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Abstract Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control inAplysia californica. Using the Synthetic Nervous System framework, we developed a model ofAplysiafeeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding. 

Abstract Opposed-flow flame spread over solid materials has been investigated in the past few decades owing to its importance in fundamental understanding of fires. These studies provided insights on the behavior of opposed-flow flames in different environmental conditions (e.g., flow speed, oxygen concentration). However, the effect of confinement on opposed-flow flames remains under-explored. It is known that confinement plays a critical role in concurrent-flow flame spread in normal and microgravity conditions. Hence, for a complete understanding it becomes important to understand the effects of confinement for opposed-flow flames. In this study, microgravity experiments are conducted aboard the International Space Station (ISS) to investigate opposed-flow flame spread in different confined conditions. Two materials, cotton-fiberglass blended textile fabric (SIBAL) and 1 mm thick polymethyl methacrylate (PMMA) slab are burned between a pair of parallel flow baffles in a small flow duct. By varying the sample-baffle distance, various levels of confinement are achieved (H = 1–2 cm). Three types of baffles, transparent, black, and reflective, are used to create different radiative boundary conditions. The purely forced flow speed is also varied (between 2.6 and 10.5 cm/s) to investigate its interplay with the confinement level. For both sample materials, it is observed that the flame spread rate decreases when the confinement level increases (i.e., when H decreases). In addition, flame spread rate is shown to have a positive correlation with flow speed, up to an optimal value. The results also indicate that the optimal flow speed for flame spread can decrease in highly confined conditions. Surface radiation on the confinement boundary is shown to play a key role. For SIBAL fabric, stronger flames are observed when using black baffles compared to transparent. For PMMA, reflective baffles yield stronger flames compared to black baffles. When comparing the results to the concurrent-flow case, it is also noticed that opposed-flow flames spread slower and blow off at larger flow speeds but are not as sensitive to the flow speed. This work provides unique long-duration microgravity experimental data that can inform the design of future opposed-flow experiments in microgravity and the development of theory and numerical models. 

Abstract Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field. 

Abstract BackgroundRNA secondary structure (RSS) can influence the regulation of transcription, RNA processing, and protein synthesis, among other processes. 3′ untranslated regions (3′ UTRs) of mRNA also hold the key for many aspects of gene regulation. However, there are often contradictory results regarding the roles of RSS in 3′ UTRs in gene expression in different organisms and/or contexts. ResultsHere, we incidentally observe that the primary substrate of miR159a (pri-miR159a), when embedded in a 3′ UTR, could promote mRNA accumulation. The enhanced expression is attributed to the earlier polyadenylation of the transcript within the hybrid pri-miR159a-3′ UTR and, resultantly, a poorly structured 3′ UTR. RNA decay assays indicate that poorly structured 3′ UTRs could promote mRNA stability, whereas highly structured 3′ UTRs destabilize mRNA in vivo. Genome-wide DMS-MaPseq also reveals the prevailing inverse relationship between 3′ UTRs’ RSS and transcript accumulation in the transcriptomes ofArabidopsis, rice, and even human. Mechanistically, transcripts with highly structured 3′ UTRs are preferentially degraded by 3′–5′ exoribonuclease SOV and 5′–3′ exoribonuclease XRN4, leading to decreased expression inArabidopsis. Finally, we engineer different structured 3′ UTRs to an endogenousFTgene and alter theFT-regulated flowering time inArabidopsis. ConclusionsWe conclude that highly structured 3′ UTRs typically cause reduced accumulation of the harbored transcripts inArabidopsis. This pattern extends to rice and even mammals. Furthermore, our study provides a new strategy of engineering the 3′ UTRs’ RSS to modify plant traits in agricultural production and mRNA stability in biotechnology.