Jurnal STAI Al-Hamidiyah
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Early elongation pausing in macrophages
No full textThis project documents the code that was used for RiboSeq analysis in “Translation initiation factor eIF3m regulates ubiquitination-dependent early elongation pausing of ribosomes” by Reitter et al. 2026
Responsible use of AI in evidence SynthEsis (RAISE): recommendations and guidance
No full text** If you would like to provide feedback on RAISE, please use this feedback form https://forms.office.com/e/EDYvBPEBrd **
The RAISE guidance is currently written in three documents (click 'Files' in the menu above). The first document, ‘RAISE 1’, provides tailored recommendations for eight of the distinct roles in the evidence synthesis ecosystem: evidence synthesists, methodologists, AI tool developments teams, organisations that produce evidence synthesis, publishers, funders, users, and trainers of evidence synthesis methods.
RAISE 2 contains guidance on building and evaluating AI evidence synthesis tools, which focuses on determining if an AI tool does what it claims to do to an acceptable standard, including how to build and validate AI tools, conduct evaluations to build a cumulative evidence base, including performance metrics to consider, and report evaluations.
The final document, RAISE 3, then offers guidance on selecting and using AI evidence synthesis tools, which focuses on understanding whether an AI tool can be used for a specific evidence synthesis, including how to assess, select and use an AI tool, including ethical, legal and regulatory considerations, and the current state of AI tools for evidence synthesis.
Please see the storage area for the RAISE documents. https://osf.io/fwaud/files/osfstorage
We invite feedback and thoughts on this work. Please use https://forms.office.com/e/EDYvBPEBrd to submit feedback on RAISE
Helping People in Times of Need Cultivates Empathy Over Time
No full textWhy is lower social class often associated with greater empathic concern? We test whether less wealthy people experience more challenges, thereby motivating them to help others as means of securing future social support, and, in turn, cultivating empathy over recurrent experiences. Study 1 (N = 513) showed wealth was negatively correlated with empathy through fewer challenges and less helping. Study 2 (N = 915) replicated this effect while revealing unexpected curvilinear associations. Among people helping less overall (measured monthly across one year), wealth led to lower empathy through fewer challenges and less helping. However, among people helping more overall, more helping led to decreasing empathy. Study 3 (N = 500) replicated the sequential indirect effect of wealth on empathic concern at low, but not high, helping in a two-wave preregistered study. Results suggest repeated acts of helping can cultivate empathic concern – but, only for those helping a moderate amount
Data-Driven Hierarchical Digital Twins of Social Interactions
No full textSocial interactions are inherently complex, shaped by dynamic trust building, biases, and adaptive strategies. Yet in laboratory settings, their study is often constrained by small datasets that nonetheless encode rich and sophisticated cognitive processes. This scarcity has historically limited modeling approaches to hand-tailored frameworks that embed strong priors about the underlying mechanisms. Recent advances in data-driven modeling of latent dynamical processes provide an alternative, extracting generative models directly from behavioral data without restrictive assumptions. Building on these methods, we derive hierarchical digital twins from sparse, non-Gaussian investment sequences in repeated trust games. Our approach proceeds in three stages. First, we demonstrate that, despite the limited data, the inferred twins can accurately predict future investments, establishing predictive validity and providing a solid baseline for subsequent mechanistic analyses. Second, we analyze the latent dynamics of these models and show that they capture mechanistic structure in how investments and choice uncertainty are organized in state space and how social versus non-social cues are represented, in a way that parallels patterns observed directly in the empirical behavior. Third, we exploit the generative nature of the hierarchical digital twins to run in-silico experiments beyond the original data, such as probing how specific cues steer participants into trusting or mistrusting states and simulating responses to novel, unobserved cue-outcome combinations and trustee strategies. Together, this work demonstrates how generative digital twins open a new path toward mechanistic, data-driven accounts of social interaction dynamics that both reproduce observed behavior and support hypothesis generation in virtual environments
The Divergent Trajectory of Environmental Politics between the United States and the United Kingdom
No full textThis study examines factors affecting the divergent environmental performances between the U.S. and the U.K. from the perspective of social movement. I look into domestic political factors, especially social movements causing different environmental performances in two countries that attain the high level of economic growth and are under the same international factors. Given that previous research dealing with domestic political factors tends to focus on political institutions and that the comparative research on the cases of the U.S. and the U.K. are limited, this research is relevant. I utilize the EPI scores, protest data, public opinion data and party manifestos. I conclude that the strong connection of environmental movement with political parties, less dispersive social movement agendas, favorable conditions from the EU and relatively weak anti-environmental movement led Britain to making a better environmental performance than the U.S
Retrieval Induced Forgetting to Augment Exposure
No full textInvestigating the use of retrieval-induced forgetting to strengthen inhibitory memories in exposur