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  • 1
    Online Resource
    Online Resource
    Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: Applications to distributed computing under disclosure constraints
    Wiley ; 2020
    In:  Statistics in Medicine Vol. 39, No. 8 ( 2020-04-15), p. 1183-1198
    Details
    In: Statistics in Medicine, Wiley, Vol. 39, No. 8 ( 2020-04-15), p. 1183-1198
    Abstract: There are many settings where individual person data (IPD) are not available, due to privacy or technical reasons, and one must work with IPD proxies, such as summary statistics, to approximate original IPD inferences, that is, the results of statistical analyses that would ideally have been performed on individual‐level data. For instance, in a distributed computing setting, as implemented in the DataSHIELD software framework, different centers can only share IPD proxies to obtain pooled IPD inferences. Such privacy requirements limit the scope of statistical investigation. For example, it can be challenging to perform between‐center random‐effect regression models. To increase modeling freedom we propose a method that only uses simple nondisclosive summaries of the original IPD as input, such as empirical marginal moments and correlation matrices, and generates artificial data compatible with those summary features. Specifically, data are generated from a Gaussian copula with marginal and joint components specified by the above summaries. The goal is to reproduce original IPD features in the artificial data, such that original IPD inferences are recovered from the artificial data. In an application example, and through simulations, we show that we can recover estimates of a multivariable IPD random‐effect logistic regression, from artificial data generated via the Gaussian copula using the above IPD summaries, suggesting the proposed approach provides a generally applicable strategy for distributed computing settings with data protection constraints.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    URL: Article
    DOI: 10.1002/sim.v39.8
    DOI: 10.1002/sim.8470
    RVK:
    XA 10000
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1491221-1
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  • 2
    Online Resource
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    The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Details
    In: Sensors, MDPI AG, Vol. 20, No. 6 ( 2020-03-21), p. 1751-
    Abstract: Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs] , and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s20061751
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 3
    Online Resource
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    Video_2.mp4
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 9 ( 2022-8-26)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 9 ( 2022-8-26)
    Abstract: Ocean observatories collect large volumes of video data, with some data archives now spanning well over a few decades, and bringing the challenges of analytical capacity beyond conventional processing tools. The analysis of such vast and complex datasets can only be achieved with appropriate machine learning and Artificial Intelligence (AI) tools. The implementation of AI monitoring programs for animal tracking and classification becomes necessary in the particular case of deep-sea cabled observatories, as those operated by Ocean Networks Canada (ONC), where Petabytes of data are now collected each and every year since their installation. Here, we present a machine-learning and computer vision automated pipeline to detect and count sablefish ( Anoplopoma fimbria ), a key commercially exploited species in the N-NE Pacific. We used 651 hours of video footage obtained from three long-term monitoring sites in the NEPTUNE cabled observatory, in Barkley Canyon, on the nearby slope, and at depths ranging from 420 to 985 m. Our proposed AI sablefish detection and classification pipeline was tested and validated for an initial 4.5 month period (Sep 18 2019-Jan 2 2020), and was a first step towards validation for future processing of the now decade-long video archives from Barkley Canyon. For the validation period, we trained a YOLO neural network on 2917 manually annotated frames containing sablefish images to obtain an automatic detector with a 92% Average Precision (AP) on 730 test images, and a 5-fold cross-validation AP of 93% (± 3.7%). We then ran the detector on all video material (i.e., 651 hours from a 4.5 month period), to automatically detect and annotate sablefish. We finally applied a tracking algorithm on detection results, to approximate counts of individual fishes moving on scene and obtain a time series of proxy sablefish abundance. Those proxy abundance estimates are among the first to be made using such a large volume of video data from deep-sea settings. We discuss our AI results for application on a decade-long video monitoring program, and particularly with potential for complementing fisheries management practices of a commercially important species.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fmars.2022.842946
    DOI: 10.3389/fmars.2022.842946.s001
    DOI: 10.3389/fmars.2022.842946.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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  • 4
    Online Resource
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    Long‐term automated visual monitoring of Antarctic benthic fauna
    Wiley ; 2022
    In:  Methods in Ecology and Evolution Vol. 13, No. 8 ( 2022-08), p. 1746-1764
    Details
    In: Methods in Ecology and Evolution, Wiley, Vol. 13, No. 8 ( 2022-08), p. 1746-1764
    Abstract: The rapid changes in the climate of Antarctica are likely to pose challenges to living communities, which makes monitoring of Antarctic fauna an urgent necessity. Benthos is particularly difficult to monitor, and is sensitive to local environmental changes. At the same time, long‐term monitoring is complicated by logistical factors. It is therefore urgent to develop advanced instruments to set up autonomous and long‐term monitoring programmes to obtain the lacking biological knowledge needed to understand this complex and remote marine environment. We present a pilot study to set up a non‐invasive and sustainable autonomous monitoring activity in Antarctica, leveraging on a specifically designed automated camera recording, computer vision and machine learning image processing techniques. We also present and analyse the high‐resolution image dataset acquired for an extended period of time encompassing both the summer and the Antarctic night and the corresponding transition periods. The results of this study demonstrate both the effectiveness of such an autonomous imaging devices for acquiring relevant long‐term visual data and the effectiveness of the proposed image analysis algorithms for extracting relevant scientific knowledge from such data. The presented results show how the extracted knowledge discloses dynamics of the observed ecosystems that can be obtained only through continuous observations extended in time, not achievable with the state‐of‐the‐art monitoring approaches commonly implemented in Antarctica. The success of this pilot study is a step towards the collection of continuous data near shore in Antarctic areas and in general in all the remote and extreme underwater habitats. Moreover, the presented stand‐alone and autonomous imaging device can be used for increasing the number of the monitoring sites in remote environments and when complemented with the acquisition of physical and bio‐chemical variables it can be used for obtaining data collections of great scientific value difficult to acquire otherwise.
    Type of Medium: Online Resource
    ISSN: 2041-210X , 2041-210X
    URL: Issue
    URL: Article
    DOI: 10.1111/mee3.v13.8
    DOI: 10.1111/2041-210X.13898
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2528492-7
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  • 5
    Online Resource
    Online Resource
    Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Data Vol. 9, No. 1 ( 2022-12-03)
    Details
    In: Scientific Data, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2022-12-03)
    Abstract: Antarctica is a remote place, the continent is covered by ice and its surrounding coastal areas are frozen for the majority of the year. Due to its peculiarity the observation of the underwater organisms is particularly difficult, complicated by logistic factors. We present a long-term dataset consisting of 755 images acquired by using a non-invasive, autonomous imaging device and encompassing both the Antarctic daylight and dark periods, including the corresponding transition phases. All images have the same field of view showing the benthic fauna and part of the water column above, including fishes present in the monitored period. All the images are manually annotated after a visual inspection performed by expert biologists. The extended monitoring period and the annotated images make the dataset a valuable benchmark suitable for studying the dynamics of the long-term Antarctic underwater fauna as well as for developing and testing algorithms for automated image analysis focused on the recognition and classification of the Antarctic organisms and the automated analysis of their long-term dynamics.
    Type of Medium: Online Resource
    ISSN: 2052-4463
    URL: Article
    DOI: 10.1038/s41597-022-01865-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2775191-0
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  • 6
    Online Resource
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    Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures
    Frontiers Media SA ; 2022
    In:  Frontiers in Marine Science Vol. 8 ( 2022-1-12)
    Details
    In: Frontiers in Marine Science, Frontiers Media SA, Vol. 8 ( 2022-1-12)
    Abstract: Deep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resources.
    Type of Medium: Online Resource
    ISSN: 2296-7745
    URL: Article
    DOI: 10.3389/fmars.2021.797140
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2757748-X
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  • 7
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    Effective and Safe Immune Checkpoint Blockade Induces Complete Responses in Murine PTCL Model of ALCL
    American Society of Hematology ; 2022
    In:  Blood Vol. 140, No. Supplement 1 ( 2022-11-15), p. 6466-6467
    Details
    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 6466-6467
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    URL: Article
    DOI: 10.1182/blood-2022-168777
    RVK:
    XA 33000
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 8
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    ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
    MDPI AG ; 2020
    In:  Journal of Marine Science and Engineering Vol. 8, No. 9 ( 2020-08-20), p. 633-
    Details
    In: Journal of Marine Science and Engineering, MDPI AG, Vol. 8, No. 9 ( 2020-08-20), p. 633-
    Abstract: The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.
    Type of Medium: Online Resource
    ISSN: 2077-1312
    URL: Article
    DOI: 10.3390/jmse8090633
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2738390-8
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  • 9
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    Meta‐analysis for aggregated survival data with competing risks: a parametric approach using cumulative incidence functions
    Wiley ; 2016
    In:  Research Synthesis Methods Vol. 7, No. 3 ( 2016-09), p. 282-293
    Details
    In: Research Synthesis Methods, Wiley, Vol. 7, No. 3 ( 2016-09), p. 282-293
    Abstract: Meta‐analysis of a survival endpoint is typically based on the pooling of hazard ratios (HRs). If competing risks occur, the HRs may lose translation into changes of survival probability. The cumulative incidence functions (CIFs), the expected proportion of cause‐specific events over time, re‐connect the cause‐specific hazards (CSHs) to the probability of each event type. We use CIF ratios to measure treatment effect on each event type. To retrieve information on aggregated, typically poorly reported, competing risks data, we assume constant CSHs. Next, we develop methods to pool CIF ratios across studies. The procedure computes pooled HRs alongside and checks the influence of follow‐up time on the analysis. We apply the method to a medical example, showing that follow‐up duration is relevant both for pooled cause‐specific HRs and CIF ratios. Moreover, if all‐cause hazard and follow‐up time are large enough, CIF ratios may reveal additional information about the effect of treatment on the cumulative probability of each event type. Finally, to improve the usefulness of such analysis, better reporting of competing risks data is needed. Copyright © 2015 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1759-2879 , 1759-2887
    URL: Issue
    URL: Article
    DOI: 10.1002/jrsm.v7.3
    DOI: 10.1002/jrsm.1165
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 2548499-0
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