Biology subjects

Rafiq, K.

Publications and source records attributed to Rafiq, K..

3 recordsLinked to original sources

bioRxiv2026

Probabilistic dietary exposure modeling and health risk assessment of heavy metals via the fodder-cattle-human continuum in Bangladesh

Dietary exposure to heavy metals (HMs) via animal-source foods is a critical environmental health pathway. In rapidly industrializing Bangladesh, contamination of the bovine food chain from agricultural feeds and industrial emissions poses an unquantified public health burden. This study evaluated exposure pathways, spatial distribution, mass-transfer dynamics, and health risks of six HMs (Cr, Cu, Cd, Pb, As, and Hg) across the fodder-cattle-human continuum. Samples of beef (n = 76), raw milk (n = 76), commercial cattle feed (n = 40), and fodder (n = 88) were collected from eight sites across industrial and non-industrial zones in Bangladesh and analysed by atomic absorption spectroscopy. Probabilistic Monte Carlo simulations (10,000 iterations) quantified estimated daily intake, target hazard quotients (THQ), cumulative hazard index (HI), and lifetime carcinogenic risk (CR) for adult and pediatric receptors. Copper (Cu) was the dominant contaminant across all matrices, peaking in beef (103.89 {+/-} 15.87 mg/kg) and milk (13.67 {+/-} 1.53 mg/L). Spatial analysis revealed distinct contamination profiles: Pb burden peaked in industrial zones while Cr was elevated in non-industrial sectors. Monte Carlo modelling identified commercial feed as the most efficient transfer vector into beef. Pediatric THQ for Cu significantly exceeded the safety threshold (THQ > 1), and upper-bound lifetime carcinogenic risk from As approached the critical USEPA 10- regulatory ceiling. These findings demonstrate that industrial and agricultural externalities efficiently contaminate the bovine food supply chain in Bangladesh, with copper and arsenic representing the most critical non-carcinogenic and carcinogenic dietary hazards, respectively. Children are disproportionately vulnerable due to lower body weight. The results underscore the need for targeted upstream interventions in commercial feed production and provide evidence to support feed-quality regulation and environmental monitoring in rapidly industrializing settings.

pharmacology and toxicology↗

bioRxiv2024

Leveraging machine learning and accelerometry to classify animal behaviours with uncertainty

O_LIAnimal-worn sensors have revolutionised the study of animal behaviour and ecology. Accelerometers, which measure changes in acceleration across planes of movement, are increasingly being used in conjunction with machine learning models to classify animal behaviours across taxa and research questions. However, the widespread adoption of these methods faces challenges from imbalanced training data, unquantified uncertainties in model outputs, shifts in model performance across contexts, and noisy classifications in continuous data streams, where predicted behaviours change abruptly within a sequence. C_LIO_LITo address these challenges, we introduce an open-source approach for classifying animal behaviour from raw acceleration data. Our approach integrates machine learning and statistical inference techniques to evaluate and mitigate class imbalances, changes in model performance across ecological settings, and noisy classifications. Importantly, we extend predictions from single behaviour classifications to prediction sets: sets of behaviour labels guaranteed to contain the true behaviour with a pre-specified probability, in a framework analogous to the use of prediction intervals in statistical analyses. C_LIO_LIWe evaluate our approach via simulation and highlight its utility using data collected from a free-ranging large carnivore, African wild dogs (Lycaon pictus), in the Okavango Delta, Botswana. We demonstrate significantly improved predictions along with associated uncertainty metrics in African wild dog behaviour classification, particularly for rare and ecologically important behaviours such as feeding, where correct classifications more than doubled following quality checks and data rebalancing introduced in our pipeline. C_LIO_LIOur approach is applicable across taxa and represents a key step towards advancing the burgeoning use of machine learning to remotely observe around-the-clock behaviours of free-ranging animals. Future work could include the integration of multiple data streams, such as accelerometer, audio, and GPS data, for model training and could be incorporated directly into our pipeline. C_LI

ecology↗

bioRxiv2024

Perceived and observed biases within scientific communities: a case study in movement ecology

Who conducts biological research, where, and how the results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical first step towards addressing them. We documented perceived and observed biases in movement ecology. Movement ecology is a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. First, we surveyed attendees of an international conference, and discussed the results at the conference (comparing uninformed vs informed perceived bias). Although most researchers identified as bias-aware, only a subset of biases were discussed in conversation. Next, by considering author affiliations from publications in the journal Movement Ecology, we found among-country discrepancies between the country of the authors affiliation and study site location related to national economics. At the within-country scale, we found that race-gender identities of postgraduate biology researchers in the USA differed from national demographics. We discuss the role of potential specific causes for the emergence of bias in the sub-discipline, e.g. parachute-science or accessibility to fieldwork. Undertaking data-driven analysis of bias within research sub-disciplines can help identify specific barriers and first steps towards the inclusion of a greater diversity of participants in the scientific process.

ecology↗