ABSTRACT
Digital transformation has unveiled new prospects for increased performance and productivity in the agricultural sector to meet rising food security needs. Continuous industrialization and unexpected disruptions (e.g., workforce mobility restrictions due to the COVID‐19 pandemic) call for the adoption of agricultural robots. However, automated solutions could be associated with societal challenges in rural areas;unemployment growth has been perceived as a major threat that jeopardizes societal welfare, potentially hindering the implementation of digital technologies. In this context, human–robot synergistic systems could act as a promising socially viable alternative. Through systems thinking, this research investigates the complex interconnections and key feedback mechanisms of automation diffusion (conventional and human–robot interactive) under the socio‐economic perceptions (drivers and barriers) of agribusinesses and rural communities. Overall, this study contributes towards eliciting the mental models that underpin the transition from agricultural robots to human–robot collaboration by transforming automation‐related societal risks into opportunities for sustainable rural development.
ABSTRACT
The underlying assumption of using investor sentiment to predict stock prices, stock market returns, and liquidity is that of synergy between stock prices and investor sentiment. However, this synergistic relationship has received little attention in the literature. This paper investigates the synergistic pattern between stock prices and investor sentiment using social media messages from stock market investors and natural language processing techniques. At the macro level, we reveal extremely significant positive synergy between investor sentiment and stock prices. That is, when a stock price rises, investor sentiment rises, and when a stock price falls, investor sentiment falls. However, this synergy may be reversed or even disappear over a specific time period. Through a segmented measurement of the synergy between stock prices and investor sentiment over the course of a day, we also find that investor sentiment on social media is forward looking. This provides theoretical support for using investor sentiment in stock price prediction. We also examine the effect of lockdowns, the most draconian response to COVID-19, on synergy between stock prices and investor sentiment through causal inference machine learning. Our analysis shows that external anxiety can significantly affect synergy between stock prices and investor sentiment, but this effect can promote either positive or negative synergy. This paper offers a new perspective on stock price forecasting, investor sentiment, behavioral finance, and the impact of COVID-19 on the stock markets. Copyright (c) 2022 Borsa Istanbul Anonim S, irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ABSTRACT
Postcovid syndrome affects 5-20% of all patients with symp-tomatic Covid-I9 infection, resulting in temporary or perma-nent disability for next weeks or months. The commonest syn-dromes after long Covid-I9, (or chronic fatigue syndrome after Covid, or as synonymum postcovid syndrome) are psychic or psychosomatic disorders known under the name Depression and Anxiety Syndrome. After the unrest and armed conflicts during the Covid era, clients or patients, mainly migrants of war, are also exposed to chronic post trauma syndrome related to previous or recent de-struction of infrastructure, temporary homelesness and escape from affected regions/country. Cumulation of those 2 syndro-mes may have devastating consequences to both, individual health and economic losses due to permanent working and eco-nomy disabilities and consumption of health and social funds. After the unrest and armed conflicts during Covid era,clients or patients, mainly migrants of war, are also exposed to chronic post trauma syndrome related to previous or recent destruction of infrastructure, temporary homelesness and escape from af-fected regions/country.
ABSTRACT
Extensive research supported the therapeutic potential of curcumin, a naturally occurring compound, as a promising cytokinesuppressive anti-inflammatory drug. This study aimed to investigate the synergistic anti-inflammatory and anti-cytokine activities by combining 6-shogaol and 10-shogaol to curcumin, and associated mechanisms in modulating lipopolysaccharides and interferon-É£-induced proinflammatory signaling pathways. Our results showed that the combination of 6-shogaol-10-shogaolcurcumin synergistically reduced the production of nitric oxide, inducible nitric oxide synthase, tumor necrosis factor and interlukin-6 in lipopolysaccharides and interferon-γ-induced RAW 264.7 and THP-1 cells assessed by the combination index model. 6-shogaol-10-shogaol-curcumin also showed greater inhibition of cytokine profiling compared to that of 6-shogaol-10-shogaol or curcumin alone. The synergistic anti-inflammatory activity was associated with supressed NFκB translocation and downregulated TLR4-TRAF6-MAPK signaling pathway. In addition, SC also inhibited microRNA-155 expression which may be relevant to the inhibited NFκB translocation. Although 6-shogaol-10-shogaol-curcumin synergistically increased Nrf2 activity, the anti-inflammatory mechanism appeared to be independent from the induction of Nrf2. 6-shogaol-10-shogaol-curcumin provides a more potent therapeutic agent than curcumin alone in synergistically inhibiting lipopolysaccharides and interferon-γ induced proinflammatory mediators and cytokine array in macrophages. The action was mediated by the downregulation of TLR4/TRAF6/MAPK pathway and NFκB translocation.
ABSTRACT
The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry's ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry. © 2023 by the authors.
ABSTRACT
The purpose of this study is to examine the lived experiences of agricultural workers in the Chardavol Township about the new world. The present study is a qualitative research that has been done using an interpretive paradigm and interpretive phenomenological method. The statistical population of the study includes all villagers active in the agricultural sector in Zanjire Sofla village in Chardavol Township in Ilam province. 14 participants were selected by purposive sampling method until theoretical saturation. The semi-structured interview method was used to collect information from participants and Van Mannen's (1990) method was used to analyze the data obtained from the semi-structured interviews. The results showed that a main theme entitled "New World" and 7 sub-themes including new lifestyle, a distinct consumption pattern, the integration and synergy of tensions, understanding the cross-sectional remedial shock, the symmetry of old and new vulnerabilities, socio-protective isolation and low government presence and the tendency to counter-value measures and the experience of the new sin is experienced by the participants.
ABSTRACT
This study presents the anti-COVID potential of bioactive compounds from Chrysopogon zizanioides thorough in-silico molecular docking approach using AutoDock Vina software. As of our knowledge, the antiviral potential of all its bioactive compounds and their synergistic potentials against SARS-CoV-2 main-protease is not reported earlier. The results were promising with beta-Sitosterol (G = -7.5 kcal/mol;Ki = 3.13 micro M);Campesterol (G = -7.4 kcal/mol;Ki = 3.71 micro M);Stigmast-4-en-3-one (G = -7.3 kcal/mol;Ki = 4.39 micro M) forming non-covalent interactions with the amino acids in the active site of Mpro causing inhibition. The synergistic potential of compounds showed a significant sign of inhibition against Mpro with -7.9 kcal/mol with the sequential combination of beta-Sitosterol;Campesterol;Stigmast-4-en-3-one. The docking protocol validation was performed by re-docking and superimposing co-crystallized ligand, and interactions visualized using Discovery Studio 2020. Moreover, all the compounds satisfied Lipinski's oral drug-likeliness properties to be used and oral drug. These bioactive compounds of Chrysopogon zizanioides showed low binding energies against SARS-CoV-2 Mpro which proved their anti-COVID potential. Thus, by incorporating Chrysopogon zizanioides for consumption in daily life, it is very likely that one can get rid of COVID-19.
ABSTRACT
(1) Background: Healthcare workers experienced rising burnout rates during and after the COVID-19 pandemic. A practice-academic collaboration between health services researchers and the surgical services program of a Canadian tertiary-care urban hospital was used to develop, implement and evaluate a potential burnout intervention, the Synergy tool. (2) Methods: Using participatory action research methods, this project involved four key phases: (I) an environmental scan and a baseline survey assessment, (II), a workshop, (III) Synergy tool implementation and (IV) a staffing plan workshop. A follow-up survey to evaluate the impact of Synergy tool use on healthcare worker burnout will be completed in 2023. (3) Results: A baseline survey assessment indicated high to severe levels of personal and work-related burnout prior to project initiation. During the project phases, there was high staff engagement with Synergy tool use to create patient care needs profiles and staffing recommendations. (4) Conclusions: As in previous research with the Synergy tool, this patient needs assessment approach is an efficient and effective way to engage direct care providers in identifying and scoring acuity and dependency needs for their specific patient populations. The Synergy tool approach to assessing patient needs holds promise as a means to engage direct care providers and to give them greater control over their practice-potentially serving as a buffer against burnout.
Subject(s)
Burnout, Professional , COVID-19 , Humans , Pandemics , COVID-19/epidemiology , Canada , Health PersonnelABSTRACT
Antiviral compounds targeting cellular metabolism are part of the therapeutic arsenal to control the spread of virus infection, either as sole treatment or in combination with direct-acting antivirals (DAA) or vaccines. Here, we describe the effect of two of them, lauryl gallate (LG) and valproic acid (VPA) both exhibiting a wide antiviral spectrum, against infection by coronaviruses such as HCoV-229E, HCoV-OC43, and SARS-CoV-2. A consistent 2 to 4-log-decrease in virus yields was observed in the presence of each antiviral, with an average IC50 value of 1.6 µM for LG and 7.2 mM for VPA. Similar levels of inhibition were observed when adding the drug 1 h before adsorption, at the time of infection or 2 h after infection, supporting a postvirus entry mechanism of action. The specificity of the antiviral effect of LG against SARS-CoV-2, relative to other related compounds such as gallic acid (G) and epicatechin gallate (ECG), predicted to be better inhibitors according to in silico studies, was also demonstrated. The combined addition of LG, VPA, and remdesivir (RDV), a DAA with a proven effect against human coronaviruses, resulted in a robust synergistic effect between LG and VPA, and to a lesser extent between the other drug combinations. These findings reinforce the interest of these wide antiviral spectrum host-targeted compounds as a first line of defense against viral diseases or as a vaccine complement to minimize the gap in antibody-mediated protection evoked by vaccines, either in the case of SARS-CoV-2 or for other possible emerging viruses.
Subject(s)
COVID-19 , Coronavirus 229E, Human , Coronavirus OC43, Human , Hepatitis C, Chronic , Humans , Antiviral Agents/pharmacology , SARS-CoV-2ABSTRACT
This study describes a novel, neutralizing monoclonal antibody (mAb), 11D7, discovered by mouse immunization and hybridoma generation, against the parental Wuhan-Hu-1 RBD of SARS-CoV-2. We further developed this mAb into a chimeric human IgG and recombinantly expressed it in plants to produce a mAb with human-like, highly homogenous N-linked glycans that has potential to impart greater potency and safety as a therapeutic. The epitope of 11D7 was mapped by competitive binding with well characterized mAbs, suggesting that it is a Class 4 RBD-binding mAb that binds to the RBD outside the ACE2 binding site. Of note, 11D7 maintains recognition against the B.1.1.529 (Omicron) RBD, as well neutralizing activity. We also provide evidence that this novel mAb may be useful in providing additional synergy to established antibody cocktails, such as Evusheld™ containing the antibodies tixagevimab and cilgavimab, against the Omicron variant. Taken together, 11D7 is a unique mAb that neutralizes SARS-CoV-2 through a mechanism that is not typical among developed therapeutic mAbs and by being produced in ΔXFT Nicotiana benthamiana plants, highlights the potential of plants to be an economic and safety-friendly alternative platform for generating mAbs to address the evolving SARS-CoV-2 crisis.
ABSTRACT
To date, the selection of a project portfolio that maximises the decision-making outcome remains essential. However, existing research on project synergy has mainly focused on two projects, while there are multiple projects in some cases. Two kinds of synergies among multiple projects are proposed. First, multiple projects must be selected together, in order to produce synergy. Second, some projects depend on synergy with other projects, leading to a synergetic increase in performance. Furthermore, we present strategic synergy, with benefits, resources, and technology, which is quantified for a procurement project concerning a COVID-19 pandemic recovery plan. A design structure matrix is used to describe the technology diffusion among the projects. Then, strategic alignment is utilised to measure the strategic contribution of projects. Next, a portfolio selection model considering uncertainty is established, based on the strategic utility. Finally, our results indicate that selecting projects considering multi-project synergy is more advantageous. © 2023, Journal of Industrial and Management Optimization. All Rights Reserved.
ABSTRACT
Assessing progress towards achieving the Sustainable Development Goals (SDGs) is among the most pressing areas for sustainability research. Both international and inter-provincial trade has substantial impacts on sustainability. However, little is known about the impacts of inter-provincial trade on progress towards achieving the SDG targets and the relationships among SDG indicators through time and space. Here we, taking Chinese inter-provincial trade as a study case, used a spatiotemporal approach and the multi-regional input-output (MRIO) model to examine changes in six SDG indicators and their relationships within China in the year 2002, 2007, 2010, 2012, 2015, and 2017. The results showed that (1) Chinese inter-provincial trade improved the trade-related SDG target scores of 16 provinces out of the evaluated 30 provinces but reduced the trade-related SDG target scores of the remaining 14 provinces. (2) Chinese inter-provincial trade and distant trade were more beneficial for achieving the trade-related SDG targets in developed provinces (e.g., Beijing), which thus improved China's overall SDG target scores. In contrast, Chinese inter-provincial trade suppressed the trade-related SDG target scores of developing provinces (e.g., Guangxi). (3) Individual SDG indicators, SDG target bundles, and interactions among SDG indicators changed across both time and space. (4) The no-trade scenario in Hubei province during the COVID-19 pandemic will have a clearly inhibiting effect on China's overall SDG target scores. Besides, trade with adjacent provinces would improve Hubei's SDG target scores, while these trades have more negative effects (approximately 50-83% of provinces suffered from greater reductions in SDG target scores) on Hubei's adjacent provinces. Our study suggests the spatiotemporal dynamic characteristics of SDG indicators and their interactions deserve more attention, which can help identify the drivers behind these changing relationships.
ABSTRACT
Assessing progress towards achieving the Sustainable Development Goals (SDGs) is among the most pressing areas for sustainability research. Both international and inter–provincial trade has substantial impacts on sustainability. However, little is known about the impacts of inter–provincial trade on progress towards achieving the SDG targets and the relationships among SDG indicators through time and space. Here we, taking Chinese inter–provincial trade as a study case, used a spatiotemporal approach and the multi–regional input–output (MRIO) model to examine changes in six SDG indicators and their relationships within China in the year 2002, 2007, 2010, 2012, 2015, and 2017. The results showed that (1) Chinese inter–provincial trade improved the trade–related SDG target scores of 16 provinces out of the evaluated 30 provinces but reduced the trade–related SDG target scores of the remaining 14 provinces. (2) Chinese inter–provincial trade and distant trade were more beneficial for achieving the trade–related SDG targets in developed provinces (e.g., Beijing), which thus improved China's overall SDG target scores. In contrast, Chinese inter–provincial trade suppressed the trade–related SDG target scores of developing provinces (e.g., Guangxi). (3) Individual SDG indicators, SDG target bundles, and interactions among SDG indicators changed across both time and space. (4) The no–trade scenario in Hubei province during the COVID–19 pandemic will have a clearly inhibiting effect on China's overall SDG target scores. Besides, trade with adjacent provinces would improve Hubei's SDG target scores, while these trades have more negative effects (approximately 50–83% of provinces suffered from greater reductions in SDG target scores) on Hubei's adjacent provinces. Our study suggests the spatiotemporal dynamic characteristics of SDG indicators and their interactions deserve more attention, which can help identify the drivers behind these changing relationships.
ABSTRACT
Three directly acting antivirals (DAAs) demonstrated substantial reduction in COVID-19 hospitalizations and deaths in clinical trials. However, these agents did not completely prevent severe illness and are associated with cases of rebound illness and viral shedding. Combination regimens can enhance antiviral potency, reduce the emergence of drug-resistant variants, and lower the dose of each component in the combination. Concurrently targeting virus entry and virus replication offers opportunities to discover synergistic drug combinations. While combination antiviral drug treatments are standard for chronic RNA virus infections, no antiviral combination therapy has been approved for SARS-CoV-2. Here, we demonstrate that combining host-targeting antivirals (HTAs) that target TMPRSS2 and hence SARS-CoV-2 entry, with the DAA molnupiravir, which targets SARS-CoV-2 replication, synergistically suppresses SARS-CoV-2 infection in Calu-3 lung epithelial cells. Strong synergy was observed when molnupiravir, an oral drug, was combined with three TMPRSS2 (HTA) oral or inhaled inhibitors: camostat, avoralstat, or nafamostat. The combination of camostat plus molnupiravir was also effective against the beta and delta variants of concern. The pyrimidine biosynthesis inhibitor brequinar combined with molnupiravir also conferred robust synergistic inhibition. These HTA+DAA combinations had similar potency to the synergistic all-DAA combination of molnupiravir plus nirmatrelvir, the protease inhibitor found in paxlovid. Pharmacodynamic modeling allowed estimates of antiviral potency at all possible concentrations of each agent within plausible therapeutic ranges, suggesting possible in vivo efficacy. The triple combination of camostat, brequinar, and molnupiravir further increased antiviral potency. These findings support the development of HTA+DAA combinations for pandemic response and preparedness. IMPORTANCE Imagine a future viral pandemic where if you test positive for the new virus, you can quickly take some medicines at home for a few days so that you do not get too sick. To date, only single drugs have been approved for outpatient use against SARS-CoV-2, and we are learning that these have some limitations and may succumb to drug resistance. Here, we show that combinations of two oral drugs are better than the single ones in blocking SARS-CoV-2, and we use mathematical modeling to show that these drug combinations are likely to work in people. We also show that a combination of three oral drugs works even better at eradicating the virus. Our findings therefore bode well for the development of oral drug cocktails for at home use at the first sign of an infection by a coronavirus or other emerging viral pathogens.
Subject(s)
COVID-19 Drug Treatment , SARS-CoV-2 , Humans , Antiviral Agents/pharmacology , Protease Inhibitors/pharmacology , Drug Combinations , PyrimidinesABSTRACT
The underlying assumption of using investor sentiment to predict stock prices, stock market returns, and liquidity is that of synergy between stock prices and investor sentiment. However, this synergistic relationship has received little attention in the literature. This paper investigates the synergistic pattern between stock prices and investor sentiment using social media messages from stock market investors and natural language processing techniques. At the macro level, we reveal extremely significant positive synergy between investor sentiment and stock prices. That is, when a stock price rises, investor sentiment rises, and when a stock price falls, investor sentiment falls. However, this synergy may be reversed or even disappear over a specific time period. Through a segmented measurement of the synergy between stock prices and investor sentiment over the course of a day, we also find that investor sentiment on social media is forward looking. This provides theoretical support for using investor sentiment in stock price prediction. We also examine the effect of lockdowns, the most draconian response to COVID-19, on synergy between stock prices and investor sentiment through causal inference machine learning. Our analysis shows that external anxiety can significantly affect synergy between stock prices and investor sentiment, but this effect can promote either positive or negative synergy. This paper offers a new perspective on stock price forecasting, investor sentiment, behavioral finance, and the impact of COVID-19 on the stock markets.
ABSTRACT
Purpose: The development of effective treatments for coronavirus infectious disease 19 (COVID-19) caused by SARS-Coronavirus-2 was hindered by the little data available about this virus at the start of the pandemic. Drug repurposing provides a good strategy to explore approved drugs' possible SARS-CoV-2 antiviral activity. Moreover, drug synergism is essential in antiviral treatment due to improved efficacy and reduced toxicity. In this work, we studied the effect of approved and investigational drugs on one of SARS-CoV-2 essential proteins, the main protease (Mpro), in search of antiviral treatments and/or drug combinations. Methods: Different possible druggable sites of Mpro were identified and screened against an in-house library of more than 4000 chemical compounds. Molecular dynamics simulations were carried out to explore conformational changes induced by different ligands' binding. Subsequently, the inhibitory effect of the identified compounds and the suggested drug combinations on the Mpro were established using a 3CL protease (SARS-CoV-2) assay kit. Results: Three potential inhibitors in three different binding sites were identified; favipiravir, cefixime, and carvedilol. Molecular dynamics simulations predicted the synergistic effect of two drug combinations: favipiravir/cefixime, and favipiravir/carvedilol. The in vitro inhibitory effect of the predicted drug combinations was established on this enzyme. Conclusion: In this work, we could study one of the promising SARS-CoV-2 viral protein targets in searching for treatments for COVID-19. The inhibitory effect of several drugs on Mpro was established in silico and in vitro assays. Molecular dynamics simulations showed promising results in predicting the synergistic effect of drug combinations.
Subject(s)
COVID-19 Drug Treatment , Coronavirus 3C Proteases , Amides , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Carvedilol , Cefixime , Cysteine Endopeptidases/chemistry , Cysteine Endopeptidases/metabolism , Drugs, Investigational , Humans , Ligands , Molecular Dynamics Simulation , Pyrazines , SARS-CoV-2 , Viral ProteinsABSTRACT
Teaching tools are playing an increasingly important role in modern education, especially in this post-pandemic era, the outbreak of Covid-19 pandemic and the social distancing policy have brought fundamental changes to the education mode in China. Fully and rationally making use of multiple teaching tools in blended classrooms is helpful in realizing teaching goals, moreover, it can increase students’ interest in learning and activate classroom atmosphere. However, current studies on the teaching tools of blended classrooms fail to well exhibit teaching content in the classrooms, the existing tools only have limited functions, and such classrooms couldn’t effectively train students’ abilities to communicate or make self-evaluations. In view of these problems, this paper aims to study the selection and utilization of multiple teaching tools in blended classrooms from the perspective of synergistic effect. At first, this paper built an Evaluation Index System (EIS) for assessing the degree of synergy of multiple teaching tools when they are used in blended classrooms;then, it calculated the degree of order of these tools in the said classrooms;at last, this paper analyzed the effectiveness of the synergistic effect of different teaching tools. © 2022. International Journal of Emerging Technologies in Learning.All Rights Reserved
ABSTRACT
The technology innovation of high-tech industries has become an important support for the innovation-driven strategy. This study introduces innovation ecosystem synergy as a moderating variable from a systemic and holistic perspective based on the traditional perspective of innovation factor input-output, and helps construct a technology innovation performance driving model based on the Cobb–Douglas knowledge production function, which enriches the discussion perspective and theoretical model research on technology innovation performance. With a sample of 28 provinces in mainland China, this study empirically analyzed the moderating mechanism of innovation performance by innovation synergy in high-tech industries during the two stages of technology development and technology transformation. The findings of the study are as follows: (1) Independent research and development has a positive and significant impact on technology development performance;product innovation has a positive and significant impact on technology transformation performance;(2) Technology introduction can weaken technology development performance due to technology dependence and the inhibitory effect on independent innovation, and inefficient technology renovation can negatively and significantly affect technology transformation performance.;(3) The degree of synergy has a positive and significant impact on the performance of technology development innovation and technology transformation innovation. The degree of synergy has a positive moderating effect on the innovation performance of independent R&D and technology development, as well as product innovation and technology renovation, and a negative moderating effect on the innovation performance of technology introduction and technology development, but no significant moderating effect on technology renovation and technology transformation performance. The research results can provide a reference for the improvement of the technology innovation performance of regional high-tech industries.
ABSTRACT
This paper presents Macquarie University’s participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task B (BioASQ10b), Phase B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the n highest-scoring sentences are returned as the answer. The Synergy Task corresponds to an end-to-end system that requires document selection, snippet selection, and finding the final answer, but it has very limited training data. For the Synergy task, we selected the candidate sentences following two phases: document retrieval and snippet retrieval, and the final answer was found by using a DistilBERT/ALBERT classifier that had been trained on the training data of BioASQ9b. Document retrieval was achieved as a standard search over the CORD-19 data using the search API provided by the BioASQ organisers, and snippet retrieval was achieved by re-ranking the sentences of the top retrieved documents, using the cosine similarity of the question and candidate sentence. We observed that vectors represented via sBERT have an edge over tf.idf. BioASQ10b Phase B focuses on finding the specific answers to biomedical questions. For this task, we followed a data-centric approach. We hypothesised that the training data of the first BioASQ years might be biased and we experimented with different subsets of the training data. We observed an improvement of results when the system was trained on the second half of the BioASQ10b training data. © 2022 Copyright for this paper by its authors.