A Web-based and Mobile Environmental Management System

This paper describes a Web-based and mobile system specifically developed to monitor fish species at risk. Such a system integrates spatial functionality to allow users not only to visualise maps and metadata of the area of concern but also to perform context-aware queries and updating of spatial datasets. The spatial datasets are provided by the Canadian Department of Fisheries and Oceans (DFO) and the prototype is customised to the specific needs of the Great Lakes Laboratory for Fisheries and Aquatic Sciences (GLLFAS) Fish Habitat Section requirements for fish species at risk assessment. Currently, researchers, habitat biologists and enforcement officers have access to the fisheries database, containing layers of biological information solely from the office. Delivering these data overlaid on base maps of the Great Lakes region to a GPS-enabled hand-held device and linking it to each task currently being investigated allows for mobile DFO biologists and enforcement officers in the field to make informed decisions immediately. In this paper we describe the system and demonstrate how it is used by the DFO in practice

Autologous Hematopoietic Stem Cell Transplantation (AHSCT): Standard of Care for Relapsing–Remitting Multiple Sclerosis Patients

Abstract Autologous hematopoietic stem cell transplantation (AHSCT) has been used in the treatment of highly active multiple sclerosis (MS) for over two decades. It has been demonstrated to be highly efficacious in relapsing–remitting (RR) MS patients failing to respond to disease-modifying drugs (DMDs). AHSCT guarantees higher rates of no evidence of disease activity (NEDA) than those achieved with any other DMDs, but it is also associated with greater short-term risks which have limited its use. In the 2019 updated EBMT and ASBMT guidelines, which review the clinical evidence of AHSCT in MS, AHSCT indication for highly active RRMS has changed from “clinical option” to “standard of care”. On this basis, AHSCT must be proposed on equal footing with second-line DMDs to patients with highly active RRMS, instead of being considered as a last resort after failure of all available treatments. The decision-making process requires a close collaboration between transplant hematologists and neurologists and a full discussion of risk–benefit of AHSCT and alternative treatments. In this context, we propose a standardized protocol for decision-making and informed consent process

The added value of simultaneous palpation during scrotal ultrasound

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A 6-DIMENSIONAL HILBERT APPROACH TO INDEX FULL WAVEFORM LiDAR DATA IN A DISTRIBUTED COMPUTING ENVIRONMENT

Laser scanning data are increasingly available across the globe. To maximize the data's usability requires proper storage and indexing. While significant research has been invested in developing storage and indexing solutions for laser scanning point clouds (i.e. using the discrete form of the data), little attention has been paid to developing equivalent solutions for full waveform (FWF) laser scanning data, especially in a distributed computing environment. Given the growing availability of FWF sensors and datasets, FWF data management solutions are increasingly needed. This paper presents an attempt towards establishing a scalable solution for handling large FWF datasets by introducing the distributed computing solution for FWF data. The work involves a FWF database built atop HBase – the distributed database system running on Hadoop commodity clusters. By combining a 6-dimensional (6D) Hilbert spatial code and a temporal index into a compound indexing key, the database system is capable of supporting multiple spatial, temporal, and spatio-temporal queries. Such queries are important for FWF data exploration and dissemination. The proposed spatial decomposition at a fine resolution of 0.05 m allows the storage of each LiDAR FWF measurement (i.e. pulse, waves, and points) on a single row of the database, thereby providing the full capabilities to add, modify, and remove each measurement record anatomically. While the feasibility and capabilities of the 6D Hilbert solution are evident, the Hilbert decomposition is not due to the complications from the combination of the data’s high dimensionality, fine resolution, and large spatial extent. These factors lead to a complex set of both attractive attributes and limitation in the proposed solution, which are described in this paper based on experimental tests using a 1.1 billion pulse LiDAR scan of a portion of Dublin, Ireland