Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection

Qualitative and semi quantitative systems approaches to complex systems modelling – Intuitive and flexible models of biological systems : A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Lincoln University

Biological systems such as cell cycle are complex systems consisting of an enormous number of elements. These elements interact in ways that produce nonlinear and complex systems behaviour such as oscillations. A number of modelling approaches have been used to explain these kinds of systems; they are classified into four classes (continuous, discrete, stochastic and hybrid). Ordinary Differential Equations (ODEs) are used to mimic the continuous dynamic behaviour of system components, while discrete models can simulate the biological elements as straightforward binary variables providing a qualitative view of system behaviour. Stochastic models are used to model the effect of noise in biological systems. Combined methods together introduce hybrid models to cover the limitations of individual models and take advantage of their strengths. This study introduces a series of advanced models with increasing resolution from discrete to continuous in a systematic way to model the mammalian cell cycle system. Each model includes the essential controllers of mammalian cell cycle. Specifically, they reveal cell regulators that control cell cycle transitions from one phase to another in cell division. In the first model, this work introduces a new biological network based on a qualitative approach-Boolean network. A new Boolean model with 13 proteins can capture the essential aspects of cell cycle phases and it can simplify cell cycle control system. The developed new, simple and intuitive Boolean model can mimic the fluctuation of Cyclins during cell cycle. Furthermore, it can show cell cycle phases based on the changes in Cyclins since each Cyclin leads the transition of cell cycle phases. Also, some important proteins that are missing in most of the current models, such as SCF ubiquitin ligase and c-Myc, have been added to our model. This study provides a deep understanding of the mechanism of mammalian cell cycle regulation, especially when growth factors are present, and it can reveal the cell cycle process in terms of flow cytometry of Cyclin proteins. In addition to the aforementioned advancements related to the developed Boolean model, the model has captured the periodic sequence of activity of cell cycle regulatory proteins over repeated cell cycles. The existing discreet models failed to represent the periodic behaviour of cell cycle phase transitions and the correct activities for system species over repeated cell cycles. In the second model, another computing model based on degree of truth rather than the usual Boolean model with true or false (1 or 0) values is implemented. This second developed model is a fuzzy logic model. It involves the use of artificial intelligence to model a fuzzy cell cycle control system. Fuzzy logic model is a rule-based model that depends on the practical knowledge and heuristic design of complex systems. Specifically, this work utilizes fuzzy inference system features in the development process. Indeed, it is expected to provide approximate continuous dynamics for the discrete events using limited available data. When applied to the cell cycle system, the model reveals the intermediate states of concentrations of proteins during each cell cycle event. In addition, applying fuzzy logic provides more accurate representation of the cell cycle system than previous Boolean approaches. It can follow/explain the flow cytometry measurement levels of protein concentrations such as Cyclins. Also, the fuzzy model is expected to simplify and realistically represent the system and address the existing shortcomings of both discrete and continuous representations, specifically, discrete models’ restriction to 0 and 1 values for the states of proteins and the scarcity of available kinetic parameters in continuous models such as ODEs. Furthermore, for more accurate results and to automate the development of the fuzzy controller, the core of the fuzzy inference system has been successfully optimized using artificial intelligent approach Particle Swarm Optimization (PSO)

An Overview on Computational Approaches to Modeling Mammalian Cell Cycle

Biological systems such as cell cycle are “complex systems “consisting of an enormous number of elements interacting in ways that produce nonlinear and complex systems behavior. Computational modelling a promising approach to study such systems. However, representing structural and functional complexity of these systems is a major challenge to these models that can range from simpler discrete models such as Boolean networks to more complex mathematical model. Modeling methods and techniques have become popular for modeling biological systems because they can provide a deep understanding and insight of the complex biological system issues. These techniques can also be used in prediction, diagnosis and treatment of diseases such as cancer. This paper overlays current and existing computational modeling approaches used in modeling mammalian cell cycle (discrete, continuous, stochastic and hybrid). In addition, it introduces a set of opened research questions related to cell cycle system. Furthermore, this paper exposes the pros and cons of the existing modelling approaches and presents a more flexible and intuitive fuzzy logic based system framework to modeling cell cycle system

Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Organism network systems provide a biological data with high complex level. Besides, these data reflect the complex activities in organisms that identifies nonlinear behavior as well. Hence, mathematical modelling methods such as Ordinary Differential Equations model (ODE's) are becoming significant tools to predict, and expose implied knowledge and data. Unfortunately, the aforementioned approaches face some of cons such as the scarcity and the vagueness in the biological knowledge to expect the protein concentrations measurements. So, the main object of this research presents a computational model such as a neural Feed Forward Network model using Back Propagation algorithm to engage with imprecise and missing biological knowledge to provide more insight about biological systems in organisms. Therefore, the model predicts protein concentration and illustrates the nonlinear behavior for the biological dynamic behavior in precise form. Also, the desired results are matched with recent ODE's model and it provides precise results in simpler form than ODEs

The Expression of the Senescence-Associated Biomarker Lamin B1 in Human Breast Cancer

Senescence is a major response to cancer chemotherapy and has been linked to unfavorable therapy outcomes. Lamin B1 is a component of the nuclear lamina that plays a pivotal role in chromatin stability. Downregulation of lamin B1 represents an established biomarker for cellular senescence. However, the protein expression level of lamin B1 in malignant tissue, particularly of the breast, has not been previously described. In this work, we investigated lamin B1 protein expression in normal breast epithelium, malignant breast tissue (including adjacent non-malignant tissue) and in malignant tissue exposed to neoadjuvant chemotherapy (NAC) using immunohistochemistry (IHC) in three patient groups (n = 15, n = 87, and n = 43, respectively). Our results indicate that lamin B1 mean positive expression was 93% in normal breast epithelium and 88% in malignant breast cells, but significantly decreased (mean: 55%, p < 0.001) in malignant breast tissue after exposure to NAC, suggestive of senescence induction. No significant association between lamin B1 expression and other clinicopathological characteristics or survival of breast cancer patients was recorded. To our knowledge, this is the first report that established the baseline protein expression level of lamin B1 in normal and malignant breast tissue, and its reduction following exposure to chemotherapy. In conclusion, lamin B1 downregulation can be used reliably as a component of multiple biomarker batteries to identify therapy-induced senescence (TIS) in clinical cancer