A Machine Learning Method to Synthesize Channel State Information Data in Millimeter Wave Networks

In millimeter-wave (MMW) networks, the channel state information (CSI) carries essential information from the user to the base station (BS). The CSI values depend highly on the geometrical and physical features of the environment. Therefore, it is impossible to generate CSI data for computer simulations or analysis through mathematical models. The CSI in MMW networks can only be acquired through physical measurement(s) or with the help of expensive and complicated ray-tracing software. For many users, both these options are infeasible. This work aims to propose a simple and fast method that can generate artificial samples from the real data samples while ensuring that the artificial samples look similar to the real ones. The proposed method helps increase the size of existing CSI datasets and likely to benefit the evolution of deep learning models that need a large amount of training/testing data. The proposed method comprises two parts. (i) The first part applies data clustering and transformations such as principal component analysis (PCA)-based dimensionality reduction and probability integral transform (PIT) to convert the real data into a multivariate normal distribution of a smaller number of variables, and (ii) The second part synthesizes artificial data by learning from the multivariate normal distribution of the first part. The last step in the second part is to apply PIT and inverse PCA transformations to transform the artificial data into the same space as the input data. We compared the proposed method’s performance with the well-known Kernel density estimation (KDE)-based methods that use Scott’s rule and Silverman’s rule to choose the bandwidth parameter value. The results show that the artificial samples generated by the proposed method exhibit very high similarity with the real ones as compared to the KDE-based methods

Modeling and simulation of the "IL-36 cytokine" and CAR-T cells interplay in cancer onset

Background: CAR-T cells are chimeric antigen receptor (CAR)-T cells; they are target-specific engineered cells on tumor cells and produce T cell-mediated antitumor responses. CAR-T cell therapy is the "first-line" therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and leukemia. This adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since, precisely for this type of tumor, the use of immune checkpoint inhibitors remained disappointing. Although CAR-T is a promising therapeutic technique, there are therapeutic limits linked to the persistence of these cells and to the tumor's immune escape. CAR-T cell engineering techniques are allowed to express interleukin IL-36, and seem to be much more efficient in antitumoral action. IL-36 is involved in the long-term antitumor action, allowing CAR-T cells to be more efficient in their antitumor action due to a "cross-talk" action between the "IL-36/dendritic cells" axis and the adaptive immunity. Methods: This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of tumor. The model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy: IL-36, "armored" CAR-T cells (i.e., engineered to produce IL-36) and the tumor cell population, focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T cells. Mathematical model was developed and numerical simulations were carried out during this research. The development of the model with stability analysis by conditions of Routh-Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment, making therapy more effective against the "solid tumor". Findings: Primary malignant bone tumors are quite rare (about 3% of all tumors) and the vast majority consist of osteosarcomas and Ewing's sarcoma and, approximately, the 20% of patients undergo metastasis situations that is the most likely cause of death. Interpretation: In bone tumor like osteosarcoma, there is a variation of the cellular mechanical characteristics that can influence the efficacy of chemotherapy and increase the metastatic capacity; an approach related to adoptive immunotherapy with CAR-T cells may be a possible solution because this type of therapy is not influenced by the biomechanics of cancer cells which show peculiar characteristics