Miracle-dz/UniFAR-FTUs huggingface dataset:
Racial Disparities in Perioperative Morbidity Following Oncological Spine Surgery

Study Design: Retrospective cohort study. Objective: To assess the impact of race on complications following spinal tumor surgery. Methods: Adults with cancer who underwent spine tumor surgery were identified in the American College of Surgeons National Surgical Quality Improvement Program datasets from 2012 to 2016. Clavien-Dindo Grade I-II (minor complications) and Clavien-Dindo Grade III-V (major complications including 30-day mortality) complications were compared between non-Hispanic Whites (NHW) and Black patients. A multivariable analysis was also conducted. Results: Of 1,226 identified patients, 85.9% were NHW (n = 1,053) and 14.1% were Black (n = 173). The overall rate of Grade I-II complications was 16.2%; 15.1% for NHW patients and 23.1% for Black patients (P = .008). On multivariable analysis, Black patients had significantly higher odds of having a minor complication (OR 1.87; 95% CI, 1.16-3.01; P = .010). On the other hand, the overall rate of Grade III-V complications was 13.3%; 12.5% for NHW patients and 16.2% for Black patients (P = .187). On multivariable analysis, Black race was not independently associated with major complications (OR 1.26; 95% CI, 0.71-2.23; P = .430). Median length of stay was 8 days (IQR 5-13) for NHW patients and 10 days (IQR 6-15) for Black patients (P = .011). Conclusion: Black patients who underwent metastatic spinal tumor surgery were at a significantly increased risk of perioperative morbidity compared to NHW patients independent of baseline and operative characteristics. Major complications did not differ between groups. Race should be further studied in the context of metastatic spine disease to improve our understanding of these disparities.
Mental health correlates and potential childhood predictors for the wish to be of the opposite sex in young autistic adults

Cross-sectional research has demonstrated the overrepresentation of gender dysphoria in children and adults with autism spectrum disorder. However, the predictors and underlying mechanisms of this co-occurrence remain unclear. This follow-up study aimed to explore baseline (childhood/adolescence) predictors for the follow-up (adulthood) self-reported wish to be of the opposite sex and to investigate its mental health correlates in a sample of 88 autistic individuals as compared with 42 typically developing controls. An item on the Adult Self-Report Inventory-4, “I wish I was the opposite sex,” was used. We compared mental health symptoms between adults with and without this item endorsement. We used prediction models to explore family and autism-related predictors in childhood/adolescence to endorse this item in adulthood. There were more adults endorsing the item in the autism spectrum disorder group compared with the typically developing group. Autistic adults who endorsed the item experienced more mental health challenges, more bullying victimization, more suicidal ideations, and worse quality of life. Lower parent-reported family support and more stereotyped/repetitive behaviors during childhood/adolescence predicted the self-reported wish to be of the opposite sex in adulthood in autistic individuals. It is necessary to raise more attention to gender development and related mental health impact in autistic individuals. Lay abstract Autistic people/people with autism spectrum disorder are more likely to experience gender dysphoria. However, the possible longitudinal predictors and underlying mechanisms of this co-occurrence are unclear. To fill this knowledge gap, we assessed 88 people with autism spectrum disorder and 42 typically developing individuals at their average ages of 13.0 (baseline, childhood/adolescence) and 20.2 years old (follow-up, adulthood). At follow-up, their endorsement on the item “I wish I was the opposite sex” was used to evaluate gender dysphoric symptoms. We compared mental health symptoms between adults with and without this item endorsement at the follow-up assessment. We explored parent-reported family and autism characteristics-related predictors in childhood/adolescence to this item endorsement in adulthood. We found that more autistic adults reported the wish to be of the opposite sex than did typically developing individuals. Autistic adults who endorsed this item experienced more mental health challenges, more school bullying and cyberbullying, more suicidal ideation, and worse quality of life. Moreover, parent-reported lower family support and more stereotyped/repetitive behaviors during childhood/adolescence predicted the self-reported wish to be of the opposite sex in adulthood in autistic individuals. More attention and support should be provided to autistic people regarding gender development and related mental health and quality of life impact, especially during the transition period to young adulthood.
miR-218-5p inhibits the malignant progression of glioma via targeting TCF12

Several studies have shown the ability of transcription factor 12 (TCF12) to promote tumor malignant progression, but its function in glioma cells has not been fully elucidated. In this study, we analyzed the data from TCGA by bioinformatics and found that in glioma tissue, TCF12 was conspicuously highly expressed while miR-218-5p was significantly low-expressed. The downregulation of miR-218-5p was correlated with adverse prognosis in patients with glioma. miR-218-5p was found to be negatively associated with TCF12 by Pearson correlation analysis, and dual luciferase assay was employed to verify that miR-218-5p and TCF12 had a targeting relationship. qRT-PCR and Western blot assays were used to verify that the expression of TCF12 was regulated by its upstream regulator miR-218-5p. Moreover, cell experiments validated that overexpressed TCF12 could promote the proliferation, migration, and invasion of glioma cells and inhibit their apoptosis, whereas overexpressing miR-218-5p at the same time could reverse this phenomenon. Our study demonstrates the regulatory mechanism of the miR-218-5p/TCF12 axis in gliomas, which lays a foundation for searching for new therapeutic approaches for glioma.
A Celebration of the Career of Dr. William E. WaltonSeptember 1, 1956 - October 18, 2020

The field of vector ecology has lost one of its most prominent, dedicated, productive, and beloved scientists. Dr. William E. (Bill) Walton, Professor of Entomology, passed away at the UC Irvine Medical Center on October 18, 2020 from B Cell Lymphoma. He was 64 years old. Bill was accomplished and active in more roles than most scientists are willing to tackle. However, the Society for Vector Ecology (SOVE), with its dual mission to learn about vectors and also manage them for the betterment of human health and well being, was in our opinion most important to him. A gifted teacher, Bill was nevertheless at heart a researcher, and his research epitomizes what SOVE is all about. So, while we will touch on his other professional contributions, this tribute will particularly attempt to summarize aspects of his research career that have been most impactful in the field of vector ecology. A more traditional obituary was published by Mullens et al. (2020). For a much more complete listing and discussion of Bill’s publications and research activities, the readers are directed to a pending paper (Metzger, submitted).
Quantification, sources, and associated risks of 16-priority polycyclic aromatic hydrocarbons from selected land-use impacted soils

Abstract This study determined the spatial compositional occurrence, sources, and the associated risks of sixteen priority PAHs in soil depths from the selected land-use environments. Samples were collected from nine generator land-use sites in the top (0-15 cm) and sub (15-30 cm) soil depths. Sample extraction was by ultrasonication with dichloromethane/n-hexane and clean-up in silica gel/alumina packed column. The level of PAHs was determined using a gas chromatography-mass spectrometer (GC-MS). The concentrations of PAHs isomers and the Σ 16 PAHs ranged from ND to 16876 μg·kg−1, and from 346 to 44052 μg·kg−1 respectively. The Σ 16PAHs occurrence showed concentration load in the order of subsoil > topsoil. The ΣPAHs concentrations exceeded the DPR-EGAPSIN target and intervention value in 91% and 11% of the samples respectively. The total cancer risk ranged from low to moderate risk-based levels. The PAHs sources were attributed to low and high petroleum combustion emissions and stationary sources around the diesel combustion electricity generator in the land-use sites. This study revealed that the land-use activities associated with diesel combustion have contributed a significant amount of Σ16PAHs to the pollution load in the land-use sites with potential for ecological and human exposure risks.
Research on English language teaching and learning in Argentina (2014–2018)

Abstract The aim of this article is to review five years (2014–2018) of research in English language teaching (ELT) in Argentina. In this overview, 115 publications (conference proceedings, journal articles, and edited collections) were analysed in terms of topic and research methodologies to map out the major areas of interest and developments in ELT in Argentina. The publications are reviewed according to nine topics: (1) interculturality and citizenship, (2) language teacher education and professional development, (3) technology-enhanced language learning, (4) learner differences, (5) language skills development, (6) language systems, (7) English for academic and specific purposes, (8) assessment, and (9) language curriculum development. The review discusses trends in research practices identified in the 2014–2018 period and suggests implications and further areas of inquiry to advance research skills as well as knowledge of ELT.
Comparative therapeutic strategies for preventing aortic rupture in a mouse model of vascular Ehlers Danlos syndrome

We created a knock-in Col3a1+/G182R mouse model with spontaneous mortality caused by thoracic aortic rupture that recapitulates a rare vascular genetic disease of type III collagen, the vascular Ehlers-Danlos syndrome (vEDS). Investigation of this model showed lower survival rate in males caused by aortic rupture, thin non-inflammatory arteries and altered arterial collagen. Transcriptomic analysis of aortas showed upregulation of genes related to inflammation and cell stress response. Compared to water, survival rate of Col3a1+/G182R mice was not affected by beta-blockers (propranolol or celiprolol). Two other vasodilating anti-hypertensive agents (hydralazine, amlodipine) gave opposite results on aortic rupture and mortality rate. There was a spectacular beneficial effect of losartan, reversed by the cessation of its administration, and a marked deleterious effect of exogenous angiotensin II. These results suggest that blockade of the renin angiotensin system should be tested as a first-line medical therapy in patients with vEDS.
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search

We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search. The framework is capable of handling the uncertainty from the initial condition, stochastic dynamics, and uncertain parameters in the model. The algorithm is compared against a risk-sensitive distributional reinforcement learning framework and demonstrates outperformance on a pendulum and cartpole with stochastic dynamics. We also showcase the applicability of the framework to robotics as an adaptive risk-sensitive controller by optimizing with respect to the fully nonlinear belief provided by a particle filter on a pendulum, cartpole, and quadcopter in simulation.
Designing a Public Experimental Terminal for Citizen Engagement

Experimental research involving human participants has received an increasing interest in the Information Systems (IS) discipline. Much of today's experimental research is done with student participants, which comes with the advantage of easy availability and homogeneity of the sample. However, non-representative student samples are also often criticized due to limited generalizability. Thus, an important challenge is to engage citizens in experimental research following an open science paradigm. In this ongoing design science research project, we propose design requirements and design principles for public experimental terminals to increase engagement of citizens in experimental research. On this basis, we create an instantiation of an operational experimental terminal that can be deployed in the public space. With our research project, we present a novel approach to raise awareness and actively engage citizens in experimental research.
Multi-agents collaboration in open system

Share constrained resources, accomplish complex tasks and achieve shared or individual goals are examples requiring collaboration between agents in multi-agent systems. The collaboration necessitates an effective team composed of a set of agents that do not have conflicting goals and express their willingness to cooperate. In such a team, the complex task is split into simple tasks, and each agent performs its assigned task to contribute to the fulfilment of the complex task. Nevertheless, team formation is challenging, especially in an open system that consists of self-interested agents performing tasks to achieve several simultaneous goals, usually clashing, by sharing constrained resources. The clashing goals obstruct the collaboration's success since the self-interested agent prefers its individual goals to the team’s shared goal. In open systems, the collaboration team construction process is impacted by the Multi-Agent System (MAS) model, the collaboration’s target, and dependencies between agents’ goals. This study investigates how to allow agents to build collaborative teams to realize a set of goals concurrently in open systems with constrained resources. This paper proposes a fully distributed approach to model the Collaborative Team Construction Model (CTCM). CTCM modifies the social reasoning model to allow agents to achieve their individual and shared goals concurrently by sharing resources in an open MAS by constructing collaborative teams. Each agent shares partial information (to preserve privacy) and models its goal relationships. The proposed team construction approach supports a distributed decision-making process. In CTCM, the agent adapts its self-interest level and adjusts its willingness to form an effective collaborative team.
Effects of ethylene glycol dimethacrylate as cross-linker in ionic liquid gel polymer electrolyte based on poly(glycidyl methacrylate)

ABSTRACT In this study, modified poly(glycidyl methacrylate)-based films for gel electrolyte were prepared by an in situ UV photopolymerization technique. The effects of adding ethylene glycol dimethacrylate (EGDMA) to the polymer host were studied through X-ray diffraction analysis and differential scanning calorimetry. The results from Fourier transform-infrared spectra indicate complete polymerization among the monomers. The addition of EGDMA to the formulation of gel polymer electrolyte increased the loading of 1-butyl-3-methylimidazolium bis(fluoromethylsulfonyl)imide up to 200 wt. % with the highest value of 8.2 × 10−4 S cm−1. All the gel polymer electrolyte membranes obeyed the Arrhenius law.
Image-Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.
Business model implications of privacy-preserving technologies in data marketplaces: The case of multi-party computation

Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to companies on data privacy and control. However, how such technologies change the business model of data marketplaces is not fully understood. This paper aims to bridge this gap by focusing on multi-party computation (MPC) as a cryptographic technology that is currently being hyped. Based on interviews with privacy and security experts, we find that MPC enables data marketplaces to employ a “privacy-as-a-service” business model, which goes beyond privacy-preserving data exchange. Depending on the architecture, MPC could transform data marketplaces into data brokers or data aggregators. More complex architectures might lead to more robust security guarantees and lower trust requirements towards data marketplace operators. Furthermore, MPC enables new offerings of privacy-preserving analytics and services as new revenue sources. Our findings contribute to developing business models of privacy-preserving data marketplaces to unlock the potential of data sharing in a digitized economy.
The Impact of the COVID-19 Lockdown on Digital Transformation in German Organizations

COVID-19 has struck the world unexpectedly in 2020. The first lockdown created an unprecedented situation, in which interpersonal contact is reduced to only the most inevitable encounters. An immense need for digitalization emerged in all areas, such as education, working from home and digital sales channels. Meanwhile, organizations struggled to finance the sudden call for digital transformation (DT), when traditional streams of income broke down. In this study, we aim to investigate the impact of the first lockdown on the drivers of DT. How did German organizations react and what can we learn from this crisis for the long term? In order to answer this question, we conduct a Systematic Literature Review (SLR) on the influencing factors on DT and conduct an interview study with digital experts from different German organizations. This research in progress paper reports on the findings of the SLR and preliminary results of the following study.
The Impact of Intelligent Personal Assistants on Work Practices

Individuals and organisations are increasingly turning to artificial intelligence (AI) technologies to enhance productivity and performance. This research considers the impact of intelligent personal assistants (IPAs) on work practices. IPAs are intelligent applications that automate and perform routine tasks, collaborate with workers, and offer actionable insights and recommendations to help workers make data-driven decisions. In theory, using IPAs can improve workers’ productivity by supporting and/or helping to transform relevant work practices. However, empirical field-based evidence substantiating such propositions is scarce, and there is little understanding of how such transformation may occur in practice. We, therefore, propose a multiple case study to investigate how work practices change when workers collaborate with IPAs. We aim to contribute to the body of knowledge that explores the relationship between human-AI collaboration and work practices. Moreover, we expect our research to provide useful insights for organisations to better understand the implications of AI in the workplace.
Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

Language model pre-training architectures have demonstrated to be useful to learn language representations. bidirectional encoder representations from transformers (BERT), a recent deep bidirectional self-attention representation from unlabelled text, has achieved remarkable results in many natural language processing (NLP) tasks with fine-tuning. In this paper, we want to demonstrate the efficiency of BERT for a morphologically rich language, Turkish. Traditionally morphologically difficult languages require dense language pre-processing steps in order to model the data to be suitable for machine learning (ML) algorithms. In particular, tokenization, lemmatization or stemming and feature engineering tasks are needed to obtain an efficient data model to overcome data sparsity or high-dimension problems. In this context, we selected five various Turkish NLP research problems as sentiment analysis, cyberbullying identification, text classification, emotion recognition and spam detection from the literature. We then compared the empirical performance of BERT with the baseline ML algorithms. Finally, we found enhanced results compared to base ML algorithms in the selected NLP problems while eliminating heavy pre-processing tasks.
Estimating economic values for beach and foreshore assets and preservation against future climate change impacts in Victoria, Australia

ABSTRACT Determining social values for protecting coastal assets, including coastal parks, beaches and foreshores, is important because of potential losses with climate change impacts. In a case study application in Victoria, Australia, the travel cost method was applied to value the use of camping parks by visitors, and the choice modelling technique was applied to estimate willingness to pay (WTP) of visitors and residents to avoid future losses in foreshore campground access and beach width, through protection and adaptation efforts. While both visitors and residents had similar values for park and campsite, and beach protection, residents valued beach protection as more important whereas visitors placed greater importance in protecting parks and campsites. Visitor values were higher for sites and beaches closer to metropolitan Melbourne. These results provide government agencies and coastal managers with better information about protection values for low lying coastal assets and will enable more informed decisions to be made about investing in coastal protection and the management of specific resources in the face of sea level rise and climate threats.
The Bahadur representation of sample quantiles for φ-mixing random variables and its application

In this paper, the Bahadur representation of sample quantiles for φ-mixing random variables is investigated without any restriction on the decaying rate of mixing coefficients. As an application, we further investigate the asymptotic normality and the Berry–Esséen bound of sample quantiles for φ-mixing random variables under some general conditions. It is shown that the rate of normal approximation is for any if the mixing coefficients satisfy for some or for any if the mixing coefficients can decay exponentially. Furthermore, a real data analysis is presented.