Andersenpetersen9516

The results showed that the increased specific IgG titers from vaccinated mice supported the vaccine immunogenicity. The increased cytokines (IFN-γ, IL-12 and TNF-α), splenic CD3+, CD4+ and CD8+ T cells and hepatic granulomas, and the decreased splenic parasitic loads (parasite reduction rates of Gp63, Kmp-11 and Amatin groups were 89%, 86% and 79%, respectively) from immunized mice post-infection were suggested the good immunoprotection of the vaccines. Our study demonstrated that vaccines based on the dominant epitopes of Gp63, Kmp-11 and Amastin with DNA prime-protein boost vaccination strategy showed significant immune effects against Leishmania, especially the Gp63 group showed a nearly 90% parasites reduction rate. This study will provide references for visceral leishmaniasis epitope vaccine design and immune strategy selection.Scientists have been warning the world of the threatening consequences of climate change for decades. Yet, only a few countries have made climate change mitigation a priority. One of the chief issues regarding climate change is its abstractness consequences for the collective in the long-term are much more abstract than consequences for the self in the here-and-now. To combat climate change, individuals, communities, and governments must work together to reduce the psychological distance of climate change and designate the future of the planet as the prime concern.

Rectus abdominis diastasis is regarded as a risk factor for abdominal muscle dysfunction and reduced quality of life postpartum. It is thought that specific exercises and additional physical support might reduce the diastasis, with a need to establish efficacy in doing so.

Determine the effect of four abdominal exercises as well as Tubigrip or taping in reducing rectus abdominis diastases three weeks postpartum.

Cross-sectional repeated measures comparison.

32 women undertook a single session of ultrasound imaging. Ultrasound measurements of inter-rectus distance were taken at rest and during 1) crook lying abdominal "drawing in" exercise; 2) crook lying trunk curl-up; 3) early Sahrmann level leg raise; 4) McGill side lying plank. The curl-up and abdominal "drawing in" exercises were assessed under two further conditions a) wearing Tubigrip, b) taping across the diastasis. Data analyses involved repeated measures ANOVA.

At rest the mean inter-rectus distance above and below the umbilicus was 3.5cm (SD1.1) and 2.6cm (SD1.2) respectively. A significant decrease (19%, p<0.05) was observed at both measurement points during the curl-up exercise. No other exercises elicited a significant difference compared to resting. At rest, wearing Tubigrip reduced the inter-rectus distance (7%, p<0.05). During exercise, there was no additional change in the inter-rectus distance (p>0.05) with supports.

The curl-up exercise was most effective in reducing inter-rectus distance. As no exercises invoked an increase in the rectus diastasis, they could not be regarded as potentially detrimental. Tubigrip and taping did not add to the effects of these exercises.

The curl-up exercise was most effective in reducing inter-rectus distance. As no exercises invoked an increase in the rectus diastasis, they could not be regarded as potentially detrimental. Tubigrip and taping did not add to the effects of these exercises.High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images.Dynamic network analysis using resting-state functional magnetic resonance imaging (rs-fMRI) provides a great insight into fundamentally dynamic characteristics of human brains, thus providing an efficient solution to automated brain disease identification. Previous studies usually pay less attention to evolution of global network structures over time in each brain's rs-fMRI time series, and also treat network-based feature extraction and classifier training as two separate tasks. To address these issues, we propose a temporal dynamics learning (TDL) method for network-based brain disease identification using rs-fMRI time-series data, through which network feature extraction and classifier training are integrated into the unified framework. Specifically, we first partition rs-fMRI time series into a sequence of segments using overlapping sliding windows, and then construct longitudinally ordered functional connectivity networks. To model the global temporal evolution patterns of these successive networks, we introduce a group-fused Lasso regularizer in our TDL framework, while the specific network architecture is induced by an ℓ1-norm regularizer. Besides, we develop an efficient optimization algorithm to solve the proposed objective function via the Alternating Direction Method of Multipliers (ADMM). Compared with previous studies, the proposed TDL model can not only explicitly model the evolving connectivity patterns of global networks over time, but also capture unique characteristics of each network defined at each segment. We evaluate our TDL on three real autism spectrum disorder (ASD) datasets with rs-fMRI data, achieving superior results in ASD identification compared with several state-of-the-art methods.The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. selleckchem Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density less then ±3%; dose less then ±20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.The YAG single crystals doped with 10 at.%, 20 at.% and 50 at.% Er3+ were successfully grown by the micro-pulling down method and spectroscopic properties of the crystals were investigated. The main interest was focus on the relation between the Er3+ concentration and ∼3.5 μm emission of Er3+YAG crystals. Room temperature absorption spectra were analyzed by the Judd-Ofelt theory. The stimulated emission cross-sections were calculated by the Füchtbauer-Ladenburg equation. The fluorescence intensities and peak emission cross-sections of the crystals at ∼3.5 μm are slightly decreasing with the increase of Er3+ concentration. The trend of the emission properties in NIR and visible region with the Er3+ concentration was also discussed and compared. The results indicate that the highly doped Er3+ concentration is beneficial to realize the ∼3.5 µm laser output.Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.

To compare the anatomical and audiological outcomes of endoscopic type I tympanoplasty using cartilage-perichondrium, with or without a customized 3D-printed guiding template.

A total of 60 patients with tympanic membrane perforation receiving endoscopic type I tympanoplasty were divided into the non-template group (group 1, n=30) and template group (group 2, n=30). Closure rate, hearing outcomes and operating time were compared between the two groups.

Group1 had a significant higher operation time compared with group2 (77.73±10.63min vs. 66.23±14.92min, p=0.001). The overall closure rate of group1 was lower than that of group2 (83.33% vs. 100%, p=0.052). The postoperative air-bone gaps (ABGs) were significantly lower than preoperative ones in each group (p<0.001, respectively).

Improvements in hearing outcomes were comparable for the two groups. The applying of customized 3D-printed guiding template resulted in a higher closure rate and a shorter operation time. Our results suggest that the customized 3D-printed guiding template can be recommended as a useful aid for endoscopic type I tympanoplasty.

Improvements in hearing outcomes were comparable for the two groups. The applying of customized 3D-printed guiding template resulted in a higher closure rate and a shorter operation time. Our results suggest that the customized 3D-printed guiding template can be recommended as a useful aid for endoscopic type I tympanoplasty.