Elastoinertial turbulence (EIT) is a chaotic state that emerges in the flows of dilute polymer solutions. Direct numerical simulation (DNS) of EIT is highly computationally expensive due to the need to resolve the multiscale nature of the system. While DNS of two-dimensional (2-D) EIT typically requires $O(10^6)$ degrees of freedom, we demonstrate here that a data-driven modelling framework allows for the construction of an accurate model with 50 degrees of freedom. We achieve a low-dimensional representation of the full state by first applying a viscoelastic variant of proper orthogonal decomposition to DNS results, and then using an autoencoder. The dynamics of this low-dimensional representation is learned using the neural ordinary differential equation (NODE) method, which approximates the vector field for the reduced dynamics as a neural network. The resulting low-dimensional data-driven model effectively captures short-time dynamics over the span of one correlation time, as well as long-time dynamics, particularly the self-similar, nested travelling wave structure of 2-D EIT in the parameter range considered.

A 54-question survey about System Healthcare Infection Prevention Programs (SHIPPs) was sent out to SHEA Research Network participants in August 2023. Thirty-eight United States-based institutions responded (38/93, 41%), of which 23 have SHIPPs. We found heterogeneity in the structure, staffing, and resources for system infection prevention (IP) programs.

Elastoinertial turbulence (EIT) is a chaotic flow resulting from the interplay between inertia and viscoelasticity in wall-bounded shear flows. Understanding EIT is important because it is thought to set a limit on the effectiveness of turbulent drag reduction in polymer solutions. Here, we analyse simulations of two-dimensional EIT in channel flow using spectral proper orthogonal decomposition (SPOD), discovering a family of travelling wave structures that capture the sheetlike stress fluctuations that characterise EIT. The frequency-dependence of the leading SPOD mode contains distinct peaks and the mode structures corresponding to these peaks exhibit well-defined travelling structures. The structure of the dominant travelling mode exhibits shift–reflect symmetry similar to the viscoelasticity-modified Tollmien–Schlichting (TS) wave, where the velocity fluctuation in the travelling mode is characterised by large-scale regular structures spanning the channel and the polymer stress field is characterised by thin, inclined sheets of high polymer stress localised at the critical layers near the channel walls. The travelling structures corresponding to the higher-frequency modes have a very similar structure, but are nested in a region roughly bounded by the critical layer positions of the next-lower-frequency mode. A simple theory based on the idea that the critical layers of mode $\kappa$ form the ‘walls’ for the structure of mode $\kappa +1$ yields quantitative agreement with the observed wave speeds and critical layer positions, indicating self-similarity between the structures. The physical idea behind this theory is that the sheetlike localised stress fluctuations in the critical layer prevent velocity fluctuations from penetrating them.

Background: Hospitals are increasingly consolidating into networks and integrating infection prevention (IP) into system infection prevention programs (SIPP). Very little has been published about these programs. This survey sheds light on the current state of SIPPs. Methods: We used the survey generator Alchemer.com for setting up the questionnaire, and tested a beta version among peers. The final version was sent out to SHEA Research Network participants in August 2023. Raw data was compiled and analyzed. Results: Forty institutions responded (40/104, 38%), of which 25 (63%) had SIPPs. These SIPPS reported health systems with a median of 4.5 acute care hospitals (range, 1-33); 16 SIPPS reported a median of 2 critical access hospitals (range, 1-8); 4 SIPPs reported 1-3 LTACHs, and 6 SIPPS reported a median of 1.5 nursing homes. All except 3 (88%) contained an academic center; 48% (11/23) of the U.S. based programs operate in multiple states. Four programs have been in place >20 years, four < 2 years, and the remainder a median of 8 years (range, 2-18). Physician directors also have clinical (20/25, 80%), teaching (19/25, 76%), research (15/25, 60%), antimicrobial stewardship (8/25, 32%), quality (8/25, 32%), and/or patient safety (5/25, 20%) roles. Seventeen (68%) report having a written job description. Nineteen (76%) report having an infection preventionist in a system IP director role; only 7/25 (28%) have a dedicated system IP team that operates independent of individual hospitals. Sixteen (64%) report administrative support, 10/25 (40%) have a data manager/analyst, and 4/25 (16%) include IT expert or programmer support. 15/25 (60%) report having done a formal system-wide IP needs assessment. While 16/25 (64%) have some automation in HAI surveillance (predominantly using Bugsy [Epic] or Theradoc [Premier]), while only 5/25 (20%) run fully automated surveillance. 10/25 (40%) have implemented centralized surveillance. 12/25 (48%) have “system IP policies” that are hierarchically above individual site policies. The biggest challenges appear to be gaps in 1) clear governing structure, 2) communication, 3) consistent staffing, 4) data management support, and 5) dedicated, empowered IP expert FTEs. Conclusions: To our knowledge, this is the first U.S. survey to explore present-day system infection prevention. In this sample of hospital networks, we found heterogeneity in the structure, staffing and resources for system IP with significant opportunities for improvement. In this era of healthcare consolidation, our findings highlight the urgent need to more clearly delineate and support system IP needs in order to enhance their functionality.

The accurate simulation of complex dynamics in fluid flows demands a substantial number of degrees of freedom, i.e. a high-dimensional state space. Nevertheless, the swift attenuation of small-scale perturbations due to viscous diffusion permits in principle the representation of these flows using a significantly reduced dimensionality. Over time, the dynamics of such flows evolves towards a finite-dimensional invariant manifold. Using only data from direct numerical simulations, in the present work we identify the manifold and determine evolution equations for the dynamics on it. We use an advanced autoencoder framework to automatically estimate the intrinsic dimension of the manifold and provide an orthogonal coordinate system. Then, we learn the dynamics by determining an equation on the manifold by using both a function-space approach (approximating the Koopman operator) and a state-space approach (approximating the vector field on the manifold). We apply this method to exact coherent states for Kolmogorov flow and minimal flow unit pipe flow. Fully resolved simulations for these cases require $O(10^3)$ and $O(10^5)$ degrees of freedom, respectively, and we build models with two or three degrees of freedom that faithfully capture the dynamics of these flows. For these examples, both the state-space and function-space time evaluations provide highly accurate predictions of the long-time dynamics in manifold coordinates.

The reactivity of colloidal particles is regulated by their surface properties. These properties affect the wettability, flocculation-dispersion characteristics, ion exchange, sorption capacities and transport of inorganic colloids. Most studies have focused on hydrophilic, charged-particle surfaces, often ignoring the alterations in surface properties produced by the adsorption of natural organic matter, surfactants and other compounds. Adsorption of these substances can potentially render a surface substantially more hydrophobic. Nevertheless, comparatively little is known about changes in surface properties and reactivity of minerals upon sorption of hydrophobic organic compounds. In this study, the properties of four minerals (kaolinite, pyrophyllite, montmorillonite and Min-U-Sil®) and two inorganic materials (X-ray amorphous Al hydroxide and X-ray amorphous Si oxide) were compared before and after treatment with the common silylating agent, trimethylchlorosilane (TMCS). The samples were characterized by measurements of total carbon, cation exchange capacity (CEC), particle size, specific surface area (SSA), electrophoretic mobility, contact angle, particle aggregation, and by X-ray diffraction and diffuse reflectance infrared spectroscopy. For the layer silicates, surface coverage was limited to ∼2% trimethyl silane (TMSi). TMSi covered 7.5% of the Min-U-Sil® surface and 33% of the X-ray amorphous Si oxide. Treatment did not affect the structure of the minerals but reduced the CEC, SSA and electrophoretic mobilities. Water contact angles increased to between 18 and 114° with treatment. While the apolar characteristic of the surfaces decreased minimally with treatment, the Lewis acid/base properties were substantially reduced and interfacial free energy shifted from positive to negative values indicating a more hydrophobic surface character. For all the samples except kaolinite, these changes affected the stability of the colloids in suspension depending upon solution pH. Although the grafting of TMSi altered colloidal mineral surface properties and increased their hydrophobicity, these changes were not sufficient to predict colloid aggregation behavior.

Because the Navier–Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than the dimension required for a resolved simulation. On this manifold, the state of the system can be exactly described in a coordinate system parameterising the manifold. Describing the system in this low-dimensional coordinate system allows for much faster simulations and analysis. We show, for turbulent Couette flow, that this description of the dynamics is possible using a data-driven manifold dynamics modelling method. This approach consists of an autoencoder to find a low-dimensional manifold coordinate system and a set of ordinary differential equations defined by a neural network. Specifically, we apply this method to minimal flow unit turbulent plane Couette flow at $Re=400$, where a fully resolved solutions requires ${O}(10^5)$ degrees of freedom. Using only data from this simulation we build models with fewer than $20$ degrees of freedom that quantitatively capture key characteristics of the flow, including the streak breakdown and regeneration cycle. At short times, the models track the true trajectory for multiple Lyapunov times and, at long times, the models capture the Reynolds stress and the energy balance. For comparison, we show that the models outperform POD-Galerkin models with $\sim$2000 degrees of freedom. Finally, we compute unstable periodic orbits from the models. Many of these closely resemble previously computed orbits for the full system; in addition, we find nine orbits that correspond to previously unknown solutions in the full system.

This study aims to extract and characterize structures in fully developed pipe flow at a friction Reynolds number of $\textit {Re}_\tau = 12\,400$. To do so, we employ data-driven wavelet decomposition (DDWD) (Floryan & Graham, Proc. Natl Acad. Sci. USA, vol. 118, 2021, e2021299118), a method that combines features of proper orthogonal decomposition and wavelet analysis in order to extract energetic and spatially localized structures from data. We apply DDWD to streamwise velocity signals measured separately via a thermal anemometer at 40 wall-normal positions. The resulting localized velocity structures, which we interpret as being reflective of underlying eddies, are self-similar across streamwise extents of 40 wall units to one pipe radius, and across wall-normal positions from $y^+=350$ to $y/R=1$. Notably, the structures are similar in shape to Meyer wavelets. Projections of the data onto the DDWD wavelet subspaces are found to be self-similar as well, but in Fourier space; the bounds of self-similarity are the same as before, except streamwise self-similarity starts at a larger length scale of $450$ wall units. The evidence of self-similarity provided in this study lends further support to Townsend's attached eddy hypothesis, although we note that the self-similar structures are detected beyond the log layer and extend to large length scales.

A survey of academic medical-center hospital epidemiologists indicated substantial deviation from Centers for Disease Control and Prevention guidance regarding healthcare providers (HCPs) recovering from coronavirus disease 2019 (COVID-19) returning to work. Many hospitals continue to operate under contingency status and have HCPs return to work earlier than recommended.

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body’s natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Only 6 to 8 % of the UK adults meet the daily recommendation for dietary fibre. Fava bean processing lead to vast amounts of high-fibre by-products such as hulls. Bean hull fortified bread was formulated to increase and diversify dietary fibre while reducing waste. This study assessed the bean hull: suitability as a source of dietary fibre; the systemic and microbial metabolism of its components and postprandial events following bean hull bread rolls. Nine healthy participants (53·9 ± 16·7 years) were recruited for a randomised controlled crossover study attending two 3 days intervention sessions, involving the consumption of two bread rolls per day (control or bean hull rolls). Blood and faecal samples were collected before and after each session and analysed for systemic and microbial metabolites of bread roll components using targeted LC-MS/MS and GC analysis. Satiety, gut hormones, glucose, insulin and gastric emptying biomarkers were also measured. Two bean hull rolls provided over 85 % of the daily recommendation for dietary fibre; but despite being a rich source of plant metabolites (P = 0·04 v. control bread), these had poor systemic bioavailability. Consumption of bean hull rolls for 3 days significantly increased plasma concentration of indole-3-propionic acid (P = 0·009) and decreased faecal concentration of putrescine (P = 0·035) and deoxycholic acid (P = 0·046). However, it had no effect on postprandial plasma gut hormones, bacterial composition and faecal short chain fatty acids amount. Therefore, bean hulls require further processing to improve their bioactives systemic availability and fibre fermentation.

There is limited documentation of cross-tenure collaborative weed management programs, and no consistent set of metrics for evaluating their performance. In this study, 12 weed management practitioners in southeast Australia participated in a qualitative social research project to discuss and document examples of cross-tenure collaborative weed management and critically reflect on whether existing metrics are suitable for evaluating the performance of their programs. Analysis of focus group discussions, project documentation, subsequent reflections, and review of the literature reveal that weed management practitioners, in Australia and elsewhere, mostly rely on metrics that measure weed management inputs, such as herbicides, labor, and costs. Metrics used to evaluate social outcomes focus on benefits for individuals rather than social relationships or achievement of equitable outcomes. Social research on collaborative governance and social science methods more broadly, such as social network analysis and collective narratives, could be used by weed management practitioners to better evaluate and explain social–ecological outcomes over time.