Search Results (1,864)

Toxoplasmosis is a widely distributed zoonosis caused by the protozoan parasite Toxoplasma gondii. Infection during pregnancy is a major public health concern due to its potential impact on both maternal health and fetal development. Early detection of maternal infection is critical to prevent adverse outcomes; however, maternal signs are often subtle, non-specific or absent, complicating timely diagnosis. This scoping review aimed to map and synthesise existing evidence on early maternal signs, pregnancy and foetal outcomes, frequently assessed risk factors, and diagnostic approaches of toxoplasmosis in expectant mothers in Africa. The review was done in accordance with the PRISMA-ScR guidelines. A literature search of PubMed, Scopus, ResearchGate, and Google Scholar was performed to identify studies published between 2000 and 2025. Retrieved records were managed using Zotero (version 8.0.4) for deduplication and screening. Only English-language studies conducted in Africa and reporting relevant maternal or clinical data were included. A total of 28 cross-sectional studies were included. Lymphadenopathy (25.0%) was the most frequently reported maternal early sign, followed by flu-like illness, asymptomatic infection, low-grade or mild fever, and fatigue or malaise (each 10.7%). Congenital anomalies (50.0%) and miscarriage or spontaneous abortion (42.9%) were the most commonly reported foetal and pregnancy outcomes. Frequently reported risk factors were exposure to cat faeces (57.1%) and ingestion of undercooked or raw meat (42.9%). Diagnostic approaches were commonly enzyme-based immunoassays (78.6%), with limited use of RDTs and molecular methods. These findings suggest the need for improved early detection and prevention strategies in high-risk, low-resource African settings. Enhancing routine screening, health education, and access to appropriate diagnostics are considered. Future studies should consider adopting standardised reporting and integrating sensitive, affordable, rapid diagnostic approaches to enhance early detection and reduce the burden of congenital toxoplasmosis. Full article

Anaerobic digestion (AD) plays an important role in the circular bioeconomy by converting organic waste into renewable methane and nutrient-rich fertilizer. However, consistent, high-quality biomethane production is hindered by four main factors: hydrolysis limitations, fluctuating feedstock quality, microbial instability, and the high cost/energy demand of purification. This review explores three key areas that improve biomethane production: (i) process intensification (pretreatments and advanced reactors), (ii) microbial regulation through additives, and (iii) biogas upgrading for pipeline use. Anaerobic digestion can be greatly improved by combining thermal or hybrid pretreatments, staged digestion, high-solids technology, and electrochemical systems. These methods speed up hydrolysis and help the system handle higher amounts of organic material more effectively. However, actual performance benefits depend on specific substrate characteristics, heat integration, and control complexity. Optimizing the C:N ratio, buffering capacity, and trace-element supplementation, while simultaneously diluting toxic inhibitors, makes co-digestion an effective and adaptable approach to enhancing anaerobic digestion processes. Additives like carbon, iron nanoparticles, enzymes, and buffers can optimize digestion, but their performance is highly dependent on dosage and substrate. Additionally, they lack validation in long-term, industrial-scale applications. Conventional physicochemical techniques continue to be standard for generating high-quality biomethane, but biological methanation and microalgal systems are playing a growing role in integrating Power-to-Gas technology and using CO₂ efficiently. Critical research needs to focus on four areas: (1) standardized reporting metrics, (2) AI-enabled monitoring and control, (3) coupled techno-economic and life-cycle analysis (TEA-LCA), and (4) long-term pilot or full-scale validation. Overall, comprehensive optimization of the entire flow is more effective than improving isolated parts. Full article

Water scarcity, climate variability, and increasing water demands are placing growing pressure on water supply and distribution systems, particularly in water-scarce environments. Optimization-based approaches have become central to improving system design, planning, and operation. This study presents a structured review of optimization techniques applied to water distribution systems under conditions of scarcity, intermittency, or aridity, and introduces a context-aware classification framework incorporating system scale, population, and scarcity severity. PRISMA (“Preferred Reporting Items for Systematic Reviews and Meta-Analyses”) principles are adopted. Relevant studies are identified through Scopus and Google Scholar, screened using criteria focused on system type, optimization relevance, and explicit consideration of scarcity, intermittency, or aridity, and classified by optimization stage, methodological approach, geographical context, and main findings. The review is dominated by benchmark network studies under water scarcity, while real-world applications in arid regions, such as Sub-Saharan Africa and parts of the Middle East, remain underrepresented. Deterministic least-cost designs are inadequate under water scarcity, whereas multi-objective approaches deliver more reliable systems. The review shows a mismatch between the optimization focus of the benchmark studies, which is mainly in the design phase, and the real-world applications, which mainly focus on optimization of the operations of the existing systems. Full article

This study examined the distribution and feeding ecology of Grey Crowned Cranes (GCCs) (Balearica regulorum gibbericeps) in the Rushebeya–Kanyabaha wetland watershed in southwestern Uganda, focusing on changes in land use and land cover (LULC) between 1986 and 2022. We documented crane distribution and foraging behaviors through field surveys and analyzed Landsat data of 1986, 1998, 2010, and 2022 using supervised classification. The findings revealed significant changes in LULC, with an increase in built-up areas and subsistence farms, while grassland, bushland, and wetland coverage steadily declined. As the human population increased, leading to a demand for food, subsistence farming emerged as the predominant land use starting in 1998. Data on crane distribution indicates that wetlands are a vital habitat for roosting and breeding; nests are typically located within 140 m of water, along the edges of wetlands, and in vegetation that averages 2.6 m in height. Subsistence farmland, primarily growing beans, potatoes, and sorghum, serves as a key food source for the cranes. The study highlights that while agricultural landscapes provide important foraging sites, crane populations are at risk due to ongoing habitat degradation and disturbances. To effectively conserve these populations, strategies that integrate sustainable land use planning within the catchment area and wetland protection will be essential. Full article

In 2024 Kenya had a population of 4.78 million camels that contributed to the livelihoods of pastoralist communities in northern Kenya. Previous studies in Kenya, Saudi Arabia and eastern Africa demonstrated high seroprevalence of Middle East respiratory syndrome coronavirus (MERS-CoV)-specific antibodies in dromedary camels, as well as sporadic transmission of MERS-CoV from camels to humans. Based on the MERS-CoV data and the very close contact between owners and their camels in northern Kenya, we speculated that camels may also transmit other zoonotic viruses, such as Rift Valley fever virus (RVFV). In this study, 493 camel and 197 human sera were collected in Marsabit, Kenya, through a cross-sectional survey in 2018 and analyzed for the presence of RVFV IgG antibodies using a laboratory-developed indirect enzyme-linked immunosorbent assay (ELISA). Overall, 15.6% of camels and 7.6% of humans were RVFV IgG-positive; IgG-positive camels were predominantly females in large population herds and IgG-positive humans were engaged in farming-related activities and were greater than 18 years old. Of the eight location groups sampled, two had high camel (site 2 and site 6) and two had high human (site 5 and site 6) RVFV seropositivity rates. These data suggest that camelids, such as dromedary camels, may serve as amplifying hosts for vector-borne zoonotic diseases, such as RVFV, and that humans with frequent farming and camel meat, milk, or camel product contact may have increased risk for RVFV exposure or infection. Full article

Spider mites (Oligonychus trichardti) are emerging as a major constraint to Urochloa forage productivity in East Africa; however, knowledge of genotypic variation and tolerance remains limited. Herein, 55 Urochloa genotypes were evaluated under field-infested and non-infested conditions across two seasons using an alpha-lattice design. Agronomic and physiological traits, including plant height (PH), tiller number (TN), the Normalized Difference Vegetation Index (NDVI), total dry weight (TDW), and mite damage indices (visual severity index (VSI) and stress tolerance index (STI)) were assessed. Infestation reduced biomass by 22.4% on average, with reductions of up to 45% in susceptible genotypes. Significant genotypic variation was detected for PH, TN, TDW, and VSI. Heritability estimates under mite infestation were moderate to high for all traits except TDW, suggesting that direct selection of these traits could be effective in breeding programs aimed at improving mite resistance. VSI showed a strong negative correlation with NDVI (r = −0.63), supporting its value as a phenotyping indicator of spider mite response. Additive main effects and multiplicative interaction (AMMI) analysis revealed significant genotype × environment interactions for TDW. The AMMI biplot identified Xaraes, ILRI_13369, and ILRI_14787 as high-yielding and stable genotypes, while the AMMI Stability Value (ASV) and the Weighted Average of Absolute Scores from the Best Linear Unbiased Prediction (WAASB) identified CIAT_16122, CIAT_664, ILRI_14801, ILRI_14787, and ILRI_13266 as the most stable and broadly adapted across environments. STI further highlighted ILRI_13751 (2.71) and ILRI_13531 (2.58) as highly tolerant under stress. Overall, the study reveals substantial exploitable genetic diversity and identifies stable, high-yielding, and mite-tolerant genotypes suitable for breeding to improve Urochloa productivity in East Africa. Full article

Deep decarbonization strategies at the local level have been extensively documented for cities in the Global North, yet little is known about how cities in Sub-Saharan Africa (SSA) pursue climate mitigation amid infrastructure constraints, limited fiscal autonomy, and pressing developmental needs. Local governments worldwide are recognized as critical actors in addressing urban greenhouse gas (GHG) emissions. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. This study examines how deep decarbonization pathways in four leading SSA cities (Accra, Addis Ababa, Lagos, and Nairobi) compare with those in the Global North. Using qualitative methods including document analysis and semi-structured interviews, we examine the technical pathways, institutional strategies, governance mechanisms, and actors involved in these cities’ climate mitigation efforts. Our findings reveal that while SSA cities pursue similar technical priorities to Global North cities (renewable energy, building efficiency, sustainable transport), their approaches diverge significantly in implementation. SSA cities innovate through decentralized waste-to-energy systems adapted to informal contexts, rely heavily on donor funding rather than municipal bonds, and uniquely leverage traditional institutions for community engagement. Governance structures are predominantly top-down and centralized, contrasting with the polycentric, multi-level governance observed in the Global North. These findings demonstrate that deep decarbonization in SSA must be reconceptualized not only as a form of climate mitigation but as an integrated strategy that addresses infrastructure gaps and building institutional capacity. This research contributes new knowledge on urban climate governance in developing regions and offers transferable lessons for cities facing similar constraints. Full article

Background: Assisted Partner Notification Services (APNS) support HIV disclosure and reduce HIV transmission. Despite this potential, APNS remains underutilized due to several barriers. A clearer understanding of APNS utilization, barriers, and the facilitators is essential to guide APNS improvement. Objectives: This study aimed to determine the level of APNS utilization, barriers, and facilitators for its uptake among people living with HIV (PLWH) in Singida. Methods: We employed a convergent mixed-methods design. A cross-sectional survey quantified APNS utilization and its associated factors among PLWH who have sexual partners, while complementary qualitative interviews explored perceived barriers and facilitators from the perspectives of both PLWH and healthcare providers. Results: In the Singida region, only forty percent of participants reported the use of APNS. Higher knowledge of HIV disclosure was positively associated with uptake (AOR = 2.65, 95% CI: 2.28–2.81; p = 0.02), whereas depressive symptoms reduced engagement (AOR = 0.95, 95% CI: 0.91–0.99; p = 0.027). Qualitative interviews in Singida identified multilevel barriers, including stigma, gender dynamics, and cultural constraints. Facilitators provided supportive medical advice, non-judgmental counseling, and assurances of confidentiality. Conclusions: Findings reveal low APNS uptake in Singida, constrained by stigma and cultural dynamics. Strengthening knowledge of disclosure, providing supportive medical advice, and ensuring confidentiality may enhance the use of APNS. Full article

Water pans and small dams play a vital role in supplying domestic water in rural regions characterised by seasonal rainfall regimes, with increasing importance as a climate change adaptation measure. Despite their small individual size, the collective impact of numerous water pans is significant. Commercially available monitoring systems are often too costly to be justified for these decentralised infrastructures, resulting in limited data availability that impedes detailed studies aimed at improving their performance. Here, we developed a low-cost monitoring station network that measures water level (JSN-SR04T ultrasonic sensor), precipitation (3D-printed tipping-bucket gauge), and air temperature and humidity (DHT22 sensor). Each station costs less than 12,000 KES (≈93 USD in March 2026), making it suitable for such decentralised multi-site monitoring. A field trial conducted from June to November 2025 at four water pans in the Kakia-Esamburmbur Catchment, Kenya, compared the collected data with an automatic weather station and manual observations. Water level measurements were more accurate than manual reference readings, while air temperature showed biases of $1.4$ to $1.8$ °C. Precipitation data were largely inaccurate due to inadequate sensor levelling. Overall operational reliability reached $83\%$, indicating potential for improvements to reduce maintenance efforts and fully exploit the advantages of its low-cost hardware. Full article

Heavy and overloaded freight traffic strongly affects pavement performance, yet short-term weigh-in-motion (WIM) measurements are not easily converted into design-oriented traffic inputs. Using the Nairobi Southern Bypass in Kenya as a case study, this study develops axle load spectrum (ALS) and equivalent single axle load (ESAL) indicators from more than 1.5 million axle group records collected between June and December 2025 and proposes an XGBoost-based conditional axle load spectrum (CA-ALS) framework. The data revealed strongly right-skewed load distributions, with a limited number of heavily loaded axle groups dominating pavement damage. Compared with the static ALS by axle group type baseline, the CA-ALS reduced log loss from 2.7563 to 2.6709 in conditional spectrum prediction. In the December 2025 tandem axle benchmark, the CA-ALS increased the ESAL-based verification input by 6.0% at b = 4 and 11.1% at b = 5 relative to the stronger static reference. A legal-load-capped counterfactual analysis further showed that, for all heavy vehicles, observed overloading increased ESAL by 161.0% at b = 4 and 239.4% at b = 5. These results indicate that the CA-ALS provides condition-sensitive traffic inputs for design traffic verification, scenario-based pavement checks, and overload-sensitive evaluation based on short-term WIM observations. Full article

Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modeling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9 M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7 M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification. Full article

Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of Africa, especially in Eastern, Central and Southern Africa. In Kenya, the disease continues to wreak havoc on cassava production leading to a significant reduction in crop yields and economic losses of up to USD 1 billion. Variation in virus populations make the control of CBSD challenging as virus genomic variation can affect the accuracy of diagnostic tests, lead to resistance breaking isolates and jeopardize strategies of breeding for resistance. CBSV and UCBSV populations obtained from cassava fields in Kenya were characterized. In total, 44 new complete sequences of CBSV and UCBSV were assembled and 40 sequences successfully submitted to GenBank. Single Nucleotide Polymorphism (SNP) analysis revealed that the cylindrical inclusion protein (CI) is the most stable region across the genome of CBSV and UCBSV. In contrast, protein 1 (PI) and the coat protein (CP) were the most hypervariable regions. Phylogenetic analysis showed three major geographical groupings for both UCBSV and CBSV isolates, suggesting a continued spread of the viruses through human-mediated movement of infected planting materials. The data obtained in this study can support the development of disease management strategies through improved molecular diagnostic tests and targets for breeding for resistance against CBSD. Full article

Apolipoprotein B mRNA editing enzyme catalytic polypeptide 3s (APOBEC3s, A3s) are single-stranded DNA cytidine deaminases with antiviral activity against diverse DNA and RNA viruses. The human APOBEC3 locus encodes seven members: A3A, A3B, A3C, A3D, A3F, A3G, and A3H. Of these, A3C, A3D, A3F, A3G, and A3H are packaged into HIV-1, lacking the viral infectivity factor (VIF, HIV-1Δvif), while A3D, A3F, A3G, and A3H hap II exhibit strong antiviral activity. Packaging of A3s into virions is critical for viral restriction, yet the underlying mechanisms remain incompletely understood. A3 incorporation requires interactions with the GAG polyprotein, especially the matrix (MA) and nucleocapsid (NC) domains, and binding to cellular or viral RNAs. Specific amino acid residues within A3 proteins mediate these contacts, and A3G localization to lipid rafts facilitates packaging. While A3F and A3G incorporation have been extensively characterized, mechanisms for other A3s remain poorly defined. This review synthesizes current knowledge on A3 packaging, emphasizing the interplay of protein, RNA, and membrane determinants in efficient virion incorporation. Full article

This research aimed to investigate livestock owners’ willingness to pay (WTP) for a hypothetical livestock vaccination program in southern Ethiopia. We used both primary and secondary data. The former data were collected from 377 livestock farmers. We used descriptive statistics and a seemingly unrelated bivariate probit model to analyze the data. The double-bounded contingent valuation format was used within a seemingly unrelated bivariate probit model to identify factors influencing WTP for livestock vaccination. It was found that the average WTP for sheep and goat vaccination was 0.38 USD (United States of America dollar) yearly, while for cattle and oxen vaccination, it was 0.64. Factors such as the household head’s education, sex, and age; annual income; total livestock holdings; and farm size affected farmers’ WTP for sheep and goat vaccination in both initial and follow-up scenarios. Market access, farm experience, and access to credit also influenced WTP for cattle and oxen vaccination across both bid values. We suggest that improving socioeconomic factors, such as household well-being and access to credit services, is vital because these elements directly influence a household’s capacity to invest in livestock health and productivity. Establishing clear vaccination rates, with vaccine costs aligned to the average household income, is recommended. Full article