bluuebunny/crossref_metadata_2025_split huggingface dataset:
Kappa opioid receptor modulation on GABAergic inputs onto ventral periaqueductal gray dopamine neurons
Authors: Li Chia , Kash Thomas L. 
Published: August 2018

AbstractThe kappa opioid receptor (KOR) system has been implicated in regulation of many behaviors including pain. While there are numerous studies suggesting KOR-regulation of pain being mediated spinally, there have been reports of pain-like behaviors regulated by central KOR signaling. In particular, oxytocin-induced analgesia appears to be mediated by KOR receptors within the ventrolateral periaqueductal gray (vlPAG). The vlPAG is a brain region that has long been known to be involved in the regulation of pain. We recently found that dopamine (DA) neurons within the vlPAG represent a specific population of neurons that can regulate pain-like behaviors. In this study, we sought to determine the impact of KOR signaling on GABAergic inputs to the vlPAG DA neurons, and determine the mechanism of inhibition. We found that activation of KOR significantly reduced GABAergic transmission onto vlPAG DA neurons. In addition, our data suggest this effect is mediated pre-synaptically via the G-protein βγ subunit. These data suggest the possibility that KOR-activation disinhibits vlPAG dopamine neurons, which could lead to altered regulation of pain-related behaviors.
Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes
Authors: Dennis Hernaus , Frank Michael J. , Brown Elliot C. , Brown Jaime K. , Gold James M. , Waltz James A. 
Published: August 2018

ABSTRACTBackgroundMotivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on “value-less” stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.MethodsHealthy volunteers (n= 38) and PSZ (n=49) completed a reinforcement learning paradigm consisting of four stimulus pairs. Reward magnitude (3/2/1/0 points) and probability (90%/80%/20%/10%) together determined each stimulus’ EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.ResultsPSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability*magnitude) between two competing stimuli increased. Using a previously-validated computational hybrid model, PSZ relied less on EV (“Q-learning”) and more on stimulus-response learning (“actor-critic”), which correlated with SANS motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability.
ConclusionsProbability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on value-less stimulus-response associations. Consistent with previous work and confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work reconfirms the importance of decreased Q-learning/increased actor-critic-type learning as an explanatory framework for a range of EV deficits in PSZ.
Hierarchical Complexity of the Adult Human Structural Connectome
Authors: Smith Keith , Bastin Mark E. , Cox Simon R. , Hernández Maria C. Valdés , Wiseman Stewart , Escudero Javier , Sudlow Catherine 
Published: August 2018

AbstractThe structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.
Hexokinase: A central player in the synergism of high-intensity intermittent exercise and every-other-day intermittent fasting regimen on energy metabolism adaptations
Authors: Real-Hohn Antonio , Navegantes Clarice , Ramos Katia , Ramos-Filho Dionisio , Cahuê Fábio , Galina Antonio , Salerno Verônica P. 
Published: August 2018

AbstractVisceral lipid accumulation, organ hypertrophy and a reduction in skeletal muscle strength are all signs associated with the severity of obesity related disease. Intermittent fasting (IF) and high-intensity intermittent exercise (HIIE) are natural strategies that, individually, can prevent and ameliorate obesity along with metabolic syndrome and its associated diseases. However, the combinatorial effect of IF and HIIF on energetic metabolism is currently not well understood. We hypothesized that their combination could have a potential for more than strictly additive benefits. Here, we show that two months of every-other-day intermittent fasting regimen combined with a high-intensity intermittent exercise protocol (IF/HIIE) produce a synergetic effect, preventing fat accumulation, enhancing physical performance and optimizing energy production. The IF/HIIE group presented increased glucose uptake, lower levels of serum insulin and a global activation of hexokinases in skeletal muscle, heart and liver comparing to control, IF and HIIE groups. IF/HIIE synergism led to activation of the FoF1 ATP synthase and promoted a more oxidative profile of mitochondria in observed skeletal muscle. Additionally, high-resolution respirometry of muscle fibers showed that animals in the IF/HIIE group presented characteristics suggestive of augmented mitochondrial mass and efficiency. Finally, an important reduction in serum oxidative stress markers were observed in IF/HIIE group. These findings provide new insights for the implementation of non-pharmaceutical strategies to prevent/treat metabolic syndrome and associated diseases.
Mitochondrial hypoxic stress induces widespread RNA editing by APOBEC3G in lymphocyte
Authors: Sharma Shraddha , Wang Jianmin , Portwood Scott , Cortes-Gomez Eduardo , Maguire Orla , Basse Per H. , Wang Eunice S. , Baysal Bora E. 
Published: August 2018

AbstractProtein recoding by RNA editing is required for normal health and evolutionary adaptation. However, de novo induction of RNA editing in response to environmental factors is an uncommon phenomenon. While APOBEC3A edits many mRNAs in monocytes/macrophages in response to hypoxia and interferons, the physiological significance of such editing is unclear. Here we show that the related APOBEC3G cytidine deaminase induces site-specific C-to-U RNA editing in natural killer (NK), CD8+ T cells and lymphoma cell lines upon cellular crowding and hypoxia. RNASeq analysis of hypoxic NK cells reveals widespread C-to-U recoding mRNA editing that is enriched for genes involved in mRNA translation. APOBEC3G promotes Warburg-like metabolic remodeling and reduces proliferation of HuT78 T cells under similar conditions. Hypoxia-induced RNA editing by APOBEC3G can be mimicked by the inhibition of mitochondrial respiration, and occurs independently of HIF-1α. Thus, APOBEC3G is an endogenous RNA editing enzyme, which is induced by mitochondrial hypoxic stress to promote adaptation in lymphocytes.
How social influence and personality affect users’ social network fatigue and discontinuance behavior
Authors: Shokouhyar Sajad , Siadat Seyed Hossein , Razavi Mojde Khazeni 
Published: August 2018

Purpose
The purpose of this paper is to focus on understanding how social influence and personality of individuals differentiate between users’ social network fatigue and discontinuance behavior. Furthermore, the most common discontinuance behavior among users was investigated.

Design/methodology/approach
The research model was tested with the data from 163 Instagram users based on online and offline surveys. The partial least squares method was used to test the proposed hypotheses of this study.

Findings
The results indicate that social influence affects users’ discontinuance behavior and social network fatigue. Social network fatigue is greater in users with higher reported social influence compared to those with a lower one. Moreover, in response to social network fatigue, users prefer to keep their activities under control instead of switching to alternative social network sites (SNSs) or a short break in social network activities.

Practical implications
By achieving a better understanding of users’ feeling and behaviors, social network providers may codify their strategies more efficiently.

Originality/value
The study is novel in exploring users’ SNS fatigue and their discontinuance behavior by integrating social influence and personality. The authors defined a new concept of effect of social influence on social network fatigue. Additionally, the authors examined which discontinuance behaviors in individuals were more prevalent.
Internet of Things adoption for reconfiguring decision-making processes in asset management
Authors: Brous Paul , Janssen Marijn , Herder Paulien 
Published: August 2018

Purpose
Managers are increasingly looking to adopt the Internet of Things (IoT) to include the vast amount of big data generated in their decision-making processes. The use of IoT might yield many benefits for organizations engaged in civil infrastructure management, but these benefits might be difficult to realize as organizations are not equipped to handle and interpret this data. The purpose of this paper is to understand how IoT adoption affects decision-making processes.

Design/methodology/approach
In this paper the changes in the business processes for managing civil infrastructure assets brought about by IoT adoption are analyzed by investigating two case studies within the water management domain. Propositions for effective IoT adoption in decision-making processes are derived.

Findings
The results show that decision processes in civil infrastructure asset management have been transformed to deal with the real-time nature of the data. The authors found the need to make organizational and business process changes, development of new capabilities, data provenance and governance and the need for standardization. IoT can have a transformative effect on business processes.

Research limitations/implications
Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the propositions further.

Practical implications
The paper shows that data provenance is necessary to be able to understand the value and the quality of the data often generated by various organizations. Managers need to adapt new capabilities to be able to interpret the data.

Originality/value
This paper fulfills an identified need to understand how IoT adoption affects decision-making processes in asset management in order to be able to achieve expected benefits and mitigate risk.
Order reduction in electrical power systems using singular perturbation in different coordinate systems
Authors: Huisinga Hauke , Hofmann Lutz 
Published: August 2018

PurposeEfficient calculations of the transient behaviour after disturbances of large-scale power systems are complex because of, among other things, the non-linearity and the stiffness of the overall state equation system (SES). Because of the rising amount of flexible transmission system elements, there is an increasing need for reduced order models with a negligible loss of accuracy. With the Extended Nodal Approach and the application of the singular perturbation method, it is possible to reduce the order of the SES adapted to the respective setting of the desired tasks and accuracy requirements.Design/methodology/approachBased on a differential-algebraic equation for the electric power system which is formulated with the Extended Nodal Approach, the automatic decomposition into reduced order models is shown in this paper. The paper investigates the effects of different coordinate systems for an automatic order reduction with the singular perturbation method, as well as a comparison of results calculated with the full and reduced order models.FindingsThe eigenvalues of the full system are approximated sufficiently by the three subsystems. A simulation example demonstrates the good agreement between the reduced order models and the full model independent of the choice of the coordinate system. The decomposed subsystems in rotating coordinates have benefits as compared to those in static coordinates.Originality/valueThe paper presents a systematic decomposition based only on a differential-algebraic equation system of the electric power system into three subsystems.
Novel hybrid excited machine with flux barriers in rotor structure
Authors: Wardach Marcin , Palka Ryszard , Paplicki Piotr , Bonislawski Michal 
Published: August 2018

Purpose
Permanent magnet (PM) electrical machines are becoming one of the most popular type of the machines used in electrical vehicle drive applications. The main drawback of permanent magnet machines, despite obvious advantages, is associated with the flux control capability, which is limited at high rotor speeds of the machine. This paper aims to present a new arrangement of permanent magnets and flux barriers in the rotor structure to improve the field weakening control of hybrid excited machines. The field weakening characteristics, back-emf waveforms and efficiency maps of this novel machine have been reported.

Design/methodology/approach
In the study, finite element analysis was used to perform simulation research. Then, based on the simulation studies, an experimental model was built. The paper also presents selected experimental results.

Findings
Obtained results show that the proposed machine topology and novel control strategy can offer an effective flux control method allowing to extend the maximal rotational speed of the machine at constant power range.

Practical implications
The proposed solution can be used in electric vehicles drive to extend its torque and speed range.

Originality/value
The paper presents original design and results of research on a new solution of a hybrid excited machine with magnetic barriers in a rotor.
Improved estimation of iron losses for non-sinusoidal voltages
Authors: Ionita Valentin , Petrescu Lucian , Cazacu Emil 
Published: August 2018

Purpose
The electrical machines connected to modern electric power grids are non-sinusoidal excited, and their augmented losses, including iron losses, limit their working characteristics. This paper aims to propose a prediction method for iron losses in non-oriented grains (NO) FeSi sheets under non-sinusoidal voltage, involving an inverse classical Preisach hysteresis model and the time-integration of each loss component.

Design/methodology/approach
The magnetic history management in inverse Preisach model is optimized and a numerical Everett function is identified from measured symmetrical hysteresis cycles. The experimental data for sinusoidal waveforms obtained by a single sheet tester were also used to identify the parameters involved in Bertotti’ losses separation method. The non-sinusoidal magnetic induction waveform, corresponding to a measured voltage in an industrial electrical grid, was the input for Preisach model, the output magnetic field being accurately computed. The hysteresis, classical and excess losses are calculated by time-integration and the total losses are compared with those obtained for sinusoidal excitation.

Findings
The proposed method allows to estimate the iron losses for non-sinusoidal magnetic induction, using carefully identified parameters of FeSi NO sheets, using experimental data from sinusoidal regimes.

Originality/value
The method accuracy is assured by using a numerical Everett function, a variable Preisach grid step (adapted for the high non-linearity of FeSi sheets) and high-order fitting polynomials for the microscopic parameters involved in the excess loss estimation. The procedure allows a better design of magnetic cores and an improved estimation of the electric machine derating for non-sinusoidal voltages.
Optimization of concrete placing operation based on competing carbon footprint, cost and production rate objectives
Authors: Moussavi Nadoushani Zahra Sadat , Akbarnezhad Ali , Rey David 
Published: August 2018

PurposeDue to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.Design/methodology/approachThe proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.FindingsThe results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.Originality/valueResults of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.
Slowly shifting out of neutral
Authors: Falter Michelle M. , Kerkhoff Shea N. 
Published: August 2018

PurposeThe purpose of this paper is twofold: to explore how preservice teachers in a young adult literature course critically conceptualize discussions in school spaces about race and police/community relations; and to understand the constraints and affordances of using the young adult (YA) novel,All American Boys, as a critical literacy tool for discussing race and police/community relations.Design/methodology/approachThis qualitative exploratory case study (Stake, 1995) investigated 24 pre-service teachers in two university YA literature courses as they read and discussedAll American Boys. Thematic analysis consisted of open coding through the theoretical lenses of critical literacy and critical race theory.FindingsPre-service English language arts teachers largely thought that while race and police relations was important and the YA book was powerful, it was too political. Their fears about what might happen lead to privileging the role of neutrality as the desired goal for teachers when tackling difficult conversations about racial injustice in America. Although students made some shifts in terms of moving from neutral to more critical stances, three sub-themes of neutrality were predominant: a need for both sides of the story, the view that all beliefs are valid and the belief that we are all humans therefore all lives matter equally.Originality/valueA search at the time of this study yielded few research tackling racial injustice and community/police relations through YA literature in the classroom. This study is important as stories of police brutality and racism are all too common and adolescents are too often the victims.
Toward understanding the topical structure of hospitality literature
Authors: Park Eunhye (Olivia) , Chae Bongsug , Kwon Junehee 
Published: August 2018

Purpose
This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning and traditional statistical analyses.

Design/methodology/approach
Abstracts from all 4,139 articles published in four top hospitality journals were analyzed using the structured topic modeling and inferential statistics. Topic correlation and community detection were applied to identify strengths of correlations and sub-groups of topics. Trend visualization and regression analysis were used to quantify the effects of the metadata (i.e. year of publication and journal) on topic proportions.

Findings
The authors found 50 topics and eight subgroups in the hospitality journals. Different evolutionary patterns in topic popularity were demonstrated, thereby providing the insights for popular research topics over time. The significant differences in topical proportions were found across the four leading hospitality journals, suggesting different foci in research topics in each journal.

Research limitations/implications
Combining machine learning techniques with traditional statistics demonstrated potential for discovering valuable insights from big text data in hospitality and tourism research contexts. The findings of this study may serve as a guide to understand the trends in the research field as well as the progress of specific areas or subfields.

Originality/value
It is the first attempt to apply topic modeling to academic publications and explore the effects of article metadata with the hospitality literature.