Notes

Application of the actual Multi-Facet Rasch Model to judge the main Resident Variety Questionnaire: The Two-Year Research.
Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew's correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.Third-generation sequencing technologies allow to sequence long reads of tens of kbp, that are expected to solve various problems. However, they display high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads, and allows to process a full human dataset, containing reads reaching up to 1.5 Mbp, in 10 days. Moreover, our experiments show that error correction with CONSENT improves the quality of Flye assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools, while providing comparable results. Furthermore, we show that, on a human dataset, assembling the raw data and polishing the assembly is less resource consuming than correcting and then assembling the reads, while providing better results. CONSENT is available at https//github.com/morispi/CONSENT .In this case control study, long-term gynecological, reproductive and sexual outcomes after uterine artery embolization (UAE) for postpartum hemorrhage (PPH) were evaluated. The study was performed in a single referral hospital for PPH in Lausanne from 2003 to 2013. Each woman whose delivery was complicated by PPH and treated by UAE was included, and compared to a control group of women whose delivery was uncomplicated. Cases were matched by maternal age, parity, ethnicity, year and mode of delivery, birth weight and gestational age in a 1-3 ratio. A total of 77 patients treated by UAE for PPH were identified in our obstetrical database. Among them, 63 were included and compared to 189 matched patients (no PPH). The mean interval time between UAE and this study was 8.1 years. Time to menstrual cycle recovery after delivery (3.9 vs 5.6 months, p = 0.66), spotting (7.9% vs 7.2%, p = 0.49), dysmenorrhea (25.4% vs 22.2%, p = 0.60) and amenorrhea (14.3% vs 12.2%, p = 0.66) were similar between the two groups. There was no difference in the FSFI score between the groups (23.2 ± 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. 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. Selleck Z-DEVD-FMK 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 0.8 (± 0.01). 3D US based ventricular volume estimation through an automatic segmentation software developed through deep learning improves the accuracy and reduces the processing time needed for manual segmentation using VOCAL. 3D US should be considered a promising tool to help deepen our current understanding of the complex evolution of PHVD.Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation and osteoporosis is the major comorbidity associated with poor prognosis in COPD. However, the effect of inhaled corticosteroids (ICS) on bone mineral density among COPD remains uncertain. There is the urgent need to examine whether the long-term ICS use may increase the risk of osteoporosis. In this nested case-control study retrieved from the Taiwan National Health Insurance Research Database from 2002 to 2017, the study aimed to investigate risk of osteoporosis associated with ICS, focusing on the dosage and duration of ICS therapy. Cases with osteoporosis or osteoporotic fractures claims were defined and matched to 3 randomly selected controls. Conditional logistic regressions were used to estimate odds ratios of osteoporosis from ICS treatment measured in 3 years before the index date. This population-based study included 891,395 patients with COPD, where after matching had 58,048 case groups and 174,144 matched control groups.
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