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- Ljungman, P., et al.
(author)
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Improved outcomes over time and higher mortality in CMV seropositive allogeneic stem cell transplantation patients with COVID-19; An infectious disease working party study from the European Society for Blood and Marrow Transplantation registry
- 2023
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In: FRONTIERS IN IMMUNOLOGY. - : Frontiers Media SA. - 1664-3224. ; 14
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Journal article (peer-reviewed)abstract
- IntroductionCOVID-19 has been associated with high morbidity and mortality in allogeneic hematopoietic stem cell transplant (allo-HCT) recipients. MethodsThis study reports on 986 patients reported to the EBMT registry during the first 29 months of the pandemic. ResultsThe median age was 50.3 years (min - max; 1.0 - 80.7). The median time from most recent HCT to diagnosis of COVID-19 was 20 months (min - max; 0.0 - 383.9). The median time was 19.3 (0.0 - 287.6) months during 2020, 21.2 (0.1 - 324.5) months during 2021, and 19.7 (0.1 - 383.9) months during 2022 (p = NS). 145/986 (14.7%) patients died; 124 (12.6%) due to COVID-19 and 21 of other causes. Only 2/204 (1%) fully vaccinated patients died from COVID-19. There was a successive improvement in overall survival over time. In multivariate analysis, increasing age (p<.0001), worse performance status (p<.0001), contracting COVID-19 within the first 30 days (p<.0001) or 30 - 100 days after HCT (p=.003), ongoing immunosuppression (p=.004), pre-existing lung disease (p=.003), and recipient CMV seropositivity (p=.004) had negative impact on overall survival while patients contracting COVID-19 in 2020 (p<.0001) or 2021 (p=.027) had worse overall survival than patients with COVID-19 diagnosed in 2022. DiscussionAlthough the outcome of COVID-19 has improved, patients having risk factors were still at risk for severe COVID-19 including death.
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- Averbuch, A. Z., et al.
(author)
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Low bit-rate efficient compression for seismic data
- 2001
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In: IEEE Transactions on Image Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1057-7149 .- 1941-0042. ; 10:12, s. 1801-1814
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Journal article (peer-reviewed)abstract
- Compression is a relatively new introduced technique for seismic data operations. The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Thus, the need to compress these very large seismic data riles is imperative. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. Up to now some of the algorithms used for seismic data compression were based on some form of wavelet or local cosine transform. while using a uniform or quasiuniform quantization scheme and they finally employ a Huffman coding scheme. Using this family of compression algorithms we achieve compression results which are acceptable to geophysicists, only at low to moderate compression ratios. For higher compression ratios or higher decibel quality, significant compression artifacts are introduced in the reconstructed images, even with high-dimensional transforms. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40 dB in the decompressed seismic data sets. Several established compression algorithms are reviewed, and some new compression algorithms are introduced. All of these compression techniques are applied to a good representation of seismic data sets, and their results are documented in this paper. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. All the described methods cover wide range of different data sets. Each data set will have his own best performed method chosen from this collection. The results were performed on four different seismic data sets. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.
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- Meyer, F. G., et al.
(author)
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Fast adaptive wavelet packet image compression
- 2000
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In: IEEE Transactions on Image Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1057-7149 .- 1941-0042. ; 9:5, s. 792-800
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Journal article (peer-reviewed)abstract
- Wavelets are ill-suited to represent oscillatory patterns: rapid variations of intensity can only be described by the small scale wavelet coefficients, which are often quantized to zero, even at high bit rates. Our goal in this paper is to provide a fast numerical implementation of the best wavelet packet algorithm [1] in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image. Emphasis in this paper has been placed on developing algorithms that are computationally efficient. We developed a new fast two-dimensional (2-D) convolution-decimation algorithm with factorized nonseparable 2-D filters. The algorithm is four times faster than a standard convolution-decimation, An extensive evaluation of the algorithm was performed on a large class of textured images. Because of its ability to reproduce textures so well, the wavelet packet coder significantly out performs one of the best wavelet coder [2] on images such as Barbara and fingerprints, both visually and in term of PSNR.
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