bluuebunny/crossref_metadata_2025_split huggingface dataset:
Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning
Authors: Medlock Gregory L. , Papin Jason A. 
Published: November 2018

AbstractMechanistic models are becoming common in biology and medicine. These models are often more generalizable than data-driven models because they explicitly represent biological knowledge, enabling simulation of scenarios that were not used to construct the model. While this generalizability has advantages, it also creates a dilemma: how should model curation efforts be focused to improve model performance? Here, we develop a machine learning-guided solution to this problem for genome-scale metabolic models. We generate an ensemble of candidate models consistent with experimental data, then performin silicoensemble simulations for which improved predictiveness is desired. We apply unsupervised and supervised learning to the simulation output to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. The resulting structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach, calledAutomatedMetabolicModelEnsemble-DrivenElimination ofUncertainty withStatistical learning (AMMEDEUS), by applying it to 29 bacterial species to identify curation targets that improve gene essentiality predictions. We then compile these curation targets from all 29 species to prioritize refinement of the entire biochemical database used to generate them. AMMEDEUS is a fully automated, scalable, and performance-driven recommendation system that complements human intuition during the curation of hypothesis-driven models and biochemical databases.SignificanceMechanistic computational models, such as metabolic and signaling networks, are becoming common in biology. These models contain a comprehensive representation of components and interactions for a given system, making them generalizable and often more predictive than simpler models. However, their size and connectivity make it difficult to identify which parts of a model need to be changed to improve performance further. Here, we develop a strategy to guide this process and apply it to metabolic models for a set of bacterial species. We use this strategy to identify model components that should be investigated, and demonstrate that it can improve predictive performance. This approach systematically aides the curation of metabolic models, and the databases used to construct them, without relying on the intuition of the curator.
A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates
Authors: Lin Ying , Rajadhyaksha Anjali M. , Potash James B. , Han Shizhong 
Published: November 2018

AbstractAutism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role ofde novomutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example,TCF20andFBOX11), but also indicated potentially novel candidates, such asDOCK3,MYCBP2andCAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.
Noninvasive prenatal detection of fetal trisomy and single gene disease by shotgun sequencing of placenta originated exosome DNA: a proof-of-concept validation
Authors: Zhang Weiting , Lu Sen , Zhao Jia , Pu Dandan , Zhang Haiping , Yang Lin , Zeng Peng , Su Fengxia , Chen Zhichao , Guo Mei , Gu Ying , Luo Yanmei , Hu Huamei , Lu Yanping , Zhang Hongyun , Chen Fang , Gao Ya 
Published: November 2018

BackgroundDuring human pregnancy, Placental trophectoderm cells can release exosomes into maternal circulation. Trophoblast cells also give rise to cell-free DNA (cfDNA) and has been used for noninvasive prenatal screening for chromosomal aneuploidy. We intended to prove the existence of exosomal DNA (exoDNA) in the exosomes of maternal blood and compared exoDNA with plasma cfDNA in terms of genome distribution, fragment length, and the possibility of detecting genetic diseases.MethodsMaternal blood from 20 euploid pregnancies, 9 T21 pregnancies, 3 T18 pregnancies, 1 T13 pregnancy and 2 pregnancies with FGFR3 mutations were obtained. Exosomes enriched from maternal plasma were confirmed by transmission electronic microscopy (TEM), western blotting and flow cytometry. ExoDNA was extracted and its fetal origin was confirmed by realtime fluorescence quantitative PCR(Q-PCR). Besides, exoDNA content was uncovered by Q-PCR. To characterize exoDNA and compare with cfDNA, pair-end whole genome sequencing was performed. Lastly, the fetal risk of genetic diseases was analyzed using the exoDNA sequencing data.ResultsExoDNA span on all 23 pairs of chromosomes and mitochondria, sharing a similar distribution pattern and higher GC content comparing with cfDNA. ExoDNA showed shorter fragments yet lower fetal fraction than cfDNA. ExoDNA could be used to determine fetal gender correctly, and all trisomies as well as de novo FGFR3 mutations.ConclusionsWe proved that fetal exoDNA could be identified in the exosomes extracted from maternal plasma. ExoDNA shared some similar features to cfDNA and could potentially be used to detect genetic diseases in fetus.
Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology
Authors: Lim Hansaim , He Di , Qiu Yue , Krawczuk Patrycja , Sun Xiaoru , Xie Lei 
Published: November 2018

AbstractAlthough remarkable progresses have been made in the cancer treatment, existing anti-cancer drugs are associated with increasing risk of heart failure, variable drug response, and acquired drug resistance. To address these challenges, for the first time, we develop a novel genome-scale multi-target screening platform 3D-REMAP that integrates data from structural genomics and chemical genomics as well as synthesize methods from structural bioinformatics, biophysics, and machine learning. 3D-REMAP enables us to discover marked drugs for dual-action agents that can both reduce the risk of heart failure and present anti-cancer activity. 3D-REMAP predicts that levosimendan, a drug for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to identify cancer cell-lines and patients that may respond to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe and effective multi-targeted cancer therapy, and demonstrate the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.Author SummaryMulti-target drug design (a.k.a targeted polypharmacology) has emerged as a new strategy for discovering novel therapeutics that can enhance therapeutic efficacy and overcome drug resistance in tackling multi-genic diseases such as cancer. However, it is extremely challenging for conventional computational tools that are either receptor-based or ligand-based to screen compounds for selectively targeting multiple receptors. Existing multi-target drug design mainly focuses on compound screening against receptors within the same gene family but not across different gene families. Here, we develop a new computational tool 3D-REMAP that enables us to identify chemical-protein interactions across fold space on a genome scale. The genome-scale chemical-protein interaction network allows us to discover dual-action drugs that can bind to two types of targets simultaneously, one for mitigating side effect and another for enhancing the therapeutic effect. Using 3D-REMAP, we predict and subsequently experiments validate that levosimendan, a drug for heart failure, is active against multiple cancers, notably, lymphoma. This study demonstrates the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.
OrthoFinder: phylogenetic orthology inference for comparative genomics
Authors: Emms David M. , Kelly Steven 
Published: November 2018

AbstractHere, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted genes trees, gene duplication events, the rooted species tree, and comparative genomic statistics. Each output is benchmarked on appropriate real or simulated datasets and, where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available athttps://github.com/davidemms/OrthoFinder.
Buzzfeed Inc: native advertising the way forward?
Authors: Rishi Bikramjit , Mehta Aditya , Banerjee Poulomi , Deepak Akshay 
Published: November 2018

Learning outcomes
This paper aims to understand the changing landscape of media and entertainment industry, to understand the difference between display advertising and native advertising, to know the standing of BuzzFeed in the industry and to know the strategic actions of BuzzFeed under the current competitive business environment.

Case overview/synopsis
Founded in 2006 as a viral lab, by Jonah Peretti and John S. Johnson, with the aim of tracking viral content, it caused disruption in the market with its entry and grew very rapidly. It was valued at $1.5bn in 2015, having raised money from numerous investors. The revenue of BuzzFeed was driven by the concept of native advertising. Catchy headlined articles conveyed the sense that BuzzFeed might be charging advertisers on basis of clicks, but this was not entirely true. Instead, BuzzFeed charged a fee from its clients for creating custom content targeting the customer base of the client. However, the year 2015 went tough for BuzzFeed when, as per the reports by Financial Times, it fell short of achieving its targeted revenue of US$250m by US$80m. It forced the company to revise and lower its target revenues for the year 2016 as well. The combined worldwide traffic to BuzzFeed saw a decline of up to 14 per cent. As Claire marketing head looked out of the window and pondered over the slashed revenue projections and the content related issues, the question on her mind was would native advertising sustain BuzzFeed in the longer run? BuzzFeed was known for its viral content and native advertising would involve finding a balance between what is good for the advertisers' brand and what will become viral. Buzzfeed ran a risk of losing brands to other modes of advertisement if they felt that native advertisement, which disguises the product within the content, was not meeting their expectations.

Complexity academic level
The case is targeted at students of post-graduation and under-graduation programs in Business Administration.

Supplementary materials
Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Subject code
Marketing
A prototype-targeting intervention for the promotion of healthy eating in adolescents
Authors: Dohnke Birte , Dewitt Tanja , Steinhilber Amina 
Published: November 2018

Purpose
Unhealthy eating among adolescents from families with lower social status is a major concern. The purpose of this paper is to describe the development and evaluation of a prototype-targeting intervention for the promotion of healthy eating in this target group.

Design/methodology/approach
The planning model intervention mapping (IM) was chosen to proceed systematically, to use theory and evidence herein and to make decisions transparent. A controlled study with three times of data assessment was conducted to evaluate process characteristics and effects (N=108).

Findings
“Provide opportunities for social comparison” (behavioural change technique 6.2) was chosen as change method and “perceived similarity” was identified as the condition for its effectiveness. An intervention unit was designed for application. The evaluation results show the feasibility of the unit; materials and activities successfully applied the change method and its condition for effectiveness; and intervention objectives were generally achieved.

Practical implications
A prototype-targeting intervention is provided that is based on theory and evidence, and is suitable for implementation. More generally, the paper can serve as a blueprint for the systematic planning of theory- and evidence-based interventions targeting specific personal determinants for behaviour change.

Originality/value
The paper makes an important contribution to the application of the prototype-related theory and a useful addition of IM to the growing field of intervention development and design.
What happens to the physical and psychosocial work environment when activity-based offices are introduced into academia?
Authors: Berthelsen Hanne , Muhonen Tuija , Toivanen Susanna 
Published: November 2018

PurposeThere is an increased interest for introducing activity-based offices at universities. The purpose of this study is to contribute to the knowledge about the importance of the built environment for the psychosocial work environment within academia by analyzing how staff at a large Swedish university experienced the physical and psychosocial work environment before and after moving to activity-based offices.Design/methodology/approachA Web-based survey was distributed to all employees at two faculties at a university three months before (2015,n= 217, response rate 51 per cent) and nine months after (2016,n= 200, response rate 47 per cent) relocation to a new activity-based university building.FindingsIn the new premises, a vast majority (86 per cent) always occupied the same place when possible, and worked also more often from home. The social community at work had declined and social support from colleagues and supervisors was perceived to have decreased. The participants reported a lower job satisfaction after the relocation and were more likely to seek new jobs. No aspects in the physical or psychosocial work environment were found to have improved after the relocation.Research/limitations implicationsThe study had a two-wave cross-sectional design, which does not allow establishing causal relations.Practical implicationsThere is reason to be cautious about relocation to activity-based offices at universities. The potential savings in costs for premises may lead to may be followed by an increase in other costs. The risk that staff cannot concentrate on their work in activity-based university workplaces and lose their sense of community with colleagues are factors, which in the long run may lead to decreased efficiency, more conflicts and poorer well-being.Originality/valueThis paper contributes with new knowledge concerning changes in the physical and psychosocial work environment when relocating from cell offices to activity-based offices in a university setting.
Psychological antecedents to customized apparel purchases
Authors: Seo Sukyung , Lang Chunmin 
Published: November 2018

Purpose
The purpose of this paper is to determine the influences of consumers’ internal-oriented psychological factors (i.e. self- promotion, need for uniqueness, self-expression and self-monitoring) and external-oriented psychological properties (i.e. social identity and other-directedness) on the perception of customization and the sense of extended-self; further to identify whether the perception of customization and the sense of extended-self lead to purchase intention toward customized apparel products.

Design/methodology/approach
Data were collected through an online survey among college students in the USA, generating a valid sample size of 338 participants. Factor analysis and Structural Equation Modeling were utilized to test proposed hypotheses.

Findings
The results indicated that need for uniqueness and self-monitoring were significantly related to consumers’ perception of customization and the sense of extended-self, whereas impacts of self-expression and other-directedness were not significant. Furthermore, the results also confirmed that consumers’ perception of customization and the sense of extended-self were positively associated with purchase intention of customized apparel products.

Originality/value
This study investigates consumers’ psychological features that influence consumers’ buying intention of customized apparel products, which is the first attempt. The study results are insightful and beneficial for marketers to construct target consumer segmentation under different cultural and social settings.
Brand equity, risk and return in Latin America
Authors: de Oliveira Marta Olivia Rovedder , Stefanan Aline Armanini , Lobler Mauri Leodir 
Published: November 2018

Purpose
This study aims to compare the performance of stocks of companies with high brand equity with the stocks of other companies listed on the stock market of emerging countries of Latin America: Brazil, Chile, Colombia, Mexico and Peru.

Design/methodology/approach
The valuable brands (brands with high brand equity) considered were the most valuable Latin America brands according to the Millward Brown reports. Carhart four-factor model was used to analyze performance and the total sample included 732 stocks in the Latin American market collected at Economatica, monthly, for a period of 10 years.

Findings
The Valuable Brands Portfolio presents the lowest investment risk, suggesting that stocks of companies with valuable brands ensure lower risk investment to shareholders in these emerging markets.

Originality/value
This study is the first to associate brand equity with the creation of shareholder value in the context of emerging Latin American countries. In addition, the proposed method has also not been used previously to study emerging countries. The association found between a marketing asset (brand equity) and stock market performance contributes to improve the relationship between marketing and finance areas. The results of this study in emerging markets corroborate previous studies in developed markets, strongly suggesting the confirmation of the effect of brand equity on the reduction of risk stock.
Brand line extensions: creating new loyalties or internal variety-seeking?
Authors: Koschmann Anthony , Sheth Jagdish 
Published: November 2018

Purpose
The purpose of this paper is to examine whether line extensions (modified brands) create their own loyalties or induce variety-seeking within the brand. Prior research has explored how the branded house strategy (i.e. multiple products bearing the same brand name) retains customers from competing brands. However, this research investigates loyalty within the brand by comparing loyalty and variety-seeking rates of modified brands.

Design/methodology/approach
Markov chains examine behavioral loyalty and switching rates of panel households in the USA over several quarters for two family brands of carbonated beverages. Emphasis is placed on the consumers who purchase the upper median of volume (heavy half) and constitute a disproportionate amount of brand’s sales (86 per cent of the volume).

Findings
Three propositions find that loyalty rates are high among modified brands with little switching to other lines within the brand. Further, loyalty and switch to rates are highest for the flagship branded product (the master modified brand).

Practical implications
Managers segment the market using the branded house strategy, yet loyalty rates vary for each product line. The switching rates can guide managers as to which products have established a loyal consumer base.

Originality/value
While brand switching is a considerable research stream, this research is believed to be the first to explore loyalty versus variety-seeking in the branded house strategy.