Fucosylated human milk oligosaccharides are valuable targets for microbial biomanufacturing. Here we engineered the probiotic Escherichia coli Nissle 1917 as a versatile chassis for synthesizing both 2′-fucosyllactose and 3-fucosyllactose. We implemented lactose preservation and blocked colanic acid precursor consumption while reinforcing the de novo GDP-L-fucose pathway. This core module was coupled with either WbgL or FutM2 to drive terminal fucosylation. Initial screening yielded a 2′-fucosyllactose titer of approximately 1.21 g/L. Integrating an additional chromosomal wbgL cassette increased the titer to approximately 1.58 g/L, whereas subsequent genomic modifications did not further improve the 2′-FL titer. Cultivation with 30 g/L glycerol and 5 g/L yeast extract significantly improved productivity, enabling the strain to produce approximately 7.71 g/L of 2′-fucosyllactose in shake-flask cultures. Conversely, the FutM2 strain produced 0.38 g/L 3-fucosyllactose without yeast extract and nutrient supplementation unexpectedly suppressed product accumulation despite promoting biomass formation. The work provides a probiotic-chassis proof of concept and identifies product-module physiology as a priority for further engineering.
Identifying the runoff generation mechanism is helpful for decision-making and for selecting the most appropriate model to simulate runoff processes. Watershed managers require practical tools to understand how stormwater responds to changes in land use, urbanization, and climate variability. Many hydrologic models estimate runoff volume, peak flow, or flood frequency, but few tools clearly identify whether runoff is primarily driven by infiltration excess, saturation excess, or a combination of processes. This manuscript presents the development and conceptual application of an automated watershed management dashboard that integrates watershed delineation, rainfall-frequency data, soil infiltration capacity, antecedent moisture, and imperviousness in one interactive workflow. The dashboard delineates the watershed for a selected USGS streamgage, retrieves rainfall data for different storm durations and return periods, calculates rainfall intensity, estimates soil infiltration capacity, and evaluates antecedent moisture conditions. The classification framework compares rainfall intensity with soil infiltration capacity while also considering soil saturation and urban imperviousness. Based on these conditions, the dashboard classifies the likely runoff response as infiltration-excess runoff, saturation-excess runoff, or mixed runoff. Results are presented through an interactive map, tables, color-coded classifications, charts, and downloadable outputs. The dashboard is intended as a screening and decision-support tool that can support engineers, watershed managers, planners, and researchers in flood mitigation, green infrastructure planning, and ecological protection.
(1) Sarcoidosis is a systemic granulomatous disease of multifaceted etiology, characterized predominantly by pulmonary parenchymal involvement. While genetic susceptibility represents a foundational prerequisite, environmental and occupational xenobiotics are increasingly recognized as pivotal etiologic triggers. In cosmetic formulation environments, prolonged inhalation of mineral micro- and nanoparticles—such as titanium dioxide, talc, and fine silicates—alongside volatile organic solvents, provides a persistent antigenic stimulus that can drive aberrant granulomatous cascades toward aggressive, fibrosing phenotypes. (2) A 54-year-old female industrial worker presented with a 17-year history of occupational exposure in cosmetic manufacturing (handling airborne inorganic powders, titanium dioxide, talc, and organic vehicles without certified particulate respiratory protection), exhibiting progressive dyspnea, marked asthenia, and weight loss. Examination revealed inspiratory crackles at the pulmonary apices, violaceous nasal plaques consistent with lupus pernio, subcutaneous nodules, and sarcoid acropathy. Imaging and histology confirmed stage IV systemic sarcoidosis with fibrosing interstitial lung disease and multiple deep lymphadenopathies. Biologically and histologically, sarcoidosis was confirmed. Complete cessation of occupational exposure was achieved through workplace reassignment. Following combination therapy with oral corticosteroids and mycophenolate mofetil, the patient demonstrated clinical stabilization, resolution of hypercalcemia, and static lung function at 6-month follow-up. (3) This case underscores the importance of considering occupational exposures, including handling cosmetic products, in the pathogenesis and progression of fibrosing sarcoidosis. Early recognition and multidisciplinary management, integrating immunosuppressive therapy and occupational health interventions, are critical to limit irreversible organ damage.
The success of COVID-19 mRNA vaccines has established mRNA as a powerful therapeutic platform. Key advantages of mRNA drugs include transient expression, rapid scalability, and universal protein-encoding capability. This review focuses on the application of mRNA therapies across a broad spectrum of diseases, including cancer, infectious diseases, genetic disorders, autoimmune diseases, and cardiovascular conditions. It systematically summarizes the latest progress in mRNA drug development, covering three major therapeutic paradigms: mRNA vaccines, protein replacement therapy, and mRNA-based gene editing. Emerging approaches, such as artificial intelligence tools for mRNA therapeutic design and advanced delivery systems, are also discussed. By integrating foundational mechanisms, engineering strategies, and translational examples, this review provides a comprehensive overview of the current landscape and future directions of mRNA-based therapeutics.
The increasing penetration of renewable resources in smart energy systems has increased the need for accurate forecasting of photovoltaic (PV) power to reduce uncertainty and improve operational planning. Rapid changes in atmospheric conditions, especially cloud movement, cause severe fluctuations and pose a significant challenge to PV power forecasting in the short term. In this paper, an integrated Deep Learning-based framework for short-term PV power forecasting is proposed that models the relationships among sky conditions, solar irradiance, and PV power. First, the cloud movement is forecasted based on the Optical Flow algorithm using sky images. Then, the forecasted images are fed into a ResNet50 network to estimate the solar irradiance. Finally, the forecasted solar irradiance, meteorological parameters, and historical PV power data are fed into the CNN-BiGRU-Attention model, whose hyperparameters are optimized using Bayesian Optimization to forecast the PV power for the next hour. The performance of the proposed model was evaluated under different atmospheric conditions and compared with other benchmark models. The results showed that the proposed model achieves high forecasting accuracy, obtaining a R2 of 0.987 and RMSE, MAE, and MAPE error values of 0.231 Kw, 0.147 Kw, and 8.69%, respectively, and outperforming benchmark models.
