Recent work on high plasma homocysteine levels in patients at risk for developing Alzheimer's disease has led to the hypothesis that folic acid supplementation might reduce risk in such patients. The authors report on the effects of folic acid 10 mg/day versus placebo on 11 patients (only 7 completers) with dementia and low-normal folic acid levels. This is the first study evaluating folic acid or placebo in patients with dementia. Subjects had low-normal baseline folic acid levels. The magnitude of change between baseline and second testing was not statistically significant between the 2 groups. However, there was a trend for the folate group to perform worse on two specific cognitive measures, suggesting a possible trend toward worsening of some cognitive abilities after the folic acid. The folic acid in very high doses was well tolerated. Larger studies are necessary before empirically administering folic acid to patients already suffering from dementia.

DHEA did not significantly improve cognitive performance or overall ratings of change in severity in this small-scale pilot study. A transient effect on cognitive performance may have been seen at month 3, but narrowly missed significance.

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Phenotyping is one of the most important processes in modern breeding, especially for maize, which is an important crop for food, feeds, and industrial uses. Breeders invest considerable time in identifying genotypes with high productivity and stress tolerance. Plant spacing plays a critical role in determining the yield of crops in production settings to provide useful management information. In this study, we propose an automated solution using unmanned aerial vehicle (UAV) imagery and deep learning algorithms to provide accurate stand counting and plant-level spacing variabilities (PSV) in order to facilitate the breeders’ decision making. A high-resolution UAV was used to train three deep learning models, namely, YOLOv5, YOLOX, and YOLOR, for both maize stand counting and PSV detection. The results indicate that after optimizing the non-maximum suppression (NMS) intersection of union (IoU) threshold, YOLOv5 obtained the best stand counting accuracy, with a coefficient of determination (R2) of 0.936 and mean absolute error (MAE) of 1.958. Furthermore, the YOLOX model subsequently achieved an F1-score value of 0.896 for PSV detection. This study shows the promising accuracy and reliability of processed UAV imagery for automating stand counting and spacing evaluation and its potential to be implemented further into real-time breeding decision making.