The opportunity to increase soybean yield has prompted Illinois farmers to plant soybean earlier than historical norms. Extending the growing season with an earlier planting date might alter the relationship between soybean growth and weed emergence timings, potentially altering the optimal herbicide application timings to minimize crop yield loss due to weed interference and ensure minimal weed seed production. The objective of this research was to examine various herbicide treatments applied at different timings and rates to assess the effect on weed control and yield in early-planted soybean. Field experiments were conducted in 2021 at three locations across central Illinois to determine effective chemical strategies for weed management in early-planted soybean. PRE treatments consisted of a S-metolachlor + metribuzin premix applied at planting or just prior to soybean emergence at 0.5X (883 + 210 g ai ha−1) or 1X (1,766 + 420 g ai ha−1) label-recommended rates. POST treatments were applied when weeds reached 10 cm tall and consisted of 1X rates of glufosinate (655 g ai ha−1) + glyphosate (1,260 g ae ha−1) + ammonium sulfate, without or with pyroxasulfone at a 0.5X (63 g ai ha−1) or 1X (126 g ai ha−1) rate. Treatments comprising both a full rate of PRE followed by a POST resulted in the greatest and most consistent weed control at the final evaluation timing. The addition of pyroxasulfone to POST treatments did not consistently reduce late-season weed emergence. The lack of a consistent effect by pyroxasulfone could be attributed to suppression of weeds by soybean canopy closure due to earlier soybean development. The full rate of PRE extended the timing of POST application 2 to 3 wk for all treatments at all locations except Urbana. Full-rate PRE treatments also reduced the time between the POST application and soybean canopy closure. Overall, a full-rate PRE reduced early-season weed interference and minimized soybean yield loss due to weed interference.

Foliar-applied postemergence applications of glufosinate are often applied to glufosinate-resistant crops to provide nonselective weed control without significant crop injury. Rainfall, air temperature, solar radiation, and relative humidity near the time of application have been reported to affect glufosinate efficacy. However, previous research may have not captured the full range of weather variability to which glufosinate may be exposed to prior to or following application. Additionally, climate models suggest more extreme weather will become the norm, further expanding this weather range glufosinate can be exposed to. The objective of this research was to quantify the probability of successful weed control (efficacy ≥85%) with glufosinate applied to some key weed species across a broad range of weather conditions. A database of >10,000 North American herbicide evaluation trials was used in this study. The database was filtered to include treatments with a single POST application of glufosinate applied to waterhemp (Amaranthus tuburculatus (Moq.) J. D. Sauer), morningglory species (Ipomoea spp.), and/or giant foxtail (Setaria faberi Herm.) <15cm in height. These species were chosen because they are well represented in the database and listed as common and troublesome weed species in both corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] (Van Wychen 2020, 2022). Individual random forest models were created. Low rainfall (≤20 mm) over the five days prior to glufosinate application was detrimental to the probability of successful control of A. tuburculatus and S. faberi. Lower relative humidity (≤70%) and solar radiation (≤23 MJ m-1 day-1) the day of application reduced the probability of successful weed control in most cases. Additionally, the probability of successful control decreased for all species when average air temperature over the first five days after application was ≤25C. As climate continues to change and become more variable, the risk of unacceptable control of several common species with glufosinate is likely to increase.

A general approach to the analysis of subjective categorical data is considered, in which agreement matrices of two or more raters are directly expressed in terms of error and agreement parameters. The method provides focused analyses of ratings from several raters for whom ratings have measurement error distributions that may induce bias in the evaluation of substantive questions of interest. Each rater's judgment process is modeled as a mixture of two components: an error variable that is unique for the rater in question as well as an agreement variable that operationalizes the “true” values of the units of observation. The statistical problems of identification, estimation, and testing of such measurement models are discussed.

The general model is applied in several special cases. The most simple situation is that underlying Cohen's Kappa, where two raters place units into unordered categories. The model provides a generalization and systematization of the Kappa-idea to correct for agreement by chance. In applications with typical research designs, including a between-subjects design and a mixed within-subjects, between-subjects design, the model is shown to disentangle structural and measurement components of the observations, thereby controlling for possible confounding effects of systematic rater bias. Situations considered include the case of more than two raters as well as the case of ordered categories. The different analyses are illustrated by means of real data sets.

We present a re-discovery of G278.94+1.35a as possibly one of the largest known Galactic supernova remnants (SNRs) – that we name Diprotodon. While previously established as a Galactic SNR, Diprotodon is visible in our new Evolutionary Map of the Universe (EMU) and GaLactic and Extragalactic All-sky MWA (GLEAM) radio continuum images at an angular size of $3{{{{.\!^\circ}}}}33\times3{{{{.\!^\circ}}}}23$, much larger than previously measured. At the previously suggested distance of 2.7 kpc, this implies a diameter of 157$\times$152 pc. This size would qualify Diprotodon as the largest known SNR and pushes our estimates of SNR sizes to the upper limits. We investigate the environment in which the SNR is located and examine various scenarios that might explain such a large and relatively bright SNR appearance. We find that Diprotodon is most likely at a much closer distance of $\sim$1 kpc, implying its diameter is 58$\times$56 pc and it is in the radiative evolutionary phase. We also present a new Fermi-LAT data analysis that confirms the angular extent of the SNR in gamma rays. The origin of the high-energy emission remains somewhat puzzling, and the scenarios we explore reveal new puzzles, given this unexpected and unique observation of a seemingly evolved SNR having a hard GeV spectrum with no breaks. We explore both leptonic and hadronic scenarios, as well as the possibility that the high-energy emission arises from the leftover particle population of a historic pulsar wind nebula.

Shiga toxin-producing Escherichia coli (STEC) is a group of bacteria that causes gastrointestinal illness and occasionally causes large foodborne outbreaks. It represents a major public health concern due to its ability to cause severe illness which can sometimes be fatal. This study was undertaken as part of a rapid investigation into a national foodborne outbreak of STEC O145. On 22 May 2024, United Kingdom (UK) public health agencies and laboratories identified an increase in stool specimens submissions and patients testing positive for Shiga toxin-producing E. coli (STEC). Whole genome sequencing (WGS) identified serotype O145:H28 stx2a/eae belonging to the same five single nucleotide polymorphism (SNP) single linkage cluster as the causative agent. By 3 July 2024, 288 cases had been linked to the cluster. Most cases were adults (87%) and females (57%), 49% were hospitalized with a further 10% attending emergency care. Descriptive epidemiology and analytical studies were conducted which identified consumption of nationally distributed pre-packed sandwiches as a common food exposure. The implicated food business operators voluntarily recalled ready-to-eat sandwiches and wraps containing lettuce on 14 June 2024.