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GC-MS-Based Metabolomics for Understanding the Metabolite Profile of Mopane Worm (Gonimbrasia belina) and Sorghum (Sorghum bicolor L)
No full textEdible insects are nutritious, having high levels of protein, essential amino acids, healthy fats, vitamins, and minerals. In areas where sorghum is a primary crop, mixing this gluten-free grain with protein-rich insect powder provides a novel approach to making nutrient-rich products. Understanding the sorghum and mopane worm flours\u27 metabolomic changes during bioprocessing can however, enhance their nutritional value and food safety. The research investigated metabolite compounds that are found in raw and processed (fermented, malted (sorghum), and ultrasonicated) mopane worms and sorghum. In the study, sorghum grains were traditionally fermented and malted at 35°C for 48 h and ultrasonicated for 10 min, at 70 Hz amplitude, while mopane worm was also traditionally fermented at 35°C for 48 h and ultrasonicated for 10 min at 70 Hz amplitudes. Obtained results showed that samples contained 67 (mopane worm) and 49 (sorghum) metabolites, which demonstrated distinct metabolic fingerprints for raw versus processed samples. The notable differences were found in lysine, threonine, and valine between the ultrasonicated sample and other treatments, while linoleic acid was the dominating compound in the ultrasonicated samples. Scyllo-inositol was the primary sugar, while lactic acid and citric acid were notable in the fermented samples. The ultrasonicated mopane worm flour had more metabolites identified than raw and fermented samples while in sorghum, the fermented sorghum had more metabolites than raw, malted, and ultrasonicated ones. These findings provide insights into optimizing processing techniques to maintain or improve the nutritional quality of both the investigated samples
The Access Assembly: Sharing Ideas and Enabling Informed Adaptations
No full textFounded in 2014, the Access Network brings together nine student-centered, university-based programs that are pursuing systemic change towards a vision of a more diverse, equitable, inclusive, and accessible STEM community. Network and program leaders are primarily students (undergraduate and graduate) and early-career faculty, many of whom started out as student leaders in Access programs. Over the last nine years, Access has brought people together (virtually or in-person) for an annual event called the “Assembly.” The environment and structures at the Assembly enable the sharing of ideas, excite people to translate those ideas to their local programs, and create space for informed and deliberate adaptations. Drawing on post-Assembly survey data, we describe where ideas come from, and where attendees intend to apply them. We also see that participants from various institutional positionalities report learning new ideas at the Assembly and having confidence to adapt them to their local contexts
Semi-Automated Extraction of Active Fire Edges from Tactical Infrared Observations of Wildfires
No full textHighlights: What are the main findings? This article presents an image processing method to semi-automatically track wildfire progression. The algorithm was successfully applied to aerial infrared imagery acquired during tactical fire management operations. What are the implications of the main finding? These results illustrate how tactical data can be used in fire behavior studies. The proposed method may facilitate real-time analysis of tactical information during wildfire emergencies. Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts
What Do Americans Think About Federal Tax Options to Support Transportation? Results from Year Sixteen of a National Survey
No full textThis report summarizes the results from the sixteenth year of a national public opinion survey asking U.S. adults questions related to their views on federal transportation taxes. A nationally representative sample of 2,539 respondents completed the online survey from February 3 to February 27, 2025. The questions test public opinions about raising the federal gas tax rate, replacing the federal gas tax with a new mileage fee, and imposing a mileage fee just on commercial travel. In addition to asking directly about support for these tax options, the survey collected data on respondents’ views on the quality of their local transportation system, their priorities for federal transportation spending, their knowledge about gas taxes, their views on privacy and equity matters related to mileage fees, travel behavior, and sociodemographic characteristics. Key findings include that large majorities supported transportation improvements across modes and wanted to see the federal government work towards making the transportation system well maintained, safe, and equitable, as well as to reduce the system’s impact on climate change. Findings related to gas taxes include that only 3% of respondents knew that the federal gas tax rate had not been raised in more than 20 years, and 75% of respondents supported increasing the federal gas tax by 10 cents per gallon if the revenue would be dedicated to maintenance. With respect to mileage fees, several options tested received support from nearly half of more than half of respondents. Also, the majority of respondents supported variable mileage fee rate structure options; 63% preferred charging low-income drivers a reduced mileage fee rate, and 49% preferred charging electric vehicles at a lower rate than gas and diesel vehicles. Finally, respondents were more likely to trust state motor vehicle departments to collect mileage fee data than tolling agencies, insurance companies, vehicle manufacturers, utility companies, and cell phone service providers. The analysis of trends across the survey series, which has run annually from 2010 to 2025, shows that support for both higher gas taxes and a hypothetical new mileage fee has risen slowly but steadily
Statistical Relevance of SIRT6 Mutation in Pancreatic Cancer and Smoking
No full textIndividuals diagnosed with Pancreatic Ductal Adenocarcinoma (PDAC) have a less than a 5% chance of surviving beyond 5 years. By the time PDAC is detected, the tumor has already advanced to an untreatable stage. The absence of reliable early detection biomarkers in pancreatic cancer motivates a comprehensive biological and statistical evaluation of the enrichment of a single nucleotide polymorphism (SNP) of interest in pancreatic cancer. Since smoking is a major risk factor for the disease, the study also explores the SNP’s association with smoking-related tumorigenesis. This non-invasive SNP detection in an individual can also point to mechanistic and epigenetic pathways relevant for early tumor development. To evaluate both SNP enrichment and its association with smoking, Fisher’s exact test is employed to account for some rarity and sparsity of the data in this exploratory analysis. The current literature supports both, the role of SNPs in epigenetic regions influencing PDAC, and specifically the impact of smoking driving PDAC. To investigate these relationships further, the mutational and clinical data from cBioPortal serves as a valuable resource. Statistically significant outcomes may warrant more investigation for a potential biomarker and help in uncovering the biological pathways involved in pancreatic cancer
Transcriptional Mechanism of Post Capillary Venule (PCVs) Identity in Peyer’s Patches
No full textHigh endothelial venules (HEVs) and Post capillary venules (PCVs) are regulators of lymphocyte trafficking in Peyer’s Patches (PP). How chicken ovalbumin upstream promoter transcription factor II (COUP-TFII) distinguishes these closely related venular endothelial types with ETS remains unknown. Using single-cell RNA sequencing of COUP-TFII overexpression and knockout endothelial cells, we identified 10 blood endothelial clusters and observed the altered COUP-TFII and ETS associated pathways across multiple vascular subsets. These findings indicate COUP-TFII modulates venular programs although additional work is required to define the specific regulatory mechanism of PCV identity with respect to ETS
Abstractions of the Game “SET”
No full textEach card in the game of SET can be represented as a point in Z43, where Z3 is the f ield of 3 elements; an in-game position without any SETs can be represented as a cap set. We find the largest cap sets in Zn 3 for n ≤ 4 and prove their uniqueness. Then, we provide more insight into Ellenberg and Gijswijt’s proof of upper bound for the maximum size of cap set in Fnq, where Fq is the field of q elements, which they find to be o(cn) for some c \u3c q. Finally, we present evidence suggesting Ellenberg and Gijswijt’s method fails to prove c ≤ 0.841434q
AI-Powered User Activity Prediction and Contextual App Recommendation System
No full textWearable sensors and smartphones continuously generate lots of data that are reflective of various aspects of human behavior. Yet, these behavioral signals are rarely translated into meaningful proactive digital support. This study introduces an AI-powered context prediction framework that predicts the next activity that a user will perform and suggests mobile applications matching the predicted context. This system uses both classical machine learning models and deep learning models for analyzing multi-modal behavioral histories, which are collected from heterogeneous sensors such as accelerometers, GPS, and screen usage logs. Contrary to traditional recommendation systems that respond to past usage patterns, the proposed approach predicts upcoming human behavior for triggering timely and personalized app recommendations that enhance productivity, health, and overall well-being. The project uses four datasets: StudentLife, College Life Experience, custom smartwatch dataset, and MobileRec. The first three provide rich real-world sensor data for context modeling, whereas MobileRec provides app metadata for semantic retrieval and recommendation. Integrating those datasets allows a leap from passive sensing toward proactive behavioral guidance. Experimental results demonstrate that predictive context modeling together with semantic retrieval substantially improves accuracy and contextual relevance of recommendations compared to state-of-the-art baselines. The findings emphasize the potential of anticipatory, context-aware assistants that are able to adapt in an intelligent manner to users\u27 goals and day-to-day life
Dual Stream Transformer based Architecture for Multimodal User Verification
No full textThis paper presents a Dual-Stream Transformer–based architecture for multi-modal user verification, leveraging both keyboard and mouse dynamics to capture complementary behavioral patterns. Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) and other sequential models have shown success in evaluating sequential relationships; however, they mainly focus on short-term patterns and can sometimes struggle to capture long-term patterns. The proposed architecture employs two parallel Transformer-based encoders, each dedicated to one behavioral modality. The two streams integrate temporal convolutional layers for local feature ex- traction and self-attention mechanisms for modeling global temporal dependencies. This allows the system to learn subtle and high-level behavioral representations. We introduce two implementations of the Dual-Stream Architecture model. The first implementation demonstrates a late fusion to allow the model to learn from each modality independently while the second implementation introduces an early fusion through a dot-product fusion mechanism for the two streams to learn from each other. The experimental results show that the late fusion architecture verifies specified genuine users from impostor users effectively while the early fusion lacks in optimization. This research highlights the potential of Transformer-based multimodal fusion as a solution for continuous and unobtrusive user authentication
The Post-War Lives of Amputee Civil War Veterans
No full textIn August 1865, Ora D. Walbridge sat down with a pen in his left hand to produce a specimen of his best business penmanship. Three years earlier, a gunshot had left Walbridge’s right arm paralyzed. When he submitted his penmanship specimen, he joined a unique group of Civil War veterans: the self-proclaimed “Left-Armed Corps.” This talk examines the lives of these veterans in the decades following the conflict to highlight the lasting resonance of missing limbs during national reconciliation. Demographic and biographical information provides insights into how the veterans’ lives were shaped by their service and their wounds.https://scholarworks.sjsu.edu/uss/1064/thumbnail.jp