4 research outputs found
Mechanisms of vision in the fruit fly
Vision is essential to maximize the efficiency of daily tasks such as feeding, avoiding predators or finding mating partners. An advantageous model is Drosophila melanogaster, since it offers tools that allow genetic and neuronal manipulation with high spatial and temporal resolution, which can be combined with behavioral, anatomical and physiological assays. Recent advances have expanded our knowledge on the neural circuitry underlying such important behaviors as color vision (role of reciprocal inhibition to enhance color signal at the level of the ommatidia); motion vision (motion-detection neurones receive both excitatory and inhibitory input), and sensory processing (role of the central complex in spatial navigation, and in orchestrating the information from other senses and the inner state). Research on synergies between pathways is shaping the field
Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
Organisms use environmental cues for directed navigation. Understanding the basic logic behind navigational decisions critically depends on the complexity of the nervous system. Due to the comparably simple organization of the nervous system of the fruit fly larva, it stands as a powerful model to study decision-making processes that underlie directed navigation. We have quantitatively measured phototaxis in response to well-defined sensory inputs. Subsequently, we have formulated a statistical stochastic model based on biased Markov chains to characterize the behavioural basis of negative phototaxis. Our experiments show that larvae make navigational decisions depending on two independent physical variables: light intensity and its spatial gradient. Furthermore, our statistical model quantifies how larvae balance two potentially-contradictory factors: minimizing exposure to light intensity and at the same time maximizing their distance to the light source. We find that the response to the light field is manifestly non-linear, and saturates above an intensity threshold. The model has been validated against our experimental biological data yielding insight into the strategy that larvae use to achieve their goal with respect to the navigational cue of light, an important piece of information for future work to study the role of the different neuronal components in larval phototaxis
Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years
publishersversionPeer reviewe
Monitoring and Forecasting COVID-19: Statistical Heuristic Regression, Susceptible-Infected-Removed model and, Spatial Stochastics
The COVID-19 pandemic has had worldwide devastating effects on human lives,
highlighting the need for tools to predict its development. Dynamics of such
public-health threats can often be efficiently analysed through simple models
that help to make quantitative timely policy decisions. We benchmark a minimal
version of a Susceptible-Infected-Removed model for infectious diseases (SIR)
coupled with a simple least-squares Statistical Heuristic Regression (SHR)
based on a lognormal distribution. We derived the three free parameters for
both models in several cases and tested them against the amount of data needed
to bring accuracy in predictions. The SHR model is approximately +/- 2%
accurate about 20 days past the second inflexion point in the daily curve of
cases, while the SIR model reaches a similar accuracy a fortnight before. All
the analyzed cases assert the utility of SHR and SIR approximants as a useful
tool to forecast the evolution of the disease. Finally, we have studied
simulated stochastic individual-based SIR dynamics, which yields a detailed
spatial and temporal view of the disease that cannot be given by SIR or SHR
methods.Comment: 33 pages, 14 figures, 3 table