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Communities in WIReDSpace

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Now showing 1 - 5 of 19

Recent Submissions

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    Commissioning and evaluation of electron Monte Carlo dose calculation algorithm in the Monaco treatment planning system
    (University of the Witwatersrand, Johannesburg, 2025-06) Motseki, Masupha; van der Merwe, Debbie; Ngcezu, Sonwabile
    This study aimed to validate and commission the electron Monte Carlo (eMC) algorithm integrated into the Monaco Treatment Planning System (TPS) (version 6.1.2) in accordance with the American Association of Physicists in Medicine (AAPM) Medical Physics Practice Guideline (MPPG) 5a of 2015. The initial task involved validating the beam data used for beam modelling, followed by three verification tests using a homogeneous water-equivalent and a locally designed inhomogeneous phantom. The computed dose distributions from the eMC algorithm were evaluated against the measured data. In the homogeneous water-equivalent phantom, profiles, output factors and percentage depth dose (PDD) curves were evaluated for multiple electron energies at standard and extended source-to-surface distances (SSDs) using different applicators, including an irregularly shaped trapezoid cutout. PDDs and profiles were also computed at an oblique gantry angle of 20°. For the heterogeneity tests, low-density polystyrene was used to simulate lung tissue and solid Plaster of Paris (POP) was utilized to simulate bone. Cross-plane profiles distal to the inhomogeneities were computed by the eMC algorithm and compared to those measured with a planar detector. The study also investigated the performance of the algorithm by varying grid spacing and the number of histories. A one dimensional gamma analysis with 3 %/3 mm criterion was utilized to compare measured and computed PDDs. The eMC algorithm demonstrated agreement within 3 %/3 mm for PDDs and profiles across all applicators and beam energies. However, discrepancies were observed for the irregular trapezoid cutout, where the agreement fell below 90 percent, indicating that limitations exist in the dosimetry of field sizes below 3 cm radius at beam energies greater than 12 MeV. Additionally, deviations in output factors were noted, with a maximum error of 6.5 percent. Overall, the eMC algorithm computed the dose distributions with sufficient accuracy in both homogeneous and inhomogeneous phantoms, and the optimal input parameters were identified as a 0.2 cm grid spacing and 500,000 histories for all energies.
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    Analyses of cassava phytohormones in biotic responses during South African cassava mosaic virus infection
    (University of the Witwatersrand, Johannesburg, 2025-06) Mokoka, Oboikanyo Austin; Sizani, Bulelani; Rey, Chrissie
    Manihot esculenta Crantz (cassava) is a starch-rich perennial tuber crop that serves as a food source, in addition to having industrial applications. Concerningly, cassava is susceptible to infection by South African cassava mosaic virus, resulting in the development of cassava mosaic disease (CMD). As CMD results in significant losses of cassava yields, it poses a threat to food security. South African cassava mosaic virus (SACMV) encodes for multifunctional proteins that aid in infection and suppress host immune responses, which are coordinated by plant hormones. Phytohormones are activated during a pathogenic infection, and include salicylic acid (SA), jasmonic acid (JA), ethylene (ET), and abscisic acid (ABA). The main aim of this research project was to quantify, using Ultra-High Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS), the phytohormone levels of SA, JA, ABA, and ET in the CMD-susceptible T200 and CMD-tolerant TME3 across the three stages of SACMV infection, namely 12-, 32-, and 67-days post infection (dpi). This was to determine if these phytohormones contribute to either susceptibility or recovery, and to relate the hormone levels of the expression of SACMV genes and cassava hormone-related genes, assessed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Infection trials and subsequent gene expression studies revealed that T200 had a significantly higher SACMV load compared to TME3 at 32- and 67- dpi, and this correlated with the significantly higher expression of SACMV genes in T200. Additionally, the activities of these viral proteins promoted virus fitness in T200 via dysregulation of cell proliferation, promotion of viral replication and transmission, disruption of global metabolism, disruption of plant defences, and the suppression of post-transcriptional gene silencing (PTGS) and transcriptional gene silencing (TGS). Contrastingly, TME3 recovered by 67 dpi and was tolerant to SACMV infection. The quantification of hormone levels and assessment hormone-related gene expression revealed that TME3 relies on an early response of ABA- and JA-mediated PTGS and SA-driven defence, while its recovery is reliant on SA-, ET-, and JA-driven defence responses and the establishment of systemic acquired resistance (SAR). Lastly, following recovery, TME3 tolerates SACMV via induced systemic resistance (ISR) that is reliant on finetuned JA-, ET-, and ABA- signalling.
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    Investigation into the molecular interaction between forkhead box P3 (FOXP3) and vitamin D receptor (VDR)
    (University of the Witwatersrand, Johannesburg, 2025-06) Hurwitz, Jessica Sarah; Meyer, Vanessa; Fanucchi, Sylvia
    Forkhead box P3 (FOXP3) is a transcription factor that is essential in the differentiation of T cells into regulatory T cells (Tregs), which play a critical role in regulating the adaptive immune system. FOXP3 expression is in turn regulated by the vitamin D receptor (VDR) and its ligand, 1,25-dihydroxyvitamin D3 (1,25(OH)D3). VDR is a transcription factor with a diverse range of functions, including immune regulation. However, whether these two proteins cooperatively regulate the immune system through direct interaction following FOXP3 expression is yet unknown. This study aimed to determine whether the VDR ligand binding domain (LBD) and the DNA-binding forkhead domain (FHD) of FOXP3 interact in the presence and absence of 1,25(OH)D3. Computational and experimental interaction studies were performed for cross-verification. An in silico study was done using LZerd and HDock to predict if the LBD of VDR interacted with the FHD of FOXP3 in the presence and absence of 1,25(OH)D3. For the experimental studies, these domains were thus overexpressed in E. coli cells, purified using immobilised metal affinity chromatography, and were then structurally and functionally characterised. After successful overexpression and purification, the interaction was further studied in vitro using fluorescence anisotropy. VDR LBD was found to interact at helix 3 (the DNA binding helix) of FOXP3 FHD in silico when both in the presence and absence of 1,25(OH)D3. Additionally, VDR LBD and FOXP3 FHD were found to interact in vitro only in the presence of 1,25(OH)D3 with a Kd of 0.2 μM. No interaction occurred in the absence of 1,25(OH)D3 in vitro. Thus, a medium affinity protein-protein interaction between the two immune regulatory proteins, VDR LBD and FOXP3 FHD, was discovered for the first time in this study, and 1,25(OH)D3 was identified as being a key player in this interaction, furthering our understanding of their important role in the coregulation of the immune system.
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    Maximising Research Impact and Visibility Through Digital Scholarship and Open Platforms
    (University of the Witwatersrand, Johannesburg, 2026-03) Matizirofa, Lazarus Gallant
    Not Available
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    Alert signal classification and prediction using natural language processing for the title calorimeter of atlas
    (University of the Witwatersrand, Johannesburg, 2025-03) Perikli, Nicholas; Mellado, Bruce
    This study investigated temporal patterns and spatial inhomogeneities in the A Toroidal Large Hadron Collider ApparatuS Tile Calorimeter Detector Control System (ATLAS TileCal DCS) alarm logs to minimize downtime and enhance data collection efficiency for double Higgs boson production during high-luminosity (HL) operations of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) post the Phase-II upgrade in 2029. By applying Natural Language Processing (NLP) to detector control signals and machine learning (ML) techniques to train Long Short Term Memory (LSTM) models for both classification of future alarm types and forecasting categorical alarm rates in problematic modules, the approach achieved Mean Absolute Percentage Error (MAPE) scores between 12–23% and an overall classification accuracy of 75%. These results demonstrate the feasibility of a ML–driven predictive maintenance strategy, providing a possible early detection mechanism to optimize di-Higgs boson detection. Although further testing is needed before full integration, this LSTM-based framework shows promise for reducing unplanned downtime and accelerating scientific progress in the HL-LHC era.