bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computational toolbox for ultrastructural quantitative analysis of filament networks in cryo-ET data
Authors: Dimchev Georgi , Amiri Behnam , Fäßler Florian , Falcke Martin , Schur Florian KM 
Published: May 2021

AbstractA precise quantitative description of the ultrastructural characteristics underlying biological mechanisms is often key to their understanding. This is particularly true for dynamic extra- and intracellular filamentous assemblies, playing a role in cell motility, cell integrity, cytokinesis, tissue formation and maintenance. For example, genetic manipulation or modulation of actin regulatory proteins frequently manifests in changes of the morphology, dynamics, and ultrastructural architecture of actin filament-rich cell peripheral structures, such as lamellipodia or filopodia. However, the observed ultrastructural effects often remain subtle and require sufficiently large datasets for appropriate quantitative analysis. The acquisition of such large datasets has been enabled by recent advances in high-throughput cryo-electron tomography (cryo-ET) methods. However, this also necessitates the development of complementary approaches to maximize the extraction of relevant biological information. We have developed a computational toolbox for the semi-automatic quantification of filamentous networks from cryo-ET datasets to facilitate the analysis and cross-comparison of multiple experimental conditions. GUI-based components simplify the manipulation of data and allow users to obtain a large number of ultrastructural parameters describing filamentous assemblies. We demonstrate the feasibility of this workflow by analyzing cryo-ET data of untreated and chemically perturbed branched actin filament networks and that of parallel actin filament arrays. In principle, the computational toolbox presented here is applicable for data analysis comprising any type of filaments in regular (i.e. parallel) or random arrangement. We show that it can ease the identification of key differences between experimental groups and facilitate the in-depth analysis of ultrastructural data in a time-efficient manner.
Phototropin-mediated perception of light direction in Arabidopsis leaves regulates blade flattening
Authors: Legris Martina , Szarzynska-Erden Bogna Maria , Trevisan Martine , Allenbach-Petrolati Laure , Fankhauser Christian 
Published: May 2021

ABSTRACTOne conserved feature among angiosperms is the development of flat thin leaves. This developmental pattern optimizes light capture and gas exchange for photosynthesis. The blue light receptors phototropins are required for leaf flattening, with the null phot1phot2 mutant showing downwards curled leaves in Arabidopsis. However, key aspects of their function in leaf development remain unknown. Here, we performed a detailed spatiotemporal characterization of phototropin function in Arabidopsis leaves. We found that phototropins perceive light direction in the leaf blade, and similar to their role in hypocotyls they control the spatial pattern of auxin signaling possibly modulating auxin transport, to ultimately regulate cell expansion. Phototropin signaling components in the leaf partially differ from hypocotyls. Moreover, the light response on the upper (adaxial) and lower (abaxial) sides of the leaf blade suggest a partially distinct requirement of phototropin signaling components on each side. In particular, NON PHOTOTROPIC HYPOCOTYL 3 (NPH3) showed an adaxial-specific function. In addition, we show a prominent role of PHYTOCHROME KINASE SUBSTRATE 3 (PKS3) in leaf flattening. Among the auxin transporters tested, PINs and AUX/LAX influence the response most prominently. Overall, our results show that directional blue light perception by the phototropins is a key aspect of leaf development, integrating endogenous and exogenous signals.One sentence summaryPhototropins perceive light direction in the leaf and control the auxin signaling pattern to regulate blade flattening.
Entrepreneurial intention among Latin American university students
Authors: Leiva Juan Carlos , Mora-Esquivel Ronald , Krauss-Delorme Catherine , Bonomo-Odizzio Adriana , Solís-Salazar Martín 
Published: May 2021

PurposeThis paper analyses how contextual factors at universities (entrepreneurship education and program learning) and cognitive variables (perceived behavioral control, implementation intentions, and attitude) influence entrepreneurial intentions among Latin American university students.Design/Methodology/ApproachThe empirical analysis employs a multilevel (hierarchical) linear model with a sample size of 9012 university students taken in 2018 from nine Latin American countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, México, Panamá, and Uruguay.FindingsOverall, the university context and cognitive variables contribute to explaining entrepreneurial intentions in university students. Whereas program learning constitutes a variable that directly and indirectly explains entrepreneurial intentions among university students, attending entrepreneurship courses negatively influences their entrepreneurial intentions.Originality/valueA central premise of this study is that the entrepreneurial process in university students is a multilevel phenomenon, given that university context and cognitive variables are key factors in entrepreneurial intentions. The findings support this premise and contribute to the existing literature on entrepreneurship in emerging economies. Nevertheless, the results reveal a more nuanced picture regarding the role of university context on the entrepreneurial intentions of students.
Social dimensions of synthetic biology in the agrifood sector: the perspective of Chinese and EU scientists
Authors: Jin Shan , Clark Beth , Li Wenjing , Kuznesof Sharron , Frewer Lynn J. 
Published: May 2021

PurposeScientists' perceptions of societal needs and priorities will shape the innovation trajectories of synthetic biology (SB). In turn, these will be shaped by the funding and regulatory environments in which their research is conducted. This study intends to investigate scientists' perspectives on co-innovation with the public regarding implementation of pathways associated with SB including its agrifood applications.Design/methodology/approachSemi-structured interviews were conducted with Chinese and EU scientists (N = 9 and 13, respectively). Six prominent themes emerged from the data based on thematic analysis method.FindingsBoth Chinese and EU scientists regarded SB as being high-benefit, low-risk and ethically acceptable, and predicted its rejection by the general public and attributed this to the public's knowledge deficit and irrationality. They endorsed the deficit model of science communication, independent of greater emphasis on responsible research and innovation (RRI) in EU research projects. The findings raised concerns that public fears might intensify once they have learned about scientists' biased risk perceptions of SB; this calls for better involvement of broader stakeholders.Research limitations/implicationsAs the sample size is relatively small, the generalisation of research findings needs to be cautious. However, the authors believe the findings have provided some insights that support increasingly RRI associated with SB.Originality/valueThis study has presented scientists' misunderstandings of societal responses to SB and science communication. It has also provided information to understand how SB-related issues and agenda can be better shaped in future.
Temporal and spatial evolution of cavitating bubbles and coherent structures in a confined submerged jet shear layer
Authors: Zhang Runqiang , Sun Guoyong , Wang Yuchuan , Leguizamón Sebastián 
Published: May 2021

PurposeThe study aims to display the bubbles' evolution in the shear layer and their relationship with the pressure fluctuations. Furthermore, the coherent structures of the first six modes are extracted, in order to provide insight into their temporal and spatial evolution and determine the relationship between cavitating bubbles and coherent structures.Design/methodology/approachIn the present study, numerical simulations of submerged jet cavitating flow were carried out at a cavitation inception condition inside an axisymmetric cavity using the large eddy simulation (LES) turbulence model and the Schnerr–Sauer (S–S) cavitation model. Based on snapshots produced by the numerical simulation, dynamic mode decomposition (DMD) was performed to extract the three-dimensional coherent structures of the first six modes in the shear layer.FindingsThe cavitating bubbles in the shear layer are deformed to elongated ellipsoid shapes by shear forces. The significant pressure fluctuations are induced by the collapse of the biggest bubble in the group. The first mode illustrates the mean characteristics of the flow field. The flow in the peripheral region of the shear layer is mainly dominated by large-scale coherent structures revealed by the second and third modes, while different small-scale coherent structures are contained in the central region. The cavitating bubbles are associated with small size coherent structures as the sixth or higher modes.Practical implicationsThis work demonstrates the feasibility of LES for high Reynolds number shear layer flow. The dynamic mode decomposition method is a novel method to extract coherent structures and obtain their dynamic information that will help us to optimize and control the flow.Originality/value(1) This paper first displays the three-dimensional coherent structures and their characteristics in the shear layer of confined jet flow. (2) The relationship of bubbles shape and pressure fluctuations is illustrated. (3) The visualization of coherent structures benefits the understanding of the mixing process and cavitation inception in jet shear layers.
A new method to optimize the truncated PCE order of collocation-based SRSM in reliability analysis and its application to geotechnical problems
Authors: Zeng Peng , Li Tianbin , Jimenez Rafael , Feng Xianda , Chen Yu , Zhang Tianlong 
Published: May 2021

PurposeThe collocation-based stochastic response surface method (CSRSM) is widely used in geotechnical reliability analyses due to its efficiency and accuracy. Determining the optimal truncated order of the associated polynomial chaos expansion (PCE) is important, as it may strongly affect the practical applicability of CSRSM.Design/methodology/approachThis study investigates the performance of different optimal order selection strategies used in the CSRSM and proposes a new cross-order validation method. First, several methods commonly used for optimal order selection are briefly reviewed, and their merits and limitations for reliability analyses are discussed. Then, an improved optimal order selection method that achieves a better trade-off between efficiency and accuracy is proposed.FindingsIn total, ten simple mathematical examples from the literature are employed to perform a preliminary test on the proposed method, and a comparative study is conducted to demonstrate its advantages with respect to some other existing methods.Practical implicationsA total of three typical geotechnical problems are employed to demonstrate the performance of the proposed method in geotechnical practice.Originality/valueAn improved optimal order selection method that achieves a better trade-off between efficiency and accuracy is proposed. The threshold value of the deterministic coefficient used for the proposed method is discussed.
The relative influence of spectator dysfunctional behavior on spectator enjoyment in Major League Baseball
Authors: Pedersen Zack P. , Kim Kyungyeol (Anthony) , Byon Kevin K. , Williams Antonio S. 
Published: May 2021

PurposeThe purpose of this study is to evaluate the perceived enjoyment that is derived from spectators observing other spectators’ dysfunctional behavior during a game.Design/methodology/approachUsing four forms (i.e. fighting, verbal assault, disrupting play and throwing missiles) of spectators dysfunctional behavior (SDB), two experiments (N = 252 for Study 1 and N = 92 for Study 2) were conducted in which video clips corresponding to the four types of SDB were used as experimental stimuli.FindingsThe findings indicate that participants enjoyed viewing spectators running onto the field of play significantly more than the other forms of SDB (i.e. fighting, verbal assaults and throwing missiles). The results also show no significant difference between how much spectators enjoyed the actions of fighting, verbal assault and throwing missiles.Originality/valueThe novelty of this study include the usage of a multidimensional approach to the concept of SDB and testing for a positive outcome pertaining to SDB that has largely, if not fully, been examined using negative inputs and outputs.
Adaptive customer relationship management contingency model under disruptive events
Authors: Lozada-Contreras Frank , Orengo-Serra Karen L. , Sanchez-Jauregui Maria 
Published: May 2021

PurposeGiven that few studies examine how disruptive events affect customer relationships during and after the event, this study examines the resilience of companies in Puerto Rico, their underlying vulnerabilities, and how they deployed customer relationship management (CRM) resilience strategies during and after Hurricane Maria.Design/methodology/approachThe study analyzed data gathered from qualitative focus groups composed of 41 firms via an exploratory approach. Participants were business owners and managers of enterprises in Puerto Rico.FindingsAll companies faced critical government infrastructure failures that affected their CRM activities. Firms implemented one or more CRM resilience strategies in response to the natural disaster. Accordingly, a comprehensive, adaptive CRM contingency model was postulated using marketing crisis management strategies discussed in the literature, existing resilience models and research studies in marketing resilience. The adaptive CRM contingency model operationalizes all processes at the business-logic level via the event-driven process chain (EPC) language, thus making it easier to understand and employ.Originality/valueThis study presents a unique model that shows the value of CRM and its capacity to evolve under disruptive environments that affect company–customer relationships. The operationalization of the model allows practitioners, policymakers and academic researchers to better understand how CRM is not only a suitable tool for managing business continuity after a natural disaster but also a mitigating technique for responding to new customer needs and expectations.
Perceptions, knowledge and self-defense behaviors regarding COVID-19 among employees at Rajamangala University of Technology Thanyaburi, Thailand
Authors: Siramaneerat Issara 
Published: May 2021

PurposeThis research aims to study self-defense behaviors from coronavirus disease 2019 (COVID-19) and to investigate factors affecting the prevention and control behavior of COVID-19 among personnel at Rajamangala University of Technology Thanyaburi.Design/methodology/approachThe sample was 405 personnel of Rajamangala University of Technology Thanyaburi. The sample was calculated by using the Yamane formula at a confidence level of 95. The researcher collected the data between July 1 and 15, 2020. Questionnaire items were developed following the health belief model (HBM). The questionnaire contains basic information, knowledge of COVID-19, perception of COVID-19 and COVID-19 preventive behaviors. Data were analyzed by descriptive statistics, correlation coefficients and multiple regression analysis at the statistical significance level of 0.05.FindingsThe results showed that the sample had an average knowledge about COVID-19 of 8.93, the perceived of risk and severity of COVID-19 was presented average of 4.22 and 3.48. The perceived of benefits and barriers of COVID-19 showed average of 4.31 and 2.72 and mean of COVID-19 prevention and control behaviors was 2.41. The multiple regression analysis showed that the model can explain the various self-defense behaviors from COVID-19 of 11.30%. Perception of the benefits of COVID-19 had a statistically significant effect on self-defense behaviors from COVID-19 at the level 0.05 (Beta = 0.232, 95% CI: 1.233–3.395, p < 0.001).Originality/valueBased on the results, the relationship between HBM and COVID-19 prevention behavior can be clearly seen. This study found the perception of benefit toward COVID-19 affected prevention practice. Thus, using HBM could be useful in improving preventive behaviors of COVID-19.
Hybrid ensemble learning approaches to customer churn prediction
Authors: Tavassoli Sara , Koosha Hamidreza 
Published: May 2021

PurposeCustomer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.Design/methodology/approachIn this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.FindingsTo examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.Originality/valueIn this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.
Scientific collaboration networks and firm innovation: the contingent impact of a dynamic environment
Authors: Yang Jing , Zhang Jing , Zeng Deming 
Published: May 2021

PurposeThe environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.Design/methodology/approachUsing a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.FindingsA broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.Practical implicationsFirms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.Originality/valueThis study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.
Life-cycle asset allocation and the peso problem: does ambiguity aversion matter?
Authors: Silva Nuno 
Published: May 2021

PurposeThe study aims to show that ambiguity aversion exerts a non-negligible effect on the investors' decisions, especially due to the possibility of sharp declines in stock prices.Design/methodology/approachThe vast majority of previous studies on life-cycle consumption and asset allocation assume that the equity premium is constant. This study evaluates the impact of rare disasters that shift the stock market to a low return state on investors' consumption and portfolio decisions. The author assumes that investors are averse to ambiguity relative to the current state of the economy and must incur a per period cost to participate in the stock market and solve their optimal consumption and asset allocation problem using dynamic programming.FindingsThe results show that most young investors choose not to invest in stocks because they have low accumulated wealth and the potential return from their stock market investments would not cover the participation costs. Furthermore, ambiguity-averse investors hold considerably fewer stocks throughout their lifetime than ambiguity-neutral ones. The fraction of wealth invested in stocks over the typical consumer's life is hump-shaped: it is low for a young individual, peaks at his early 30s and then decreases until his retirement age.Originality/valueTo the best of the author’s knowledge, this is the first study that assesses the impact of negative stock price jumps on the optimal portfolio of an ambiguity-averse investor.