bluuebunny/crossref_metadata_2025_split huggingface dataset:
Near-infrared light increases functional connectivity with a non-thermal mechanism
Authors: Dmochowski Grzegorz M. , Shereen Ahmed (Duke) , Berisha Destiny , Dmochowski Jacek P. 
Published: November 2018

AbstractAlthough techniques for non-invasive brain stimulation are under intense investigation, an approach that has received limited attention is transcranial photobiomodulation (tPBM), the delivery of near-infrared light to the brain with a laser directed at the scalp. Here we employed functional magnetic resonance imaging (fMRI) to measure the Blood-Oxygenation-Level Dependent (BOLD) signal in n = 20 healthy humans while concurrently stimulating their right frontal pole with a near-infrared laser. We failed to detect an evoked BOLD response at illumination. However, functional connectivity with the illuminated region increased by an average of 10% during stimulation, with some connections strengthening by as much as 40%. 23% of connections with the illuminated region experienced a significant acute increase, with the time course of connectivity exhibiting a sharp rise at illumination onset. Brain-wide connectivity increases were also observed, with connections in the stimulated hemisphere showing a significantly larger increase than those in the non-stimulated hemisphere. We subsequently employed MR Thermometry to measure brain temperature during tPBM (separate cohort, n = 20), and found no significant temperature differences between active and sham stimulation. Our findings suggest that near-infrared light synchronizes brain activity with a non-thermal mechanism, underscoring the promise of tPBM as a new technique for stimulating brain function.
Integrative inference of brain cell similarities and differences from single-cell genomics
Authors: Welch Joshua , Kozareva Velina , Ferreira Ashley , Vanderburg Charles , Martin Carly , Macosko Evan 
Published: November 2018

SummaryDefining cell types requires integrating diverse measurements from multiple experiments and biological contexts. Recent technological developments in single-cell analysis have enabled high-throughput profiling of gene expression, epigenetic regulation, and spatial relationships amongst cells in complex tissues, but computational approaches that deliver a sensitive and specific joint analysis of these datasets are lacking. We developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity, allowing flexible modeling of highly heterogeneous single-cell datasets. We demonstrated its broad utility by applying it to four diverse and challenging analyses of human and mouse brain cells. First, we defined both cell-type-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis, an anatomically complex brain region that plays important roles in sex-specific behaviors. Second, we analyzed gene expression in the substantia nigra of seven postmortem human subjects, comparing cell states in specific donors, and relating cell types to those in the mouse. Third, we jointly leveraged in situ gene expression and scRNA-seq data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical scRNA-seq profiles with single-cell DNA methylation signatures, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using the LIGER algorithm promise to accelerate single-cell investigations of cell-type definition, gene regulation, and disease states.
Imaging of sub-cellular fluctuations provides a rapid way to observe bacterial viability and response to antibiotics
Authors: Bermingham Charlotte R , Murillo Isabel , Payot Alexandre D J , Balram Krishna C , Kloucek Maximilian B , Hanna Simon , Redmond Niamh M , Baxter Helen , Oulton Ruth , Avison Matthew B , Antognozzi Massimo 
Published: November 2018

AbstractDetermining the viability of bacteria in a sample is an essential microbiological technique used in healthcare, industrial bioprocesses and research. Increasingly, attention has been focussing on antimicrobial susceptibility tests (AST), allowing rapid and appropriate prescribing of antibiotics. Current AST are limited in speed as they rely on detecting growth of microorganisms. Faster AST could be enabled by the recent discovery that living bacteria manifest nano-scale fluctuations, which reduce when the bacteria die. Here, we demonstrate a direct method of visualising fluctuations within bacterial cells using Sub-Cellular Fluctuation Imaging (SCFI), which is based on Total Internal Reflection Microscopy (TIRM). We show that SCFI can measure the viability of bacterial samples within minutes, distinguishing not only between live and dead bacteria but also live bacteria in different metabolic states. Importantly, we subsequently show that SCFI can rapidly distinguish antibiotic-treated resistant and susceptible bacteria, and therefore has particular application as a rapid AST.
Respiratory heterogeneity shapes biofilm formation and host colonization in uropathogenic Escherichia coli
Authors: Beebout Connor J. , Eberly Allison R. , Werby Sabrina H. , Reasoner Seth A. , Brannon John R. , De Shuvro , Fitzgerald Madison J. , Huggins Marissa M. , Clayton Douglass B. , Cegelski Lynette , Hadjifrangiskou Maria 
Published: November 2018

AbstractBiofilms are multicellular bacterial communities encased in a self-secreted extracellular matrix comprised of polysaccharides, proteinaceous fibers, and DNA. Organization of these components lends spatial organization to the biofilm community such that biofilm residents can benefit from the production of common goods, while being protected from exogenous insults. Spatial organization is driven by the presence of chemical gradients, such as oxygen. Here we quantified and localized the expression of two Escherichia coli cytochrome oxidases in cells found in the biofilm state and defined their contribution to biofilm architecture. These studies elucidated a role for the high-affinity quinol oxidase cytochrome bd in matrix production and biofilm resident protection. Cytochrome bd was the most abundantly expressed respiratory complex in the biofilm community and was localized in the biofilm interior. Depletion of the cytochrome bd-expressing subpopulation led to decreased extracellular matrix and increased sensitivity of the community to exogenous stresses. Interrogation of the distribution of cytochrome oxidases in the planktonic state revealed that ∼15% of the population expresses cytochrome bd at atmospheric oxygen concentration, and this population dominates during acute urinary tract infection. These data point towards a bet-hedging mechanism in which heterogeneous expression of respiratory complexes ensures respiratory plasticity of E. coli across diverse host niches.
Simulation and reconstruction ofmetabolite-metabolite association networks usinga metabolic dynamic model and correlation based-algorithms
Authors: Jahagirdar Sanjeevan , Suarez-Diez Maria , Saccenti Edoardo 
Published: November 2018

AbstractBiological networks play a paramount role in our understanding of complex biological phenomena and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS and modifications of the MR-NET algorithm, together with standard Pearson’s and Spearman’s correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample size and noise levels. We found that inference algorithms based on resampling and bootstrapping to perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns which are more similar to those observed in real metabolomics data.
A machine-learning approach for accurate detection of copy-number variants from exome sequencing
Authors: Pounraja Vijay Kumar , Jayakar Gopal , Jensen Matthew , Kelkar Neil , Girirajan Santhosh 
Published: November 2018

ABSTRACTCopy-number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome sequencing data are limited by high false positive rates and low concordance due to the inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn-diagram approaches to identify “high-confidence” CNVs. However, this approach is inadequate, as it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework (https://github.com/girirajanlab/CN_Learn) that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted by four exome-based CNV callers (CANOES, CODEX, XHMM and CLAMMS) from 503 samples, we demonstrate that CN-Learn identifies true CNVs at higher precision (~90%) and recall (~85%) rates while maintaining robust performance even when trained with minimal data (~30 samples). CN-Learn recovers twice as many CNVs compared to individual callers or Venn diagram-based approaches, with features such as exome capture probe count, caller concordance and GC content providing the most discriminatory power. In fact, about 58% of all true CNVs recovered by CN-Learn were either singletons or calls that lacked support from at least one caller. Our study underscores the limitations of current approaches for CNV identification and provides an effective method that yields high-quality CNVs for application in clinical diagnostics.
Corrosion inhibition of N80 steel in 10% HCl + 8% HBF4solution
Authors: Du Juan , He Yuning , Liu Pingli , Liu Yigang , Meng Xianghai , Zhao Liqiang 
Published: November 2018

PurposeThis paper aims to analyze the corrosion and corrosion inhibition of N80 in 10 per cent HCl + 8 per cent fluoroboric acid (HBF4) solution for acidizing operation.Design/methodology/approachThe corrosion rate, kinetic parameters (Ea, A) and thermodynamic parameters (ΔH, ΔS) of N80 steel in fresh acid and spent acid, 10 per cent HCl + 8 per cent HBF4, 10 per cent HCl and 8 per cent HBF4solutions were calculated through immersion tests. The corrosion and inhibition properties were studied through X-ray diffraction and electrochemical measurements. The corrosion morphology of the corrosion product was examined by scanning electron microscopy (SEM).FindingsThe results demonstrated that the spent acid was the main cause of acidification corrosion, and the HBF4would cause serious corrosion to N80 steel. The results showed that the N80 steel was more seriously corroded in the spent acid than in fresh acid, and the hydrolysis of HBF4accelerates the dissolution process of N80 steel anode to control the corrosion reaction. The results showed that the acidification will definitely cause serious corrosion to the oil tube; therefore, necessary anti-corrosion measures must be taken in the acidification process.Originality/valueThe results showed that acidizing the formation with 10 per cent HCl + 8 per cent HBF4will definitely cause serious corrosion to the oil tube, especially when the spent acid flows back. Therefore, necessary anti-corrosion measures must be taken in the acidification process, especially in the spent acid flowback stage.
Factors affecting the development of accounting practices in Jordan: an institutional perspective
Authors: Tahat Yasean , Omran Mohamed A. , AbuGhazaleh Naser M. 
Published: November 2018

Purpose
Based on the institutional theory, the purpose of this paper is to examine institutional factors that affect the development of accounting practices in Jordan.

Design/methodology/approach
The current study surveys the perceptions of 306 participants and 20 interviewees.

Findings
First, the early formation of accounting practices in Jordan has been affected by the legacy of Ottoman Empire’s and the British Colony’s accounting systems. Second, the results indicate that government of Jordan (regulatory frameworks), pressures from international donors and large economic organizations (politico-economic factors), education and training/development (cultural inputs), and the efforts to attract foreign investments and getting access into the international fund and trade (economic factors) have been influential influences in the development of accounting practices and the adoption of International Accounting Standards/International Financial Reporting Standards (IAS/IFRS) in Jordan. Finally, the findings reveal that “Secrecy” construct (a culture input) has been a problematic in the implementation of IAS/IFRS.

Practical implications
The current study provides policy implications for the Jordanian policy makers and for other developing countries that are working hard to improve the quality of financial reporting of their business entities. Finally, the authors suggest some great opportunities for future research.

Originality/value
First, this paper contributes to Jordan’s policy developments including fundamental strategies in terms of attracting foreign investments to expand the economy and the international and regional trade. Second, it fills a gap in the international accounting research by empirically assessing how institutional factors affect the development of accounting practices in emerging country such as Jordan.
Hygiene behavior assessment of a hazelnut processing plant
Authors: Botondi Rinaldo 
Published: November 2018

PurposeThe purpose of this paper is to evaluate the possible microbiological contamination in terms of total counts and representative microbial groups found in the hazelnuts as well as in work areas and on the working surfaces of a hazelnut processing plant.Design/methodology/approachSome microbiological parameters related to the manufacturing process (e.g. aerobic colony count yeast and fungi) were evaluated. Indoor air samples were tested in order to evaluate the possible contamination of fungal strains (i.e. mycotoxin producing fungi).FindingsThe results showed that the highest values (>of 500 CFU/m3) for bacteria and fungi were only observed in the shelling and sorting areas. Some species such as Aspergillus and Penicillium (potential mycotoxin-producers) as well as some noxious colonies ofAspergillus fumigatuswere detected along the processing line. No occurrence of aflatoxins was observed in the finished product. Microbial loads obtained through surface analyses were in accordance to the reference data.Originality/valueThe aim of this study was to monitor and improve the procedures currently used in a hazelnut processing plant.
Benchmarking of TQM practices in the Jordanian pharmaceutical industry (a comparative study)
Authors: Yaseen Maysoon Mohammed , Sweis Rateb J. , Abdallah Ayman Bahjat , Obeidat Bader Yousef , Sweis Nadia J. 
Published: November 2018

PurposeThe purpose of this paper is to establish practical guidelines for benchmarking eight total quality management (TQM) practices vital to pharmaceutical companies’ performance. The paper also proposes the use of an analytic total quality index (TQI) as a benchmarking tool and illustrates the importance and effectiveness of this benchmarking methodology by applying it in two comparative studies of three Jordanian pharmaceutical companies.Design/methodology/approachIn order to achieve the above-mentioned purpose, the data were gathered through a questionnaire that was used to evaluate the gap between the ideal and current status of the quality management system and distributed to the quality units from three companies: pharmaceutical manufacturing company, a pharmaceutical manufacturing company working in the same field and a pharmaceutical service providing research services to a pharmaceutical manufacturing companies. And the mean differences between the current and ideal states for the eight critical TQM practices were compared for these two comparative studies using thet-test.FindingsEach of the two comparisons reveals statistically significant differences regarding the perceptions of actual and ideal scores for manufacturing and service companies on five out of eight critical factors and, on two out of eight critical factors for manufacturing and manufacturing companies.Practical implicationsThe pharmaceutical companies, regardless of whether they are manufacturing or service company, can adopt benchmarking techniques which were applied in this case study to improve their performance and their product/service quality.Originality/valueThe consequences of this research can support organization managers and policy makers in effectively benchmarking the identified TQM practices in their organizations using the proposed TQI benchmarking tool.
The influence of social media marketing activities on customer loyalty
Authors: Yadav Mayank , Rahman Zillur 
Published: November 2018

PurposeThe purpose of this paper is to examine the impact of perceived social media marketing activities (SMMAs) on customer loyalty via customer equity drivers (CEDs) in an e-commerce context.Design/methodology/approachThe study surveyed 371 students from a large university in India. The data were analyzed via confirmatory factor analysis and the research hypotheses were examined using SEM.FindingsThe study revealed three key findings. First, perceived SMMAs of e-commerce comprise five dimensions, namely, interactivity, informativeness, word-of-mouth, personalization and trendiness. Second, perceived SMMAs of e-commerce have significantly and positively influenced all the drivers of customer equity (CEDs). Third, the CEDs of e-commerce exhibit a significant and positive influence on customer loyalty toward the e-commerce sites.Practical implicationsThis study will help e-commerce managers to boost customer loyalty toward the e-commerce sites through perceived SMMA.Originality/valueThe study is the first to identify five dimensions of e-commerce perceived SMMA. The current study also introduces the stimulus–organism–response model as a theoretical support to connect perceived SMMAs of e-commerce to customers’ loyalty via CEDs. This is supposed to be the first study to examine the impact of perceived SMMA on customer loyalty toward the e-commerce sites via CEDs in the e-commerce industry.
Determinants of customer loyalty: a study with customers of a Brazilian bank
Authors: Milan Gabriel Sperandio , Slongo Luiz Antonio , Eberle Luciene , De Toni Deonir , Bebber Suélen 
Published: November 2018

PurposeThe purpose of this paper is to analyze customer loyalty in the context of existing relationships between Brazilian banking service provider and its customers in the context of B2C (Business-to-Consumer) relationships. Hence, a theoretical model was proposed and tested with banking services private individual customers taking into account perceived value, service provider reputation, financial bonding tactics, structural bonding tactics, social bonding tactics and switching costs as customer loyalty determinants.Design/methodology/approachA multivariate statistical approach with structural equations modeling was used in a 505 customer sample of the one prominent bank in Brazil.FindingsResults indicate that the proposed theoretical model confirming a satisfactory fit, presenting a good explanatory power (R2=0.738) and supporting that perceived value influences the service provider reputation; financial bonding tactics, structural bonding tactics and social bonding tactics influence perceived value; service provider reputation influences switching costs; switching costs influence customer loyalty and the social bonding tactics influence customer loyalty.Practical implicationsThe results evidenced in the present research could serve as benchmarking for other researchers or managers connected to the financial service sector (or bank service) when looking for a better understanding about the antecedents of customer loyalty, adapting strategies and actions to stimulate and generate better market and economic–financial results for the institutions of this sector.Originality/valueFinding out which constructs better explain customer loyalty, and its possible relations, is something relevant for the banking sector, once it can generate more effective managerial insights, positively making an impact in a customer portfolio performance, or the financial institution itself, from the construction, maintenance and strengthening of the relationships with customers.
Benchmarking the interactions among performance indicators in dairy supply chain
Authors: Mor Rahul S. , Bhardwaj Arvind , Singh Sarbjit 
Published: November 2018

Purpose
The purpose of this paper is to explore the key performance indicators (PIs) that serve as a decision support tool in case of dairy supply chain practices and to analyze their interactions in the context of Indian dairy industry sector. A total of 11 PIs have been identified through the literature review and the opinions of an expert team consisting of managerial and technical experts from dairy industry and academics.

Design/methodology/approach
A solution methodology based on the interpretive structure modeling (ISM) technique is used to analyze the interactions among PIs and to propose a structural model. The developed model not only helps in understanding the contextual relationship among the PIs, but also in determining their interdependence to assess the supply chain performance in dairy industry. Further, the importance of PIs has been determined based on their driving and dependence power by using MICMAC analysis.

Findings
The ISM-based model suggests four PIs at first level, three PIs at second level, one PI at third level as well as one PI at fourth level and two PIs at fifth level. Model allocates to the effective information technology, brand management, responsiveness in shipment and accuracy and a control over wastages as the key PIs in the dairy industry sector. The effective traceability systems, cold chain infrastructure, quality management and the support for technological innovations are the next major PIs. There exists no autonomous PI in MICMAC analysis which proves the importance of identified PIs in the case study.

Research limitations/implications
The proposed model is an attempt to capture the dynamics of milk processing sector and to incorporate all relevant constraints related to internal and external environments that would significantly improve the supply chain performance in the dairy industry.

Practical implications
The model developed in this study has been tested in the cooperative milk processing units based in India and also discussed with the experts from academics. This work may help practitioners, regulators and dairy industry professionals to focus their efforts toward achieving high performance by the effective implementation of the identified PIs.

Originality/value
In this study, 11 PIs are considered. Interactions among PIs are evaluated with the help of the ISM matrix. Out of the 11 PIs, six demonstrate both strong driving and dependence power as explained in the MICMAC analysis.