Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios

Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression

The Infrared Atmospheric Sounding Interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System (EPS). Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, IASI collects rich spectral information to derive temperature and moisture profiles –among other relevant trace gases–, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bitrates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors

Statistical atmospheric parameter retrieval largely benefits from spatial-spectral image compression

The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors

Hyperspectral IASI L1C data compression

The Infrared Atmospheric Sounding Interferometer (IASI), implemented on the MetOp satellite series, represents a significant step forward in atmospheric forecast and weather understanding. The instrument provides infrared soundings of unprecedented accuracy and spectral resolution to derive humidity and atmospheric temperature profiles, as well as some of the chemical components playing a key role in climate monitoring. IASI collects rich spectral information, which results in large amounts of data (about 16 Gigabytes per day). Efficient compression techniques are requested for both transmission and storage of such huge data. This study reviews the performance of several state of the art coding standards and techniques for IASI L1C data compression. Discussion embraces lossless, near-lossless and lossy compression. Several spectral transforms, essential to achieve improved coding performance due to the high spectral redundancy inherent to IASI products, are also discussed. Illustrative results are reported for a set of 96 IASI L1C orbits acquired over a full year (4 orbits per month for each IASI-A and IASI-B from July 2013 to June 2014) . Further, this survey provides organized data and facts to assist future research and the atmospheric scientific community

Universidad y sociedad: comunicación e integración en empresas e instituciones públicas y organizaciones no lucrativas. Nuevas orientaciones

Proyecto de Innovación docente 2021Depto. de Teorías y Análisis de la ComunicaciónFac. de Ciencias de la InformaciónFALSEsubmitte

A clinically compatible drug-screening platform based on organotypic cultures identifies vulnerabilities to prevent and treat brain metastasis

We report a medium‐throughput drug‐screening platform (METPlatform) based on organotypic cultures that allows to evaluate inhibitors against metastases growing in situ. By applying this approach to the unmet clinical need of brain metastasis, we identified several vulnerabilities. Among them, a blood–brain barrier permeable HSP90 inhibitor showed high potency against mouse and human brain metastases at clinically relevant stages of the disease, including a novel model of local relapse after neurosurgery. Furthermore, in situ proteomic analysis applied to metastases treated with the chaperone inhibitor uncovered a novel molecular program in brain metastasis, which includes biomarkers of poor prognosis and actionable mechanisms of resistance. Our work validates METPlatform as a potent resource for metastasis research integrating drug‐screening and unbiased omic approaches that is compatible with human samples. Thus, this clinically relevant strategy is aimed to personalize the management of metastatic disease in the brain and elsewhere

Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging

19 Páginas.-- 7 FigurasATP-citrate lyase is a central integrator of cellular metabolism in the interface of protein, carbohydrate, and lipid metabolism. The physiological consequences as well as the molecular mechanisms orchestrating the response to long-term pharmacologically induced Acly inhibition are unknown. We report here that the Acly inhibitor SB-204990 improves metabolic health and physical strength in wild-type mice when fed with a high-fat diet, while in mice fed with healthy diet results in metabolic imbalance and moderated insulin resistance. By applying a multiomic approach using untargeted metabolomics, transcriptomics, and proteomics, we determined that, in vivo, SB-204990 plays a role in the regulation of molecular mechanisms associated with aging, such as energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, while global alterations on histone acetylation are absent. Our findings indicate a mechanism for regulating molecular pathways of aging that prevents the development of metabolic abnormalities associated with unhealthy dieting. This strategy might be explored for devising therapeutic approaches to prevent metabolic diseases.This work was funded by grants from the Ministerio de Economía y Competitividad, Instituto de Salud Carlos III, co-funded by Fondos FEDER (PI15/00134, PI18/01590, CPII19/00023 to A.M.M.) and the Ministerio de Ciencia e Innovación (PID2021-123965OB-100 to A.M.M.). A.M.M. is funded by the Junta de Andalucía P20_00480, the Spanish Society of Diabetes, and CSIC 202220I059. M.S.K. is funded by the Nordea Foundation (#02-2017-1749), the Novo Nordisk Foundation (#NNF17OC0027812), the Neye Foundation, the Lundbeck Foundation (#R324-2019-1492), the Ministry of Higher Education and Science of Denmark (#0238-00003B). V.C.G. is funded by the Instituto de Salud Carlos III (CP19/00046), co-funded by FEDER. F.M. is funded by the CIBERDEM of the Instituto de Salud Carlos III. A.M.M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. We acknowledge the support of the group of basic research on diabetes of the Spanish Society of Diabetes.Peer reviewe

Universidad y sociedad: comunicación e integración en empresas e instituciones públicas y organizaciones no lucrativas. Renovación y vanguardia

Depto. de Teorías y Análisis de la ComunicaciónFac. de Ciencias de la InformaciónFALSEsubmitte

Universidad y sociedad: comunicación e integración en empresas e instituciones públicas y organizaciones no lucrativas: innovación y progreso

Depto. de Teorías y Análisis de la ComunicaciónFac. de Ciencias de la InformaciónFALSEsubmitte