bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-level expression of the monomeric SARS-CoV-2 S protein RBD 320-537 in stably transfected CHO cells by the EEF1A1-based plasmid vector
Authors: Sinegubova Maria V. , Orlova Nadezhda A. , Kovnir Sergey V. , Dayanova Lutsia K. , Vorobiev Ivan I 
Published: November 2020

AbstractThe spike (S) protein is one of the three proteins forming the coronaviruses’ viral envelope. The S protein of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has a spatial structure similar to the S proteins of other mammalian coronaviruses, except for a unique receptor-binding domain (RBD), which is a significant inducer of host immune response. Recombinant SARS-CoV-2 RBD is widely used as a highly specific minimal antigen for serological tests. Correct exposure of antigenic determinants has a significant impact on the accuracy of such tests – the antigen has to be correctly folded, contain no potentially antigenic non-vertebrate glycans, and, preferably, should have a glycosylation pattern similar to the native S protein. Based on the previously developed p1.1 vector, containing the regulatory sequences of the Eukaryotic translation elongation factor 1 alpha gene (EEF1A1) from Chinese hamster, we created two expression constructs encoding SARS-CoV-2 RBD with C-terminal c-myc and polyhistidine tags. RBDv1 contained a native viral signal peptide, RBDv2 – human tPA signal peptide. We transfected a CHO DG44 cell line, selected stably transfected cells, and performed a few rounds of methotrexate-driven amplification of the genetic cassette in the genome. For the RBDv2 variant, a high-yield clonal producer cell line was obtained. We developed a simple purification scheme that consistently yielded up to 30 mg of RBD protein per liter of the simple shake flask cell culture. Purified proteins were analyzed by polyacrylamide gel electrophoresis in reducing and non-reducing conditions and gel filtration; for RBDv2 protein, the monomeric form content exceeded 90% for several series. Deglycosylation with PNGase F and mass spectrometry confirmed the presence of N-glycosylation. The antigen produced by the described technique is suitable for serological tests and similar applications.
Thermodynamic evaluation of the impact of DNA mismatches in PCR-type SARS-CoV-2 primers and probes
Authors: Miranda Pâmella , Weber Gerald 
Published: November 2020

AbstractBackgroundDNA mismatches can affect the efficiency of PCR techniques if the intended target has mismatches in primers or probes regions. The accepted rule is that mismatches are detrimental as they reduce the hybridization temperatures, yet a more quantitative assessment is rarely performed.MethodsWe calculate the hybridization temperatures of primer/probe sets after aligning to SARS-COV-2, SARS-COV-1 and non-SARS genomes, considering all possible combinations of single, double and triple consecutive mismatches. We consider the mismatched hybridization temperature within a range of 5 °C to the fully matched reference temperature.ResultsWe obtained the alignments of 19 PCR primers sets that were recently reported for the detection of SARS-CoV-2 and to 21665 SARS-CoV-2 genomes as well as 323 genomes of other viruses of the coronavirus family of which 10 are SARS-CoV-1. We find that many incompletely aligned primers become fully aligned to most of the SARS-CoV-2 when mismatches are considered. However, we also found that many cross-align to SARS-CoV-1 and non-SARS genomes.ConclusionsSome primer/probe sets only align substantially to most SARS-CoV-2 genomes if mismatches are taken into account. Unfortunately, by the same mechanism, almost 75% of these sets also align to some SARS-CoV-1 and non-SARS viruses. It is therefore recommended to consider mismatch hybridization for the design of primers whenever possible, especially to avoid undesired cross-reactivity.
Tracing production instability in a clonally-derived CHO cell line using single cell transcriptomics
Authors: Tzani Ioanna , Herrmann Nicolas , Carillo Sara , Spargo Cathy A. , Hagan Ryan , Barron Niall , Bones Jonathan , Dilmore W. Shannon , Clarke Colin 
Published: November 2020

AbstractA variety of mechanisms including transcriptional silencing, gene copy loss and increased susceptibility to cellular stress have been associated with a sudden or gradual loss of monoclonal antibody (mAb) production in Chinese hamster ovary (CHO) cell lines. In this study, we utilised single cell RNA-seq (scRNA-seq) to study a clonally-derived CHO cell line that underwent production instability leading to a dramatic reduction of the levels of mAb produced. From the scRNA-seq data we identified sub clusters associated with variations in the mAb transgenes and observed that heavy chain gene expression was significantly lower than that of the light chain across the population. Using trajectory inference, the evolution of the cell line was reconstructed and was found to correlate with a reduction in heavy and light chain gene expression. Genes encoding for proteins involved in the response to oxidative stress and apoptosis were found to increase in expression as cells progressed along the trajectory. Future studies of CHO cell lines using this technology have the potential to dramatically enhance our understanding of the characteristics underpinning efficient manufacturing performance as well as product quality.HighlightsA clonally-derived CHO cell line in our laboratory had undergone production instability – in that the amount of intact monoclonal antibody had reduced dramatically to levels at which reliable quantitation was no longer possible. We were, however, able to detect mAb heavy and light chain protein, as well as dimerised light chain species in the cell culture media.Single cell RNA-seq was utilised to capture > 3,800 gene expression profiles from the cell line at 72hrs post seeding.Analyses of the scRNA-seq data uncovered transcriptional heterogeneity and revealed the presence of multiple intra cell line clusters. The heavy chain transcript was detected at a significantly lower level in comparison to light chain transcripts. Light chain gene expression was not only more abundant, but also expressed more uniformly across the cell population.Using unsupervised trajectory analysis, the emergence of heterogeneity in the cell population was traced from those cells most similar to the original isolated clone to those where transcription of the mAb heavy and light chain was undetectable.Subsequent analysis of CHO cell gene expression patterns revealed a correlation between the progression of cells along the trajectory and the upregulation of genes involved in the cellular response to oxidative stress.
Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction
Authors: Miningou Zobon Nadiatou T. , Jędrzejewska-Szmek Joanna , Blackwell Kim T. 
Published: November 2020

ABSTRACTLong-lasting long-term potentiation (L-LTP) is a cellular mechanism of learning and memory storage. Studies have demonstrated a requirement for extracellular signal-regulated kinase (ERK) activation in L-LTP produced by a diversity of temporal stimulation patterns. Multiple signaling pathways converge to activate ERK, with different pathways being required for different stimulation patterns. To answer whether and how different temporal patterns select different signaling pathways for ERK activation, we developed a computational model of five signaling pathways (including two novel pathways) leading to ERK activation during L-LTP induction. We show that calcium and cAMP work synergistically to activate ERK and that stimuli given with large inter-trial intervals activate more ERK than shorter intervals. Furthermore, these pathways contribute to different dynamics of ERK activation. These results suggest that signaling pathways with different temporal sensitivity facilitate ERK activation to diversity of temporal patterns.
Derivation of predator functional responses using a mechanistic approach in a natural system
Authors: Beardsell Andréanne , Gravel Dominique , Berteaux Dominique , Gauthier Gilles , Clermont Jeanne , Careau Vincent , Lecomte Nicolas , Juhasz Claire-Cécile , Royer-Boutin Pascal , Bêty Joël 
Published: November 2020

AbstractThe functional response is central to our understanding of any predator–prey system as it establishes the link between trophic levels. Most functional responses are evaluated using phenomenological models linking predator acquisition rate and prey density. However, our ability to measure functional responses using such an approach is often limited in natural systems and the use of inaccurate functions can profoundly affect the outcomes of population and community models. Here, we develop a mechanistic model based on extensive data to assess the functional response of a generalist predator, the arctic fox (Vulpes lagopus), to various tundra prey species (lemmings and the nests of geese, passerines and sandpipers). We found that predator acquisition rates derived from the mechanistic model were consistent with field observations. Although sigmoidal functional responses were previously used to model fox-prey population dynamics, none of our simulations resulted in a saturating response in all prey species. Our results highlight the importance of predator searching components in predator-prey interactions, especially predator speed, while predator acquisition rates were not limited by handling processes. By combining theory with field observations, our study provides evidences that predator acquisition rate is not systematically limited at the highest prey densities observed in a natural system. We reinforce the idea that functional response categories, typically types I, II, and III, should be considered as particular cases along a continuum. Specific functions derived with a mechanistic approach for a range of densities observed in natural communities should improve our ability to model and understand predator-prey systems.
Green market segmentation and consumer profiling: a cluster approach to an emerging consumer market
Authors: Jaiswal Deepak , Kaushal Vikrant , Singh Pankaj Kumar , Biswas Abhijeet 
Published: November 2020

PurposeIn the present era of green consumerism, consumers are shifting towards sustainable consumption with the rising demand of green products. Despite consumers' demand of such products, their attitudes towards eco-friendly practices can neither be the same for different consumer groups nor can be treated as they all are equally green. The purpose of this study is to operationalize the green market segmentation based on demographic, psychographic and behavioural characterization in the Indian context of green consumerism.Design/methodology/approachThe deductive approach of questionnaire survey method has been adapted to collect the responses from convenience sampling of Indian consumers using the measured constructs concerning to green consumer psychology. The data were analyzed by applying multivariate techniques of confirmatory factor analysis (CFA), cluster and discriminant analysis.FindingsThe results revealed that the three distinctive set of consumer groups are evolved as “keen greens”, “moderate greens” and “reluctant greens” based on the eight cognitive variables used in this study, namely environmental concern (EC), perceived environmental knowledge (PEK), perceived consumer effectiveness (PCE), perception of eco-label, perception of eco-brand and environmental advertisements, green purchase intention (GPI) and green purchasing behaviour (GPB) in the Indian context.Research limitations/implicationsThe research findings may lack its generalizability in the Indian context, as the survey strategy is confined with the most populated territory of India. To provide its robustness, the future studies need more heterogeneous sample across the country. The research findings could provide the key insights into policymakers and enterprises in the framing of marketing strategies to promote green consumerism in the setting of emerging economies.Originality/valueThere is dearth of literature concerning to green consumer segmentation based on the “attitude–intention–behaviour” approach in the Indian context. Therefore, the present study endeavours to manifest a holistic description of green consumer classification based on demographic, psychographic and behavioural characterization. At Prima facie, the study is the first that elucidates consumers' segments profile by incorporating environmental cognitive factors from both the perspectives; “consumers” inner stimuli' and “external green marketing cues”, especially in the indigenous Indian setting.
Lean-green performance management in Indian SMEs: a novel perspective using the best-worst method approach
Authors: Ahmad Shaad , Abdullah Ahmad , Talib Faisal 
Published: November 2020

PurposeIn a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.Design/methodology/approachThe study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.FindingsThe results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.Practical implicationsThe prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.Originality/valueThe study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.
Enhancing lifetime of wireless multimedia sensor networks using modified lion algorithm–based image transmission model
Authors: Wankhade Mahesh P. , Jondhale KC 
Published: November 2020

PurposeIn the past few decades, the wireless sensor network (WSN) has become the more vital one with the involvement of the conventional WSNs and wireless multimedia sensor networks (WMSNs). The network that is composed of low-power, small-size, low-cost sensors is said to be WSN. Here, the communication information is handled using the multiple hop and offers only a simple sensing data, such as humidity, temperature and so on, whereas WMSNs are referred as the distributed sensing networks that are composed of video cameras, which contain the sector sense area. These WMSNs can send, receive and process the video information data, which is more intensive and complicated by wrapping with wireless transceiver. The WSNs and the WMSNs are varied in terms of their characteristic of turnablity and directivity.Design/methodology/approachThe main intention of this paper is to maximize the lifetime of network with reduced energy consumption by using an advanced optimization algorithm. The optimal transmission radius is achieved by optimizing the system parameter to transmit the sensor information to the consequent sensor nodes, which are contained within the range. For this optimal selection, this paper proposes a new modified lion algorithm (LA), the so-called cub pool-linked lion algorithm (CLA). The next contribution is on the optimal selection of cluster head (CH) by the proposed algorithm. Finally, the performance of proposed model is validated and compared over the other traditional methods in terms of network energy, convergence rate and alive nodes.FindingsThe proposed model's cost function relies in the range of 74–78. From the result, it is clear that at sixth iteration, the proposed model’s performance attains less cost function, that is, 11.14, 9.78, 7.26, 4.49 and 4.13% better than Genetic Algorithm (GA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly (FF), correspondingly. The performance of the proposed model at eighth iteration is 14.15, 7.96, 4.36, 7.73, 7.38 and 3.39% superior to GA, DA, PSO, GSO, FF and LA, correspondingly with less convergence rate.Originality/valueThis paper presents a new optimization technique for increasing the network lifetime with reduced energy consumption. This is the first work that utilizes CLA for optimization problems.
Big data, technology capability and construction project quality: a cross-level investigation
Authors: Sang Linhua , Yu Mingchuan , Lin Han , Zhang Zixin , Jin Ruoyu 
Published: November 2020

PurposeEmbracing big data has been at the forefront of research for project management. Although there is a consensus that the adoption of big data has significantly positive impact on project performance, far less is known about how this innovative information technology becomes an effective driver of construction project quality improvement. This study aims to better understand the mechanism and conditions under which big data can effectively improve project quality performance.Design/methodology/approachAdopting Chinese construction enterprises as samples, the theoretical framework proposed in this paper is verified by the empirical results of the two-level hierarchical linear model. The moderated mediation analysis is also conducted to test the hypotheses. Finally, the empirical findings are validated by a comparative case study.FindingsThe results show that big data facilitates the development of technology capability, which further produces remarkable quality performance. That is, a project team's technology capability acts as a mediator in the relationship between organizational adaptability of big data and predictive analytics and project quality performance. It is also observed that two types of project team interdependence (goal and task interdependence) positively moderate the mediation effect.Research limitations/implicationsThe questionnaire study from China only represents the relationship within a short time interval in the current context. Future studies should apply longitudinal designs to properly test the causality and use multiple data sources to ensure the validity and robustness of the conclusions.Practical implicationsThe value of big data in terms of quality improvement could not be determined in a vacuum; it also depends on the internal capability development and elaborate design of project governance.Originality/valueThis study provides an extension of the existing big data studies and fuels the ongoing debate on its actual outcomes in project management. It not only clarifies the direct effect of big data on project quality improvement but also identifies the mechanism and conditions under which the adoption of big data can play an effective role.
Testing a social-psychological model of public acceptance towards highway infrastructure projects: a case study from China
Authors: Ge Yujia , Cui Caiyun , Zhang Chunqing , Ke Yongjian , Liu Yong 
Published: November 2020

PurposeTo test a social-psychological model of public acceptance of highway infrastructure projects in the Chinese architecture/engineering/construction industry.Design/methodology/approachThrough a comprehensive literature review, we established a social-psychological model of public acceptance related to benefit perception, risk perception and public trust. We empirically validated our model by using structural equation model analysis based on a questionnaire survey in the S35 Yongjin Highway Infrastructure Project in Yunnan Province, China.FindingsBenefit, trust and risk perception had a significant influence on local residents' public acceptance of highway infrastructure projects; benefit perception and trust perception had a greater influence than risk perception. Public acceptance among local male residents over the age of 35 or those with higher education levels was more likely to be determined by the relative dominance of risk and benefit perceived.Research limitations/implicationsThis study contributes empirical evidence to the theoretical literature related to locally unwanted land use (LULU) siting and stakeholders in the field of project management from the public perspective. This study also suggests valuable practical implications to authorities, project managers and the public in decision-making and risk communication.Originality/valueAlthough previous studies addressed factors affecting public acceptance towards potentially hazardous facilities, understanding of the implications of these social-psychological factors and their effects are still far from sufficient. This study bridges this gap by exploring the determinants of public acceptance towards highway infrastructure projects based on a selected case in China.
Sustainability perceptions of home modification service providers for older Australians
Authors: Hu Xin 
Published: November 2020

PurposeThe majority of older Australians prefer living in their own home. Home modifications support older adults' ageing-in-place through mitigating environmental hazards of their home, and there is an increased interest in modifying older adults' residential environment in sustainable ways. However, an in-depth understanding of sustainability perceptions of home modification service providers (HMSPs) is lacking in Australia. Thus, the study aims to address this research gap.Design/methodology/approachQualitative content analysis was adopted to identify sustainability perceptions through analyzing the retrieved home modification business information from the selected HMSPs' official websites. The results of qualitative content analysis were further analyzed by using independent-samples t-test and chi-square contingency table analysis to investigate the relationships between HMSP type and sustainability.FindingsIn total, 54 sustainability perceptions were identified, with the crucial ones being safe living environment, independent living and service diversity. Overall, there are no significant differences in sustainability perceptions between charitable and non-charitable HMSPs. Nevertheless, charitable HMSPs place more importance on social sustainability in comparison with non-charitable HMSPs.Originality/valueThe study results facilitate understanding about HMSPs' sustainability perceptions and sustainable home modifications in the ageing society, which facilities theory advancement and industry practice in the home modification area.