Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Acifluorfen is a nonsystemic PPO-inhibiting herbicide commonly used for POST Palmer amaranth control in soybean, peanut, and rice across the southern United States. Concerns have been raised regarding herbicide selection pressure and particle drift, increasing the need for application practices that optimize herbicide efficacy while mitigating spray drift. Field research was conducted in 2016, 2017, and 2018 in Mississippi and Nebraska to evaluate the influence of a range of spray droplet sizes [150 μm (Fine) to 900 μm (Ultra Coarse)], using acifluorfen to create a novel Palmer amaranth management recommendation using pulse width modulation (PWM) technology. A pooled site-year generalized additive model (GAM) analysis suggested that 150-μm (Fine) droplets should be used to obtain the greatest Palmer amaranth control and dry biomass reduction. Nevertheless, GAM models indicated that only 7.2% of the variability observed in Palmer amaranth control was due to differences in spray droplet size. Therefore, location-specific GAM analyses were performed to account for geographical differences to increase the accuracy of prediction models. GAM models suggested that 250-μm (Medium) droplets optimize acifluorfen efficacy on Palmer amaranth in Dundee, MS, and 310-μm (Medium) droplets could sustain 90% of maximum weed control. Specific models for Beaver City, NE, indicated that 150-μm (Fine) droplets provide maximum Palmer amaranth control, and 340-μm (Medium) droplets could maintain 90% of greatest weed control. For Robinsonville, MS, optimal Palmer amaranth control could be obtained with 370-μm (Coarse) droplets, and 90% maximum control could be sustained with 680 μm (Ultra Coarse) droplets. Differences in optimal droplet size across location could be a result of convoluted interactions between droplet size, weather conditions, population density, plant morphology, and soil fertility levels. Future research should adopt a holistic approach to identify and investigate the influence of environmental and application parameters to optimize droplet size recommendations.