University of Illinois at Chicago

Human herpesviruses (HHVs) are notorious, ubiquitous intracellular pathogens that establish lifelong infections in the host. They tightly manipulate host signaling pathways that play central roles in key cellular processes such as cell survival, metabolism, immune responses, and oncogenic transformation. Among the many pathways explored, the phosphatidylinositol-3-kinase (PI3K)/Akt signaling axis has emerged as a central and conserved target exploited by all eight HHVs. Herpesviruses can induce PI3K/Akt signaling at multiple stages of their life cycle, beginning at viral entry and extending through lytic replication, latency maintenance, immune evasion, and virus-associated tumorigenesis. Mechanistically, herpesviruses engage both host cell receptors and viral effector proteins to activate PI3K, drive Akt phosphorylation, and thereby orchestrate downstream signaling pathways that favor viral replication, survival, and immune evasion. Transient activation of this pathway supports viral replication, whereas sustained signaling promotes latent infection and oncogenesis, particularly in Epstein-Barr virus and Kaposi's sarcoma-associated herpesvirus. This review provides a comparative analysis of PI3K/Akt pathway manipulation across all HHVs, highlighting shared strategies and virus-specific adaptations. We further discuss ongoing clinical trials and therapeutic opportunities targeting the PI3K/Akt axis, emphasizing its potential as a host-directed antiviral and anticancer strategy.

Changes in gut microbiota composition due to diet impact health. Fiber-rich diets promote beneficial microbiota and reduce the risk of metabolic diseases, while low-fiber, calorie-dense diets are linked to dysbiosis and increased disease risk. This study examines the effects of a Western diet (WD) and explores dietary fiber supplements as potential modifiers of those effects. 10-week-old C57Bl/6J male mice were fed control (low-fat) or WD (high-fat, high-sucrose) containing 0% fermentable fiber (FF) or WD supplemented with 20% FF (fructooligosaccharides, FOS; guar gum, GG, or pectin, Pec). After 19 weeks, analysis of the cecal metagenome using whole-genome shotgun sequencing, metabolome by untargeted and targeted LC-MS/MS, and tissue RNA and protein expression by RT-PCR and immunoblotting was undertaken. WD-FF reduced metabolic derangements from WD while also improving GM diversity and altering cecal metabolites, particularly tryptophan metabolism. A profound increase in cecal indole levels (targeted metabolomics) was noted in WD vs WD-FF groups. As the primary indole-oxidizing enzyme, CYP2E1 generates indoxyl sulfate, which contributes to oxidative stress and a leaky gut. Mice on WD displayed higher expression of Cyp2e1 mRNA in the gut. In the liver, the levels of both CYP2E1 protein and mRNA were higher in the WD group compared to the WD-FOS group, with protein levels also higher than in the WD-Pec group and mRNA levels higher than in the WD-GG group. mRNA expression of markers of oxidative stress, inflammation, and leaky barrier was significantly higher in the liver and intestine of the WD vs the WD-FF groups. FFs reduced high plasma indoxyl sulfate levels (except in WD-GG), and boosted short-chain fatty acids and indole acetic acid. Our data suggest that WD disrupts GM tryptophan metabolism, possibly by altering the balance between indole-producing and utilizing gut bacteria. Dietary fiber supplementation exerts protective effects, in part, by mitigating this imbalance.

Separation and recovery of nitrous oxide (N2O) from nitric acid tail gases is a pressing environmental challenge due to the dual role of N2O as a potent greenhouse gas and as the leading ozone-depleting substance. While catalytic destruction is the current industrial practice, it often lacks selectivity and generates secondary emissions, overlooking the added value of recovering N2O as a chemical feedstock. In this work, we employ atomistic nonequilibrium Molecular Dynamics (MD) simulations to investigate the selective retention and transport of N2O in zeolitic membranes. Three zeolite types all-silica Linde Type A (LTA), all-silica CDO-type zeolite, and calcium-exchanged Linde Type A (Ca-LTA, Si/Al = 1.0) were examined, and our results show that Ca-LTA exhibits superior recovery performance. Simulations were conducted across a wide range of temperatures (300–600 K) and pressures (100–932 atm) for equimolar binary, ternary, and quaternary mixtures, as well as five cases closely matching industrial gas composition (N2 mole fraction ≈ 0.4–0.8), providing direct insight into realistic operating conditions. Our results demonstrate that Ca-LTA retains N2O through strong interactions with Ca2+, while N2, NO, and NO2 permeate more readily. N2O permeation was negligible under most conditions, but trace breakthrough appeared in equimolar binary mixtures at 500 K, in ternary and quaternary mixtures at 450 K, and under industrial-like feeds with N2 mole fraction = 0.8 at 400 K, where only very few molecules of N2O permeated. In contrast, NO exhibited the highest mobility, while N2 and NO2 showed modest to intermediate permeability, both increasing systematically with temperature. Importantly, our results capture the experimentally observed transport behavior of pure gases in Ca-LTA membranes, and also predict the separation factors (SF), permeability, diffusion coefficients, and adsorption/permeation selectivity. Finally, our results provide the first molecular-level predictions for binary, ternary, quaternary, and industrial-composition mixtures in the absence of experimental data. Together, these findings establish Ca-LTA as a robust recovery medium for N2O, maintaining high selectivity across diverse feed compositions, ranging from simple gases to industrial-like mixtures, and aligning with experimental expectations for zeolitic membranes.

Bike-sharing use has been growing because it improves personal mobility, offers an alternative to walking, and strengthens connections to transit. Demand forecasting is crucial for bike-sharing services because it enables operators to anticipate empty stations and full docks, improve vehicle rebalancing and staffing, and deliver more reliable service at lower operating cost. In this paper, we propose a cluster-based, hour-ahead demand forecasting methodology that (1) groups stations into geographically coherent areas using K-means clustering method, (2) constructs hourly arrival and departure demand time series for each cluster while explicitly preserving zero-demand hours, and (3) incorporates exogenous factors such as temperature and weather-event type. We analyze multi-year trip records from Chicago’s Divvy bike-sharing system (2014–2017) to characterize network expansion and assess spatial stability over time. We then use the period (1 August 2016–31 December 2017), during which the number of active stations is stable, to conduct our predictive modeling. We compare three machine learning-based predictive models—linear regression (LR), time series (TS), and random forest (RF)—and assess their out-of-sample performance using the root mean squared error (RMSE). Results show that TS and RF models consistently outperform LR, achieving up to 80% R2 values and substantially lower RMSE across all 10 clusters, with particular improvements in high-variability central areas. By forecasting net demand (arrivals minus departures) at the cluster level, the approach supports practical identification of likely surplus/deficit areas to guide rebalancing decisions.

Abstract Text segmentation is a foundational process in NLP, but Myanmar presents a particularly difficult case due to its non-segmented, syllabic writing system and complex morphophonology. While syllable-level tokenization has seen notable progress, word- or meaningful phrase-level segmentation remains underexplored. Existing approaches largely rely on dictionary-based algorithms that ignore grammatical structure and contextual cues. This research proposes a new text segmentation method that integrates linguistic features such as conjunctions, post-particles, and whitespace usage, which carry implicit syntactic and semantic information. The proposed method produces stable and linguistically motivated segmentation across both formal and informal text styles, achieving an intrinsic boundary-level segmentation F1-score of 0.9 and a token-level segmentation F1-score of 0.8 across three different datasets. Extrinsic evaluation using named entity recognition (NER) as a downstream task demonstrates competitive performance across multiple entity types, with F1-scores of approximately 0.7 for DATE, NUM, LOC, PER, and TIME, even when applied without retraining the NER model. These results highlight the effectiveness and practical applicability of grammar-aware segmentation for Myanmar and underscore its potential for downstream NLP tasks in low-resource settings.