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Vem är den goda läraren? : Föränderliga lärarideal i undervisning om sexualitet och relationer
No full textThis paper explores the construction of teacher ideals in the context ofsexuality education – a curricular area marked by numerous, and oftenconflicting, expectations on the teachers responsible for teaching it. Using a curriculum theory approach, a thematic analysis of Swedish teacher guides on sexuality education from 1956 to 2014 was conducted. The study examines how these manuals shape conceptions of both the knowledge domain and teacher ideals. The findings identify four main teacher ideals across the guides, revealing a mix of divergent and recurring ideas about teachers’ roles, encompassing both personal and professional qualities. Notably, the teacher ideals in the earlier guides (from 1956 and 1977) arepresented implicitly, while the 1995 guide explicitly outlines the attributes of a good teacher. The study suggests that shifts in teacher ideals may belinked to broader changes in the content focus of the guides, though this relationship must be understood within the often-fragile amalgamation of competing ideals
PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography
No full textObjectivesTo develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA).Materials and methodsCCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Link & ouml;ping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance.ResultsPlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684).ConclusionWe developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Key PointsQuestionA tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination.FindingsSegmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance.Clinical relevanceThis novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.Funding Agencies|VINNOVA; Medis Medical Imaging for software development</p
A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR
No full textBackgroundThere is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors.MethodsWe collected demographic data, medication use, serological indicators, and baseline cardiac ultrasound data of all included subjects, totaling 50 variables. The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. We built a prediction model for AF using three machine learning methods. Finally, the significance of each variable was analyzed by Shapley additive explanations method.ResultsAfter screening, we found the optimal variable set consisting of 10 variables. The model we built achieved good predictive performance (AUC = 0.870, 95%CI 0.858 to 0.882), and had specificity of 0.851 (95%CI 0.844 to 0.858) and sensitivity of 0.716 (95%CI 0.676 to 0.755). Good predictive performance was stably achieved in different age subgroups and different gender subgroups. LA and NT-proBNP were the two most important variables for predicting paroxysmal and persistent AF in all models, except for the female subgroup aged less than 60 years.ConclusionsOur model makes it possible to predict paroxysmal and persistent AF based on baseline data at admission. Early and individualized intervention strategies based on our model may help to improve clinical outcomes in AF patients.Funding Agencies|National Natural Science Foundation of China</p
Ecology and Spatial Distribution of Magnetotactic Bacteria in Araguaia River Floodplain
No full textMagnetotactic bacteria (MTB) are Gram-negative, ubiquitous, aquatic, flagellated, microaerophilic, or anaerobic microorganisms exhibiting magnetotactic behaviour based on magnetosomes, which are the structural signature of the group. Magnetosomes are ferrimagnetic nanocrystals surrounded by a lipid bilayer, usually aligned in chain(s) within the cell. Environmental abiotic conditions such as salinity, dissolved oxygen, pH, and oxidation-reduction potential may drive the diversity of MTB populations in environments. Our results reported the first evidence of MTB in sediments sampled from the Araguaia River floodplain in the Amazon-Cerrado biome. Light microscopy showed at least six morphotypes of South-seeking MTB. Transmission electron microscopy and energy-dispersive X-ray spectroscopy observations demonstrated magnetite cuboctahedral, prismatic, and anisotropic magnetosomes. PCA ordination demonstrated a more significant influence of depth, ORP (oxidation-reduction potential), and transparency in sampled data from the river main channel (MC). Non-metric multidimensional scaling (NMDS) ordination and correlation analysis demonstrated a difference between MTB populations inhabiting MC and lakes and affluent (LA). NGS and bioinformatic analysis revealed higher richness and diversity among magnetotactic cocci and the majority phylogenetic assignment of MTB affiliated to Pseudomonadota phylum. Hence, the complete acquisition of these results will provide further insight into magnetotaxis characterisation and the abiotic factors that impact MTB spatial distribution.Funding Agencies|Fundacao de Apoio a Pesquisa do Distrito Federal; Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro [0193-001.627/2017]; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior</p
Maternal uniparental disomy of chromosome 7 : how chromosome 7-encoded imprinted genes contribute to the Silver-Russell phenotype
No full textBackground Silver–Russell syndrome (SRS) is a rare congenital growth disorder which is associated with molecular alterations affecting imprinted regions on chromosome 11p15 and maternal uniparental disomy of chromosome 7 (upd(7)mat). In 11p15, imprinted regions contributing to the SRS phenotype could be identified, whereas on chromosome 7 at least two regions in 7q32 and 7p13 are in discussion as SRS candidate regions. We report on DNA and RNA data from upd(7)mat patients and a monozygotic twin pair with a postnatal SRS phenotype carrying a small intragenic deletion within GRB10 to delineate the contribution of upd(7)mat and imprinted genes on this chromosome to the SRS phenotype. Results Genome sequencing in the monozygotic twins revealed a 18 kb deletion within the paternal allele of the GRB10 gene. Expression of GRB10 in blood of the twins as well as in cells from upd(7)mat and upd(7q)mat patients was not altered, whereas RNAseq indicates noticeable changes of the expression of other genes encoded by chromosomes 7 and other genomic regions. Conclusions Our data indicate that intrauterine growth restriction as the prenatal phenotype of upd(7)mat is caused by defective paternal alleles of the 7q32 region, as well as by overexpression of the maternal GRB10 allele whereas a defective GRB10 paternal allele does not cause this feature. The altered expression of MEST in 7q32 by upd(7)mat is associated with the complete SRS phenotype, whereas maternalization or deletion of the paternal GRB10 copy and duplication of the chromosomal region 7p12 are associated with a postnatal SRS-like phenotype
The anti-diabetic PPARγ agonist Pioglitazone inhibits cell proliferation and induces metabolic reprogramming in prostate cancer
No full textProstate cancer (PCa) and Type 2 diabetes (T2D) often co-occur, yet their relationship remains elusive. While some studies suggest that T2D lowers PCa risk, others report conflicting data. This study investigates the effects of peroxisome proliferator-activated receptor (PPAR) agonists Bezafibrate, Tesaglitazar, and Pioglitazone on PCa tumorigenesis. Analysis of patient datasets revealed that high PPARG expression correlates with advanced PCa and poor survival. The PPARγ agonists Pioglitazone and Tesaglitazar notably reduced cell proliferation and PPARγ protein levels in primary and metastatic PCa-derived cells. Proteomic analysis identified intrinsic differences in mTORC1 and mitochondrial fatty acid oxidation (FAO) pathways between primary and metastatic PCa cells, which were further disrupted by Tesaglitazar and Pioglitazone. Moreover, metabolomics, Seahorse Assay-based metabolic profiling, and radiotracer uptake assays revealed that Pioglitazone shifted primary PCa cells' metabolism towards glycolysis and increased FAO in metastatic cells, reducing mitochondrial ATP production. Furthermore, Pioglitazone suppressed cell migration in primary and metastatic PCa cells and induced the epithelial marker E-Cadherin in primary PCa cells. In vivo, Pioglitazone reduced tumor growth in a metastatic PC3 xenograft model, increased phosho AMPKα and decreased phospho mTOR levels. In addition, diabetic PCa patients treated with PPAR agonists post-radical prostatectomy implied no biochemical recurrence over five to ten years compared to non-diabetic PCa patients. Our findings suggest that Pioglitazone reduces PCa cell proliferation and induces metabolic and epithelial changes, highlighting the potential of repurposing metabolic drugs for PCa therapy
Polymeric Ionic Liquid-Enabled In Situ Protection of Li Anodes for High-Performance Li-O2 Batteries
No full textRedox mediators (RMs) have shown promise in enhancing Li-O2 battery cycling stability by reducing overpotential. However, their application is hindered by the shuttle effect, leading to RM loss and Li anode corrosion. Here, we introduce a polyionic liquid, poly (1-Butyl-3-vinylimidazolium bis(trifluoromethanesulfonylimine)) ([PBVIm]-TFSI) as an additive, showcasing a novel Li anode protection strategy for LiI-mediated Li-O2 batteries. [PBVIm]+ cations migrate to the Li anode, forming a protective cationic shield that promotes uniform Li+ deposition. The addition of [PBVIm]-TFSI enhances the cycling stability, achieving 105 cycles at 200 mA⋅g−1, compared to the cell with LiI which exhibited 38 cycles under the same conditions. Synchrotron X-ray tomography reveals the evolution of this protective layer, providing insights into its formation mechanism, in conjunction with XPS analysis. Our findings offer a new approach to Li anode protection in Li-O2 batteries, emphasizing the critical role of interfacial engineering for battery performance
Evaluation of a home-based parenting support programme-Parenting Young Children-For parents with intellectual and developmental disabilities when there is a risk for neglect: Study protocol for a multi-centre study
No full textIntroduction Parents with intellectual and developmental disabilities (IDDs) often need parenting support, but there are few evidence-based programmes adapted to their cognitive needs. Parenting Young Children (PYC), a home-based programme for parents with IDDs, is perceived as beneficial by parents and practitioners, but it is unclear if PYC improves parenting. The purpose of the proposed mixed-methods study is therefore to evaluate the PYC programme for improved parenting in parents with IDDs.Methods and analysis The quantitative evaluation will have a multi-centre, pretest-posttest study design and include parents with IDDs (children aged 0-9) in need of adapted parenting support. Goal-attainment in parenting skills, parental self-efficacy and child mental health will be measured outcomes. Interviews will be used to explore the perspectives of parents and children on PYC.Ethics and dissemination Particpation is based on informed consent from parents and guardians of the participating children. Ethical approval was granted by the Swedish Ethical Review Authority.Funding Agencies|Swedish Research Council for Health, Working Life and Welfare (FORTE) [2020-01333]</p
The role of agriculture in a sustainable energy system : The farmers’ perspective
No full textAgriculture plays a pivotal role in the sustainable transition. The current trend within the agricultural sector is that actors are often suppliers of energy. This places the farmer in the intersection between the agricultural and energy systems. The present study assesses the farmers’ role to be part of the transition toward a sustainable agricultural and energy system. Departing from socio-technical theory, it puts the emphasis on the farmer as an actor within the systems. Designed as a case study in Östergötland, Sweden, interviews were conducted with eleven farmers for their perspectives on the sustainable energy system transition and analyzed through Reflexive Thematic Analysis.Funding and the financial aspects of investing in renewable energy sources was seen as crucial. Simultaneously, institutional support was experienced as cumbersome and complicated. Long-term, reliable institutional support for farmers was emphasized and shows the need for policymakers and researchers to further investigate the agricultural energy system and the intersection of the systems. Further analysis showed that the motivation for institutional support is to foster resilience, self-sufficiency and safeguard against uncertainties. Glimpses of tensions within the current agriculture and energy system regimes could be seen and the study suggests that landscape changes related to the energy system can drive niche development and implementation of renewable energy sources.Funding Agencies|Swedish Energy Agency through the Biogas Solutions Research Center [52669-1, P2021-90266]</p
Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning
No full textDiffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods. Furthermore, we employ a measure based on the earth mover’s distance to assess the accuracy of the upsampled fODFs. At low signal-to-noise ratios, our super-resolution method provides more accurate estimates of the fODF compared to data collected with 8 times smaller voxel volume.Funding Agencies|Linkping University [2021-01954]; ITEA/VINNOVA project ASSIST (Automation)</p