Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700

Plant-Mycorrhiza Percent Infection as Evidence of Coupled Metabolism

A common feature of mycorrhizal observation is the growth of the infection on the plant root as a percent of the infected root or root tip length. Often, this is measured as a logistic curve with an eventual, though usually transient, plateau. It is shown in this paper that the periods of stable percent infection in the mycorrhizal growth cycle correspond to periods where both the plant and mycorrhiza growth rates and likely metabolism are tightly coupled.Comment: 11 pages; accepted by the Journal of Theoretical Biology (in press

Specialist laboratory networks as preparedness and response tool - The emerging viral diseases-expert laboratory network and the chikungunya outbreak, Thailand, 2019

We illustrate the potential for specialist laboratory networks to be used as preparedness and response tool through rapid collection and sharing of data. Here, the Emerging Viral Diseases-Expert Laboratory Network (EVD-LabNet) and a laboratory assessment of chikungunya virus (CHIKV) in returning European travellers related to an ongoing outbreak in Thailand was used for this purpose. EVD-LabNet rapidly collected data on laboratory requests, diagnosed CHIKV imported cases and sequences generated, and shared among its members and with the European Centre for Disease Prevention and Control. Data across the network showed an increase in CHIKV imported cases during 1 October 2018-30 April 2019 vs the same period in 2018 (172 vs 50), particularly an increase in cases known to be related to travel to Thailand (72 vs 1). Moreover, EVD-LabNet showed that strains were imported from Thailand that cluster with strains of the ECSA-IOL E1 A226 variant emerging in Pakistan in 2016 and involved in the 2017 outbreaks in Italy. CHIKV diagnostic requests increased by 23.6% between the two periods. The impact of using EVD-LabNet or similar networks as preparedness and response tool could be improved by standardisation of the collection, quality and mining of data in routine laboratory management systems

Gymnotic Delivery of LNA Mixmers Targeting Viral SREs Induces HIV-1 mRNA Degradation

Transcription of the HIV-1 provirus generates a viral pre-mRNA, which is alternatively spliced into more than 50 HIV-1 mRNAs encoding all viral proteins. Regulation of viral alternative splice site usage includes the presence of splicing regulatory elements (SREs) which can dramatically impact RNA expression and HIV-1 replication when mutated. Recently, we were able to show that two viral SREs, G(I3)-2 and ESEtat, are important players in the generation of viral vif, vpr and tat mRNAs. Furthermore, we demonstrated that masking these SREs by transfected locked nucleic acid (LNA) mixmers affect the viral splicing pattern and viral particle production. With regard to the development of future therapeutic LNA mixmer-based antiretroviral approaches, we delivered the G(I3)-2 and the ESEtat LNA mixmers nakedly, without the use of transfection reagents (gymnosis) into HIV-1 infected cells. Surprisingly, we observed that gymnotically-delivered LNA mixmers accumulated in the cytoplasm, and seemed to co-localize with GW bodies and induced degradation of mRNAs containing their LNA target sequence. The G(I3)-2 and the ESEtat LNA-mediated RNA degradation resulted in abrogation of viral replication in HIV-1 infected Jurkat and PM1 cells as well as in PBMCs

Pol-InSAR-Island - A Benchmark Dataset for Multi-frequency Pol-InSAR Data Land Cover Classification (Version 2)

Pol-InSAR-Island is the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) benchmark dataset for land cover classification. The strong scientific interest and the accompanying rapid development of machine learning, in particular deep learning, has led to a significant improvement in automatic image interpretation in recent years. Research generally focuses on classification or segmentation of optical images, but there are already several successful approaches that apply deep learning techniques to the analysis of PolSAR or Pol-InSAR images. While the success of learning-based methods for the analysis of optical images has been strongly driven by public benchmark datasets such as ImageNet and Cityscapes, which contain a large number of annotated training and test data, comparable datasets for the PolSAR and especially the Pol-InSAR domain are almost non-existent. To fill this gap, this work presents a new multi-frequency Pol-InSAR benchmark dataset for training and testing learning-based methods. The dataset contains Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. To allow interferometric analysis a repeat-pass configuration with a time offset of several minutes and a vertical baseline of 40 m is used. The image data are given as geocoded 6 × 6 coherency matrices on a 1 m × 1 m grid and is labeled by 12 different land cover classes. The Pol-InSAR-Island dataset is intended to improve the development of new learning-based approaches for multi-frequency Pol-InSAR classification. To ensure the comparability of various approaches, a defined division of the data into training and testing sections is given. For more information, refer to the corresponding research article: https://doi.org/10.1016/j.ophoto.2023.100047 Pol-InSAR-Island - A Benchmark Dataset for Multi-frequency Pol-InSAR Data Land Cover Classification (Version 2) is the updated version of the dataset. The PolSAR as well as the label images remain unchanged, but additional files containing the corresponding incidence angle and the vertical wavenumbers are added