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Differential privacy is a popular privacy-enhancing technology that has been deployed both by industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data subjects, which depend on the choice of deployment model. We design and evaluate new explanations of differential privacy for the local and central models, drawing inspiration from prior work explaining other privacy-enhancing technologies such as encryption. We reflect on the challenges in evaluating explanations and on the tradeoffs between qualitative and quantitative evaluation strategies. These reflections offer guidance for other researchers seeking to design and evaluate explanations of privacy-enhancing technologies. 

Novice programming students frequently engage in help-seeking to find information and learn about programming concepts. Among the available resources, generative AI (GenAI) chatbots appear resourceful, widely accessible, and less intimidating than human tutors. Programming instructors are actively integrating these tools into classrooms. However, our understanding of how novice programming students trust GenAI chatbots-and the factors influencing their usage-remains limited. To address this gap, we investigated the learning resource selection process of 20 novice programming students tasked with studying a programming topic. We split our participants into two groups: one using ChatGPT (n=10) and the other using a human tutor via Discord (n=10). We found that participants held strong positive perceptions of ChatGPT's speed and convenience but were wary of its inconsistent accuracy, making them reluctant to rely on it for learning entirely new topics. Accordingly, they generally preferred more trustworthy resources for learning (e.g., instructors, tutors), preferring ChatGPT for low-stakes situations or more introductory and common topics. We conclude by offering guidance to instructors on integrating LLM-based chatbots into their curricula-emphasizing verification and situational use-and to developers on designing chatbots that better address novices' trust and reliability concerns. 

Abstract Over the past decade, lead halide perovskites have gained significant interest for ionizing radiation detection, owing to their exceptional performance, and cost-effective fabrication in a wide range of form factors, from thick films to large single crystals. However, the toxicity of lead, limited environmental and thermal stability of these materials, as well as dark current drift due to ionic conductivity, have prompted the development of alternative materials that can address these challenges. Bismuth-based compounds (including perovskite derivatives and nonperovskite materials) have similarly high atomic numbers, leading to strong X-ray attenuation, but have lower toxicity, tend to be more environmentally stable, and can have lower ionic conductivity, especially in low-dimensional materials. These materials are also advantageous over commercial direct X-ray detectors by being able to detect lower dose rates of X-rays than amorphous selenium by at least two orders of magnitude, are potentially more cost-effective to mass produce than cadmium zinc telluride, and can operate at room temperature (unlike high-purity Ge). Given the strong interest in this area, we here discuss recent advances in the development of bismuth-based perovskite derivatives (with 3D, 2D and 0D structural dimensionality), and other bismuth-based perovskite-inspired materials for direct X-ray detection. We discuss the critical properties of these materials that underpin the strong performances achieved, particularly the ability to detect low-dose rates of X-rays. We cover key strategies for enhancing the performance of these materials, as well as the challenges that need to be overcome to commercialize these emerging technologies. Graphical abstract 

The practice of thermoforming plastics relies on understanding the effects of temperature. Although simulations can predict these effects with precise material and equipment parameters, they often fail to communicate experiential knowledge of how different materials and processes interact. Tactile feedback and visual cues are central to determining whether a material is malleable, a skill that simulations cannot replicate. Our work explores the use of a heat-sensitive spray-on smart material made from polydiacetylene (PDA) to improve heat perception. This sensor exhibits reversible colorimetric changes in response to temperature variations from 100ºC to 200ºC, acting as a visual cue perceivable by humans. This study evaluates the sensitivity, accuracy, and practicality of PDAs in real-time temperature monitoring during vacuum forming and acrylic bending. Our findings demonstrate that PDA based sensors enhance visibility of material dispersion, provide safeguards to critical temperatures, and illustrate heat flow and conductivity, thereby improving accessibility, literacy, and relationships with materials in thermoforming practices. 

ABSTRACT Quorum sensing (QS) is a population density-dependent mechanism of intercellular communication, whereby microbes secrete and detect signals to regulate behaviors such as virulence and biofilm formation. Although QS is well-studied in bacteria, little is known about cell-cell communication in archaea. The model archaeonHaloferax volcaniican transition from motile rod-shaped cells to non-motile disks as population density increases. In this report, we demonstrate that this transition is induced by a secreted small molecule present in cell-free conditioned medium (CM). The CM also elicits a response from a bacterial QS bioreporter, suggesting the potential for inter-domain crosstalk. To investigate theHfx. volcaniiQS response, we performed quantitative proteomics and detected significant differential abundances of 236 proteins in the presence of CM, including proteins involved in cell structure, motility, glycosylation, and two-component systems. We also demonstrate that a mutant lacking the cell shape regulatory factor DdfA does not undergo shape and motility transitions in the presence of CM, allowing us to identify protein abundance changes in the QS response pathway separate from those involved in shape and motility. In the ∆ddfAstrain, only 110 proteins had significant differential abundance, and comparative analysis of these two proteomics experiments enabled us to identify proteins dependent on and independent of DdfA in the QS response pathway. Our study provides the first detailed analysis of QS pathways in any archaeon, strengthening our understanding of archaeal communication as well as providing the framework for studying intra- and interdomain crosstalk. IMPORTANCEUnderstanding the complex signaling networks in microbial communities has led to many invaluable applications in medicine and industry. Yet, while archaea are ubiquitous and play key roles in nutrient cycling, little is known about the roles of archaeal intra- and interspecies cell-cell communication in environments such as the human, soil, and marine microbiomes. In this study, we established the first robust system for studying quorum sensing in archaea by using the model archaeonHaloferax volcanii. We demonstrated that different behaviors, such as cell shape and motility, are mediated by a signal molecule, and we uncovered key regulatory components of the signaling pathway. This work advances our understanding of microbial communication, shedding light on archaeal intra- and interdomain interactions, and contributes to a more complete picture of the interconnected networks of life on Earth. 

Abstract Human chondrocytes are responsible for cartilage repair and homeostasis through metabolic production of precursors to collagen and other matrix components. This metabolism is sensitive both to the availability of media energy sources as well as the local temperature. Central carbon metabolites such as glucose and glutamine are essential not only for producing energetic compounds such as ATP and NADH, but also for assembling collagen and aggrecan from non-essential amino acid precursors. The rate at which this metabolism takes place directly relates to temperature: a moderate increase in temperature results in faster enzyme kinetics and faster metabolic processes. Furthermore, these biological processes are exothermic and will generate heat as a byproduct, further heating the local environment of the cell. Prior studies suggest that mechanical stimuli affect levels of central metabolites in three-dimensionally cultured articular chondrocytes. But these prior studies have not determined if articular chondrocytes produce measurable heat. Thus,the goal of this studyis to determine if three-dimensionally encapsulated chondrocytes are capable of heat production which will improve our knowledge of chondrocyte central metabolism and further validate in vitro methods. Here we show the results of microcalorimetric measurements of heat generated by chondrocytes suspended in agarose hydrogels over a 2-day period in PBS, glucose, and glutamine media. The results show that a significant amount of heat is generated by cells (Cells Only: 3.033 ± 0.574 µJ/cell, Glucose: 2.791 ± 0.819 µJ/cell, Glutamine: 1.900 ± 0.650 µJ/cell) versus the absence of cells (No Cells: 0.374 ± 0.251 µJ/cell). This suggests that cells which have access to carbon sources in the media or as intracellular reserves will generate a significant amount of heat as they process these metabolites, produce cellular energy, and synthesize collagen precursors. The length of the microcalorimeter experiment (48 h) also suggests that the metabolism of articular chondrocytes is slower than many other cells, such as human melanoma cells, which can produce similar quantities of heat in less than an hour. These data broadly suggest that chondrocyte metabolism is sensitive to the available nutrients and has the potential to alter cartilage temperature through metabolic activity. 

The existence of organic pollutants in our environment is a growing concern. Many processes (e.g., textiles, painting, and printing) release waste effluents with organic pollutants (e.g., synthetic dyes) that harm aquatic systems. However, detecting and removing them efficiently and effectively is challenging. This study addressed this by developing a dual-functional plasmonic membrane using biowaste-derived nanocellulose for both detection and removal. The plasmonic nanomaterial was integrated with surface-enhanced Raman spectroscopy (SERS) to identify and quantify three organic pollutants (basic red 9, BR9; malachite green, MG; and methylene blue, MB). The nanocellulose removed these pollutants through electrostatic attraction. The organic pollutants were detected down to 0.05 mg/L, 0.25 mg/L, and 0.05 mg/L for BR9, MG, and MB, respectively; these concentrations are well below those considered to be environmentally hazardous. SERS analysis was performed in spiked streamwater samples to demonstrate detection in an environmentally relevant matrix. The nanomaterial was also used to remove the pollutants from aqueous matrices; removal efficiencies were 99.54 ± 0.16% for BR9, 99.50 ± 0.25% for MG, and 99.84 ± 0.10% for MB. For pollutant-spiked stream samples, removal efficiencies were 98.76 ± 1.26% for BR9, 97.50 ± 2.29% for MG, and 98.33 ± 1.59% for MB. This study demonstrates the high potential of this nanomaterial for the simultaneous detection and removal of organic contaminants, which provides the first example of using biowaste-derived functional nanomaterial for water testing and remediation concurrently. 

Large Language Model (LLM) conversational agents are increasingly used in programming education, yet we still lack insight into how novices engage with them for conceptual learning compared with human tutoring. This mixed‑methods study compared learning outcomes and interaction strategies of novices using ChatGPT or human tutors. A controlled lab study with 20 students enrolled in introductory programming courses revealed that students employ markedly different interaction strategies with AI versus human tutors: ChatGPT users relied on brief, zero‑shot prompts and received lengthy, context‑rich responses but showed minimal prompt refinement, while those working with human tutors provided more contextual information and received targeted explanations. Although students distrusted ChatGPT’s accuracy, they paradoxically preferred it for basic conceptual questions due to reduced social anxiety. We offer empirically grounded recommendations for developing AI literacy in computer science education and designing learning‑focused conversational agents that balance trust‑building with maintaining the social safety that facilitates uninhibited inquiry.