PIM kinase inhibition: co-targeted therapeutic approaches in prostate cancer

PIM kinases have been shown to play a role in prostate cancer development and progression, as well as in some of the hallmarks of cancer, especially proliferation and apoptosis. Their upregulation in prostate cancer has been correlated with decreased patient overall survival and therapy resistance. Initial efforts to inhibit PIM with monotherapies have been hampered by compensatory upregulation of other pathways and drug toxicity, and as such, it has been suggested that co-targeting PIM with other treatment approaches may permit lower doses and be a more viable option in the clinic. Here, we present the rationale and basis for co-targeting PIM with inhibitors of PI3K/mTOR/AKT, JAK/STAT, MYC, stemness, and RNA Polymerase I transcription, along with other therapies, including androgen deprivation, radiotherapy, chemotherapy, and immunotherapy. Such combined approaches could potentially be used as neoadjuvant therapies, limiting the development of resistance to treatments or sensitizing cells to other therapeutics. To determine which drugs should be combined with PIM inhibitors for each patient, it will be key to develop companion diagnostics that predict response to each co-targeted option, hopefully providing a personalized medicine pathway for subsets of prostate cancer patients in the future

Co-targeting PIM and PI3K/mTOR using multikinase inhibitor AUM302 and a combination of AZD-1208 and BEZ235 in prostate cancer

PIM and PI3K/mTOR pathways are often dysregulated in prostate cancer, and may lead to decreased survival, increased metastasis and invasion. The pathways are heavily interconnected and act on a variety of common efectors that can lead to the development of resistance to drug inhibitors. Most current treatments exhibit issues with toxicity and resistance. We investigated the novel multikinase PIM/PI3K/mTOR inhibitor, AUM302, versus a combination of the PIM inhibitor, AZD-1208, and the PI3K/mTOR inhibitor BEZ235 (Dactolisib) to determine their impact on mRNA and phosphoprotein expression, as well as their functional efcacy. We have determined that around 20% of prostate cancer patients overexpress the direct targets of these drugs, and this cohort are more likely to have a high Gleason grade tumour (≥Gleason 8). A co-targeted inhibition approach ofered broader inhibition of genes and phosphoproteins in the PI3K/mTOR pathway, when compared to single kinase inhibition. The preclinical inhibitor AUM302, used at a lower dose, elicited a comparable or superior functional outcome compared with combined AZD-1208 +BEZ235, which have been investigated in clinical trials, and could help to reduce treatment toxicity in future trials. We believe that a co-targeting approach is a viable therapeutic strategy that should be developed further in pre-clinical studies

Multi-UAV Allocation Framework for predictive crime deterrence and data acquisition

The recent decline in the number of police and security force personnel has raised a serious security issue that could lead to reduced public safety and delayed response to crimes in urban areas. This may be alleviated in part by utilizing micro or small unmanned aerial vehicles (UAVs) and their high-mobility on-board sensors in conjunction with machine-learning techniques such as neural networks to offer better performance in predicting times and places that are high-risk and deterring crimes. The key to the success of such operation lies in the suitable placement of UAVs. This paper proposes a multi-UAV allocation framework for predictive crime deterrence and data acquisition that consists of the overarching methodology, a problem formulation, and an allocation method that work with a prediction model using a machine learning approach. In contrast to previous studies, our framework provides the most effective arrangement of UAVs for maximizing the chance to apprehend offenders whilst also acquiring data that will help improve the performance of subsequent crime prediction. This paper presents the system architecture assumed in this study, followed by a detailed description of the methodology, the formulation of the problem, and the UAV allocation method of the proposed framework. Our framework is tested using a real-world crime dataset to evaluate its performance with respect to the expected number of crimes deterred by the UAV patrol. Furthermore, to address the engineering practice of the proposed framework, we discuss the feasibility of the simulated deployment scenario in terms of energy consumption and the relationship between data analysis and crime prediction

Performance Assessment of Global Optimization Algorithms in Automatic Calibration of Groundwater Models

Calibration of groundwater models can be done manually by comparing the measured and computed groundwater heads or automatically using algorithms which do the work for you. Modules for this purpose are included in or linked externally to traditional groundwater modelling software and used. One such module PEST (Parameter ESTimation) uses the Levenburg Marquardt algorithm which is a combination of Gauss Newton and Gradient Descent methods to find the minima of a function. However, PEST is capable of finding only a local minimum for the objective function. A possibility exists that there is a global minimum that better fits our function and could give even better results for the optimization problem in, in our case, parameter estimation in groundwater models. A performance assessment has been done with a Genetic algorithm on a groundwater modelling problem in this study

Protean authentication scheme - a time-bound dynamic KeyGen authentication technique for IoT edge nodes in outdoor deployments

IoT edge/sensor nodes are exposed to large attack surface and could easily succumb to several well-known attacks in the wireless sensor network (WSN) domain. Authenticating edge nodes before they join a network especially after a sleep state is a critical step to maintain the overall trust of any given IoT Local Area Network (IoT LAN). The low resources and computational constraints of such IoT nodes make this a challenging and non-trivial problem. As many IoT deployments are in uncontrolled environments, IoT devices are often placed in the open so that physical access to them is inevitable. Due to easy physical access of the devices, common attacks including cloning of devices or stealing secret keys stored in an edge node are some of the most common attacks on IoT deployments. This paper focuses on developing an extremely lightweight authentication scheme for constrained end-devices that are part of a given IoT LAN. Authentication occurs between the end-device and the gateway which acts as an edge computing device. The proposed authentication scheme is put through both formal and informal security verification. Voltage drop, current, and power are measured to gauge the overall impact of the security scheme. All the three parameters were measured while configuring the edge node as an end-device or as a router. Our testing results show that the impact on the resources was minimal