bluuebunny/crossref_metadata_2025_split huggingface dataset:
Initial experience in staging primary oesophageal/gastro-oesophageal cancer with 18F-FDG PET/MRI
Authors: Sharkey Amy R. , Sah Bert-Ram , Withey Samuel J. , Bhuva Shaheel , Neji Radhouene , Jeljeli Sami , Green Adrian , Cook Gary J. R. , Goh Vicky ,   On behalf of the Guy’s & St Thomas’ Oesophago-Gastric Research Group, Baker C. R. , Chang F. , Chicklore S. , Cominos M. , Coombes A. , Davies A. R. , George S. , Gill-Barman B. , Dunn J. N. , Gossage J. A. , Griffin N. , Hill M. , Hynes O. , Iezzi C. , Jacques A. , Kelly M. , Mahadeva U. , Maisey N. , McEwan R. , Meenan J. , Ngan S. , Owczarczyk K. , Qureshi A. , Reyhani A. , Subesinghe M. , Tham G. , Waters J. , Zeki S. S. 
Published: December 2021

Abstract
Background
18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) may improve cancer staging by combining sensitive cancer detection with high-contrast resolution and detail. We compared the diagnostic performance of 18F-FDG PET/MRI to 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for staging oesophageal/gastro-oesophageal cancer. Following ethical approval and informed consent, participants with newly diagnosed primary oesophageal/gastro-oesophageal cancer were enrolled. Exclusions included prior/concurrent malignancy. Following 324 ± 28 MBq 18F-FDG administration and 60-min uptake, PET/CT was performed, immediately followed by integrated PET/MRI from skull base to mid-thigh. PET/CT was interpreted by two dual-accredited nuclear medicine physicians and PET/MRI by a dual-accredited nuclear medicine physician/radiologist and cancer radiologist in consensus. Per-participant staging was compared with the tumour board consensus staging using the McNemar test, with statistical significance at 5%.

Results
Out of 26 participants, 22 (20 males; mean ± SD age 68.8 ± 8.7 years) completed 18F-FDG PET/CT and PET/MRI. Compared to the tumour board, the primary tumour was staged concordantly in 55% (12/22) with PET/MRI and 36% (8/22) with PET/CT; the nodal stage was concordant in 45% (10/22) with PET/MRI and 50% (11/22) with PET/CT. There was no statistical difference in PET/CT and PET/MRI staging performance (p > 0.05, for T and N staging). The staging of distant metastases was concordant with the tumour board in 95% (21/22) with both PET/MRI and PET/CT. Of participants with distant metastatic disease, PET/MRI detected additional metastases in 30% (3/10).

Conclusion
In this preliminary study, compared to 18F-FDG PET/CT, 18F-FDG PET/MRI showed non-significant higher concordance with T-staging, but no difference with N or M-staging. Additional metastases detected by 18F-FDG PET/MRI may be of additive clinical value.
Evolutionary understanding of airfoil lift
Authors: Liu Tianshu 
Published: December 2021

AbstractThis review attempts to elucidate the physical origin of aerodynamic lift of an airfoil using simple formulations and notations, particularly focusing on the critical effect of the fluid viscosity. The evolutionary development of the lift problem of a flat-plate airfoil is reviewed as a canonical case from the classical inviscid circulation theory to the viscous-flow model. In particular, the physical aspects of the analytical expressions for the lift coefficient of the plate-plate airfoil are discussed, including Newton’s sine-squared law, Rayleigh’s lift formula, thin-airfoil theory and viscous-flow lift formula. The vortex-force theory is described to provide a solid foundation for consistent treatment of lift, form drag, Kutta condition, and downwash. The formation of the circulation and generation of lift are discussed based on numerical simulations of a viscous starting flow over an airfoil, and the evolution of the flow topology near the trailing edge is well correlated with the realization of the Kutta condition. The presented contents are valuable for the pedagogical purposes in aerodynamics and fluid mechanics.
A square root information filter for multi-GNSS real-time precise clock estimation
Authors: Zuo Xiang , Jiang Xinyuan , Li Pan , Wang Jungang , Ge Maorong , Schuh Harald 
Published: December 2021

AbstractReal-time satellite orbit and clock estimations are the prerequisite for Global Navigation Satellite System (GNSS) real-time precise positioning services. To meet the high-rate update requirement of satellite clock corrections, the computational efficiency is a key factor and a challenge due to the rapid development of multi-GNSS constellations. The Square Root Information Filter (SRIF) is widely used in real-time GNSS data processing thanks to its high numerical stability and computational efficiency. In real-time clock estimation, the outlier detection and elimination are critical to guarantee the precision and stability of the product but could be time-consuming. In this study, we developed a new quality control procedure including the three standard steps: i.e., detection, identification, and adaption, for real-time data processing of huge GNSS networks. Effort is made to improve the computational efficiency by optimizing the algorithm to provide only the essential information required in the processing, so that it can be applied in real-time and high-rate estimation of satellite clocks. The processing procedure is implemented in the PANDA (Positioning and Navigation Data Analyst) software package and evaluated in the operational generation of real-time GNSS orbit and clock products. We demonstrated that the new algorithm can efficiently eliminate outliers, and a clock precision of 0.06 ns, 0.24 ns, 0.06 ns, and 0.11 ns can be achieved for the GPS, GLONASS, Galileo, and BDS-2 IGSO/MEO satellites, respectively. The computation time per epoch is about 2 to 3 s depending on the number of existing outliers. Overall, the algorithm can satisfy the IGS real-time clock estimation in terms of both the computational efficiency and product quality.
Rare benign tumor in a prepubescent accessory breast: a case report
Authors: Tulsian Akriti , Basu Syamantak , Makan Apoorva , Joseph Vini , Gandhi Suraj , Shenoy Neha Sisodiya , Shah Hemanshi 
Published: December 2021

Abstract
Background
Polymastia or accessory breast is a congenital condition seen in 1% of the population at birth and in 2–6% of the female population. The most common presentation is in the pubertal age group when secondary sexual characters begin to develop or during pregnancy and lactation. Giant cell fibroblastoma is an exceedingly rare benign soft tissue neoplasm in the pediatric age group, usually seen over the back and thigh. We report a case of giant cell fibroblastoma in accessory breast tissue in a 4-year-old, female child. This case is rare in its presentation at the age of 4 years with an enlarging accessory breast without any secondary sexual characters. Giant cell fibroblastoma in the accessory breast tissue is unreported yet.

Case presentation
A 4-year-old female child presented with a progressively enlarging accessory breast. A wide local excision of the accessory breast with underlying growth was done. The histopathological examination revealed a giant cell fibroblastoma within the breast tissue.

Conclusions
Enlargement of an accessory or normal breast at a prepubescent age with the absence of secondary sexual characters should arouse suspicion of benign tumors in the breast tissue.
Documenting the Everyday Hidden Resistance of Ride-Hailing Platform Drivers to Algorithmic Management in Lagos, Nigeria
Authors: Arubayi Daniel 
Published: December 2021

Ride-hailing platforms such as Uber, an integral component in the global platform economy, are not only facilitating fluidity and so-called autonomy of labor; they are also creating an unfair working environment for workers. This phenomenon indicates the strength of a highly temporal and mobile capital, pitted against workers not just in Lagos but around the world. This article adopts James Scott’s notion of everyday resistance in exposing some of the hidden practices of platform drivers in Lagos. It finds that sabotaging and falsely complying through manipulating algorithms and gaming spaces for rewards are facilitated by social media and communication networks, are deliberate, hidden practices to subvert algorithmic control. While Lagos is a unique case in the global South, examples from global North cities highlight some peculiarity. A robust qualitative methodology was conducted comprising semistructured interviews, focus group discussions, and participant observations from forty Uber and Bolt trips. Other primary data sources include driver forums, attending driver training sessions and listening to transport radio programs.This article identifies temporal and spatial dynamics in recognizing everyday hidden practices as not always hidden, but dispersed and inconsistent because of the mutual learning capabilities between platform drivers and algorithmic managers. The hidden transcripts of platform drivers delve into public realms and back following, enabling platform drivers to develop new hidden practices, typifying a continuous power struggle in Lagos.
The Abl-interactor Abi suppresses the function of the BRAG2 GEF family member Schizo
Authors: Lübke Stefanie , Braukmann Carina , Rexer Karl-Heinz , Cigoja Lubjinka , Rout Pratiti , Önel Susanne F. 
Published: December 2021

ABSTRACT
Guanine nucleotide exchange factors (GEF) of the BRAG subfamily activate small Arf GTPases, which are pivotal regulators of intracellular membrane traffic and actin dynamics. Consequently, BRAG proteins have been implicated to regulate the surface levels of adhesive and signaling receptors. However, not much is known about the mechanism leading to the regulation of these surface proteins. In this study, we found that the Drosophila BRAG GEF Schizo interacts physically with the Abl-interactor (Abi). schizo mutants display severe defects in myoblast fusion during syncytial muscle formation and show increased amounts of the cell adhesion protein N-cadherin. We demonstrate that the schizo myoblast fusion phenotype can be rescued by the expression of the Schizo GEF (Sec7) and membrane-binding (pleckstrin homology) domain. Furthermore, the expression of the Sec7-PH domain in a wild-type background decreases the amounts of N-cadherin and impairs myoblast fusion. These findings support the notion that the Sec7-PH domain serves as a constitutive-active form of Schizo. Using a yeast-two hybrid assay, we show that the SH3 domain of Abi interacts with the N-terminal region of Schizo. This region is also able to bind to the cytodomain of the cell adhesion molecule N-cadherin. To shed light on the function of Schizo and Abi in N-cadherin removal, we employed epistasis experiments in different developmental contexts of Drosophila. These studies point towards a new model for the regulation of Schizo. We propose that the binding of Abi to the N-terminal part of Schizo antagonizes Schizo function to inhibit N-cadherin removal.
Reframing organizations in the digital age: A qualitative study exploring institutional social media adoption involving emergency physicians and other researchers
Authors: Yilmaz Yusuf , Ruan Brandon , Thomas Priya , Tran Victoria , M. Chan Teresa 
Published: December 2021

Background: Social media is changing the modern academic landscape; this study sought to explore how organizational structures support or inhibit the harnessing of social media use in academic contexts and knowledge translation. Methods: A qualitative study was conducted using framework analysis based on the Bolman and Deal’s Four-Frame Model—structural, human resources, political and symbolic. The research team used the snowball sampling technique to recruit participants following the completion of each participant’s semi-structured interview. A member check was completed to ensure rigour. Results: 16 social media educators and experts from several countries participated in the study. Study findings showed that within the Structural Frame, participants’ organizations were reported to have with diverse hierarchical structures, ranging hospital-based (strict), education institutional-based and online only groups (malleable). The Human Resources Frame revealed that most participants’ social media organizations operated on unpaid volunteer staff. The training of these staff was primarily via role-modeling and mentorship. Regarding the Political Frame, social media helped participants accumulate scholarly currency and influence within their field of practice. The Symbolic Frame showed a wide range of traditional to non-traditional organizational supports, which interacted with both intrinsic to extrinsic motivation. Conclusions: Bolman and Deal’s Four-Frame Model framework may serve as an effective guideline for academic leaders who wish to strategically implement or enhance social media use into their organizations. The key insights that we have gained from our participants are how new emerging forms of scholarly pursuits can be more effectively enabled or hindered by the attributes of the organization within which these are occurring.
Performance profiling of the unit trust funds in Malaysia with data mining techniques
Authors: Khairuddin Aida Farah , Ng Keng-Hoong , Khor Kok-Chin 
Published: December 2021

Background: Millennials are exposed to many investment opportunities, and they have shown their interest in gaining more income via investments. One popular investment avenue is unit trusts. However, analysing unit trusts’ financial data and gaining valuable insights may not be as simple because not everyone has the required financial knowledge and adequate time to perform in-depth analytics on the numerous financial data. Furthermore, it is not easy to compile the performance of each unit trust available in Malaysia. The primary objective of this research is to identify unit trust funds that provide higher returns than their average peers via performance profiling. Methods: This research proposed a performance profiling on Malaysia unit trust funds using the two data mining techniques, i.e., Expectation Maximisation (EM) and Apriori, to assist amateur retail investors to choose the right unit trust based on their risk tolerance. EM clustered the unit trust funds in Malaysia into several groups based on their annual financial performances. This was then followed by finding the rules associated with each cluster by applying Apriori. The resulted rules shall serve the purpose of profiling the clustered unit trust funds. Retail investors can then select their preferred unit trust funds based on the performance profile of the clusters. Results: The yearly average total return of the financial year 2018 and 2019 was used to evaluate unit trust funds’ performance in the clusters. The evaluation results indicated that the profiling could provide valuable and insightful information to retail investors with varying risk appetites. Conclusions: This research has demonstrated that the financial performance profiling of unit trust funds could be acquired via data mining approaches. This valuable information is crucial to unit trust investors for selecting suitable funds in investment.
Customer churn prediction for telecommunication industry: A Malaysian Case Study
Authors: Mustafa Nurulhuda , Sook Ling Lew , Abdul Razak Siti Fatimah 
Published: December 2021

Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry. Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables. Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction. Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.
The impact of coronavirus disease 2019 (COVID-19) on older adults with an intellectual disability during the first wave of the pandemic in Ireland
Authors: McCarron Mary , McCausland Darren , Luus Retha , Allen Andrew , Sheerin Fintan , Burke Eilish , McGlinchy Eimear , Flannery Fidelma , McCallion Philip 
Published: December 2021

Background: People with intellectual disability have increased risk of exposure to and adverse outcomes from coronavirus disease 2019 (COVID-19).They also face challenges to mental health and well-being from COVID-19-related social restrictions and service closures. Methods: Data from a supplemental COVID-19 survey from the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA) (n=710) was used to assess outcomes from the first infection wave of COVID-19 among adults with intellectual disability aged 40+ years in Ireland. Data was gathered on testing, for symptoms and outcomes; procedures to manage COVID-19; and both stress/anxiety and positive experiences during the pandemic. Demographic and health-related data from the main IDS-TILDA dataset was included in analyses. Results: High rates were identified of health conditions associated with poorer COVID-19 outcomes, including overweight/obesity (66.6%, n=365), high cholesterol (38.6%, n=274) and cardiovascular disease (33.7%, n=239). Over half (53.5%, n=380) reported emotional, nervous or psychiatric disorders. Almost two-thirds (62.4%, n=443) were tested for COVID-19, with 10% (n=71) reporting symptoms and 2.5% (n=11) testing positive. There were no instances of COVID-19 related mortality. Common symptoms included fatigue, fever, and cough. Some participants (7.8%, n=55) moved from their usual home, most often to isolate (n=31) or relocate to a family home (n=11). Three-quarters (78.7%) of those who were symptomatic or who tested positive had plans to manage self-isolation and two-thirds were able to comply with guidelines. Over half (55%, n=383) reported some COVID-19 related stress/anxiety; and a similar proportion reported positive aspects during this period (58%, n=381). Conclusions: Our data suggests that people with intellectual disability avoided the worst impacts of COVID-19 during the first infection wave in Ireland. Nevertheless, participants’ health profiles suggest that this population remains at high risk for adverse infection outcomes. Repeated measures are needed to track health and well-being outcomes across multiple infection waves.
SHEDR: An End-to-End Deep Neural Event Detection and Recommendation Framework for Hyperlocal News Using Social Media
Authors: Hu Yuheng , Hong Yili 
Published: December 2021

Residents often rely on newspapers and television to gather hyperlocal news for community awareness and engagement. More recently, social media have emerged as an increasingly important source of hyperlocal news. Thus far, the literature on using social media to create desirable societal benefits, such as civic awareness and engagement, is still in its infancy. One key challenge in this research stream is to timely and accurately distill information from noisy social media data streams to community members. In this work, we develop SHEDR (social media–based hyperlocal event detection and recommendation), an end-to-end neural event detection and recommendation framework with a particular use case for Twitter to facilitate residents’ information seeking of hyperlocal events. The key model innovation in SHEDR lies in the design of the hyperlocal event detector and the event recommender. First, we harness the power of two popular deep neural network models, the convolutional neural network (CNN) and long short-term memory (LSTM), in a novel joint CNN-LSTM model to characterize spatiotemporal dependencies for capturing unusualness in a region of interest, which is classified as a hyperlocal event. Next, we develop a neural pairwise ranking algorithm for recommending detected hyperlocal events to residents based on their interests. To alleviate the sparsity issue and improve personalization, our algorithm incorporates several types of contextual information covering topic, social, and geographical proximities. We perform comprehensive evaluations based on two large-scale data sets comprising geotagged tweets covering Seattle and Chicago. We demonstrate the effectiveness of our framework in comparison with several state-of-the-art approaches. We show that our hyperlocal event detection and recommendation models consistently and significantly outperform other approaches in terms of precision, recall, and F-1 scores. Summary of Contribution: In this paper, we focus on a novel and important, yet largely underexplored application of computing—how to improve civic engagement in local neighborhoods via local news sharing and consumption based on social media feeds. To address this question, we propose two new computational and data-driven methods: (1) a deep learning–based hyperlocal event detection algorithm that scans spatially and temporally to detect hyperlocal events from geotagged Twitter feeds; and (2) A personalized deep learning–based hyperlocal event recommender system that systematically integrates several contextual cues such as topical, geographical, and social proximity to recommend the detected hyperlocal events to potential users. We conduct a series of experiments to examine our proposed models. The outcomes demonstrate that our algorithms are significantly better than the state-of-the-art models and can provide users with more relevant information about the local neighborhoods that they live in, which in turn may boost their community engagement.
Do Board Gender Quotas Matter? Selection, Performance, and Stock Market Effects
Authors: Ferrari Giulia , Ferraro Valeria , Profeta Paola , Pronzato Chiara 
Published: December 2021

From business to politics and academia, the economic effects of gender quotas are under scrutiny. We provide new causal evidence based on the introduction of mandatory gender quotas for boards of directors of Italian listed companies. Exploiting staggered board elections, we find that quotas are associated with a new selection of board members, characterized by higher education and lower age, and no significant costs on firm performance or the stock market. This paper was accepted by Yan Chen, behavioral economics and decision analysis.