Parasite fate and involvement of infected cells in the induction of CD4+ and CD8+ T cell responses to Toxoplasma gondii

During infection with the intracellular parasite Toxoplasma gondii, the presentation of parasite-derived antigens to CD4+ and CD8+ T cells is essential for long-term resistance to this pathogen. Fundamental questions remain regarding the roles of phagocytosis and active invasion in the events that lead to the processing and presentation of parasite antigens. To understand the most proximal events in this process, an attenuated non-replicating strain of T. gondii (the cpsII strain) was combined with a cytometry-based approach to distinguish active invasion from phagocytic uptake. In vivo studies revealed that T. gondii disproportionately infected dendritic cells and macrophages, and that infected dendritic cells and macrophages displayed an activated phenotype characterized by enhanced levels of CD86 compared to cells that had phagocytosed the parasite, thus suggesting a role for these cells in priming naïve T cells. Indeed, dendritic cells were required for optimal CD4+ and CD8+ T cell responses, and the phagocytosis of heat-killed or invasion-blocked parasites was not sufficient to induce T cell responses. Rather, the selective transfer of cpsII-infected dendritic cells or macrophages (but not those that had phagocytosed the parasite) to naïve mice potently induced CD4+ and CD8+ T cell responses, and conferred protection against challenge with virulent T. gondii. Collectively, these results point toward a critical role for actively infected host cells in initiating T. gondii-specific CD4+ and CD8+ T cell responses

Least-Squares Fitting of Multidimensional Spectra to Kubo Lineshape Models

We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of dephasing parameters is between 8× to 50× better for nonlinear model fitting compared to the CLS method, which effectively increases data acquisition efficiency by one to two orders of magnitude. Whereas the center-line-slope (CLS) method requires sequential fitting of both the nonlinear and linear spectra, our model fitting algorithm only requires nonlinear spectra, but accurately predicts the linear spectrum. We show an experimental example in which the CLS time constants differ by 60% for independent measurements of the same system, while the Kubo time constants differ by only 10% for model fitting. This suggests that model fitting is a far more robust method of measuring spectral diffusion than the CLS method, which is more susceptible to structured residual signals that are not removable by pure solvent subtraction. Statistical analysis of the CLS method reveals a fundamental oversight in accounting for the propagation of uncertainty by Kubo time constants in the process of fitting to the linear absorption spectrum. A standalone desktop app and source code for the least-squares fitting algorithm are freely available with example lineshape models and data. We have written the MATLAB source code in a generic framework where users may supply custom lineshape models. Using this application, a standard desktop fits a 12-parameter generalized Kubo model to a 106 data-point spectrum in a few minutes

Least-Squares Fitting of Multidimensional Spectra to Kubo Lineshape Models

We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of dephasing parameters is between 8× to 50× better for nonlinear model fitting compared to the CLS method, which effectively increases data acquisition efficiency by one to two orders of magnitude. Whereas the center-line-slope (CLS) method requires sequential fitting of both the nonlinear and linear spectra, our model fitting algorithm only requires nonlinear spectra, but accurately predicts the linear spectrum. We show an experimental example in which the CLS time constants differ by 60% for independent measurements of the same system, while the Kubo time constants differ by only 10% for model fitting. This suggests that model fitting is a far more robust method of measuring spectral diffusion than the CLS method, which is more susceptible to structured residual signals that are not removable by pure solvent subtraction. Statistical analysis of the CLS method reveals a fundamental oversight in accounting for the propagation of uncertainty by Kubo time constants in the process of fitting to the linear absorption spectrum. A standalone desktop app and source code for the least-squares fitting algorithm are freely available with example lineshape models and data. We have written the MATLAB source code in a generic framework where users may supply custom lineshape models. Using this application, a standard desktop fits a 12-parameter generalized Kubo model to a 106 data-point spectrum in a few minutes.</jats:p

Sensor-based ore sorting in 2020

Abstract Sensor-based ore sorting is not a new technology. It has been around since more than 70 years, mainly for diamond concentration, where it was applied to eliminate the security risk of diamonds being stolen from the previously applied grease-tables [13]. Despite a few installations in uranium ore processing, it had no further widespread acceptance in the minerals industry, mainly due to low design capacity. Besides that, sensor-based colour sorters were used in the food industry for small particle sizes (e. g., rice cleaning). It is fact that the first machine designs appropriate for coarse bulk materials were not developed for the minerals industry, but for the upcoming recycling industry for plastics, glass, paper, metals in the late 1980s. In this sector, besides some magnetic separators, all the work was done by manual hand-picking, and it needed automation. After some years of optimization, these machines showed reliable performance under harsh conditions in scrap yards and recycling plants. Then, finally, the minerals industry, which at first was not convinced that this rather complicated machines were suited to be used with minerals, began with the first applications. These first installations of sensor-based ore sorters around the late 1990, all of them equipped with line-scan optical cameras, were mainly in industrial minerals, such as calcite, magnesite, quartz or rock salt. Since then, the technology has seen an enormous development in terms of available sensors, design capacity and availability, and the number of installations for minerals is growing – steadily but slower than expected, considering the many advantages it brings.</jats:p

Sensor‐Based Ore Sorting Technology in Mining - Past, Present and Future

While the deposit qualities for mineral raw materials are constantly decreasing, the challenges for sustainable raw material processing are increasing. This applies not only to the demand for minimizing the consumption of energy, water, and reagents, but also to the reduction of residual materials, especially fine and difficult-to-landfill materials. Sensor-based ore sorting can be used as a separation process for coarser grain sizes before the application of fine comminution and separation technologies and is applicable for a large variety of mineral raw materials. Sensor-based ore sorting applies at various points in the process flow diagram and is suitable for waste elimination, for material diversion into different process lines, for the production of pre- and final concentrates, as well as for the reprocessing of coarse-grained waste dumps and other applications. The article gives an overview of the development and state of the art of sensor-based ore sorting for mineral raw materials and introduces various applications