Input significance analysis: feature ranking through synaptic weights manipulation for ANNS-based classifiers

Due to the ANNs architecture, the ISA methods that can manipulate synaptic weights selectedare Connection Weights (CW) and Garson’s Algorithm (GA). The ANNs-based classifiers thatcan provide such manipulation are Multi-Layer Perceptron (MLP) and Evolving Fuzzy NeuralNetworks (EFuNNs). The goals for this work are firstly to identify which of the twoclassifiers works best with the filtered/ranked data, secondly is to test the FR method by usinga selected dataset taken from the UCI Machine Learning Repository and in an onlineenvironment and lastly to attest the FR results by using another selected dataset taken fromthe same source and in the same environment. There are three groups of experimentsconducted to accomplish these goals. The results are promising when FR is applied, someefficiency and accuracy are noticeable compared to the original data.Keywords: artificial neural networks, input significance analysis; feature selection; featureranking; connection weights; Garson’s algorithm; multi-layer perceptron; evolving fuzzyneural networks

Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

The Shared Genetic Architecture of Modifiable Risk for Dementia and its Influence on Brain Health

Targeting modifiable risk factors for dementia may prevent or delay dementia. However, the mechanisms by which risk factors influence dementia remain unclear and current research often ignores commonality between risk factors. Therefore, my thesis aimed to model the shared genetic architecture of modifiable risk for dementia and explored how these shared pathways may influence dementia and brain health. I used linkage disequilibrium score regression and genomic structural equation modelling (SEM) to create a multivariate model of the shared genetics between Alzheimer’s disease (AD) and its modifiable risk factors. Although AD was genetically distinct, there was widespread genetic overlap between most of its risk factors. This genetic overlap formed an overarching Common Factor of general modifiable dementia risk, in addition to 3 subclusters of distinct sets of risk factors. Next, I performed two multivariate genome-wide association studies (GWASs) to identify the risk variants that underpinned the Common Factor and the 3 subclusters of risk factors. Together, these uncovered 590 genome-wide significant loci for the four latent factors, 34 of which were novel findings. Using post-GWAS analyses I found evidence that the shared genetics between risk factors influence a range of neuronal functions, which were highly expressed in brain regions that degenerate in dementia. Pathway analysis indicated that shared genetics between risk factors may impact dementia pathogenesis directly at specific loci. Finally, I used Mendelian randomisation to test whether the shared genetic pathways between modifiable dementia risk factors were causal for AD. I found evidence of a causal effect of the Common Factor on AD risk. Taken together, my thesis provides new insights into how modifiable risk factors for dementia interrelate on a genetic level. Although the shared genetics between modifiable risk factors for dementia seem to be distinct from dementia pathways on a genome-wide level, I provide evidence that they influence general brain health, and so they may increase dementia risk indirectly by altering cognitive reserve. However, I also found that shared genetics risk between risk factors in certain genomic regions may directly influence dementia pathogenesis, which should be explored in future work to determine whether these regions represent targets to prevent dementia