The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium's Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.
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http://dx.doi.org/10.3390/s17122923 | DOI Listing |
Comput Vis ECCV
November 2024
University of Minnesota, Minneapolis.
Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated noise schedules that involve more frequent iterations of timesteps at lower noise levels, thereby improving image generation and convergence speed.
View Article and Find Full Text PDFBioinformatics
January 2025
Geneis Beijing Co., Ltd, Beijing 100102, China.
Motivation: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple Instance Learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension.
View Article and Find Full Text PDFMicrob Cell Fact
January 2025
Chair of Technical Biochemistry, Technische Universität Dresden, Bergstraße 66, 01069, Dresden, Germany.
Background: The biosynthesis of the natural product family of the polycyclic tetramate macrolactams (PoTeMs) employs an uncommon iterative polyketide synthase/non-ribosomal peptide synthetase (iPKS/NRPS). This machinery produces a universal PoTeM biosynthetic precursor that contains a tetramic acid moiety connected to two unsaturated polyene side chains. The enormous structural and hence functional diversity of PoTeMs is enabled by pathway-specific tailoring enzymes, particularly cyclization-catalyzing oxidases that process the polyene chains to form distinct ring systems, and further modifying enzymes.
View Article and Find Full Text PDFBioresour Bioprocess
January 2025
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Aspergillus oryzae is a widely used host for heterologous expression of fungal natural products. However, the vectors previously developed are not convenient for use and screening positive transformants by PCR and fermentation is time- and effort-consuming. Hence, three plug-and-play vectors were developed here for multi-gene expression and liquid chromatography mass spectrometry detection was introduced to screen positive transformants.
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