Bronze Age–Early Iron Age tin ingots recovered from four Mediterranean shipwrecks off the coasts of Israel and southern France can now be provenanced to tin ores in south-west Britain. These exceptionally rich and accessible ores played a fundamental role in the transition from copper to full tin-bronze metallurgy across Europe and the Mediterranean during the second millennium BC. The authors’ application of a novel combination of three independent analyses (trace element, lead and tin isotopes) to tin ores and artefacts from Western and Central Europe also provides the foundation for future analyses of the pan-continental tin trade in later periods.

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.

A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

In practice, nondestructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation, and associated with independent data. In contrast, this work looks to capture the interdependencies between acoustic emission (AE) experiments (as meta-models) and then use the resulting functions to predict the model hyperparameters for previously unobserved systems. We utilize a Bayesian multilevel approach (similar to deep Gaussian Processes) where a higher-level meta-model captures the inter-task relationships. Our key contribution is how knowledge of the experimental campaign can be encoded between tasks as well as within tasks. We present an example of AE time-of-arrival mapping for source localization, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. We constrain the meta-model based on domain knowledge, then use the inter-task functions for transfer learning, predicting hyperparameters for models of previously unobserved experiments (for a specific design).

Background: CAP is often inappropriately treated with agents active against multidrug-resistant organisms (MDRO; methicillin-resistant S. aureus [MRSA] and P. aeruginosa [PSA]) and for prolonged duration. We assessed the relationship between antibiotic use with ATS/IDSA guideline-unjustified empiric and definitive MDRO therapy and prolonged duration in non-ICU inpatients with CAP at 105 VA Medical Centers. Methods: From VA Corporate Data Warehouse data, we identified patients with discharge ICD-10-CM codes consistent with CAP from 1/2022-3/2023, excluding cases with 14 days of antibiotic therapy, ICU admission, concurrent infections, or severe immunocompromise. We considered as jultified empiric (≤third day of hospitalization) therapy: anti-MRSA therapy for patients with prior positive MRSA cultures, anti-PSA therapy for patients with prior positive PSA cultures, and both anti-MRSA & anti-PSA therapy in patients with severe pneumonia and intravenous antibiotics in the prior 3 months. Definitive (>third day of hospitalization) anti-MDRO therapy was considered unjustified in patients who had achieved clinical stability and whose cultures did not grow MRSA or PSA. Prolonged duration (>6 days of therapy) was unjustified if patients were clinically stable or discharged by day 5. Results: The median age of the 29,260 patients was 75 (IQR 69,81); 4.6% were women. While 33% and 22% of patients received empiric or definitive MDRO therapy, such therapy was jultified in 12% and 0.5%, respectively. Median facility use of empiric and definitive MDRO therapy was 31% (IQR 25%,38%) and 20% (15%,23%), respectively (Figure 1); this use was unjustified in 89% (85%,93%) and 100% (100%,100%), respectively. Pearson’s correlation coefficient between MDRO therapy and rates of unjustified empiric and definitive MDRO therapy for CAP was 0.54 and 0.61, respectively (Figure 2). Although 99% of patients were discharged or stable by day 5, 42% received prolonged therapy. The median frequency of prolonged therapy was 39% (33%,48%); facility rates of prolonged therapy had a correlation of 0.56 with total antibiotic use and 0.46 with MDRO therapy (Figure 3). Discussion: Based on electronic documentation, we identified 1) substantial opportunities to reduce unjustified anti-MDRO therapy and the duration of therapy in hospitalized non-ICU patients with CAP; 2) a moderate correlation of unjustified anti-MDRO therapy with increased MDRO antibiotic use and of prolonged duration of therapy with increased total and MDRO antibiotic use. The correlation of lower quality prescribing with increased antibiotic use provides further impetus for tools such as dashboards (Figure 4) to assist antibiotic stewards in designing and monitoring interventions to reduce unjustified therapy.

Marine litter poses a complex challenge in Indonesia, necessitating a well-informed and coordinated strategy for effective mitigation. This study investigates the seasonality of plastic concentrations around Sulawesi Island in central Indonesia during monsoon-driven wet and dry seasons. By using open data and methodologies including the HYCOM and Parcels models, we simulated the dispersal of plastic waste over 3 months during both the southwest and northeast monsoons. Our research extended beyond data analysis, as we actively engaged with local communities, researchers and policymakers through a range of outreach initiatives, including the development of a web application to visualize model results. Our findings underscore the substantial influence of monsoon-driven currents on surface plastic concentrations, highlighting the seasonal variation in the risk to different regional seas. This study adds to the evidence provided by coarser resolution regional ocean modelling studies, emphasizing that seasonality is a key driver of plastic pollution within the Indonesian archipelago. Inclusive international collaboration and a community-oriented approach were integral to our project, and we recommend that future initiatives similarly engage researchers, local communities and decision-makers in marine litter modelling results. This study aims to support the application of model results in solutions to the marine litter problem.

OBJECTIVES/GOALS: Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by dysregulated collagen accumulation in the lung parenchyma. Our goal is to investigate the role of O-linked N-Acetylglucosamine (O-GlcNAc) transferase (OGT) in pulmonary fibrosis to ultimately discover novel therapies for fibrosis resolution. METHODS/STUDY POPULATION: Lung tissue from IPF and non-IPF donors was subjected to immunohistochemistry (IHC) to assess O-GlcNAc levels. Primary human lung fibroblasts were treated with OGT or O-GlcNAcase (OGA) inhibitors followed by transforming growth factor-beta 1 (TGF-β1) stimulation to assess O-GlcNAc regulation of fibroblast-to-myofibroblast transition (FMT) markers [alpha smooth muscle actin (α-SMA) and type 1 and type 3 collagen (COL1α1, COL3α1)] In Drosophila melanogaster, OGT knockdown (KD)/overexpression (OE) was conditionally induced to assess pericardin, a type IV collagen-like protein, regulation by immunofluorescence. Lastly, a mouse model of bleomycin-induced pulmonary fibrosis was examined following OGT KD and assessed for fibrosis resolution via histology, hydroxyproline assay, and western blotting. RESULTS/ANTICIPATED RESULTS: O-GlcNAc staining was increased in IPF lung tissue compared to non-IPF control lungs. In primary human lung fibroblasts, TGF-α1 administration resulted in increased FMT markers (α-SMA, COL1α1, and COL3α1), which were reduced or increased by OGT or OGA inhibition, respectively. Genetic manipulation in the Drosophila models showed decreased pericardin expression with OGT KD compared to the wild-type, whereas OGT OE increased pericardin compared to control. Additionally, OGT KD in bleomycin treated aged mice resulted in reduced collagen levels at the transcript and protein level and concurrent fibrosis resolution as assessed by Masson’s trichrome staining and total hydroxyproline analysis. Collectively, showing OGT/O-GlcNAc regulating collagen in fibrosis resolution. DISCUSSION/SIGNIFICANCE: These data suggest that the OGT/O-GlcNAc axis regulates collagen deposition in pulmonary fibrosis, and we show that O-GlcNAc is implicated in the pathogenesis of IPF. We identified OGT as a therapeutic target to overcome current drug limitations, opening new horizons for biomedical treatment.

Despite the growing availability of sensing and data in general, we remain unable to fully characterize many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as “big,” they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a derivation path for a spectrum of possible Gaussian process models is introduced and used to highlight how and where different levels of expert knowledge of a system is likely best exploited. Each of the models highlighted in the spectrum have been explored in different ways across communities; novel examples in a structural assessment context here demonstrate how these approaches can significantly reduce reliance on expensive data collection. The increased interpretability of the models shown is another important consideration and benefit in this context.

Globally, mental disorders account for almost 20% of disease burden and there is growing evidence that mental disorders are socially determined. Tackling the United Nations Sustainable Development Goals (UN SDGs), which address social determinants of mental disorders, may be an effective way to reduce the global burden of mental disorders. We conducted a systematic review of reviews to examine the evidence base for interventions that map onto the UN SDGs and seek to improve mental health through targeting known social determinants of mental disorders. We included 101 reviews in the final review, covering demographic, economic, environmental events, neighborhood, and sociocultural domains. This review presents interventions with the strongest evidence base for the prevention of mental disorders and highlights synergies where addressing the UN SDGs can be beneficial for mental health.

Wastewater-based epidemiology (WBE) has proven to be a powerful tool for the population-level monitoring of pathogens, particularly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For assessment, several wastewater sampling regimes and methods of viral concentration have been investigated, mainly targeting SARS-CoV-2. However, the use of passive samplers in near-source environments for a range of viruses in wastewater is still under-investigated. To address this, near-source passive samples were taken at four locations targeting student hall of residence. These were chosen as an exemplar due to their high population density and perceived risk of disease transmission. Viruses investigated were SARS-CoV-2 and its variants of concern (VOCs), influenza viruses, and enteroviruses. Sampling was conducted either in the morning, where passive samplers were in place overnight (17 h) and during the day, with exposure of 7 h. We demonstrated the usefulness of near-source passive sampling for the detection of VOCs using quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS). Furthermore, several outbreaks of influenza A and sporadic outbreaks of enteroviruses (some associated with enterovirus D68 and coxsackieviruses) were identified among the resident student population, providing evidence of the usefulness of near-source, in-sewer sampling for monitoring the health of high population density communities.

Like the polar bear beleaguered by global warming, artificial intelligence (AI) serves as the charismatic megafauna of an entangled set of local and global histories of science, technology and economics. This Themes issue develops a new perspective on AI that moves beyond conventional origin myths – AI was invented at Dartmouth in the summer of 1956, or by Alan Turing in 1950 – and reframes contemporary critique by establishing plural genealogies that situate AI within deeper histories and broader geographies. ChatGPT and art produced by AI are described as generative but are better understood as forms of pastiche based upon the use of existing infrastructures, often in ways that reflect stereotypes. The power of these tools is predicated on the fact that the Internet was first imagined and framed as a ‘commons’ when actually it has created a stockpile for centralized control over (or the extraction and exploitation of) recursive, iterative and creative work. As with most computer technologies, the ‘freedom’ and ‘flexibility’ that these tools promise also depends on a loss of agency, control and freedom for many, in this case the artists, writers and researchers who have made their work accessible in this way. Thus, rather than fixate on the latest promissory technology or focus on a relatively small set of elite academic pursuits born out of a marriage between logic, statistics and modern digital computing, we explore AI as a diffuse set of technologies and systems of epistemic and political power that participate in broader historical trajectories than are traditionally offered, expanding the scope of what ‘history of AI’ is a history of.

We tested 85 isolates of β-hemolytic Streptococcus spp. against trimethoprim/sulfamethoxazole (TMP/SMX), clindamycin, and doxycycline by broth microdilution (BMD) and BD Phoenix. Susceptibility rates via BMD for TMP/SMX, clindamycin, and doxycycline were 100%, 85.5%, and 56.6%, respectively. TMP/SMX is a potential monotherapy agent for β-hemolytic Streptococcus skin and soft tissue infections.