Rhodes University is a public research university located in Makhanda (Grahamstown) in the Eastern Cape Province of South Africa. It is one of four universities in the province. Established in 1904, Rhodes University is the province's oldest university, and it is the sixth oldest South African university in continuous operation, being preceded by the University of the Free State (1904), University of Witwatersrand (1896), University of South Africa (1873) as the University of the Cape of Good Hope, Stellenbosch University (1866) and the University of Cape Town (1829). Rhodes was founded in 1904 as Rhodes University College, named after Cecil Rhodes, through a grant from the Rhodes Trust. It became a constituent college of the University of South Africa in 1918 before becoming an independent university in 1951.The university had an enrolment of over 8,000 students in the 2015 academic year, of whom just over 3,600 lived in 51 residences on campus, with the rest (known as Oppidans) taking residence in digs (off-campus residences) or in their own homes in the town.
Early detection of fruit fly (Diptera: Tephritidae) infestation in traded fruit is important for maintaining phytosanitary security, particularly for quarantine pests. This study evaluated the integration of micro-focus X-ray computed tomography (& micro;CT) and deep learning as a non-destructive, automated, and scalable solution for postharvest pest detection. The Mediterranean fruit fly (medfly), Ceratitis capitata Wiedemann, 1824, was used as a model species and Navel oranges, Citrus sinensis (Osbeck), served as the host commodity. Controlled infestation experiments were conducted using both artificially punctured fruit to promote oviposition and intact fruit to avoid confounding effects associated with mechanical damage. Oranges were exposed to varying adult medfly densities to generate different infestation levels. Following exposure, the infested oranges were subjected to varying incubation periods to simulate different infestation stages. High-resolution & micro;CT scans were first assessed manually and subsequently analyzed using a staged deep learning pipeline incorporating convolutional neural networks (CNNs), 3D CNNs, CNN-Long Short-Term Memory (LSTM) hybrids and Transformer-based models. Manual mu CT inspection achieved mean correct classification rates of 0.84 in punctured fruit and 0.69 in intact fruit, with detection performance increasing with infestation stage. Deep learning analyses were conducted on a curated punctured-fruit dataset using a slice-level classification and voting-based aggregation strategy. Among the evaluated transfer-learning architectures, DenseNet121 achieved the best overall performance, reaching an accuracy of 0.88, a precision of 0.86, a sensitivity of 1.00, and an AUC of 0.80; because the manual and automated analyses were performed on different sets of fruit, these accuracies are complementary rather than directly comparable. Grad-CAM and attention visualizations confirmed that model predictions were driven by biologically meaningful tissue regions associated with larval damage. Although the deep learning workflow was developed and validated only on punctured fruit, the results demonstrate that mu CT images contain sufficient information for automated detection of medfly infestation when infestation-associated damage is visible. These findings support the potential of combining mu CT imaging and artificial intelligence (AI) for automated, reproducible and nondestructive postharvest pest detection and enhanced phytosanitary compliance in postharvest supply chains.
The fruit-piercing moth, Serrodes partita (Fabricius), is an economically important pest of citrus and other commercial fruit in South Africa, particularly during outbreaks. Adults puncture fruit to feed, causing direct damage and fruit decay. Other fruit-feeding moth species may also be attracted to damaged fruit, contributing to the overall fruit-feeding moth complex. This study evaluated bait types, bait presentation methods and trap designs to develop an effective trapping system for S. partita and other fruit-feeding moths under citrus orchard conditions. Fresh banana consistently outperformed the synthetic and banana-derived attractants evaluated. In the bait trial, fresh banana captured approximately 4.2-, 5.0-, and 25.0-fold more S. partita than banana essence and two Australian proprietary synthetic bait formulations, respectively. Combining banana with agar maintained its attractiveness to other fruit-feeding moths, with captures approximately 1.8- and 2.2-fold higher than those for fresh banana and banana formulated with a superabsorbent polymer, respectively. Funnel traps consistently outperformed bucket, delta and disc traps. Modification of the funnel trap with an electric grid further increased captures of other fruit-feeding moths approximately 4.3-fold relative to conventional funnel traps. These results demonstrate that fresh banana and banana and agar combined are effective attractants, and that the funnel trap is the most effective trap design of those investigated. These relatively simple and potentially low-cost components provide a practical basis for improved monitoring of fruit-feeding moths and for managing outbreak populations in commercial orchards.
Cancer is a multifactorial disease and remains a significant problem owing to its complex nature. There is an urgent need to identify and design hybrid anticancer drugs that achieve a desired biological effect by combining two or more pharmacophores into a single molecule. Herein, we utilized MOLHYBRID program to generate a chemical library of 46740 unique molecules based on known anticancer pharmacophores and targeting the cancer protein, using the crystal structure of the B-Raf V600E oncogenic mutant in complex PLX4033 (vemurafenib) (PDB ID: 3OG7). The B-Raf V600E mutant is prevalent cancer-associated kinase mutation, particularly in melanoma, where it promotes the activation of the RAF/MEK/ERK. A comprehensive analysis of the chemical library and virtual screening was conducted using several computational methods such as molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT). To further elucidate the distinctiveness and three-dimensionality (3D) of the molecules, several other techniques such as principal moment of inertia (PMI), Murcko scaffold analysis, molecular frameworks (MFs), and multi-fusion similarity maps (MFSM) were used. The filtering cut-off was performed using the synthetic accessibility score (SAS) and quantitative estimation of drug-likeness (QED) using the criteria SAS values < 6 and QED values > 0.4. After intensive filtering of the molecules also based on drug-likeness rules, molecule 40254 had the strongest binding energy of -8.70 kcal/mol. The pharmacological evaluation suggested that the selected molecules showed adherence to Lipinski's rule with minimal violations (0 - 1). The four molecules (40254, 39155, 11090, and 7879), which were selected as potential hits, exhibited stability in the binding pocket of the protein during the MD simulations. Overall, the root mean square deviation (RMSD) remained stable at around 4.0 & Aring;. The analyses of evaluating stability also include the root mean square fluctuation (RMSF), protein-ligand contacts, and radius of gyration (Rg). DFT was used to investigate the chemical reactivity, and it suggested that the most reactive molecule was molecule 14186 with a small energy gap of 3.09 eV, a high softness of 0.64 eV, and a greater electrophilicity index of 4.46 eV. Collectively, these findings justify further experimental work on the four promising candidates for anti-cancer agents.
The 2025 Third United Nations Oceans Conference (UNOC3) called for “a strong science-policy interface to provide timely, credible and salient scientific and socioeconomic information to inform policies and action”. Renowned naturalist David Attenborough's narration of the recent film Ocean illustrates an important challenge to achieving that goal. It was released with much publicity in the lead-up up to UNOC3. The film presented a clear agenda and was intended to influence world leaders ahead of the Conference. In our view, while the film had merits, it presented over-simplistic and misleading messages about the nature of the challenges to the oceans and their solutions. Focused on the goal of a strong science-policy interface, this contribution examines the use of celebrities to influence public policy, the reasons for their influence, and the impacts they can have on the public and policy-makers. These impacts can lead to misguided and biased policies that will not achieve important societal objectives, such as the Sustainable Development Goals. The likelihood of flawed policies increases when the influence of celebrities on policy is based on other than the best available scientific information. To avoid or reduce the risk of this, we make recommendations on strengthening scientific advice and improving communication between scientists, policy-makers and the public. The media and celebrities could be important intermediaries in achieving this. Recommendations include scientists making greater efforts to improve communication and public trust in science, as well as other building blocks needed to ensure a sound scientific basis for policy and its implementation.
Solar photovoltaic (PV) systems can provide a clean and sustainable alternative to fossil fuel energy, but the uptake of solar PV systems is often constrained by perceived and actual barriers. Despite South Africa's very high solar generation potential, solar PV adoption remains low, accounting for less than 10% of the country's energy mix. Central to renewable energy debates is making sure that the transition is equitable, affordable, and inclusive. Yet, despite high levels of inequality, there is no empirical standing on the factors hindering solar PV adoption among a socially differentiated household gradient. Using household interviews, the study investigated barriers to solar PV adoption across an income gradient in four towns located in the Eastern Cape province of South Africa. Results showed that significantly more high-income (82%) than low-income (63%) households had considered solar PV adoption, primarily to mitigate power cuts (89%). Both income groups were constrained by financial, institutional, and social barriers, but low-income households were disproportionately affected. While 86% of high-income households cited initial financial constraints, low-income households were nearly twice as likely to worry about high maintenance costs (48% vs. 26%) and system unfamiliarity (36% vs. 14%). High-income respondents were more concerned with institutional and technical barriers, such as a lack of supportive policies and grid-feed difficulties. Nearly all (98%) low-income households lacked knowledge regarding solar PV system capacity, compared to 60% for high-income households. Altogether, the results highlight that heterogeneity matters in our understanding of barriers to solar PV adoption and should be a key consideration in designing differentiated approaches to address barriers and ensure equity in renewable energy adoption.
