Is Area-Wide Pest Management Useful? The Case of Citrus Greening.

Citrus greening currently poses a severe threat to citrus production worldwide. No treatment or management strategy is yet available to cure the disease. Scientists recommend controlling the vector of the disease, and area-wide pest management has been proposed as a superior alternative to individual pest management. We analyzed a unique dataset of farm-level citrus yields that allowed us to test this hypothesis. We found that yields of blocks located in an area with higher participation in coordinated sprays were 28%, 73% and 98% percent higher in 2012/13, 2013/14, and 2014/15, respectively, compared to the yields of blocks under the same management but located in an area with lower participation; providing evidence on the efficiency of a well-performing pest management area to deal with HLB. However, participation in CHMAs has not been commensurate with this evidence. We present survey data that provide insights about producers’ preferences and attitudes toward the area-wide pest management program. Despite the economic benefit we found area-wide pest management can provide, the strategic uncertainty involved in relying on neighbors seems to impose too high of a cost for most growers, who end up not coordinating sprays

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine