Biology subjects

Kiware, S. S.

Publications and source records attributed to Kiware, S. S..

5 recordsLinked to original sources

bioRxiv2026

An improved generic schema for high fidelity data linkage and sample tracing across complex multi-assay medical entomology studies

Evidence-based decision making on malaria vector control strategies increasingly rely on triangulation of data which requires informatics systems that can integrate data from complex, multi-stage studies involving mosquitoes. This manuscript describes a performance evaluation of an extended version of the generic schema underpinning the VBDs360 platform, specifically improved to accommodate multiple distinct entomological assays spanning the field, insectary and laboratory. The utility of this extension, with respect to high-fidelity data linkage and robust sample traceability across complex entomological workflows, was evaluated through a case study conducted in southern Tanzania. Wild female mosquitoes were collected from 40 locations across a >4,000 km{superscript 2} area and then reared through multiple generations in an insectary before derived iso-female lineages were tested for phenotypic susceptibility to a pyrethroid insecticide. Such multi-generational lineages (F to F where n [≥] 2) were propagated to prevent non-heritable maternal effects on phenotype and produce enough progeny for standard WHO susceptibility assays. All samples were subsequently archived in a molecular laboratory, where all F specimens were tested for sibling species identity. A paper-based implementation of the extended schema enabled successful integration of 77,017 lines of data distributed across 6 different tables that spanned 3 distinct field, insectary, and laboratory workflows, implemented by three different teams working in different locations. At each step, fully independent and redundant primary and secondary keys enabled high fidelity error correction and sample tracing. Consistently perfect linkage between assay design and sample sorting data was achieved for F0 wild-caught adults, with 100% of 66,108 record successfully linked between field capture and morphological categorization. This complete traceability extended to the propagation of derived Fn lineages, with all 100 and 243 records from 9 adult-derived and 13 larval-derived lineages, respectively, correctly linked. Insecticide susceptibility phenotype further confirmed 100% linkage for 5,654 records between exposure history and recorded mortality outcome data in the insectary. Although such cross-cleaned linkages to sample analysis and storage data recorded by the laboratory team were not entirely perfect and could be improved, they were nevertheless of very high fidelity (97.3% (1967/2,022) for F0 samples and 99.3% (437/440) for Fn samples). Overall, this pilot application of the extended generic schema ensured robust data provenance and minimized transcription errors in this complex study distributed across multiple teams and locations. These findings demonstrate how this generic informatics framework may be scaled and adapted to support data integrity across diverse, large-scale, multi-team entomological research workflows.

bioinformatics↗

bioRxiv2026

Implications of the trade-offs between negative density-dependence and Allee effects for vector control

BackgroundUnderstanding population dynamics is fundamental to predicting species persistence and extinction, yet remains challenging due to the complex interplay between ecological, environmental and anthropogenic factors. Population dynamics are regulated by intrinsic factors such as negative density-dependence and Allee effects. While negative density-dependence is a well-understood process, Allee effects have received less attention but can have important implications for conservation and species management. For control of disease vectors, negative density-dependence and Allee effects can drive vectors to elimination or to rebound after control. For malaria mosquitoes, negative density-dependence at the larval stages is well known to limit population growth, but the implications of Allee effects and the trade-offs between negative density-dependence and Allee effects remain unknown. It is hypothesised that, depending on the vector control strategy, Allee effects could be triggered and push populations closer to extinction. MethodsA stochastic state-structured population simulation model was developed, which followed a simplified mosquito life cycle. Negative density-dependence and Allee effects were included as parameters influencing larval survival and total fecundity, respectively. The aims were addressed by varying the strength of negative density-dependence and Allee effects parameters, independently and simultaneously, under different vector control scenarios: control, sustained and shorter-term interventions (here, immolating larvicides) that reduced the larval population. ResultsWhile in isolation, the strength of negative density-dependence and Allee effects did not have a long-term impact on the population dynamics, their combination accelerated population extinction as both the strength of negative density-dependence and Allee effects increased. As Allee effects act on small population sizes, sustained interventions were able to activate Allee effects, increasing the probability of population extinction, but short-term interventions can lead to populations rebounding, driven by negative density-dependence. ConclusionUnderstanding less-studied regulatory processes like Allee effects can support vector control by highlighting aspects of the vectors life cycle that are either resilient or vulnerable to interventions. If present, Allee effects could potentially be harnessed to accelerate the elimination of vectors and diseases such as malaria.

ecology↗

bioRxiv2025

Characterization of a new laboratory colony of Anopheles funestus mosquitoes established in Ifakara, Tanzania

BackgroundAnopheles funestus, a major vector of malaria in Africa, has proven difficult to colonize in laboratory settings, impeding research on its biology and control. After several attempts, our team recently succeeded in colonizing a strain of An. funestus from Tanzania (FUTAZ). The objective of this study was to analyse the key fitness and genotypic characteristics of these mosquitoes during multiple filial generations of laboratory adaptation and compare them to wild An. funestus from Tanzania and a pre-existing colony of An. funestus from Mozambique (FUMOZ). MethodsMeasures of mating success (percentage of female mosquitoes inseminated), body size (wing length), fecundity (number of eggs laid per female), and insecticide susceptibility (percentage of 24-hour mortality after exposure to insecticides) were compared between the newly established colonies of Tanzanian An. funestus (FUTAZ colonies), the long-established FUMOZ colonies, and a colony of Anopheles arabiensis maintained in the same laboratory. The maternal lineages of the An. funestus mosquitoes were investigated through a hydrolysis probe analysis of their mitochondrial DNA to identify distinct clades, I and II. Additionally, other intragenomic variations were examined through a PCR analysis of restriction fragment length polymorphisms (RFLP) on the third domain of 28S ribosomal DNA. These molecular markers were used to compare the FUTAZ colonies, FUMOZ colonies in Tanzania and South Africa, and the wild-collected An. funestus from Tanzania. ResultThe mating success and body size of FUTAZ females declined significantly from filial generations F1 to F6 relative to the founder population (F0), but then increased from F7 onwards eventually matching FUMOZ by F9. Fecundity was similar across all colonies tested. However, it took significantly longer for 50% of the females in the FUTAZ and FUMOZ colonies (over 10 days) to mate compared to females in the An. arabiensis colony (approximately 5 days). Insecticide resistance appeared to be lost during colonization, but this varied with insecticide classes. Majority of mosquitoes in the FUTAZ colony, as well as the wild-caught Tanzanian An. funestus belonged to Clade I (80.4-89.4%) and RFLP type "Y" (90.5-91.4%), while the FUMOZ colonies were mostly Clade II (65.5-88.5%) and RFLP type "MW" (90.5-91.5%). ConclusionThis study suggests that the mating success and body size of An. funestus decreases significantly during the early stages of colonization, then increase as the mosquitoes adapt to laboratory conditions. It is therefore crucial to have a large enough founder population to persist through these early generations in order to achieve stable colonization of An. funestus. The Clade and RFLP genotyping demonstrated the genetic similarities between the FUTAZ mosquitoes and wild-caught Tanzanian An. funestus, but also showed that the new colony can be distinguished from the FUMOZ colony.

ecology↗

bioRxiv2020

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

BackgroundAfter mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. Methods and resultsIn this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 {+/-} 2.8% (N=927), 68.7 {+/-} 4.8% (N=140), 80.3 {+/-} 2.0% (N=158), and 75.7 {+/-} 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 {+/-} 2.2%, (N=927), 89.8 {+/-} 1.7% (N=140), 93.3 {+/-} 1.2% (N=158), and 92.7 {+/-} 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

bioinformatics↗

bioRxiv2018

Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks

BackgroundNear infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or [≥] 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier.\n\nMethods and findingsWe explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy.\n\nA total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. Based on ten-fold Monte Carlo cross-validation, an ANN regression model scored root mean squared error (RMSE) of 1.6 {+/-} 0.2 for An. gambiae and 2.8 {+/-} 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 {+/-} 0.2 for An. gambiae, and 4.5 {+/-} 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 {+/-} 1.0 % for An. gambiae, and 90.2 {+/-} 1.7 % for An. arabiensis; while PLS regression model scored the accuracy of 83.9 {+/-} 2.3% for An. gambiae, and 80.3 {+/-} 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 {+/-} 1.0 for An. gambiae, and 99.0 {+/-} 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 {+/-} 1.2% for An. gambiae, and 88.7 {+/-} 1.1% for An. arabiensis.\n\nConclusionTraining both regression and binary classification age models using ANNs yields models with higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers score higher accuracy on classifying age of mosquitoes than a regression model translated as binary classifier. Therefore, we recommend training models to estimate age of An. gambiae and An. arabiensis using ANN model architectures and direct training of binary classifier instead of training a regression model and interpret it as a binary classifier.

bioinformatics↗