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Article ; Online: The unintended consequences of exemptions in conservation and management measures for fisheries management

Haas, Bianca / Azmi, Kamal / Hanich, Quentin

Ocean and Coastal Management. 2023 Apr., v. 237 p.106544-

2023

Abstract: The duty to recognize the special requirements of developing states, and ensure that conservation and management measures avoid placing a disproportionate burden on them, has been firmly anchored in the United Nations Convention on the Law of the Sea and ...

Abstract	The duty to recognize the special requirements of developing states, and ensure that conservation and management measures avoid placing a disproportionate burden on them, has been firmly anchored in the United Nations Convention on the Law of the Sea and the United Nations Fish Stocks Agreement. Coastal developing states, particularly small island developing states (SIDS), are often economically and socially dependent on marine resources, and their development aspirations have been recognized by the international community. Ideally, members of regional fisheries management organizations (RFMOs) will meet their duty to avoid placing a disproportionate conservation burden on SIDS by designing and agreeing upon conservation and management measures that are equitable in terms of both their ease of implementation and their substantive impact on each participating state, such as through the equitable allocation of fishing opportunities. Where RFMOs are unable to adopt equitable measures, they may rely on the use of exemptions from conservation and management measures for developing states as a second-best alternative. However, exemptions have the potential to threaten the sustainability of the respective target stocks by creating loopholes in catch and effort limits. They can also undermine the scarcity value created by strong catch and effort limits, which can generate higher access fees for SIDS. In this paper, we analysed the conservation and management measures of RFMOs that include exemptions from catch, effort and capacity limits and found that they are used most commonly in the Western and Central Pacific Fisheries Commission. We argue that the use of exemptions due to the failure of RFMOs to adopt equitable allocation frameworks has the potential to negatively impact marine resources and their development opportunities. Instead, alternatives, such as equitable allocations of science-based catch and effort limits, transferability and phased adjustments, should be developed.
Keywords	United Nations ; coastal zone management ; design ; fish ; fisheries ; fisheries management ; islands ; marine resources ; oceans ; resource management ; wills ; Ocean governance ; Pacific ocean ; Tuna fishery
Language	English
Dates of publication	2023-04
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ISSN	0964-5691
DOI	10.1016/j.ocecoaman.2023.106544
Database	NAL-Catalogue (AGRICOLA)

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Article: Regional fisheries management: COVID-19 calendars and decision making.

Haas, Bianca / Davis, Ruth / Campbell, Brooke / Hanich, Quentin

Marine policy

2021 Volume 128, Page(s) 104474

Abstract: In 2020 the management of transboundary fisheries was severely impacted by the global COVID-19 pandemic. Most annual meetings of regional fisheries and marine management organizations were held virtually, postponed, or cancelled. Even though most ... ...

Abstract	In 2020 the management of transboundary fisheries was severely impacted by the global COVID-19 pandemic. Most annual meetings of regional fisheries and marine management organizations were held virtually, postponed, or cancelled. Even though most organizations managed to meet virtually in 2020, many important decisions were postponed to 2021. Consequently, regional secretariats and delegations face a difficult calendar with substantial agendas and complex decision-making challenges. This commentary provides a brief overview of the virtual meeting processes that have been implemented by regional organisations in response to COVID-19 and provides a calendar of their plans for 2021.
Language	English
Publishing date	2021-03-06
Publishing country	Netherlands
Document type	Journal Article
ISSN	0308-597X
ISSN	0308-597X
DOI	10.1016/j.marpol.2021.104474
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Distinct Roles of Sialyltransferases in Cancer Biology and Onco-Immunology.

Hugonnet, Marjolaine / Singh, Pushpita / Haas, Quentin / von Gunten, Stephan

Frontiers in immunology

2021 Volume 12, Page(s) 799861

Abstract: Aberrant glycosylation is a key feature of malignant transformation. Hypersialylation, the enhanced expression of sialic acid-terminated glycoconjugates on the cell surface, has been linked to immune evasion and metastatic spread, eventually by ... ...

Abstract	Aberrant glycosylation is a key feature of malignant transformation. Hypersialylation, the enhanced expression of sialic acid-terminated glycoconjugates on the cell surface, has been linked to immune evasion and metastatic spread, eventually by interaction with sialoglycan-binding lectins, including Siglecs and selectins. The biosynthesis of tumor-associated sialoglycans involves sialyltransferases, which are differentially expressed in cancer cells. In this review article, we provide an overview of the twenty human sialyltransferases and their roles in cancer biology and immunity. A better understanding of the individual contribution of select sialyltransferases to the tumor sialome may lead to more personalized strategies for the treatment of cancer.
MeSH term(s)	Animals ; Glycosylation ; Humans ; Isoenzymes ; Neoplasms/enzymology ; Neoplasms/immunology ; Neoplasms/metabolism ; Protein Processing, Post-Translational ; Selectins/metabolism ; Sialic Acid Binding Immunoglobulin-like Lectins/metabolism ; Sialic Acids/metabolism ; Sialyltransferases/metabolism ; Substrate Specificity
Chemical Substances	Isoenzymes ; Selectins ; Sialic Acid Binding Immunoglobulin-like Lectins ; Sialic Acids ; Sialyltransferases (EC 2.4.99.-)
Language	English
Publishing date	2021-12-17
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2021.799861
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research.

Borissov, Nikolay / Haas, Quentin / Minder, Beatrice / Kopp-Heim, Doris / von Gernler, Marc / Janka, Heidrun / Teodoro, Douglas / Amini, Poorya

Systematic reviews

2022 Volume 11, Issue 1, Page(s) 172

Abstract: Background: Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related ...

Abstract	Background: Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related to manual deduplication, we developed an automated, efficient, and rapid artificial intelligence-based algorithm named Deduklick. Deduklick combines natural language processing algorithms with a set of rules created by expert information specialists. Methods: Deduklick's deduplication uses a multistep algorithm of data normalization, calculates a similarity score, and identifies unique and duplicate references based on metadata fields, such as title, authors, journal, DOI, year, issue, volume, and page number range. We measured and compared Deduklick's capacity to accurately detect duplicates with the information specialists' standard, manual duplicate removal process using EndNote on eight existing heterogeneous datasets. Using a sensitivity analysis, we manually cross-compared the efficiency and noise of both methods. Discussion: Deduklick achieved average recall of 99.51%, average precision of 100.00%, and average F1 score of 99.75%. In contrast, the manual deduplication process achieved average recall of 88.65%, average precision of 99.95%, and average F1 score of 91.98%. Deduklick achieved equal to higher expert-level performance on duplicate removal. It also preserved high metadata quality and drastically reduced time spent on analysis. Deduklick represents an efficient, transparent, ergonomic, and time-saving solution for identifying and removing duplicates in SRs searches. Deduklick could therefore simplify SRs production and represent important advantages for scientists, including saving time, increasing accuracy, reducing costs, and contributing to quality SRs.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Biomedical Research ; Humans ; Natural Language Processing ; Systematic Reviews as Topic
Language	English
Publishing date	2022-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2662257-9
ISSN	2046-4053 ; 2046-4053
ISSN (online)	2046-4053
ISSN	2046-4053
DOI	10.1186/s13643-022-02045-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Cartography of Siglecs and Sialyltransferases in Gynecologic Malignancies: Is There a Road Towards a Sweet Future?

Haas, Quentin / Simillion, Cedric / von Gunten, Stephan

Frontiers in oncology

2018 Volume 8, Page(s) 68

Abstract: Altered surface glycosylation is a key feature of cancers, including gynecologic malignancies. Hypersialylation, the overexpression of sialic acid, is known to promote tumor progression and to dampen antitumor responses by mechanisms that also involve ... ...

Abstract	Altered surface glycosylation is a key feature of cancers, including gynecologic malignancies. Hypersialylation, the overexpression of sialic acid, is known to promote tumor progression and to dampen antitumor responses by mechanisms that also involve sialic acid binding immunoglobulin-like lectins (Siglecs), inhibitory immune receptors. Here, we discuss the expression patterns of Siglecs and sialyltransferases (STs) in gynecologic cancers, including breast, ovarian, and uterine malignancies, based on evidence from The Cancer Genome Atlas. The balance between sialosides generated by specific STs within the tumor microenvironment and Siglecs on leukocytes may play a decisive role for antitumor immunity. An interdisciplinary effort is required to decipher the characteristics and biological impact of the altered tumor sialome in gynecologic cancers and to exploit this knowledge to the clinical benefit of patients.
Language	English
Publishing date	2018-03-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2018.00068
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.

Knafou, Julien / Haas, Quentin / Borissov, Nikolay / Counotte, Michel / Low, Nicola / Imeri, Hira / Ipekci, Aziz Mert / Buitrago-Garcia, Diana / Heron, Leonie / Amini, Poorya / Teodoro, Douglas

Systematic reviews

2023 Volume 12, Issue 1, Page(s) 94

Abstract: Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health ... ...

Abstract	Background: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. Methods: In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results: The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion: This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently and support epidemiological curation and review. The ensemble consistently and significantly outperforms any standalone model. Fine-tuning the voting strategy thresholds is an interesting alternative to annotate a subset with higher predictive confidence.
MeSH term(s)	Humans ; Deep Learning ; COVID-19 ; Pandemics ; Retrospective Studies ; Language
Language	English
Publishing date	2023-06-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2662257-9
ISSN	2046-4053 ; 2046-4053
ISSN (online)	2046-4053
ISSN	2046-4053
DOI	10.1186/s13643-023-02247-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

Julien Knafou / Quentin Haas / Nikolay Borissov / Michel Counotte / Nicola Low / Hira Imeri / Aziz Mert Ipekci / Diana Buitrago-Garcia / Leonie Heron / Poorya Amini / Douglas Teodoro

Systematic Reviews, Vol 12, Iss 1, Pp 1-

2023 Volume 16

Abstract: Abstract Background The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy ... ...

Abstract	Abstract Background The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process. Methods In this retrospective study, five different pre-trained deep learning-based language models were fine-tuned on a dataset of 6365 publications manually classified into two classes, three subclasses, and 22 sub-subclasses relevant for epidemiological triage purposes. In a k-fold cross-validation setting, each standalone model was assessed on a classification task and compared against an ensemble, which takes the standalone model predictions as input and uses different strategies to infer the optimal article class. A ranking task was also considered, in which the model outputs a ranked list of sub-subclasses associated with the article. Results The ensemble model significantly outperformed the standalone classifiers, achieving a F1-score of 89.2 at the class level of the classification task. The difference between the standalone and ensemble models increases at the sub-subclass level, where the ensemble reaches a micro F1-score of 70% against 67% for the best-performing standalone model. For the ranking task, the ensemble obtained the highest recall@3, with a performance of 89%. Using an unanimity voting rule, the ensemble can provide predictions with higher confidence on a subset of the data, achieving detection of original papers with a F1-score up to 97% on a subset of 80% of the collection instead of 93% on the whole dataset. Conclusion This study shows the potential of using deep learning language models to perform triage of COVID-19 references efficiently ...
Keywords	COVID-19 ; Living systematic review ; Literature screening ; Text classification ; Language model ; Deep learning ; Medicine ; R
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Daratumumab monotherapy in refractory warm autoimmune hemolytic anemia and cold agglutinin disease.

Jalink, Marit / Jacobs, Chaja F / Khwaja, Jahanzaib / Evers, Dorothea / Bruggeman, Coty / Fattizzo, Bruno / Michel, Marc / Crickx, Etienne / Hill, Quentin A / Jaeger, Ulrich / Kater, Arnon P / Mäkelburg, Anja B U / Breedijk, Anouk / Te Boekhorst, Peter A W / Hoeks, Marlijn P A / de Haas, Masja / D'Sa, Shirley P / Vos, Josephine M I

Blood advances

2024

Abstract: Autoimmune hemolytic anemia (AIHA) is a rare autoantibody-mediated disease. For steroid and/or rituximab-refractory AIHA, there is no consensus on optimal treatment. Daratumumab, a monoclonal antibody targeting CD38, could be beneficial by suppression of ...

Abstract	Autoimmune hemolytic anemia (AIHA) is a rare autoantibody-mediated disease. For steroid and/or rituximab-refractory AIHA, there is no consensus on optimal treatment. Daratumumab, a monoclonal antibody targeting CD38, could be beneficial by suppression of CD38+ plasmacells and thus autoantibody secretion. In addition, since CD38 is also expressed by activated T-cells, daratumumab may also act via immunomodulatory effects. We evaluated efficacy and safety of daratumumab monotherapy in an international retrospective study including 19 adult patients with heavily pretreated refractory AIHA. In warm AIHA (wAIHA, n=12), overall response was 50% with a median response duration of 5.5 months (range, 2-12 months) including ongoing response in 2 patients after 6 and 12 months. Of 6 non-responders, 4 had Evans syndrome. In cold AIHA (cAIHA, n=7) overall hemoglobin (Hb) response was 57%, with ongoing response in 3/7 patients. One additional non-anemic cAIHA patient was treated for severe acrocyanosis and reached a clinical acrocyanosis response as well as a Hb increase. Of 6 cAIHA patients with acrocyanosis, 4 had improved symptoms after daratumumab treatment. In two patients with wAIHA treated with daratumumab in whom we prospectively collected blood samples, we found complete CD38+ T cells depletion after daratumumab, as well as altered T-cell subset differentiation and a severely diminished capacity for cell activation and proliferation. Reappearance of CD38+ T-cells coincided with disease relapse in one patient. In conclusion, our data show that daratumumab therapy may be a treatment option for refractory AIHA. The observed immunomodulatory effects that may contribute to the clinical response deserve further exploration.
Language	English
Publishing date	2024-03-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2915908-8
ISSN	2473-9537 ; 2473-9529
ISSN (online)	2473-9537
ISSN	2473-9529
DOI	10.1182/bloodadvances.2024012585
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Vaccine Development in the Time of COVID-19: The Relevance of the Risklick AI to Assist in Risk Assessment and Optimize Performance.

Haas, Quentin / Borisov, Nikolay / Alvarez, David Vicente / Ferdowsi, Sohrab / von Mayenn, Leonhard / Teodoro, Douglas / Amini, Poorya

Frontiers in digital health

2021 Volume 3, Page(s) 745674

Abstract: The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence ...

Abstract	The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence (AI), we estimated the risks associated with all types of COVID-19 vaccine during the early phase of vaccine development. We then performed a postmortem analysis of the probability and the impact matrix calculations by comparing the 2020 prognosis to the contemporary situation. We used the Risklick AI to evaluate the risks and their incidence associated with vaccine development in the early stage of the COVID-19 pandemic. Our analysis revealed the diversity of risks among vaccine technologies currently used by pharmaceutical companies providing vaccines. This analysis highlighted the current and future potential pitfalls connected to vaccine production during the COVID-19 pandemic. Hence, the Risklick AI appears as an essential tool in vaccine development for the treatment of COVID-19 in order to formally anticipate the risks, and increases the overall performance from the production to the distribution of the vaccines. The Risklick AI could, therefore, be extended to other fields of research and development and represent a novel opportunity in the calculation of production-associated risks.
Language	English
Publishing date	2021-11-02
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2021.745674
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.

Haas, Quentin / Alvarez, David Vicente / Borissov, Nikolay / Ferdowsi, Sohrab / von Meyenn, Leonhard / Trelle, Sven / Teodoro, Douglas / Amini, Poorya

Pharmacology

2021 Volume 106, Issue 5-6, Page(s) 244–253

Abstract: Introduction: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. ... ...

Abstract	Introduction: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. Methods: In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. Results: Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention. Discussion: The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.
MeSH term(s)	Artificial Intelligence/statistics & numerical data ; Artificial Intelligence/trends ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19/therapy ; Clinical Trials as Topic/statistics & numerical data ; Data Interpretation, Statistical ; Drug Development/statistics & numerical data ; Drug Development/trends ; Evidence-Based Medicine/statistics & numerical data ; Evidence-Based Medicine/trends ; Humans ; Pharmacology/statistics & numerical data ; Pharmacology/trends ; Registries
Language	English
Publishing date	2021-04-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	206671-3
ISSN	1423-0313 ; 0031-7012
ISSN (online)	1423-0313
ISSN	0031-7012
DOI	10.1159/000515908
Database	MEDical Literature Analysis and Retrieval System OnLINE

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