Search

The positive links between children's theory of mind (ToM), emotion understanding, and positive peer relationships are well established. However, the existing literature lacks comprehensive studies investigating the longitudinal interplay between these components in preschool-aged children. This study aimed to fill this gap by examining the concurrent and longitudinal associations between young children's social cognition and their positive peer relationships at three different time points over the course of 7 months. A sample of 211 preschool children (age in months: M[subscript T1] = 43.2, SD[subscript T1] = 6.6) underwent standardized assessments evaluating their ToM and emotion understanding, while playgroup educators reported on children's positive peer relationships. Using multivariate latent growth modeling, we expected to find that higher levels of ToM and emotion understanding would be associated with a greater rate of change in positive peer relationships and that higher levels of positive peer relationships would be associated with a higher rate of change in ToM and emotion understanding. Contrary to our expectations, the results did not support the anticipated longitudinal associations. Nevertheless, a noteworthy correlation emerged between children's emotion understanding and positive peer relationships at T1, in line with previous research and social-constructivist theories.

This research explores the impact of an innovative social media-based project on students' digital activism and their perspectives on societal issues, particularly domestic terrorism. A mixed methods approach, combining structural equation modeling and qualitative data, revealed that higher levels of ethnocultural empathy led to increased engagement in digital activism, strengthening students' belief in their ability to effect meaningful societal change. Results show that students' belief in their project contribution dips initially due to unexpected challenges but later recovers and strengthens, underscoring the importance of sustained engagement. Qualitative data also highlight enhanced awareness, ethnocultural empathy, and activism skills. This research demonstrates the pedagogical potential of integrating social media with societal impact campaigns, offering a replicable framework for business educators seeking to link marketing education with social responsibility.

Summary: Background: Short interpregnancy interval (IPI) following caesarean delivery is associated with uterine rupture in subsequent pregnancies. However, the interval required to minimise this risk is unknown. We investigated how the interval between pregnancies and induction or augmentation of labour affect the likelihood of uterine rupture among parturients with one previous livebirth by caesarean delivery who had a subsequent trial of labour. Methods: In this population-based cohort study, we used data from U.S National Vital Statistics System from 2011 to 2021. Multiple pregnancies and births of infants with congenital abnormalities were excluded. A linear spline logistic regression with one knot was used to assess the relationship between uterine rupture and interpregnancy interval for spontaneous and for induced/augmented labours. Multivariable logistic regression was performed with multiple imputation and stepwise backward elimination to adjust for maternal demographic and clinical factors including maternal age, height, and BMI and gestational age. The predicted risk of uterine rupture was tabulated for interpregnancy intervals between zero and 21 months. Adverse outcomes were compared between labours with and without uterine rupture. Findings: We examined 491,998 trials of labour among parturients with one previous livebirth by caesarean delivery and no previous vaginal births. The odds ratio (OR) of uterine rupture per three months interpregnancy interval was 0.91 (95% CI 0.88–0.94) between zero and 21 months after adjusting for confounders, with no further change in risk detected beyond 21 months. The OR was 2.51 (95% CI 2.27–2.78) for induced or augmented labours compared with spontaneous labours. Other factors associated with uterine rupture included older maternal age, shorter maternal height, more advanced gestational age (from 35 to 43 weeks), and heavier birthweight. Predicted rates of uterine rupture ranged from 0.36% at zero to 0.19% at 21 months’ interpregnancy interval for spontaneous labours and from 0.91% to 0.47% for induced/augmented labours for parturients with a typical clinical and demographic background. When uterine rupture occurred, the rates of unplanned hysterectomy, intrapartum or neonatal death, and neonatal seizures were 4.0% (95% CI 3.2–5.1%), 3.7% (95% CI 2.7–5.1%), and 2.6% (95% CI 1.8–3.3%) respectively. Interpretation: The risk of uterine rupture progressively decreases as IPI increases until about 21 months and then stabilises. Counselling should advise that for women choosing between a planned TOLAC or a planned caesarean delivery after one previous caesarean delivery and no previous vaginal births waiting until 21 months or longer after a prior low transverse caesarean delivery might minimise the risk of uterine rupture. The absolute risk of certain serious maternal and fetal/neonatal complications such as unplanned hysterectomy and perinatal death is low. Funding: No funding.

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel.

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.

IMPORTANCE:. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has evolved through multiple phases in the United States, with significant differences in patient centered outcomes with improvements in hospital strain, medical countermeasures, and overall understanding of the disease. We describe how patient characteristics changed and care progressed over the various pandemic phases; we also emphasize the need for an ongoing clinical network to improve the understanding of known and novel respiratory viral diseases. OBJECTIVES:. To describe how patient characteristics and care evolved across the various COVID-19 pandemic periods in those hospitalized with viral severe acute respiratory infection (SARI). DESIGN:. Severe Acute Respiratory Infection-Preparedness (SARI-PREP) is a Centers for Disease Control and Prevention Foundation-funded, Society of Critical Care Medicine Discovery-housed, longitudinal multicenter cohort study of viral pneumonia. We defined SARI patients as those hospitalized with laboratory-confirmed respiratory viral infection and an acute syndrome of fever, cough, and radiographic infiltrates or hypoxemia. We collected patient-level data including demographic characteristics, comorbidities, acute physiologic measures, serum and respiratory specimens, therapeutics, and outcomes. Outcomes were described across four pandemic variant periods based on a SARS-CoV-2 sequenced subsample: pre-Delta, Delta, Omicron BA.1, and Omicron post-BA.1. SETTING:. Multicenter cohort of adult patients admitted to an acute care ward or ICU from seven hospitals representing diverse geographic regions across the United States. PARTICIPANTS:. Patients with SARI caused by infection with respiratory viruses. MAIN OUTCOMES AND RESULTS:. Eight hundred seventy-four adult patients with SARI were enrolled at seven study hospitals between March 2020 and April 2023. Most patients (780, 89%) had SARS-CoV-2 infection. Across the COVID-19 cohort, median age was 60 years (interquartile range, 48.0–71.0 yr) and 66% were male. Almost half (430, 49%) of the study population belonged to underserved communities. Most patients (76.5%) were admitted to the ICU, 52.5% received mechanical ventilation, and observed hospital mortality was 25.5%. As the pandemic progressed, we observed decreases in ICU utilization (94% to 58%), hospital length of stay (median, 26.0 to 8.5 d), and hospital mortality (32% to 12%), while the number of comorbid conditions increased. CONCLUSIONS AND RELEVANCE:. We describe increasing comorbidities but improved outcomes across pandemic variant periods, in the setting of multiple factors, including evolving care delivery, countermeasures, and viral variants. An understanding of patient-level factors may inform treatment options for subsequent variants and future novel pathogens.

This study examined strategies teachers applied to promote peer interactions between children with and without Special Educational Needs (SEN) in an inclusive preschool in Shanghai, China. Qualitative case study was used in which seven teachers from four inclusive classrooms participated. In-depth participatory observation and online documents were collected and qualitative content analysis was applied to analyse the data. The key finding suggests both preventive and interventive strategies are identified from five different levels: cooperation with different community stakeholders level; the classroom environment level; the curriculum plan level; the activity design level and the individual children with SEN level. Implications of the findings to promote peer interactions in inclusive early childhood settings and directions for future research are discussed.

Quality of educator-child interaction is a well-established predictor of children's well-being and development. Situational and personal characteristics seem to play an important role in the quality of interactions. More challenging situations, such as a large number of children or activities like meals or transitions, show lower quality. One important personal characteristic might be educator self-efficacy. However, empirical findings of the relation between self-efficacy and interaction quality are inconsistent. Situational characteristics might explain these inconsistent findings. We investigate the effects of educator self-efficacy as well as situational characteristics on the observed interaction quality and the moderating role of the situation on the relation between educators' self-efficacy and interaction quality. A total of 245 early childhood care educators from 103 groups in Switzerland participated. A self-report questionnaire was used to assess educators' self-efficacy. Interaction quality was observed using a standardized observation tool (CLASS Toddler). Results confirm that the presence of many children and activities like meals, routines, and transitions are related to lower interaction quality. Including situational characteristics like group size or mealtimes yielded some significant -- and partly contradictory -- associations between educator self-efficacy and interaction quality. We discuss the still unclear and partly problematic transmission of self-efficacy into interaction quality.

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.

Training studies have shown the positive effects of play tutoring on children's social skills. This study investigates whether the theoretically suggested mechanism of change--social pretend play quality--explains the effect of play tutoring on social pretend play competence and social skills. Twenty-seven Swiss playgroups (N = 214 three- to four-year-olds) participated in a randomized intervention study with three conditions: the intervention group (play tutoring), the material group (half-dose), and the control group (treatment as usual). Weekly treatment sessions took place for six consecutive weeks. Pre-tests, post-tests, and a follow-up were made. Playgroup educators reported on children's pretend play competence and social skills using a questionnaire. Children's pretend play quality during treatment sessions was assessed by standardized behavioral observations. Using latent change models with indirect effects, we investigated whether the intervention effects of play tutoring on children's pretend play competence and social skills are mediated by their social pretend play quality shown during the intervention sessions. The results indicate mediating effects of social pretend play quality on children's change in social pretend play competence and self-oriented social skills (sociability and setting limits). The study supports social pretend play as a beneficial ground to promote social development but also indicates a more complex interplay of different change mechanisms.

The present study observed 86 three-year-old children (M = 43.7, SD = 6.4) from 15 Swiss childcare groups, to investigate multiple individual and contextual contributions to toddlers' positive engagement with peers. The children's individual characteristics (age, sex and social skills) and childcare-related predictors (emotional and behavioural support from caregivers, and structural group features) were assessed. We employed the child-by-environment perspective and tested the hypothesis that high-quality behavioural and emotional support provided by caregivers benefits children with deficits in social abilities. Results of the multilevel structural equation modelling indicated that toddlers rated by caregivers as sociable and assertive showed more positive situation-specific peer engagement, especially with a concomitant higher quality of caregiver emotional and behavioural support. By contrast, being prosocial-cooperative was negatively associated with observed peer engagement. Thus, children's social skills were found to be the most important factor for peer engagement in childcare settings. Important suggestions for future research are made, and practical implications are discussed.

Abstract Polarimetry is an optical characterization technique capable of analyzing the polarization state of light reflected by materials and biological samples. In this study, we investigate the potential of Müller matrix polarimetry (MMP) to analyze fresh pancreatic tissue samples. Due to its highly heterogeneous appearance, pancreatic tissue type differentiation is a complex task. Furthermore, its challenging location in the body makes creating direct imaging difficult. However, accurate and reliable methods for diagnosing pancreatic diseases are critical for improving patient outcomes. To this end, we measured the Müller matrices of ex-vivo unfixed human pancreatic tissue and leverage the feature-learning capabilities of a machine-learning model to derive an optimized data representation that minimizes normal-abnormal classification error. We show experimentally that our approach accurately differentiates between normal and abnormal pancreatic tissue. This is, to our knowledge, the first study to use ex-vivo unfixed human pancreatic tissue combined with feature-learning from raw Müller matrix readings for this purpose.

High-flow nasal cannula oxygen therapy (HFNCOT) system consists of an air/oxygen supply system capable of delivering up to 100% humidified and heated oxygen at a flow rate of up to 80 L/min. The system includes a blender, active humidifier, single heated tube, and nasal cannula. HFNCOT has many physiological advantages compared with other standard oxygen therapies, such as anatomical dead space washout, more constant fraction of inspired oxygen, positive end-expiratory (PEEP) effect, supplement of adequate humidification and maintenance of muco-ciliary function. HFNCOT is mostly used for hypoxemic acute respiratory failure, although it also has other indications. HFNCOT is a common choice of physicians as its technology makes it more silent and comfortable. Though HFNCOT is used in many clinical settings, there is a lack of publications addressing devices and initial settings. We present a review on HFNCOT, with focus on device and application methodology.