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Timelines are critical in space exploration. Timelines facilitate planning, resource management, and automation of uncrewed missions. As NASA and other space agencies increasingly rely on timelines for autonomous spacecraft operations, ensuring their understandability and verifiability is essential for mission success. However, interdisciplinary design teams face challenges in interpreting timelines due to variations in cultural and educational backgrounds, leading to communication barriers and potential system mismatches. This work-in-progress research explores time-oriented data visualizations to improve timeline comprehension in space systems. We contribute (1) a survey of visualization techniques, identifying patterns and gaps in historic time-oriented data visualizations and industry tools, (2) a focus group pilot study analyzing user interpretations of timeline visualizations, and (3) a novel method for visualizing aggregate runs of a timeline on a complex system, including identification of key features for usability of aggregate-data visuals. Our findings inform future visualization strategies for debugging and verifying timelines in uncrewed systems. While focused on space, this research has broader implications for aerospace, robotics, and emergency response systems. 

Highly pathogenic avian influenza (HPAI) viruses cross species barriers and have the potential to cause pandemics. In North America, HPAI A(H5N1) viruses related to the goose/Guangdong 2.3.4.4b hemagglutinin phylogenetic clade have infected wild birds, poultry, and mammals. Our genomic analysis and epidemiological investigation showed that a reassortment event in wild bird populations preceded a single wild bird–to-cattle transmission episode. The movement of asymptomatic or presymptomatic cattle has likely played a role in the spread of HPAI within the United States dairy herd. Some molecular markers that may lead to changes in transmission efficiency and phenotype were detected at low frequencies. Continued transmission of H5N1 HPAI within dairy cattle increases the risk for infection and subsequent spread of the virus to human populations. 

Cell counting in immunocytochemistry is vital for biomedical research, supporting the diagnosis and treatment of diseases such as neurological disorders, autoimmune conditions, and cancer. However, traditional counting methods are manual, time-consuming, and error-prone, while deep learning solutions require costly labeled datasets, limiting scalability. We introduce the Immunocytochemistry Dataset Cell Counting with Segment Anything Model (IDCC-SAM), a novel application of the Segment Anything Model (SAM), designed to adapt the model for zero-shot-based cell counting in fluorescent microscopic immunocytochemistry datasets. IDCC-SAM leverages Meta AI’s SAM, pre-trained on 11 million images, to eliminate the need for annotations, enhancing scalability and efficiency. Evaluated on three public datasets (IDCIA, ADC, and VGG), IDCC-SAM achieved the lowest Mean Absolute Error (26, 28, 52) on VGG and ADC and the highest Acceptable Absolute Error (28%, 26%, 33%) across all datasets, outperforming state-of-the-art supervised models like U-Net and Mask R-CNN, as well as zero-shot benchmarks like NP-SAM and SAM4Organoid. These results demonstrate IDCC-SAM’s potential to improve cell-counting accuracy while reducing reliance on specialized models and manual annotations. 

Real-world quantitative reasoning problems are complex, often including extra information irrelevant to the question (or “IR noise” for short). State-of-the-art (SOTA) prompting methods have increased the Large Language Model’s ability for quantitative rea-soning on grade-school Math Word Problems (MWPs). To assess how well these SOTA methods handle IR noise, we constructed four new datasets with IR noise, each consisting of 300 problems from each of the four public datasets: MAWPS, ASDiv, SVAMP, and GSM8K, with added IR noise. We called the collection of these new datasets “MPN”—Math Word Problems with IR Noise. We evaluated SOTA prompting methods using MPN. We propose Noise Reduction Prompting (NRP) and its variant (NRP+) to reduce the impact of IR noise. Findings: Our IR noise significantly degrades the performance of Chain-of-Thought (CoT) Prompting on three different backend models: ChatGPT (gpt-3.5-turbo-0613), PaLM2, and Llama3-8B-instruct. Among them, ChatGPT offers the best accuracy on MPN with and without IR noise. With IR noise, the performances of CoT, Least-To-Most Prompting, Progressive-Hint Prompting, and Program-aided Language Models with ChatGPT were significantly impacted, each with an average accuracy drop of above 12%. NRP is least impacted by the noise, with a drop in average accuracy to only around 1.9%. Our NRP+ and NRP perform comparably in the presence of IR noise. 

Graph neural networks are powerful graph representation learners in which node representations are highly influenced by features of neighboring nodes. Prior work on individual fairness in graphs has focused only on node features rather than structural issues. However, from the perspective of fairness in high-stakes applications, structural fairness is also important, and the learned representations may be systematically and undesirably biased against unprivileged individuals due to a lack of structural awareness in the learning process. In this work, we propose a pre-processing bias mitigation approach for individual fairness that gives importance to local and global structural features. We mitigate the local structure discrepancy of the graph embedding via a locally fair PageRank method. We address the global structure disproportion between pairs of nodes by introducing truncated singular value decomposition-based pairwise node similarities. Empirically, the proposed pre-processed fair structural features have superior performance in individual fairness metrics compared to the state-of-the-art methods while maintaining prediction performance. 

Highly pathogenic avian influenza (HPAI) H5N1 haemagglutinin clade 2.3.4.4b was detected in the USA in 2021. These HPAI viruses caused mortality events in poultry, wild birds and wild mammals. On 25 March 2024, HPAI H5N1 clade 2.3.4.4b was confirmed in a dairy cow in Texas in response to a multistate investigation into milk production losses¹. More than 200 positive herds were identified in 14 US states. The case description included reduced feed intake and rumen motility in lactating cows, decreased milk production and thick yellow milk^2,3. The diagnostic investigation revealed viral RNA in milk and alveolar epithelial degeneration and necrosis and positive immunoreactivity of glandular epithelium in mammary tissue. A single transmission event, probably from birds, was followed by limited local transmission and onward horizontal transmission of H5N1 clade 2.3.4.4b genotype B3.13 (ref. ⁴). Here we sought to experimentally reproduce infection with genotype B3.13 in Holstein yearling heifers and lactating cows. Heifers were inoculated by an aerosol respiratory route and cows by an intramammary route. Clinical disease was mild in heifers, but infection was confirmed by virus detection, lesions and seroconversion. Clinical disease in lactating cows included decreased rumen motility, changes to milk appearance and production losses. Infection was confirmed by high levels of viral RNA detected in milk, virus isolation, lesions in mammary tissue and seroconversion. This study provides the foundation to investigate additional routes of infection, pathogenesis, transmission and intervention strategies.