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The current study analysed the relationships between socio-economic status, migration background, and non-cognitive factors and PISA reading and mathematics achievement. The results from multi-level analyses on Sweden's PISA survey from 2018 indicate that both mathematics achievement and reading achievement are affected by SES, migration background, reading abilities, growth mindset, and the ability to master the content. Between-school level differences are explained by reading motivation and the mother's educational level. Our findings stress the importance of both socio-demographic, socio-economic, and non-cognitive factors such as reading self-concept and growth mindset for both mathematics achievement and reading achievement.

The use of alternative diesel fuels has increased due to the demand for renewable energy sources. There is limited knowledge regarding the potential health effects caused by exhaust emissions from biodiesel- and renewable diesel-fueled engines. This study investigates the toxic effects of particulate matter (PM) emissions from a diesel engine powered by conventional petroleum diesel fuel (SD10) and two biodiesel and renewable diesel fuels in vitro. The fuels used were rapeseed methyl ester (RME), soy methyl ester (SME), and Hydrogenated Vegetable Oil (HVO), either pure or as 50% blends with SD10. Additionally, a 5% RME blend was also used. The highest concentration of polycyclic aromatic hydrocarbon emissions and elemental carbon (EC) was found in conventional diesel and the 5% RME blend. HVO PM samples also exhibited a high amount of EC. A dose-dependent genotoxic response was detected with PM from SD10, pure SME, and RME as well as their blends. Reactive oxygen species levels were several times higher in cells exposed to PM from SD10, pure HVO, and especially the 5% RME blend. Apoptotic cell death was observed in cells exposed to PM from SD10, 5% RME blend, the 50% SME blend, and HVO samples. In conclusion, all diesel PM samples, including biodiesel and renewable diesel fuels, exhibited toxicity.

Breast-conserving surgery accompanied by adjuvant radiotherapy is the standard of care for patients with early-stage breast cancer. However, re-excision is reported in 20–30 % of cases, largely because of close or involved tumor margins in the specimen. Several intraoperative tumor margin assessment techniques have been proposed to overcome this issue, however, none have been widely adopted. Furthermore, tumor margin assessment of the excised specimen provides only an indirect indication of residual cancer in the patient following excision of the primary tumor. Handheld optical coherence tomography (OCT) probes and their functional extensions have the potential to detect residual cancer in vivo in the surgical cavity. Until now, validation of in vivo OCT has been achieved through correlation with ex vivo histology performed on the specimen removed during surgery that is adjacent to the tissue scanned in vivo. However, this indirect approach cannot accurately validate in vivo imaging performance. To address this, we present a method for robust co-registration of in vivo OCT scans with histology performed, not on the main specimen, but on cavity shavings corresponding directly to the tissue scanned in vivo. In this approach, we use ex vivo OCT scans as an intermediary, surgical sutures as fiducial markers, and extend the in vivo field-of-view to 15 × 15 mm2 by acquiring partially overlapping scans. We achieved successful co-registration of 78 % of 139 in vivo OCT scans from 16 patients. We present a detailed analysis of three cases, including a case where a functional extension of OCT, quantitative micro-elastography, was performed.

BackgroundMexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.PurposeTo explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.MethodsWe grouped features into five data modalities corresponding to paediatric population exogenous factors, in two multimodal machine learning pipelines, against a unimodal early fusion baseline. The supervised pipeline employed four methods: Linear classifier with Elastic Net regularisation, k-Nearest Neighbour, Decision Tree, and Random Forest. The unsupervised pipeline used traditional methods with k-Means and hierarchical clustering, with the optimal number of clusters calculated to be k = 2.ResultsThe decision tree classifier in the supervised early fusion approach produced the best quantitative results. The top five most important features for classifying child or adolescent health were measures of an adult in the household, selected at random: BMI, obesity diagnosis, being single, seeking care at private healthcare, and having paid TV in the home. Unsupervised learning approaches varied in the optimal number of clusters but agreed on the importance of home environment features when analysing inter-cluster patterns. Main findings from this study differed from previous studies using only traditional statistical methods on the same database. Notably, the BMI of a randomised adult within the household emerged as the most important feature, rather than maternal BMI, as reported in previous literature where unwanted cultural bias went undetected.ConclusionOur general conclusion is that multimodal machine learning is a promising approach for comprehensively analysing obesogenic environments. The modalities allowed for a multimodal approach designed to critically analyse data signal strength and reveal sources of unwanted bias. In particular, it may aid in developing more effective public health policies to address the ongoing paediatric obesity epidemic in Mexico.

In recent years, some educational researchers have questioned the theory of a robust relationship between test scores in international large-scale assessments such as PISA and lagged economic growth. However, both the proponents and opponents of such a human capital approach have mainly focused on earlier growth patterns during the twentieth century. Hence, the current article analysed the relationship between PISA 2000 and economic growth in 2010–2019 for 31 countries, and composite cognitive skills for 80 countries and their relationships with economic growth in 2010–2019. The results indicate that PISA scores or other cognitive skill measures could to some extent predict economic growth between 2010 and 2019, even when controlling for a significant number of variables. In a consecutive analysis, the relations were substantially associated with Middle Eastern countries because many countries from this region experienced political instability and decreasing oil revenues throughout this period. Hence, it is possible that regional and contextual relationships affect these patterns.

Objective: Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment. Methods: We use 1) commonly used time-independent methods (i.e., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. This is done with symptom scores from 6436 ICBT patients from regular care, using robust multiple imputation and nested cross-validation methods. Results: The models had a 14 %–12 % root mean squared error (RMSE) in predicting the post-treatment outcome, corresponding to a balanced accuracy of 67–74 %. Time-dependent models did not have higher accuracies. Using data for the initial treatment period (b) instead of only from before treatment (a) increased prediction results by 1.3 % percentage points (12 % to 10.7 %) RMSE and 6 % percentage points BACC (69 % to 75 %). Conclusion: Training prediction models on only symptom scores of the first few weeks is a promising avenue for symptom predictions in treatment, regardless of which model is used. Further research is necessary to better understand the interaction between model complexity, dataset length and width, and the prediction tasks at hand.

Charitable organizations are increasingly soliciting donors to engage in word of mouth (WOM) as a strategy to foster future contributions. While some organizations encourage donors to share WOM that focuses on their own donations (donor-focused WOM; e.g., "I just donated to the kids of @StJude. Join me in saving children's lives."), others prompt donors to share WOM that focuses on the organization itself (charity-focused WOM; e.g., "Smile Train gives children with clefts the #PowerOfASmile."). Contrary to the common belief that people mostly want to talk about themselves, the current research demonstrates that donor-focused WOM backfires, such that donors are less likely to share donor- than charity-focused WOM. This effect is driven by their belief that donor-focused WOM is less altruistic and is therefore less efficacious in persuading others to contribute to the same cause. In addition to sharing, the type of WOM solicited exerts far-reaching impact, with donor-focused WOM attracting fewer new donors than charity-focused WOM. The current research improves the understanding of WOM type, its effect, and the underlying processes. [ABSTRACT FROM AUTHOR]

Abstract Background Concern over medical students’ well-being is a global issue, with studies showing high psychological distress rates. Impostor Phenomenon (IP), i.e., underestimating one’s abilities, attributing success to external factors, and feeling like a fraud, has been implicated as one reason behind these troubling findings. Meanwhile, resilience has been suggested to protect against psychological distress. This study aimed to investigate the prevalence of IP and its association with resilience among undergraduate medical students. Methods The Clance Impostor Phenomenon Scale (CIPS), the Brief Resilience Scale (BRS), and sociodemographic questions were completed by 457 medical students registered in their 2-10th semester at a Swedish university. Of the respondents, 62.6% identified as women, 36.1% as men, and 1.3% as others. Results The prevalence of IP was 58.4% (defined as CIPS score ≥ 62). According to the CIPS scoring guidelines, 10.3% of participants had low IP, 29.5% moderate, 41.6% frequent, and 18.6% intense IP. Of all participants, almost 90% experienced at least moderate and 60.2% frequent to intense IP. Women had significantly higher CIPS scores and lower BRS scores than men. In contrast, neither attending semester nor age group significantly impacted CIPS scores. Finally, there was a moderate inverse correlation between the level of resilience and the prevalence of IP. Conclusions Our findings suggest that the majority of Swedish medical students feels like an impostor, and of these students, most do so frequently. Furthermore, IP is more prevalent among female students – who also show lower levels of resilience. Moreover, our results indicated that IP could hinder achieving higher resilience. Future longitudinal studies should investigate how gender norms may contribute to IP feelings and explore the potential advantages of counteracting IP and strengthening resilience among medical students. However, targeted interventions addressing individual students’ IP and resilience are insufficient. There is also a need to address structural aspects of the educational environment, such as gender stereotypes, that may contribute to IP.

Finite element analysis (FEA) is a computational method used to predict the behaviour (stresses, strains and deformation) of a structure under predefined loading conditions. It can be applied to different biological structures, such as bone, to study defined muscle-driven scenarios. However, as muscle is an extremely complex structure to model, evolutionary biologists usually model muscle forces indirectly. In 2007, the BONELOAD MATLAB routine was developed to distribute muscle forces on a surface defined by the user. This routine then had to be coupled with a pre-existing FEA software (e.g. Strand7) to perform the analyses and has been widely used ever since. In this manuscript, we present a new method to run muscle-driven finite element simulations on a bone by distributing muscle forces on their insertions area, all within a single environment. We apply this protocol in three different situations: two biting simulations (unilateral and bilateral) and a shoulder flexion simulation. We demonstrate how to prepare the mesh, delineate the muscle origins and insertions, define the constraints, adjust material properties, choose a loading scenario (uniform, tangential or tangential-plus-normal), and extract the results. Our automated script meshes the 3D model, defines the constraints and distributes muscle forces within a single simulation software: ‘Metafor’ (nonlinear solver, owned and distributed by Gesval S.A) or ‘Fossils’ (a new open-source linear static solver developed in the frame of this work). ‘Metafor’ and ‘Fossils’ can perform the entire protocol (from the meshing to the muscle-induced simulations) on high-resolution volumetric meshes (millions of tetrahedra) and rapidly, exceeding the processing time of other widely used software protocols by up to four times. We demonstrate that the results obtained from our protocol are highly congruent with brands such as Strand7. Thus, our protocol opens up the possibility to routinely and rapidly simulate the behaviour of high-precision muscle-driven FE models containing millions of tetrahedra.

It is well established that socioeconomic status, cognitive ability, and non-cognitive abilities such as self-efficacy are substantially associated with academic achievement. However, the specific relationships of these variables remain a relatively unexplored topic in regard to more recent and representative samples. The current study examined such relations by taking advantage of two cohorts (total N = 12,315) of Swedish students at the elementary (Grade 6) and lower-secondary school levels (Grade 8) in the compulsory school. The regression models showed that all three variables explained a substantial portion of grade variance, with cognitive ability having the strongest relationship, followed by non-cognitive abilities, and SES. Longitudinal associations, which accounted for previous academic achievement, showed that the three variables did still explain a substantial amount of grade variance.

The next generation of central receivers is expected to reach high outlet temperatures of 800 °C and above, maintain stable operation and react to a varying incoming flux magnitude caused by hourly and seasonal variations, endure high-temperature operation, and remain cost-effective. Particle-based central receivers are being considered due to their high-temperature durability and favorable thermal properties of particle materials such as bauxite particles. This paper describes a new particle-based central receiver concept and provides an initial exploration of its performance in comparison with the existing CentRec® technology. Discrete Element Method (DEM) simulations demonstrated that a stable falling and sliding particle film can be achieved inside the SlideRec. The residence time of particle flow within the SlideRec is estimated to be higher than a falling particle receiver and similar to that of an obstructed-flow receiver. The results of the thermal model (incorporating reflective, radiative, convective and conductive heat losses) indicate that the SlideRec demonstrates a higher thermal efficiency than the CentRec® under an incoming aperture flux of between 0.1 MW/m2 and 1.7 MW/m2. The concept therefore is promising and is recommended for further experimental exploration.