bluuebunny/crossref_metadata_2025_split huggingface dataset:
Migratory hoverflies orientate north during spring migration
Authors: Hawkes Will L. S. , Weston Scarlett T. , Cook Holly , Doyle Toby , Massy Richard , Guri Eva Jimenez , Wotton Jimenez Rex E. , Wotton Karl R. 
Published: August 2022

AbstractMigratory hoverflies are long-range migrants that, in the northern hemisphere, move seasonally to higher latitudes in the spring and lower latitudes in the autumn. The preferred migratory direction of hoverflies in the autumn has been the subject of radar and flight simulator studies while spring migration has proved to be more difficult to characterise due to a lack of ground observations. Consequently, the preferred migratory direction during spring has only been inferred from entomological radar studies and patterns of local abundance and currently lacks ground confirmation. Here, during a springtime arrival of migratory insects onto the Isles of Scilly and mainland Cornwall, UK, we provide ground proof that spring hoverfly migrants have an innate northward preference. Captured migratory hoverflies displayed northward vanishing bearings when released under sunny conditions under both favourable wind and zero-wind conditions. In addition, and unlike autumn migrants, spring individuals were also able to orientate when the sun was obscured. Analysis of winds suggests an origin for insects arriving on the Isles of Scilly in from western France. These findings of spring migration routes and preferred migration directions are likely to extend to the diverse set of insects found within the western European migratory assemblage.
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
Authors: Lakrisenko Polina , Stapor Paul , Grein Stephan , Paszkowski Łukasz , Pathirana Dilan , Fröhlich Fabian , Lines Glenn Terje , Weindl Daniel , Hasenauer Jan 
Published: August 2022

AbstractDynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.
Cancer Serum Atlas supported precise pan-targeted proteomics enable multi-cancer detection
Authors: Hu Anqi , Zhang Lei , Wang Zhenxin , Yuan Chunyan , Lin Ling , Zhang Jiayi , Gao Xia , Chen Xuguang , Guo Wei , Yang Pengyuan , Shen Huali 
Published: August 2022

AbstractThe wide dynamic range of serum proteome restrained discovery of the clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput and precise pan-targeted serum proteomic strategy for high-efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas,www.cancerserumatlas.com). Then, the standard peptides anchored parallel reaction monitoring (SPA-PRM) method was developed with support of Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serum of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy, and discovered considerable potential biomarkers benefit for early detection of cancer. Finally, a proteomics based multi-cancer detection model was built, demonstrating high sensitivity (87.2%), specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and high-efficient serological studies of cancer, and the Cancer Serum Atlas supported pan-targeted proteomic strategy enables high-efficient biomarker discovery and multi-cancer detection, thus can be a powerful tool for liquid biopsy.
RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness
Authors: Youssef Alexey , Zhu Tingting , Thakur Anshul , Watkinson Peter , Horby Peter , Eyre David W , Clifton David A 
Published: August 2022

AbstractCOVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we proposeRapiD_AI: a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD_AI framework allows us to build high-performing ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD_AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.
Examining neurophysiological markers of apathy and processing speed in late premanifest and early-stage manifest Huntington’s disease
Authors: Davis Marie-Claire , Hill Aron T. , Fitzgerald Paul B. , Bailey Neil W. , Stout Julie C. , Hoy Kate E. 
Published: August 2022

AbstractObjectiveTo find sensitive biological markers of non-motor symptoms in Huntington’s disease (HD), which are essential for the development and assessment of novel treatments.MethodsWe used resting state EEG to examine differences in oscillatory activity (analysing the isolated periodic as well as the complete EEG signal) and functional connectivity in 22 late premanifest and early stage people with HD and 20 neurotypical controls. We then assessed the correlations between these neurophysiological markers and clinical measures of apathy and cognitive functioning.ResultsSignificantly lower theta and greater delta resting state power was seen in the HD group, as well as significantly greater delta connectivity. There was a significant positive correlation between theta power and processing speed, however there were no associations between the neurophysiological and apathy measures.ConclusionsWe speculate that these changes in oscillatory power and connectivity reflect ongoing, frontally concentrated degenerative and compensatory processes associated with HD.SignificanceOur findings support the potential utility of quantitative EEG as a proximate marker for non-motor symptoms in HD.HighlightsWe examined EEG oscillatory power and functional connectivity as a marker of non-motor symptoms in HD.The HD group had lower theta power, higher delta power and connectivity, and theta power was correlated with processing speed.Our findings support the use of quantitative EEG metrics as potential non-motor symptom markers for clinical research in HD.
Metagenomic insight into taxonomic composition, environmental filtering, and functional redundancy for shaping worldwide modern microbial mats
Authors: Viladomat M. , García-Ulloa M. , Zapata-Peñasco I. , Eguiarte L. E , Souza V. 
Published: August 2022

AbstractAlthough microbial mats are considered relictual communities that are nowadays mostly constrained in their distribution by predation and phosphorus availability, they are still found in a wide range of environmental conditions. Their ancestral history, geographical isolation, stratified community composition and interspecies dynamics make them an interesting model to study community ecological processes and concepts. In this study, we analyzed different metagenomic datasets from worldwide modern microbial mats to compare community structure and functions. We found significant differentiation in both alpha and beta diversity of taxonomic and functional categories without significant correlation with temperature and pH. Differences depended more on the presence of very highly abundant cyanobacteria and some generalist microorganisms. Our results suggest that there is more than just Grinnellian niche dynamics in the determination of microbial mat community assembly, opening the hypothesis of interactions as the driver behind these ancient communities. We also discuss the influence of niche dynamics and environmental filtering in the community assembly of microbial mats.
A rapid review of barriers and facilitators to cancer screening uptake (breast, cervical and bowel) in underserved populations
Authors: Okolie Chukwudi , Hookway Amy , Wale Alesha , Everitt Jordan , Shaw Hannah , Lewis Ruth , Cooper Alison , Edwards Adrian 
Published: August 2022

AbstractIn the United Kingdom (UK), the National Health Service (NHS) provides population-based screening programmes for breast, bowel, and cervical cancer. These programmes were temporarily paused in March 2020, due to the COVID-19 pandemic, resulting in large numbers of the eligible population having their invitations delayed. This disruption may have had a disproportionate impact on underserved populations for whom there was a lower uptake prior to the pandemic. Some people may also be less willing to attend screening after the pandemic. Interventions and campaigns designed to encourage people to take part in cancer screening may need to be adapted after the pandemic, in particular those targeting underserved populations.This rapid review aimed to identify the barriers and facilitators to breast, bowel, and cervical screening uptake in underserved populations (e.g. clinically vulnerable, shielding, multi-morbidities, ethnic minorities, social deprivation, gender, age) during and since the onset of the pandemic, using evidence from the UK and other countries with similar cancer screening programmes (such as Australia and Netherlands), and to compare with the pre-pandemic literature. The pre-pandemic literature was identified using a supplementary scoping search for published systematic reviews.Three primary studies (two published and one ongoing trial) conducted during the pandemic were identified. Five systematic reviews of pre-pandemic evidence were also included. Two qualitative studies conducted during the pandemic were appraised as high quality but both included sample populations with limited representation.No primary studies specifically exploring the impact of the pandemic on barriers and facilitators to screening uptake among underserved groups were identified. The findings did not show marked differences in the barriers and facilitators for screening uptake before and during the COVID-19 pandemic in underserved populations. However, it is unclear whether this is because these genuinely remain unchanged or reflects the lack of available evidence. The findings may only be transferable to the population groups studied.
Prevalence and correlates of alcohol and tobacco consumption among research scholars: evidence from a cross-sectional survey of three Indian universities
Authors: Usman Mohd , Anand Enu , Akhtar Saddaf Naaz , Umenthala Srikanth Reddy , Anwar Tarique , Unisa Sayeed 
Published: August 2022

Purpose
Alcohol and tobacco consumption are significant public health concerns and considered some of the riskiest behaviors among students. Despite strong indications of heavy consumption of these substances, certain sections of the student population remain unexplored regarding their consumption patterns and associated factors, including research scholars pursuing Doctor of Philosophy degrees. This study aims to explore the patterns and correlations of consumption of alcohol and tobacco among doctoral students in three Indian universities.

Design/methodology/approach
A cross-sectional study was conducted on a total of 530 scholars from three central universities, Aligarh Muslim University, Banaras Hindu University and Jawahar Lal Nehru University, and focused on two substances (alcohol and tobacco) frequently used by students in India. Bi-variate analysis and penalized logistic regressions were applied to analyse the prevalence and associated factors of alcohol, smoking and smokeless tobacco among doctoral students.

Findings
Tobacco consumption was observed to be the dominant form of substance use among doctoral students. Findings revealed that 34% of scholars smoked and 25% consumed smokeless tobacco daily and around 14% consumed alcohol at least once a week. Major significant correlates of substance consumption among scholars were found to be gender, religion, parental mortality, dissatisfaction with academic performance and stress about future career/employment. Significant variation in substance use patterns was observed among universities.

Originality/value
This study provides first-hand evidence on substance use patterns and the correlates among doctoral students in three Indian universities. Findings indicate the implementation of specific intervention programs for mental health and counselling for this section of students to prevent substance use addiction would be beneficial.
Applying Kansei engineering to the design of headwear pieces with traditional shape features
Authors: Tsai Hung-Cheng , Hsu Yuan-Chin 
Published: August 2022

PurposeIt is desirable that shape appeal in craft design takes people's cultural and emotional influencers into consideration. Five Royal Lords worship, prevalent in the southwest coastal part of Taiwan and a source of religious and spiritual support to the local residents, offers such a shape appeal. This study takes the design of Taiwan's cultural handicrafts as the main point of discussion and uses Kansei engineering with semantic technique to promote the linkage between shape and the mental image of the Five Royal Lords' headwear. There are only two types of traditional headwear for the Five Royal Lords: the Imperial Crown and Lord Crown, despite the different personal characteristics of the five deities. This study aims to design a crown for each that matches their individuality.Design/methodology/approachIn the first stage of the research process, the Kawakita Jiro method was used to arrive at appropriate descriptions representative of the deities' individuality. Fuzzy set theory was then applied to convert the relationship between the representative descriptions and headwear shape features into a quantitative one, after which the headwear could be redesigned and validated.FindingsThe study results show that: (1) analysis of the relationship between shape features and representative deity descriptions offered guidance to the redesign. (2) A method combining fuzzy theory and description terms could generate quantitative data that helped to provide design suggestions and result validation, supporting both scientific rationality and designers' sensibility. (3) The validation revealed that the redesigned headwear was better than the original headwear.Originality/valueThe study successfully established a design and development process featuring collaboration by folklore experts, designers, craftspeople and worshippers, and helping to promote new cultural product development. The success of the research process can serve as a reference to the development of other different products with shape features.
How do human resource practices help employees alleviate stress in enforced remote work during lockdown?
Authors: Li Gang , Zheng Qiqi , Xia Mengyao 
Published: August 2022

PurposeDue to the fact that most employees have been forced to work remotely during the lockdown resulting from the COVID-19 pandemic, there is great concern about how to alleviate increased stress among employees through human resource (HR) practices. Drawing upon the job demands-control (JDC) model and the job demands-resources (JDR) model, this study empirically investigated the direct effect of HR practices on employee stress in enforced remote work and the mediating role of sources of stress (SoS) and sense of control (SoC).Design/methodology/approachData were collected through an online survey platform called Wenjuanxing from March 15 to 22, 2020 in Hubei, China and from April 22 to 29, 2022 in Shanghai, China. Respondents scanned the QR code on WeChat to enter the platform. A total of 511 valid questionnaires were received with a response rate of 75.4%. After controlling demographic variables, the authors used the mediation modeling and PROCESS tool to test the proposed hypotheses.FindingsHR practices negatively affect stress in enforced remote work among employees. Both SoS and SoC partially mediate the relationship between HR practices and stress. HR practices can alleviate stress via decreasing SoS and enhancing SoC, respectively. Moreover, employee care and training are found to be two key factors of HR practices to help employees alleviate stress in enforced remote work.Originality/valueLockdown as an extreme external condition has brought great challenges in employee work arrangement as well as HR practices. Although the relationship between HR practices and job stress was studied previously, there is a lack of research on the effects of HR practices on stress in enforced remote work due to lockdown. It advances knowledge on HR practices' stress-reducing effect in the context of remote work and provides suggestions for HR practitioners on ways of alleviating employee stress in remote work.
How does domestic market fragmentation affect enterprise innovation performance? Empirical evidence from China
Authors: Qi Xiao-Feng , Zhou Lihong 
Published: August 2022

PurposeThis paper aims to explore the impact of domestic market fragmentation on the innovation performance of enterprises and its mechanism from the perspective of market segmentation, a government behavior with Chinese characteristics.Design/methodology/approachIn order to verify the theoretical hypothesis proposed in the previous article, that is, whether domestic market fragmentation can effectively improve the innovation performance of enterprises, this paper bases on the data of listed companies from 2010 to 2016, empirically testing the theoretical hypothesis by constructing a measurement model.FindingsDomestic market fragmentation has a significant inhibitory effect on enterprise innovation performance. Domestic market fragmentation has heterogeneous effects on innovation performance of enterprises and regions. It is undeniable that domestic market fragmentation does have a certain support effect on state-owned enterprises but the support effect is achieved by distorting regional resource allocation and creating an unfair market environment.Originality/valueFirstly, this paper explores the impact mechanism of domestic market fragmentation on corporate innovation performance from the perspective of market segmentation, a government behavior with Chinese characteristics, so as to expand and enrich the relevant research on enterprise innovation. Secondly, from the perspective of corporate innovation performance, this paper provides new evidence for the “curse effect” of domestic market fragmentation. Thirdly, this paper tries to shake the domestic market fragmentation support theory from the perspective of distortion effect brought by the “hand of support” of domestic market fragmentation.