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4% female), 92 had bHFpEF (35.9% female) and 282 had HFpEF (42.5% female). Rates of shockable rhythms were 44.5%, 48.9% and 27.0% for HFrEF, bHFpEF and HFpEF, respectively (P less then 0.001). Compared with HFpEF, adjusted odds ratios for shockable rhythm were 1.86 (95% confidence interval, CI, 1.27-2.74; P=0.002) in HFrEF and 2.26 (95% CI, 1.35-3.77; P=0.002) in bHFpEF. Rates of survival to hospital discharge were 10.6% in HFrEF, 22.8% in bHFpEF and 9.9% in HFpEF (P=0.003). Conclusion Rates of shockable rhythm during SCA depend on the HF clinical sub-type. Patients with bHFpEF had the highest likelihood of shockable rhythm, correlating with highest rates of survival.The 2-in-1 adaptive design [Chen et al. 2018] allows seamless expansion of an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program. An intermediate endpoint can be used for the adaptive decision. Under a mild assumption that is expected to generally hold in practice, both the Phase 2 trial (in case of no expansion) and the Phase 3 trial (in case of expansion) can be tested at the full alpha level without inflating the overall Type I error of the study. Due to its flexibility and robustness, the design has quickly generated a lot of interest since its recent publication. We make three major extensions to the 2-in-1 design in this paper 1) an increase of adaptive decisions from two to any number; 2) incorporation of the group sequential method for data monitoring; 3) expansion of the univariate design to multivariate. These extensions not only facilitate the application of 2-in-1 design in practice but also stimulate research interest in related statistical issues. Although we mainly use hypothetical trials in oncology and neuroscience for illustration, the application of the 2-in-1 design and its extensions is not limited to the two therapeutic areas.Photonics is among the most promising emerging technologies for providing fast and energy-efficient Deep Learning (DL) implementations. LOXO-195 purchase Despite their advantages, these photonic DL accelerators also come with certain important limitations. For example, the majority of existing photonic accelerators do not currently support many of the activation functions that are commonly used in DL, such as the ReLU activation function. Instead, sinusoidal and sigmoidal nonlinearities are usually employed, rendering the training process unstable and difficult to tune, mainly due to vanishing gradient phenomena. Thus, photonic DL models usually require carefully fine-tuning all their training hyper-parameters in order to ensure that the training process will proceed smoothly. Despite the recent advances in initialization schemes, as well as in optimization algorithms, training photonic DL models is still especially challenging. To overcome these limitations, we propose a novel adaptive initialization method that employs auxiliary tasks to estimate the optimal initialization variance for each layer of a network. The effectiveness of the proposed approach is demonstrated using two different datasets, as well as two recently proposed photonic activation functions and three different initialization methods. Apart from significantly increasing the stability of the training process, the proposed method can be directly used with any photonic activation function, without further requiring any other kind of fine-tuning, as also demonstrated through the conducted experiments.Lipid droplets (LDs) are key organelles in cancer cells proliferation, growth, and response to stress. These nanometric structures can aggregate to reach the size of microns becoming important cell components. Although it is known that LDs contain various lipids, their chemical composition is still under investigation. Moreover, their function in cell's response to exogenous factors is also not fully understood. Raman spectroscopy, together with chemometrics, has been shown to be a powerful tool for analytical analyses of cancer cell components on the subcellular level. It provides the opportunity to analyse LDs in a label-free manner in live cells. In the current study, this method was applied to investigate LDs composition in untreated and irradiated with X-ray beams prostate cancer cells. Raman mapping technique proved lipids accumulation in PC-3 cells and allowed visualization of LDs spatial distribution in cytoplasm. A heterogeneous composition of LDs was revealed by detailed analysis of Raman spectra. Interestingly, PC-3 cells were found to accumulate either triacylglycerols or cholesteryl esters. Finally, effect of X-ray radiation on the cells was investigated using Raman spectroscopy and fluorescence staining. Significant influence of LDs in the process of cell response was confirmed and time dependence of this phenomenon was determined.Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b ​= ​0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (Cy major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 ​b ​= ​0 images and 90 DWIs measures around 1-1.5 ​mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.