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The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
Objectives: Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline. Methods: Here, we used longitudinal cognitive data from 1256 late–middle aged adults from the Wisconsin Registry for Alzheimer’s Prevention study to examine the effects of sex, apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a set of models with different combinations of interactions among sex, APOE, literacy, and age. Results: For a list-learning test, model-averaged results showed better performance for women versus men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an association with cognitive performance in this age range (∼40–70 years). Conclusions: These results illustrate the utility of the IT approach and point to literacy as a potential modifier of cognitive decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work. (JINS, 2019, 25, 119–133)
Objectives: A major challenge in cognitive aging is differentiating preclinical disease-related cognitive decline from changes associated with normal aging. Neuropsychological test authors typically publish single time-point norms, referred to here as unconditional reference values. However, detecting significant change requires longitudinal, or conditional reference values, created by modeling cognition as a function of prior performance. Our objectives were to create, depict, and examine preliminary validity of unconditional and conditional reference values for ages 40–75 years on neuropsychological tests. Method: We used quantile regression to create growth-curve–like models of performance on tests of memory and executive function using participants from the Wisconsin Registry for Alzheimer’s Prevention. Unconditional and conditional models accounted for age, sex, education, and verbal ability/literacy; conditional models also included past performance on and number of prior exposures to the test. Models were then used to estimate individuals’ unconditional and conditional percentile ranks for each test. We examined how low performance on each test (operationalized as <7th percentile) related to consensus-conference–determined cognitive statuses and subjective impairment. Results: Participants with low performance were more likely to receive an abnormal cognitive diagnosis at the current visit (but not later visits). Low performance was also linked to subjective and informant reports of worsening memory function. Conclusions: The percentile-based methods and single-test results described here show potential for detecting troublesome within-person cognitive change. Development of reference values for additional cognitive measures, investigation of alternative thresholds for abnormality (including multi-test criteria), and validation in samples with more clinical endpoints are needed. (JINS, 2019, 25, 1–14)
Research was conducted to determine the efficacy of aminocyclopyrachlor in comparison to glyphosate, clopyralid, fluroxypyr, and triclopyr for silk tree (commonly known as mimosa) control. In the greenhouse, aminocyclopyrachlor was applied at 8.75, 17.5, 35, and 70 g ha−1 with and without methylated seed oil (MSO) at 0.5% v/v. Efficacy of these treatments was compared to glyphosate and triclopyr at 1,350 g ha−1, fluroxypyr at 103 g ha−1, and clopyralid at 100 g ha−1. Few differences in silk tree control were detected by 28 d after treatment (DAT), as aminocyclopyrachlor with MSO controlled silk tree 87 to 100% compared to 53 to 100% for aminocyclopyrachlor without MSO. Aminocyclopyrachlor at 35 g ha−1 provided silk tree control similar to glyphosate, triclopyr, clopyralid, and fluroxypyr regardless of adjuvant. Inclusion of MSO enhanced initial activity of aminocyclopyrachlor after application. At 7 DAT, 8.75 g ha−1 of aminocyclopyrachlor plus MSO controlled silk tree similar to aminocyclopyrachlor alone at 70 g ha−1. In laboratory studies, absorption of 14C-aminocyclopyrachlor 2 h after treatment (HAT) with MSO measured 93% compared to only 62% for 14C-aminocyclopyrachlor without MSO. By 24 HAT, absorption of 14C-aminocyclopyrachlor measured 99 and 71% for applications with and without MSO, respectively. Increased foliar absorption with MSO may explain enhanced activity observed 7 DAT in greenhouse studies, as no effects in 14C-aminocyclopyrachlor translocation due to adjuvant were observed. Responses suggest MSO increased the speed of silk tree control with aminocyclopyrachlor and may also improve rainfastness of aminocyclopyrachlor applications for control of silk tree and other woody species.
Objectives: Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. Methods: Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer’s Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer’s disease). The primary outcome was Wave 4 cognitive status (“cognitively normal” vs. “impaired”) determined by consensus conference; “impaired” included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: “6 Factor IICV” and “4 Test IICV”. Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. Results: Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. Conclusions: While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016–1025)