bluuebunny/crossref_metadata_2025_split huggingface dataset:
Conserved transcriptional programming across sex and species after peripheral nerve injury predicts treatments for neuropathic pain
Authors: Ghazisaeidi Shahrzad , Muley Milind M. , Tu YuShan , Kolahdouzan Mahshad , Sengar Ameet S. , Ramani Arun K. , Brudno Michael , Salter Michael W. 
Published: May 2022

AbstractChronic pain is a devastating problem affecting 1 in 5 individuals around the globe, with neuropathic pain the most debilitating and poorly treated type of chronic pain. Advances in transcriptomics and data mining have contributed to cataloging diverse cellular pathways and transcriptomic alterations in response to peripheral nerve injury but have focused on phenomenology and classifying transcriptomic responses. Here, with the goal of identifying new types of pain-relieving agents, we compared transcriptional reprogramming changes in the dorsal spinal cord after peripheral nerve injury cross-sex and cross-species and imputed commonalities, as well as differences in cellular pathways and gene regulation. We identified 93 transcripts in the dorsal horn that were increased by peripheral nerve injury in male and female mice and rats. Following gene ontology and transcription factor analyses, we constructed a pain interactome for the proteins encoded by the differentially expressed genes, discovering new, conserved signaling nodes. We interrogated the interactome with the Drug-Gene database to predict FDA-approved medications that may modulate key nodes within the network. The top hit from the analysis was fostamatinib, the molecular target of which is the non-receptor tyrosine kinase Syk, which our analysis had identified as a key node in the interactome. We found that intrathecally administrating the active metabolite of fostamatinib, R406, significantly reversed pain hypersensitivity in both sexes. Thus, we have identified and shown the efficacy of an agent that could not have been previously predicted to have analgesic properties.One sentence summaryUnbiased approach to predicting safe therapies for neuropathic pain
Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle
Authors: Xiang Ruidong , Fang Lingzhao , Liu Shuli , Macleod Iona M. , Liu Zhiqian , Breen Edmond J. , Gao Yahui , Liu George E. , Tenesa Albert ,   CattleGTEx Consortium, Mason Brett A , Chamberlain Amanda J. , Wray Naomi R. , Goddard Michael E. 
Published: May 2022

AbstractMany quantitative trait loci (QTL) are located in non-coding genomic regions. Therefore, QTL are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription so QTL changing gene expression (eQTL) or RNA splicing (sQTL) are expected to significantly contribute to phenotypic variations. Here, we quantify the contribution of eQTL and sQTL detected from 16 tissues (N~5,000) to 37 complex traits of ~120k cattle. Using Bayesian methods, we show that including more regulatory variants in the model explains larger proportions of heritability. Across traits, cis and trans eQTL and sQTL detected from 16 tissues jointly explain ~70% (SE=0.5%) of heritability, 44% more than expected from the same number of random variants, where trans e/sQTL contribute 24% (14% more than expected). Multi-tissue cis and trans e/sQTL also explain 71% (SE=0.3%) of heritability for the metabolome, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.
DrDimont: Explainable drug response prediction from differential analysis of multi-omics networks
Authors: Hiort Pauline , Hugo Julian , Zeinert Justus , Müller Nataniel , Kashyap Spoorthi , Rajapakse Jagath C. , Azuaje Francisco , Renard Bernhard Y. , Baum Katharina 
Published: May 2022

AbstractMotivationWhile it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem.ResultsWe present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont’s predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite, and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response.AvailabilityDrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont.Contactkatharina.baum@hpi.de
Within-individual changes reveal increasing social selectivity with age in rhesus macaques
Authors: Siracusa Erin R. , Negron-Del Valle Josué E. , Phillips Daniel , Platt Michael L. , Higham James P. , Snyder-Mackler Noah , Brent Lauren J. N. 
Published: May 2022

AbstractAccumulating evidence in humans and other mammals suggests older individuals tend to have smaller social networks. Uncovering the cause of these declines is important as it can inform how changes in social relationships with age might affect health and fitness in later life. Smaller social networks might be detrimental, but may also be the result of greater selectivity in partner choice, reflecting an adaptive solution to physical or physiological limitations imposed by age. While greater selectivity with age has been shown in humans, the extent to which active ‘social selectivity’ within an individual’s lifetime occurs across the animal kingdom remains an open question. Using 8 years of longitudinal data from a population of free-ranging rhesus macaques we provide the first evidence in a non-human animal for within-individual increases in social selectivity with age. Going beyond previous cross-sectional studies, our within-individual analyses revealed that adult female macaques actively reduced the size of their networks as they aged and focused on partners previously linked to fitness benefits, including kin and partners to whom they were strongly and consistently connected earlier in life. Females spent similar amounts of time socializing as they aged, suggesting that network shrinkage does not result from lack of motivation or ability to engage. Furthermore, females remained attractive companions and were not isolated by withdrawal of social partners. Taken together, our results provide rare empirical evidence for social selectivity in non-humans, suggesting patterns of social aging in humans may be deeply rooted in primate evolution and may have adaptive value.Significance statementThe narrowing of social networks and prioritization of meaningful relationships with age is commonly observed in humans. Determining whether social selectivity is exhibited by other animals remains critical to furthering our understanding of the evolution of late-life changes in sociality. Here we test key predictions from the social selectivity hypothesis and demonstrate that female rhesus macaques show within-individual changes in sociality with age that resemble the human social aging phenotype. Our use of longitudinal data to track how individuals change their social behavior within their lifetimes offers the most conclusive evidence to date that social selectivity is not a phenomenon unique to humans and therefore might have deeper evolutionary underpinnings.
Multiomic deep delve of synthesis and secretion processes in a model peptidergic system
Authors: Bárez-López Soledad , Mecawi André S. , Bryan Natasha , Gillard Benjamin T. , Pauza Audrys G. , Murphy David , Greenwood Michael P. 
Published: May 2022

AbstractThe cell bodies of hypothalamic magnocellular neurones are densely packed in the hypothalamic supraoptic nucleus (SON) whereas their axons project to the anatomically discrete posterior pituitary gland. We have taken advantage of this unique anatomical structure to establish proteome and phosphoproteome dynamics in neuronal cell bodies and axonal terminals in response to physiological stimulation. We have found that proteome and phosphoproteome responses are very different between somatic and axonal neuronal compartments, indicating the need of each cell domain to differentially adapt. In particular, changes in the phosphoproteome in the cell body are involved in the reorganisation of the cytoskeleton and in axonal terminals the regulation of synaptic and secretory processes. We have identified that prohormone precursors including vasopressin and oxytocin are phosphorylated in axonal terminals and become hyperphosphorylated following stimulation. By multi-omic integration of transcriptome and proteomic data we identify changes to proteins present in afferent inputs to this nucleus.Graphical abstract
Spontaneous Variations in Arousal Modulate Subsequent Visual Processing and Local Field Potential Dynamics in the Ferret during Quiet Wakefulness
Authors: Klaver Lianne M.F. , Brinkhof Lotte P. , Sikkens Tom , Casado-Román Lorena , Williams Alex G. , van Mourik-Donga Laura , Mejías Jorge F. , Pennartz Cyriel M.A. , Bosman Conrado A. 
Published: May 2022

AbstractBehavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent, but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in primary visual cortex (V1) and posterior parietal cortex (PPc) in awake ferrets, using the pupil diameter as an indicator of arousal. We observed that during low arousal, low- frequency power increases during visual stimulation, and that the peak alpha frequency shifted depending on the arousal state. High arousal increased gamma power as well as low-frequency inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area PPc and supragranular layers in V1. Neurons in V1 displayed higher firing rates at their preferred orientations on high-arousal trials. Broad-spiking cells in V1 entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons phase-locked to low (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.
Probabilistic fatigue life prediction of an aero-engine turbine shaft
Authors: Wu Jun , Huang Hong-Zhong , Li Yan-Feng , Bai Song , Yu Ao-Di 
Published: May 2022

PurposeAero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty.Design/methodology/approachTo study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model.FindingsThe results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation.Practical implicationsThe proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft.Originality/valueThe novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.
Effect of varied spray drying parameters on physicochemical, micrometric and microstructural characteristics of pear powder employing response surface approach
Authors: Gani Gousia , Naik Haroon , Amin Tawheed , Jan Nusrat , Hussain Syed Zameer , Bashir Omar , Jabeen Abida 
Published: May 2022

PurposeCurrently, there is a great demand for those food products that are easy to prepare or ready for direct consumption. Making pear fruit/juice available round the year is desirous owing to pears' high-nutritional value and specific pleasant taste. Pear is, however, a seasonal fruit and under ambient conditions has a limited shelf life rendering it available as fresh fruit for a specific period.Design/methodology/approachThe study aimed to optimize the spray drying process parameters using response surface methodology for the development of pear juice powder. The process variables included the inlet air temperature of 140–210°C, maltodextrin levels of 4–25%, atomization speed of 11,400–28,000 rpm, feed flow rate of 180–630 mL/hr, and feed total soluble solids (TSS) of 13–30°Brix. The dependent responses were powder yield, solubility, antioxidant activity {% 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity}, dispersibility, hygroscopicity and particle density.FindingsAmong independent variables, inlet air temperature showed a predominant effect. The optimum processing conditions for the development of pear juice powder with optimum quality were 163.02°C inlet air temperature, 13.50% maltodextrin, 28,000 rpm atomization speed, 390.94 mL/h feed flow rate, and 25.5°Brix feed TSS. Under these optimum conditions, pear powder with desirable properties could be produced. The experimental and predicted values were found to be in agreement, indicating the suitability of the model in predicting optimizing responses of pear powder. Glass transition temperature of pear powder was found to be 36.60 ± 0.40°C, which is much higher than that of ambient temperature, suggesting better shelf stability.Originality/valueThe processing of pear fruit has resulted in the increased demand for pear juice powder in both domestic and international markets as a primer of new food products. The optimum conditions obtained in the current study could provide a new insight to the food industry in developing spray-dried pear powder of optimum quality. This can open up a new horizon in the field of food industry for the common masses of Jammu and Kashmir, India.
Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework
Authors: Elghaish Faris , Matarneh Sandra T. , Edwards David John , Pour Rahimian Farzad , El-Gohary Hatem , Ejohwomu Obuks 
Published: May 2022

Purpose
This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)) and the construction industry’s gradual transition into a circular economy (CE) system to foster the adoption of circular economy in the construction industry.

Design/methodology/approach
A critical and thematic analysis conducted on 115 scientific papers reveals a noticeable growth in adopting digital technologies to leverage a CE system. Moreover, a conceptual framework is developed to show the interrelationship between different I4.0 technologies to foster the implantation of CE in the construction industry.

Findings
Most of the existing bodies of research provide conceptual solutions rather than developing workable applications and the future of smart cities. Moreover, the coalescence of different technologies is highly recommended to enable tracking of building assets’ and components’ (e.g. fixtures and fittings and structural components) performance, which enables users to optimize the salvage value of components reusing or recycling them just in time and extending assets’ operating lifetime. Finally, circular supply chain management must be adopted for both new and existing buildings to realise the industry's CE ambitions. Hence, further applied research is required to foster CE adoption for existing cities and infrastructure that connects them.

Originality/value
This paper investigates the interrelationships between most emerging digital technologies and circular economy and concludes with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI into the operation of assets to direct future practical research applications
Assessment framework for workforce agility in higher education institutions
Authors: Menon Shalini , Suresh M. 
Published: May 2022

PurposeThe higher education sector has been lately facing new challenges. The ease and speed with which the workforce adapts to these changes determine their agility level. This study attempts to develop a conceptual model to measure workforce agility in higher education.Design/methodology/approachAn extensive literature review revealed limited research on workforce agility assessment. This study addresses the research gap by developing a workforce agility assessment framework and testing the model in a higher education institution (HEI). The study applied the fuzzy logic method for the assessment.FindingsThe computed value of the fuzzy-workforce agility index (3.72, 5.27, 6.86) indicates that the HEI chosen for the assessment is Agile. The fuzzy performance importance index helped identify the six-weak attributes that require immediate attention and improvement.Practical implicationsThe proposed framework would help the institution's management understand the current agility level of their workforce and take corrective actions to improve the identified weak attributes.Originality/valueThe study has contributed to the literature of workforce agility assessment by creating a conceptual model for evaluating workforce agility in higher education. This model can be applied to any HEI.
A decision model for sustainable informal entrepreneurship in cities
Authors: Khan Eijaz Ahmed , Chowdhury Md Maruf Hossan , Royhan Pradip , Gowan Sunaina , Rahman Mohammed Mizanur , Mahdavi Mehregan 
Published: May 2022

Purpose
Sustainable development goals and the climate change agenda are becoming widely promoted topics of research for the 21st century. The role of cities is increasingly recognised as central to investigating these topics. Yet, the field of informal sector entrepreneurship which so many urban entrepreneurs in developing countries depend upon is seldom considered. To redress this imbalance, this study aims to develop a decision model in accordance with institutional theory (IT) and resource dependency theory (RDT) for city managers to deploy. The model identifies and prioritises optimal strategies to address the three areas of sustainability requirements environment society and economy within the study context of Bangladesh.

Design/methodology/approach
This study used a mixed methods research design. In the qualitative part, the authors identified the three areas of sustainability requirements (i.e. environment, society and economy) and their corresponding strategies involving the informal sector that operates within the urban environment. In the quantitative part, the authors applied fuzzy quality function deployment (QFD) integrated with the 0-1 non-linear optimisation technique to identify optimal strategies.

Findings
The findings show that strategies such as legitimate frameworks, waste management, allocation of urban public space and training programs contribute in important ways to the three areas of sustainability requirements.

Practical implications
The proposed decision model will assist policy-makers and city managers to prioritise sustainability requirements and implement optimal strategies to address those requirements.

Originality/value
Through the integration of IT and RDT, the decision model developed in this study is unique in its application to urban-based informal entrepreneurship in the context of developing countries. The effective application of the fuzzy QFD approach and the optimisation model in the context of urban-based informal entrepreneurship also offers unique contributions to the field of study.
An integrative model of facial recognition check-in technology adoption intention: the perspective of hotel guests in Singapore
Authors: Boo Huey Chern , Chua Bee-Lia 
Published: May 2022

Purpose
This study aims to explain how hotel guests form attitudes toward facial recognition technology in Singapore by integrating technology acceptance model (TAM), privacy calculus theory and personal innovativeness.

Design/methodology/approach
A self-administered online questionnaire was developed with measurements adopted from past research. Guests who stayed in four- or five-star hotels in Singapore were recruited via systematic random sampling. Structural equation modeling was conducted to examine the proposed integrated models.

Findings
Results showed that hotel guests performed calculative cognitive processes, weighing the benefits and risks of using facial recognition check-in system. Contradictory to the past research which suggested that trust activates both perceived risk and benefits, this study demonstrated that trust independently directed consumer attention on the benefits gained while risk perception was triggered by privacy concern. Furthermore, the current study revealed that the ease of use of facial recognition check-in system could possibly backfire.

Practical implications
The research indicates that the effort to adopt new technology in the hotel industry is promising in view of the growing millennials and Generation Z population who are digital natives. Furthermore, the current study highlights ways to elevate institutional trust and divert consumers’ attention from risk perception to enhance their positive attitude and behavior toward accepting facial recognition check-in system.

Originality/value
This study integrated TAM with privacy calculus theory and personal innovativeness in examining the acceptance of facial recognition check-in system in the hotel industry in Singapore. This study is also the first, to the best of the authors’ knowledge, to investigate the relationships among privacy concern, perceived risk, institutional trust and perceived benefits, as well as their effects on consumers’ attitudes and behavior toward the biometric system.