Background: The Agency for Healthcare Research and Quality Safety Program for MRSA Prevention Surgical Services cohort aimed to reduce surgical site infections (SSIs) and prevent methicillin-resistant Staphylococcus aureus (MRSA) in teams performing surgeries at high risk for infection with and high morbidity due to MRSA (cardiac, knee or hip replacement, and spinal fusion) using evidence-based infection prevention interventions and the Comprehensive Unit-based Safety Program (CUSP) framework. We report process and outcome measures associated with program participation. Methods: The Surgical Services Safety Program for MRSA Prevention was implemented from January 2023 to June 2024. The aim was to increase teamwork and collaboration, reinforce safety culture, implement evidence-based infection prevention practices, and decrease SSIs and MRSA. The project team provided 22 live webinars, supporting materials, and other tools to assist surgical teams (Table 1). Teams were also assigned an implementation advisor who provided support through monthly coaching calls.

Teams submitted baseline and endline information on patient safety culture and on infrastructure at the team- and hospital-level, as well as monthly data regarding process measures and SSIs. Teams submitted SSI data from 12 months prior to the start of the program and for 18 months after program implementation. Changes were assessed using pre-post comparisons with Chi-squared test and linear mixed effect models with random intercept. Results: 104 surgical teams (18 cardiac, 19 neurosurgical spinal fusion, 16 orthopedic spinal fusion, 51 knee/hip replacement) from 63 hospitals completed the program. Significant improvements in team-based process measures of surgical team infrastructure (Figure 1) and in teams’ reporting that patients received evidence-based practices (Figure 2) were observed across several areas from baseline to endline, including preoperative decolonization, appropriate antibiotic prophylaxis, and intraoperative infection prevention procedures. While SSI rates did not significantly change, the observed 23% decrease in overall deep or organ space SSI rates approached statistical significance (95% CI -0.46, 0.01) (Table 2 and Table 3). Conclusions: The AHRQ Safety Program for MRSA Prevention supported implementation of evidence-based infection prevention practices to prevent MRSA and SSIs in high-risk surgeries. Participating teams showed improvements in team-based process measures and observed a reduction in deep or organ space SSI rates.

Rayleigh–Taylor instability (RTI) caused by rarefaction waves not only features variable acceleration but also incorporates time-dependent density, which introduces great challenges in predicting the finger growth behaviours. In this work, we propose a model for predicting the single-mode finger behaviours by extending the Layzer potential-flow framework to account for time-dependent acceleration and density. Relative to the previous models, the present model can evaluate the effect of time-dependent density on finger growth, and can describe the growth behaviours of both bubbles and spikes in rarefaction-driven RTI flows. In addition, the time-dependent curvature of the finger tip as it evolves from its initial value to the quasi-steady value is quantified. To validate the model, rarefaction-tube experiments and numerical simulations are conducted across a wide range of initial conditions. The results show that the present model can accurately capture the amplitude growth and curvature evolution of bubbles and spikes across various density ratios. Moreover, both the present model and experiments demonstrate that the continuous density reduction in rarefaction-driven flows causes larger asymptotic velocities of bubbles and spikes, leading to higher Froude numbers relative to those under constant or time-dependent acceleration.

Rayleigh–Taylor (RT) stability occurs when a single-mode light/heavy interface is accelerated by rarefaction waves, exhibiting a sustained oscillation in perturbation amplitude. If the perturbation is accelerated again by a shock propagating in the same direction as the rarefaction waves, the interface evolution will shift from RT stability to Richtmyer–Meshkov (RM) instability. Depending upon the interface state when the shock arrives, the perturbation growth can be actively manipulated through controlling the magnitudes of vorticity deposited by rarefaction and shock waves. The present work first theoretically analyses the 12 different growth possibilities of a light/heavy interface accelerated by co-directional rarefaction and shock waves. A theoretical model is established by combining the RT growth rate with the RM growth rate, providing the conditions for the different possibilities of the perturbation growth. Based on the model, extensive experiments are designed and conducted in the specially designed rarefaction-shock tube. By precisely controlling the shock arrival time at the interface, the different growth possibilities, including promotion, reduction and freeze-out, are realised in experiments. This work verifies the feasibility of manipulating the light/heavy perturbation via co-directional rarefaction and shock waves, which sheds light on control of hydrodynamic instabilities in practical applications.

In recent years, there has been growing interest in developing robots and autonomous systems that can interact with human in a more natural and intuitive way. One of the key challenges in achieving this goal is to enable these systems to manipulate objects and tools in a manner that is similar to that of humans. In this paper, we propose a novel approach for learning human-style manipulation skills by using adversarial motion priors, which we name HMAMP. The approach leverages adversarial networks to model the complex dynamics of tool and object manipulation and the aim of the manipulation task. The discriminator is trained using a combination of real-world data and simulation data executed by the agent, which is designed to train a policy that generates realistic motion trajectories that match the statistical properties of human motion. We evaluated HMAMP on one challenging manipulation task: hammering, and the results indicate that HMAMP is capable of learning human-style manipulation skills that outperform current baseline methods. Additionally, we demonstrate that HMAMP has potential for real-world applications by performing real robot arm hammering tasks. In general, HMAMP represents a significant step towards developing robots and autonomous systems that can interact with humans in a more natural and intuitive way, by learning to manipulate tools and objects in a manner similar to how humans do.

Safety is an essential requirement as well as a major bottleneck for legged robots in the real world. Particularly for learning-based methods, their trial-and-error nature and unexplainable policy have raised widespread concerns. Existing methods usually treat this challenge as a trade-off between safety assurance and task performance. One reason for this drawback stems from the inaccurate inference for the robot’s safety. In this paper, we re-examine the segmentation of the robot’s state space in terms of safety. According to the current state and the prediction of the state transition trajectory, the states of legged robots are classified into safe, recoverable, unsafe, and failure, and a safety verification method is introduced to online infer the robot’s safety. Then, task, recovery, and fall protection policies are trained to ensure the robot’s safety in different states, forming a safety supervision framework independently from the learning algorithm. To validate the proposed method and framework, experiment results are conducted both in the simulation and on the real-world robot, indicating improvements in terms of safety and efficiency.

Characterised by the extensive use of obsidian, a blade-based tool inventory and microblade technology, the late Upper Palaeolithic lithic assemblages of the Changbaishan Mountains are associated with the increasingly cold climatic conditions of Marine Isotope Stage 2, yet most remain poorly dated. Here, the authors present new radiocarbon dates associated with evolving blade and microblade toolkits at Helong Dadong, north-east China. At 27 300–24 100 BP, the lower cultural layers contain some of the earliest microblade technology in north-east Asia and highlight the importance of the Changbaishan Mountains in understanding changing hunter-gatherer lifeways in this region during MIS 2.

OBJECTIVES/GOALS: Platform trials gain efficiency by sharing placebo controls among different study arms. However, the varying routes of administration make it unclear whether participants exposed to different placebos have similar outcomes. As such, we seek to compare outcomes between participants receiving tablet and inhaler placebos in the ACTIV-6 trial. METHODS/STUDY POPULATION: ACTIV-6 is a large, decentralized platform trial exploring repurposed drugs for the treatment of adults with mild to moderate COVID-19. Enrolled participants were randomly assigned to a study arm vs. placebo and then mailed the study drug. They were monitored until symptom resolution or Day 28. Here, we compare outcomes for control participants contributing to the fluticasone furoate study arm, in which 251 were assigned to a tablet placebo and 370 an inhaler placebo. Time to sustained recovery and time to resolution of individual symptoms are compared between groups using Kaplan-Meier curves and unadjusted log-rank tests. A step-down procedure is applied to control the false discovery rate. RESULTS/ANTICIPATED RESULTS: Control participants assigned to tablet placebos had shorter time to sustained recovery (adjusted hazard ratio (HR) 1.34 (95% CI 1.11, 1.62)). When examining each of the eleven individually reported symptoms on study Day 14, nasal symptoms (adjusted odds ratio (OR) 0.44 (0.27, 0.72), p<0.01), dyspnea (OR 0.44 (0.22, 0.87), p = 0.02), and cough (OR 0.54 (0.35, 0.83), p<0.01) were identified as symptoms in which the tablet-placebo group performed notably better than those who received inhaler-placebos. In the follow-up, longitudinal analysis, we anticipate similar results. DISCUSSION/SIGNIFICANCE: Among ACTIV-6 control participants, those receiving a tablet placebo had a significantly shorter time to sustained recovery than those receiving an inhaler placebo. Platform trials using shared controls should consider efficiency in the context of the additional variability when sharing controls with a different route of administration.

Rodents and shrews are major reservoirs of various pathogens that are related to zoonotic infectious diseases. The purpose of this study was to investigate co-infections of zoonotic pathogens in rodents and shrews trapped in four provinces of China. We sampled different rodent and shrew communities within and around human settlements in four provinces of China and characterised several important zoonotic viral, bacterial, and parasitic pathogens by PCR methods and phylogenetic analysis. A total of 864 rodents and shrews belonging to 24 and 13 species from RODENTIA and EULIPOTYPHLA orders were captured, respectively. For viral pathogens, two species of hantavirus (Hantaan orthohantavirus and Caobang orthohantavirus) were identified in 3.47% of rodents and shrews. The overall prevalence of Bartonella spp., Anaplasmataceae, Babesia spp., Leptospira spp., Spotted fever group Rickettsiae, Borrelia spp., and Coxiella burnetii were 31.25%, 8.91%, 4.17%, 3.94%, 3.59%, 3.47%, and 0.58%, respectively. Furthermore, the highest co-infection status of three pathogens was observed among Bartonella spp., Leptospira spp., and Anaplasmataceae with a co-infection rate of 0.46%. Our results suggested that species distribution and co-infections of zoonotic pathogens were prevalent in rodents and shrews, highlighting the necessity of active surveillance for zoonotic pathogens in wild mammals in wider regions.

This study compares judgments of the fairness of economic actions among survey populations in Switzerland, and both student and non-student groups in the People’s Republic of China, with the earlier Kahneman, Knetsch and Thaler (1986a) surveys of Canadians. The findings suggest that fairness concerns matter among all of these groups, and the general patterns of what was and was not considered to be fair were similar. However, there were also some significant differences with the influence of fairness being weaker in the two Chinese samples than in the groups from the Western countries, with the influence being weakest in the Chinese student population for the wage related topics. On the whole, almost no significant gender differences were found in any of the new surveys.

The Belt and Road Initiative (BRI) may potentially reduce trade barriers between China and countries along the Belt and Road (BR) route, affecting the positions in Global Value Chains (GVCs) of agricultural products of these countries. This study explores the BRI influence on the GVC positions using a global computable general equilibrium (CGE) model and focusing on China and the BR countries. The study finds that the reduction of tariff barriers and non-tariff barriers between countries along ‘the Belt and Road’ results in increased producer prices and volumes of almost all agricultural products exported from China, which improves the position of China in the GVCs of these agricultural products in our best estimate scenario. Countries along the BR route also benefit from the reduction in trade barriers, with improved positions in the GVCs of agricultural products in the best estimate scenario, especially those products that have comparative advantages in GVCs.

In order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Atomization is often accompanied by phase change, which could significantly affect performance parameters such as the cooling efficiency and combustion efficiency of atomization. Nevertheless, the effect of phase change on jet atomization is rarely numerically studied due to the complexity of the coupling of the aerodynamics and the thermodynamics as well as the modelling difficulty caused by the cross-scale flow. In this study, comprehensive direct numerical simulations were carried out to evaluate the effects of phase change on the primary breakup and secondary atomization. Two methods dealing with phase-interface movement and mass transfer across the interface are built to meet the requirements of different modelling scales and Weber numbers. Simulation results indicate that phase change affects the flow behaviours and volume distribution of broken droplets in the primary breakup. In the secondary atomization, phase change leads to significantly different deforming morphologies of droplets with low Weber number and a more thorough breakup of droplets with high Weber number.

A two-dimensional model of a hydraulic fracture propagating in a weakly consolidated, highly permeable reservoir rock during a waterflooding operation is described in this paper. The model recognizes the essential differences that exist between this class of fractures and conventional hydraulic fracturing treatments of oil and gas wells, namely: (i) the large-scale perturbations of pore pressure and the associated poroelastic effects caused by extended injection time; (ii) the extremely small volume of fluid stored in the fracture compared with the injected volume; and (iii) the leakage of water from both the borehole and the propagating fracture. The model consists of a set of equations encompassing linear elastic fracture mechanics, porous media flow and lubrication theory. Three asymptotic solutions applicable at different time regimes are found theoretically, and numerical results are obtained from the discretized governing equations. The solution reveals that the injection pressure does not evolve monotonically, as it increases with time in the early time radial-flow regime but decreases in the late time fracture-flow regime. Thus, the peak injection pressure does not correspond to a breakdown of the formation, as usually assumed, but rather to a transition between two regimes of porous media flow. However, this problem exhibits an extreme sensitivity of the time scales on a dimensionless injection rate $\mathcal {I}$. If $\mathcal {I} \lessapprox 1$, the time to reach the peak pressure could become so large that it cannot be observed in field operations, i.e. the fracture remains hydraulically invisible. Finally, it is found that poroelasticity significantly affects the response of the system, by increasing the injection pressure and delaying the time at which the peak pressure takes place.

An aircraft wing is the carrier of imaging payload (interferometric synthetic aperture radar (SAR) or array SAR) of a high-resolution aerial remote sensing system, and high-precision estimation of wing deformation is the key. There are two main traditional modelling methods for wing deformation, namely stochastic theory modelling and material mechanics modelling only dealing with single disturbance, of which the model parameters are derived from empirical values. Aiming at the complex multi-source disturbance of an aircraft wing, this paper separately probes the influence of external disturbance (air disturbance) and internal disturbance (engine vibration) based on the real-time observation of sensors and classifies the wing deformation on the basis of auto-regressive (AR) modelling for parameter identification. With the authentic flight data of a certain types of aircraft, the experimental analysis shows that the wing deformation under the influence of engine vibration is the 14th-order AR model, and the wing deformation under the influence of turbulence is the fifth-order AR model. Meanwhile, this paper also provides an experimental verification idea for the wing deflection modelling built on the second- or third-order Markov model.

Microglia, the main immune cell of the central nervous system (CNS), categorized into M1-like phenotype and M2-like phenotype, play important roles in phagocytosis, cell migration, antigen presentation, and cytokine production. As a part of CNS, retinal microglial cells (RMC) play an important role in retinal diseases. Diabetic retinopathy (DR) is one of the most common complications of diabetes. Recent studies have demonstrated that DR is not only a microvascular disease but also retinal neurodegeneration. RMC was regarded as a central role in neurodegeneration and neuroinflammation. Therefore, in this review, we will discuss RMC polarization and its possible regulatory factors in early DR, which will provide new targets and insights for early intervention of DR.

ABSTRACT IMPACT: Screening the effect of thousands of non-coding genetic variants will help identify variants important in the etiology of diseases OBJECTIVES/GOALS: Massively parallel reporter assays (MPRAs) can experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders and use those results to help understand their contributions to disease etiology. METHODS/STUDY POPULATION: To choose variants to evaluate with the MPRA, we first gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value < 10-5. For each SNP, we identified the region that was in linkage disequilibrium (r2 > 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We used an MPRA to measure the impact of these 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. RESULTS/ANTICIPATED RESULTS: Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. Of the 84 SNPs significantly impacted in both cells, the direction of impact was the same in 81. The direction of eQTL in GTEx tissues agreed with the assay SNP effect in SH-SY5Y cells but not microglial cells. The deep-learning model predicted sequence activity level correlated with the experimental activity level (Spearman’s corr = 0.45). The deep-learning model identified several predictive motifs similar to motifs of RNA-binding proteins. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that MPRAs can be used to evaluate the effect of non-coding variants, and the results can be used to train a machine learning model and interpret its predictions. Together, these can help identify causal variants and further understand the etiology of diseases.