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We present a novel approach for the radiometric calibration of phase-measuring 3D sensors. Our approach can be applied to a broad variety of scenes and calculates the calibration parameters “on-the-fly” from the actual object measurement images, meaning that no additional calibration session is required. 

Knowledge of locations and activities ofcis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state regulatory potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbor distinctive transcription factor binding motifs that are similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we show that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment. 

Interest in the deep Arctic Ocean is rapidly increasing from governments, policy makers, industry, researchers, and conservation groups, accentuated by the growing accessibility of this remote region by surface vessel traffic. In this review, our goal is to provide an updated taxonomic inventory of benthic taxa known to occur in the deep Arctic Ocean and relate this inventory to habitat diversity. To achieve this goal, we collected data for Arctic metazoan deep-sea taxa from open-access databases, information facilities, and non-digitised scientific literature, limiting the collection to the area north of 66°N and below 500 m depth (excluding all shelf seas). Although notable progress has been made in understanding the deep Arctic using novel technologies and infrastructure, this data gathering shows that knowledge of deep-sea benthic Arctic communities remains very limited. Yet, through our compilation of habitat maps, we show that the Arctic contains a high diversity of geomorphological features, including slopes, deep basins, submarine canyons, ridges, and seamounts, as well as chemosynthesis-based and biogenic (biologically engineered) ecosystems. To analyse taxon richness and density, using both morphological and molecular data, we compiled 75,404 faunal records with 2,637 taxa. Phyla with the most records were the Arthropoda (21,405), Annelida (13,763) and Porifera (12,591); phyla with the most documented taxa were the Arthropoda (956), Annelida (566) and Mollusca (351). An overview of the dominant groups inhabiting the different geomorphological features highlights regions in the deep Arctic where data are particularly scarce and increased research efforts are needed, particularly the deep basins of the central Arctic Ocean. This scarcity of deep benthic Arctic biodiversity data creates a bottleneck for developing robust management and conservation measures in a rapidly changing region, leading to a call for international collaboration and shared data to ensure understanding and preservation of these fragile Arctic ecosystems. 

Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species’ co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs. 

Abstract Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.