Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.

Pseudomonas fragi (P. fragi) is usually detected in low-temperature meat products, and seriously threatens food safety and human health. Therefore, the study investigated the antibacterial mechanism of linalool against P. fragi from membrane damage and metabolic disruption. Results from field-emission transmission electron microscopy (FETEM) and atomic force microscopy (AFM) showed that linalool damage membrane integrity increases surface shrinkage and roughness. According to Fourier transform infrared (FTIR) spectra results, the components in the membrane underwent significant changes, including nucleic acid leakage, carbohydrate production, protein denaturation and modification, and fatty acid content reduction. The data obtained from amino acid metabolomics indicated that linalool caused excessive synthesis and metabolism of specific amino acids, particularly tryptophan metabolism and arginine biosynthesis. The reduced activities of glucose 6-phosphate dehydrogenase (G6PDH), malate dehydrogenase (MDH), and phosphofructokinase (PFK) suggested that linalool impair the respiratory chain and energy metabolism. Meanwhile, genes encoding the above enzymes were differentially expressed, with pfkB overexpression and zwf and mqo downregulation. Furthermore, molecular docking revealed that linalool can interact with the amino acid residues of G6DPH, MDH and PFK through hydrogen bonds. Therefore, it is hypothesized that the mechanism of linalool against P. fragi may involve cell membrane damage (structure and morphology), disturbance of energy metabolism (TCA cycle, EMP and HMP pathway) and amino acid metabolism (cysteine, glutamic acid and citrulline). These findings contribute to the development of linalool as a promising antibacterial agent in response to the food security challenge.

Coccidioidomycosis (Valley fever, VF) is a climate-sensitive infectious disease caused by inhaling soil-dwelling fungus Coccidioides , mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies—precipitation and air temperature—that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers—including SM, ST, and wind speed from the North American Land Data Assimilation phase 2 (NLDAS-2), and PM10-based dusty-day counts—in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi-year soil hydrothermal cycles—alternating wet–dry and cool–hot periods along with concurrent dry, dusty conditions—significantly influenced incidence. Notably, no antecedent dry–cool seasons were linked to increased incidence, indicating moisture or heat as prerequisites for fungal growth or dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS-2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi-year soil indicators—with up to three-year lead times—into early-warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.

When hypotheses are tested in a stream and real-time decision-making is needed, online sequential hypothesis testing procedures are needed. Furthermore, these hypotheses are commonly partitioned into groups by their nature. For example, RNA nanocapsules can be partitioned based on the therapeutic nucleic acids (siRNAs) being used, as well as the delivery nanocapsules. When selecting effective RNA nanocapsules, simultaneous false discovery rate control at multiple partition levels is needed. In this paper, we develop hypothesis testing procedures which control the false discovery rate (FDR) simultaneously for multiple partitions of hypotheses in an online fashion. We provide rigorous proofs for their FDR or modified FDR (mFDR) control properties and use extensive simulations to demonstrate their performances.

Coccidioidomycosis (Valley fever) is a soilborne mycosis endemic to the US Southwest whose incidence has increased markedly in recent decades. Environmental conditions are thought to influence the soil-dwelling lifecycle of Coccidioides; however, most prior studies have relied on above-ground meteorological conditions - primarily precipitation and air temperature (AT) - with few examining subsurface soil moisture (SM) and soil temperature (ST), which may more directly influence the fungal lifecycle. No study of coccidioidomycosis, or any other environment-sensitive soilborne mycosis, has examined how deeper-layer soil conditions relate to disease incidence, despite the prevailing soil-sterilisation hypothesis implicating deeper soil as a potential fungal refugium. Furthermore, nonlinear exposure-lag-response relationships for key dust-dispersion exposures - including PM10, a potential proxy for airborne spore concentration, and wind speed - remain uncharacterised. We aimed to estimate and compare the associations between coccidioidomycosis incidence and environmental exposures across multiple above- and below-ground layers, and to evaluate their independent and combined predictive performance. This ecological time-series study analysed 185,486 reported cases of coccidioidomycosis in Arizona's hyperendemic tri-county region (Maricopa, Pima, and Pinal) during 1997-2024. We developed a mechanism-informed multilayer environmental framework comprising one dust-dispersion layer (PM10, wind speed) and four soil-climate layers - meteorological (precipitation, AT), topsoil (0-10 cm), midsoil (10-40 cm), and deepsoil (40-100 cm) SM and ST. We fitted distributed lag non-linear models (DLNMs) with season-specific interaction terms to estimate exposure-lag-response associations between each environmental layer and coccidioidomycosis incidence. We then developed a two-stage stacked ensemble machine learning framework to assess each layer's independent predictive performance (stage 1) and integrate them into a unified forecast (stage 2), which was evaluated using a strictly held-out test period. At concurrent lags (1-3 months prior to reporting), coccidioidomycosis incidence was primarily associated with dustier, windier, and drier conditions, cooler air temperatures, and warmer topsoil. For each IQR increase, PM10 showed the most consistent concurrent associations, with significant positive incidence rate ratios (IRRs) across all four incidence seasons at lags 1-2 (ranging from 1.04 [95% CI 1.00-1.08] to 1.37 [1.26-1.48]). Across lags 1-36 months, all four soil-climate layers exhibited nonlinear, non-monotonic, and season-dependent associations with coccidioidomycosis incidence, characterised by alternating wet-dry and cool-warm oscillations. Topsoil displayed the most frequent significant associations, with moisture-temperature signals attenuating progressively from topsoil through midsoil to deepsoil. A depth-dependent lag structure was observed for both moisture and temperature, in which significant positive IRRs emerged at progressively shorter lags with increasing soil depth, accompanied by vertical divergence across depths at the same lag windows. For example, for fall incidence, positive moisture IRRs appeared at precipitation lag 15 (1.10 [1.00-1.21]), topsoil SM lag 9 (1.22 [1.15-1.30]), midsoil SM lags 8-9 (up to 1.23 [1.11-1.37]), and deepsoil SM lags 4-5 (up to 1.08 [1.01-1.16]); at these same lags, deepsoil SM was positively associated with incidence whereas topsoil SM and precipitation remained negatively associated. During the held-out test period (2021-2024), the multilayer ensemble generally captured seasonal and interannual variation well, including the timing and approximate magnitude of most peaks, outperforming all single-layer models. Although individual layers had slightly lower test RMSEs, their test gap ratios were substantially higher (0.27-0.67 vs 0.00), indicating that the ensemble generalised far more reliably. All five environmental layers contributed to the final ensemble forecast; the dust-dispersion and topsoil layers received the highest importance, with PM10 ranked as the most important predictor group. The best-performing of four pipeline configurations relied solely on environmental inputs available within one week, enabling the model to function as a near-real-time nowcast. This study provides the first evidence linking multilayer environmental exposures to coccidioidomycosis incidence across both temporal and vertical dimensions, offering new quantitative support for the prevailing soil-sterilisation and grow-and-blow hypotheses and demonstrating that a multilayer framework could improve both mechanistic understanding and predictive performance. The framework could be generalised to other endemic settings and readily extended with new data and methods to inform surveillance and public health preparedness. These findings support incorporating multilayer lagged environmental exposures into both effect estimation and forecasting systems to better prepare endemic regions for anticipated warming, drying, and increasingly variable climatic conditions.