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Sublethal Pyriproxyfen Exposure Alters Anopheles arabiensis Fitness and Pyrethroid Susceptibility Without Trans-Generational Carry-Over
(MDPI, 2026) Simoni Twaha Mnzava, Augustino Thabiti Mmbaga, Anitha Mutashobya, Letus Laurian Muyaga, Mwema Felix Mwema, Halfan Ngowo, Dickson Wilson Lwetoijera
to 9.3% and 3.3%, and deltamethrin KDT60 from 79.7% to 66.7% and 65%. No significant
differences were observed in subsequent generations (p > 0.05). PPF exposure also induced
notable fitness costs in the first generation: mean wing length decreased from 3.07 mm in
controls to 2.88–2.66 mm (6–13% reduction), mean egg production dropped from 30.1 to
13.9–18.8 eggs per female (37–54% reduction), and egg hatching rate declined from 87% to
79–82% (6–9% reduction). Conclusions: These findings suggest that sublethal PPF doses
can temporarily enhance insecticide resistance without leading to heritable resistance and
negatively impact key mosquito fitness traits. PPF may thus be a valuable addition to
integrated vector management strategies.
A hybrid machine learning framework for land use carbon accounting: A case study of Tanzania
(PLOS Climate, 2026) Talemwa Byomutonzi Johansen, Mwema Felix Mwema, Silas Steven Mirau, Verdiana Grace Masanja
This study presents a structured integration of land-use carbon accounting with regression, time-series, and machine-learning models to examine historical patterns and prospective trajectories of land-use related CO2 emissions in data-constrained settings. Rather than proposing a new accounting methodology, the framework demonstrates how established modeling approaches can be combined to support comparative analysis and scenario exploration. The study demonstrate the frame work’s application through a case study of Tanzania, integrating multi-source land use and socio-economic data within a hybrid ensemble of multiple linear regression, ARIMA time-series modeling, and machine learning approaches (Random Forest and XGBoost).The analysis indicates that land-use change explains a substantial share of modeled emissions variability within the accounting-consistent framework, with cropland-to-forest conversion associated with comparatively larger modeled emission reductions under the explored scenarios. These results reflect model-based associations rather than causal dominance and are conditional on the accounting structure and scenario assumptions. In contrast, Socio-economic drivers, particularly urbanization and economic growth, were associated with variations in modeled emissions, although the magnitude and direction of these effects depend on model specification. Scenario analysis suggests that a 20% conversion of cropland to forest is associated with a reduction in modeled emissions from 24,339–18,041 metric tons, whereas combined urbanization and GDP growth increase projected emissions. Machine-learning models, particularly XGBoost, exhibited lower prediction errors under the adopted validation design; however, because the data are temporally indexed, these results should be interpreted as measures of internal predictive consistency rather than strict out-of-sample forecasting accuracy. Overall, the framework provides evidence-informed and conditional insights that may support prioritization of land-based mitigation options in developing-country contexts under the Paris Agreement, while remaining contingent on model specification, data structure, and validation design.
Rare earth element systematics–based multi-tracer hydrogeochemistry reveals controls on potentially harmful inorganic constituents in groundwater from a volcanic rift system in Tanzania
(Elsevier, 2026) Shinji Nakaya, Harue Masuda, Jun Yasumoto, Koichi Sakakibara, Ryogo Takada, Ryuichi Shinjo, Innocent Lugodisha
This study investigated potentially harmful inorganic constituents (PHICs) and the hydrogeochemical properties of drinking water from wells in a densely populated area on the slopes of Mount Meru in the East African Rift Valley. A multi-tracer approach, including rare earth elements (REEs), strontium isotope ratios, and stable oxygen and hydrogen isotope ratios (δ18O and δD), was applied to elucidate groundwater sources and controlling processes in the volcanic area. Groundwater is enriched in light REEs (La–Nd) and exhibits a positive Gd anomaly, interpreted as being of geogenic origin. Integration of REE systematics, Sr isotope signatures, and groundwater flow-path length indicators inferred from δ18O and δD suggests interaction with rocks derived from highly differentiated volcanic lithologies and reveals two distinct groundwater flow systems characterized by contrasting leaching and transport behaviors of potentially toxic trace elements. REE patterns indicate that six of eighteen well water samples are influenced by surface water infiltration. This surface contribution dilutes Fe and Pb concentrations, whereas fluoride (F−) remains largely unaffected, while nitrate (NO3−) increases with increasing infiltration, reflecting superimposed anthropogenic inputs. Notably, elevated concentrations of PHICs (Fe, Pb, F−, and NO3−) exceeding World Health Organization drinking-water guideline values were detected in several residential wells (8/18 for Fe, 7/18 for Pb, 13/18 for F−, and 6/18 for NO3−). The lack of systematic covariation between NO3− and Cl− concentrations, and Pb, Fe, and CI chondrite–normalized REEs supports a predominantly geogenic control on Pb, Fe, and REE. Overall, this study demonstrates that integrated geochemical tracers provide a robust framework for disentangling geogenic and anthropogenic controls on groundwater quality in volcanic rift settings.
Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification
(Frontiers in Artificial Intelligence, 2026) Erica Hargety Kimei, Devotha Godfrey Nyambo, Neema Mduma, Shubi Felix Kaijage
Reliable and accurate farm-level forecasting of greenhouse gas concentrations from dairy cattle is important to the formulation of climate change miti-gation strategies and policy planning in livestock systems. This study proposed an uncertainty-aware deep learning model that integrated data from multiple sources, including remote sensing and ground-based sensors, to forecast hourly concentrations of nitrous oxide, methane, and carbon dioxide in a controlled zero-grazing dairy system. The forecasting was implemented by formulating a causal multivariate time series using a 24-h lookback window with direct one-step-ahead prediction. A two-stage model evaluation protocol, model hyper-parameter tuning via rolling cross-validation, and holdout Test Evaluation were implemented during model development. The model was evaluated using the coefficient of determination, root-mean-square error, and mean squared error.To evaluate model reliability, the study employed a dual-output gated recurrent unit to estimate the conditional mean and heteroscedastic variance, and Monte Carlo dropout and Gaussian Negative Log-Likelihood to quantify epistemic and aleatoric uncertainty. Results indicate stable generalisation across temporal folds and strong probabilistic calibration, with empirical 95% coverage ranging from 93.6 to 94.8% on the holdout test set. Feature selection indicates that rainfall, normalised difference vegetation index, humidity, temperature, trend, and season
influence the prediction of concentration. The study shows that incorporating exogenous variables improves model performance. The proposed framework
demonstrates proof of concept for controlled zero-grazing systems, with poten-tial for broader application following multi-site validation.
Dynamics, stability, and bifurcation analysis of a fractional-order HIV–TB co-infection model with environmental transmission
(Elsevier, 2026) Dickson D. Luambano, Mussa A. Stephano, Maranya M. Mayengo
Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection remain a significant public health challenge, especially in high-burden regions where environmental contamination sustains Mycobacterium tuberculosis. To incorporate memory effects in disease progression and environmental transmission, we develop a fractional-order compartmental model using the Caputo derivative. The model includes an environmental reservoir for TB bacteria and differentiates between HIV, TB, and co-infected stages. We establish positivity and boundedness, derive the basic reproduction number , and conduct stability analysis. Global sensitivity analysis with Latin Hypercube Sampling and Partial Rank Correlation Coefficients highlights key parameters influencing disease spread. The model exhibits backward bifurcation, suggesting that reducing below unity does not necessarily eliminate the disease. Memory effects (lower fractional order) slow disease progression and decrease infection burdens, while environmental transmission considerably sustains disease persistence. HIV transmission rate (), TB transmission rate (), and environmental transmission rate () are the primary drivers of , whereas antiretroviral therapy (), TB treatment (), and environmental clearance rate () are the most effective suppressors. These findings emphasize the importance of integrated intervention strategies that combine direct transmission control, expanded treatment coverage, and environmental sanitation to effectively manage HIV–TB co-infection.