Hickman Chang (coffeewhite39)

A random forest machine learning model was built to predict compound growth rate after a SIP order and was found to have an accuracy of 92.3%. The random forest found that population, longitude, and population per square mile were the most important features when predicting the effect of SIP. Conclusions SIP orders were found to be effective at reducing the growth rate of COVID-19 cases in the US. Counties with a large population or a high population density were found to benefit the most from a SIP order.Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.American Trypanosomiasis, a parasitic disease produced by Trypanosoma cruzi (T. cruzi), endemic in Latin America, infects about 6 million people. During the chronic stage of the infection, approximately 30% of infected people will develop Chagas Disease, the clinical manifestation. Few decades ago it was reported that, during the chronic stage, the parasite interferes with the development of solid tumors. However, the identification of parasite molecules responsible for such effects remained elusive. Years later, we described T.cruzi Calreticulin (TcCalr), an endoplasmic reticulum resident chaperone that infective trypomastigotes translocate to the parasite exterior, where it displays anticomplement activities. Most likely, at least some of these activities are related with the antitumor properties of TcCalr, as shown in in vitro, ex vivo, in ovum, and in vivo models. In this context we, we have seen that in vivo subcutaneous peritumoral inoculation of rTcCalr enhances local infiltration of T cells and slows tumor development. Based on these precedents, we propose that in vitro treatment of a mammary adenocarcinoma (TA3 cell line) with rTcCalr, will enhance tumor immunogenicity. In agreement with this proposal, we have shown that i). rTcCalr binds to TA3 cells in a concentration-dependent fashion, ii). C1q binds to TA3 cells in an rTcCalr-dependent fashion, confirmed by the reversion attained using anti-TcS (a central TcCalr domain that binds C1) F(ab')2 antibody fragments, iii). incubation of TA3 cells with rTcCalr, promotes cell phagocytosis by murine macrophages and, iv). rTcCalr decreases the membrane expression of MHC class II, m-Dectin-1, Galectin-9 and PD-L1, while increasing the expression of Rae-1γ. In synthesis, herein we show that in vitro treatment of a murine mammary adenocarcinoma with rTcCalr enhances phagocytosis and modulates the expression of a variety of membrane molecules that correlates with increased tumor immunogenicity.CD8+ T cells are crucial for immunity against viral infections, including HIV. Several characteristics of CD8+ T cells, such as polyfunctionality and cytotoxicity, have been correlated with effective control of HIV. However, most of these correlates have been established in the peripheral blood. Meanwhile, HIV primarily replicates in lymphoid tissues. Therefore, it is unclear which aspects of CD8+ T cell biology are shared and whi