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More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
Species introductions shift seed dispersal potential more than extinctions across 120 island plant–frugivore communities
Heinen, Julia H.; Drake, Donald R.; McConkey, Kim; Hume, Julian P.; Albert, Sébastien; Ando, Haruko; Baider, Cláudia; Bellingham, Peter J.; Case, Samuel B.; Chimera, Charles G.; Florens, F. B. Vincent; Fricke, Evan C.; Gawel, Ann Marie; González-Castro, Aaron; Heleno, Ruben; Hervias-Parejo, Sandra; Hruska, Amy; Imada, Clyde T.; de Lima, Ricardo F.; Nogales, Manuel; Rogers, Haldre S.; Rumeu, Beatriz; Strasberg, Dominique; Traveset, Anna; Valido, Alfredo; Watanabe, Kenta; Wotton, Debra; Yoshikawa, Tetsuro; Rahbek, Carsten; Borregaard, Michael K. (Proceedings of the National Academy of Sciences, 2025-10-01)
Oceanic islands are hotspots of both species extinctions and introductions, which led to marked changes in species composition. This may disrupt key ecological interactions, such as animal-mediated seed dispersal, with potential long-term impacts on ecosystem structure and functioning. While some recent studies on individual taxa and islands report functional shifts, there has been no globally comprehensive study of how these changes vary in intensity and direction across islands. Importantly, it remains unclear how changes in traits of animal communities actually translate to ecologically relevant mismatches with native plant species. We report widespread functional remodeling of frugivore communities based on frugivory-specific traits of all native, extinct, and introduced vertebrate frugivores (birds, mammals, reptiles) from 120 islands in 22 archipelagos. There is a trend for taxonomic and functional substitution, mainly of nonvolant terrestrial mammalian omnivores replacing large-gaped flying frugivores, which caused a mismatch between gape size and seed size. This shift in seed dispersal potential risks underestimation in single-taxon studies. Overall, vertebrate introductions outnumbered extinctions both in terms of species (44 vs. 23%) and islands affected (92 vs. 76%). Moreover, introductions have driven stronger shifts in frugivore trait space compared to extinctions. However, the general patterns are modulated by substantial spatial variation and idiosyncratic functional shifts within frugivore communities on some islands. This, coupled with differences in plant seed size distributions, leads to variability in realized functional mismatches among islands. These results emphasize challenges with predicting functional responses to anthropogenic activities, while highlighting that remodeling of ecosystem interactions is a global concern.
Molecular characterization of soybean meal trypsin inhibitors and lectins as a basis for developing approaches to mitigate their anti-nutritional effects
Okedigba, Ayoyinka Oluwaseun (Virginia Tech, 2025-12-03)
Soybean is a crop that is widely eaten because of its nutritive properties. However, soybean cannot be consumed in its raw form because it contains anti-nutrients like trypsin inhibitors which bind to serine proteases (chymotrypsin, elastase and trypsin) and restrain their digestive functions leading to indigestion and stunted growth. It also contains lectin which binds to intestinal cells containing N-acetyl galactosamine (GalNAc) to impede nutrient uptake into the blood. Soybean needs to be processed to get rid of these anti-nutrients before human or animal consumption. The conventional method of soybean processing which involves the moist heat treatment of soybeans is time and energy demanding, leads to the loss of nutrients, and residual amount of anti-nutrients are retained in the processed soybean meal, making this process relatively inefficient.
This research work aimed to provide alternative solutions to the conventional soybean processing methods. Using quick purification techniques, we isolated TIs [Bowman-Birk trypsin inhibitor (BBTI) and Kunitz trypsin inhibitor (KTI)] and lectin from soybean meal. Using biophysical techniques, we characterized the interactions between soybean anti-nutrients and their host ligands to offer alternative solutions to improve the nutritional quality of soybean meal. The known anti-nutritional pathway of soybean lectin involves its binding to GalNAc containing intestinal cells; however, we believe soybean lectin could be also targeting another ligand called sulfatide besides GalNAc in the small intestine due to some similarities between soybean lectin and galectin-4, a mammalian lectin that binds to sulfatide in the small intestine. Hence, we also explored soybean lectin to sulfatide interactions to find a new soybean lectin anti-nutritional pathway.
Results from a soybean meal cultivar showed that KTI had a binding preference for chymotrypsin, while BBTI preferred binding to trypsin and elastase. This provides insights into the unique roles that both TIs play in soybean and why the plant retains both. After screening TIs from several soybean meal lines for their affinity to trypsin, we identified BBTI soybean lines with ~4 to 6-fold lower affinity for trypsin and a KTI soybean line with ~8-fold lower affinity for trypsin, compared to the standard BBTI and KTI, respectively. These promising soybean meal lines have been designed for soybean crossbreeding to produce hybrid soybean meal lines that would require little to no processing before animal consumption. We also identified the KTI amino acid sequence motif, SPLHALFI as a suitable motif for gene editing to produce soybean meal lines with improved nutritional qualities.
After screening GalNAc and GalNAc analogs against lectin to find a higher lectin affinity ligand than GalNAc that could be introduced as an additive in soybean meal to bind to lectin and prevent lectin from binding to GalNAc containing intestinal cells, we found no higher affinity ligand than GalNAc for lectin. However, we found out that a suitable substitution on the anomeric carbon of GalNAc would lead to the design of higher lectin affinity ligands than GalNAc that could serve as additives in soybean meal to mitigate lectin adverse effects. We also found out that soybean lectin crosslinks and binds to sulfatides with a high affinity, in a process that could result in unwanted signaling events in the body, making this another soybean lectin antinutritional pathway. This discovery could also lead to the design of suitable additives to target the sulfatide binding site on soybean lectin and prevent soybean lectin from binding to sulfatide in the body. It is recommended that this newly discovered soybean lectin-sulfatide anti-nutritional pathway should also be considered when assessing the nutritional quality of soybean meal.
Taken together, this research provides alternative solutions that would lead to the enhancement of the nutritional quality of soybean meal and the mitigation of the adverse effects of soybean's anti-nutrients.
Marketing at the Human-Technology Frontier: Essays on AI Acceptance, Digital Twins, and Brain-Computer Interfaces
Li, Bingqing (Virginia Tech, 2025-12-03)
5G-MAP: Demystifying the Performance Implications of Cloud-Based 5G Core Deployments
Atalay, Tolga; Stojadinovic, Dragoslav; Famili, Alireza; Stavrou, Angelos; Wang, Haining (ACM, 2025-11-03)
The Fifth Generation (5G) core network is designed as a set of Virtual Network Functions (VNFs) hosted on Commercial- Off-the-Shelf (COTS) hardware. This creates a growing demand for general-purpose computing resources. Given their elastic infrastructure, cloud services like Amazon Web Services (AWS) are attractive platforms to address this need. Therefore, it is crucial to understand the Quality of Service (QoS) requirements associated with deploying the 5G core in the cloud. We developed the 5G-MAP (5G Measurement and Assessment Platform) to understand the trade-offs between different deployment strategies. Our framework facilitates detailed control and user plane performance assessments in varied deployment scenarios. We integrated 5G-MAP with the OpenAirInterface (OAI) 5G core and utilized it in a series of deployments across seven countries, leveraging eight AWS regions and eighteen edge zones. Our evaluations cover from HTTP transactions to user plane throughput and packet loss. We identify topologies that can considerably lower the 5G core service chain latencies due to a significant reduction in the number of inter-site hops. Such actionable performance improvements illustrate how operators can leverage 5G-MAP to optimize their cloud-based 5G deployments.
Zero-Knowledge AI Inference with High Precision
Riasi, Arman; Wang, Haodi; Behnia, Rouzbeh; Vo, Viet; Hoang, Thang (ACM, 2025-11-19)
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.
In this work, we present ZIP, an efficient and precise commit and prove zero-knowledge SNARK for AIaaS inference (both linear and non-linear layers) that natively supports IEEE-754 double-precision floating-point semantics while addressing reliability and privacy challenges inherent in AIaaS. At its core, ZIP introduces a novel relative-error-driven technique that efficiently proves the correctness of complex non-linear layers in AI inference computations without any loss of precision, and hardens existing lookup-table and range proofs with novel arithmetic constraints to defend against malicious provers. We implement ZIP and evaluate it on standard datasets (e.g., MNIST, UTKFace, and SST-2). Our experimental results show, for non-linear activation functions, ZIP reduces circuit size by up to three orders of magnitude while maintaining the full precision required by modern AI workloads.