Error-based Analysis of VEP EEG Signal using LMS

Electroencephalography (EEG) involves the usage of electrodes placed on the human scalp to record electrical impulses generated by the brain. One of the many components that are present in EEG signals is the Visually Evoked Potential (VEP), whereby brief electrical impulses are generated as a result of the presence of visual stimuli. The aim of this project is to analyse EEG signals that contain VEP using the least-mean squares (LMS) method and differentiate between alcoholic and non-alcoholic subjects based on the resultant error signal. This LMS method is a form of adaptive filter that minimizes the mean square of the cost function for every iteration it undergoes and is widely used in many signal imaging applications due to its simplicity in implementation and low computational complexity. The EEG recording with VEP components is already available so the scope of the project only covers the adaptation of the LMS adaptive filter and the analysis of the VEP EEG error signals for 5 alcoholic and non-alcoholic subjects. The analysis of the results indicate that there is a certain range of standard deviation values in which it is possible to classify the condition of the subject into either alcoholic or non-alcoholic condition. vi

Error-based Analysis of VEP EEG Signal using LMS

Electroencephalography (EEG) involves the usage of electrodes placed on the human scalp to record electrical impulses generated by the brain. One of the many components that are present in EEG signals is the Visually Evoked Potential (VEP), whereby brief electrical impulses are generated as a result of the presence of visual stimuli. The aim of this project is to analyse EEG signals that contain VEP using the least-mean squares (LMS) method and differentiate between alcoholic and non-alcoholic subjects based on the resultant error signal. This LMS method is a form of adaptive filter that minimizes the mean square of the cost function for every iteration it undergoes and is widely used in many signal imaging applications due to its simplicity in implementation and low computational complexity. The EEG recording with VEP components is already available so the scope of the project only covers the adaptation of the LMS adaptive filter and the analysis of the VEP EEG error signals for 5 alcoholic and non-alcoholic subjects. The analysis of the results indicate that there is a certain range of standard deviation values in which it is possible to classify the condition of the subject into either alcoholic or non-alcoholic condition. vi

Experimental and CFD Modelling: Impact of the Inlet Slug Flow on the Horizontal Gas–Liquid Separator

For a gas-liquid separator sizing, many engineers have neglected the flow pattern of incoming fluids. The impact of inlet slug flow which impeded onto the separator’s liquid phase will cause a separator fails to perform when sloshing happened in the separator. To date, the study on verifying the impact of inlet slug flow in a separator remains limited. In this paper, the impact of inlet momentum and inlet slug flow on the hydrodynamics in a separator for cases without an inlet device were investigated. The experimental and Computational Fluid Dynamics (CFD) results of cavity formation and sloshing occurrence in the separator in this study were compared. A User Defined Function (UDF) was used to describe the inlet slug flow at the separator inlet. Inlet slug flow occurred at inlet momentum from 200 to 1000 Pa, and sloshing occurred in the separator at 1000 Pa. Both experimental and simulated results showed similar phenomena

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.ASTAR (Agency for Sci., Tech. and Research, S’pore