pilot evaluation stage.

Using a weight of evidence approach, natural capital outcomes associated with regenerative grazing and silvopastoral systems were compared to those associated with conventional grazing systems. The aim of the review was to better understand how grazing management influences 16 natural capital indicators likely to be material from both an economic and sustainability perspective for grazing enterprises and to assess the evidence for associated impacts, positive or negative, on the natural capital resources required to sustain the system. Material natural capital issues reviewed included water availability and security, water quality, soil health and pasture productivity, biodiversity and climate change and greenhouse gas emissions.The review confirms previous evidence that moderate to high levels of grazing tends to degrade natural capital, relative to light to moderate levels of grazing. In relation to regenerative grazing practices the responses were less clear. Regenerative grazing practices may have a positive impact on natural capital, particularly in relation to soil biodiversity and live weight gain on a per head basis. However, the evidence base suggests that the effect size may be small and may take some years to be realised. For 12 of the 16 indicators reviewed, the natural capital outcomes were inconclusive or unresolved even though for five of these indicators, the evidence base was considered to be robust. For the remainder of these the evidence base was insufficient to support an analysis of potential impacts. Similarly, for silvopastoral grazing management systems, there was some evidence to suggest that this type of grazing management could lead to improved natural capital outcomes. However, the evidence base was generally insufficient to definitively attribute the changes in grazing management practices to natural capital outcomes.Although the evidence base for many of the natural capital outcomes associated with differing grazing management practices is incomplete, it should not be interpreted that this indicates a lack of a beneficial response, where a beneficial response would be characterised by improved natural capital and enterprise productivity. Rather it highlights shortcomings in the underlying evidence. Very few studies have the capacity to fully account for the benefits of changes in management practices on the natural capital outcomes and the productivity of the enterprise simultaneously and over a sufficient period of time to observe change. This was particularly evident in relation to carbon storage. While there have been many studies that have examined changes in the individual stocks, e.g. soil carbon or emissions in relation to grazing management very few studies have examined the whole farm carbon balance. Increased adoption of processes such as natural capital accounting, although in their infancy, could help to address this challenge and facilitate a more systematic analysis an enterprises natural capital and financial performance.

Abstract BackgroundThe increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs. ObjectiveThe primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public. MethodsParticipants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session. ResultsIn total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59‐6.05 and control: mean intensity 5.19, 95% CI 4.97‐5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition. ConclusionsThe findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity.

Summary: Human neural organoid models have become an important tool for studying neurobiology. However, improving the representativeness of neural cell populations in such organoids remains a major effort. In this work, we compared Matrigel, a commercially available matrix, to a neural cadherin (N-cadherin) peptide-functionalized gelatin methacryloyl hydrogel (termed GelMA-Cad) for culturing cortical neural organoids. We determined that peptide presentation can tune cell fate and diversity in gelatin-based matrices during differentiation. Of particular note, cortical organoids cultured in GelMA-Cad hydrogels mapped more closely to human fetal populations and produced neurons with more spontaneous excitatory postsynaptic currents relative to Matrigel. These results provide compelling evidence that matrix-tethered signaling peptides can influence neural organoid differentiation, opening an avenue to control stem cell fate. Moreover, outcomes from this work showcase the technical utility of GelMA-Cad as a simple and defined hydrogel alternative to Matrigel for neural organoid culture.

The challenge of learning to be a teacher in a pandemic stymied world calls for focus and access to pedagogies of critical empathy to ensure the wellbeing of students is paramount in classrooms K to 12. Additional to facilitating the development of skills and competencies in curriculum disciplines, teachers are required to meet the emotional and social needs of their learners and prepare them to be active citizens in an increasingly complex and chaotic world. Making certain all students in their care are nurtured in environments that establish emotional safety and growth is a central tenet in the practice of all good teaching. Empathy, it seems is at the forefront of our western thinking given its prevalence in common parlance and language. Superficially, this is no bad thing. Kindness, being more humane, thinking of others and reflection are all arguably positive attributions or ways to behave to better and create a more humane society. However, empathy needs to be activated conceptually in productive and transformational ways, to be defined, problematised and critiqued. Grounding this work in the affordances of drama rich pedagogies (Ewing, 2019) and by drawing on the work of Greene (1995) and C. Stanislavski (1936), this article argues that creating a coherent and empathic classroom requires a praxis of critical empathy, facilitated by careful attendance to methods and processes steeped in and informed by theatrical traditions. In addition to a theoretical and contextual positioning of the work, this article will elucidate the method of practice undertaken in a research project, developed to inform the practice of teachers and educators.

The funding model for further education and training (FET) in the Republic of Ireland currently describes provider and learner success in narrow quantifiable terms as captured on the national Programme Learner Support System (PLSS). FET practitioners have grappled with the restrictive rigidity of these outcomes and measures of success for adult learners. This article investigates the concept of Psychological Capital (PsyCap) as a means of widening outcomes and measures in FET.