Many children are insufficiently active, and children with a migration background appear to be even less active and at a higher risk of developing obesity. This study evaluated the weight status, and ...the frequencies and intensities of objectively assessed physical activity (PA) of children with and without a migration background.
Cross-sectional study.
PA was assessed objectively for 6 days in 273 children (aged 7.1 ± 0.6 years). In total, 74 children (27%) were classified as having a migration background. PA was grouped in light and moderate-to-vigorous (MVPA) intensities. Body mass index (BMI) percentiles (BMIPCT) were determined.
Children without a migration background spent more time in MVPA compared with children with a migration background (138.2 ± 62.6 vs 121.7 ± 54.9 min, respectively; P < 0.01). On weekends, time in MVPA decreased significantly for all children (112.3 ± 66.0 min, P < 0.01), especially for children with a migration background (97.7 ± 56.7 min, P < 0.01). Children with a migration background displayed significantly higher BMIPCT than children without a migration background (55.7 ± 29.6 vs 44.3 ± 26.8, respectively; P < 0.01) and were significantly more often overweight and/or obese (13.5% vs 8.5%, respectively; P < 0.02).
Children with a migration background are less physically active and more often overweight, resulting in higher risks of developing secondary diseases. The results of this study should be considered when designing interventions to increase PA in children with a migration background.
Trial registration: German Clinical Trials Register (DRKS), DRKS-ID: DRKS00000494;
•Children with a migration background spent significantly less time in moderate-to-vigorous physical activity (MVPA).•On weekends, children spent significantly more time being sedentary.•Children with a migration background reached the MVPA guidelines more often.•Children with a migration background displayed significantly higher body mass index percentiles.
Recent investigations of the magnetic field vector properties in the solar internetwork have provided diverging results. While some works found that the internetwork is mostly pervaded by horizontal ...magnetic fields, other works argued in favor of an isotropic distribution of the magnetic field vector. Motivated by these seemingly contradictory results and by the fact that most of these works have employed spectropolarimetric data at disk center only, we have revisited this problem employing high-quality data (noise level σ ≈ 3 × 10-4 in units of the quiet-Sun intensity) at different latitudes recorded with the Hinode/SP instrument. Instead of applying traditional inversion codes of the radiative transfer equation to retrieve the magnetic field vector at each spatial point on the solar surface and studying the resulting distribution of the magnetic field vector, we surmised a theoretical distribution function of the magnetic field vector and used it to obtain the theoretical histograms of the Stokes profiles. These histograms were then compared to the observed ones. Any mismatch between them was ascribed to the theoretical distribution of the magnetic field vector, which was subsequently modified to produce a better fit to the observed histograms. With this method we find that Stokes profiles with signals above 2 × 10-3 (in units of the continuum intensity) cannot be explained by an isotropic distribution of the magnetic field vector. We also find that the differences between the histograms of the Stokes profiles observed at different latitudes cannot be explained in terms of line-of-sight effects. However, they can be explained by a distribution of the magnetic field vector that inherently varies with latitude. We note that these results are based on a series of assumptions that, although briefly discussed in this paper, need to be considered in more detail in the future.
Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic ...examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54–0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.
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