Miracle-dz/UniFAR-FTUs huggingface dataset:
Pediatric Neural Changes to Physical and Emotional Pain After Intensive Interdisciplinary Pain Treatment: A Pilot Study

Brain areas activated during pain can contribute to enhancing or reducing the pain experience, showing a potential connection between chronic pain and the neural response to pain in adolescents and youth. This study examined changes in brain activation associated with experiencing physical pain, and the observation of physical and emotional pain in others, by using functional magnetic resonance imaging (fMRI) before and after intensive interdisciplinary pain treatment (IIPT). Eighteen youth (age 14 to 18) with widespread chronic pain completed fMRI testing before and after IIPT to assess changes in brain activation in response to physical and emotional pain. Broadly, brain activation changes were observed in frontal, somatosensory, and limbic regions. These changes suggest improvements in descending pain modulation via thalamus and caudate, and the different pattern of brain activation after treatment suggests better discrimination between physical and emotional pain. Brain activation changes were also correlated with improvements in clinical outcomes of catastrophizing (reduced activation in right caudate, right mid-cingulate, and postcentral gyrus) and pain-related disability (increased activation in precentral gyrus, left hippocampus, right middle occipital cortex, and left superior frontal gyrus). These changes support interpretation that reduced brain protective responses to pain were associated with treatment-related improvements. This pilot study highlights the need for larger trials designed to better understand the brain mechanisms involved in pediatric widespread pain treatment.
Contextual Gaps in Machine Learning for Mental Illness Prediction: The Case of Diagnostic Disclosures

Getting training data for machine learning (ML) prediction of mental illness on social media data is labor intensive. To work around this, ML teams will extrapolate proxy signals, or alternative signs from data to evaluate illness status and create training datasets. However, these signals' validity has not been determined, whether signals align with important contextual factors, and how proxy quality impacts downstream model integrity. We use ML and qualitative methods to evaluate whether a popular proxy signal, diagnostic self-disclosure, produces a conceptually sound ML model of mental illness. Our findings identify major conceptual errors only seen through a qualitative investigation -- training data built from diagnostic disclosures encodes a narrow vision of diagnosis experiences that propagates into paradoxes in the downstream ML model. This gap is obscured by strong performance of the ML classifier (F1 = 0.91). We discuss the implications of conceptual gaps in creating training data for human-centered models, and make suggestions for improving research methods.
POTASSIUM CHLORIDE ADMINISTERED VIA FOUR ROUTES FOR EUTHANASIA OF ANESTHETIZED GOLDFISH (CARASSIUS AURATUS)

Abstract: Immersion overdose in tricaine methanesulfonate (MS-222) is ineffective for euthanasia of adult goldfish (Carassius auratus), and investigation of alternative techniques is warranted. This study evaluated potassium chloride (KCl) administered via four routes for goldfish euthanasia. Thirty clinically healthy adult goldfish (17.2–41.9 g) were anesthetized via immersion in buffered MS-222 (300 mg/L) for 5 min beyond anesthetic induction. Fish were then randomly administered one of five treatments: KCl (333 mg/mL, 4.5 mmol/mL, 4.5 mEq/mL) via intracardiac injection at 10 mEq/kg (IC), intracoelomic injection (ICe) or bilateral topical delivery over the gill filaments (T) at 100 mEq/kg, or 90-min immersion at 4,500 mEq/L (saturated solution) dissolved in induction solution (water [W]), or no treatment (control [C]). Following treatment, all fish were moved to anesthetic-free freshwater. Serial heart rates were collected via Doppler device until sound cessation or recovery. Median (range) time to perform treatments was 5.3 (1.2–8.0) min, 3 (3–3) s, and 10 (10–10) s in IC, ICe, and T, respectively. Doppler cessation occurred in 6/6, 6/6, 6/6, 6/6, and 0/6 fish in median (range) times of 3 (0–210), 18 (10–45), 118 (90–390), and 150 (60–240) min in IC, ICe, T, W, and C, respectively; this was significantly different between groups (P = 0.002). Following or during treatment, 1/6, 2/6, 6/6, and 4/6 fish in IC, ICe, T, and W, respectively, exhibited intermittent transient jerking movements. Median (range) time to recovery in C was 5.5 (3.5–6.5) min. All administration routes were effective, but time to Doppler cessation varied and transient movements were noted. Preliminary follow-up research determined that return to MS-222 induction solution following ICe KCl treatment abolished movements. ICe KCl at 100 mEq/kg was technically simple and resulted in Doppler sound cessation in a clinically relevant time frame in 6/6 anesthetized goldfish. Return to MS-222 immersion following KCl administration is recommended.
Alternate bar response to sediment supply termination

[1] Sediment supply is widely held to be one of the primary controls on bar topography in alluvial channels, yet quantitative linkages between sediment supply and bar topography are not well developed. We explore the conditions under which alternate bars form and how they respond to the elimination of sediment supply in two linked laboratory experiments. The first set of experiments was conducted in a 28 m long, 0.86 m wide flume channel using a unimodal sand-gravel mix. The second set of experiments was conducted at field scale in a 55 m long, 2.74 m wide channel using a unimodal gravel mixture. In both experiments, alternate bars and patchy surface grain-size distributions developed under steady flow and sediment supply conditions. The cessation of the sediment supply induced a reduction in the surface grain-size heterogeneity and the bars were eliminated. In both flumes, mean boundary shear stress had declined, but were capable of moving sediments after the bars disappeared, albeit at relatively small rates compared to when the bars were present. In the smaller flume, the previously stationary bars migrated out of the flume and were not replaced with new bars. A nearly featureless bed formed with limited surface grain-size heterogeneity, a slightly coarsened surface and a slightly reduced slope. In the larger flume, the formation of alternate bars was induced by an imposed upstream flow constriction and as such, the bars did not migrate. Termination of sediment supply led to progressive erosion of bed topography and loss of the bars, coarsening of the bed surface, loss of bed texture patchiness and significant slope reduction. The original alternate bar topography redeveloped when the sediment supply was restored once sufficient deposition had occurred to reconstruct the original channel slope. This shows that the bar loss was reversible by establishing the previous conditions and highlights the importance of sediment supply for bar formation. The role of sediment supply in bar formation and stability is not often recognized in stream restoration. Our results suggest that the loss of sediment supply can significantly affect alternate bar topography and that considerable volumes of sediment may be needed restore channel bars.
"Our Job is to be so Temporary": Designing Digital Tools that Meet the Needs of Care Managers and their Patients with Mental Health Concerns

Digital tools have potential to support collaborative management of mental health conditions, but we need to better understand how to integrate them in routine healthcare, particularly for patients with both physical and mental health needs. We therefore conducted interviews and design workshops with 1) a group of care managers who support patients with complex health needs, and 2) their patients whose health needs include mental health concerns. We investigate both groups' views of potential applications of digital tools within care management. Findings suggest that care managers felt underprepared to play an ongoing role in addressing mental health issues and had concerns about the burden and ambiguity of providing support through new digital channels. In contrast, patients envisioned benefiting from ongoing mental health support from care managers, including support in using digital tools. Patients' and care managers' needs may diverge such that meeting both through the same tools presents a significant challenge. We discuss how successful design and integration of digital tools into care management would require reconceptualizing these professionals' roles in mental health support.
Knowing Unknown Teammates: Exploring Anonymity and Explanations in a Teammate Information-Sharing Recommender System

A growing organizational trend is to utilize ad-hoc team formation which allows for teams to intentionally form based on the member skills required to accomplish a specific task. Due to the unfamiliar nature of these teams, teammates are often limited by their understanding of one another (e.g., teammate preferences, tendencies, attitudes) which limits the team's functioning and efficiency. This study conceptualizes and investigates the use of a teammate information-sharing recommender system which selectively shares interpersonal recommendations between unfamiliar teammates (e.g., "Your voice may be overshadowed by this teammate when making decisions.") to promote teammate understanding. Through a mixed-methods approach involving 105 participants working on actual unfamiliar teams, this study explores how presentation elements such as anonymity and explanations influence system perceptions and how anonymity influences team outcomes. Results indicate that anonymizing recommendations was associated with worse team measures, particularly team satisfaction and team cohesion. Qualitative results shed light on why team members perceived privacy concerns and team benefits associated with using the system. We contribute to CSCW through a better understanding of how to support unfamiliar teams, the conceptualization and empirical investigation of a novel teammate information-sharing recommender system, and foundational design recommendations associated with such a system.
Characterization of Illegal Wildlife Trade Networks

The legal and illegal trade in wild animals and their products is a multi-billion dollar industry that threatens the health and well-being of humans and animals alike. The management of the wildlife trade is a crisis-driven area, where decisions are made quickly, and, often, inefficiently. In particular, the regulation and control of the illegal wildlife trade is hampered by a dearth of formal quantitative analysis of the nature of the trade. This thesis represents a preliminary attempt to rectify that knowledge gap. It describes an investigation into the factors that support and promote the trade and is based upon information in two databases: CITES (the legal trade) and HealthMap (the illegal trade). The study 1) quantified the relationship between the illegal wildlife trade and several key factors thought to contribute to the illegal wildlife trade, namely road development, unemployment, and Corruption Perception Index (a score related to the perceived level of corruption); 2) measured the extent to which the product types, origins, destinations, and trade routes in the legal and the illegal wildlife trade are alike; and 3) identified locations to place resources to (a) restrict trade by causing the greatest network destabilization and (b) disseminate an educational message that would cause the greatest impact to the network. Several key factors and the legal trade were associated with the magnitude of various indices of the illegal trade at a country-level, but no generalizable findings can be asserted at this time. With regard to the best placement of regulatory resources, China was key with respect to network disruption and information dissemination targets. This thesis has begun the urgently needed analysis of the complex relationships of the illegal wildlife trade and identified specific ways to bring about change using network science. These findings offer hope for regulatory and enforcement agencies, NGOs, and governments that it will be possible to find more effective ways of combating the illegal wildlife trade and problems it brings with it. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Epidemiology & Biostatistics First Advisor Gary Smith
Playing with Emotions: A Systematic Review Examining Emotions and Emotion Regulation in Esports Performance

The massive growth of esports has vitalized the need to study human performance in competitive video gaming. The pressure of competitive play elicits a range of emotional experiences, which can affect players during and beyond a gaming session. In this work, we review the state of the literature concerning the role emotions play in esports performance as well as highlight coping strategies players use to regulate emotions during competitive play. We review the findings of N=32 peer-reviewed articles pertaining to emotions and esports, finding that the emotional experiences elicited by competitive play affect esports performance. In response, players attempt to regulate their emotions to maintain performance; however, efforts to do so vary, as they currently lack effective coping strategies. Lastly, we review the potential of technical interventions in esports training for improving emotion regulation among players. Our findings support knowledge development in esports, and present avenues towards promoting the emotional wellbeing of competitive gamers.
Identifying Impactful Pairs of Insertion Mutations in Proteins

Understanding how amino acid insertion or deletion (InDel) mutations affect a protein's structure can inform pharmaceutical efforts targeting diseases that are caused by protein mutants. In silico simulation of mutations can complement and inform wet lab experiments performed on physical proteins which are time and cost prohibitive. We have computationally generated the exhaustive sets of pairwise insertion amino acid mutations for three proteins, including the 99-residue Aspartyl Protease from the Human Immunodeficiency Virus type 1. To probe and identify which pairs of InDels are most impactful, we analyze a variety of structural metrics available for each mutant. We show that certain pairs of InDels have a far more pronounced effect on protein structure than do other pairs. Our approach has the potential to increase understanding of drug-target interactions and drug resistance for pharmaceutically important targets.
From Points to Progression: A Scoping Review of Game Elements in Gamification Research with a Content Analysis of 280 Research Papers

We lack a shared and detailed understanding in gamification of what game elements are. To address this, we provide a scoping review of the last five years of gamification research, focusing primarily on how game elements have been applied and characterized. We retrieved the definitions of game elements from 280 research papers, conducted a content analysis, and identified their features. On the basis of this information, we provide responses regarding the frequently cited game elements, whether they are consistently characterized in the literature, and the frequently stated features of these elements. Our research has identified 15 game elements in the literature, with points, badges, and leaderboards being the most prevalent. As a first step toward clear definitions, we suggest a set of properties to characterize these game elements. The results of our review contribute to the formation of a consensus among gamification scholars about the application and definition of game elements.
Working Together (to Undermine Democratic Institutions): Challenging the Social Bot Paradigm in SSIO Research

Unlike most other forms of coordinated, inauthentic behavior occurring online, the goals of state-sponsored information operations, or SSIOs, are often complex and multifaceted. These goals range from flooding conversations with a certain narrative, to increasing the public's engagement with news sources of questionable quality, to stoking tensions between ideologically opposed groups to weaken public trust. The prevailing theoretical framework for understanding SSIOs is to treat them as a social botnet: a behaviorally homogeneous cluster of coordinated activity. However, the social bot framework is both at odds with some of the behaviors observed in early SSIOs and more broadly with the wide swathe of goals these operations set out to accomplish. To examine the fit of the social bot framework in the SSIO context, we develop a novel bag-of-words based method for clustering and describing user activity traces. Applying this method to a comprehensive repository of SSIOs conducted on Twitter over the last decade, we find that SSIOs violate both the core assumption of the social bot framework, and how it is operationalized in practical work. Instead, we find that SSIOs exhibit a clear division of labor and propose cooperative work with social roles as a more effective theoretical framework for understanding SSIOs. Through applying this framework, we find that the roles that SSIO agents take on have become more stable and simple over time, which holds substantial implications for developing methods for detection of these operations in the wild.
Recent development of assessment tools for urban climate and heat‐island investigation especially based on experiences in Japan

In recent years, problems concerning the urban climate–including the urban heat‐island effect and urban air pollution—have attracted much attention in terms of the widespread urbanization in many parts of the world, including Japan. Assessment tools for urban climate are very important to understand effective countermeasures for heat‐island mitigation. In this paper, various assessment tools are introduced and classified according to corresponding modelling scales. Special attention is given to the recent progress in Japan. It is shown that these assessment tools are very powerful at estimating the effectiveness of countermeasures. Furthermore, these assessment tools should become design tools for practical applications in the future. Copyright © 2007 Royal Meteorological Society
'Don't Fall for This': Communications about Cybersafety from the AARP

Older adults face unique risks in trying to secure their online activities. They are not only the frequent targets of scams and fraud; they are the targets of a barrage of cybersafety communiqués whose impact is unclear. AARP, the United States advocacy group focusing on issues facing older adults over the age of 50, is among those educators whose strategies remain underexplored, yet their reach makes it imperative that we understand what they are saying, to whom, and to what effect. Drawing on an analysis of AARP publications about cybersafety and privacy, we sought to better understand their discourse on the topic. We report on findings that AARP's language may have the effect of portraying bad actors ("fraudsters") as individuals, rather than enterprises, which at the target end, personalizes interactions, placing too much onus on individual users to assess and deflect threats. AARP's positioning of, and guidance about, threats may sometimes prompt a thought process that puts users at the center of the narrative and may encourage engagement. Instructing older Americans, or anyone, on the forensics of cyber-sleuthing is enormously difficult. We conclude with a discussion of different approaches to cybersafety, one that involves educating older adults about the rudiments of surveillance capitalism.
Optimizing data integration improves Gene Regulatory Network inference in Arabidopsis thaliana

Motivations Gene Regulatory Networks (GRN) are traditionnally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. Results We address this issue for two common regression-based GRN inference models, an integrative Random Forest (weigthedRF) and a generalized linear model with stability selection estimated under a weighted LASSO penalty (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis for optimizing data integration strength in a hypothesis-driven, gene-specific manner. The resulting integration scheme reveals a strong diversity of optimal integration intensities between genes. In addition, it provides a good trade-off between prediction error minimization and validation on experimental interactions, while master regulators of nitrate induction can be accurately retrieved. Availability and implementation The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.
Marginalization and the Construction of Mental Illness Narratives Online: Foregrounding Institutions in Technology-Mediated Care

People experiencing mental illness are often forced into a system in which their chances of finding relief are largely determined by institutions that evaluate whether their distress deserves treatment. These governing institutions can be offline, such as the American healthcare system, and can also be online, such as online social platforms. As work in Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) frames technology-mediated support as one method to fill structural gaps in care, in this study, we ask the question: how do online and offline institutions influence how people in resource-scarce areas understand and express their distress online? We situate our work in U.S. Mental Health Professional Shortage Areas (MHPSAs), or areas in which there are too few mental health professionals to meet expected needs. We use an analysis of illness narratives to answer this question, conducting a large scale linguistic analysis of social media posts to understand broader trends in expressions of distress online. We then build on these analyses via in-depth interviews with 18 participants with lived experience of mental illness, analyzing the role of online and offline institutions in how participants express distress online. Through our findings, we argue that a consideration of institutions is crucial in designing effective technology-mediated support, and discuss the implications of considering institutions in mental health support for platform designers.