Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial DNA haplogroup H association with endometriosis and possible role in inflammation and pain

Introduction: Endometriosis is an inflammatory disease characterised by the presence of endometrial-like tissue outside the uterus and affects approximately 10%–15% of women in their reproductive years. Pain is one of the predominant symptoms of the disease. Oxidative stress is involved in the pathophysiology of endometriosis and develops when there is an imbalance between the reactive oxygen species and reactive nitrogen species production, and the elimination capacity of antioxidants in the reproductive tract. High levels of reactive oxygen species can induce pain indirectly through oxidative stress-associated inflammation or directly through sensitising the nociceptive neurons that transmit the signals to the cerebral sensory cortex which are perceived as a feeling of pain. Mitochondria are the main source of reactive oxygen species, which generate through oxidative phosphorylation. Given that the mitochondria are involved in reactive oxygen species formation and energy production, which are required for the activation and proliferation of peripheral lymphocytes, it has been suggested that mitochondrial DNA variants are involved in the pathogenesis of endometriosis. This study has provided a better understanding of maternally inherited risk factors which contribute to the pain mechanisms associated with endometriosis. Results: Mitochondrial DNA haplogroup H was found to be significantly higher in women with endometriosis. This study was the first to report the association between the European mitochondrial haplogroup H and the risk of pain associated with endometriosis. Discussion: The results suggest that there are maternally inherited risk factors in women with endometriosis causing high reactive oxygen species production and oxidative stress, which facilitate pain generation in women with endometriosis.
The Asian Paradigm in Hospitality and Tourism

The emergence of Asian countries, powered by economic growth, has set the stage for Asia’s tourist destinations and hospitality companies to stand out in the global sphere. This study contemplates the shifting center of modern hospitality and tourism gravity toward Asia by implementing the three pillars of capital theory: economic, cultural, and social. The concept of the Asian paradigm was revisited to demonstrate the unique service provision and care for guests that forms the basis for Asian hospitality and tourism. This study offers perspectives about how Asian tradition and evolving Asian identities influence the leisure industry and create competitive advantage. The cycle of economic, cultural, and social capital that drives the Asian paradigm can provide implications for both hospitality practitioners and policymakers.
Book Review: Organizational Moral Learning: A Communication Approach

When I purchased Ryan Bisel’s Organizational Moral Learning in 2018, the news cycle was dominated by ethical issues such as the murder of Jamal Khashoggi and ransomware attacks. Today, during a time when ethical puzzles span systemic racial inequities, face mask mandates, and the forced closing of businesses in the interest of public health, a closer examination of ethics within organizations is imperative, and I again found myself reflecting deeply on these issues. My return to ethics is typical behavior, according to Bisel, as most people do not deeply consider ethical dilemmas until a disaster arises. It is this same tendency toward inertia within organizations that Bisel addresses with Organizational Moral Learning. In this book, Bisel argues that current ethics training programs fail to reinforce ethical behavior within organizations, and he unveils a theoretical framework aimed to improve ethics training and discussions in organizations. The investigation of how ethics are taught and implemented in organizations remains timely and critical work, as examples in Organizational Moral Learning reveal how organizations typically incorporate ethics teachings with ineffective results. As Bisel notes,
The Norwegian remote intervention programme for problem gambling: Short- and long-term outcomes

Background and aim: Although problem gambling typically involves substantial distress, few seek face-to-face treatment. In Norway, problem gamblers can participate in a governmental supported internet- and telephone-based intervention programme. The current study aimed to evaluate the outcomes of this programme in terms of gambling behaviour, gambling-related cognitions and mental health in a one group pre-test post-test design with a follow-up assessment. Methods: The sample consisted of the 67 participants who completed the intervention programme within a one-year timeframe. Gambling behaviour (SOGS-R), gambling-related cognitions (GBQ) and mental health (SCL-90-R) were measured pre-intervention, post-intervention and at a 6−12 months follow-up. A total of 25 (37.3%) participants completed the follow-up assessment. T-tests were conducted to investigate development in gambling behaviour, gambling-related cognitions and mental health from pre-intervention to post-intervention and follow-up. Results: The analyses showed a significant reduction in gambling behaviour, gambling problems, gambling-related cognitive distortions and mental health symptoms from pre-intervention to post-intervention and follow-up. The corresponding effect sizes for the reductions in gambling and gambling-related cognitive distortions were very large, while the effect sizes for the reductions in mental distress were moderate. Conclusion: The internet/telephone programme appears to have several positive outcomes including reduction in gambling behaviour, gambling problems, gambling-related cognitive distortions and symptoms of mental disorders both in the short and long term. Another positive outcome of participation appears to be a lowered threshold for seeking additional treatment. The current study entails, however, important limitations, and future studies should investigate the outcomes of the programme while including a control group.
The Effects of Environment-Based Education on Students’ Achievement Motivation

This mixed-methodology study examined the relationship between environment-based education and high school students’ achievement motivation. Four hundred 9th- and 12th-grade students from 11 Florida high schools participated in the study. A Pretest-Posttest Nonequivalent Comparison Group Design (9th grade) and a Posttest Only Nonequivalent Comparison Group Design (12th grade) were used in the study. Interviews of students and teachers were used to support and explain the quantitative data. Data collection took place over the 2001–2002 school year. When controlling for pretest score, GPA, gender, and ethnicity, environment-based programs had a positive effect on 9th grade students’ achievement motivation. When controlling for GPA, gender, and ethnicity, environment-based programs had a positive effect on 12th-grade students’ achievement motivation. The results of this study support the use of environment-based education for improving achievement motivation and can be used to guide future program implementation. Implications for formal educators, environmental educators, and interpreters are discussed.
Feedback Between Psychological Science and Policy in the Context of Same-Sex Couples

Psychological science informed recent policy changes granting increased rights for same-sex couples. Understanding that the link between science and policy goes both ways, how should policy inform the next generation of research concerning same-sex couples and sexual minority individuals? This article presents ways that psychological research influenced marriage policy and then puts forth suggestions for future research for the attention of scholars, funders, and policymakers. These include examinations of minority stress in the age of marriage equality; new stressors for same-sex couples including legal divorce; relationship expectations and experiences for emerging adults; and the potential impact of marriage equality for mixed-sex couples including a further challenge to strict gender roles in marriage. The article ends by acknowledging the need for continued application of research to emerging policy questions affecting same-sex couples and sexual minority individuals.
Designing a Self-help Mobile App to Cope with Avoidance Behavior in Panic Disorder

Panic disorder (PD) is an anxiety disorder that in recent years has spread worldwide. PD is diagnosed when a person has recurring panic attacks, characterized by physical symptoms and disturbing thoughts and feelings that arise rapidly, reach their peak in a few minutes and soon disappear. Panic attacks, despite being harmless and relatively short, are highly distressing and deeply affect the lives of patients, who very often develop agoraphobia, an anxiety disorder that leads to systematic avoidance of places where previous attacks have occurred. PD is often a chronic condition that does not respond well to pharmacological treatment. However, psychotherapeutic approaches such as mindfulness have proved to be quite effective and their delivery through self-care eHealth tools has been encouraged by the World Health Organization. In this paper, we present a self-aid mobile app designed by and for patients affected by PD with mild agoraphobia. The app is aimed at helping users cope with avoidance behavior. Thanks to geolocation, the app automatically detects the proximity of a "critical place (i.e., where a previous attack has occurred) and suggests mindfulness strategies for coping with stress, in order to prevent anxiety escalation and panic. This paper describes the therapeutic background of the proposed application, as well as the mHealth best practices we strove to adopt in the design phase. Preliminary trials conducted with one patient are encouraging; nonetheless, we point out the need for further and more extensive tests to fully assess the effectiveness of our approach.
How Walkable Neighborhoods Promote Physical Activity: Policy Implications for Development and Renewal

Mitigating physical inactivity is vital for public health. Neighborhoods that include visually engaging, eye-catching objects and locations increase the frequency, duration, and vigorousness of resident and visitors’ exercise. Three findings are key: First, individuals in neighborhoods that include features directly relevant to exercise—including dense mixed-use developments, greenspaces, parks, sidewalks, and connected streets—are more active and maintain better health. Second, when neighborhoods include visually interesting contents that are indirectly relevant to exercise, individuals believe exercise is more feasible, and this change in psychological mindset predicts increased physical activity. Third, as individuals become more physically active, they are less tempted by unhealthy food, which may counteract the detrimental effects on healthy eating that having proximal fast-food restaurants in neighborhoods poses. Race and socioeconomic disparities co-exist with the contents of neighborhoods. We highlight implications for urban planners, developers, community groups, and individuals selecting and designing public spaces that are conducive to healthy lifestyles.
Jumping concepts and other transpositions: the Keller effect in/on discourses of living systems

Evelyn Fox Keller's work has long focused on the discontinuities that seem to punctuate discourses on living systems. The discourse of gene action, for example, focuses on the uncanny shift of agency from organism to genome, where the empirical ‘action’ of a living system becomes attributed to its genes. Shifts in these regimes of description become akin to conceptual transpositions, ‘jumping concepts’ parallel to the ‘jumping genes’ perceived by McClintock. This chapter will investigate Keller's treatment of these shifts in parallel with her early work on slime mold aggregation, as both the holistic coherence of a living system or a discourse becomes visible through its sudden shifts - the Keller Effect.
Digital Methods for Hashtag Engagement Research

This article seeks to contribute to the field of digital research by critically accounting for the relationship between hashtags and their forms of grammatization—the platform techno-materialization process of online activity. We approach hashtags as sociotechnical formations that serve social media research not only as criteria in corpus selection but also displaying the complexity of the online engagement and its entanglement with the technicity of web platforms. Therefore, the study of hashtag engagement requires a grasping of the functioning of the platform itself (technicity) along with the platform grammatization. In this respect, we propose the three-layered (3L) perspective for addressing hashtag engagement. The first contemplates potential differences between high-visibility and ordinary hashtag usage culture, its related actors, and content. The second focuses on hashtagging activity and the repurposing of how hashtags can be differently embedded into social media databases. The last layer looks particularly into the images and texts to which hashtags are brought to relation. To operationalize the 3L framework, we draw on the case of the “impeachment-cum-coup” of Brazilian president Dilma Rousseff. When cross-read, the three layers add value to one another, providing also difference visions of the high-visibility and ordinary groups.
Discussion of Active Risk Models

Although investment managers and quants have deep understanding for risk, the common investors still have questions on what exactly the risk is: how to estimate it and how to define the sources of risk. The risk theory started with Markowitz, passing though CAPM and developing Multi-factor risk models. However, in a multi-factor setting correlation is always a problem. This problem has been solved in two different ways: First, developing Global Risk Attribute Model (GMFM). GRAM deals with correlation applying the technique called Cascading regressions. This technique basically removes the correlation between factors and allows us to use them directly in the equation. Second, BARRA US E3 categorize exposure to multiple factors as a specific (active) risk. Here total active risk is separated in the risk due to other risk factors (Active systematic risk) and the company-specific risk (Active residual risk). Based on the limitations of previous approaches we develop our own approach to the investment risk by separating it into three components. It is done so that the three main sources of risk are clear and from here the model can be used also for return attribution to any kind of fund. The three components are: Benchmark risk – it represents the risk from exposure to the market. It is most used for appraisal of passive funds that sole goal is to track the benchmark. It consist of Passive risk and Market timing risk; Beta risk – these are the risk of exposure to other types of systematic factors. For passive managers these betas should be the same as the market, if they are to replicate its performance. This kind of risk also is the base of Smart Beta strategies; Active risk – if the goal of active fund manager is to get “pure alpha” all he needs to do is neutralize the exposure to the previous two types of risk, or very often his results will get mixed up with the returns generated by timing the other systematic factors. From here the active risk is deconstructed into pure tracking error and strategy risk. Deconstructing the active risk is very interesting and has some very important nuances. First of all pure tracking error is the “desired risk” especially for active investors. After all you can’t get abnormal return if you do not let your portfolio deviate from the market. On the other hand the “strategy risk” measures what is the risk in your forecasting model. This is undesirable risk, and the main goal of this is to be able to compare strategies on how accurate they are. Such a definition of the active risk is needed in order for active managers to make informed decisions.
When High Fidelity Matters: AR and VR Improve the Learning of a 3D Object

Virtual and Augmented Reality Environments have long been seen as having strong potential for educational applications. However, research showing actual evidences of their benefits is sparse. Indeed, some recent studies point to unnoticeable benefits, or even a detrimental effect due to an increase of cognitive demand for the students when using these environments. In this work, we question if a clear benefit of AR and VR can be robustly measured for a specific education-related task: learning a 3D object. We ran a controlled study in which we compared three interaction techniques. Two techniques are VR- and AR-based; they offer a High Fidelity (HF) virtual reproduction of observing and manipulating physical objects. The third technique is based on a multi-touch tablet and was used as a baseline. We selected a task of 3D object learning as one potentially benefitting from the HF reproduction of object manipulation. The experiment results indicate that VR and AR HF techniques can have a substantial benefit for education as the object was recognized more than 27% faster when learnt using the HF techniques than when using the tablet.
Improving FPGA-Based Logic Emulation Systems through Machine Learning

We present a machine learning (ML) framework to improve the use of computing resources in the FPGA compilation step of a commercial FPGA-based logic emulation flow. Our ML models enable highly accurate predictability of the final place and route design qualities, runtime, and optimal mapping parameters. We identify key compilation features that may require aggressive compilation efforts using our ML models. Experiments based on our large-scale database from an industry’s emulation system show that our ML models help reduce the total number of jobs required for a given netlist by 33%. Moreover, our job scheduling algorithm based on our ML model reduces the overall time to completion of concurrent compilation runs by 24%. In addition, we propose a new method to compute “recommendations” from our ML model to perform re-partitioning of difficult partitions. Tested on a large-scale industry system on chip design, our recommendation flow provides additional 15% compile time savings for the entire system on chip. To exploit our ML model inside the time-critical multi-FPGA partitioning step, we implement it in an optimized multi-threaded representation.
Mapping Road Safety Features from Streetview Imagery

Each year, an average of around 6 million car accidents occur in the United States. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for federal or state transportation agencies, helping traffic engineers identify locations to invest in safety infrastructure. In current practice, mapping road safety features is largely done manually (e.g., observations on the road or visual interpretation of streetview imagery), which is both expensive and time consuming. In this article, we propose a deep learning approach to automatically map road safety features from streetview imagery. Unlike existing convolutional neural networks that classify each image individually, we propose to further add a recurrent neural network (long short-term memory) to capture geographic context of images (spatial autocorrelation effect along linear road network paths). Evaluations on real-world streetview imagery show that our proposed model outperforms several baseline methods.
Quantitative trait loci mapping reveals pleiotropic effect for grain iron and zinc concentrations in wheat

Malnutrition because of the deficiency of minerals such as iron (Fe) and zinc (Zn) afflicts over 2 billion people worldwide. Wheat is a major staple crop, providing 20% of dietary energy and protein consumption worldwide. Breeding wheat with elevated levels of grain Zn and Fe concentrations (GZn and GFe) represents a significant opportunity to increase the intake of these micronutrients for the resource poor people who depend on it as a major source of dietary energy. Synthetic hexaploid wheats (SHWs) have large genetic variation for GZn and GFe, which can be exploited for developing wheat varieties with higher concentrations of these minerals. The objective of this study was to localise genomic regions associated with GZn and GFe, thousand kernel weight (TKW) and test weight (TW) in a mapping population derived from the cross of Seri M82 and the SHW CWI76364. Major quantitative trait loci (QTL) on chromosome 4BS were detected for GZn and GFe; the QTL explained up to 19.6% of the total phenotypic variation for GZn and showed pleiotropic effects on GFe. This indicates that simultaneous improvement of GZn and GFe is feasible. Three and five QTL for TW and TKW were detected, respectively. One of the QTL for TKW was also located on chromosome 4BS. Positive correlations between plant height and GZn/GFe were observed. The 4BS QTL is of great interest for breeding biofortified wheat by means of marker-assisted selection.
Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations

We propose Compadre, a tool for visual analysis for comparing distances of high-dimensional (HD) data and their low-dimensional projections. At the heart is a matrix visualization to represent the discrepancy between distance matrices, linked side-by-side with 2D scatterplot projections of the data. Using different examples and datasets, we illustrate how this approach fosters (1) evaluating dimensionality reduction techniques w.r. t. how well they project the HD data, (2) comparing them to each other side-by-side, and (3) evaluate important data features through subspace comparison. We also present a case study, in which we analyze IEEE VIS authors from 1990 to 2018, and gain new insights on the relationships between coauthors, citations, and keywords. The coauthors are projected as accurately with UMAP as with t-SNE but the projections show different insights. The structure of the citation subspace is very different from the coauthor subspace. The keyword subspace is noisy yet consistent among the three IEEE VIS sub-conferences.
A Multistaged Hyperparallel Optimization of the Fuzzy-Logic Mechanistic Model of Molecular Regulation

Motivation Although it circumvents hyperparameter estimation of ordinary differential equation (ODE) based models and the complexities of many other models, the computational time complexity of a fuzzy logic regulatory model inference problem, particularly at higher order of interactions, quickly approaches those of computationally intractable problems. This undermines the benefits inherent in the simplicity and strength of the fuzzy logic-based molecular regulatory inference approach. Results For a sample inference problem – molecular regulation of vorinostat resistance in the HCT116 colon cancer cell lines, our modeled, designed and implemented “multistaged-hyperparallel” optimization approach significantly shortened the time to model inference from about 485.6 hours (20.2 days) to approximately 9.6 hours (0.4 days), compared to an optimized version of a previous implementation. Availability The multistaged-hyperparallel method is implemented as a plugin in the JFuzzyMachine tool, freely available at the GitHub repository locations https://github.com/paiyetan/jfuzzymachine and https://github.com/paiyetan/jfuzzymachine/releases/tag/v1.7. 21. Source codes and binaries are freely available at the specified URLs. Contact paiyetan@gmu.edu