Fangzhou Li

Societal Impact Statement: Agricultural management and environmental conditions influence the biochemical composition of food crops; however, the specific drivers of this variation remain insufficiently understood, despite their importance for climate change adaptation and human health. This review synthesizes evidence on upstream agricultural and environmental drivers of food crop composition and analyzes subdrivers identified in the literature. The main findings indicate that most research addresses heavy metal bioaccumulation and the application of amendments in major crops such as vegetables and cereals, whereas relatively few studies examine agroecological management, atmospheric conditions, or impacts on lipids and broader phytochemical diversity. These findings highlight persistent knowledge gaps, particularly concerning the effects of less‐studied drivers on food quality. The results emphasize the need for integrative, cross‐disciplinary research, and standardized foodomics tools within global food systems. Summary: The biomolecular composition of our food, a critical determinant of human health and nutrition, is shaped by complex interactions between plants, agricultural management, and the environment. Understanding the drivers of variation in food composition is essential for addressing major global challenges including malnutrition, food safety, and sustainability. Following the PRISMA‐ScR protocol, we conducted a scoping review to investigate the agricultural and environmental drivers that impact food composition across 482 included studies to map research trends and knowledge gaps. Our analysis reveals that agricultural drivers' impact on food composition were the most examined across the literature (51.7% of studies, 249 studies), followed by studies examining both environmental and agricultural drivers (31.3%, 151) and environmental drivers alone the least investigated (17.0%, 82). Among agricultural drivers, nutrient management (185 studies) was the most studied, and agroecology (23 studies) was the least examined subdriver. Among environmental drivers, ecological conditions (232 studies) were most studied, whereas atmospheric conditions (28 studies) were the least examined subdriver. Overall, the literature reflected 154 unique food crops, with vegetable and cereal crops the most represented, and underutilized crops the least represented. The literature further focused on food components with significant public health implications, including heavy metals (259 studies), micronutrient minerals (188), and broad classes of macronutrients, such as total protein (106) and total sugars (100). This scoping review disaggregates the research gaps and opportunities to further elucidate how agricultural management and environmental drivers contribute to variation in food composition, informing nutrition security interventions and the development of resilient food systems for human and planetary health.

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4,120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0%) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R ² = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

Introduction: Biogenic amines play important roles throughout cellular metabolism. This study explores a role of biogenic amines in glioblastoma pathogenesis. Here, we characterize the plasma levels of biogenic amines in glioblastoma patients undergoing standard-of-care treatment. Methods: We examined 138 plasma samples from 36 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma at multiple stages of treatment. Untargeted gas chromatography–time of flight mass spectrometry (GC-TOF MS) was used to measure metabolite levels. Machine learning approaches were then used to develop a predictive tool based on these datasets. Results: Surgery was associated with increased levels of 12 metabolites and decreased levels of 11 metabolites. Chemoradiation was associated with increased levels of three metabolites and decreased levels of three other metabolites. Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). The metabolites sorbitol and N-methylisoleucine were identified as important predictive features and confirmed via SHAP. Conclusion: To our knowledge, this is the first study to describe plasma biogenic amine signatures throughout the treatment of patients with glioblastoma. A larger study is needed to confirm these results with hopes of developing a diagnostic algorithm.

Introduction: This study aims to characterize changes in biogenic amines patterns in glioblasto-ma patients undergoing surgery and concurrent chemoradiation to illustrate how this class of metabolites changes during different stages of standard care treatment protocol. Methods: We examined 138 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, immediately after chemoradiation, and after adju-vant chemotherapy treatment) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics of biogenic amines was used given its superiority for identifying and quantitating small metabolites; this yielded 340 structurally identified metabolites. Results: Comparing post-surgery to pre-surgery showed increased level of 12 metabolites: Gly-codeoxycholic acid (P=7.79E-05), Betonicine (P=9.23E-05), Glycocholic acid (P=3.18E-03), 3-Cysteinylaectaminophen (P=4.95E-03), S-Methyl-3-thioacetaminophen (P=5.77E-03), Tau-rocholic acid (P=9.97E-03), N-glycine (P=0.0013), p-acetamidophenyl.beta.-D-glucuronide (P=0.0048), 2-Hydroxy-5-sulfopyridine-3-carboxylic acid (P=0.0074), Acetaminophen sulfate (P=0.0095), 2-Amino-3-methoxybenzoic acid (P=0.02), and Dehydrofelodipine (P=0.024). There were 11 compounds that were downregulated, which included Mannitol (P=1.96E-19), Sorbitol (P=2.75E-019), Linoleic acid (P=1.51E-10), 1-Methylnicotinamide (P=1.78E-09), Nudifloramide (P=2.81E-08), Hexadecanedioic acid (P=5.49E-05), 3-Hydroxybutyric acid (P=0.0002), Riluzole (0.0036), Bupivacaine (P=0.0041), Lactitol (P=0.008), and 1-hydroxymidazolam.beta.-D-glucuronide (P=0.0414). After chemoradiation, significant de-crease was uncovered in Famotidine (P=0.0074), N-Isovalerylglycine (P=0.012), and 3-Methylcrotonylglycine (P=0.0131). While significant increase was detected in N-Methylisoleucine (P=0.0011), 4-Methyl-5-thiazoleethanol (P=0.042), and 6-Hydroxycaproic acid (P=0.049). Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). One significant insight gleamed from these models were that the metabolites Sorbitol and N-methylisoleucine were identified as important predictive features and confirmed by SHAP analysis. Conclusion: To our knowledge, this is the first study to describe plasma biogenic amines signa-tures during different treatment phases in patients with glioblastoma. A larger study is needed to confirm the results and the potential application of this algorithm for classification of treatment responses.

Interest in animal cell-based meat (ACBM) or laboratory-grown meat has been increasing; however, the economic viability of these potential products has not been thoroughly vetted. Recent studies suggest monoclonal antibody production technology can be adapted for the industrialization of ACBM production. This study provides a scenario-based assessment of the projected cost per kilogram of ACBM produced in the United States based on cellular metabolic requirements and process/chemical engineering conventions. A sensitivity analysis of the model identified the nine most influential cost factors for ACBM production out of 67 initial parameters. The results indicate that technological performance will need to approach technical limits for ACBM to achieve profitably as a commodity. However, the model also suggests that low-volume high-value specialty products could be viable based on current technology.

Interest in animal cell-based meat (ACBM) or laboratory grown meat has been increasing, however the economic viability of these potential products has not been thoroughly vetted. Recent studies suggest monoclonal antibody production technology can be adapted for the industrialization of ACBM production. This study provides a scenario-based assessment of the projected cost per kilogram of ACBM based on cellular metabolic requirements and process/chemical engineering conventions. A sensitivity analysis of the model identified the nine most influential cost factors for ACBM production out of 67 initial parameters. The results indicate that technological performance will need to approach technical limits for ACBM to achieve profitably as a commodity. However, the model also suggests that low-volume high-value specialty products could be viable based on current technology. One Sentence Summary: A model based upon cellular metabolism and engineering conventions was created to examine the economic viability of animal cell-based meat. Significance statement: Animal cell-based meat (ACBM) has received a significant amount of media attention (as well as corporate investment) in recent years based on its perceived potential to displace traditional meat production, whether beef, poultry, or fish. However, a robust techno-economic assessment (TEA) of these potential products is not publicly available. Our study examined the capital and operating expenditures for potential ACBM products based upon fundamental cellular attributes, the use of proposed near-term/existing technology, and process engineering conventions. Our findings suggest that the current production pathways are far from producing cost-competitive ACBM products, as well as highlight the technical metrics that must be achieved for an ACBM product to become economically viable.