Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.Comment: 14 pages, 4 figures, 5 table

The Kidney Transplant Evaluation Process in the Elderly: Reasons for Being Turned down and Opportunities to Improve Cost-Effectiveness in a Single Center

Background. The kidney transplant evaluation process for older candidates is complex due to the presence of multiple comorbid conditions. Methods. We retrospectively reviewed patients ≥60 years referred to our center for kidney transplantation over a 3-year period. Variables were collected to identify reasons for patients being turned down and to determine the number of unnecessary tests performed. Statistical analysis was performed to estimate the association between clinical predictors and listing status. Results. 345 patients were included in the statistical analysis. 31.6% of patients were turned down: 44% due to coronary artery disease (CAD), peripheral vascular disease (PVD), or both. After adjustment for patient demographics and comorbid conditions, history of CAD, PVD, or both (OR = 1.75, 95% CI (1.20, 2.56), p=0.004), chronic obstructive pulmonary disease (OR = 8.75, 95% CI (2.81, 27.20), p=0.0002), and cancer (OR 2.59, 95% CI (1.18, 5.67), p=0.02) were associated with a higher risk of being turned down. 14.8% of patients underwent unnecessary basic testing and 9.6% underwent unnecessary supplementary testing with the charges over a 3-year period estimated at $304,337. Conclusion. A significant number of older candidates are deemed unacceptable for kidney transplantation with primary reasons cited as CAD and PVD. The overall burden of unnecessary testing is substantial and potentially avoidable

Why do semi-analytic models predict higher scatter in the stellar mass-halo mass relation than cosmological hydrodynamic simulations?

Semi-analytic models (SAMs) systematically predict higher stellar-mass scatter at a given halo mass than hydrodynamical simulations and most empirical models. Our goal is to investigate the physical origin of this scatter by exploring modifications to the physics in the SAM Dark Sage. We design two black hole formation models that approximate results from the IllustrisTNG 300-1 hydrodynamical simulation. In the first model, we assign a fixed black hole mass of $10^{6}\, \mathrm{M}_{\odot}$ to every halo that reaches $10^{10.5}\, \mathrm{M}_{\odot}$. In the second model, we disregard any black hole growth as implemented in the standard Dark Sage model. Instead, we force all black hole masses to follow the median black hole mass-halo mass relation in IllustrisTNG 300-1 with a fixed scatter. We find that each model on its own does not significantly reduce the scatter in stellar mass. To do this, we replace the native Dark Sage AGN feedback model with a simple model where we turn off cooling for galaxies with black hole masses above $10^{8}\, \mathrm{M}_{\odot}$. With this additional modification, the SMBH seeding and fixed conditional distribution models find a significant reduction in the scatter in stellar mass at halo masses between $10^{11-14}\, \mathrm{M}_{\odot}$. These results suggest that AGN feedback in SAMs acts in a qualitatively different way than feedback implemented in cosmological simulations. Either or both may require substantial modification to match the empirically inferred scatter in the Stellar Mass Halo Mass Relation (SMHMR).Comment: 21 pages, 16 figure

Code Comparison in Galaxy Scale Simulations with Resolved Supernova Feedback: Lagrangian vs. Eulerian Methods

We present a suite of high-resolution simulations of an isolated dwarf galaxy using four different hydrodynamical codes: {\sc Gizmo}, {\sc Arepo}, {\sc Gadget}, and {\sc Ramses}. All codes adopt the same physical model which includes radiative cooling, photoelectric heating, star formation, and supernova (SN) feedback. Individual SN explosions are directly resolved without resorting to sub-grid models, eliminating one of the major uncertainties in cosmological simulations. We find reasonable agreement on the time-averaged star formation rates as well as the joint density-temperature distributions between all codes. However, the Lagrangian codes show significantly burstier star formation, larger supernova-driven bubbles, and stronger galactic outflows compared to the Eulerian code. This is caused by the behavior in the dense, collapsing gas clouds when the Jeans length becomes unresolved: gas in Lagrangian codes collapses to much higher densities than in Eulerian codes, as the latter is stabilized by the minimal cell size. Therefore, more of the gas cloud is converted to stars and SNe are much more clustered in the Lagrangian models, amplifying their dynamical impact. The differences between Lagrangian and Eulerian codes can be reduced by adopting a higher star formation efficiency in Eulerian codes, which significantly enhances SN clustering in the latter. Adopting a zero SN delay time reduces burstiness in all codes, resulting in vanishing outflows as SN clustering is suppressed.Comment: accepted version by ApJ (including a new simulation in Appendix B suggested by the referee

The Diversity and Variability of Star Formation Histories in Models of Galaxy Evolution

Understanding the variability of galaxy star formation histories (SFHs) across a range of timescales provides insight into the underlying physical processes that regulate star formation within galaxies. We compile the SFHs of galaxies at $z=0$ from an extensive set of models, ranging from cosmological hydrodynamical simulations (Illustris, IllustrisTNG, Mufasa, Simba, EAGLE), zoom simulations (FIRE-2, g14, and Marvel/Justice League), semi-analytic models (Santa Cruz SAM) and empirical models (UniverseMachine), and quantify the variability of these SFHs on different timescales using the power spectral density (PSD) formalism. We find that the PSDs are well described by broken power-laws, and variability on long timescales ($\gtrsim1$ Gyr) accounts for most of the power in galaxy SFHs. Most hydrodynamical models show increased variability on shorter timescales ($\lesssim300$ Myr) with decreasing stellar mass. Quenching can induce $\sim0.4-1$ dex of additional power on timescales $>1$ Gyr. The dark matter accretion histories of galaxies have remarkably self-similar PSDs and are coherent with the in-situ star formation on timescales $>3$ Gyr. There is considerable diversity among the different models in their (i) power due to SFR variability at a given timescale, (ii) amount of correlation with adjacent timescales (PSD slope), (iii) evolution of median PSDs with stellar mass, and (iv) presence and locations of breaks in the PSDs. The PSD framework is a useful space to study the SFHs of galaxies since model predictions vary widely. Observational constraints in this space will help constrain the relative strengths of the physical processes responsible for this variability.Comment: 31 pages, 17 figures (+ appendix). Resubmitted to MNRAS after responding to referee's comments. Comments are welcome

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer

Summary: Thousands of cancer exomes are currently being sequenced, yielding millions of non-synonymous single nucleotide variants (SNVs) of possible relevance to disease etiology. Here, we provide a software toolkit to prioritize SNVs based on their predicted contribution to tumorigenesis. It includes a database of precomputed, predictive features covering all positions in the annotated human exome and can be used either stand-alone or as part of a larger variant discovery pipeline

Small CCI – Exploring App Evaluation with Preschoolers

Child-Computer Interaction (CCI) is predominantly studied with school aged children. Working with preschool children, generally unable to read or write, involves addressing many challenges around planning, recruitment, and interpretation of findings. There are few examples in the literature of the challenges faced when conducting evaluations of technology with preschool children and very few evaluations conducted for commercial software companies. Our case study paper describes a six-week, twelve session, evaluation study of a commercial app (Lingokids) with children aged three and four in two nursery (preschool / kindergarten) schools. We describe challenges we met and describe how we adapted our plans to fit the context. We show how we were able to explore engagement and learning without gathering any personal data. With our practical tips and reflections, we hope our work will encourage others to work with young children in ways that respect their limited ability to understand assent and participation