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.
In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a practically relevant situation in which additional covariates are required for identification while the reduced form and first-stage regressions implicitly restrict the effects of the instrument to be homogeneous, and are thus possibly misspecified. I show that the weights on some conditional LATEs are negative and the IV estimand is no longer interpretable as a causal effect under a weaker version of monotonicity, i.e. when there are compliers but no defiers at some covariate values and defiers but no compliers elsewhere. The problem of negative weights disappears in the overidentified specification of Angrist and Imbens (1995) and in an alternative method, termed “reordered IV,” that I also develop. Even if all weights are positive, the IV estimand in the just identified specification is not interpretable as the unconditional LATE parameter unless the groups with different values of the instrument are roughly equal sized. I illustrate my findings in an application to causal effects of college education using the college proximity instrument. The benchmark estimates suggest that college attendance yields earnings gains of about 60 log points, which is well outside the range of estimates in the recent literature. I demonstrate that this result is driven by the existence of defiers and the presence of negative weights. Corrected estimates indicate that attending college causes earnings to be roughly 20% higher.
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.
