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Purpose: Education for Sustainable Development (EDS) has highlighted the need to generate instruments to assess the sustainability competencies of different populations. In this regard, Physical Education (PE) has been recognized as a subject with unique characteristics that allow students to develop competencies that favor sustainability. However, in previous literature, there are no specific instruments to assess the competencies of future Physical Education teachers (PETs). Therefore, this research aims to design and validate an instrument to assess sustainable competencies in future PETs. Design/methodology/approach: This paper shows the validation process of a questionnaire designed ad hoc to measure the future teacher's sustainable competencies. In total, 341 students completed the Physical Education Scale for Sustainable Development in Future Teachers (PESD-FT) questionnaire consisting of 20 items and an eight-point Likert scale. To ensure the relationship of the instrument's items with the sustainable development, the specific targets that compose the 17 Sustainable Development Goals were considered. Findings: The results showed that the instrument had very high reliability (0.949), excellently fulfilling the validity criteria (0.929). Furthermore, the principal component factor analysis results showed that the PESD-FT comprises three factors, which coincide with the three dimensions of sustainability. Originality/value: It is concluded that PESD-FT is an instrument that reliably assesses the sustainable competencies of prospective PET and expands the possibilities of PE as a valuable instrument to promote sustainable competencies in an integrated way with PE learning objectives. As such, it can be a robust and valuable measurement tool for proposing effective education for sustainable development policies and programs in initial teacher education for PET.

The objective of this study is to provide a positron emission tomography (PET) imaging modality targeting vascular endothelial growth factor receptors (VEGFR) for the early noninvasive detection and assessment of pulmonary arterial hypertension (PAH) severity. To validate the effectiveness of the [18F]VEGFR PET tracer, we utilized a monocrotaline (MCT)‐induced PAH rat model. Molecular optical imaging, using a Cy5.5‐conjugated VEGFR targeting agent, was employed to demonstrate the uptake of the agent at pulmonary arterioles, correlating with the onset and progression of PAH. Histological examinations of the MCT‐PAH rat lung revealed a significant correlation between VEGFR2 expression and the pathogenesis of PAH. Molecular optical imaging demonstrated heightened uptake of the Cy5.5‐conjugated VEGFR targeting agent at pulmonary arterioles, corresponding with the onset and progression of PAH. [18F]VEGFR PET showed increased lung uptake detectable in early‐stage PAH before increase in pulmonary artery pressures, and this uptake correlated with increased PAH severity. Moreover, when compared to [18F]FDG PET, [18F]VEGFR PET exhibited markedly lower background cardiac signal, enhancing imaging sensitivity for lung abnormalities. Our study provides a compelling evidence for the potential utility of the innovative [18F]VEGFR PET tracer, in non‐invasively detecting early signs of PAH, and monitoring its progression. The observed correlations between VEGFR2 expression, molecular optical imaging results, and [18F]VEGFR PET findings support the use of this tracer for early detection, and assessment of PAH severity. The lower background cardiac signal observed with [18F]VEGFR PET further enhances its imaging sensitivity, emphasizing its potential clinical significance. [ABSTRACT FROM AUTHOR]

Background: Contrastive learning, a successful form of representational learning, has shown promising results in pretraining deep learning (DL) models for downstream tasks. When working with limited annotation data, as in medical image segmentation tasks, learning domain‐specific local representations can further improve the performance of DL models. Purpose: In this work, we extend the contrastive learning framework to utilize domain‐specific contrast information from unlabeled Magnetic Resonance (MR) images to improve the performance of downstream MR image segmentation tasks in the presence of limited labeled data. Methods: The contrast in MR images is controlled by underlying tissue properties (e.g., T1 or T2) and image acquisition parameters. We hypothesize that learning to discriminate local representations based on underlying tissue properties should improve subsequent segmentation tasks on MR images. We propose a novel constrained contrastive learning (CCL) strategy that uses tissue‐specific information via a constraint map to define positive and negative local neighborhoods for contrastive learning, embedding this information in the representational space during pretraining. For a given MR contrast image, the proposed strategy uses local signal characteristics (constraint map) across a set of related multi‐contrast MR images as a surrogate for underlying tissue information. We demonstrate the utility of the approach for downstream: (1) multi‐organ segmentation tasks in T2‐weighted images where a DL model learns T2 information with constraint maps from a set of 2D multi‐echo T2‐weighted images (n = 101) and (2) tumor segmentation tasks in multi‐parametric images from the public brain tumor segmentation (BraTS) (n = 80) dataset where DL models learn T1 and T2 information from multi‐parametric BraTS images. Performance is evaluated on downstream multi‐label segmentation tasks with limited data in (1) T2‐weighted images of the abdomen from an in‐house Radial‐T2 (Train/Test = 30/20), (2) public Cartesian‐T2 (Train/Test = 6/12) dataset, and (3) multi‐parametric MR images from the public brain tumor segmentation dataset (BraTS) (Train/Test = 40/50). The performance of the proposed CCL strategy is compared to state‐of‐the‐art self‐supervised contrastive learning techniques. In each task, a model is also trained using all available labeled data for supervised baseline performance. Results: The proposed CCL strategy consistently yielded improved Dice scores, Precision, and Recall metrics, and reduced HD95 values across all segmentation tasks. We also observed performance comparable to the baseline with reduced annotation effort. The t‐SNE visualization of features for T2‐weighted images demonstrates its ability to embed T2 information in the representational space. On the BraTS dataset, we also observed that using an appropriate multi‐contrast space to learn T1+T2, T1, or T2 information during pretraining further improved the performance of tumor segmentation tasks. Conclusions: Learning to embed tissue‐specific information that controls MR image contrast with the proposed constrained contrastive learning improved the performance of DL models on subsequent segmentation tasks compared to conventional self‐supervised contrastive learning techniques. The use of such domain‐specific local representations could help understand, improve performance, and mitigate the scarcity of labeled data in MR image segmentation tasks. [ABSTRACT FROM AUTHOR]

It has been reported that hyaluronic acid (HA) with a 35 kDa molecular weight (HA35) acts biologically to protect tissue from injury, but its biological properties are not yet fully characterized. This study aimed to evaluate the cellular effects and biodistribution of HA35 compared to HA with a 1600 kDa molecular weight (HA1600). We assessed the effects of HA35 and HA1600 on cell migration, NO and ROS generation, and gene expression in cultured macrophages, microglia, and lymphocytes. HA35 was separately radiolabeled with 99mTc and 125I and administered to C57BL/6J mice for in vivo biodistribution imaging. In vitro studies indicated that HA35 and HA1600 similarly enhanced cell migration through HA receptor binding mechanisms, reduced the generation of NO and ROS, and upregulated gene expression profiles related to cell signaling pathways in immune cells. HA35 showed a more pronounced effect in regulating a broader range of genes in macrophages and microglia than HA1600. Upon intradermal or intravenous administration, radiolabeled HA35 rapidly accumulated in the liver, spleen, and lymph nodes. In conclusion, HA35 not only exhibits effects on cellular bioactivity comparable to those of HA1600 but also exerts biological effects on a broader range of immune cell gene expression. The findings herein offer valuable insights for further research into the therapeutic potential of HA35 in inflammation-mediated tissue injury. [ABSTRACT FROM AUTHOR]

This article introduces a dataset on the covert use of social media to influence politics by promoting propaganda, advocating controversial viewpoints, and spreading disinformation. Influence efforts (IEs) are defined as: (i) coordinated campaigns by a state, or the ruling party in an autocracy, to impact one or more specific aspects of politics at home or in another state, (ii) through media channels, including social media, by (iii) producing content designed to appear indigenous to the target state. Our data draw on more than 1,000 media reports and 500 research articles/reports to identify IEs, track their progress, and classify their features. The data cover 78 foreign influence efforts (FIEs) and 25 domestic influence efforts (DIEs) – in which governments targeted their own citizens – against 51 different countries from 2011 through early 2021. The Influence Effort dataset measures covert information campaigns by state actors, facilitating research on contemporary statecraft. [ABSTRACT FROM AUTHOR]

In this work, 1 eV Ga0.7In0.3As inverted metamorphic (IMM) solar cells were analyzed to achieve a deeper understanding of the mechanism limiting their improvement. For this purpose, high-resolution X-ray diffraction (HRXRD), transmission electron microscopy (TEM), high-resolution cross-sectional cathodoluminescence (CL), and transient in situ surface reflectance were carried out. Additionally, the photovoltaic responses of the complete devices were measured using the external quantum efficiency (EQE) and numerically simulated through Silvaco TCAD ATLAS. The combination of structural characterization of the semiconductor layers and measurements of the solar cell photovoltaic behavior, together with device modeling, allows us to conclude that the lifetime of the bulk minority carriers is the limiting factor influencing the PV response since the recombination at the interfaces (GaInP window–GaInAs emitter and GaInAs base–GaInP back surface field (BSF)) does not impact the carrier recombination due to the favorable alignment between the conduction and the valance bands. The advanced characterization using cross-sectional cathodoluminescence, together with transient in situ surface reflectance, allowed the rejection of the formation of traps related to the GaInAs growth conditions as being responsible for the decrement in the minority-carrier lifetime. Conversely, the TEM and HRXRD revealed that the presence of misfit dislocations in the GaInAs layer linked to strain relaxation, which were probably formed due to an excessive tensile strain in the virtual substrate or an incorrect combination of alloy compositions in the topmost layers, was the dominant factor influencing the GaInAs layer's quality. These results allow an understanding of the contributions of each characterization technique in the analysis of multi-junction solar cells. [ABSTRACT FROM AUTHOR]

One of the most promising emerging photovoltaic technologies is represented by perovskite materials essentially due to their outstanding performance. However, the complex fundamental understanding of relevant device physics is challenging, making it harder to obtain correlations with efficiency and long-term stability, and thus definitely transforming the landscape of solar energy. In electrical terms, perovskite solar cells often show different types of experimental behaviors at long timescales (light-enhanced capacitance and chemical inductor) in separate voltage domains, but with permanent deviations from the ideal pattern (Cole–Cole relaxation processes, fractional dynamics, and beyond). Here, we reevaluate the dynamical behavior of a photovoltaic perovskite model that leads to the two versions of constant-phase element behavior in the impedance response. Our general theory is, therefore, able to explain naturally the vast majority of results concerning the nonlinear polarization mechanisms of perovskite solar cells, extending the mathematical framework from the perspective of fractional-order electrical circuits. In this context, we discover a novel property that reveals the anomalous electrical coupling of memory effects in photovoltaic perovskites. We hope that this work can provide a useful tool for modeling experts and device physicists belonging to the photovoltaic community, moving forward toward addressing the outstanding challenges in this fast-developing field. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to describe the activities designed and conducted with primary school, secondary school and university students to study the integration of alternative assessment and self-assessment online tools in mathematics courses using iTest. iTest is an educational software tool born to support the continuous evaluation system promoted by the European Space for Higher Education (ESHE). Design/methodology/approach: To cyclically design, implement and analyze the results of the activities a design-based research methodology was followed which also facilitates the collaboration between researchers and teachers. Findings: Students and Professors involved in the activities had positive attitudes towards the practical use of the tool. It was detected that iTest offers a good testing tool for units that focus on skills and procedural development as well as for units that are problem-based and require visualization and manipulation of the geometric representation. Originality/value: iTest is found to be an appropriate complementary software for evaluation in the ESHE. After all the experiences described in this paper, it is now possible to begin the design of a virtual learning environment for mathematics implementing iTest as the assessment and self-assessment tool. (Contains 7 figures.)