Muni University (MU) is a public multi-campus university in Uganda. It is one of the public universities and degree-awarding institutions in the country, licensed and supervised by the Uganda National Council for Higher Education (UNCHE).
Abstract Cassava (Manihot esculenta) is an important staple food in sub–Saharan Africa. Over 60% of the global cassava production occurs in Africa, with the southern African region contributing approximately 15% to this total. Compared to west and central Africa, the southern Africa region has not fully realised its potential for cassava production. This study examines cassava-related research in southern Africa focusing on current trends, challenges, and contributions of cassava to food security, economic development, and climate resilience. It also explores industrial applications, the implications for sustainable agriculture, and identifies critical policy and research gaps through a bibliographic network analysis. One hundred and ninety-two eligible research manuscripts published between 2000 and 2024 were retrieved from the Scopus and Web of Science databases. Most cassava research has been conducted in South Africa, followed by Zambia, Mozambique and Malawi. Cassava pests and diseases, crop management, yield and food security were the most researched themes. The region’s average actual yield (9.5 t/ha) compared with the potential yield (75–80 t/ha) highlights the untapped potential in cassava production. Findings from climatic studies predict a substantial increase in the area suitable for cassava production in the region. Some studies have highlighted the potential of cassava as a raw material for biofuel and industrial starch. This study highlights the current body of knowledge and identifies research gaps concerning cassava, which various stakeholders can explore to achieve significant advancements in promoting cassava cultivation in the region.
The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) provides global Radio Occultation (RO) measurements of ionospheric total electron content (TEC), but these values are systematically underestimated relative to ground-based Global Navigation Satellite System (GNSS)-derived TEC due to the exclusion of the plasmaspheric contribution. This study presents a machine learning calibration framework that transforms COSMIC TEC into GNSS-equivalent values. Using co-located COSMIC and GNSS observations from 2006 to 2025, we developed neural network models (ROTEC-A and ROTEC-B) trained on (19 and 22) input features respectively, including COSMIC profile parameters, spatiotemporal descriptors, and optionally, solar and geomagnetic activity indices. Results show that the calibration effectively mitigates systematic underestimation, reducing mean bias from 6.97 TECU (uncalibrated COSMIC) to near zero (0.02-0.03 TECU). The calibrated products also substantially reduce skewness in residuals, yielding nearly symmetric error distributions suitable for data assimilation. Across various latitudinal, local time, and seasonal sectors, mean absolute errors were reduced by 50-75%, with the best performance at mid-latitudes and slightly elevated errors in high-latitude and equatorial regions. Although, the inclusion of solar and geomagnetic indices yielded marginal improvements, statistical tests confirmed no significant advantage over the baseline model. The operationally oriented framework outputs calibrated GNSS-equivalent TEC in near real-time, providing enhanced ionospheric monitoring capability, especially over GNSS-sparse regions such as oceans and deserts.These results demonstrate the potential of COSMIC RO data, once calibrated, to serve as a reliable complement to GNSS observations for ionospheric research, space weather monitoring, and operational applications. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
BACKGROUND:Hunger and malnutrition are major global issues particularly in conflict and disaster-affected areas. Own food production has been considered as effective strategy to improve nutrition outcomes. However, evidence on its contribution to dietary diversity and nutrient intake adequacy, especially in refugee settings with limited access to land remains limited. This study assessed the contribution of own food production to dietary diversity and nutrient intake adequacy among refugees in Palorinya settlement, northwestern Uganda. METHODS:A cross-sectional survey was conducted among 316 randomly selected households in Palorinya refugee settlement, northwestern Uganda. Current household dietary diversity score (HDDS) was assessed using a 24-hour dietary recall of food groups consumed. Nutrient intake adequacy was estimated using food composition tables based on annual food production and consumption. Poisson regression with robust standard errors and multiple linear regression were employed to identify factors associated with current HDDS and nutrient intake adequacy. Beta coefficients with their 95% confidence intervals (CIs) were presented. RESULTS:Own food production greatly contributed to vegetable consumption (85.0%) but was low for animal-sourced foods (3% for milk/dairy products; 7.5% for meat). Own food production contributed minimally to nutrient intake adequacy, with most households falling short of annual recommended dietary allowance (RDA) for calcium (100%), iron (95%), zinc and protein (95.2%), and energy (99%). Education level, access to agricultural land, and kitchen garden were key predictors of both HDDS and nutrient intake adequacy. Additionally, size of agricultural, household income and occupation also influenced HDDS and nutrient intake adequacy. CONCLUSION:Own food production contributes moderately to dietary diversity and minimally to nutrient intake adequacy. Education level, land access, and kitchen gardening play important roles in shaping HDDS and nutrient intake adequacy. Interventions should focus on promoting kitchen gardening, nutrition-sensitive agriculture, and improving refugee land policy to enhance nutrient intake in refugee settlements.
BackgroundSerum Alanine aminotransferase to High density lipoprotein cholesterol ratio (ALT-to-HDL-C ratio) has been identified as a significant predictor of non-alcoholic fatty liver disease, a hepatic manifestation of metabolic syndrome. This study investigated the association between serum aminotransferases to high-density lipoprotein cholesterol ratios and metabolic syndrome (MetS) among people living with HIV (PLWH) on Dolutegravir (DTG)-based antiretroviral therapy (ART) in South Western Uganda.MethodsWe conducted a secondary analysis study from June 15, 2025 to August 20, 2025 using a dataset generated from hospital-based cross-sectional study that investigated an association between aspartate aminotransferase to alanine aminotransferase ratio and MetS among 377 PLWH who were on DTG-based ART at Ruhoko Health Centre IV, South Western Uganda.ResultsThe prevalence of MetS was 44.6%(168/377); 95% CI: 39.6 - 49.6 and significantly increased from the lowest to the highest ALT-to-HDL-C ratio tertiles (30.2% vs 47.7% vs 56.1%, p < 0.001). In the adjusted model, higher ALT-to-HDL-C ratio was significantly associated with MetS. Individuals in the second tertile had 2.35-fold higher odds (aOR 2.35, 95% CI: 1.26-4.41, p = 0.008), and those in the third tertile had over fourfold higher odds (aOR 4.65, 95% CI: 2.25-9.61, p < 0.001) of MetS compared to the lowest tertile. ALT-to-HDL-C ratio at an optimal cutoff of 0.33 had a significant ability (AUC=0.820, 95%CI: 0.782 - 0.861) to differentiate between participants with MetS from those without MetS at a sensitivity of 92% and specificity of 54%.ConclusionHigher ALT-to-HDL-C ratio is potentially associated with MetS. Since both ALT and HDL-C are routine measurements in HIV Care, this warrants further studies on the potential of ALT-to-HDL-C ratio as a biomarker for MetS.
Malaria remains a major public health concern in Uganda, with prevalence in Lira City rising sharply in recent years despite ongoing interventions. Women of reproductive age are particularly vulnerable, yet little is known about their use of available preventive measures. This study assessed knowledge, attitudes, perceptions, and utilization of malaria control interventions, and examined factors influencing their uptake.A cross-sectional study was conducted with 629 randomly selected women of reproductive age in Lira City. Quantitative data were collected using semi structured questionnaires, while qualitative insights were obtained through focus group discussions. Descriptive statistics were used to summarize knowledge, attitudes, perceptions, and use of insecticide-treated nets (ITNs), intermittent preventive treatment (IPT), and indoor residual spraying (IRS). Chi-square tests were used to examine associations, and multivariate logistic regression was applied to identify predictors of utilization. Qualitative data were analyzed thematically to explore barriers to uptake, and findings were triangulated with quantitative results for validation and deeper interpretation.ITN utilization was high (84.1%), IPT uptake moderate (68%), and IRS coverage low (32.8%). Almost all participants (96.8%) were knowledgeable about ITNs, mainly gained through health workers, and expressed positive attitudes toward malaria prevention (mean score: 4.34, SD = 0.67). While most (91.9%) perceived IRS as effective, concerns included bad odor and discomfort (51.7%), doubt about safety (21.9%), and effectiveness (17.3%). Age, type of house, challenges faced with nets, marital status, and exposure to health education were significant predictors of utilization.Despite widespread ITN use, IRS uptake remains limited due to persistent concerns and misconceptions. Strengthening targeted health education, addressing community fears, and improving access to interventions are critical for enhancing malaria control in high-transmission settings like Lira City.
