The Hector Galaxy Survey is a new optical integral field spectroscopy (IFS) survey currently using the Anglo-Australian Telescope to observe up to 15 000 galaxies at low redshift ($z \lt 0.1$). The Hector instrument employs 21 optical fibre bundles feeding into two double-beam spectrographs, AAOmega and the new Spector spectrograph, to enable wide-field multi-object IFS observations of galaxies. To efficiently process the survey data, we adopt the data reduction pipeline developed for the SAMI Galaxy Survey, with significant updates to accommodate Hector’s dual-spectrograph system. These enhancements address key differences in spectral resolution and other instrumental characteristics relative to SAMI and are specifically optimised for Hector’s unique configuration. We introduce a two-dimensional arc fitting approach that reduces the root-mean-square (RMS) velocity scatter by a factor of 1.2–3.4 compared to fitting arc lines independently for each fibre. The pipeline also incorporates detailed modelling of chromatic optical distortion in the wide-field corrector, to account for wavelength-dependent spatial shifts across the focal plane. We assess data quality through a series of validation tests, including wavelength solution accuracy (1.2–2.7 km s$^{-1}$ RMS), spectral resolution (FWHM of 1.2–1.4 Å for Spector), throughput characterisation, astrometric precision ($\lesssim$ 0.03 arcsec median offset), sky subtraction residuals (1–1.6% median continuum residual), and flux calibration stability (4% systematic offset when compared to Legacy Survey fluxes). We demonstrate that Hector delivers high-fidelity, science-ready datasets, supporting robust measurements of galaxy kinematics, stellar populations, and emission-line properties and provide examples. Additionally, we address systematic uncertainties identified during the data processing and propose future improvements to enhance the precision and reliability of upcoming data releases. This work establishes a robust data reduction framework for Hector, delivering high-quality data products that support a broad range of extragalactic studies.

Understanding the developmental and occupational histories of Ancestral Maya settlements is crucial for interpreting their roles in broader social, political, and economic dynamics. This article presents 62 new accelerator mass spectrometry (AMS) 14C dates from residential groups in the outlying settlement zone at Alabama, a major inland Ancestral Maya center in East-Central Belize. Alabama is a rare example of a “boomtown” in the Maya lowlands, experiencing rapid development primarily during the 8th and 9th century CE, corresponding to the Late to Terminal Classic periods. Using Bayesian stratigraphic sequence models, we construct detailed developmental and occupational histories for the townsite, clarifying the timing of its development, occupation, and abandonment. Our analysis reveals complex residential histories, confirming a rapid tempo of Late and Terminal Classic settlement growth and indicating continuities in occupation into the 10th century CE and beyond. Furthermore, we identify two separate periods of occupation during the Early Classic (cal AD 345–545) and the Late Postclassic (cal AD 1325–1475), demonstrating that parts of the settlement were inhabited at different intervals over many centuries. These results offer the first detailed deep-history perspective for the East-Central Belize region, establishing a framework that addresses challenges in chronology-building posed by poor pottery preservation and the complexities of earthen-core architecture at the site and enabling future chronological modeling in this lesser-known frontier of the eastern Maya lowlands.

Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and priming biases in three experimental designs that vary when moderators are measured: pre-treatment, post-treatment, or a randomized choice between the two. We derive nonparametric bounds for interactions between the treatment and the moderator under each design and show how to use substantive assumptions to narrow these bounds. These bounds allow researchers to assess the sensitivity of their empirical findings to priming and post-treatment bias. We then apply the proposed methodology to a survey experiment on electoral messaging.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401–409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

Migrants and refugees face elevated risks for mental health problems but have limited access to services. This study compared two strategies for training and supervising nonspecialists to deliver a scalable psychological intervention, Group Problem Management Plus (gPM+), in northern Colombia. Adult women who reported elevated psychological distress and functional impairment were randomized to receive gPM+ delivered by nonspecialists who received training and supervision by: 1) a psychologist (specialized technical support); or 2) a nonspecialist who had been trained as a trainer/supervisor (nonspecialized technical support). We examined effectiveness and implementation outcomes using a mixed-methods approach. Thirteen nonspecialists were trained as gPM+ facilitators and three were trained-as-trainers. We enrolled 128 women to participate in gPM+ across the two conditions. Intervention attendance was higher in the specialized technical support condition. The nonspecialized technical support condition demonstrated higher fidelity to gPM+ and lower cost of implementation. Other indicators of effectiveness, adoption and implementation were comparable between the two implementation strategies. These results suggest it is feasible to implement mental health interventions, like gPM+, using lower-resource, community-embedded task sharing models, while maintaining safety and fidelity. Further evidence from fully powered trials is needed to make definitive conclusions about the relative cost of these implementation strategies.

Accelerating COVID-19 Treatment Interventions and Vaccines (ACTIV) was initiated by the US government to rapidly develop and test vaccines and therapeutics against COVID-19 in 2020. The ACTIV Therapeutics-Clinical Working Group selected ACTIV trial teams and clinical networks to expeditiously develop and launch master protocols based on therapeutic targets and patient populations. The suite of clinical trials was designed to collectively inform therapeutic care for COVID-19 outpatient, inpatient, and intensive care populations globally. In this report, we highlight challenges, strategies, and solutions around clinical protocol development and regulatory approval to document our experience and propose plans for future similar healthcare emergencies.

This article explores the financial and geopolitical networks behind the independence of Gran Colombia. It shows that the failure to obtain official British government support for independence was compensated for by the development of a network of private individuals and partnerships that supplied large quantities of arms, equipment and men. A Colombian government document granting ‘Powers’ to London intermediaries was crucial to the construction of this network. We analyse who the key players were and how the network operated. By exploring the decisions and actions of merchants through the lens of risk, trust, credit and networks, we provide a fresh insight into the wider process of independence in Gran Colombia.

OBJECTIVES/GOALS: Super refractory status epilepticus (SRSE) is associated with high mortality, often due to withdrawal of life sustaining therapy (WLST) based on perceived poor neurological prognosis. Factors influencing decision making are underreported and poorly understood. We surveyed clinicians who treat SRSE to identify factors that influence WLST. METHODS/STUDY POPULATION: Health care providers (HCP), including physicians, pharmacists, and advanced practice providers, who treat SRSE answered a 51-question survey on respondent demographics, institutional characteristics and SRSE management that was distributed though professional societies. Respondents described approaches to prognostication and rated the importance of clinical factors in the management of two hypothetical clinical cases followed by their prediction of recovery potential for the same two cases. Neurointensivists and other HCP responses were compared using descriptive statistics to differentiate group characteristics; a p-value <0.05 was considered significant. Logistical regression models were employed to identify associations between clinician specific factors and prognostication. RESULTS/ANTICIPATED RESULTS: One-hundred and sixty-four respondents were included in the analysis. Compared to other HCPs (neurologists, epileptologists, neurosurgeons, other intensivists; n=122, 74%), neurointensivists (n=42, 26%) [Odds ratio (OR) 0.3, 95% confidence interval (CI) 0.14-0.68), p=.004)] were less likely to use prognostic severity scores and were less likely to prognosticate likelihood of good functional recovery (OR: 0.28 (95% CI: 0.13-0.62), p=.002) compared to non-neurointensivist HCPs, controlling for potential confounders including professional degree, years of experience, country of practice, and annual volume of SRSE cases. There was, however, significant overlap in factors deemed necessary for determining futility in care escalation. DISCUSSION/SIGNIFICANCE: Neurointensivists value similar clinical factors to other HCPs when evaluating medical futility in SRSE but are less likely to predict definitive outcomes. Pending final survey results, future studies aimed at understanding why neurointensivists may be less likely to decisively prognosticate (i.e. avoiding nihilism) in SRSE may be warranted.