Fast and efficient identification is critical for reducing the likelihood of weed establishment and for appropriately managing established weeds. Traditional identification tools require either knowledge of technical morphological terminology or time-consuming image matching by the user. In recent years, deep learning computer vision models have become mature enough to enable automatic identification. The major remaining bottlenecks are the availability of a sufficient number of high-quality, reliably identified training images and the user-friendly, mobile operationalization of the technology. Here, we present the first weed identification and reporting app and website for all of Australia. It includes an image classification model covering more than 400 species of weeds and some Australian native relatives, with a focus on emerging biosecurity threats and spreading weeds that can still be eradicated or contained. It links the user to additional information provided by state and territory governments, flags species that are locally reportable or notifiable, and allows the creation of observation records in a central database. State and local weed officers can create notification profiles to be alerted of relevant weed observations in their area. We discuss the background of the WeedScan project, the approach taken in design and software development, the photo library used for training the WeedScan image classifier, the model itself and its accuracy, and technical challenges and how these were overcome.

Small angle X-ray scattering curves have been obtained for a series of Na Wyoming Bentonite clay samples containing 10% clay by weight and NaPO3 in concentrations ranging from 0 to 100 meq/1. From the scattering data, the relative probability of spacings between parallel clay platelets was computed. For the sample containing no NaPO3, the probability distribution showed a relatively broad maximum at an interparticle spacing of about 180Å. As the concentration of NaPO3 increased, the maximum became sharper and occurred for smaller interparticle distances. At NaPO3 concentrations between 25 and 100 meq/1, the position d of the maximum was given approximately by the equation d = 21 + 18.4c-1/2, where d is in angstroms, and c is the NaPOs concentration in eq/1. The similarity of this relation to the dependence of d on the concentration of NaCl (Norrish and Rausell-Colom, 1963; Norrish, 1954) suggests that the interparticle spacing depends primarily on the sodium ion concentration and not on the concentration of the anion. The value of d appears to be independent of whether the gel was prepared by the method of Norrish and Rausell-Colom, in which a dried flake was allowed to come to equilibrium with an electrolyte solution, or whether, as in this investigation, the gel was obtained by centrifuging a dilute suspension. Since the Na ions act to reduce the double-layer repulsion between platelets, while the anions tend to be adsorbed on the platelet edges and thus reduce the edge-to-face linkages (H. van Olphen, 1962), the value of the most probable interparticle distance appears to be determined primarily by the magnitude of the double-layer repulsion, even though other properties of the clay gels, such as the rheological behavior, are governed mainly by edge-to-face attractions.

Appropriateness of transmission-based precautions after positive result for a non-SARS-CoV-2 virus was evaluated. Most patients (77.2%) lacked appropriate precautions within 3 hours of virus detection; 36.9% remained without appropriate precautions during their stay. With recent cessation of universal masking, adherence to infection control best practices is needed to optimize safety.

We examine the Holocene loess record in the Heye Catchment on the margins of the Tibetan Plateau (TP) and China Loess Plateau (CLP) to determine: the region to which the Heye Catchment climate is more similar; temporal change in wind strength; and modification of the loess record by mass wasting and human activity. Luminescence and radiocarbon dating demonstrate loess deposited in two periods: >11–8.6 ka and <5.1 ka. The 8.6–5.1 ka depositional hiatus, which coincides with the Mid-Holocene Climatic Optimum, is more similar to the loess deposition cessation in the TP than to the loess deposition deceleration in the CLP. Grain-size analysis suggests the Heye loess is a mixture of at least three different grain-size distributions and that it may derive from multiple sources. A greater proportion of coarse sediments in the older loess may indicate stronger winds compared with the more recent depositional period. Gravel incorporated into younger loess most likely comes from bedrock exposed in slump scarps. Human occupation of the catchment, for which the earliest evidence is 3.4 ka, postdates the onset of slumping; thus the slumps may have created a livable environment for humans.

Seed production from weeds that are missed by herbicide application can affect future weed populations and management decisions. It may be possible to expand the utility of computerized weed management decision aids to include an estimate of weed seed production resulting from selected treatments based on crop yield potential. Field studies were conducted in soybean near Columbia, MO, to determine whether weed control recommendations based on crop yield potential from a computerized weed management decision aid influence weed seed production in two soybean row spacings. At approximately 28 d after planting, weed densities and heights were entered into WeedSOFT® to generate a list of treatments ranked by predicted crop yields. Treatments included: (1) highest predicted crop yield in a glyphosate-resistant system, (2) highest predicted crop yield in a nonglyphosate-resistant system, (3) a 10% yield reduction, (4) a 20% yield reduction, and (5) an untreated control. These treatments were applied to soybean grown in 38- and 76-cm rows. Treatments that provided 90% or higher control of an individual species at 22 d after treatment usually produced less seed than untreated checks. Weed seed production based on early-season herbicide efficacy showed a linear relationship and was relatively predictable (r2 ≥ 0.52) for the predominant weed species. For less dominant weed species, weed seed production was not strongly correlated (r2 ≤ 0.27) to early-season herbicide efficacy but apparently influenced by control of other weed species. Narrow row spacing reduced giant foxtail biomass both years but did not reduce common ragweed and ivyleaf morningglory biomass. Narrow rows did not decrease giant foxtail, common ragweed, and ivyleaf morningglory seed production.

Field studies were conducted in 2000 and 2001 to evaluate corn yield-loss predictions generated by WeedSOFT, a computerized weed management decision aid. Conventional tillage practices were used to produce corn in 76-cm rows in Illinois, Indiana, Kansas, Michigan, Missouri, Nebraska, and Wisconsin. A total of 21 site-years from these seven states were evaluated in this study. At 4 wk after planting, weed densities and size, crop-growth stage, estimated weed-free yield, and environmental conditions at the time of application were entered into WeedSOFT to generate POST treatments ranked by percent maximum yield (PMY). POST treatments were chosen with yield losses ranging from 0 to 20%. Data were subjected to linear regression analysis by state and pooled over all states to determine the relationship between actual and predicted yield loss. A slope value equal to one implies perfect agreement between actual and predicted yield loss. Slope value estimates for Illinois and Missouri were equal to one. Actual yield losses were higher than the software predicted in Kansas and lower than predicted in Michigan, Nebraska, and Wisconsin. Slope value estimate from a data set containing all site years was equal to one. This research demonstrated that variability in yield-loss predictions occurred at sites that contained a high density of a single weed specie (>100/m2) regardless of its competitive index (CI); at sites with a predominant broadleaf weed with a CI greater than five, such as Palmer amaranth, giant ragweed, common sunflower, and common cocklebur; and at sites that experience moderate to severe drought stress.

Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.