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Purpose. Bacterial cancer therapy (BCT) research using engineered Salmonella typhimurium has increased in recent years. 2-Deoxy-2[18F] fluoro-D-glucose positron emission tomography (FDG PET) is widely used in cancer patients to detect cancer, monitor treatment responses, and predict prognoses. The aim of this pilot study was to investigate FDG uptake patterns in a mouse tumor model after BCT. Procedures. BCT was performed via the intravenous injection of attenuated S. typhimurium (SL Δ ppGpp/lux) into female mice bearing a tumor (derived from CT26 murine colon cancer cells) in the right thigh. 18F-FDG PET images acquired before BCT and at different time points after BCT. In vivo bioluminescence imaging confirmed bacterial presence in the tumor. The tumor volume, standardized uptake value (SUV) of FDG (SUVmax and SUVmean), early SUV reduction%, and normalized tumor volume change were analyzed. Results. Early after BCT (1 or 2 days post-injection (dpi)), FDG tumor uptake decreased in 10 out of 11 mice and then increased at later stages. FDG uptake before BCT was correlated with normalized tumor volume change after BCT. Early FDG reduction% after BCT was correlated with normalized volume change after BCT. Conclusions. Early after BCT, FDG tumor uptake decreased and then increased at later stages. The higher the FDG tumor uptake before BCT, the better the BCT response. FDG uptake patterns were related to tumor volume change after BCT. Therefore, FDG uptake was a good candidate for evaluating BCT. [ABSTRACT FROM AUTHOR]

Here, we provide a brief overview of the approaches and strategies underlying bacteria-based cancer immunotherapy (BCiT). We also describe and summarize research in the field of synthetic biology, which aims to regulate bacterial growth and gene expression for immunotherapeutic use. Finally, we discuss the current clinical status and limitations of BCiT. [ABSTRACT FROM AUTHOR]

This study proposes a new method to classify image data for two groups and effectively apply it in identifying people infected with COVID-19 based on their lung image. First, we extract each image into a two-dimensional interval based on the Gray Level Co-occurrence Matrix (GLCM). Next, find the prior probability based on the fuzzy relationship between the classified element and the established groups. Finally, combining the above improvements, we propose a new principle similar to the Bayesian method for classification. An image is classified into a group if it has the most significant value for prior probability and the smallest value for the overlap distance between the representative interval for the image and the groups. Applying the set of lung X-ray images to distinguish people infected with COVID-19, the proposed algorithm has given outstanding results compared with many well-known methods. The result also shows that this research can be applied in practice, and has the potential for different fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach to determine seven parameters of the Helmert transformation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984 (WGS84) to the International Terrestrial Reference Frame. This includes (1) converting the coordinates of common points from the global coordinate system to the local coordinate system, (2) identifying and eliminating outliers by the Dikin estimator, and (3) estimating seven parameters of the Helmert transformation by least squares (LS) estimation with the "clean" data (i.e. outliers removed). Herein, the local coordinate system provides a platform to separate points' horizontal and vertical components. Then, the Dikin estimator identifies and eliminates outliers in the horizontal or vertical component separately. It is significant because common points in a continuous GNSS network may contain outliers. The proposed approach is tested with the Géoazur GNSS network with the results showing that the Dikin estimator detects outliers at 6 out of 18 common points, among which three points are found with outliers in the vertical component only. Thus, instead of eliminating all coordinate components of these six common points, we only eliminate all coordinate components of three common points and only the vertical component of another three common points. Finally, the classical LS estimation is applied to "clean" data to estimate seven parameters of the Helmert transformation with a significant accuracy improvement. The Dikin estimator's results are compared to those of other robust estimators of Huber and Theil-Sen, which shows that the Dikin estimator performs better. Furthermore, the weighted total least-squares estimation is implemented to assess the accuracy of the LS estimation with the same data. The inter-comparison of the seven estimated parameters and their standard deviations shows a small difference at a few per million levels (E-6). [ABSTRACT FROM AUTHOR]

In this paper we shall prove a theorem on the existence of a linear feedback maximizing the real stability radius of a linear positive discrete-time system while preserving positivity of the system. [ABSTRACT FROM AUTHOR]

In this paper we shall prove a theorem on existence of global solutions for multi-valued intergral equations in Banach spaces. We shall consider also integral equations containing parameters and prove a continuity result for the set of global solutions. [ABSTRACT FROM AUTHOR]