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Sensemaking is conceptualized as a trajectory to develop better understanding and is advocated as one of the fundamental practices in science education. However, the field is lacking of a framework to view the prolonged process of sensemaking that starts from a raise of uncertainty of a target phenomenon to a grasping of a better understanding of a target phenomenon. The process requires teachers to recognize the role of scientific uncertainty in different phases of sensemaking and develop responsive instructional supports to help students navigate the uncertainties. With an attention on student scientific uncertainty as a potential driver of the trajectory of sensemaking, this study aims to identify different phases of sensemaking that can be developed with students’ scientific uncertainty. This study especially attends to two types of scientific uncertainty—conceptual and epistemic uncertainties. Conceptual uncertainty refers to student struggle of using conceptual understanding (e.g., mastery of content and everyday knowledge) to respond to an encountered phenomenon. Epistemic uncertainty emerges from struggles in using epistemic understanding to generate new ideas. Based on the multiple case study method, we examined sensemaking activities in two Korean science classrooms and one American science classroom and identified three phases of sensemaking: (a) focusing on a driving question related to a target phenomenon, (b) delving into multiple resources to develop plausible explanation(s), and (c) examining the successfulness of the new understanding and concretizing it. Based on the findings, we discuss two emerging themes. First, sensemaking progresses through three distinctive phases driven by students’ dynamically evolving scientific uncertainty. Second, attending to both epistemic and conceptual uncertainties can support developing sensemaking coherent with students’ view. 

3D object detection (OD) is a crucial element in scene understanding. However, most existing 3D OD models have been tailored to work with light detection and ranging (LiDAR) and RGB-D point cloud data, leaving their performance on commonly available visual-inertial simultaneous localization and mapping (VI-SLAM) point clouds unexamined. In this paper, we create and release two datasets: VIP500, 4772 VI-SLAM point clouds covering 500 different object and environment configurations, and VIP500-D, an accompanying set of 20 RGB-D point clouds for the object classes and shapes in VIP500. We then use these datasets to quantify the differences between VI-SLAM point clouds and dense RGB-D point clouds, as well as the discrepancies between VI-SLAM point clouds generated with different object and environment characteristics. Finally, we evaluate the performance of three leading OD models on the diverse data in our VIP500 dataset, revealing the promise of OD models trained on VI-SLAM data; we examine the extent to which both object and environment characteristics impact performance, along with the underlying causes. 

Building a skilled cybersecurity workforce is paramount to building a safer digital world. However, the diverse skill set, constantly emerging vulnerabilities, and deployment of new cyber threats make learning cybersecurity challenging. Traditional education methods struggle to cope with cybersecurity's rapidly evolving landscape and keep students engaged and motivated. Different studies on students' behaviors show that an interactive mode of education by engaging through a question-answering system or dialoguing is one of the most effective learning methodologies. There is a strong need to create advanced AI-enabled education tools to promote interactive learning in cybersecurity. Unfortunately, there are no publicly available standard question-answer datasets to build such systems for students and novice learners to learn cybersecurity concepts, tools, and techniques. The education course material and online question banks are unstructured and need to be validated and updated by domain experts, which is tedious when done manually. In this paper, we propose CyberGen, a novel unification of large language models (LLMs) and knowledge graphs (KG) to generate the questions and answers for cybersecurity automatically. Augmenting the structured knowledge from knowledge graphs in prompts improves factual reasoning and reduces hallucinations in LLMs. We used the knowledge triples from cybersecurity knowledge graphs (AISecKG) to design prompts for ChatGPT and generate questions and answers using different prompting techniques. Our question-answer dataset, CyberQ, contains around 4k pairs of questions and answers. The domain expert manually evaluated the random samples for consistency and correctness. We train the generative model using the CyberQ dataset for question answering task.

 

Leaves of the carnivorous sundew plants (Droseraspp.) secrete mucilage that hosts microorganisms, but whether this microbiota contributes to prey digestion is unclear. We identified the acidophilic fungusAcrodontium crateriformeas the dominant species in the mucilage microbial communities, thriving in multiple sundew species across the global range. The fungus grows and sporulates on sundew glands as its preferred acidic environment, and its presence in traps increased the prey digestion process.A. crateriformehas a reduced genome similar to other symbiotic fungi. DuringA. crateriforme–Drosera spatulatacoexistence and digestion of prey insects, transcriptomes revealed significant gene co-option in both partners. Holobiont expression patterns during prey digestion further revealed synergistic effects in several gene families including fungal aspartic and sedolisin peptidases, facilitating prey digestion in leaves, as well as nutrient assimilation and jasmonate signalling pathway expression. This study establishes that botanical carnivory is defined by adaptations involving microbial partners and interspecies interactions.

 

Candidate bacterial phylum CSP1-3 has not been cultivated and is poorly understood. Here, we analyzed 112 CSP1-3 metagenome-assembled genomes and showed they are likely facultative anaerobes, with 3 of 5 families encoding autotrophy through the reductive glycine pathway (RGP), Wood–Ljungdahl pathway (WLP) or Calvin-Benson-Bassham (CBB), with hydrogen or sulfide as electron donors. Chemoautotrophic enrichments from hot spring sediments and fluorescence in situ hybridization revealed enrichment of six CSP1-3 genera, and both transcribed genes and DNA-stable isotope probing were consistent with proposed chemoautotrophic metabolisms. Ancestral state reconstructions showed that the ancestors of phylum CSP1-3 may have been acetogens that were autotrophic via the RGP, whereas the WLP and CBB were acquired by horizontal gene transfer. Our results reveal that CSP1-3 is a widely distributed phylum with the potential to contribute to the cycling of carbon, sulfur and nitrogen. The name Sysuimicrobiota phy. nov. is proposed.