Notes

Statistical custom modeling rendering from the SARS-CoV-2 outbreak in Qatar as well as impact on the country's a reaction to COVID-19.
2 ± 0.6 vs 23.8 ± 0.4; p = 0.41). However, the interval time to subsequent pregnancy was longer for patients after UAE than the control group (35 vs 18 months, p = 0.002). In case of pregnancy desire, the success rate was lower after UAE compared to controls (55% vs 93.5%, p  less then  0.001). The rate of PPH was higher in those with previous PPH (6.6% vs 36.4%, p = 0.010). Patients treated by UAE for PPH did not report higher rates of gynecological symptoms or sexual dysfunction compared to patients with uneventful deliveries. The inter-pregnancy interval was increased and the success rate was reduced. In subsequent pregnancies, a higher rate of PPH was observed in those that underwent UAE.Based on questionnaires from 197,825 non-diabetic participants in a large Japanese cohort, we determined impact of (1) habit of exercise, (2) habit of active physical activity (PA) and (3) walking pace on new-onset of type 2 diabetes mellitus. Unadjusted and multivariable-adjusted logistic regression models were used to determine the odds ratio of new-onset diabetes mellitus incidence in a 3-year follow-up. There were two major findings. First, habits of exercise and active PA were positively associated with incidence of diabetes mellitus. Second, fast walking, even after adjusting for multiple covariates, was associated with low incidence of diabetes mellitus. In the subgroup analysis, the association was also observed in participants aged ≥ 65 years, in men, and in those with a body mass index ≥ 25. Proteasome inhibitor review Results suggest that fast walking is a simple and independent preventive factor for new-onset of diabetes mellitus in the health check-up and guidance system in Japan. Future studies may be warranted to verify whether interventions involving walking pace can reduce the onset of diabetes in a nation-wide scale.We investigated a multicenter registry to identify estimated event rates according to CHA2DS2-VASc scores in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). The additional effectiveness of antiplatelets (APs) plus oral anticoagulants (OACs) compared with OACs alone considering the CHA2DS2-VASc scores was also explored. This study retrospectively analyzed a multicenter stroke registry between Jan 2011 and Nov 2017, identifying patients with acute ischemic stroke with AF. The primary outcome event was a composite of recurrent stroke, myocardial infarction, and all-cause mortality within 1 year. A total of 7395 patients (age, 73 ± 10 years; men, 54.2%) were analyzed. The primary outcome events at one year ranged from 5.99% (95% CI 3.21-8.77) for a CHA2DS2-VASc score of 0 points to 30.45% (95% CI 24.93-35.97) for 7 or more points. After adjustments for covariates, 1-point increases in the CHA2DS2-VASc score consistently increased the risk of primary outcome events (aHR 1.10 [1.06-1.15]) at 1-year. Among OAC-treated patients at discharge (n = 5500), those treated with OAC + AP (vs. OAC alone) were more likely to experience vascular events, though among patients with a CHA2DS2-VASc score of 5 or higher, the risk of primary outcome in the OAC + AP group was comparable to that in the OAC alone group (Pint = 0.01). Our study found that there were significant associations of increasing CHA2DS2-VASc scores with the increasing risk of vascular events at 1-year in AIS with AF. Further study would be warranted.Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.Rapid thermal annealing is an effective way to improve the optical properties of semiconductor materials and devices. In this paper, the emission characteristics of GaAs0.92Sb0.08/Al0.3Ga0.7As multiple quantum wells, which investigated by temperature-dependent photoluminescence, are adjusted through strain and interfacial diffusion via rapid thermal annealing. The light-hole (LH) exciton emission and the heavy-hole (HH) exciton emission are observed at room temperature. After annealing, the LH and HH emission peaks have blue shift. It can be ascribed to the variation of interfacial strain at low annealing temperature and the interfacial diffusion between barrier layer and well layer at high annealing temperature. This work is of great significance for emission adjustment of strained multiple quantum wells.To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We trained a 2D convolutional neural network (CNN) for automatic segmentation ventricular volume from 3D US of preterm infants with PHVD. The method was validated with the Dice similarity coefficient (DSC) and the intra-class coefficient (ICC) compared to manual segmentation. The mean birth weight of the included patients was 1233.1 g (SD 309.4) and mean gestational age was 28.1 weeks (SD 1.6). A total of 152 serial 3D US from 10 preterm infants with PHVD were analysed. 230 ventricles were manually segmented. Of these, 108 were used for training a 2D CNN and 122 for validating the methodology for automatic segmentation. The global agreement for manual versus automated measures in the validation data (n = 122) was excellent with an ICC of 0.944 (0.874-0.971). The Dice similarity coefficient was 0.
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