Toews, M. | Search bioRxiv

bioRxiv2019

Cover crops improve early season natural enemy recruitment and pest management in cotton production

A shift to more ecologically based farming practices would improve the sustainability and economic stability of agricultural systems. Habitat management in and around agricultural fields can provide stable environments that aid in the proliferation of natural enemy communities that moderate pest populations and injury. Winter cover crops offer a potentially cost-effective approach to improving habitat that supports natural enemy communities early in the growing season. We investigated the effects of winter cover crops including cereal rye (Secale cereal L.) and crimson clover (Trifolium incarnatum L.) on the abundance and diversity of natural enemies, key pest populations, biological control services, and cotton yield. Winter cover crops were established on 0.4 ha replicated field plots in the fall of 2017 and 2018. Suction sampling during each cotton development stage demonstrated that a rye cover crop promoted greater abundance and diversity of natural enemy communities in early cotton stages. Extensive leaf sampling of seedling cotton showed that cover crops significantly reduced thrips infestations. Furthermore, stink bug boll injury decreased on plots prepared with a rye cover compared to cotton lacking this additional habitat. Combining end of season yield results and management practices with an economic analysis of the costs of production, the value of cotton grown into a cover crop was cost competitive with conventional (no cover) cotton production. These results suggest that conventional growers utilizing cover crops could reduce insecticide inputs through natural reductions in pest pressure, and overall do not incur additional production costs.

ecology↗

bioRxiv2017

Multi-modal brain fingerprinting: a manifold approximation based framework

This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features, which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold, using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis. In addition, taking inspiration from the computer vision community, the proposed rank retrieval evaluation based on the task of twin/sibling identification and using Mean Average Precision (MAP) can be used for a standardized comparison of future brain fingerprints.

neuroscience↗