Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories.

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods' free movement, and birds' migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)2041-172310.1038/s41467-021-26320-w]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

Objective comparison of methods to decode anomalous diffusion.

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Characterizing the Feeding Injury Caused by Phylloscelis rubra (Hemiptera: Dictyopharidae) to Cranberries.

Due to changes in pest management practices, farmers' reports of severe feeding injury to cranberries, Vaccinium macrocarpon Aiton Ericales: Ericaceae, caused by the cranberry toad-bug, Phylloscelis rubra Ball, have increased in recent years in New Jersey (United States). Currently, however, limited information is available on the effects of P. rubra feeding or density of individuals needed to cause injury to cranberry vines and fruit. In 2015‒2017, we conducted studies to characterize injury to cranberry at a range of P. rubra densities by using cages in a screen-house and field, to establish a correlation between P. rubra density and crop injury in an open field experiment, and to measure the effects of P. rubra injury on the nutritional content (i.e., amounts of macro- and microelements) of cranberry vines. Phylloscelis rubra feeding on cranberry vines produced typical injury symptoms at relatively low densities (i.e., 2 individuals per vine in field cages or <10 individuals per sweep net sample in open fields), which included discolored (yellowish or reddish) or dead (brown) vines. This vine injury could lead to reductions in fruit mass and total fruit number. However, P. rubra injury to cranberry vines did not alter their nutritional composition. In general, this study highlights the ability of P. rubra to cause substantial injury to cranberry vines even when population densities were relatively low, which could result in declines in fruit production (quality and quantity). Therefore, infestations by P. rubra in cranberries must be considered when making pest management decisions in regions where this insect is present.

Interpreting Temporal and Spatial Variation in Spotted-Wing Drosophila (Diptera: Drosophilidae) Trap Captures in Highbush Blueberries.

Integrated pest management (IPM) programs for the spotted-wing drosophila Drosophila suzukii (Diptera: Drosophilidae) rely on insecticide applications to reduce adult populations and prevent fruit infestation. Although monitoring traps are used for early D. suzukii adult detection to time the start of insecticide applications, it remains unclear whether trap counts can be used to determine the efficacy of these programs and predict the risk of fruit infestation. To address this, a 2-yr study (2016-2017) was conducted in highbush blueberries in New Jersey (USA) to interpret D. suzukii trap count variation in relation to the frequency of insecticide applications and proximity to forest habitats. We also correlated trap counts with fruit infestation and used traps to determine the maximum dispersive distance traveled by D. suzukii adults within blueberry fields by using mark-release-capture studies. Using a trapping network across nine farms, we demonstrated that insecticide applications reduce D. suzukii trap counts, but this varied according to seasonality, and that traps placed closer to forest habitats within farms had higher fly counts than those placed in farm interiors. Moreover, blueberry fields that had zero fruit infestation also had predictably lower trap counts than fields with infested fruit, and the maximum dispersive distance for D. suzukii within blueberry fields was 90 m. In summary, while D. suzukii trap counts in blueberry farms could predict the frequency of insecticide applications and fruit infestation, the predictive power of our trap data was too variable across the blueberry harvest period to make it a reliable tool.

Exploring an Odor-Baited "Trap Bush" Approach to Aggregate Plum Curculio (Coleoptera: Curculionidae) Injury in Blueberries.

This 2-year study (2013-2014) assessed the efficacy of an odor-baited "trap bush" approach to aggregate plum curculio, Conotrachelus nenuphar, adult injury, i.e., number of oviposition-scared fruit, in four commercial highbush blueberry farms in New Jersey (USA). In each farm, we compared fruit injury in bushes baited with grandisoic acid and benzaldehyde along the perimeter of trap-bush plots versus unbaited bushes in control plots. We also measured the amount of fruit injury in neighboring bushes (i.e., spillover effect) and in the plots' interior. In both years, the amount of fruit injury by C. nenuphar adults was greater on and near odor-baited bushes in trap-bush plots compared with those on and near unbaited bushes in control plots, indicative of aggregation. Injury in unbaited bushes neighboring trap bushes was often greater than unbaited bushes in control plots, providing some evidence for a spillover effect. However, no difference in fruit injury was found between interior trap-bush and control plots. Therefore, odor-baited trap bushes can be used in blueberries to manipulate C. nenuphar foraging behavior, i.e., aggregate adults, without compromising injury in field interiors. Under this approach, insecticides could then be targeted at only a few (perimeter-row) bushes within fields rather than entire fields.