Abstract: Downregulated miR-99b-5p and upregulated mTOR cooperatively promotes the African American (AA) PCa aggressiveness and drug resistance. Nuclear mTOR, AR, and SMARCD1 are highly expressed in AA PCa (MDA PCa 2b) compared to EA PCa (LNCaP) cell line. miR-99b-5p inhibited protein levels of mTOR, AR/AR-V7 and SMARCD1 in cytoplasm and nuclei of EA and AA PCa. miR-99b-5p effectively inhibits cell proliferation/survival and induced cell apoptosis in EA and AA PCa cells. Moreover, combination of miR-99b-5p and enzalutamide (Enz) synergistically enhances the cytotoxicity against aggressive AA PCa and castration resistant prostate cancer (CRPC). miR-99b-5p or miR-99b-5p/Enz significantly reduces the recruitment of mTOR to the genes involved in the metabolic reprogramming in CRPC. miR-99b-5p can function as an epigenomic driver to modulate the mTOR/AR/SMARCD1 signaling axis in AA PCa and resistant CRPC. miR-99b-5p can be utilized as a biomarker for identifying the presence of prostate cancer.

Abstract: This disclosure is related to using network flow information of a network to determine the trajectory of an attack. In some examples, an adjacency data structure is generated for a network. The adjacency data structure can include a machine of the network that has interacted with another machine of the network. The network can further include one or more deception mechanisms. The deception mechanisms can indicate that an attack is occurring when a machine interacts with one of the deception mechanisms. When the attack is occurring, attack trajectory information can be generated by locating in the adjacency data structure the machine that interacted with the deception mechanism. The attack trajectory information can correlate the information from the interaction with the deception mechanism, the interaction information of the network, and machine information for each machine to determine a possible trajectory of an adversary.

Abstract: This disclosure is related to using network flow information of a network to determine the trajectory of an attack. In some examples, an adjacency data structure is generated for a network. The adjacency data structure can include a machine of the network that has interacted with another machine of the network. The network can further include one or more deception mechanisms. The deception mechanisms can indicate that an attack is occurring when a machine interacts with one of the deception mechanisms. When the attack is occurring, attack trajectory information can be generated by locating in the adjacency data structure the machine that interacted with the deception mechanism. The attack trajectory information can correlate the information from the interaction with the deception mechanism, the interaction information of the network, and machine information for each machine to determine a possible trajectory of an adversary.