bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 virus mutation prediction with LSTM and attention mechanisms
Authors: Burukanli Mehmet , Yumuşak Nejat 
Published: June 2024

Abstract
Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2, is an emerging and rapidly spreading type of coronavirus. One of the most important reasons for the rapid spread of the COVID-19 virus are the frequent mutations of the COVID-19 virus. One of the most important methods to overcome mutations of the COVID-19 virus is to predict these mutations before they occur. In this study, we propose a robust HyperMixer and long short-term memory based model with attention mechanisms, HyperAttCov, for COVID-19 virus mutation prediction. The proposed HyperAttCov model outperforms several state-of-the-art methods. Experimental results have showed that the proposed HyperAttCov model reached accuracy 70.0%, precision 92.0%, MCC 46.5% on the COVID-19 testing dataset. Similarly, the proposed HyperAttCov model reached accuracy 70.2%, precision 90.4%, MCC 46.2% on the COVID-19 testing dataset with an average of 10 random trail. Besides, When the proposed HyperAttCov model with 10 random trail has been compared with compared to the study in the literature, the average of performance values has been increased by accuracy 7.18%, precision 37.39%, MCC 49.51% on the testing dataset. As a result, the proposed HyperAttCov can successfully predict mutations occurring on the COVID-19 dataset in the 2022 year.
Factors associated with intent to stay in the profession: an exploratory cluster analysis across healthcare professions in Switzerland
Authors: Roth Leonard , Gilles Ingrid , Antille Emilie , Jubin Jonathan , Jolidon Vladimir , Oulevey-Bachmann Annie , Peytremann-Bridevaux Isabelle 
Published: June 2024

Abstract
Retention issues are widespread within the health workforce. This cross-sectional study used data collected from 1707 healthcare professionals in 2022–23 to identify with k-means clustering groups of individuals sharing similar working experiences. These profiles were linked with varying levels of turnover intentions and a range of healthcare professions. While occupational therapists and paramedics reported in average better working conditions, registered nurses and intermediate caregivers reported the poorest experiences. In other clusters, salaries were high where work–life balance was low, and inversely. By learning from similarities and differences in the working conditions of diverse healthcare professionals, shared initiatives aimed at improving retention across professions can be facilitated.
A Debate of the Highest Order: The Brexit Referendum as Second-Order Role Contestation
Authors: Martill Benjamin , Rogstad Adrian 
Published: June 2024

Abstract
The 2016 Brexit referendum reignited debate on the UK’s international role. Yet the stakes were complex, since neither side challenged Britain’s global leadership role or its strong ties with non-European partners. Research on role contestation has thus struggled to account for the politics of Brexit, focusing instead on non-role-based conflict. We argue that Brexit debates can be understood by reference to second-order role contestation where role compatibility itself is the subject of political disagreements, a phenomenon role theory scholarship has missed by studying role conflict and role contestation in isolation. We distinguish between inclusive and exclusive second-order conceptions, which capture the respective positions of the Leave and Remain campaigns regarding Britain’s European role and its relation to other (shared) roles. Our argument shows that role conflict is often contested politically and that role contestation operates at higher levels of abstraction than conventionally acknowledged.
Searching for choice and control: Western Australian service provider experiences of health, housing and migration
Authors: Connor Elizabeth , Blackford Krysten , McCausland Kahlia , Lobo Roanna , Crawford Gemma 
Published: June 2024

Abstract
This research aimed to inform approaches to increase access to secure housing and improve mental health outcomes for migrants from culturally and linguistically diverse backgrounds (hereafter migrants) who are generally invisible in health and social policy and service provision in Western Australia. We used semi-structured, in-depth interviews (n = 11) and interpretative phenomenological analysis to explore service provider experiences and perspectives of issues impacting service provision and the needs of migrants in this context. Five superordinate themes reveal complex experiences for both service providers and the migrants with whom they work. Findings reflect tensions between contemporary notions of choice and control and a social service system that is difficult to navigate, reflects systemic racism and appears to rely heavily on the non-government sector. Insights have important and practical implications for health promotion policy, practice and research. Recommendations include improvements to housing access, provision, funding and policies; addressing service barriers via staff training and more accessible community resources; and co-design and community outreach approaches.
Influences on COVID-19 booster uptake among adults intending to receive a booster: a qualitative study
Authors: Moore Ramey , Purvis Rachel S , Willis Don E , Li Ji , Selig James P , Ross Jeanne , McElfish Pearl A 
Published: June 2024

Abstract
Bivalent COVID-19 vaccine boosters have been recommended for all Americans 12 years of age and older. However, uptake remains suboptimal with only 17% of the United States (US) population boosted as of May 2023. This is a critical public health challenge for mitigating the ongoing effects of COVID-19 infection. COVID-19 booster uptake is not currently well understood, and few studies in the US have explored the vaccination process for booster uptake in a ‘post-pandemic’ context. This study fills gaps in the literature through qualitative analysis of interviews with a racially/ethnically diverse sample of Arkansans who received the COVID-19 vaccine main series and expressed intent to receive a booster (n = 14), but had not yet received the COVID-19 booster at the time we recruited them. All but one did not receive the booster by the time of the interview. Participants described influences on their vaccination behavior and uptake of boosters including reduced feelings of urgency; continued concerns about the side effects; social contagion as a driver of urgency; increasing practical barriers to access and missing provider recommendations. Our findings highlight the importance of considering vaccination as an ongoing, dynamic process drawing on past/current attitudes, prior experience, perceptions of risk and urgency and practical barriers. Based on these findings, healthcare providers should continue to provide strong, consistent recommendations for COVID-19 boosters to patients, even among those with histories of vaccine uptake.
Associations between noticing public health education campaigns about cannabis and risk perceptions in the northern Canadian territories: a cross-sectional study
Authors: Schwartz Naomi , Poon Theresa , Hammond David , Hobin Erin 
Published: June 2024

Abstract
This study investigated whether noticing cannabis education campaigns was associated with increased cannabis risk perceptions in Canada’s three territories following non-medical cannabis legalization. Data were from the Cannabis Policy Study in the Territories, including 2452 participants, age ≥16 years residing in Yukon, Northwest Territories and Nunavut. Poisson regression with robust standard errors were used to estimate associations between noticing cannabis education campaigns and moderate to very high risk perceptions of daily cannabis smoking, vaping, edible use and exposure to second-hand cannabis smoke, adjusting for sociodemographic characteristics and cannabis-use frequency. Results were compared with associations with risk perceptions of daily alcohol consumption and cigarette smoking, not included in cannabis education campaigns. Interactions were examined between noticing education campaigns and age group and cannabis-use frequency. Cannabis education campaigns were noticed by 40.4% of respondents, with lower awareness among those with lower education and income. Noticing campaigns was associated with higher risk perceptions of daily cannabis smoking [adjusted risk ratio (RRadj) = 1.09, 95% confidence interval (CI): 1.02–1.16] and vaping (RRadj = 1.09, 95%CI: 1.02–1.16). Significant interactions were not found with age group or cannabis-use frequency. Findings are consistent with modest effects of cannabis education campaigns. Approaches are needed to increase reach of cannabis education campaigns, including among groups with lower education and income.
Era of the Female Witness: Jewish Women and the Trial of Klaus Barbie
Authors: Valanzola Ashley 
Published: June 2024

Abstract
In May 1987, Sabine Zlatin and Simone Lagrange became household names in France after they testified against the infamous Nazi Klaus Barbie, “the Butcher of Lyon,” during his trial for crimes against humanity. On the witness stand, Zlatin’s testimony revealed her perseverance as a Polish-Jewish immigrant involved extensively in wartime rescue and resistance. Meanwhile, Lagrange shared her encounters as a thirteen-year-old Jewish girl whom Klaus Barbie tortured. Throughout Barbie’s trial, national and international media outlets reported frequently on Zlatin and Lagrange’s wartime and postwar lives. The enormous media attention the trial received made it a crucial event during the resurgence of Holocaust memory in France, yet what made this trial unique regarding the role of Jewish women as witnesses was its timing in the aftermath of the women’s rights movement. The feminist movement allowed people to better understand the gendered nature of Zlatin and Lagrange’s testimonies and recognize their persecution and perseverance as women during and after the war. Going forward, the centrality of experiences shared by women shaped how the trial would be remembered, and arguably even influenced a greater consideration of crimes against women within the statutes for crimes against humanity.
Genome resequencing reveals the genetic basis of population evolution, local adaptation, and rewiring of the rhizome metabolome in Atractylodes lancea
Authors: Zhang Chengcai , Wang Sheng , Sun Jiahui , Li Xiangkong , Wang Hongyang , Guo Xiuzhi , Wang Yuefeng , Jiang Daiquan , Lyu Chaogeng , Kang Chuanzhi , Zhang Yan , Xiang Zengxu , Yuan Qingjun , Yan Binbin , Qin Ming , Huang Luqi , Guo Lanping 
Published: June 2024

Abstract
The formation of high-quality Chinese medicinal materials is a micro-evolutionary process of multiple genes involving quantitative inheritance under environmental stress. Atractylodes lancea is a traditionally used medicinal plant in China that is broadly distributed and possesses a considerable amount of essential oils. However, to date, limited research has been conducted to characterize the genetics and metabolites of A. lancea shaped by natural variation. Hence, we assembled a high-quality genome of A. lancea, featuring a contig N50 of 1.18 Mb. We further integrated population resequencing of A. lancea and conducted analyses to characterize its genetic diversity, population evolution, and rewiring of volatile metabolites. The natural variation effect exerted significant pressure on A. lancea from different geographic locations, resulting in genetic differentiation among three groups. Correlation analysis of metabolites in A. lancea revealed significant natural variations of terpenoids, heterocyclic compounds, ketones, and esters. We also found that 427 metabolites displayed noteworthy divergence due to directional selection. Additionally, our genome-wide association studies on the metabolome for medicinal quality traits identified several candidate genes, such as AlZFP706 and AlAAHY1, exhibiting significant correlations with atractylodin and hinesol levels, respectively. Overall, this study provides an intricate genomic resource for A. lancea, thereby expanding our understanding of the effect of natural variation on metabolites and facilitating the genetic improvement of its medicinal properties.
The gapless genome assembly and multi-omics analyses unveil a pivotal regulatory mechanism of oil biosynthesis in the olive tree
Authors: Lv Jiaojiao , Jiang Chengying , Wu Wenjun , Mao Kaili , Wei Qianqian , Zheng Yuming , Gao Chengyu , Niu Zhiming , Jin Gaoming , Zhang Rong , Mao Juan , Chen Baihong , Ren Guangpeng , Yang Yongzhi , Wan Dongshi 
Published: June 2024

Abstract
Olive is a valuable oil-bearing tree with fruits containing high levels of fatty acids. Oil production is a multifaceted process involving intricate interactions between fatty acid biosynthesis and other metabolic pathways that are affected by genetics and the developmental stages of the fruit. However, a comprehensive understanding of the underlying regulatory mechanisms is still lacking. Here, we generated a gap-free telomere-to-telomere assembly for Olea europaea cv. ‘Leccino’, representing an olive genome with the highest contiguity and completeness to date. The combination of time-course metabolomics and transcriptomics datasets revealed a negative correlation between fatty acid and flavonoid biosynthesis in the initial phase of olive fruit development, which was subject to an opposing regulatory mechanism mediated by the hub transcription factor MYC2. Multifaceted molecular assays demonstrated that MYC2 is a repressor of fatty acid biosynthesis by downregulating the expression of BCCP2 (biotin carboxylase carrier protein 2), while it acts as an activator of FLS (flavonol synthase), leading to an increase in flavonoid synthesis. Furthermore, the expression of MYC2 is regulated by fluctuations of methyl jasmonate content during olive fruit development. Our study completes a high-quality gapless genome of an olive cultivar, and provides new insight into the regulatory mechanisms underlying the biosynthesis of fatty acids and flavonoids in its fruit.
Drivers of bitter crab disease occurrence in eastern Bering Sea snow crab (Chionoecetes opilio)
Authors: Balstad Laurinne J , Fedewa Erin J , Szuwalski Cody S 
Published: June 2024

Abstract
A recent population collapse of eastern Bering Sea (EBS) snow crab (Chionoecetes opilio) led to the first-ever closure of the snow crab fishery in 2022. The population collapse, caused, in part, by unprecedented warming, was preceded by peaks in juvenile snow crab density (2018) and bitter crab disease (BCD, Hematodinium sp.; 2016), a fatal crustacean disease. Annual bottom trawl surveys in the EBS show high year-to-year spatiotemporal variation in BCD-infected crab, yet it remains unclear what ecological drivers might explain the variation. We used statistical models of BCD presence/absence to examine the relative importance of intrinsic and extrinsic factors as drivers of BCD. We found a dome-shaped relationship between temperature and BCD presence, and results suggest that 2–4°C bottom temperatures are more likely to support BCD. Matching with past work across the globe, we find that stations with high population density of small, new shell crab are most likely to be BCD-positive. While our work highlights the challenges of disease monitoring in the EBS, our results indicate that indirect management measures related to snow crab rebuilding and recruitment may be more appropriate than directed fisheries management in mitigating BCD impacts.
hp-version C1-continuous Petrov–Galerkin method for nonlinear second-order initial value problems with application to wave equations
Authors: Wang Lina , Zhang Mingzhu , Tian Hongjiong , Yi Lijun 
Published: June 2024

Abstract
We introduce and analyze an $hp$-version $C^{1}$-continuous Petrov–Galerkin (CPG) method for nonlinear initial value problems of second-order ordinary differential equations. We derive a-priori error estimates in the $L^{2}$-, $L^{\infty }$-, $H^{1}$- and $H^{2}$-norms that are completely explicit in the local time steps and local approximation degrees. Moreover, we show that the $hp$-version $C^{1}$-CPG method superconverges at the nodal points of the time partition with regard to the time steps and approximation degrees. As an application, we apply the $hp$-version $C^{1}$-CPG method to time discretization of nonlinear wave equations. Several numerical examples are presented to verify the theoretical results.
Connecting detailed photosynthetic kinetics to crop growth and yield: a coupled modelling framework
Authors: He Yufeng , Wang Yu , Friedel Douglas , Lang Meagan , Matthews Megan L 
Published: June 2024

Abstract
Photosynthesis and crop growth are inseparable processes that govern plant carbon assimilation and allocation. An accurate model description of these processes can bridge dynamics at the leaf and canopy levels, assisting in identifying potential photosynthetic improvements that can be converted into increased yield. Integrating multiscale biophysical processes and achieving computational effectiveness for seasonal simulations, however, are challenging. Here, we present a fully coupled modelling framework that integrates a metabolic model of C3 photosynthesis (ePhotosynthesis) and a semi-mechanistic crop growth model (BioCro). We replaced the leaf-level Farquhar photosynthesis model in BioCro with the ePhotosynthesis model that mechanistically describes the photosystem electron transport processes and the C3 carbon metabolism including the Calvin–Benson–Bassham cycle and the photorespiratory pathway. The coupled BioCro-ePhotosynthesis model was calibrated to represent a soybean cultivar and developed to be operationally fast for seasonal simulations. As an example of model application, we conducted a global sensitivity analysis of 26 enzymes under an average daytime intercepted radiation of 400 µmol m−2 s−1, identifying 2 enzymes, phosphoglycerate kinase (PGK) and phosphoribulokinase (PRK), which had the largest impact on the leaf-level assimilation. Increasing PGK and PRK by 2-fold was predicted to increase the leaf-level assimilation by 8.3 % and the final seed yield by 6.75 % ± 0.5 % over 4 years of observed field climate data. The coupled BioCro-ePhotosynthesis model provides a seamless and efficient integration between the leaf-level metabolism and the field-level yield over a full growing season. The coupled model could be further applied to investigate non-steady-state photosynthetic processes such as non-photochemical quenching.
LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction
Authors: Zhou Huixue , Li Mingchen , Xiao Yongkang , Yang Han , Zhang Rui 
Published: June 2024

Abstract

Objective
To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness.

Materials and Methods
Our study was conducted in two phases. Initially, we analyzed a range of demonstration components vital for LLMs’ biomedical data capabilities, including task descriptions and examples, experimenting with various combinations. Subsequently, we introduced the LLM instruction-example adaptive prompting (LEAP) framework, including instruction adaptive tuning, example adaptive tuning, and instruction-example adaptive tuning methods. This framework aims to systematically investigate both adaptive task descriptions and adaptive examples within the demonstration. We assessed the performance of the LEAP framework on the DDI, ChemProt, and BioRED datasets, employing LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B.

Results
Our findings indicated that Instruction + Options + Example and its expanded form substantially improved F1 scores over the standard Instruction + Options mode for zero-shot LLMs. The LEAP framework, particularly through its example adaptive prompting, demonstrated superior performance over conventional instruction tuning across all models. Notably, the MedLLAMA_13B model achieved an exceptional F1 score of 95.13 on the ChemProt dataset using this method. Significant improvements were also observed in the DDI 2013 and BioRED datasets, confirming the method’s robustness in sophisticated data extraction scenarios.

Conclusion
The LEAP framework offers a compelling strategy for enhancing LLM training strategies, steering away from extensive fine-tuning towards more dynamic and contextually enriched prompting methodologies, showcasing in biomedical relation extraction.
Using machine learning to improve anaphylaxis case identification in medical claims data
Authors: Kural Kamil Can , Mazo Ilya , Walderhaug Mark , Santana-Quintero Luis , Karagiannis Konstantinos , Thompson Elaine E , Kelman Jeffrey A , Goud Ravi 
Published: June 2024

Abstract

Objectives
Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of “Big Data” for healthcare or public health purposes.

Materials and methods
This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical databases.

Results
Resulting machine learning model accuracies ranged from 47.7% to 94.4% when tested on ground truth data. Finally, we found new features to help experts enhance existing case-finding algorithms.

Discussion
Developing precise algorithms to detect medical outcomes in claims can be a laborious and expensive process, particularly for conditions presented and coded diversely. We found it beneficial to filter out highly potent codes used for data curation to identify underlying patterns and features. To improve rule-based algorithms where necessary, researchers could use model explainers to determine noteworthy features, which could then be shared with experts and included in the algorithm.

Conclusion
Our work suggests machine learning models can perform at similar levels as a previously published expert case-finding algorithm, while also having the potential to improve performance or streamline algorithm construction processes by identifying new relevant features for algorithm construction.
Electromembrane extraction of drugs of abuse and prescription drugs from micropulverized hair
Authors: Schüller Maria , Bergh Marianne Skov-Skov , Pedersen-Bjergaard Stig , Øiestad Elisabeth Leere 
Published: June 2024

Abstract
Hair analysis can provide chronological insights into past drug use for months to years after drug administration. In comparison to analyses from other biological matrices, such as blood and urine, sample pretreatment is often tedious and not environmental friendly. In this study, we present a more environmental friendly approach to hair analysis using micropulverized hair and electromembrane extraction for the efficient extraction of 15 drugs of abuse, prescription drugs, and metabolites from hair. The optimized extraction method, involving micropulverization, demonstrated comparable yields to the standard approach of cutting and overnight incubation. A 15-min extraction method using a commercial electromembrane extraction prototype was developed and validated according to forensic guidelines, using only 10 µL of organic solvent per sample. The final method, employing HPLC–MS–MS with a biphenyl column, exhibited good linearity, precision, and sensitivity. An AgreePrep assessment comparing the environmental impact of our method with the standard routine method, involving overnight incubation and conventional liquid–liquid extraction, was conducted. This is the first time micropulverized hair has been subjected to electromembrane extraction.