ABSTRACT IMPACT: Use of this novel murine model of inflammatory bowel disease (IBD) and C. difficile infection (CDI) will aid in developing new clinical approaches to predict, diagnose, and treat CDI in the IBD population. OBJECTIVES/GOALS: IBD is associated with intestinal inflammation and alterations of the gut microbiota, both of which can diminish colonization resistance to C. difficile. Here, we sought to determine if IBD is sufficient to render mice susceptible to C. difficile colonization and infection in the absence of other perturbations, such as antibiotic treatment. METHODS/STUDY POPULATION: C57BL/6 IL-10-/- mice were colonized with Helicobacter hepaticus to trigger colonic inflammation akin to human IBD. Control mice, not colonized with H. hepaticus, were pretreated with the antibiotic cefoperazone to render the gut microbiota susceptible to CDI. Mice were then gavaged with spores of the toxigenic C. difficile strain VPI 10463 and monitored for C. difficile colonization and disease. The fecal microbiota at the time of C. difficile exposure was profiled by 16S rRNA gene sequencing and analyzed using mothur. Statistical analyses were performed using R. RESULTS/ANTICIPATED RESULTS: Mice with IBD harbored significantly distinct intestinal microbial communities compared to non-IBD controls at the time of C. difficile spore exposure (14 days post-IBD trigger). Mice with IBD were susceptible to persistent C. difficile colonization, while genetically identical non-IBD controls were resistant to C. difficile colonization. Concomitant IBD and CDI was associated with significantly worse clinical and intestinal disease than unaccompanied IBD. DISCUSSION/SIGNIFICANCE OF FINDINGS: Patients with IBD who develop concurrent CDI experience increased morbidity and mortality. These studies in a novel mouse model of IBD and CDI emphasize the dual importance of host responses and alterations of the gut microbiota in susceptibility to C. difficile colonization and infection in the setting of IBD.

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

The UK faces a number of economic challenges in the short to medium term. Prior to COVID-19, renegotiation of trading arrangements with the European Union was the most prominent of these. We build on existing macroeconomic analysis by assessing prospects for the UK’s regions generated by combining a global macroeconometric model and a regional computable general equilibrium of the UK. A central macroeconomic scenario shows a national average annual GDP growth rate of 1.7 per cent to 2044. When the macroeconomic scenario is applied across regions, growth rates range from 1.6 per cent for Cambridge to 2.2 per cent for Pembrokeshire; the standard deviation is low at 0.07 per cent and the coefficient of variation is 0.04 per cent. In contrast, much wider variation is observed in the standard deviation for exports (0.36 per cent), investment (0.11 per cent) and consumption (0.14 per cent). The country results favour Scotland, which grows at an annual rate of 1.8 per cent, whereas Wales is the slowest growing of the countries at 1.7 per cent. Consistent with the macroeconomic analysis, international trade is the most important contributor to the regional variation in growth rates. We also analyse the effects of higher government consumption relative to the forecasts and find most regions are predicted to experience lower economic activity except the handful in which government consumption is a much higher share of GDP than average.

OBJECTIVES/SPECIFIC AIMS: The first aim of the study is to evaluate the accuracy of serum biomarkers of acute GVHD measured after four weeks of corticosteroid therapy to predict 6 month NRM. The second aim of this study is to compare the accuracy of the biomarker algorithm to that of clinical response to corticosteroids after four weeks. The third aim of the study is to develop a novel regression model that uses weekly biomarker measurements over the first month of corticosteroid therapy to predict 6 month NRM. METHODS/STUDY POPULATION:. Patients who received HCT at one of 22 IRB-approved centers and provided blood samples to the Mount Sinai Acute GVHD International Consortium (MAGIC) biorepository and developed GVHD between January 2008 to May 2018 are included in this study. Patients were divided by time into a training set (Jan 2008-Dec 2015, n=233) for model development and a validation set (Jan 2015-May 2018, n=357) to evaluate the predictive performance of the model. The later time of the validation set was chosen deliberately to model contemporaneous GVHD treatment practices. The size of each group was designed so that there would be roughly equal numbers of deaths in both groups. RESULTS/ANTICIPATED RESULTS:. Serum concentrations of GVHD biomarkers after one month of corticosteroid therapy were measured in the validation set, and the predicted probability of NRM ($\hat{\rm p}$) was computed according to the previously published algorithm: $\log[-\log(1 - \hat{\rm p})]=-11.263 + 1.844({\rm logST}2)+ 0.577({\rm logREG}3\alpha)$. The performance of the biomarker algorithm was evaluated by creating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) in the validation set. The AUC of the biomarker algorithm was a significantly better predictor of 6 month NRM than clinical response to treatment after four weeks of corticosteroids (0.84 vs. 0.64, p<0.001), which is a clinically relevant improvement in accuracy. To evaluate serial biomarker monitoring, serum biomarker concentrations will be measured weekly at five time points from treatment initiation to one month after corticosteroid therapy. We will use these values in the training set to develop a regression model for 6 month NRM that accounts for repeated biomarker measurements. The performance of this model will be tested in the validation set and the accuracy of the serial biomarker measurements will be compared to the accuracy of measuring biomarkers at the single time point after four weeks of corticosteroid therapy. An AUC improvement of 0.05 would be considered clinically significant. DISCUSSION/SIGNIFICANCE OF IMPACT: Clinical response to treatment after four weeks has been the standard endpoint in GVHD interventional trials for decades. If biomarkers measured at the same time more accurately predict long term mortality, this study would provide the basis for a novel endpoint in GVHD trials and enable more accurate determination of effect size of experimental interventions. An accurate biomarker algorithm will prove useful in guiding immunosuppressive treatment decisions for patients with GVHD. Patients identified by the algorithm as low-risk may benefit from reduced-dose corticosteroid therapy, potentially reducing lethal opportunistic infections. Patients identified as high-risk will be candidates for more intensive immunosuppression or investigational therapies. This precision medicine approach tailors therapy to the individual patient’s biology.

Large discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

Parkinson's disease (PD) is characterized by proteinaceous aggregates named Lewy Bodies and Lewy Neurites containing α-synuclein fibrils. The underlying aggregation mechanism of this protein is dominated by a secondary process at mildly acidic pH, as in endosomes and other organelles. This effect manifests as a strong acceleration of the aggregation in the presence of seeds and a weak dependence of the aggregation rate on monomer concentration. The molecular mechanism underlying this process could be nucleation of monomers on fibril surfaces or fibril fragmentation. Here, we aim to distinguish between these mechanisms. The nature of the secondary processes was investigated using differential sedimentation analysis, trap and seed experiments, quartz crystal microbalance experiments and super-resolution microscopy. The results identify secondary nucleation of monomers on the fibril surface as the dominant secondary process leading to rapid generation of new aggregates, while no significant contribution from fragmentation was found. The newly generated oligomeric species quickly elongate to further serve as templates for secondary nucleation and this may have important implications in the spreading of PD.

Field experiments were conducted over 3 yr at three locations in Illinois to evaluate the efficacy of glyphosate in glyphosate-resistant soybean planted in rows spaced 19, 38, and 76 cm. Minimal soybean injury (less than 10%) was observed from any glyphosate treatment. Glyphosate treatments controlled 82 to 99% of giant foxtail. Common waterhemp control was increased as soybean row spacing was decreased. Applying sequential glyphosate applications or increasing the glyphosate rate from 420 g ae/ha to 840 g/ha frequently increased common waterhemp control in 76-cm rows. Velvetleaf control with glyphosate was variable, ranging from 48 to 99%. Decreasing soybean row spacing, utilizing sequential glyphosate applications, or increasing the glyphosate rate improved velvetleaf control in at least four of eight site-years. Glyphosate treatments generally resulted in weed control and soybean yield equal to or greater than the standard herbicide treatments. However, glyphosate treatments yielded less than the hand-weeded control in four of eight site-years, suggesting that weed control from glyphosate treatments was sometimes inadequate.

Recent changes in herbicide registrations and governmental restrictions on field burning raised many management questions for Kentucky bluegrass seed producers, particularly the extent to which useful lives of their stands might be shortened by decreasing crop yields or increasing weed pressure. Tests conducted over the lives of two grass seed stands (1993–1997) evaluated three contrasting methods of postharvest residue management (vacuum sweep, bale/flail chop/rake, and field burn) and 13 herbicide treatments. Downy brome was the primary weed at both the Madras and LaGrande, OR, sites. In nontreated checks and the four least effective herbicide treatments, downy brome populations increased exponentially over time, with year-to-year increases in density averaging 13.1-fold. Competition had easily detected effects on Kentucky bluegrass seed yield at densities of 30 downy brome plants/m2, and crop stands were destroyed beyond 100 to 200 weeds/m2. Both PRE terbacil at 840 g/ha and early POST (EPOST)/late POST (LPOST) split-applied primisulfuron at 20 g/ha per application contained downy brome during the first 2 yr but not the third, when crop injury from terbacil forced reduction in terbacil rate and changes in weed populations overcame primisulfuron. PRE terbacil followed by LPOST primisulfuron, EPOST terbacil plus primisulfuron followed by LPOST primisulfuron, and EPOST/LPOST split-applied terbacil plus primisulfuron achieved excellent control of downy brome until the final years of the study, when control became increasingly erratic as primisulfuron-resistant downy brome proliferated in specific individual plots. Injury from combination terbacil plus primisulfuron treatments reduced yield relative to safest treatments in early years when downy brome population densities were low.

Field studies were conducted from 1996 to 1998 to evaluate grass control in no-till corn (Zea mays) with herbicides applied early preplant (EPP), preemergence (PRE), and postemergence (POST) at the Belleville Research Center at Belleville, IL. Grass control was affected by application timing rather than herbicide. The herbicides applied PRE provided more consistent giant foxtail (Setaria faberi) and barnyardgrass (Echinochloa crus-galli) control (90 to 98%) than the same herbicides applied EPP (0 to 92%). There also was no difference in giant foxtail and barnyardgrass control between the emulsifiable concentrate (EC) formulation and microencapsulated (ME) formulation of acetochlor. Rimsulfuron plus thifensulfuron applied POST provided 90 to 97% control of giant foxtail and barnyardgrass. Metolachlor, EC-acetochlor, SAN 582H, and rimsulfuron plus thifensulfuron provided 85 to 92% control of yellow nutsedge (Cyperus esculentus) compared with 63 to 74% control for BAY FOE 5043 plus metribuzin and ME-acetochlor. Corn grain yield was greater with herbicides applied either PRE or POST than applied EPP. Grass control and grain yield were greater with herbicides applied either PRE or POST compared with EPP.

Ten years of Oregon Seed Certification Service (OSCS) preharvest field inspections converted from a nonspatial database to a geographic information system (GIS) were analyzed for patterns in spatial distribution of occurrence and severity of the 36 most common weeds of grass seed crops. This was done under the assumptions that those patterns would be primarily consequences of interactions among farming practices, soil properties, and biological traits of the weeds, and that improved understanding of the interactions would benefit the grass seed industry. Kriging, Ripley's K-function, and both Moran's I spatial autocorrelation and Getis-Ord General G high/low clustering using the multiple fixed distance band option all produced roughly similar classifications of weeds possessing strongest and weakest spatial clustering patterns. When Moran's I and General G analyses of maximum weed severity observed within individual fields over the life of stands were conducted using the inverse distance weighting option, however, results were highly sensitive to the presence of a small number of overlapping fields in the 10-yr record. Addition of any offset in the range from 6 to 6,437 m to measured distances between field centroids in inverse distance weighting matrices removed this sensitivity, and produced results closely matching those for the multiple fixed distance band method. Clustering was significant for maximum severity within fields over the 10-yr period for all 43 weeds and in 78% of single-year analyses. The remaining 22% of single-year cases showed random rather than dispersed distribution patterns. In decreasing order, weeds with strongest inverse-distance spatial autocorrelation were German velvetgrass, field bindweed, roughstalk bluegrass, annual bluegrass, orchardgrass, common velvetgrass, Italian ryegrass, Agrostis spp., and perennial ryegrass. Of these nine weeds, distance for peak spatial autocorrelation ranged from 2 km for Agrostis spp. to 34 km for common velvetgrass. Weeds with stronger spatial autocorrelation had greater range between distance of peak spatial autocorrelation and maximum range of significance. Z-scores for General G high/low clustering were substantially lower than corresponding values for Moran's I spatial autocorrelation, although the same two weeds (German velvetgrass and field bindweed) showed strongest clustering using both measures. Simultaneous patterns in Moran's I and General G implied that management practices relatively ineffective in controlling weeds usually played a greater role in causing weeds to cluster than highly effective practices, although both types of practices impacted Italian ryegrass distribution. Distance of peak high/low clustering among perennial weeds was smallest (1 to 3 km) for Canada thistle, field bindweed, Agrostis spp., and western wildcucumber, likely indicating that these weeds occurred in patchy infestations extending across neighboring fields. Although both wild carrot and field bindweed doubled in average severity over the period from 1994 to 2003, wild carrot was the only weed clearly undergoing an increase in spatial autocorrelation. Soil chemical and physical properties and dummy variables for soil type and crop explained small but significant portions of total variance in redundancy and canonical correspondence analysis of weed occurrence and severity. Fitch-Morgoliash tree diagrams and Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) ordinations revealed substantial differences among soil types in weed occurrence and severity. Gi∗ local hot-spot clustering combined with feature class to raster conversion protected grower expectations of confidentiality while describing dominant spatial features of weed distribution patterns in maps released to the public.

Field studies were conducted from 1997 to 1999 to evaluate the influence of weed competition and herbicides on glyphosate-resistant soybean at the Belleville Research Center at Belleville, IL. Season-long weed competition did not delay maturity or reduce soybean population. However, season-long weed competition reduced soybean height by 10% and grain yield by 68%. Competition from weeds 30 cm in height or less did not increase days to maturity or reduce plant population, height, or grain yield. Chlorimuron plus thifensulfuron caused 6% height reduction at 21 d after treatment. Despite the injury from herbicides, none increased days to maturity or reduced plant height, population, or grain yield of soybean. The soil-applied herbicides alachlor, imazaquin, and pendimethalin and the postemergence herbicides acifluorfen, bentazon, chlorimuron, imazethapyr, and thifensulfuron did not reduce yield of glyphosate-resistant soybean.

The 1985 Farm Bill departs from recent farm bills in moving toward more restrictive acreage control. The change from a two- to a five-year average in calculating base acreage and enforcement of limited cross-compliance appear to significantly alter crop mix decisions on representative Alabama cotton farms.

As one of the largest marathons worldwide, the Bank of America Chicago Marathon (BACCM; Chicago, Illinois USA) accumulates high volumes of data. Race organizers and engaged agencies need the ability to access specific data in real-time. This report details a data visualization system designed for the Chicago Marathon and establishes key principles for event management data visualization. The data visualization system allows for efficient data communication among the organizing agencies of Chicago endurance events. Agencies can observe the progress of the race throughout the day and obtain needed information, such as the number and location of runners on the course and current weather conditions. Implementation of the system can reduce time-consuming, face-to-face interactions between involved agencies by having key data streams in one location, streamlining communications with the purpose of improving race logistics, as well as medical preparedness and response.

HankenT, YoungS, SmilowitzK, ChiampasG, WaskowskiD. Developing a Data Visualization System for the Bank of America Chicago Marathon (Chicago, Illinois USA). Prehosp Disaster Med. 2016;31(5):572–577.