bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can DyeCycling break the photobleaching limit in single-molecule FRET?
Authors: Vermeer Benjamin , Schmid Sonja 
Published: February 2022

AbstractBiomolecular systems, such as proteins, crucially rely on dynamic processes at the nanoscale. Detecting biomolecular nanodynamics is therefore key to obtaining a mechanistic understanding of the energies and molecular driving forces that control biomolecular systems. Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique to observe in real-time how a single biomolecule proceeds through its functional cycle involving a sequence of distinct structural states. Currently, this technique is fundamentally limited by irreversible photobleaching, causing the untimely end of the experiment and thus, a prohibitively narrow temporal bandwidth of ≤ 3 orders of magnitude. Here, we introduce ‘DyeCycling’, a measurement scheme with which we aim to break the photobleaching limit in single-molecule FRET. We introduce the concept of spontaneous dye replacement by simulations, and as an experimental proof-of-concept, we demonstrate the intermittent observation of a single biomolecule for one hour with a time resolution of milliseconds. Theoretically, DyeCycling can provide >100-fold more information per single molecule than conventional smFRET. We discuss the experimental implementation of DyeCycling, its current and fundamental limitations, and specific biological use cases. Given its general simplicity and versatility, DyeCycling has the potential to revolutionize the field of time-resolved smFRET, where it may serve to unravel a wealth of biomolecular dynamics by bridging from milliseconds to the hour range.
ACh signaling modulates activity of the GABAergic signaling network in the basolateral amygdala and behavior in stress-relevant paradigms
Authors: Mineur Yann S. , Mose Tenna N. , Maibom Kathrine Lefoli , Pittenger Steven T. , Soares Alexa R. , Wu Hao , Huang Yaqing , Picciotto Marina R 
Published: February 2022

ABSTRACTBalance between excitatory and inhibitory (E/I) signaling is important for maintaining homeostatic function in the brain. Indeed, dysregulation of inhibitory GABA interneurons in the amygdala has been implicated in human mood disorders. We hypothesized that acetylcholine (ACh) signaling in the basolateral amygdala (BLA) might alter E/I balance resulting in changes in stress-sensitive behaviors. We therefore measured ACh release as well as activity of calmodulin-dependent protein kinase II (CAMKII)-, parvalbumin (PV)-, somatostatin (SOM)- and vasoactive intestinal protein (VIP)-expressing neurons in the BLA of awake, behaving mice. ACh levels and activity of both excitatory and inhibitory BLA neurons increased when animals were actively coping, and decreased during passive coping, in the light-dark box, tail suspension and social defeat. Changes in neuronal activity preceded behavioral state transitions, suggesting that BLA activity may drive the shift in coping strategy. In contrast to exposure to escapable stressors, prolonging ACh signaling with a cholinesterase antagonist changed the balance of activity among BLA cell types, significantly increasing activity of VIP neurons and decreasing activity of SOM cells, with little effect on CaMKII or PV neurons. Knockdown of α7 or β2-containing nAChR subtypes in PV and SOM, but not CaMKII or VIP, BLA neurons altered behavioral responses to stressors, suggesting that ACh signaling through nAChRs on GABA neuron subtypes contributes to stress-induced changes in behavior. These studies show that ACh modulates the GABAergic signaling network in the BLA, shifting the balance between SOM, PV, VIP and CaMKII neurons, which are normally activated coordinately during active coping in response to stress. Thus, prolonging ACh signaling, as occurs in response to chronic stress, may contribute to maladaptive behaviors by shifting the balance of inhibitory signaling in the BLA.
Neuronal growth regulator 1 (NEGR1) promotes synaptic targeting of glutamic acid decarboxylase 65 (GAD65)
Authors: Amanda Tan Rui Ping , Kozlova Irina , Su Feifei , Sah Saroj , Keable Ryan , Hagan D. Walker , Bustamante Sonia , Du Ximing , Osborne Brenna , Turner Nigel , Clemens Kelly J. , Begg Denovan , Phelps Edward A. , Yang Hongyuan , Leshchyns’ka Iryna , Sytnyk Vladimir 
Published: February 2022

AbstractNeuronal growth regulator 1 (NEGR1) is a glycosylphosphatidylinositol-anchored cell adhesion molecule encoded by an obesity susceptibility gene. We demonstrate that NEGR1 accumulates in GABAergic inhibitory synapses in hypothalamic neurons, a GABA-synthesizing enzyme GAD65 attaches to the plasma membrane, and NEGR1 promotes clustering of GAD65 at the synaptic plasma membrane. GAD65 is removed from the plasma membrane with newly formed vesicles. The association of GAD65 with vesicles results in increased GABA synthesis. In NEGR1 deficient mice, the synaptic targeting of GAD65 is decreased, the GABAergic synapse densities are reduced, and the reinforcing effects of food rewards are blunted. In mice fed a high fat diet, levels of NEGR1 are increased and GAD65 abnormally accumulates at the synaptic plasma membrane. Our results indicate that NEGR1 regulates a previously unknown step required for synaptic targeting and functioning of GAD65, which can be affected by bidirectional changes in NEGR1 levels causing disruptions in the GABAergic signaling controlling feeding behavior.
Facebook medical support groups: the communication privacy management perspective
Authors: Avizohar Carmel , Gazit Tali , Aharony Noa 
Published: February 2022

PurposeExploring the considerations that guide and influence members of Facebook medical support groups to disclose private information.Design/methodology/approachThe sample included 293 participants, aged 18–85 years, who answered an online survey, 155 members of Facebook medical support groups and 138 members of other Facebook groups.FindingsMembers of medical support groups attach greater importance to privacy policy, give greater value to privacy and reveal more private information, compared to members of other groups. Members of medical support groups tend to feel that they receive more support and feel that the privacy policy preserves their privacy much more. These feelings are associated with greater self-disclosure of groups' members.Originality/valueThe research insights will help medical support groups' members and admins to put into action the fundamental right to privacy and build a set of rules tailored to their individual and group needs.
Cyber resilience in supply chain system security using machine learning for threat predictions
Authors: Yeboah-Ofori Abel , Swart Cameron , Opoku-Boateng Francisca Afua , Islam Shareeful 
Published: February 2022

PurposeCyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.Design/methodology/approachThe approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.FindingsThe paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.Research limitations/implicationsTherefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.Practical implicationsML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.Social implicationsThere are no social implications rather it has serious implications for organizations and third-party vendors.Originality/valueThe originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.
Robust multifocus deep neural network for progression prediction on patient trajectory data
Authors: Arunkumar K. , Vasundra S. 
Published: February 2022

PurposePatient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research. In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies. Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approachMultifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture. Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant. The performance of disease progression learning progress utilizes the precision of the constituent classifiers, which usually has larger generalization benefits than those optimized classifiers.FindingsDeep learning architecture uses weight function, bias function on input layers and max pooling. Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease, and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network. Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/valueProposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory. Then, the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory. The model tries to produce the accurate prognosis outcomes by employing data conditional probability function. The originality of the work defines 70% and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.
Prediction and evaluation of fatigue life considering material parameters distribution characteristic
Authors: Liu Xintian , Liu Jiazhi , Wang Haijie , Yang Xiaobing 
Published: February 2022

PurposeTo improve the accuracy of parameter prediction for small-sample data, considering the existence of error in samples, the error circle is introduced to analyze original samples.Design/methodology/approachThe influence of surface roughness on fatigue life is discussed. The error circle can treat the original samples and extend the single sample, which reduces the influence of the sample error.FindingsThe S-N curve obtained by the error circle method is more reliable; the S-N curve of the Bootstrap method is more reliable than that of the Maximum Likelihood Estimation (MLE) method.Originality/valueThe parameter distribution and characteristics are statistically obtained based on the surface roughness, surface roughness factor and intercept constant. The original sample is studied by an error circle and discussed using the Bootstrap and MLE methods to obtain corresponding S-N curves. It provides a more trustworthy basis for predicting the useful life of products.
Measuring the time spent on data curation
Authors: Perry Anja , Netscher Sebastian 
Published: February 2022

PurposeBudgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.Design/methodology/approachWe make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.FindingsWe identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.Originality/valueThe different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.
Understanding the determinants of users’ continuance intention to buy low-cost airline flights online
Authors: Tam Carlos , Barroso Maria , Cruz-Jesus Frederico 
Published: February 2022

Purpose
Low-cost carriers’ (LCC) worldwide market share is gaining momentum. This paper aims to propose using the expectation confirmation model (ECM) combined with brand awareness and image, website quality and visual appeal to evaluate the continuance use intention of low-cost airlines (LCA) websites.

Design/methodology/approach
The empirical approach was based on an online survey of 272 individuals collected in Portugal. The data were analyzed using structural equation modeling.

Findings
Brand awareness, brand image and satisfaction are the variables that best explain continuance intention. The leftover constructs can empirically explain ECM.

Originality/value
This study seeks to understand the significance of brand awareness and image, website quality and visual appeal in LCA websites’ confirmation and continuance use intention. Understanding the influence on confirmation and long-term viability, strategies based on these conclusions are proposed for LCC companies to better promote consumers’ continuance use intention.
Fatigue life optimization for 17-4Ph steel produced by selective laser melting
Authors: Linares Jean-marc , Chaves-Jacob Julien , Lopez Quentin , Sprauel Jean-Michel 
Published: February 2022

Purpose
The mechanical characterization of selective laser melting (SLM) parts is an industrial challenge. This paper aims to propose a methodology to control the fatigue life of 17-4Ph stainless steel by selecting the most relevant manufacturing parameters: i.e. laser power, laser travel speed, hatch spacing and laser defocusing.

Design/methodology/approach
A rough and refined design of experiment (DOE) is carried out to target the best combination of process parameters. A response surface model is then constructed to predict the parameter combination that optimizes the fatigue performance.

Findings
This study results show that the fatigue limit of the specimens manufactured by SLM (471.7 MPa at 107 cycles) has reached near 90% of the value found in samples machined from a bar. This demonstrates the applicability of the method proposed to optimize the SLM process and control the fatigue life of 17-4Ph stainless steel. The study results are compared with other research works and provide an increase of 18% to the fatigue limit.

Originality/value
This study showcases a DOE methodology to optimize the SLM parameters to achieve fatigue performance as great as that of solid 17-4Ph stainless steel.
The effect of big data in transforming to learning organization a single-case study in IT sector
Authors: Başak Sena , Kılınç İzzet , Ünal Aslıhan 
Published: February 2022

Purpose
The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

Design/methodology/approach
The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

Findings
As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

Research limitations/implications
The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

Originality/value
In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.
Maternal liver dysfunction in early pregnancy predisposes to gestational diabetes mellitus independent of preconception overweight: A prospective cohort study
Authors: Chen Xiaotian , Chen Hongyan , Zhang Yi , Jiang Yuan , Wang Yin , Huang Xiangyuan , Wang Dingmei , Li Mengru , Dou Yalan , Sun Xupeng , Huang Guoying , Yan Weili 
Published: February 2022

AbstractObjectiveTo evaluate whether the associations of maternal liver dysfunction and liver function biomarkers (LFBs) with gestational diabetes mellitus (GDM) are independent of overweight.DesignProspective cohort study.MethodsA sub‐cohort of pregnant women with seven LFBs examined at 9–13 weeks of gestation and with complete GDM evaluation at mid‐gestation were extracted from the prospective Shanghai Preconception Cohort Study. Associations of liver dysfunction, defined as having any elevated LFB levels, and individual LFB levels with GDM incidence were assessed by adjusting body mass index and other covariates in the multivariable logistic regression model. Odds ratios (ORs) and 95% CI were reported.Main outcome measuresIncident GDM.ResultsAmong 6211 pregnant women, 975 (15.7%) developed GDM. Liver dysfunction was associated with increased odds of GDM (OR 1.63; 95% CI 1.38–1.92). This association persisted after adjustment for BMI (adjusted OR [aOR] 1.37; 95% CI 1.15–1.63). Higher γ‐glutamyl transferase, alanine aminotransferase, alkaline phosphatase, and albumin levels were also linked with GDM (aOR per 1 SD: 1.15, 95% CI 1.08–1.23; 1.10, 1.03–1.17; 1.21, 1.13–1.29 and 1.19, 1.11–1.27, respectively). Similar magnitudes of associations were observed between normal weight and overweight pregnant women.ConclusionMaternal liver dysfunction in early pregnancy predisposes women to subsequent GDM, and this association is independent of being overweight preconception. Our findings of an increased risk even in normal‐weight pregnant women adds new mechanistic insights about the pathophysiological role of liver function in GDM aetiology.Tweetable abstractMaternal liver dysfunction in early pregnancy is associated with GDM incidence independent of preconception overweight.
Risk factors for RhD immunisation in a high coverage prevention programme of antenatal and postnatal RhIg: a nationwide cohort study
Authors: Slootweg Y. M. , Zwiers C. , Koelewijn J. M. , van der Schoot E. , Oepkes D. , van Kamp I. L. , de Haas M. 
Published: February 2022

AbstractObjectiveTo evaluate which risk factors for RhD immunisation remain, despite adequate routine antenatal and postnatal RhIg prophylaxis (1000 IU RhIg) and additional administration of RhIg. The second objective was assessment of the current prevalence of RhD immunisations.DesignProspective cohort study.SettingThe Netherlands.PopulationTwo‐year nationwide cohort of alloimmunised RhD‐negative women.MethodsRhD‐negative women in their first RhD immunised pregnancy were included for risk factor analysis. We compared risk factors for RhD immunisation, occurring either in the previous non‐immunised pregnancy or in the index pregnancy, with national population data derived from the Dutch perinatal registration (Perined).ResultsIn the 2‐year cohort, data from 193 women were eligible for analysis. Significant risk factors in women previously experiencing a pregnancy of an RhD‐positive child (n = 113) were: caesarean section (CS) (OR 1.7, 95% CI 1.1–2.6), perinatal death (OR 3.5, 95% CI 1.1–10.9), gestational age >42 weeks (OR 6.1, 95% CI 2.2–16.6), postnatal bleeding (>1000 ml) (OR 2.0, 95% CI 1.1–3.6), manual removal of the placenta (MRP) (OR 4.3, 95% CI 2.0–9.3); these factors often occurred in combination. The miscarriage rate was significantly higher than in the Dutch population (35% versus 12.‐5%, P < 0.001).ConclusionComplicated deliveries, including cases of major bleeding and surgical interventions (CS, MRP), must be recognised as a risk factor, requiring estimation of fetomaternal haemorrhage volume and adjustment of RhIg dosing. The higher miscarriage rate suggests that existing RhIg protocols need adjustment or better compliance.Tweetable abstractComplicated delivery (caesarean section, manual removal placenta, major bleeding) is the most valid risk factor for RhD immunization despite antenatal and postnatal RhIg.