A 16-year-old male with newly diagnosed granulomatosis with polyangiitis presented to the emergency room with chest pain. He was found to have a myocardial infarction involving the right coronary artery and the left circumflex artery. He underwent mechanical thrombectomy and stent placement without significant sequelae. This is a rare complication associated with granulomatosis with polyangiitis.

Estimates of test size (probability of Type I error) were obtained for several specific repeated measures designs. Estimates were presented for configurations where the underlying covariance matrices exhibited varying degrees of heterogeneity. Conventional variance ratios were employed as basic statistics in order to produce estimates of size for a conventional test, an ∊-adjusted test, and ∊-adjusted test and a conservative test. Indices for empirical distributions of two estimators of ∊j, a measure of covariance heterogeneity, were also provided.

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.

Furrow-irrigated rice (Oryza sativa L.) hectares are increasing in the Midsouth. The lack of sustained flooding creates a favorable environment for weed emergence and persistence, which makes Palmer amaranth (Amaranthus palmeri S. Watson) difficult to control throughout the growing season. The negative yield impacts associated with season-long A. palmeri interference in corn, cotton, and soybean have been evaluated. However, there is limited knowledge of the weed’s ability to influence rice grain yield. Research was initiated in 2022 and 2023 to determine the effect of A. palmeri time of emergence relative to rice on weed seed production and grain yield. Cotyledon stage A. palmeri plants were marked every seven days, beginning one week before rice emergence through four weeks after rice emergence. Amaranthus palmeri seed production decreased exponentially as emergence timing was delayed relative to rice, and seed production increased by 447 seed plant-1 for every one-gram increase in weed biomass. Without rice competition and from the earliest emergence timing, A.palmeri produced 540,000 seeds plant-1. Amaranthus palmeri that emerged one week before the crop had the greatest spatial influence on rice, with grain yield loss of 5% and 50% at a distance of 1.4 m and 0.40 m from the weed, respectively. As A. palmeri emergence was delayed, the area of influence decreased. However, A. palmeri plants emerging 3.5 weeks after rice emergence still negatively affected grain yield and produce sufficient seed to replenish the soil seedbank, potentially impacting long-term crop management decisions. These results show that the time of A. palmeri emergence is a crucial factor influencing rice grain yield and weed seed production, which can be used to determine the consequences of escapes in rice.

Social impact has been widely discussed by the engineering community, but studies show that there is currently little systematic consideration of the social impact of products in both academia and in industry beyond social impacts on health and safety. While Failure Mode and Effect Analysis (FMEA) is useful for evaluating health and safety risks, new developments are needed to create an FMEA-style evaluation that can be applied to a wide range of social impacts for engineered products. The authors describe necessary modifications to traditional FMEA that transform it into a tool for social impact analysis. The modification of FMEA involves the introduction of positive and negative impacts, the inclusion of discrete and continuous impacts, the consideration of various stakeholder types, and the inclusion of uncertainty in place of detectability. This modified FMEA is referred to in this paper as Social Impact Effects Analysis (SIEA). The paper describes how SIEA is performed and articulates the potential benefits of SIEA.

We examine whether the “privileged coordinates” of a geometric space encode its “amount of structure.” In doing so, we compare this coordinate approach to comparing amounts of structure to the more familiar automorphism approach. We first show that on a natural understanding of the former, it faces one of the same well-known problems as the latter. We then capture a precise sense in which the two approaches are closely related to one another, and we conclude by discussing whether they might still prove useful in cases of philosophical interest, despite their shortcomings.

Ask most oncologists (especially radiation oncologists), and you may be hard pressed to find one who professes ignorance of Madame Curie and her historic contributions to medicine. Innumerable publications and media over the past century have correctly memorialised Madame Curie as one of the most brilliant minds in world history. However, do any of us really know the woman and the true extent of her sphere of influence? How, as well, does she fit into modern life and contemporary scientific advancement? Let us try to understand and learn from this complex scientist as more than just one of the most brilliant minds in human history.

Herbicides that persist in the forest litter and soil following their use for managing invasive plant species may negatively affect restoration efforts as well as minimize reinvasion via their residual phytotoxic activity. This study determined the impact of an herbicide mixture comprising triclopyr, dicamba, picloram, and aminopyralid (TDPA) for the control of dense infestations of a woody invader, Pinus contorta Douglas ex Loudon, on the germination of reinvading P. contorta and three New Zealand native species (Chionochloa rubra Zotov, Nothofagus cliffortiodes (Hook. f.) Oerst., and Leptospermum scoparium J.R. Forst. & G. Forst.) used in restoration. Given the essential role of ectomycorrhizal fungi in facilitating conifer reinvasion, the impact of residual herbicides present in mineral soil on the ectomycorrhizal infection of P. contorta seedling roots was also examined. Germination trials were conducted using intact forest litter–soil cores collected at 27, 112 and 480 d (after herbicide spraying) from sprayed and adjacent unsprayed dense P. contorta infestations. At the same time, mineral soil was also collected for the ectomycorrhizal infection study. Post-spray herbicide residue bound in the litter significantly decreased survival, germination rate, root and shoot growth, and also caused malformation of P. contorta seedlings. Similar results were recorded for native species’ germination; however, overall viability of native seed was poor, resulting in low germination rates. There was no difference in levels of ectomycorrhizal infection rates of P. contorta between treatments. Results indicate residual levels of TDPA herbicide in forest floor litter negatively affect P. contorta reinvasion, native recruitment, and active restoration management. Ectomycorrhizal fungi, however, are unaffected by this herbicide mixture and therefore remain a risk to facilitating reinvasion as residual herbicide declines.

The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.

Caribbean health research has overwhelmingly employed measures developed elsewhere and rarely includes evaluation of psychometric properties. Established measures are important for research and practice. Particularly, measures of stress and coping are needed. Stressors experienced by Caribbean people are multifactorial, as emerging climate threats interact with existing complex and vulnerable socioeconomic environments. In the early COVID-19 pandemic, our team developed an online survey to assess the well-being of health professions students across university campuses in four Caribbean countries. This survey included the Perceived Stress Scale, 10-item version (PSS-10) and the Brief Resilient Coping Scale (BRCS). The participants were 1,519 health professions students (1,144 females, 372 males). We evaluated the psychometric qualities of the measures, including internal consistency, concurrent validity by correlating both measures, and configural invariance using confirmatory factor analysis (CFA). Both scales had good internal consistency, with omega values of 0.91 for the PSS-10 and 0.81 for the BRCS. CFA suggested a two-factor structure of the PSS-10 and unidimensional structure of the BRCS. These findings support further use of these measures in Caribbean populations. However, the sampling strategy limits generalizability. Further research evaluating these and other measures in the Caribbean is desirable.