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.

In 2022, the Camden Coalition Medical-Legal Partnership began providing civil and criminal legal services to substance use disorder patients at Cooper University Health Care’s Center for Healing. This paper discusses early findings from the program’s first year on the efficacy of the provision of criminal-legal representation, which is uncommon among MLPs and critical for this patient population. The paper concludes with takeaways for other programs providing legal services in an addiction medicine setting.

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning – they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information.

It is known that the water entry of a body with a recessed, cupped nose can suppress the splash and air cavity typically observed for solid body entry (Mathai, Govardhan & Arakeri, Appl. Phys. Lett., vol. 106, 2015, 064101). However, the interplay between the captive gas in the cup, the cavity and the splash is quite subtle and has not been thoroughly explored. Here we study the cavity and splash dynamics associated with the vertical water entry of cups and find a variety of regimes over a range of Weber numbers ($We_D$) and dimensionless cup depths. Our parameter space spans a transition between slow-developing cavities with long closure times (low $We_D$) to fast-sealing cavities (high $We_D$). An important dynamic event is the evacuation of trapped gas from within the cup, which drives the ensuing cavity and splash behaviour. Through modelling, we predict the conditions for which the evacuating gas inflates a cavity that opens to the atmosphere versus inflating a submerged cavity that suppresses air entrainment from above the surface. We also compare our cup water entry findings to the impact phenomena observed for flat disks, which entrap gas on the front surface similar to cups. In doing so, we reveal the sensitivity of disk splash and cavity behaviour to impact angle, and show that disks share a common regime with cups, in which a thin splash quickly seals on the body. We deduce the mechanisms by which increasing cup depth delays the cavity seal time in this regime. These findings reveal that cups may in fact promote or suppress cavity growth, depending on the cup depth and impact conditions.

Laboratory studies of choice and decision making among real monetary rewards typically use smaller real rewards than those common in real life. When laboratory rewards are large, they are almost always hypothetical. In applying laboratory results meaningfully to real-life situations, it is important to know the extent to which choices among hypothetical rewards correspond to choices among real rewards and whether variation of the magnitude of hypothetical rewards affects behavior in meaningful ways. The present study compared real and hypothetical monetary rewards in two experiments. In Experiment 1, participants played a temporal discounting game that incorporates the logic of a repeated prisoner’s-dilemma (PD) game versus tit-for-tat; choice of one alternative (“defection” in PD terminology) resulted in a small-immediate reward; choice of the other alternative (“cooperation” in PD terminology) resulted in a larger reward delayed until the following trial. The larger-delayed reward was greater for half of the groups than for the other half. Rewards also differed in type across groups: multiples of real nickels, hypothetical nickels, or hypothetical hundred-dollar bills. All groups significantly increased choice of the larger delayed reward over the 40 trials of the experiment. Over the last 10 trials, cooperation was significantly higher when the difference between larger and smaller hypothetical rewards was greater. Reward type (real or hypothetical) made no significant difference in cooperation on most measures. In Experiment 2, real and hypothetical rewards were compared in social discounting—the decrease in value to the giver of a reward as social distance increases to the receiver of the reward. Social discount rates were well described by a hyperbolic function. Discounting rates for real and hypothetical rewards did not significantly differ. These results add to the evidence that results of experiments with hypothetical rewards validly apply in everyday life.