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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.

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.

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.

Background Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders.

The extent to which people with intellectual disabilities (ID) are voting is an under-researched area. In order to fill this gap, we conducted a systematic literature review with a focus on voter turnout and democratic inclusiveness among people with ID. In total, we found N = 9 studies that met our inclusion criteria, all of which were based on samples from Western countries such as Croatia, Ireland, Netherlands, Sweden, Switzerland, UK and US. Our findings suggest that people with ID have substantially lower voter turnout than ordinary voters. Constraining factors for voting and other instances of democratic inclusion include educational limitations and physical obstacles at ballot places, as well as being under full guardianship. On the other hand, living in a household with a voter constitutes a facilitating factor. Because of the limited number of studies, we suggest that more basic quantitative and qualitative research in many countries ought to be conducted.

While well-being does generally constitute a moderate predictor of school achievement, research on the predictive validity of cognitive ability for well-being in school contexts remains scant. The current study analyzed longitudinal relations between cognitive ability measured at age 13 (Grade 6) and well-being measured at age 18 (Grade 12, valid N = 2,705) in a Swedish sample, using several multivariate model techniques. The results indicate that cognitive ability was not a statistically significant predictor when several predictors were entered in a multiple regression model. However, gender was a significant covariate as girls and young women have a substantially lower degree of self-reported well-being. This casts light on the limitations of cognitive ability as a construct for some non-cognitive outcomes, at least in shorter and narrower spatial–temporal contexts.

ObjectivesTo determine the association of prior use of renin-angiotensin-aldosterone system inhibitors (RAASIs) with mortality and outcomes in hospitalized patients with COVID-19.DesignRetrospective observational study.SettingMulticenter, international COVID-19 registry.SubjectsAdult hospitalized COVID-19 patients on antihypertensive agents (AHAs) prior to admission, admitted from March 31, 2020, to March 10, 2021.InterventionsNone.Measurements and main resultsData were compared between three groups: patients on RAASIs only, other AHAs only, and those on both medications. Multivariable logistic and linear regressions were performed after controlling for prehospitalization characteristics to estimate the effect of RAASIs on mortality and other outcomes during hospitalization. Of 26,652 patients, 7,975 patients were on AHAs prior to hospitalization. Of these, 1,542 patients (19.3%) were on RAASIs only, 3,765 patients (47.2%) were on other AHAs only, and 2,668 (33.5%) patients were on both medications. Compared with those taking other AHAs only, patients on RAASIs only were younger (mean age 63.3 vs 66.9 yr; p < 0.0001), more often male (58.2% vs 52.4%; p = 0.0001) and more often White (55.1% vs 47.2%; p < 0.0001). After adjusting for age, gender, race, location, and comorbidities, patients on combination of RAASIs and other AHAs had higher in-hospital mortality than those on RAASIs only (odds ratio [OR] = 1.28; 95% CI [1.19-1.38]; p < 0.0001) and higher mortality than those on other AHAs only (OR = 1.09; 95% CI [1.03-1.15]; p = 0.0017). Patients on RAASIs only had lower mortality than those on other AHAs only (OR = 0.87; 95% CI [0.81-0.94]; p = 0.0003). Patients on ACEIs only had higher mortality compared with those on ARBs only (OR = 1.37; 95% CI [1.20-1.56]; p < 0.0001).ConclusionsAmong patients hospitalized for COVID-19 who were taking AHAs, prior use of a combination of RAASIs and other AHAs was associated with higher in-hospital mortality than the use of RAASIs alone. When compared with ARBs, ACEIs were associated with significantly higher mortality in hospitalized COVID-19 patients.

Mechanical load imparted to tissue, for example via handheld imaging probes, leads to tissue deformation, altering the distribution of tissue microstructure and, consequently, attenuation of light and image formation in optical imaging. In mechanically heterogeneous tissue, the load can result in spatially varying deformation and, therefore, spatially varying changes in the attenuation of light, which may provide additional image contrast. To investigate this potential, an assessment of the spatially resolved impact of mechanical deformation of the tissue on optical imaging is critical; however, it is challenging to incorporate stress mapping into optical imaging without obscuring the detection of photons. To address this, we present the novel integration of stress imaging using optical palpation with attenuation imaging based on optical coherence tomography (OCT). The method was implemented using a compliant silicone sensor incorporated into a custom handheld OCT probe, providing two-dimensional stress imaging with concurrent attenuation imaging. Attenuation imaging with varying mechanical loads was demonstrated on 19 tissue regions acquired from eight freshly excised human breast specimens. The results demonstrated distinct characteristics for different breast tissue types: benign stroma showed relatively large increases in attenuation (e.g., ∼0.3 to 0.4 mm−1/kPa) over a low stress range (∼2 to 10 kPa), while cancerous tissue showed markedly small increases in attenuation (e.g., ∼0.005 to 0.02 mm−1/kPa) mainly over a medium to high stress range (∼10 to 90 kPa). The integration of stress imaging with attenuation imaging provided a pilot assessment of the spatially resolved impact of tissue mechanical heterogeneity on optical attenuation, providing novel image contrast by encoding variations in mechanical properties on optical attenuation in tissue.

Imbalanced datasets are one of the main challenges in digital soil mapping. For these datasets, machine learning techniques commonly overestimate the majority classes and underestimate the minority ones. In general, this generates maps with poor precision and unrealistic results. Considering these maps for land use decision-making can have dire consequences. This is the case of acid sulfate (AS) soils, a type of harmful soil that can generate serious environmental damage when drained in agricultural or forestry activities. Therefore, it is necessary to create high-precision maps to avoid environmental damage. Although most soil class datasets in nature are imbalanced, this problem has hardly been studied. One of the main objectives of this work is the evaluation of different techniques to address the problem of imbalanced datasets. The methods considered to balance the dataset are an undersampling technique, the addition of more samples, and the combination of both. For increasing the number of samples from the minority class, we develop a new technique by creating artificial samples from the quaternary geological map. The method used for the modeling is Random Forest, one of the best methods for the classification of AS soils. Balancing the dataset improves the performance of the model in all the studied cases, where the values of the metrics for both classes are above 80%. The consideration of artificial non-AS soil samples improves the prediction of the model for the AS soils. Furthermore, we create AS soil probability maps for the four balanced datasets and the imbalanced dataset. The modeled AS soil probability maps created from the balanced datasets have high precision. A detailed comparison between the maps is made. The predictions of some of these maps match between 75%–80% of the study area. In addition, the extent of the AS soils obtained in all the cases is compared with the extent of the AS soils in the conventionally produced occurrence map. The good results of this study confirm the importance of balancing the dataset to improve the prediction and classification of AS soils.

Background: Rheumatoid arthritis (RA) is an autoimmune connective tissue disease which affects joint function and multiple body systems. Poor foot health for people with RA has a negative impact on mobility, functional capacity, and quality of life. Additional to joint pain, people with RA may develop vascular disease e.g., vasculitis, which can have increasingly detrimental effects on foot health, quality of life and longevity. If identified early, adverse effects may be prevented. However, the link between foot health symptoms and ultrasound identified precursory changes for vasculitis is yet to be established. Objectives: This study aimed to examine the association between foot health symptoms and the integrity of the dorsalis pedis artery (DPA). Methods: We recruited 47 participants with RA and 47 characteristic-matched controls. All participants completed an online survey which included demographic questions and Foot Health Status Questionnaire (FHSQ). Ultrasound imaging measured participants’ proximal DPA in terms of degree of wall inflammation, lumen diameter, artery diameter, lumen-to-artery diameter ratio and peak systolic velocity. Results: Significant differences (p

Relating to world culture theory, this article aimed to discern similarities and differences between the South Korean national curriculum (1981, 2015) and the Swedish national curriculum (1980, 2018) and a set of complementary documents, both more broadly and with special emphasis on middle school level mathematics education. The findings suggest that the latest curricula accentuate knowledge and skills. However, Sweden's national curriculum underlines knowledge and values, while the South Korean counterpart stresses character education and a 'well-rounded personality'. Mathematics education has not changed much since 1980 in both countries. However, cosmopolitan tendencies are more present in the latest Korean curriculum. Overall, an unequivocal converging effect has not been discerned.

Acid sulfate soils (ASS) cause big problems worldwide due to their potential to form sulfuric acid during oxidation of sulfidic materials, resulting in very acid soil (pH

Established international soil classification systems have not properly accommodated acid sulfate soils (ASS) and soil materials in Finland and Sweden because: (1) in these soils some diagnostic ASS properties are too deep to meet the depth requirements, and (2) there is a lack of defined diagnostic soil classification criteria for acidic and potentially acidic soil materials that do not completely fulfill the diagnostic pH criterion of pH < 4.0. In this paper, two new ASS materials are introduced with the prefix “para” for parasulfuric material (oxidized material) and parahypersulfidic material (reduced material). These materials have diagnostic pH-criteria of pH 4.0–4.5 and 3.0–3.5 (field-pH for parasulfuric material and incubation-pH for parahypersulfidic material) for mineral and organic soil materials (here defined as > 20% organic matter; peat and gyttja), respectively. The term “para-acid sulfate soil (para-ASS) material” is introduced for soil materials which may have a considerable environmental impact due to mobilization of acidity and dissolved metals. Because organic acids may lower pH to values below the established pH-value of < 4.0 for ASS materials, a pH of < 3.0 is used in the Finnish-Swedish ASS classification for organic soil materials. These changes and new additions to existing diagnostic ASS materials have consequently also led to a slight modification of the required field-pH values of the existing terms “hypersulfidic material” and “sulfuric material”. The Finnish-Swedish ASS classification further includes a systematic way for classification of the entire soil profile and no depth requirements for diagnostic ASS materials are present; what matters is the current or potential environmental impact that the soil has or may have. It is proposed that the Finnish-Swedish ASS classification may serve as a framework for establishing a unified ASS classification globally and that the new diagnostic ASS materials are included in relevant international soil classification systems.

Abstract The chirality-flow formalism, combined with good choices of gauge reference vectors, simplifies tree-level calculations to the extent that it is often possible to write down amplitudes corresponding to Feynman diagrams immediately. It has also proven to give a very sizable speedup in a proof of concept implementation of massless tree-level QED in MadGraph5_aMC@NLO. In the present paper we extend this analysis to QCD, including massive quarks. We define helicity-dependent versions of the gluon vertices, derive constraints on the spinor structure of propagating gluons, and explore the Schouten identity to simplify the four-gluon vertex further. For massive quarks, the chirality-flow formalism sheds light on how to exploit the freedom to measure spin along any direction to shorten the calculations. Overall, this results in a clear speedup for treating the Lorentz structure at high multiplicities.

Health Equity Action Plans (HEAPs) are a recent strategy employed across health and human services to promote health equity. To inform the development of future HEAPs, as well as to build upon previous initiatives, we evaluated 52 health equity plans and resources from Oregon counties using five criteria: creation date, process orientation, racial equity lens, metrics, and community engagement. When developing future HEAPs, we recommend explicit commitments to collaborate with marginalized communities, to establish measurable goals and defined metrics for assessing progress, to include voices and perspectives of those affected by health inequities, and to detail community strengths, assets, and resources.