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.

We present new Australia Telescope Compact Array (ATCA) radio observations towards N 49, one of the brightest extragalactic supernova remnants (SNRs) located in the Large Magellanic Cloud (LMC). Our new and archival ATCA radio observations were analysed along with Chandra X-ray data. These observations show a prominent ‘bullet’ shaped feature beyond the southwestern boundary of the SNR. Both X-ray morphology and radio polarisation analysis support a physical connection of this feature to the SNR. The ‘bullet’ feature’s apparent velocity is estimated at $\sim$1 300 km s$^{-1}$, based on its distance ($\sim$10 pc) from the remnant’s geometric centre and estimated age ($\sim$7 600 yr). we estimated the radio spectral index, $\alpha= -0.55 \pm 0.03$ which is typical of middle-age SNRs. Polarisation maps created for N 49 show low to moderate levels of mean fractional polarisation estimated at 7$\pm$1% and 10$\pm$1% for 5.5 and 9 GHz, respectively. These values are noticeably larger than found in previous studies. Moreover, the mean value for the Faraday rotation of SNR N 49 from combining CABB data is 212$\pm$65 rad m$^{-2}$ and the maximum value of RM is 591$\pm$103 rad m$^{-2}$.

The crystal structure of bannisterite, a modulated, mica-like mineral species, of general composition Ca0.5(K,Na)0.5(Mn,Fe,Mg,Zn)10(Si,Al)16O38(OH)8·nH2O, has been solved and refined for specimens from Franklin Furnace, New Jersey (FF), and Broken Hill, Australia (BH). The crystals are mono-clinic in space group A2/a, with (for FF) a = 22.265(1) Å, b = 16.368(1) Å, c = 24.668(2) Å, β = 94.285(5)°; and (for BH) a = 22.286(1) Å, b = 16.386(1) Å, c = 24.575(2) Å, β = 94.355(7)°; Z = 8. Refinement with anisotropic thermal factors reached Rw = 0.034 (FF) and 0.039 (BH). Like stilpnomelane and ganophyllite, bannisterite has a modified 2:1 trioctahedral layer structure in which some of the tetrahedra are inverted towards the interlayer region and linked to inverted tetrahedra in the opposite layer. The octahedral sheet is strongly corrugated along b. The tetrahedral sheet consists of 5-, 6-, and 7-fold rings, and bond distance calculations indicate that Al is concentrated into two of the four inverted tetrahedra. The interlayer Ca, K, and H2O species are highly disordered, as indicated by anomalously large temperature factors and partial occupancies. Localized differences in the Al/Si arrangements in the inverted tetrahedra induce disorder among the interlayer cations.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN–human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN–human correspondences in vision and other domains of cognition. We explore all these issues in this response.

In COVID’s immediate wake, the 2020 death toll from a different enemy of the public’s health — gun violence — ticked up by 15 percent in the United States from the previous year. Meanwhile, the U.S. Supreme Court issued an opinion in Caniglia v. Strom that will allow people who have recently threatened suicide — with a gun — to keep unsecured guns in their home unless police take time to obtain a search warrant to remove them.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

Seed retention, and ultimately seed shatter, are extremely important for the efficacy of harvest weed seed control (HWSC) and are likely influenced by various agroecological and environmental factors. Field studies investigated seed-shattering phenology of 22 weed species across three soybean [Glycine max (L.) Merr.]-producing regions in the United States. We further evaluated the potential drivers of seed shatter in terms of weather conditions, growing degree days, and plant biomass. Based on the results, weather conditions had no consistent impact on weed seed shatter. However, there was a positive correlation between individual weed plant biomass and delayed weed seed–shattering rates during harvest. This work demonstrates that HWSC can potentially reduce weed seedbank inputs of plants that have escaped early-season management practices and retained seed through harvest. However, smaller individuals of plants within the same population that shatter seed before harvest pose a risk of escaping early-season management and HWSC.

Potential effectiveness of harvest weed seed control (HWSC) systems depends upon seed shatter of the target weed species at crop maturity, enabling its collection and processing at crop harvest. However, seed retention likely is influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed-shatter phenology in 13 economically important broadleaf weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after physiological maturity at multiple sites spread across 14 states in the southern, northern, and mid-Atlantic United States. Greater proportions of seeds were retained by weeds in southern latitudes and shatter rate increased at northern latitudes. Amaranthus spp. seed shatter was low (0% to 2%), whereas shatter varied widely in common ragweed (Ambrosia artemisiifolia L.) (2% to 90%) over the weeks following soybean physiological maturity. Overall, the broadleaf species studied shattered less than 10% of their seeds by soybean harvest. Our results suggest that some of the broadleaf species with greater seed retention rates in the weeks following soybean physiological maturity may be good candidates for HWSC.

Seed shatter is an important weediness trait on which the efficacy of harvest weed seed control (HWSC) depends. The level of seed shatter in a species is likely influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed shatter of eight economically important grass weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after maturity at multiple sites spread across 11 states in the southern, northern, and mid-Atlantic United States. From soybean maturity to 4 wk after maturity, cumulative percent seed shatter was lowest in the southern U.S. regions and increased moving north through the states. At soybean maturity, the percent of seed shatter ranged from 1% to 70%. That range had shifted to 5% to 100% (mean: 42%) by 25 d after soybean maturity. There were considerable differences in seed-shatter onset and rate of progression between sites and years in some species that could impact their susceptibility to HWSC. Our results suggest that many summer annual grass species are likely not ideal candidates for HWSC, although HWSC could substantially reduce their seed output during certain years.

Posthodiplostomum minimum utilizes a three-host life cycle with multiple developmental stages. The metacercarial stage, commonly known as ‘white grub’, infects the visceral organs of many freshwater fishes and was historically considered a host generalist due to its limited morphological variation among a wide range of hosts. In this study, infection data and molecular techniques were used to evaluate the host and tissue specificity of Posthodiplostomum metacercariae in centrarchid fishes. Eleven centrarchid species from three genera were collected from the Illinois portion of the Ohio River drainage and necropsied. Posthodiplostomum infection levels differed significantly by host age, host genera and infection locality. Three Posthodiplostomum spp. were identified by DNA sequencing, two of which were relatively common within centrarchid hosts. Both common species were host specialists at the genus level, with one species restricted to Micropterus hosts and the other preferentially infecting Lepomis. Host specificity is likely dictated by physiological compatibility and deviations from Lepomis host specificity may be related to host hybridization. Posthodiplostomum species also differed in their utilization of host tissues. Neither common species displayed strong genetic structure over the scale of this study, likely due to their utilization of bird definitive hosts.

A passive seismology experiment was conducted across the main overdeepening of Storglaciären in the Tarfala valley, northern Sweden, to investigate the spatial and temporal distribution of basal microseismic waveforms in relation to known dynamics of this small polythermal sub-arctic glacier. The high ablation rate made it difficult to keep geophones buried and well coupled to the glacier during the experiment and reduced the number of days of good-quality data collection. The characterization of typical and atypical waveforms showed that the dominant waveforms were from near-surface events such as crevassing. Waveforms resembling basal microseismic signals were very rare, and seldom observed on more than two seismic stations simultaneously. The analysis of waveforms, amplitudes and particle motions suggested a near-field origin for most events. Even though basal sliding is known to occur in the overdeepening, no convincing examples of basal waveforms were detected, suggesting basal microseismic signals are rare or difficult to detect beneath polythermal glaciers like Storglaciären. We discuss the reasons for failing to locate basal signals, consider the origin of common waveforms and make recommendations for setting up passive seismology experiments on glaciers with high ablation rates.