Around 1000 years ago, Madagascar experienced the collapse of populations of large vertebrates that ultimately resulted in many species going extinct. The factors that led to this collapse appear to have differed regionally, but in some ways, key processes were similar across the island. This review evaluates four hypotheses that have been proposed to explain the loss of large vertebrates on Madagascar: Overkill, aridification, synergy, and subsistence shift. We explore regional differences in the paths to extinction and the significance of a prolonged extinction window across the island. The data suggest that people who arrived early and depended on hunting, fishing, and foraging had little effect on Madagascar’s large endemic vertebrates. Megafaunal decline was triggered initially by aridification in the driest bioclimatic zone, and by the arrival of farmers and herders in the wetter bioclimatic zones. Ultimately, it was the expansion of agropastoralism across both wet and dry regions that drove large endemic vertebrates to extinction everywhere.

Objectives/Goals: The aim of the study is to identify resistance factors for substance use (i.e., factors that explicitly help to avoid or reduce drug use). Identification of resistance factors could inform strategies that seek to reduce the prevalence of substance use and related disorders. Methods/Study Population: Adult twins aged 30–70 years were recruited from the Mid-Atlantic Twin Registry. A mixed-method approach, group concept mapping, was used to identify factors influencing participants to resist using substances. Approximately 155 participants produced 97 statements reflecting substance use resistance factors. Hierarchical cluster analysis and multidimensional scaling assessed how participants sorted and rated statements for their lifetime and current importance. Factor analysis was used to reduce data dimensionality. Reliability analyses were conducted to identify a subset of statements anticipated to consistently represent each cluster. Results were shared with participants to assess accuracy with their experiences. Results/Anticipated Results: Participants sorted 97 statements into 9 thematic clusters: (1) Controlling Personal, Negative Consequences; (2) Concern About Health and Well-being; (3) Lack of Desire; (4) Outside Influences; (5) Social Norms and My Reputation; (6) Career and Legal Impacts, (7) Avoiding Harm to Family and Relationships; (8) Preserving Family Relationships; and (9) Family and Friends Impact on Me. Participants consistently identified health concerns as an important substance use resistance factor. The statements will be further reduced to represent a smaller subset for future use as a scale to measure exposure to resistance factors. Discussion/Significance of Impact: Health concerns related to substance use were identified as an important resistance factor. This has been supported by research on smoking cessation and implemented in smoking prevention campaigns. Therefore, prioritizing health-related outcomes in prevention may be important to reduce substance use prevalence.

Acute Appendicitis (AA) is an inflammatory condition of the vermiform appendix in the caecum of the colon. Genetic polymorphisms have been suggested as risk factors predisposing to AA susceptibility but have remained relatively unknown, due to insufficient sample size in previous analyses. Therefore, the primary research aim was to identify genetic variants associated with AA. It was hypothesised that gene polymorphisms associated with AA will provide a connection to other diet-related inflammatory diseases. Genetic variants associated with AA were studied via a Genome-Wide Association Scan (GWAS) using the Global Biobank Meta-Analysis Initiative (GBMI). The GBMI is a collaborative consortium of 23 biobanks with a publicly released repository of de-identified genetic data linked with digital health records spanning 4 continents with a study population size of over 2.2 million consented individuals of multiple ancestral backgrounds1. A linear regression model was used to estimate the association between single nucleotide polymorphisms (SNPs), across the human genome, and AA by each contributing biobank. The results were then meta-analysed with a total of 32,706 cases and 1,075,763 controls. In the present study, the free open-source Complex Traits Genetic Virtual Lab (CTG-VL) platform was used to access, analyse, and visualise the GWAS summary statistics of AA2. Genome-wide significantly associated SNPs (p-value < 5 x 10-8) were further searched for their associations with health-related traits in publicly available GWAS summary statistics. Upon analysis, significantly associated SNPs for AA were identified within or nearby nine genes. HLX, NKX2-3, LTBR, and DLEU1 are genes involved in immune responses; IRF8 associated with maturation of myeloid cells; OSR-1 responsible for transmembrane ion transporter activity; NCALD a regulator of G protein-coupled signal transduction. In addition, based on the hypothesis, the SNP of key clinical importance was the HLA-C rs2524046 (p-value = 2.38 x 10-8), with the AA risk-increasing allele C being also strongly associated with a higher risk of coeliac disease (CD). The CD is an autoimmune condition where gluten, a protein present in grains such as barley, rye, and wheat, elicits an inflammatory response that results in damage to the small intestine lining. Considering how both AA and CD share the same SNP, it is possible to speculate whether gluten initiates a similar pathophysiological mechanism that exacerbates inflammation in the vermiform appendix in AA. In conclusion, the top AA associated SNPs suggests its development could be due to immunological responses influenced by dietary nutrient intake. The HLA-C SNP is common to AA and CD, suggesting that the gluten protein found in certain cereal grains possibly contributes to the pathophysiology of AA like CD. This warrants further investigations into whether dietary gluten could play a key role in AA development.

In our article, we share the lessons we have learned after creating and running a successful legal laboratory over the past seven years at Yale Law School. Our legal laboratory, which focuses on the intersection of law and severe brain injury, represents a unique pedagogical model for legal academia, and is closely influenced by the biomedical laboratory.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

Background: Recently, we showed cathodal high-definition transcranial direct current stimulation (HD-tDCS) inhibits spikes in refractory status epilepticus (RSE) patients. We sought to characterize relative power changes within regional frequency bands to explore connectivity throughout stimulation. Methods: 28 ICU-EEGs from 10 patients were filtered with 1Hz-lower/127Hz-upper/60Hz-notch. Power spectral density was computed for each individual bipolar-longitudinal (excluding midline) electrode chain in delta/theta/alpha/beta/gamma frequency bands. Relative power was extracted throughout recordings by Welch’s method with 2.5-second Hanning window size with 50% overlap every 10 seconds. The average of relative powers from ≤20-minute pre/during/post stimulation windows were calculated per session/chain/band. Absolute maximal relative power change between pre-during and during-post were computed. ANOVA statistically tested for differences. Results: Change in pre-during for delta/theta/alpha/beta/gamma were respectively -0.7%/-3.1%/-2.8%/-2.1%/8.2%, and during-post were 1.3%/2.8%/2.3%/1.7%/-7%. Positive average changes are increases in average relative power where negative changes show decreases. Changes in absolute maximal average relative power between pre-during and during-post stimulation between frequency bands were significant respectively (p=0.0011, p=0.0139). Conclusions: Relative power in all bands drop pre-during and increase during-post except gamma which correlates with desynchronization being a mechanism for HD-tDCS efficacy in RSE. These frequency band power changes may imply brain connectivity changes that need to be further explored.

The coronavirus disease 2019 (COVID-19) pandemic highlighted the lack of agreement regarding the definition of aerosol-generating procedures and potential risk to healthcare personnel. We convened a group of Massachusetts healthcare epidemiologists to develop consensus through expert opinion in an area where broader guidance was lacking at the time.

While unobscured and radio-quiet active galactic nuclei are regularly being found at redshifts $z > 6$ , their obscured and radio-loud counterparts remain elusive. We build upon our successful pilot study, presenting a new sample of low-frequency-selected candidate high-redshift radio galaxies (HzRGs) over a sky area 20 times larger. We have refined our selection technique, in which we select sources with curved radio spectra between 72–231 MHz from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey. In combination with the requirements that our GLEAM-selected HzRG candidates have compact radio morphologies and be undetected in near-infrared $K_{\rm s}$ -band imaging from the Visible and Infrared Survey Telescope for Astronomy Kilo-degree Infrared Galaxy (VIKING) survey, we find 51 new candidate HzRGs over a sky area of approximately $1200\ \mathrm{deg}^2$ . Our sample also includes two sources from the pilot study: the second-most distant radio galaxy currently known, at $z=5.55$ , with another source potentially at $z \sim 8$ . We present our refined selection technique and analyse the properties of the sample. We model the broadband radio spectra between 74 MHz and 9 GHz by supplementing the GLEAM data with both publicly available data and new observations from the Australia Telescope Compact Array at 5.5 and 9 GHz. In addition, deep $K_{\rm s}$ -band imaging from the High-Acuity Widefield K-band Imager (HAWK-I) on the Very Large Telescope and from the Southern Herschel Astrophysical Terahertz Large Area Survey Regions $K_{\rm s}$ -band Survey (SHARKS) is presented for five sources. We discuss the prospects of finding very distant radio galaxies in our sample, potentially within the epoch of reionisation at $z \gtrsim 6.5$ .

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Potential loss of energetic ions including alphas and radio-frequency tail ions due to classical orbit effects and magnetohydrodynamic instabilities (MHD) are central physics issues in the design and experimental physics programme of the SPARC tokamak. The expected loss of fusion alpha power due to ripple-induced transport is computed for the SPARC tokamak design by the ASCOT and SPIRAL orbit-simulation codes, to assess the expected surface heating of plasma-facing components. We find good agreement between the ASCOT and SPIRAL simulation results not only in integrated quantities (fraction of alpha power loss) but also in the spatial, temporal and pitch-angle dependence of the losses. If the toroidal field (TF) coils are well-aligned, the SPARC edge ripple is small (0.15–0.30 %), the computed ripple-induced alpha power loss is small (${\sim } 0.25\,\%$) and the corresponding peak surface power density is acceptable ($244\ \textrm{kW}\ \textrm {m}^{-2}$). However, the ripple and ripple-induced losses increase strongly if the TF coils are assumed to suffer increasing magnitudes of misalignment. Surface heat loads may become problematic if the TF coil misalignment approaches the centimetre level. Ripple-induced losses of the energetic ion tail driven by ion cyclotron range of frequency (ICRF) heating are not expected to generate significant wall or limiter heating in the nominal SPARC plasma scenario. Because the expected classical fast-ion losses are small, SPARC will be able to observe and study fast-ion redistribution due to MHD including sawteeth and Alfvén eigenmodes (AEs). SPARC's parameter space for AE physics even at moderate $Q$ is shown to reasonably overlap that of the demonstration power plant ARC (Sorbom et al., Fusion Engng Des., vol. 100, 2015, p. 378), and thus measurements of AE mode amplitude, spectrum and associated fast-ion transport in SPARC would provide relevant guidance about AE behaviour expected in ARC.