Models which consider changes in the composition of biomass in response to environmental changes are called Structured models. They provide a more comprehensive description of microbial behavior than unstructured models. Compared with the unstructured modeling efforts, very little has so far been done on the theory and practice of structured model building. In most of the works reported so far, no experimental data were provided, and hence no means of testing the proposed models were offered. Others only reported macroscopic response data and not the cellular composition. In an attempt to fill some of the gaps in this field, in this work, first the general formal approach to structured modeling is developed in matrix notation. Then, a simple two-compartmental model, i.e., a structured model describing the activity of the biomass with two variables, is described. The cell is divided into two fractions, one of which relates to the RNA fraction. The proposed model was then critically evaluated with experimental data, including the RNA data, obtained from fed-batch and continuous-culture experiments. The importance of using cellular structure data for model verification, i.e., RNA data in this case, is shown. Shortcomings and capabilities of the developed model are discussed.
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Ecotoxicol Environ Saf
January 2025
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China. Electronic address:
Honeybees, essential pollinators for maintaining biodiversity, are experiencing a sharp population decline, which has become a pressing environmental concern. Among the factors implicated in this decline, neonicotinoid pesticides, particularly those belonging to the fourth generation, have been the focus of extensive scrutiny due to their potential risks to honeybees. This study investigates the molecular basis of these risks by examining the binding interactions between Apis mellifera L.
View Article and Find Full Text PDFComput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFJ Breath Res
January 2025
School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Rd, Qingdao, Shandong, 266003, CHINA.
Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities.
View Article and Find Full Text PDFPLoS One
January 2025
National Institute of Public Health, University of Southern Denmark, Copenhagen K, Denmark.
Latent transition analysis (LTA) is a useful statistical modelling approach for describe transitions between latent classes over time. LTA may be characterized in terms of prevalence at each time point and through transition probabilities over time. Investigating predictors of these transitions is often of key interest.
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