Butterfly scales contribute to a butterfly's vibrant coloration and play crucial roles in functions such as thermoregulation, water repellency, and aerodynamics. However, the underlying mechanisms that drive scale structure formation in vivo are not well understood. In this perspective, we propose that mechanical instabilities are central to the morphogenesis of scales and can lead to the observed wide variety of scale morphologies in adult butterflies. We specifically focus on the interplay between a growing soft compartment formed by the plasma membrane and an epicuticular envelope, the constraints imposed on this compartment by the actin cytoskeleton, and the spatio-temporally heterogeneous sclerotization of the cuticle precursors. We discuss hypotheses on how intracellular processes control the composition of the cuticle precursor secreted into soft compartments and how mechanical instabilities may lead to the morphological diversity of ridges, lamellae, and other scale structures. Putting forward a set of hypotheses about the fundamental mechanical processes that enable the secretion of non-living functional biological matter, we aim to inspire novel fabrication approaches in material science and engineering.
This study examines the inclusion-moderation thesis within the context of Israeli populism, focusing on how government participation influences the communication styles of populist legislators. By analyzing a comprehensive dataset of tweets from Israeli lawmakers between 2015 and 2022, we explore whether holding office leads to a moderation of populist rhetoric. Our findings indicate that while coalition members generally exhibit reduced populist communication, this moderation varies significantly between ministers and backbenchers. Most importantly, in populist radical-right parties (PRRPs) backbench coalition legislators do not moderate: they maintain a populist communication style akin to their opposition counterparts. This research contributes to the understanding of populism in a non-European context and highlights the complexities of integrating radical parties into democratic governance, suggesting that moderation is not uniformly achieved across party lines.
We examined the challenges of inclusive teaching during wartime through a case study of a high school attended by Jewish citizens of Israel (JCI) and Palestinian citizens of Israel (PCI) during the Israel-Gaza war. While inclusive education aims to promote belonging and equity for marginalized students, most research assumes peacetime and stable Western democratic contexts. Using a qualitative case study design, we examine how JCI teachers without inclusive education training navigate teaching PCI students. We find that teachers operated under a principal's directive to remain silent about PCI students' presence and avoid their Palestinian identities, prioritizing classroom calm and conflict avoidance. These practices contribute to institutional silence that may harm PCI students' well-being. At the same time, when teachers emphasized personal and professional relationships over national identity, this potentially enabled their work and the PCI students' presence at the school during the war, despite the unsupportive environment.
BACKGROUND:The use of generative large language models (LLMs) with electronic health record (EHR) data is rapidly expanding to support clinical and research tasks. This systematic review characterizes the clinical fields and use cases that have been studied and evaluated to date. METHODS:We followed the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to conduct a systematic review of articles from PubMed and Web of Science published between January 1, 2023, and November 9, 2024. Studies were included if they used generative LLMs to analyze real-world EHR data and reported quantitative performance evaluations. Through data extraction, we identified clinical specialties and tasks for each included article, and summarized evaluation methods. RESULTS:Of the 18 735 articles retrieved, 196 met our criteria. Most studies focused on radiology (26.0%), oncology (10.7%), and emergency medicine (6.6%). Regarding clinical tasks, clinical decision support made up the largest proportion of studies (62.2%), while summarizations and patient communications made up the smallest, at 5.6% and 5.1%, respectively. In addition, GPT-4 and GPT-3.5 were the most commonly used generative LLMs, appearing in 60.2% and 57.7% of studies, respectively. Across these studies, we identified 22 unique non-NLP metrics and 35 unique NLP metrics. While NLP metrics offer greater scalability, none demonstrated a strong correlation with gold-standard human evaluations. CONCLUSION:Our findings highlight the need to evaluate generative LLMs on EHR data across a broader range of clinical specialties and tasks, as well as the urgent need for standardized, scalable, and clinically meaningful evaluation frameworks.
