Sunlight plays an important role in the inactivation of pathogenic microorganisms such as bacteria and viruses in water. Here we present a model that is able to predict the kinetics of direct virus inactivation (i.e. inactivation triggered by sunlight absorption by the virion, without the role played by photochemically produced reactive intermediates generated by water-dissolved photosensitizers) on a global scale (from 60 °S to 60 °N latitude) and for the different months of the year. The model is based on the equivalent monochromatic wavelength (EMW) approach that was introduced recently, and which largely simplifies complex polychromatic calculations by approximating them with a monochromatic equation at the proper wavelength, the EMW. The EMW equation was initially established for mid-July conditions at a mid-latitude, and was then extended to different seasons and to the latitude belt where the day-night cycle is always observed throughout the year. By so doing, the first-order rate constant of direct virus photoinactivation can be predicted on a global scale, with the use of a relatively simple equation plus tables of pre-calculated input data, as a function of latitude, month, and key water parameters. The model was here applied to the virus organism phiX174, a somatic phage that is often used as proxy for pathogenic viruses undergoing fast direct inactivation, and for which a wide array of published inactivation data is available. Model predictions are validated by comparison with field data of inactivation of somatic phages by sunlight.
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http://dx.doi.org/10.1016/j.watres.2021.117837 | DOI Listing |
Transl Oncol
January 2025
Johns Hopkins Greenberg Bladder Cancer Institute, Brady Urological Institute, Johns Hopkins University, Baltimore, MD, USA. Electronic address:
Bladder cancer (BLCA) genomic profiling has identified molecular subtypes with distinct clinical characteristics and variable sensitivities to frontline therapy. BLCAs can be categorized into luminal or basal subtypes based on their gene expression. We comprehensively characterized nine human BLCA cell lines (UC3, UC6, UC9, UC13, UC14, T24, SCaBER, RT4V6 and RT112) into molecular subtypes using orthotopic xenograft models.
View Article and Find Full Text PDFAm J Trop Med Hyg
January 2025
Division of Vector Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado.
Plague is a rare, potentially fatal flea-borne zoonosis endemic in the western United States. A previous model described interannual variation in human cases based on temperature and lagged precipitation. We recreated this model in northeastern Arizona (1960-1997) to evaluate its capacity to predict recent cases (1998-2022).
View Article and Find Full Text PDFJCO Clin Cancer Inform
January 2025
SimBioSys Inc, Chicago, IL.
Purpose: Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.
Methods: Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency.
This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.
View Article and Find Full Text PDFPLoS One
January 2025
Academy of Fine Arts, Jiangsu Second Normal University, Nanjing, China.
Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view images to study the relationship between visual landscape features and human perception of urban waterfront areas, employing linear regression and random forest models to predict human perception along urban coastal roads. Based on aesthetic and distinctiveness perception, urban coastal roads in Xiamen were classified into four types with different emphasis and priorities for improvement.
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