bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses
Authors: Bączyńska E. , Zaręba-Kozioł M. , Ruszczycki B. , Krzystyniak A. , Wójtowicz T. , Bijata K. , Pochwat B. , Magnowska M. , Roszkowska M. , Figiel I. , Masternak J. , Pytyś A. , Dzwonek J. , Worch R. , Olszyński K.H. , Wardak A.D. , Szymczak P. , Labus J. , Radwańska K. , Jahołkowski P. , Hogendorf A. , Ponimaskin E. , Filipkowski R.K. , Szewczyk B. , Bijata M. , Włodarczyk J 
Published: May 2022

AbstractStress resilience is the ability of neuronal networks to maintain their function despite the stress exposure. Using a mouse model we here investigate stress resilience phenomenon. To assess the resilient and anhedonic behavioral phenotypes developed after the induction of chronic unpredictable stress, we quantitatively characterized the structural and functional plasticity of excitatory synapses in the hippocampus using a combination of proteomic, electrophysiological, and imaging methods. Our results indicate that stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses. We reveal that chronic stress influences palmitoylation of synaptic proteins, whose profiles differ between resilient and anhedonic animals. The changes in palmitoylation are predominantly related with the glutamate receptor signaling thus affects synaptic transmission and associated structures of dendritic spines. We show that stress resilience is associated with structural compensatory plasticity of the postsynaptic parts of synapses in CA1 subfield of the hippocampus.One Sentence SummaryCompensatory remodeling of dendritic spines at the structural and molecular levels underlies stress resilience.
Reproducing extracellular matrix adverse remodelling of non-ST myocardial infarction in a large animal model
Authors: Contessotto Paolo , Spelat Renza , Vysockas Vaidas , Krivickienė Aušra , Jin Chunsheng , Chantepie Sandrine , Chinello Clizia , Pauza Audrys G. , Rackauskas Mindaugas , Zigmantaitė Vilma , Magni Fulvio , Papy-Garcia Dulce , Karlsson Niclas G. , Ereminienė Eglė , Pandit Abhay , Da Costa Mark 
Published: May 2022

AbstractThe rising incidence of non-ST-segment elevation myocardial infarction (NSTEMI) and associated long-term high mortality constitutes an urgent clinical issue. Unfortunately, the study of possible interventions to treat this pathology lacks a reproducible pre-clinical model. Indeed, currently adopted small and large animal models of MI mimic only full-thickness, ST-segment-elevation (STEMI) infarcts, and hence cater only for investigation into therapeutics and interventions directed at this subset of MI. Thus, we developed an ovine model of NSTEMI by ligating the myocardial muscle at precise intervals parallel to the left anterior descending coronary artery. After validating the presented model both by histology and functional analysis with clinical data, further omics analyses highlighted the distinctive features of post-NSTEMI tissue remodelling. Here, by looking at the transcriptome and proteome-derived pathways emerging at acute (7 days) and late (28 days) post-surgery timepoints, we discovered specific alterations in cardiac post-ischaemic extracellular matrix (ECM). Together with the rise of well-known markers of inflammation and fibrosis, NSTEMI ischaemic regions showed distinctive patterns in the expression of complex N-glycans and glycosaminoglycans in cellular membranes and ECM. Identifying such changes in molecular moieties accessible to infusible and intra-myocardial injectable drugs sheds light on the development of targeted pharmacological solutions to contrast adverse fibrotic remodelling.
Gene expression profiling reveals B cells are highly educated by the pancreatic environment during autoimmune diabetes
Authors: Boldison Joanne , Hopkinson Jessica , Davies Joanne , Pearson James A , Leete Pia , Richardson Sarah , Morgan Noel G , Wong F. Susan 
Published: May 2022

AbstractB cells play an important role in driving the development of type 1 diabetes, however, it remains unclear how they contribute to local beta-cell destruction during disease progression. Using gene expression profiling of B cell subsets in the pancreas and pancreatic lymph nodes, we reveal that B cells are highly modified by the inflamed pancreatic tissue and can be distinguished by their transcriptional profile from those in the lymph node. We identified both a discrete and a core shared gene expression profile in islet CD19+CD138- and CD19+CD138+ B cell subsets, the latter known to have enriched autoreactivity during diabetes development. Upon localisation to pancreatic islets, CD138+ B cells overexpressed genes associated with adhesion molecules and growth factors compared to CD138- B cells. Their shared signature displayed gene expression changes related to the differentiation of antibody-secreting cells and gene regulatory networks associated with interferon signalling pathways, pro-inflammatory cytokines and toll-like receptor activation. Finally, abundant TLR7 expression was detected in islet B cells, and was enhanced specifically in CD138+ B cells. Our study, therefore, provides a detailed transcriptional analysis of islet B cells identifying specific gene signatures and interaction networks that point towards a functional role for B cells in driving autoimmune diabetes.
Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
Authors: Benjamin Lucas , Fló Ana , Roumi Fosca Al , Dehaene-Lambertz Ghislaine 
Published: May 2022

AbstractSuccessive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here We used the formalism proposed by network science to study the representation of local and higher order structures, and their interaction, in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order structures such as network communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but compressed representations of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly account for previous findings, including local transition probabilities as well as high order network structures, unifying statistical learning across scales. We finally propose putative brain implementations of such bias.
Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes
Authors: Pedigo Benjamin D. , Winding Michael , Priebe Carey E. , Vogelstein Joshua T. 
Published: May 2022

AbstractGraph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes – in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm which allows it to solve what we call the bisected graph matching problem. This modification allows us to leverage the connections between the brain hemispheres when predicting neuron pairs. Via simulations and experiments on real connectome datasets, we show that this approach improves matching accuracy when sufficient edge correlation is present between the contralateral (between hemisphere) subgraphs. We also show how matching accuracy can be further improved by combining our approach with previously proposed extensions to graph matching, which utilize edge types and previously known neuron pairings. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises.
Quantitative Characterization of Duodenal Gastrinoma Autofluorescence using Multi-photon Microscopy
Authors: Knapp Thomas G. , Duan Suzann , Merchant Juanita L. , Sawyer Travis W. 
Published: May 2022

AbstractDuodenal gastrinomas (DGASTs) are neuroendocrine tumors that develop in the submucosa of the duodenum and produce the hormone gastrin. Surgical resection of DGASTs is complicated by the small size of these tumors and the tendency for them to develop diffusely in the duodenum. Endoscopic mucosal resection of DGASTS is an increasingly popular method for treating this disease due to its low complication rate but suffers from poor rates of pathologically negative margins. Multiphoton microscopy (MPM) is capable of capturing high-resolution images of biological tissue with contrast generated from endogenous fluorescence (autofluorescence) through two-photon excited fluorescence (2PEF). Second harmonic generation (SHG) is another popular method of generating image contrast with MPM and is a light-scattering phenomenon that occurs predominantly from structures such as collagen in biological samples. Some molecules that contribute to autofluorescence change in abundance from processes related to the cancer disease process (e.g., metabolic changes, oxidative stress, angiogenesis). MPM was used to image 12 separate patient samples of formalin-fixed and paraffinized DGAST slides with a SHG channel 4 2PEF channels, each tuned to capture fluorescence from NADH, FAD, lipofuscin, and porphyrin. We found that there was a significant difference in the relative abundance of signal generated in the 2PEF in comparison to the neighboring tissues of the duodenum. Texture extraction was used to create linear discriminant classifiers for tumor vs all other tissue classes before and after principal component analysis (PCA) of the texture feature dataset. PCA improved the classifier accuracy and reduced the number of features required to achieve maximum accuracy of the classifier. The LDA classifier after PCA distinguished between tumor and other tissue types with an accuracy of 90.6 - 93.8%. These results suggest that MPM 2PEF and SHG imaging is a promising label-free method for discriminating between DGAST tumors and normal duodenal tissue which has implications for future applications of in vivo assessment of resection margins with endoscopic MPM.
Review of metal carbide nanoprecipitate effects on hydrogen embrittlement of high strength martensitic steel
Authors: Zheng Yaojie , Huili Sun , Yan Luchun , Pang Xiaolu , Volinsky Alex A. , Gao Kewei 
Published: May 2022

Purpose
High-strength martensitic steels having strong hydrogen embrittlement (HE) susceptibility and the metal carbide (MC) nanoprecipitates of microalloying elements such as Nb, V, Ti and Mo in the steel matrix can effectively improve the HE resistance of steels. This paper aims to review the effect of MC nanoprecipitates on the HE resistance of high-strength martensitic steels.

Design/methodology/approach
In this paper, the effects of MC nanoprecipitates on the HE resistance of high-strength martensitic steels are systematically described in terms of the types of MC nanoprecipitates, the influencing factors, along with numerical simulations.

Findings
The MC nanoprecipitates, which are fine and semicoherent with the matrix, effectively improve the HE resistance of steel through the hydrogen trapping effects and microstructure optimization, but its effect on the HE resistance of steel is controlled by its size, number and distribution state.

Originality/value
This paper summarizes the effects and mechanisms of MC nanoprecipitates on HE performance of high-strength martensitic steel and provides the theoretical basis for corrosion engineers to design high-strength martensitic steels with excellent HE resistance and improve production processes.
Public governance and tunneling: evidence from a quasi-experiment in China
Authors: Ma Xiaofang , Wang Wenming , Zhou Gaoguang , Chen Jun 
Published: May 2022

PurposeThis study aims to take advantage of the unprecedented anti-corruption campaign launched in China in December 2012 and examine the effect of improved public governance on tunneling.Design/methodology/approachThis study uses a sample of Shanghai and Shenzhen Stock Exchange listed companies from 2010 to 2014 and conduct regression analyses to investigate the effect of improved public governance attributed to the anti-corruption campaign on tunneling.FindingsThis study finds that the level of tunneling decreased significantly after the anti-corruption campaign, suggesting that increased public governance effectively curbs tunneling. Cross-sectional results show that this mitigating effect is more pronounced for non-SOE firms, especially non-SOE firms with political connections, firms audited by non-Big 8 auditors, firms with a large divergence between control rights and cash flow rights and firms located in areas with lower marketization.Practical implicationsThis study highlights the importance of anti-corruption initiatives in improving public governance and in turn reducing tunneling. This study provides important implications for many other emerging economies to improve public governance.Originality/valueThis study contributes to the literature on the role of public governance in constraining corporate agency problems and advances the understanding of the economic consequences of China's anti-corruption campaign in the context of tunneling.
Physical internet enabled two-tier city logistics solution in the new retail era
Authors: Luo Hao , Wang Yilun , Luo Zhixing 
Published: May 2022

PurposeThe paper aims to study the challenges and solutions of city logistics in the new retail era. The new retail, which is characterized by omni-channel, fragmented orders and decentralized 2C distribution, is becoming the mainstream of the retail industry worldwide. In order to achieve a comprehensive breakthrough in new retail, the change of order fulfillment mode is the most noteworthy issue. The aim of this paper is to design a city logistics operation model for new retail and verify its feasibility and efficiency.Design/methodology/approachA physical internet (PI) enabled two-tier city logistics solution is proposed by redefining the key facilities in city logistics with the PI concept. A “Container-as-a-Warehouse” operation mode is designed to provide a more flexible store and transfer solution. A mathematical model of the proposed solution is established. An adaptive large neighborhood search (ALNS) is designed based upon an iterative procedure, which ensures consistent and optimal results.FindingsTo quantitatively assess the feasibility of the proposed solution, a computational experiment is designed to compare the performance of the proposed model against the conventional two-tier city logistics operation. The effects of geographical location pattern, utilization of PI-hub as well as the fluctuation of customer orders are analyzed. The results show that the PI-enabled city logistics is more advantageous than the conventional solution.Research limitations/implicationsThis study does not consider the impact of new technologies in city logistics; for example, the replenishment problem of unmanned vending machines and the charging problem of electric vehicles.Practical implicationsThe proposed PI-enabled solution and analysis results in this paper have positive guiding significance for future practical application.Originality/valueBased on the concept of PI, this paper proposes an innovative and practical operation model to solve the city logistics challenges.
“Dear diary … the covid-19 is turning us into hybrids”: exploring consumers’ hybridity facets during the pandemic
Authors: Hakim Masmoudi Manel , Jmour Arij , ElAoud Nibrass 
Published: May 2022

Purpose
This study aims to examine different levels of consumer’s hybridity, which is gaining popularity during the current COVID-19 pandemic.

Design/methodology/approach
A qualitative approach was adopted using two main data collection methods: netnography and semistructured interviews. Three main communities and 20 semistructured interviews with hybrid consumers were performed to fully understand new levels of consumers’ bipolarity. Thematic analysis was used to identify groups representing different facets of new hybridity. Similarity index and co-occurrences (Jaccard coefficient) were interpreted through QDA Miner software.

Findings
Four main facets of consumers’ hybridity were highlighted during the current COVID-19 pandemic: “up vs down,” “utilitarian vs hedonic,” “impulsive vs planned” and “responsible vs irresponsible.”

Practical implications
These findings have practical implications for marketing managers seeking to design and to improve their branding strategies and their positioning. Businesses usually offer a coherent mix targeted to specific consumers. However, these results show that providing and highlighting some contradictions in their offerings may be interesting for consumers who are trying to cope with this pandemic.

Originality/value
The study extends the contemporary consumer literature by investigating paradoxical behaviors that are still fertile. The marketing literature examines consumers’ profiles as a homogeneous concept without allowing for contradictions in consumers’ preferences. Additionally, this study recognizes important changes in consumer behavior elicited by COVID-19 pandemic. It fills that research gap by examining not only “up vs down” hybridity but new levels of hybridity as well.
An assessment of users’ satisfaction with a smart building on university campus through post-occupancy evaluation
Authors: Hassanain Mohammad A. , Sanni-Anibire Muizz Oladapo , Mahmoud Abubakar Sadiq 
Published: May 2022

Purpose
This study aims to present the post-occupancy evaluation (POE) of a smart building on a university campus.

Design/methodology/approach
Literature review was conducted to report on the smart building concept and POE. A total of 49 performance indicators, classified according to the technical, functional and behavioral elements, were investigated. A case study of a smart building in a university campus was selected to assess the users’ satisfaction through POE. A total of 90 users from the academic and administrative staff of the building were contacted through a Web-based questionnaire survey, while 35 usable responses were obtained.

Findings
The findings revealed that the occupants were satisfied with several aspects of the building performance indicators; however, issues related to ventilation, control of thermostats, amount of natural lighting through low-E windows and privacy were identified. Ultimately, the study reveals that, although the adoption of smart technologies has the potential to provide an excellent and comfortable work environment, the efficient selection and/or maintenance of the adopted technologies is crucial.

Originality/value
The concept of smart buildings is perceived to be the future of the industry. Despite this growing need for “smartization,” the performance of a building ultimately depends on how it satisfies the needs of those who use them. The value and implication of this study is inherent in its emphasis on the satisfaction of buildings users in futuristic designs of buildings. The study shows that POEs can be used to assess the satisfaction of users of smart buildings to understand what works, as well as what needs redesign or improvement.
Development of pavement roughness regression models based on smartphone measurements
Authors: Al-Suleiman (Obaidat) Turki I. , Alatoom Yazan Ibrahim 
Published: May 2022

Purpose
The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Design/methodology/approach
Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

Findings
According to the study results, several regression models were developed with a big variation in the coefficients of determination (R2). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R2 equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

Practical implications
The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

Originality/value
To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.