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Childhood is marked by the rapid accumulation of knowledge and the prolific production of drawings. We conducted a systematic study of how children create and recognize line drawings of visual concepts. We recruited 2-10-year-olds to draw 48 categories via a kiosk at a children’s museum, resulting in >37K drawings. We analyze changes in the category-diagnostic information in these drawings using vision algorithms and annotations of object parts. We find developmental gains in children’s inclusion of category-diagnostic information that are not reducible to variation in visuomotor control or effort. Moreover, even unrecognizable drawings contain information about the animacy and size of the category children tried to draw. Using guessing games at the same kiosk, we find that children improve across childhood at recognizing each other’s line drawings. This work leverages vision algorithms to characterize developmental changes in children’s drawings and suggests that these changes reflect refinements in children’s internal representations. Children produce drawings prolifically throughout childhood. Here, the authors conducted a systematic study of how children create and recognize line drawings across development and suggest that changes in children’s drawings reflect refinements in how children represent visual concepts.

Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models significantly enhance the efficiency of biomolecular analysis and synthesis, attracting widespread attention from academics and medicine. In this paper, we systematically investigate the application of prompt-based methods with LLMs to biological sequences, including DNA, RNA, proteins, and drug discovery tasks. Specifically, we focus on how prompt engineering enables LLMs to tackle domain-specific problems, such as promoter sequence prediction, protein structure modeling, and drug-target binding affinity prediction, often with limited labeled data. Furthermore, our discussion highlights the transformative potential of prompting in bioinformatics while addressing key challenges such as data scarcity, multimodal fusion, and computational resource limitations. Our aim is for this paper to function both as a foundational primer for newcomers and a catalyst for continued innovation within this dynamic field of study.

Investigating structural and functional dynamics across large spatial scales in living organisms calls for volumetric microscopy that combines a wide field of view (FOV), high spatial resolution, and sufficient temporal resolution. Swept confocally-aligned planar excitation (SCAPE) microscopy offers a compelling balance of optical sectioning, cellular resolution, and high-speed volumetric imaging. However, extending SCAPE to mesoscopic scales has been hindered by the limited tilting angle of the oblique illumination light sheet achievable with commercial low-magnification objectives. Here, we present a mesoscopic SCAPE microscope that overcomes this constraint by implementing a super-oblique light sheet—tilted up to ~60° relative to the optical axis of a 0.5-NA primary objective—enabled by an air-to-water bridging reflector. In addition, we introduce a single-galvanometer, self-conjugating rescanning strategy that allows arbitrary customization of the optical-to-mechanical angle ratio. This innovation accommodates the reversed and modulated scan displacement of the reflected light sheet and preserves descanned epi-fluorescence detection. Together, these advancements enable our mesoscopic SCAPE microscope to achieve a lateral FOV of 5.0 mm × 2.9 mm, cellular-level spatial resolution (~9.4 μm × ~5.9 μm lateral, and ~6.0 μm axial), and a volumetric imaging rate of 9 volumes per second.