Innovation to limit seafood fraud post-COVID-19.

Seafood is an important source of protein and micronutrients, but fishery stocks are increasingly under pressure from both legitimate and illegitimate fishing practices. Sustainable management of our oceans is a global responsibility, aligning with United Nations Sustainable Development Goal 14, Life Below Water. In a post-COVID-19 world, there is an opportunity to build back better, where locally sourced food via transparent supply chains are ever-more important. This article summarises emerging research of two innovative case studies in detecting and validating seafood provenance; and using alternative supply chains to minimise the opportunity for seafood fraud in a post-COVID-19 world.

Truth in an Age of Information

Many of the issues in the modern world are complex and multifaceted: migration, banking, not to mention climate change and Covid. Furthermore, social-media, which at first seemed to offer more reliable 'on the ground' citizen journalism, has instead become a seedbed of dis-information. Trust in media has plummeted, just when it has become essential. This is a problem, but also an opportunity for research in HCI that can make a real difference in the world. The majority of work in this area, from various disciplines including datascience, AI and HCI, is focused on combatting misinformation - fighting back against bad actors. However, we should also think about doing better - helping good actors to curate, disseminate and comprehend information better. There is exciting work in this area, but much still to do. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

Reptile: Aggregation-level Explanations for Hierarchical Data

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user "zoom-in" on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from. In each drill-down iteration, Reptile must train a large number of predictive models. We thus extend factorised learning from countjoin queries to aggregation-join queries, and develop a suite of optimizations that leverage the data's hierarchical structure. These optimizations reduce runtimes by >6x compared to a Lapack-based implementation. When applied to real-world Covid-19 and African farmer survey data, Reptile correctly identifies 21/30 (vs 2 using existing explanation approaches) and 20/22 errors. Reptile has been deployed in Ethiopia and Zambia, and used to clean nationwide farmer survey data;the clean data has been used to design national drought insurance policies.

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

The ethical use of digital technology in teaching anatomy: A southern African perspective.

BACKGROUND: The use of 21st Century technology in anatomy teaching and the recent crisis caused by the Coronavirus pandemic has stimulated anatomists to ponder the ethics surrounding the utilisation of digital images from human bodies of known and unknown provenance in teaching. AIM: This novel study explores the awareness of southern African anatomy educators regarding the provenance and ethical use of human material in digital resources for E-learning purposes. MATERIALS AND METHOD: Anatomy educators (both members and non-members of the Anatomical Society of Southern Africa including postgraduate students in anatomy) located in 15 health sciences facilities in southern Africa were asked to participate in the survey which consisted of an anonymous, cross-sectional, questionnaire conducted on an online research data system, REDCap. RESULTS: While 52% of respondents used E-learning resources sourced from their own departments for teaching, only 58% of these had knowledge of the provenance of the human material used. Of the 72% of respondents using images from external E-learning resources, 64% did not know the provenance of the human material in these resources. Some southern African anatomists considered anonymity as equivalent to informed consent. Regarding the acceptability of unclaimed bodies for online images, 37% of respondents were against the use of these bodies, while 20% indicated that it was acceptable. Personal internal moral conflict was acknowledged regarding the use of material from unclaimed bodies, particularly during crises such as the Coronavirus pandemic when digital resources were limited. DISCUSSION AND CONCLUSIONS: Factors such as lack of awareness of provenance, the law in South Africa and using anonymity for consent, influence the ethical behaviour of southern African anatomists. Clear guiding principles would be of value for anatomists globally with respect to consent to the taking and distribution of images, and transparency on the source of the digital images provided in digital texts and online platforms. The establishment of both an oversight and ethics committee at institutions where digital imaging will be used is recommended.

Consuming Location: The Sustainable Impact of Transformational Experiential Culinary and Wine Tourism in Chianti Italy

Tourists visit wine and culinary destinations for unique, geographically indicated experiences that are place specific. The objective of this research is to understand how the transformational potential of experiential wine and culinary tourism best promotes sustainability in the context of international educational travel. Our case study in the iconic Chianti Region of Italy applies a ‘Hopeful Tourism Enquiry’ perspective and focuses on participatory, co-transformative learning, and mindful sustainability. A mixed qualitative research strategy was implemented that integrates the results of in-depth interviews with industry experts, excerpts from expository travel journals simultaneously captured during the experience, and focus group dialogues with participating students at the end of the field course. This case study revealed three overlapping thematic results that illustrate the influence of experiential educational tourism on the sensory and cultural experience of sustainable food and wine to produce co-transformative learning. The co-creation of memorable experiences establishes a unique sensual representation of provenance through the interaction with the region through narrative so that not only is the food and wine being consumed, but also the consumption of place through the storyscape of a positive and memorable experience.

Splendors of the Serenissima in a Digital Age: The Master of the Murano Gradual Reconsidered

The manuscript has not been digitized, and only six of the illuminations have been reproduced, often in black and white with a single color reproduction of the only full-page miniature.2 A fenestra (or window) label on the binding indicates that this gradual was the fourth in the set for San Mattia.3 The original series of songbooks survives in a dispersed and fragmentary condition as follows: * The Berlin volume containing chants of the temporale portion of the liturgical year (those related to the life of Christ) from Easter Sunday until the twenty-third Sunday after Pentecost;* Dispersed cuttings of over fifty known historiated initials by the Murano Master(s) in twenty-six collections across Europe, Russia, and the United States that likely formed part of multiple volumes, including the sanctorale feasts (those commemorating the lives of saints), a hymnal, and an antiphonary for services of the Divine Office (Appendix A);4 and * A second temporale volume, from the first Sunday in Advent to the second Sunday in Lent, presently in the Biblioteca Nazionale Braidense in Milan (MS AB. The authors have embraced virtual messaging and meeting platforms to simultaneously study many of the manuscripts and cuttings discussed below on multiple occasions, thereby creating a real-time method for international collaboration even before the COVID-19 pandemic necessitated an increase in digital approaches to collaborative research. [...]in the initial with Saint Margaret (Appendix A, no. 28), the Murano Master used the dragon to form part of the bar of the letter itself-another testament to the artist's creativity. The volume was purchased by the Kupferstichkabinett in 1888, auctioned by the German government in 1898, then reacquired by the state library in 1903.11 The Milan Gradual was recorded at the Palazzo Brera in 1810 but without additional evidence for how it arrived there.12 At least sixteen of the initials from the remaining dispersed volumes were sold at Sotheby's, London, in 1838 in the public sale of William Young Ottley (1771-1836), who likely acquired them during his travels in Italy in the 1790s (he mentions acquiring them from Murano;Appendix B).

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

Memory and the Representation of Public Health Crises: Remembering the Plague of Provence in the Tricentennial

From May 1720 through the summer of 1722, the French region of Provence and surrounding areas experienced one of the last major epidemics of plague to strike Western Europe. The Plague of Provence (or "Great Plague of Marseille") represents a major eighteenth-century disaster that left in its wake as many as 126,000 deaths. Over the last three hundred years, commemorative artworks have memorialized the disaster and helped define how it is remembered. To help mark the tricentennial, this essay will examine a series of images portraying different aspects of the Plague of Provence. In particular, it will analyze how these images depict what I argue are two central themes in the art of the Plague of Provence: Marseillais civic virtue and the medical profession. Doing so reveals how these subjects were valued or perceived in the first decades of the eighteenth century, and why this matters today as we confront new contagious diseases including COVID-19. © 2021 The Author(s). Published by Oxford University Press on behalf of the American Society for Environmental History and the Forest History Society. All rights reserved.

CoviChain: A Blockchain Based Framework for Nonrepudiable Contact Tracing in Healthcare Cyber-Physical Systems During Pandemic Outbreaks.

With the world facing the new virus SARS-CoV-2, many countries have introduced instant Internet applications to identify people carrying the infection. Internet-of-Medical-Things (IoMT) have proven useful in collecting medical data as well in tracing an individual carrying the virus. The data collected or traced belongs to an individual and should be revealed to themselves and hospital providers, but not to any third-party unauthorized agencies. In this paper we use an off-chain distributed storage solution for loading large medical data sets and a blockchain implementation to securely transfer the data from the infected person to the hospital system using the edge infrastructure, and call it CoviChain. The Coronavirus Disease (COVID-19) statistics are loaded on to the edge, and moved to InterPlanetary File Systems (IPFS) storage to retrieve the hash of the data file. Once the hash is obtained, it is moved to the blockchain by means of smart contracts. As the information is being hashed twice, CoviChain addresses the security and privacy issues and avoid exposing individuals' data while achieving larger data storage on the blockchain with reduced cost and time.

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach.

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.