bluuebunny/crossref_metadata_2025_split huggingface dataset:
MEMS-Actuated Carbon Fiber Microelectrode for Neural Recording
Authors: Zoll Rachel S. , Schindler Craig B. , Massey Travis L. , Drew Daniel S. , Maharbiz Michel M. , Pister Kristofer S. J. 
Published: August 2018

AbstractMicrowire and microelectrode arrays used for cortical neural recording typically consist of tens to hundreds of recording sites, but often only a fraction of these sites are in close enough proximity to firing neurons to record single-unit activity. Recent work has demonstrated precise, depth-controllable mechanisms for the insertion of single neural recording electrodes, but these methods are mostly only capable of inserting electrodes which elicit adverse biological response. We present an electrostatic-based actuator capable of inserting individual carbon fiber microelectrodes which elicit minimal to no adverse biological response. The device is shown to insert a carbon fiber recording electrode into an agar brain phantom and can record an artificial neural signal in saline. This technique provides a platform generalizable to many microwire-style recording electrodes.
Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory
Authors: Moezzi Bahar , Hordacre Brenton , Berryman Carolyn , Ridding Michael C. , Goldsworthy Mitchell R. 
Published: August 2018

AbstractMetrics of brain network organization can be derived from neuroimaging data using graph theory. We explored the test-retest reliability of graph metrics of functional networks derived from resting-state electroencephalogram (EEG) recordings. Data were collected within two designs: (1) within sessions (WS) design where EEG data were collected from 18 healthy participants in four trials within a few hours and (2) between sessions (BS) design where EEG data were collected from 19 healthy participants in three trials on three different days at least one week apart. Electrophysiological source activity was reconstructed and functional connectivity between pairs of sensors or brain regions was determined in different frequency bands. We generated undirected binary graphs and used intra-class correlation coefficient (ICC) to estimate reliability. We showed that reliabilities ranged from poor to good. Reliability at the sensor-level was significantly higher than source-level. The most reliable graph metric at the sensor-level was cost efficiency and at the source-level was global efficiency. At the sensor-level: WS reliability was significantly higher than BS reliability; high beta band in WS design had the highest reliability; in WS design reliability in gamma band was significantly lower than reliability in low and high beta bands. At the source-level: low beta band in BS design had the highest reliability; there was no significant main effect of frequency band on reliability; reliabilities of WS and BS designs were not significantly different. These results suggest that these graph metrics can provide reliable outcomes, depending on how the data were collected and analysed.
Evolutionary inference fromQST-FSTcomparisons: disentangling local adaptation from altitudinal gradient selection in snapdragon plants
Authors: Marin Sara , Archambeau Juliette , Bonhomme Vincent , Lascoste Mylène , Pujol Benoit 
Published: August 2018

ABSTRACTPhenotypic differentiation among natural populations can be explained by natural selection or by neutral processes such as drift. There are many examples in the literature where comparing the effects of these processes on multiple populations has allowed the detection of local adaptation. However, these studies rarely identify the agents of selection. Whether population adaptive divergence is caused by local features of the environment, or by the environmental demand emerging at a more global scale, for example along altitudinal gradients, is a question that remains poorly investigated. Here, we measured neutral genetic (FST) and quantitative genetic (QST) differentiation among 13 populations of snapdragon plants (Antirrhinum majus) in a common garden experiment. We found low but significant genetic differentiation at putatively neutral markers, which supports the hypothesis of either ongoing pervasive homogenisation via gene flow between diverged populations or reproductive isolation between disconnected populations. Our results also support the hypothesis of local adaptation involving phenological, morphological, reproductive and functional traits. They also showed that phenotypic differentiation increased with altitude for traits reflecting the reproduction and the phenology of plants, thereby confirming the role of such traits in their adaptation to environmental differences associated with altitude. Our approach allowed us to identify candidate traits for the adaptation to climate change in snapdragon plants. Our findings imply that environmental conditions changing with altitude, such as the climatic envelope, influenced the adaptation of multiple populations of snapdragon plants on the top of their adaptation to local environmental features. They also have implications for the study of adaptive evolution in structured populations because they highlight the need to disentangle the adaptation of plant populations to climate envelopes and altitude from the confounding effects of selective pressures acting specifically at the local scale of a population.
Analysis and Prediction of Unplanned Intensive Care Unit Readmission using Recurrent Neural Networks with Long Short-Term Memory
Authors: Lin Yu-Wei , Zhou Yuqian , Faghri Faraz , Shaw Michael J. , Campbell Roy H. 
Published: August 2018

AbstractBackgroundUnplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians.Methods and FindingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model.ConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.
CausalTab: PSI-MITAB 2.8 updated format for signaling data representation and dissemination
Authors: Perfetto L. , Acencio M.L. , Bradley G. , Cesareni G. , Toro N.Del , Fazekas D. , Hermjakob H. , Korcsmaros T. , Kuiper M. , Lægreid A. , Surdo P. Lo , Lovering R.C. , Orchard S. , Porras P. , Thomas PD. , Touré V. , Zobolas J. , Licata L. 
Published: August 2018

AbstractCombining multiple layers of information underlying biological complexity into a structured framework, and in particular deciphering the molecular mechanisms behind cellular phenotypes, represent two challenges in systems biology. A key task is the formalisation of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signaling information, such as SIGNOR, SignaLink and IntAct, focus on capturing, organising and displaying signaling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources, through the development of a standardized exchange format, ontologies and controlled vocabularies supporting the domain of causal interactions. Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signaling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated MITAB2.8. We define the new term “causal interaction”, and related child terms, which are children of the PSI-MI “molecular interaction” term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signaling networks more precisely and with higher coverage than before.
How much fear is in anxiety?
Authors: Genewsky Andreas J. , Albrecht Nina , Bura Simona A. , Kaplick Paul M. , Heinz Daniel E. , Nußbaumer Markus , Engel Mareen , Grünecker Barbara , Kaltwasser Sebastian F. , Riebe Caitlin J. , Bedenk Benedikt T. , Czisch Michael , Wotjak Carsten T. 
Published: August 2018

AbstractThe selective breeding for extreme behavior on the elevated plus-maze (EPM) resulted in two mouse lines namely high-anxiety behaving (HAB) and low-anxiety behaving (LAB) mice. Using novel behavioral tests we demonstrate that HAB animals additionally exhibit maladaptive escape behavior and defensive vocalizations, whereas LAB mice show profound deficits in escaping from approaching threats which partially results from sensory deficits. We could relate these behavioral distortions to tonic changes in brain activity within the periaqueductal gray (PAG) in HAB mice and the superior colliculus (SC) in LAB mice, using in vivo manganese-enhanced MRI (MEMRI) followed by pharmacological or chemogenetic interventions. Therefore, midbrain-tectal structures govern the expression of both anxiety-like behavior and defensive responses. Our results challenge the uncritical use of the anthropomorphic termsanxietyoranxiety-likefor the description of mouse behavior, as they imply higher cognitive processes, which are not necessarily in place.
DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning
Authors: Ni Peng , Huang Neng , Luo Feng , Wang Jianxin 
Published: August 2018

AbstractThe Oxford Nanopore sequencing enables to directly detect methylation sites in DNA from reads without extra laboratory techniques. In this study, we develop DeepSignal, a deep learning method to detect DNA methylated sites from Nanopore sequencing reads. DeepSignal construct features from both raw electrical signals and signal sequences in Nanopore reads. Testing on Nanopore reads of pUC19, E. coli and human, we show that DeepSignal can achieve both higher read level and genome level accuracy on detecting 6mA and 5mC methylation comparing to previous HMM based methods. Moreover, DeepSignal achieves similar performance cross different methylation bases and different methylation motifs. Furthermore, DeepSignal can detect 5mC and 6mA methylation states of genome sites with above 90% genome level accuracy under just 5X coverage using controlled methylation data.
Altered microRNA expression profles are involved in Storage Lesions of Apheresis Platelet
Authors: Yuan Zhaohu , Wu Qianyu , Chen Xiaojie , Wei Yaming 
Published: August 2018

ABSTRACTAlthough platelet is anucleate cell, it contains a large amount miRNAs. This study aims to explore the relationship between miRNAs expression profles and platelets function, as well as miRNAs potential roles during platelet storage lesions. Platelets were collected from 15 healthy men with O blood types. MiRNAs profiles in platelets were detected by Agilent Human miRNA Array Differential miRNA levels were studied using human platelets after apheresis and stored for 2, 5 and 8 days using microarray. There were 167 and 230 altered miRNAs during the 5 and 8 day storage, respectively. In addition, the number of reduced miRNAs was much greater than that of increased. And many of them are involved in functions of platelet activation, degranulation, PDGF receptor signaling pathway and cell differentiation. The results of RT-PCR showed that the expression of miR-21-5p, miR-21-3p and miR-155 decreased on the 5th day, while miR-223, miR-3162 and let-7b increased. Flow cytometry results revealed that with increase of storage time, the expression of P2Y12 increased and phosphorylation level of VASP reduced. Meanwhile, the platelet reactivity index (PRI) decreased from 72.7% to 18.2%, while the apoptotic percentage of platelet significantly increased. For the first time, we found altered miRNAs are closely related to platelet aggregation, including P2Y12, VASP and GPⅡb/Ⅲa. Through KEGG database prediction, we verified there were many miRNAs impacting the pathway of platelet aggregation, such as miR-223, miR-21 and let-7b, which indicated miRNAs might serve as potential biomarkers of storage lesion in platelet. These target miRNAs are related to activation, degranulation and PDGF receptor signaling pathway of platelet.
Management accounting change in developing countries: evidence from Libya
Authors: Lasyoud Alhashmi Aboubaker , Haslam Jim , Roslender Robin 
Published: August 2018

Purpose
The purpose of this paper is to investigate the change in management accounting and control systems (MACSs) within two large public manufacturing companies in Libya so-called Trucks and Buses Company (TBC) and National Trailers Company (NTC).

Design/methodology/approach
The paper is based on semi-structured interviews, an analysis of documents and observations. It draws on New Institutional Sociology (NIS) perspective (DiMaggio and Powell’s 1983) as theoretical framework to provide explanations regarding how the MACS in the two companies were shaped by various factors.

Findings
The main factors identified in shaping the operations of the MACS were the need to comply with the political pressures, the Libyan Government’s laws and regulations, the instructions imposed by the management committee in both companies, leading organizations’ pressures (ISO), customer satisfaction (coercive isomorphism), the influence of professional associations (normative isomorphism) and the need to imitate efficient organizations in order to be more legitimate and successful (mimetic isomorphism).

Research limitations/implications
The findings of the study have implications for understanding the operations of MACS in developing countries. Future research could focus on alternative theoretical perspectives for the investigation of the process of change in MACS such as structuration theory, agency theory and actor-network theory.

Originality/value
The proposed theoretical framework provides insights into the process of change by focusing on the interplay between the institutional forces, market forces and intra – organizational power relationships to overcome the criticism of NIS that it downplays the role of market forces and intra – organizational power relations.
Corporate social responsibility (CSR) practices by SIN firms
Authors: Sharma Zenu , Song Liang 
Published: August 2018

Purpose
The purpose of this paper is to examine corporate social responsibility (CSR) practices of SIN firms. SIN firms are firms from controversial sectors such as tobacco, alcohol, gambling and firearms.

Design/methodology/approach
This paper explains contrasts CSR practices of SIN firms with similar size and industry non-SIN counterparts.

Findings
This paper shows that SIN firms conduct more CSR practices than non-SIN firms. This paper also finds that CSR practices of SIN firms are value relevant only when these firms are performing below their peers.

Originality/value
The motivation for SIN firms to engage in higher CSR is the competitive advantage hypothesis and not moral rebalancing.
Marginal abatement cost of agricultural carbon emissions in China: 1993-2015
Authors: Wu Xianrong , Zhang Junbiao , You Liangzhi 
Published: August 2018

Purpose
The purpose of this paper is to estimate shadow prices of agricultural carbon emissions produced by agricultural inputs, rice paddy and burning crop residue, and to explore the impact of cropping pattern on marginal abatement cost (MAC).

Design/methodology/approach
The shadow price of agricultural carbon emissions is estimated by applying directional distance function and non-parametric methods.

Findings
The estimated shadow price of agricultural carbon emissions ranges from 6.78 to 557.83 yuan/ton, and the average value is 62.50 yuan/ton (or $10.18/ton). The MAC value varies in different provinces and years. The regional difference of MAC shows a decreasing trend during the investigation period. Cropping pattern shows a significant negative impact on agricultural MAC. A 1 percent decrease of rice proportion leads to a 0.31 percent increase in MAC value. This implies that the higher the proportion of rice is, the lower the economic cost to reduce agricultural carbon emissions would be.

Practical implications
It is feasible to draw up appropriate mechanisms for the allocation of emission reduction responsibilities according to conditions in various regions, with emphasis on the local cropping patterns. There is a trade-off between reducing carbon emission and increasing crop yields.

Originality/value
This study calculates agricultural MAC by using the shadow price approach, taking agricultural carbon emissions as undesired environmental output. The study also provides a reference emission right price and provides guidance to make use of cropping structure adjustment and optimization for exploring the emission reduction strategy.
Technology adoption among farmers in Jilin Province, China
Authors: Zheng Shi , Wang Zhigang , Wachenheim Cheryl Joy 
Published: August 2018

Purpose
The purpose of this paper is to investigate factors influencing intent to adopt unmanned aerial vehicles for use in agricultural operations in Jilin Province, China.

Design/methodology/approach
This paper uses probit analysis to estimate the influence of factors on intent to adopt unmanned aerial vehicles for plant protection application operations in Jilin Province, China based on the Technology Acceptance Model.

Findings
UAV technology is acceptable to most participating farmers. Gender (male), agricultural income ratio, understanding of new technology, perceived ease-of-use and perceived usefulness have a positive relationship with farmers’ willingness to adopt UAVs.

Practical implications
For application, crop monitoring and other farm operations, UAVs have clear advantages. There remain challenges associated with the development of applicable technology and facilitating adoption among farmers. Understanding factors affecting adoption will contribute to the latter, and brings forth the following recommendations: conduct research on the cost effectiveness of UAVs for agriculture; accelerate development of the information service system; and increase financial support.

Originality/value
This paper is among the first to consider adoption intent among Chinese farmers of use of UAVs for agricultural operations.
Investigating accommodation experience in budget hotels
Authors: Ren Lianping , Qiu Hanqin , Ma Caiwei , Lin Pearl M.C. 
Published: August 2018

Purpose
This paper aims to explore the accommodation experience of customers in the budget hotel sector – a less explored but rapidly expanding sector in the hospitality industry. This study investigates the effects of such experience on customer behavior.

Design/methodology/approach
The study adopts a quantitative approach and draws on a comprehensive data set (N = 414) by using a quota sampling approach. The context of the study is the Chinese market, where budget hotels and the number of mass tourists (inbound and outbound) have increased tremendously.

Findings
This study identifies a behavioral pattern that differs from trends reported in prior studies. The finding shows that the relationship between customer experience and behavioral intentions is mediated by customer satisfaction, which similarly mediates the relationship between perceived value and behavioral intentions.

Research limitations/implications
The results provide significant implications for hoteliers in the budget hotel industry by calling for a differentiated marketing approach for this segment of customers. Furthermore, the study provides insights into the accommodation behavior of Chinese mass tourists. These findings can serve as reference for international budget hotel practitioners, especially those involved in hotels that target the increasing number of travelers from China.

Originality/value
The budget hotel sector is the first accommodation choice for many Chinese mass tourists. This study is the first to conduct a systematic exploration of the behavioral patterns of customers toward budget hotels in this context. Moreover, this study adopts a holistic approach by adopting an experiential view in measuring the overall experience of customers in budget hotels.
Post-migratory risk factors and asylum seekers’ mental health
Authors: Korup Kjærgaard Thomas , Koitzsch Jensen Natasja 
Published: August 2018

Purpose
The purpose of this paper is to examine if the post-migrational risk factors, namely length of stay and number of relocations, are associated with asylum seekers’ mental health.

Design/methodology/approach
The review includes seven main studies published later than 2006 examining the effect of the asylum procedure on the mental health of asylum seekers. The articles were identified through the search databases PubMed, Embase and PsycINFO. A systematic search strategy based on the concepts of Patient, Intervention, Comparison, and Outcome has laid the groundwork for the findings of relevant articles.

Findings
Two out of three articles investigating the association between number of relocations and mental health among asylum seekers observed an effect on mental health. Three out of six studies found associations between length of stay in asylum centres and poor mental health. The overall assessment of the studies indicates an effect of the post-migrational risk factors.

Research limitations/implications
The included studies vary in study populations, outcome measures and methodical soundness.

Practical implications
The review suggests that length of stay in asylum centres and the number of relocations should be considered as risk factors for poor mental health of asylum seekers and, hence, considered in the organisational procedures in the asylum process.

Originality/value
To the best of the authors’ knowledge, this is the first review to specifically examine the literature on the association between the post-migratory risk factors, number of relocations and length of stay, in asylum centres and mental health among non-detained asylum seekers in Europe.