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Generative artificial intelligence (GenAI) systems introduce new possibilities for enhancing professionals’ workflows, enabling novel forms of human–AI co-creation. However, professionals often strug- gle to learn to work with GenAI systems effectively. While research has begun to explore the design of interfaces that support users in learning to co-create with GenAI, we lack systematic approaches to investigate the effectiveness of these supports. In this paper, we present a systematic approach for studying how to support learn- ing to co-create with GenAI systems, informed by methods and concepts from the learning sciences. Through an experimental case study, we demonstrate how our approach can be used to study and compare the impacts of different types of learning supports in the context of text-to-image GenAI models. Reflecting on these results, we discuss directions for future work aimed at improving interfaces for human–AI co-creation. 

ABSTRACT Trypanosoma brucei , the causative agent of human and animal African trypanosomiasis, cycles between a mammalian host and a tsetse fly vector. The parasite undergoes huge changes in morphology and metabolism during adaptation to each host environment. These changes are reflected in the different transcriptomes of parasites living in each host. However, it remains unclear whether chromatin-interacting proteins help mediate these changes. Bromodomain proteins localize to transcription start sites in bloodstream parasites, but whether the localization of bromodomain proteins changes as parasites differentiate from bloodstream to insect stages remains unknown. To address this question, we performed cleavage under target and release using nuclease (CUT&RUN) against bromodomain protein 3 (Bdf3) in parasites differentiating from bloodstream to insect forms. We found that Bdf3 occupancy at most loci increased at 3 h following onset of differentiation and decreased thereafter. A number of sites with increased bromodomain protein occupancy lie proximal to genes with altered transcript levels during differentiation, such as procyclins, procyclin-associated genes, and invariant surface glycoproteins. Most Bdf3-occupied sites are observed throughout differentiation. However, one site appears de novo during differentiation and lies proximal to the procyclin gene locus housing genes essential for remodeling surface proteins following transition to the insect stage. These studies indicate that occupancy of chromatin-interacting proteins is dynamic during life cycle stage transitions and provide the groundwork for future studies on the effects of changes in bromodomain protein occupancy. Additionally, the adaptation of CUT&RUN for Trypanosoma brucei provides other researchers with an alternative to chromatin immunoprecipitation (ChIP). IMPORTANCE The parasite Trypanosoma brucei is the causative agent of human and animal African trypanosomiasis (sleeping sickness). Trypanosomiasis, which affects humans and cattle, is fatal if untreated. Existing drugs have significant side effects. Thus, these parasites impose a significant human and economic burden in sub-Saharan Africa, where trypanosomiasis is endemic. T. brucei cycles between the mammalian host and a tsetse fly vector, and parasites undergo huge changes in morphology and metabolism to adapt to different hosts. Here, we show that DNA-interacting bromodomain protein 3 (Bdf3) shows changes in occupancy at its binding sites as parasites transition from the bloodstream to the insect stage. Additionally, a new binding site appears near the locus responsible for remodeling of parasite surface proteins during transition to the insect stage. Understanding the mechanisms behind host adaptation is important for understanding the life cycle of the parasite. 

Abstract Active biofluid management is central to the realization of wearable bioanalytical platforms that are poised to autonomously provide frequent, real-time, and accurate measures of biomarkers in epidermally-retrievable biofluids (e.g., sweat). Accordingly, here, a programmable epidermal microfluidic valving system is devised, which is capable of biofluid sampling, routing, and compartmentalization for biomarker analysis. At its core, the system is a network of individually-addressable microheater-controlled thermo-responsive hydrogel valves, augmented with a pressure regulation mechanism to accommodate pressure built-up, when interfacing sweat glands. The active biofluid control achieved by this system is harnessed to create unprecedented wearable bioanalytical capabilities at both the sensor level (decoupling the confounding influence of flow rate variability on sensor response) and the system level (facilitating context-based sensor selection/protection). Through integration with a wireless flexible printed circuit board and seamless bilateral communication with consumer electronics (e.g., smartwatch), contextually-relevant (scheduled/on-demand) on-body biomarker data acquisition/display was achieved.