AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals

As sequenced genomes become larger and sequencing process becomes faster, there is a need to develop a tool to analyze sequences in the whole genomic scale. However, on-memory algorithms such as suffix tree and suffix array are not applicable to the analysis of whole genome sequence set, since the size of individual whole genome ranges from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench called SequeX for the analysis and visualization of whole genome sequences using SSB-tree (Static SB-tree). It consists of two parts: the analysis query subsystem and the visualization subsystem. The query subsystem supports various transactions such as pattern matching, k-occurrence, and k-mer analysis. The visualization subsystem helps biologists to easily understand whole genome structure and feature by sequence viewer, annotation viewer, CGR (Chaos Game Representation) viewer, and k-mer viewer. The system also supports a user-friendly programming interface based on Java script for batch processing and the extension for a specific purpose of a user. SequeX can be used to identify conserved genes or sequences by the analysis of the common k-mers and annotation. We analyze the common k-mer for 72 microbial genomes announced by Entrez, and find an interesting biological fact that the longest common k-mer for 72 sequences is 11-mer, and only 11 such sequences exist. Finally we note that many common k-mers occur in conserved region such as CDS, rRNA, and tRNA.

This paper reports preliminary findings from an ongoing, campus wide research project on effective methods for generative AI applicability in pursuit of effective and engaging teaching and learning activities. Generative AI has had a tremendous adoption rate since the public release of ChatGPT 3.5 on November 30th 2022. This has necessitated that educators and administrators consider the potential opportunities and threats usage of generative AI by students and faculty may have on higher education. Recognizing the inevitability of generative AI, the researchers have proposed a university-wide research project to ascertain the changes in faculty and students perspectives when using generative AI The research project is two-fold. First, a longitudinal survey has been developed to address research questions about usage and perceptions of generative AI change over time. The second prong of this research project focuses on the implementation of new and continuing generative AI professional development workshops. These “AI Institutes” are targeted educational opportunities to provide faculty, staff, and students with hands-on experiences that model appropriate ways to teach and learn with generative AI tools. Workshops change based on audience needs, but will be designed to support such processes as introductory and advanced lessons on building learning activities which engage students with generative AI, administrative shortcuts, best practices for writing, and our university’s AI policy and principles. The longitudinal survey, thus, allows the research team to gauge changes in perspectives as the “AI Institutes'' are deployed and widespread adoption of generative AI tools become more mainstream. This paper reports on the first year of this research project, including one survey and one AI Institute. This research on integrating generative AI technologies into teaching and learning has important implications for the field of networked learning. As the paper explores, rapid advances in AI are changing how students and faculty interact with content and each other. Findings from the longitudinal survey and AI Institutes could provide insights into how to thoughtfully leverage these emerging tools to enhance connections, dialogue, collaboration, and co-creation of knowledge within digital learning networks. While further research is needed, this project takes an important first step in assessing faculty and student perceptions that can inform appropriate AI integration. Lessons learned could guide other institutions exploring the potentials and pitfalls of weaving generative AI into networked learning ecosystems.

The rapid evolution of large-language models such as ChatGPT, Claude, and Gemini is reshaping the methodological landscape of obstetrics and gynecology (OBGYN) research. This narrative review provides a comprehensive account of generative AI capabilities, key use cases, and recommended safeguards for investigators. First, generative AI expedites hypothesis generation, enabling researchers to interrogate vast corpora and surface plausible, overlooked questions. Second, it streamlines systematic reviews by composing optimized search strings, screening titles and abstracts, and identifying full-text discrepancies. Third, AI assistants can draft reproducible analytic code, perform preliminary descriptive or inferential analyses, and create publication-ready tables and figures. Fourth, the models support scholarly writing by suggesting journal-specific headings, refining prose, harmonizing references, and translating technical content for multidisciplinary audiences. Fifth, they augment peer-review and editorial workflows by delivering evidence-focused critiques. In educational settings, these models can create adaptive curricula and interactive simulations for trainees, fostering digital literacy and evidence-based practice early in professional development among clinicians. Integration into clinical decision-support pipelines is also foreseeable, warranting proactive governance. Notwithstanding these opportunities, responsible use demands vigilant oversight. Large-language models occasionally fabricate citations or misinterpret domain-specific data ("hallucinations"), potentially propagating misinformation. Outputs are highly prompt-dependent, creating a reliance on informed prompt engineering that may disadvantage less technical clinicians. Moreover, uploading protected health information or copyrighted text raises privacy, security, and intellectual property concerns. We outline best-practice recommendations: maintain human verification of all AI-generated content; cross-validate references with primary databases; employ privacy-preserving, on-premises deployments for sensitive data; document prompts for reproducibility; and disclose AI involvement transparently. In summary, generative AI offers a powerful adjunct for OBGYN scientists by accelerating topic formulation, evidence synthesis, data analysis, manuscript preparation, and peer review. When coupled with rigorous oversight and ethical safeguards, these tools can enhance productivity without compromising scientific integrity. Future studies should quantify accuracy, bias, and downstream patient impact. · Generative AI supports various research stages in OBGYN, such as hypothesis generation, systematic review assistance, data analysis, and scientific writing, demonstrating its potential to streamline research workflows and improve research efficiency.. · Generative AI has notable limitations, including the risk of generating inaccurate references ("hallucinations") and the need for careful supervision.. · Effective usage requires skill in prompt engineering, posing a challenge for those without technical expertise.. · Utilizing generative AI in sensitive fields like OBGYN raises privacy, security, and ethical concerns..

This paper presents the key results of a large-scale empirical study on the adoption of Generative AI (GenAI) by the Joint Research Centre (JRC), the European Commission’s science-for-policy department. Since spring 2023, the JRC has developed and deployed GPT@JRC, a platform providing safe and compliant access to state-of-the-art Large Language Models for over 10,000 knowledge workers. While the literature highlighting the potential of GenAI to enhance productivity for knowledge-intensive tasks is abundant, there is a scarcity of empirical evidence on impactful use case types and success factors. This study addresses this gap and proposes the JRC GenAI Compass conceptual framework based on the lessons learnt from the JRC’s GenAI adoption journey. It includes the concept of AI-IQ, which reflects the complexity of a given GenAI system. This paper thus draws on a case study of enterprise-scale AI implementation in European public institutions to provide approaches to harness GenAI’s potential while mitigating the risks.

Monitoring of minimal residual disease in early T‐cell precursor acute lymphoblastic leukaemia by next‐generation sequencing

In this article, we explore the integration of generative AI systems into knowledge work, focusing on how these systems augment key information practices. Drawing on a mixed-method approach that includes digital diary studies and interviews, our human-centred exploration of interactions between knowledge workers and generative AI identifies the roles these systems play in complementing workers’ contributions to information representation, generation, evaluation, explanation, and communication. We find that generative AI systems are increasingly taking on augmentative roles such as synthesiser, organiser, brainstormer, reviewer, interpreter, and clarifier. Our findings also highlight the active role of knowledge workers, revealing a dynamic human – AI partnership that improves the efficiency of knowledge work, fosters creativity, and supports decision-making processes. This partnership not only leverages generative AI’s capabilities to augment information practices but also amplifies and draws upon distinct human expertise in critiquing, integrating, contextualising, communicating, and individualising AI-generated outputs.

Investigation of a Serratia Marcescens bacteremia Cluster in a Cardiothoracic Intensive Care Unit (CICU) Utilizing Whole Genome Sequencing

The effect of second-generation pneumococcal conjugate vaccines on invasive pneumococcal disease (IPD) strain distributions have not yet been well described. We analysed IPD isolates recovered from children aged <5 years through Active Bacterial Core surveillance before (2008–2009; n = 828) and after (2011–2013; n = 600) 13-valent pneumococcal conjugate vaccine (PCV13) implementation. We employed conventional testing, PCR/electrospray ionization mass spectrometry and whole genome sequence (WGS) analysis to identify serotypes, resistance features, genotypes, and pilus types. PCV13, licensed in February 2010, effectively targeted all major 19A and 7F genotypes, and decreased antimicrobial resistance, primarily owing to removal of the 19A/ST320 complex. The strain complex contributing most to the remaining β-lactam resistance during 2011–2013 was 35B/ST558. Significant emergence of non-vaccine clonal complexes was not evident. Because of the removal of vaccine serotype strains, positivity for one or both pilus types (PI-1 and PI-2) decreased in the post-PCV13 years 2011–2013 relative to 2008–2009 (decreases of 32–55% for PI-1, and >95% for PI-2 and combined PI-1 + PI-2). β-Lactam susceptibility phenotypes correlated consistently with transpeptidase region sequence combinations of the three major penicillin-binding proteins (PBPs) determined through WGS analysis. Other major resistance features were predictable by DNA signatures from WGS analysis. Multilocus sequence data combined with PBP combinations identified progeny, serotype donors and recipient strains in serotype switch events. PCV13 decreased the frequency of all PCV13 serotype clones and concurrently decreased the frequency of strain subsets with resistance and/or adherence features conducive to successful carriage. Our results serve as a reference describing key features of current paediatric IPD strains in the USA after PCV13 implementation.

Introduction: In April and May 2014, two suspected egg-related outbreaks of Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) were investigated by the Belgian National Reference Laboratory of Foodborne Outbreaks. Both the suspected food and human isolates being available, and this for both outbreaks, made these the ideal case study for a retrospective whole genome sequencing (WGS) analysis with the goal to investigate the feasibility of this technology for outbreak investigation by a National Reference Laboratory or National Reference Centre without thorough bioinformatics expertise.Methods: The two outbreaks were originally investigated epidemiologically with a standard questionnaire and with serotyping, phage typing, multiple-locus variable-number of tandem repeats analysis (MLVA) and antimicrobial susceptibility testing as classical microbiological methods. Retrospectively, WGS of six outbreak isolates was done on an Illumina HiSeq. Analysis of the WGS data was performed with currently available, user-friendly software and tools, namely CLC Genomics Workbench, the tools available on the server of the Center for Genomic Epidemiology and BLAST Ring Image Generator (BRIG).Results: To all collected human and food outbreak isolates, classical microbiological investigation assigned phage type PT4 (variant phage type PT4a for one human isolate) and MLVA profile 3-10-5-4-1, both of which are common for human isolates in Belgium. The WGS analysis confirmed the link between food and human isolates for each of the outbreaks and clearly discriminated between the two outbreaks occurring in a same time period, thereby suggesting a non-common source of contamination. Also, an additional plasmid carrying an antibiotic resistance gene was discovered in the human isolate with the variant phage type PT4a.Discussion: For the two investigated outbreaks occurring at geographically separated locations, the gold standard classical microbiological subtyping methods were not sufficiently discriminative to distinguish between or assign a common origin of contamination for the two outbreaks, while WGS analysis could do so. This case study demonstrated the added value of WGS for outbreak investigations by confirming and/or discriminating food and human isolates between and within outbreaks. It also proved the feasibility of WGS as complementary or even future replacing (sub)typing method for the average routine laboratory.

Understanding Mycobacterium tuberculosis (Mtb) transmission is essential to guide efficient tuberculosis control strategies. Traditional strain typing lacks sufficient discriminatory power to resolve large outbreaks. Here, we tested the potential of using next generation genome sequencing for identification of outbreak-related transmission chains. During long-term (1997 to 2010) prospective population-based molecular epidemiological surveillance comprising a total of 2,301 patients, we identified a large outbreak caused by an Mtb strain of the Haarlem lineage. The main performance outcome measure of whole genome sequencing (WGS) analyses was the degree of correlation of the WGS analyses with contact tracing data and the spatio-temporal distribution of the outbreak cases. WGS analyses of the 86 isolates revealed 85 single nucleotide polymorphisms (SNPs), subdividing the outbreak into seven genome clusters (two to 24 isolates each), plus 36 unique SNP profiles. WGS results showed that the first outbreak isolates detected in 1997 were falsely clustered by classical genotyping. In 1998, one clone (termed "Hamburg clone") started expanding, apparently independently from differences in the social environment of early cases. Genome-based clustering patterns were in better accordance with contact tracing data and the geographical distribution of the cases than clustering patterns based on classical genotyping. A maximum of three SNPs were identified in eight confirmed human-to-human transmission chains, involving 31 patients. We estimated the Mtb genome evolutionary rate at 0.4 mutations per genome per year. This rate suggests that Mtb grows in its natural host with a doubling time of approximately 22 h (400 generations per year). Based on the genome variation discovered, emergence of the Hamburg clone was dated back to a period between 1993 and 1997, hence shortly before the discovery of the outbreak through epidemiological surveillance. Our findings suggest that WGS is superior to conventional genotyping for Mtb pathogen tracing and investigating micro-epidemics. WGS provides a measure of Mtb genome evolution over time in its natural host context.

Whole Genome Sequencing Identifies Small Deletions in Ribosomal Genes Causing Diamond Blackfan Anemia

Objectives – To understand how many undergraduate and graduate students use generative AI as part of their academic work, how often they use it, and for what tasks they use it. We also sought to identify how trustworthy students find generative AI and how they would feel about a locally maintained generative AI tool. Finally, we explored student interest in trainings related to using generative AI in academic work. This survey will help librarians better understand the rate at which generative AI is being adopted by university students and the need for librarians to incorporate generative AI into their work. Methods – A team of three library staff members and one student intern created, executed, and analyzed a survey of 360 undergraduate and graduate students at Harvard University. The survey was distributed via email lists and at cafes and libraries throughout campus. Data were collected and analyzed using Qualtrics. Results – We found that nearly 65% of respondents have used or plan to use generative AI chatbots for academic work, even though most respondents (65%) do not find their outputs trustworthy enough for academic work. The findings show that students actively use these tools but desire guidance around effectively using them. Conclusion – This research shows students are engaging with generative AI for academic work but do not fully trust the information that it produces. Librarians must be at the forefront of understanding the significant impact this technology will have on information-seeking behaviors and research habits. To effectively support students, librarians must know how to use these tools to advise students on how to critically evaluate AI output and effectively incorporate it into their research.

This study considers the potential effects of Generative AI on the skills development of ‎professionals in the social media advertising and marketing industry (particularly around ‎creativity and adaptability). More specifically, Generative AI technologies are rapidly disrupting ‎social media platforms and advertising practices; therefore, the need to understand their impact ‎on the professional skills development landscape is essential. To that end, this study employed ‎both qualitative and empirical lenses to identify how advertising professionals respond to ‎Generative AI's increasing presence and the adaptation of their skill development from that ‎recognized role. This research provides significant evidence of the growing skills with social media advertising ‎bequeathed by Generative AI, interconnecting workplace learning theories, industry strategies ‎, and barriers in innovation and the digital realm. It gives practical advice to organizations about ‎basic competencies necessary to develop and the repercussions of relying on generative tools in ‎order to remain competitive. The research identifies Generative AI not only as a strategic ‎investment for the organization, enhancing marketing efficacy and overall performance, but also ‎enhancing innovation and learning. This proves its financial worth with tangible proof of lower ‎campaign costs, faster content production, and greater conversion resulting from organizations ‎putting AI-enhanced tools into operation. In demonstrating that the evolving Generative AI ‎capabilities are indicators of an increase in productivity, measures such as increased marketing return ‎on investment will place inordinate importance on building GenAI skills‎.

Legionella pneumophila is the main pathogen responsible for outbreaks of Legionnaires' disease, which can be related to contaminated water supplies such as cooling towers or water pipes. We combined conventional molecular methods and whole genome sequence (WGS) analysis to investigate an outbreak of L. pneumophila in a large Australian hospital. Typing of these isolates using sequence-based typing and virulence gene profiling, was unable to discriminate between outbreak and non-outbreak isolates. WGS analysis was performed on isolates during the outbreak, as well as on unlinked isolates from the Public Health Microbiology reference collection. The more powerful resolution provided by analysis of whole genome sequences allowed outbreak isolates to be distinguished from isolates that were temporally and spatially unassociated with the outbreak, demonstrating that this technology can be used in real-time to investigate L. pneumophila outbreaks.

This mixed-methods approach study investigates the potential of introducing generative AI (ChatGPT 4 and Bard) as part of a deductive qualitative research design that requires coding, focusing on possible gains in cost-effectiveness, coding throughput time, and inter-coder reliability (Cohen’s Kappa). This study involved semi-structured interviews with five domain experts and analyzed a dataset of 122 respondents that required categorization into six predefined categories. The results from using generative AI coders were compared with those from a previous study where human coders carried out the same task. In this comparison, we evaluated the performance of AI-based coders against two groups of human coders, comprising three experts and three non-experts. Our findings support the replacement of human coders with generative AI ones, specifically ChatGPT for deductive qualitative research methods of limited scope. The experimental group, consisting of three independent generative AI coders, outperformed both control groups in coding effort, with a fourfold (4x) efficiency and throughput time (15x) advantage. The latter could be explained by leveraging parallel processing. Concerning expert vs. non-expert coders, minimal evidence suggests a preference for experts. Although experts code slightly faster (17%), their inter-coder reliability showed no substantial advantage. A hybrid approach, combining ChatGPT and domain experts, shows the most promise. This approach reduces costs, shortens project timelines, and enhances inter-coder reliability, as indicated by higher Cohen’s Kappa values. In conclusion, generative AI, exemplified by ChatGPT, offers a viable alternative to human coders, in combination with human research involvement, delivering cost savings and faster research completion without sacrificing notable reliability. These insights, while limited in scope, show potential for further studies with larger datasets, more inductive qualitative research designs, and other research domains. Received: 30 March 2024 | Revised: 17 May 2024 | Accepted: 18 June 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are not publicly available due to privacy concerns. However, anonymous data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Frédéric Pattyn: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization, Project administration.