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The Three Basic Dimensions model, theorizes three mediators for the effect of teaching quality dimensions on student outcomes. However, the proposed mediating paths and their effects have largely not been empirically tested. This study investigated the mediating role of depth of-processing, time-on-task, and need satisfaction between student-perceived teaching quality and student mathematics achievement and interest, expanding the TBD model to include mediation paths suggested by theories of motivation, cognition, and effort. Data from the TALIS Video Study for Germany, comprising 958 secondary school students in 41 classrooms, were used to run multilevel longitudinal and correlational mediation analyses. The results only found mediation effects at the student level; there were no mediating effects at the classroom level. Not all of the hypothesized relationships thought to exist between the mediators and achievement and interest outcomes were confirmed. The conceptual sequence of the variables, the choice of correlational vs. longitudinal evidence, and the level of analysis were all shown to have an impact on the results. The study thus confirms some of the assumptions of the TBD model, identifies new paths between teaching quality and student outcomes, and provides suggestions for how to proceed with further investigation of a model which should be expanded and more thoroughly empirically tested.

Recent publications emphasize the need to take greater account of differences in teaching quality between subjects. The empirical analysis of this topic requires a comparison of teaching quality in different subjects to distinguish generic aspects of teaching quality from subject-specific ones. In this paper, we compare teaching quality in mathematics and German lessons using observational data from primary schools in Switzerland (N[subscript Math] = 319; N[subscript German] = 237). Data were collected using an observation instrument reflecting the teaching dimensions of the MAIN-TEACH model, which was developed based on a synthesis of established observation frameworks. The dimensions of classroom management, motivational-emotional support, selection and implementation of content, cognitive activation, support for consolidation, assessment and feedback, and adaptation were tested for subject-related measurement invariance. With a two-fold measurement invariance approach, differences between the subjects were investigated at both a global and an indicator level. When applying alpha accumulation correction, no significant subject-related differences in factor loadings or intercepts were found. The factorial structure of our data was basically identical for the two subjects. The comparison of latent factor means revealed no mean differences between the subjects. We discuss the implications for both the MAIN-TEACH model and research into subject-related differences of teaching in general.

There have been major advances in the science to predict the likely environmental concentrations of nanomaterials, which is a key component of exposure and subsequent risk assessment. Considerable progress has been since the first Material Flow Analyses (MFAs) in 2008, which were based on very limited information, to more refined current tools that take into account engineered nanoparticle (ENP) size distribution, form, dynamic release, and better-informed release factors. These MFAs provide input for all environmental fate models (EFMs), that generate estimates of particle flows and concentrations in various environmental compartments. While MFA models provide valuable information on the magnitude of ENP release, they do not account for fate processes, such as homo- and heteroaggregation, transformations, dissolution, or corona formation. EFMs account for these processes in differing degrees. EFMs can be divided into multimedia compartment models (e.g., atmosphere, waterbodies and their sediments, soils in various landuses), of which there are currently a handful with varying degrees of complexity and process representation, and spatially-resolved watershed models which focus on the water and sediment compartments. Multimedia models have particular applications for considering predicted environmental concentrations (PECs) in particular regions, or for developing generic "fate factors" (i.e., overall persistence in a given compartment) for life-cycle assessment. Watershed models can track transport and eventual fate of emissions into a flowing river, from multiple sources along the waterway course, providing spatially and temporally resolved PECs. Both types of EFMs can be run with either continuous sources of emissions and environmental conditions, or with dynamic emissions (e.g., temporally varying for example as a new nanomaterial is introduced to the market, or with seasonal applications), to better understand the situations that may lead to peak PECs that are more likely to result in exceedance of a toxicological threshold. In addition, bioaccumulation models have been developed to predict the internal concentrations that may accumulate in exposed organisms, based on the PECs from EFMs. The main challenge for MFA and EFMs is a full validation against observed data. To date there have been no field studies that can provide the kind of dataset(s) needed for a true validation of the PECs. While EFMs have been evaluated against a few observations in a small number of locations, with results that indicate they are in the right order of magnitude, there is a great need for field data. Another major challenge is the input data for the MFAs, which depend on market data to estimate the production of ENPs. The current information has major gaps and large uncertainties. There is also a lack of robust analytical techniques for quantifying ENP properties in complex matrices; machine learning may be able to fill this gap. Nevertheless, there has been major progress in the tools for generating PECs. With the emergence of nano- and microplastics as a leading environmental concern, some EFMs have been adapted to these materials. However, caution is needed, since most nano- and microplastics are not engineered, therefore their characteristics are difficult to generalize, and there are new fate and transport processes to consider.

Background: Ménière’s Disease (MD) is a disease that may be difficult to diagnose and manage. Our UK survey showed variability in the practice of UK Otolaryngology consultants. We hence surveyed Otolaryngology consultants internationally, to assess their confidence levels in diagnosing MD, their use of the AAO-HNS guidelines and current diagnostic and treatment modalities. Methods: An online questionnaire was distributed internationally over four weeks. The questionnaire asked respondents to anonymously rank their confidence in diagnosing MD, identify the minimum investigations required to make a diagnosis, describe their use of the AAO-HNS criteria, share their preferred treatment modalities for acute attacks, and state their 1st and 2nd-line preventative treatment options. Results: A total of 173 responses were collected with 77% of respondents reporting high levels of confidence in diagnosing MD. Most respondents stated the minimum tests required were “History, Otoscopy, Clinical Vestibular testing, and Pure Tone Audiometry” although some chose as few as 1 test. Regarding the use of the AAO-HNS criteria, responses ranged from “always” (20.2%) to “never” (22.5%). Cinnarizine was the first-line treatment for acute attacks followed by betahistine. Betahistine (30.1%) and dietary restrictions (28.3%) were recommended almost equally as first-line preventative measures. The most popular second-line measure was intratympanic steroids injection (30.1%). Conclusion: Our survey revealed disparities in the diagnosis of MD and its management, like the results of our previously conducted UK survey. This suggests the need for an international consensus regarding the diagnosis and subsequent management strategies for this disease.

Early identification of human pathogens is crucial for the effective treatment of bloodstream infections to prevent sepsis. Since pathogens that are present in small numbers are usually difficult to detect directly, we hypothesize that the behavior of the immune cells that are present in large numbers may provide indirect evidence about the causative pathogen of the infection. We previously applied time-lapse microscopy to observe that neutrophils isolated from human whole-blood samples, which had been infected with the human-pathogenic fungus Candida albicans or C. glabrata, indeed exhibited a characteristic morphodynamic behavior. Tracking the neutrophil movement and shape dynamics over time, combined with machine learning approach, the accuracy for the differentiation between the two Candida species was about 75%. In this study, the focus is on improving the classification accuracy of the Candida species using advanced deep learning methods. We implemented (i) gated recurrent unit (GRU) networks and transformer-based networks for video data, and (ii) convolutional neural networks (CNNs) for individual frames of the time-lapse microscopy data. While the GRU and transformer-based approaches yielded promising results with 96% and 100% accuracy, respectively, the classification based on videos proved to be very time-consuming and required several hours. In contrast, the CNN model for individual microscopy frames yielded results within minutes, and, utilizing a majority-vote technique, achieved 100% accuracy both in identifying the pathogen-free blood samples and in distinguishing between the Candida species. The applied CNN demonstrates the potential for automatically differentiating bloodstream Candida infections with high accuracy and efficiency. We further analysed the results of the CNN using explainable artificial intelligence (XAI) techniques to understand the critical features and patterns, thereby shedding light on potential key morphodynamic characteristics of neutrophils in response to different Candida species. This approach could provide new insights into host-pathogen interactions and may facilitate the development of rapid, automated diagnostic tools for differentiating fungal species in blood samples.

Abstract Background This study explores the impact of decentralized management on the sickness absence among healthcare professionals. Sickness absence is a reliable indicator of employees’ wellbeing and it is linked to management quality. However, the influence of decentralized management on sickness absence has not been adequately studied. Methods The research design combined a two-wave, web-survey of frontline managers in two Danish university hospitals with administrative data on sickness absence at the ward-level. The first and second wave included data from 163165 and 137 frontline managers linked to 121 wards and 108 wards. Data was analysed using an ordinal logistic regression model. Results Wards where frontline managers had the highest level of decentralised decision authority compared to none showed lower odds of ward-level sickness absence (ORcrude: 0.20, 95% CI: 0.05–0.87). A very high extent of cross-functional decision authority showed lower odds of sickness absence (ORcrude: 0.08, 95% CI: 0.01–0.49). Overall, the results showed a clear data trend, although not all results were statistically significant. Conclusion Higher levels of decentralized management in wards were positively associated with lower risks of sickness absence in hospital wards. The study supports future research on how to empower decision autonomy at the frontline level of management.

The pathology of respiratory syncytial virus (RSV) infection remains unclear. An unbalanced immune response to RSV infection can lead to immunopathology, causing airway damage and impaired exchange of oxygen and carbon dioxide between the air and the bloodstream. We aimed to evaluate the association of the expression of inflammatory and antiviral genes in peripheral blood with severe hypoxemia in children with RSV infection seen in the hospital emergency room. We conducted a cross-sectional study on 121 RSV-infected children seen in hospital emergency rooms between 2015 and 2023. Total RNA was extracted from whole blood samples, and gene expression (IL-6, TNFα, CXCL8, ISG15, IFIT1, RIGI, IFNβ, CCL5, and CXCL10) was quantified using quantitative RT-PCR. The outcome variable was having severe hypoxemia (SpO2 ≤ 90%). The association analysis was performed using a volcano plot, adjusted logistic regression, and orthogonal partial least squares discriminant analysis (OPLS-DA). We found that 26 of 121 children had severe hypoxemia (SpO2 ≤ 90%). CXCL8 was overexpressed [fold changes (FC) > 2; q-value < 0.05], and ISG15, IFIT1, RIGI, IFNβ, CCL5, and CXCL10 were underexpressed (FC

This paper presents PREDICTOR (PREDICting Take-Over Response time): an interactive open-source research software tool to predict the timing of various stages of a transition of control, or take-over, in semi-automated driving. Although previous work has investigated extensively what factors affect the minimum time needed for a successful take-over by the driver, less is known about how specific stages within the take-over process are affected by those factors. PREDICTOR applies a theoretical framework that describes the take-over process as interruption handling through a series of stages. It then ties this theory to a database that summarizes results from previous take-over studies. PREDICTOR can be used to interactively predict through simulation how specific human factors (e.g., alert modality, alert onset time) impact four distinct stages of the take-over response process. The tool simulates and visualizes expected reaction time distributions for each stage of the take-over process. The use of distributions also highlights the likelihood of an accident – as long responses (“outliers”) are quantifiable. Moreover, it can help understand at which stage drivers might take relatively longer or shorter, and which stages are most impacted by a specific factor (e.g., alert modality). PREDICTOR also allows users to add their own data, and to define their own dependent variables for analysis. As a tool that allows exploration of various scenarios, PREDICTOR can aid in the prediction and analysis of potential future accidents.

Abstract Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image data. The focus of the present study is on the imaging-based evaluation of the posterior cricoarytenoid muscle (PCA) influenced by long-term functional electrical stimulation (FES), which may assist the inspiration of patients with bilateral vocal fold paresis. To this end, muscle cross-sections of the PCA of sheep were examined by quantitative image analysis. Previous investigations of the muscle fibers and the collagen amount have not revealed signs of atrophy and fibrosis due to FES by a laryngeal pacemaker. It was therefore hypothesized that regardless of the stimulation parameters the fat in the muscle cross-sections would not be significantly altered. We here extending our previous investigations using quantitative imaging of intramuscular fat in cross-sections. In order to perform this analysis both reliably and faster than a qualitative evaluation and time-consuming manual annotation, the selection of the automated method was of crucial importance. To this end, our recently established deep neural network IMFSegNet, which provides more accurate results compared to standard machine learning approaches, was applied to more than 300 H&E stained muscle cross-sections from 22 sheep. It was found that there were no significant differences in the amount of intramuscular fat between the PCA with and without long-term FES, nor were any significant differences found between the low and high duty cycle stimulated groups. This study on a human-like animal model not only confirms the hypothesis that FES with the selected parameters has no negative impact on the PCA, but also demonstrates that objective and automated deep learning-based quantitative imaging is a powerful tool for such a challenging analysis.

Abstract Background In the United States there are roughly three million births a year, ranging from cesarean to natural births. A major aspect of the birthing process is related to the healing environment, and how that helps or harms healing for the mother and child. Using the theoretical framework, Theory of Supportive Care Settings (TSCS), this study aimed to explore what is necessary to have a safe and sacred healing environment for mothers. Method This study utilized an updated Qualitative Interpretive Meta-synthesis (QIMS) design called QIMS-DTT [deductive theory testing] to answer the research question, What are mother’s experiences of environmental factors contributing to a supportive birthing environment within healthcare settings? Results Key terms were run through multiple databases, which resulted in 5,688 articles. After title and abstract screening, 43 were left for full-text, 12 were excluded, leaving 31 to be included in the final QIMS. Five main themes emerged from analysis: 1) Service in the environment, 2) Recognizing oneself within the birthing space, 3) Creating connections with support systems, 4) Being welcomed into the birthing space, and 5) Feeling safe within the birthing environment. Conclusions Providing a warm and welcoming birth space is crucial for people who give birth to have positive experiences. Providing spaces where the person can feel safe and supported allows them to find empowerment in the situation where they have limited control.

Abstract Background Somatisation is a highly prevalent psychiatric syndrome in both women and men, in which psychological distress is manifested in physical symptoms without a medical explanation. Many patients with somatisation disorder are high healthcare utilisers, particularly at emergency departments. Unnecessary investigations and diagnostic operations occur frequently, which cause both patient suffering and a significant burden on the healthcare system. Emergency department physicians’ awareness of somatisation and its manifestations has not previously been studied. This study aimed to investigate awareness about somatisation disorder among physicians working at emergency departments in western Sweden, and to explore differences between gender, specialty, and work experience. Methods A web-based, cross-sectional survey consisting of six dichotomous questions about somatisation disorder was conducted, in December 2021 – January 2022, among licensed physicians of various specialties working at emergency departments in western Sweden. Descriptive analyses and comparative analyses were performed to investigate differences between gender, type of specialty, and years of practice. Data were analysed using chi2 tests and Fisher’s exact test. Results Of the 526 eligible physicians who received the survey, 241 responded; response rate 45.8%. The majority of the respondents (56.4%) were women, and most (35.3%) were specialised in obstetrics/gynaecology. Average years of work experience was 11.1 (SD 8.7) years. Although 71% of respondents were aware of the diagnosis, only 7% knew the diagnostic criteria and only 6% had ever diagnosed a patient with somatisation disorder. Female physicians were more aware of underlying factors than their male colleagues (55.7% vs. 38.2%; p = .010). Type of specialty or years of practice did not affect awareness. Conclusions Awareness of somatisation disorder is low among physicians working at emergency departments in western Sweden. The findings suggest a need to increase awareness and knowledge and provide training in diagnosing the condition, to ensure correct decisions and optimal patient management. Clinical guidelines need to be developed to support diagnosis, investigation, and treatment, in Sweden as well as internationally.

Abstract Purpose Resistance to fatigue is a key factor in injury prevention that needs to be considered in return-to-sport (RTS) scenarios, especially after severe knee ligament injuries. Fatigue should be induced under game-like conditions. The SpeedCourt (SC) is a movement platform for assessing multidirectional sprint performance, typical of game-sports, due to change-of-direction movements in response to a visual stimulus. Designing adequate fatigue protocols requires the suitable arrangement of several loading variables such as number of intervals, sprint distance or work/relief ratio (W:R). Therefore, this study analysed the acute fatigue effects of different SC protocols on metabolic load, cognitive function and neuromuscular performance. Methods Eighteen female soccer players (mean ± SD; age: 23.1 ± 4.6 years) of the 1st German Division participated in this randomised, crossover study. Using a random allocation sequence, players completed four volume-equated protocols differing in W:R and sprint distance per interval (P1:12 × 30 m, W:R = 1:2 s; P2:12 × 30 m, W:R = 1:3 s; P3:18 × 20 m, W:R = 1:2 s; P4:18 × 20 m, W:R = 1:3 s). Pre- and post-exercise, metabolic load was measured per blood lactate concentration (BLaC), cognitive function per reaction time (RT), and neuromuscular performance including multiple rebound jumps (MRJ height, primary outcome variable; Reactive Strength Index, RSI) and 5 m sprint times (SP5). Results Repeated-measures ANOVA revealed significant main time effects (p

Founding populations of the first Americans likely occupied parts of Beringia during the Last Glacial Maximum (LGM). The timing, pathways, and modes of their southward transit remain unknown, but blockage of the interior route by North American ice sheets between ~26 and 14 cal kyr BP (ka) favors a coastal route during this period. Using models and paleoceanographic data from the North Pacific, we identify climatically favorable intervals when humans could have plausibly traversed the Cordilleran coastal corridor during the terminal Pleistocene. Model simulations suggest that northward coastal currents strengthened during the LGM and at times of enhanced freshwater input, making southward transit by boat more difficult. Repeated Cordilleran glacial-calving events would have further challenged coastal transit on land and at sea. Following these events, ice-free coastal areas opened and seasonal sea ice was present along the Alaskan margin until at least 15 ka. Given evidence for humans south of the ice sheets by 16 ka and possibly earlier, we posit that early people may have taken advantage of winter sea ice that connected islands and coastal refugia. Marine ice-edge habitats offer a rich food supply and traversing coastal sea ice could have mitigated the difficulty of traveling southward in watercraft or on land over glaciers. We identify 24.5 to 22 ka and 16.4 to 14.8 ka as environmentally favorable time periods for coastal migration, when climate conditions provided both winter sea ice and ice-free summer conditions that facilitated year-round marine resource diversity and multiple modes of mobility along the North Pacific coast.