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Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Mosquito breeding sites are complex aquatic environments with wide microbial diversity and physicochemical parameters that can change over time during the development of immature insect stages. Changes in biotic and abiotic conditions in water can alter life-history traits of adult mosquitos but this area remains understudied. Here, using microbial genomic and metabolomics analyses, we explored the metabolites associated with Aedes aegypti breeding sites as well as the potential contribution of Klebsiella sp., symbiotic bacteria highly associated with mosquitoes. We sought to address whether breeding sites have a signature metabolic profile and understand the metabolite contribution of the bacteria in the aquatic niches where Ae. aegypti larvae develop. An analysis of 32 mosquito-associated bacterial genomes, including Klebsiella, allowed us to identify gene clusters involved in primary metabolic pathways. From them, we inferred metabolites that could impact larval development (e.g., spermidine), as well as influence the quality assessment of a breeding site by a gravid female (e.g., putrescine), if produced by bacteria in the water. We also detected significant variance in metabolite presence profiles between water samples representing a decoupled oviposition event (oviposition by single females and manually deposited eggs) versus a control where no mosquito interactions occurred (PERMANOVA: p < 0.05; R2 = 24.64% and R2 = 30.07%). Five Klebsiella metabolites were exclusively linked to water samples where oviposition and development occurred. These data suggest metabolomics can be applied to identify compounds potentially used by female Ae. aegypti to evaluate the quality of a breeding site. Elucidating the physiological mechanisms by which the females could integrate these sensory cues while ovipositing constitutes a growing field of interest, which could benefit from a more depurated list of candidate molecules.
Trypanosoma cruzi, the causative agent of Chagas disease (American trypanosomiasis), colonizes the intestinal tract of triatomines. Triatomine bugs act as vectors in the life cycle of the parasite and transmit infective parasite stages to animals and humans. Contact of the vector with T. cruzi alters its intestinal microbial composition, which may also affect the associated metabolic patterns of the insect. Earlier studies suggest that the complexity of the triatomine fecal metabolome may play a role in vector competence for different T. cruzi strains. Using high-resolution mass spectrometry and supervised machine learning, we aimed to detect differences in the intestinal metabolome of the triatomine Rhodnius prolixus and predict whether the insect had been exposed to T. cruzi or not based solely upon their metabolic profile. We were able to predict the exposure status of R. prolixus to T. cruzi with accuracies of 93.6%, 94.2% and 91.8% using logistic regression, a random forest classifier and a gradient boosting machine model, respectively. We extracted the most important features in producing the models and identified the major metabolites which assist in positive classification. This work highlights the complex interactions between triatomine vector and parasite including effects on the metabolic signature of the insect.