This study evaluated the impact of four cover crop species and their termination timings on cover crop biomass, weed control, and corn yield. A field experiment was arranged in a split-plot design in which cover crop species (wheat, cereal rye, hairy vetch, and rapeseed) were the main plot factor, and termination timings [4, 2, 1, and 0 wk before planting corn (WBP)] was the subplot factor. In both years (2021 and 2022), hairy vetch produced the most biomass (5,021 kg ha–1) among cover crop species, followed by cereal rye (4,387 kg ha–1), wheat (3,876 kg ha–1), and rapeseed (2,575 kg ha–1). Regression analysis of cover crop biomass with accumulated growing degree days (AGDDs) indicated that for every 100 AGDD increase, the biomass of cereal rye, wheat, hairy vetch, and rapeseed increased by 880, 670, 780, and 620 kg ha–1, respectively. The density of grass and small-seeded broadleaf (SSB) weeds at 4 wk after preemergence herbicide (WAPR) application varied significantly across termination timings. The grass and SSB weed densities were 56% and 36% less at 0 WBP compared with 2 WBP, and 67% and 61% less compared with 4 WBP. The sole use of a roller-crimper did not affect the termination of rapeseed at 0 WBP and resulted in the least corn yield (3,046 kg ha–1), whereas several different combinations of cover crops and termination timings resulted in greater corn yield. In conclusion, allowing cover crops to grow longer in the spring offers more biomass for weed suppression and impacts corn yield.

Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.

The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the Gaia satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the whole wavelength range, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels to all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from Gaia and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.

An important component of post-release monitoring of biological control of invasive plants is the tracking of species interactions. During post-release monitoring following the initial releases of the weevil Ceutorhynchus scrobicollis Nerenscheimer and Wagner (Coleoptera: Curculionidae) on garlic mustard, Alliaria petiolata (Marschall von Bieberstein) Cavara and Grande (Brassicaceae), in Ontario, Canada, we identified the presence of larvae of the tumbling flower beetle, Mordellina ancilla Leconte (Coleoptera: Mordellidae), in garlic mustard stems. This study documents the life history of M. ancilla on garlic mustard to assess for potential interactions between M. ancilla and C. scrobicollis as a biological control agent. Garlic mustard stems were sampled at eight sites across southern Ontario and throughout the course of one year to record the prevalence of this association and to observe its life cycle on the plant. We found M. ancilla to be a widespread stem-borer of late second–year and dead garlic mustard plants across sampling locations. This is the first host record for M. ancilla on garlic mustard. The observed life cycle of M. ancilla indicates that it is unlikely to negatively impact the growth and reproduction of garlic mustard and that it is unlikely to affect the use of C. scrobicollis as a biological control agent.

Understanding deformation and slip at ice streams, which are responsible for 90% of Antarctic ice loss, are vital for accurately modelling large-scale ice flow. Ice crystal orientation fabric (COF) has a first-order effect on ice stream deformation. For the first time, we use shear-wave splitting measurements of basal icequakes at Whillans Ice Stream (WIS), Antarctica, to determine a shear-wave anisotropy with an average delay time of 7 ms and fast S-wave polarisation (φ) of 29.3°. The polarisation is expected to align perpendicular to ice flow, whereas our observation is oblique to the current ice flow direction (${\sim}280^{\circ}$). This suggests that ice at WIS preserves upstream fabric caused by palaeo-deformation developed over at least the past 450 years, which provides evidence of the concept of microstructural fading memory. Our results imply that changes in the shape of WIS occur on timescales shorter than COF re-equilibration. The ‘palaeo-fabric’ can somewhat control present-day ice flow, which we suggest may somewhat contribute to the long-term slowdown at WIS. Our findings suggest that seismic anisotropy can provide information on past ice sheet dynamics, and how past ice dynamics can play a role in controlling current deformation.

Commercial targeted sprayer systems allow producers to reduce herbicide inputs but risks the possibility of not treating emerging weeds. Currently, targeted applications with the John Deere system have five spray sensitivity settings, and no published literature discusses the effects of these settings on detecting and spraying weeds of varying species, sizes, and positions in crops. Research was conducted in Arkansas, Illinois, Indiana, Mississippi, and North Carolina on plantings of corn, cotton, and soybean to determine how various factors might influence the ability of targeted applications to treat weeds. These data included 21 weed species aggregated to six classes with height, width, and densities ranging from 25 to 0.25 cm, 25 to 0.25 cm, and 14.3 to 0.04 plants m−2, respectively. Crop and weed density did not influence the likelihood of treating the weeds. As expected, the sensitivity setting alters the ability to treat weeds. Targeted applications (across sensitivity settings, median weed height and width, and density of 2.4 plants m−2) resulted in a treatment success of 99.6% to 84.4% for Convolvulaceae, 99.1% to 68.8% for decumbent broadleaf weeds, 98.9% to 62.9% for Malvaceae, 99.1% to 70.3% for Poaceae, 98.0% to 48.3% for Amaranthaceae, and 98.5% to 55.8% for yellow nutsedge. Reducing the sensitivity setting reduced the ability to treat weeds. The size of weeds aided targeted application success, with larger weeds being more readily treated through easier detection. Based on these findings, various conditions can affect the outcome of targeted multinozzle applications. Additionally, the analyses highlight some of the parameters to consider when using these technologies.

Objectives/Goals: Transmission-blocking vaccines hold promise for malaria elimination by reducing community transmission. But a major challenge that limits the development of efficacious vaccines is the vast parasite’s genetic diversity. This work aims to assess the genetic diversity of the Pfs25 vaccine candidate in complex infections across African countries. Methods/Study Population: We employed next-generation amplicon deep sequencing to identify nonsynonymous single nucleotide polymorphisms (SNPs) in 194 Plasmodium falciparum samples from four endemic African countries: Senegal, Tanzania, Ghana, and Burkina Faso. The individuals aged between 1 and 74 years, but most of them ranged from 1 to 19 years, and all presented symptomatic P. falciparum infection. The genome amplicon sequencing was analyzed using Geneious software and P. falciparum 3D7 as a reference. The SPNs were called with a minimum coverage of 500bp, and for this work, we used a very sensitive threshold of 1% variant frequency to determine the frequency of SNPs. The identified SNPs were threaded to the crystal structure of the Pfs25 protein, which allowed us to predict the impact of the novel SNP in the protein or antibody binding. Results/Anticipated Results: We identified 26 SNPs including 24 novel variants, and assessed their population prevalence and variant frequency in complex infections. Notably, five variants were detected in multiple samples (L63V, V143I, S39G, L63P, and E59G), while the remaining 21 were rare variants found in individual samples. Analysis of country-specific prevalence showed varying proportions of mutant alleles, with Ghana exhibiting the highest prevalence (44.6%), followed by Tanzania (12%), Senegal (11.8%), and Burkina Faso (2.7%). Moreover, we categorized SNPs based on their frequency, identifying dominant variants (>25%), and rare variants (Discussion/Significance of Impact: We identified additional SNPs in the Pfs25 gene beyond those previously reported. However, the majority of these newly discovered display low variant frequency and population prevalence. Further research exploring the functional implications of these variations will be important to elucidate their role in malaria transmission.

We present a chemo-dynamical study conducted with 2dF$+$AAOmega of $\sim 6\,000$ Gaia DR3 non-variable candidate metal-poor stars that lie in the direction of the Galactic plane. Our spectral analysis reveals 15 new extremely metal-poor (EMP) stars, with the lowest metallicity at $\left[\text{Fe/H}\right] = -4.0 \pm 0.2$ dex. Two of the EMP stars are also carbon enhanced, with the largest enhancement of $\left[\text{C/Fe}\right] = 1.3 \pm 0.1$ occurring in a dwarf. Using our $\left[\text{C/Fe}\right]$ results, we demonstrate that the number of carbon-depleted stars decreases with lower metallicities, and the fraction of carbon-enhanced stars increases, in agreement with previous studies. Our dynamical analysis reveals that the fraction of prograde and retrograde disk stars, defined as $z_{\mathrm{max}} \lt 3$ kpc, with $J_{\phi}/J_{\mathrm{tot}} \gt 0.75$ and $J_{\phi}/J_{\mathrm{tot}} \lt -0.75$, respectively, changes as metallicities decrease. Disk stars on retrograde orbits make up $\sim 10$% of all the stars in our sample with metallicities below $-2.1$ dex. Interestingly, the portion of retrograde disk stars compared with the number of kinematically classified halo stars is approximately constant at $4.6$% for all metallicities below $-1.5$ dex. We also see that $J_{\phi}$ increases from $380 \pm 50$ to $1320 \pm 90$ km s$^{-1}$ kpc across metallicity range $-1.5$ to $-1.1$, consistent with the spin-up of the Galactic disk. Over the metallicity range $-3.0 \lt \left[\text{Fe/H}\right] \lt -2.0$, the slopes of the metallicity distribution functions for the prograde and retrograde disk stars are similar and comparable to that for the halo population. However, detailed chemical analyses based on high-resolution spectra are needed to distinguish the accreted versus in situ contributions. Finally, we show that our spectroscopic parameters reveal serious systematics in the metallicities published in recent studies that apply various machine learning techniques to Gaia XP spectra.

In this methodological study we analyze price adjustment processes in multi-period laboratory asset markets with five distinct fundamental value (FV) regimes in a unified framework. Minimizing the effect of between-treatment variations we run markets with deterministically decreasing, constant, randomly fluctuating and—as main innovation—markets with deterministically increasing FVs. We find (i) efficient pricing in markets with constant FVs, (ii) overvaluation in markets with decreasing FVs, and (iii) undervaluation in markets with increasing FVs. (iv) Markets with randomly fluctuating fundamentals show overvaluation when FVs predominantly decline and undervaluation when FVs are mostly upward-sloping. Finally, we document that (v) bid-ask spreads and volatility of price changes are positively correlated with mispricing across regimes. The main contribution of the paper is to provide clean comparisons between distinct FV regimes, in particular between markets with increasing FVs and other regimes.

We review bubble measures which are commonly used in the experimental asset market literature. It seems sensible to require that measures of mispricing should (i) relate the fundamental value and price, (ii) be monotone in the difference between fundamental value and price, and (iii) be independent of the total number of periods and the absolute level of fundamental value. We show that none of the measures currently used fulfills all these criteria. To facilitate comparability across different experimental settings with different parameterizations we propose two alternative measures which fulfill all evaluation criteria. The measure for mispricing, RAD (relative absolute deviation), is calculated by averaging absolute differences between the (volume-weighted) mean price and the fundamental value across all periods and normalizing it with the absolute value of the average FV of the market. The measure for overvaluation, RD (relative deviation), is calculated analogously, but uses raw difference between (volume-weighted) mean prices and fundamental values. Hence, it provides information on whether the mispricing stems from over- or undervaluation of the asset.

We run laboratory experiments to analyze the impact of prior investment experience on price efficiency in asset markets. Before subjects enter the asset market they gain either no, positive, or negative investment experience in an investment game. To get a comprehensive picture about the role of experience we implement two asset market designs. One is prone to inefficient pricing, exhibiting bubble and crash patterns, while the other exhibits efficient pricing. We find that (i) both, positive and negative, experience gained in the investment game lead to efficient pricing in both market settings. Further, we show that (ii) the experience effect dominates potential effects triggered by positive and negative sentiment generated by the investment game. We conjecture that experiencing changing price paths in the investment game can create a higher sensibility on changing fundamentals (through higher salience) among subjects in the subsequently run asset market.

Vaccines have revolutionised the field of medicine, eradicating and controlling many diseases. Recent pandemic vaccine successes have highlighted the accelerated pace of vaccine development and deployment. Leveraging this momentum, attention has shifted to cancer vaccines and personalised cancer vaccines, aimed at targeting individual tumour-specific abnormalities. The UK, now regarded for its vaccine capabilities, is an ideal nation for pioneering cancer vaccine trials. This article convened experts to share insights and approaches to navigate the challenges of cancer vaccine development with personalised or precision cancer vaccines, as well as fixed vaccines. Emphasising partnership and proactive strategies, this article outlines the ambition to harness national and local system capabilities in the UK; to work in collaboration with potential pharmaceutic partners; and to seize the opportunity to deliver the pace for rapid advances in cancer vaccine technology.

Psychologists and other behavioral scientists are frequently interested in whether a questionnaire measures a latent construct. Attempts to address this issue are referred to as construct validation. We describe and extend nonparametric hypothesis testing procedures to assess matrix structures, which can be used for construct validation. These methods are based on a quadratic assignment framework and can be used either by themselves or to check the robustness of other methods. We investigate the performance of these matrix structure tests through simulations and demonstrate their use by analyzing a big five personality traits questionnaire administered as part of the Health and Retirement Study. We also derive rates of convergence for our overall test to better understand its behavior.