DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Recently, convolutional neural networks (CNNs) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution (SR) from multitemporal unregistered imagery have received little attention so far. This article proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire SR process relies on a single CNN with three main stages: shared 2-D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3-D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images. The method presented in this article is the winner of the PROBA-V SR challenge issued by the European Space Agency (ESA)

Detection of Solar Coronal Mass Ejections from Raw Images with Deep Convolutional Neural Networks

Coronal Mass Ejections (CMEs) are massive releases of plasma from the solar corona. When the charged material is ejected towards the Earth, it can cause geomagnetic storms and severely damage electronic equipment and power grids. Early detection of CMEs is therefore crucial for damage containment. In this paper, we study detection of CMEs from sequential images of the solar corona acquired by a satellite. A low-complexity deep neural network is trained to process the raw images, ideally directly on the satellite, in order to provide early alerts

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort.

BACKGROUND: Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets. RESULTS: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs. CONCLUSION: Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits

GMLD: A TOOL TO INVESTIGATE AND DEMONSTRATE THE USE OF ML IN VARIOUS AREAS OF GNSS DOMAIN

This paper presents relevant results achieved during the NAVISP- EL1-035.02 project funded by the European Space Agency, which aimed to investigate the possible uses of Machine Learning (ML) based techniques for the processing of data in the field of Global Navigation Satellite Systems (GNSSs). For this purpose, we explored different kind of data present in the entire chain of the positioning process and different kind of ML approaches. In particular, this paper presents the system architecture and technologies adopted for developing the GNSS ML Demonstrator (GMLD), as well as the approaches and the results obtained for one of the most promising GNSS implemented applications, which is the prediction of daily maps of the ionosphere. Results show how, based on the historical data and the time correlation of the values, ML methods outperformed benchmark methods for the majority of the applications approached, improving the positioning performance at GNSS user level. Since the GMLD has been designed and implemented providing the general data management and ML capabilities as part of the framework, it can be easily reused to execute further investigation and implement new applications

In Vitro and In Vivo Antitumor Effect of Anti-CD33 Chimeric Receptor-Expressing EBV-CTL against CD33+ Acute Myeloid Leukemia

Genetic engineering of T cells with chimeric T-cell receptors (CARs) is an attractive strategy to treat malignancies. It extends the range of antigens for adoptive T-cell immunotherapy, and major mechanisms of tumor escape are bypassed. With this strategy we redirected immune responses towards the CD33 antigen to target acute myeloid leukemia. To improve in vivo T-cell persistence, we modified human Epstein Barr Virus-(EBV-) specific cytotoxic T cells with an anti-CD33.CAR. Genetically modified T cells displayed EBV and HLA-unrestricted CD33 bispecificity in vitro. In addition, though showing a myeloablative activity, they did not irreversibly impair the clonogenic potential of normal CD34+ hematopoietic progenitors. Moreover, after intravenous administration into CD33+ human acute myeloid leukemia-bearing NOD-SCID mice, anti-CD33-EBV-specific T cells reached the tumor sites exerting antitumor activity in vivo. In conclusion, targeting CD33 by CAR-modified EBV-specific T cells may provide additional therapeutic benefit to AML patients as compared to conventional chemotherapy or transplantation regimens alone

Synergic combination of the sol-gel method with dip coating for plasmonic devices

Biosensing technologies based on plasmonic nanostructures have recently attracted significant attention due to their small dimensions, low-cost and high sensitivity but are often limited in terms of affinity, selectivity and stability. Consequently, several methods have been employed to functionalize plasmonic surfaces used for detection in order to increase their stability. Herein, a plasmonic surface was modified through a controlled, silica platform, which enables the improvement of the plasmonic-based sensor functionality. The key processing parameters that allow for the fine-tuning of the silica layer thickness on the plasmonic structure were studied. Control of the silica coating thickness was achieved through a combined approach involving sol-gel and dip-coating techniques. The silica films were characterized using spectroscopic ellipsometry, contact angle measurements, atomic force microscopy and dispersive spectroscopy. The effect of the use of silica layers on the optical properties of the plasmonic structures was evaluated. The obtained results show that the silica coating enables surface protection of the plasmonic structures, preserving their stability for an extended time and inducing a suitable reduction of the regeneration time of the chip