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1. Microbial Community Profiling Protocol with Full‐length 16S rRNA Sequences and Emu

   https://doi.org/10.1002/cpz1.978  

   Curry, Kristen D.; Soriano, Sirena; Nute, Michael G.; Villapol, Sonia; Dilthey, Alexander; Treangen, Todd J. (March 2024, Current Protocols)

   Abstract 16S rRNA targeted amplicon sequencing is an established standard for elucidating microbial community composition. While high‐throughput short‐read sequencing can elicit only a portion of the 16S rRNA gene due to their limited read length, third generation sequencing can read the 16S rRNA gene in its entirety and thus provide more precise taxonomic classification. Here, we present a protocol for generating full‐length 16S rRNA sequences with Oxford Nanopore Technologies (ONT) and a microbial community profile with Emu. We select Emu for analyzing ONT sequences as it leverages information from the entire community to overcome errors due to incomplete reference databases and hardware limitations to ultimately obtain species‐level resolution. This pipeline provides a low‐cost solution for characterizing microbiome composition by exploiting real‐time, long‐read ONT sequencing and tailored software for accurate characterization of microbial communities. © 2024 Wiley Periodicals LLC. Basic Protocol: Microbial community profiling with Emu Support Protocol 1: Full‐length 16S rRNA microbial sequences with Oxford Nanopore Technologies sequencing platform Support Protocol 2: Building a custom reference database for Emu 

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2. Graph neural network-based water contamination detection from community housing information

   https://doi.org/10.3389/fenve.2025.1488965  

   Anaadumba, Raphael; Bozkurt, Yigit; Sullivan, Connor; Pagare, Madhavi; Kurup, Pradeep; Liu, Benyuan; Alam, Mohammad_Arif Ul (March 2025, Frontiers in Environmental Engineering)

   Introduction:Detecting water contamination in community housing is crucial for protecting public health. Early detection enables timely action to prevent waterborne diseases and ensures equitable access to safe drinking water. Traditional methods recommended by the Environmental Protection Agency (EPA) rely on collecting water samples and conducting lab tests, which can be both time-consuming and costly. Methods:To address these limitations, this study introduces a Graph Attention Network (GAT) to predict lead contamination in drinking water. The GAT model leverages publicly available municipal records and housing information to model interactions between homes and identify contamination patterns. Each house is represented as a node, and relationships between nodes are analyzed to provide a clearer understanding of contamination risks within the community. Results:Using data from Flint, Michigan, the model demonstrated higher performance compared to traditional methods. Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67. Discussion:In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. This approach offers a practical tool for policymakers and public health officials to assess and mitigate contamination risks, ultimately improving community health and safety. 

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3. Cultivating a culture of inclusivity in heliophysics

   https://doi.org/10.3389/fphy.2023.1061683  

   Halford, Alexa J.; Burrell, Angeline G.; Liemohn, Michael W.; Jones, McArthur; Maute, Astrid; Pulkkinen, Tuija I.; Bard, Christopher M.; McGranaghan, Ryan M.; Wilson, Lynn B.; Allen, Robert C.; et al (February 2023, Frontiers in Physics)

   A large number of heliophysicists from across career levels, institution types, and job titles came together to support a poster at Heliophysics 2050 and the position papers for the 2024 Heliophysics decadal survey titled “Cultivating a Culture of Inclusivity in Heliophysics,” “The Importance of Policies: It’s not just a pipeline problem,” and “Mentorship within Heliophysics.” While writing these position papers, the number of people who privately shareddisturbing stories and experiences of bullying and harassmentwas shocking. The number of people who privately expressed howburned outthey were was staggering. The number of people who privately spoke about how theyconsidered leaving the field for their and their family’s healthwas astounding. And for as much good there is in our community, it is still atoxic environmentfor many. If we fail to do something now, our field will continue to suffer. While acknowledging the ongoing growth that we as individuals must work toward, we call on our colleagues to join us in working on organizational, group, and personal levels toward a truly inclusive culture, for the wellbeing of our colleagues and the success of our field. This work includes policies, processes, and commitments to promote:accountabilityfor bad actors;financial securitythrough removing the constant anxiety about funding;prioritizationof mental health and community through removing constant deadlines and constant last-minute requests;a collaborative culturerather than a hyper-competitive one; anda community where people can thrive as whole personsand do not have to give up a healthy or well-rounded life to succeed. 

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4. Deconstructing Conceptions of Rigor in Computer Science Education

   https://doi.org/10.1145/3776542  

   Everson, Jayne; Kivuva, F Megumi; Sherif, Eman; Oleson, Alannah; Ko, Amy (November 2025, ACM Transactions on Computing Education)

   ObjectivesIn calls for excellent and equitable Computer Science (CS) education, the wordrigoroften appears, but it often goes undefined. The goal of this work is to understand how CS teachers, instructors, and students conceive of rigor. Research Questions:1) What do CS instructors think rigor is? and 2) What do students think rigor is? Methods:Using the principles of phenomenological research, we conducted a semi-structured interview study with 10 post-secondary CS students, 10 secondary CS teachers, and 9 post-secondary CS instructors, to understand their conceptions of rigor. Results:Analysis showed that no participants had the same understanding of rigor. We found that participants had abstractPrinciples of Rigorwhich included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concreteObservations of Rigorthat included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also sharedConditions for Rigorwhich included Expectations, Standards, Community Support, and Resources. Implications:Our data supports prior work that educators are using different definitions of rigor. This implies that each educator holds different expectations for students, without necessarily communicating these expectations to their students. In the best case, this might confuse students; in the worst case, it reinforces hegemonic norms which can lead to gatekeeping which prevents students from fully participating in the CS field. Based on these insights, we argue that to commit to the idea of quality CS learning, the community must discard the use of this concept of rigor to justify student learning and re-imagine alternate benchmarks. 

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5. The State of the Art in User‐Adaptive Visualizations

   https://doi.org/10.1111/cgf.15271  

   Yanez, Fernando; Conati, Cristina; Ottley, Alvitta; Nobre, Carolina (February 2025, Computer Graphics Forum)

   Abstract Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user‐adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub‐domains of the visualization community. A user‐adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user‐adaptive visualizations into five components: Capture ⒶInputfrom the user and any relevant peripheral information. Perform computational ⒷUser Modellingwith this input to construct a ⒸUser Representation. Employ ⒹAdaptation Assignmentlogic to identify when and how to introduce ⒺInterventions. Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work. 

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