Real materials are disordered. This disorder influences the properties of these materials and the chemical processes that occur at their interfaces. Gaining a molecular-level understanding of the underlying physical manifestations caused by disordered materials is crucial to unraveling and ultimately controlling the efficiency and performance of these materials in a range of energy-related devices. This understanding necessitates measurement techniques through which disorder can be detected, quantified, and monitored. However, such quantitative measurements are notoriously difficult, as effects often average out in ensemble measurements. In this work, we describe how a combination of electrochemical and spatially resolved surface spectroscopy measurements illuminate a molecular-level picture of disorder in materials. Using amorphous carbon as an intrinsically disordered material, we covalently attached a monolayer of ferrocene. click here Interfacial electron transfer across the amorphous carbon-ferrocene interface is highly sensitive to disruptions of order. By systematically varying linker properties and surface loadings, the influence of lateral interactions between nonuniformly distributed ferrocene headgroups on ensemble electrochemical measurements is demonstrated. Electrochemical and imaging data collectively indicate that conformational flexibility of the ferrocene moieties provides a mechanism to elude repulsive and unbalanced lateral interactions, while rigid linkages provide direct information about the underlying disorder of the material. This study is the first of its kind to quantify and visualize molecular disorder and heterogeneity with an experimental model accessed through ensemble measurements.Treating fibrous feed ingredients with exogenous feed enzymes may improve their utilization in monogastric animals. An in vitro study was conducted to determine the effects of steeping corn distillers dried grains with solubles (DDGS) or wheat middlings (WM) with exogenous feed enzymes. Four treatments were arranged as follows 1) co-product steeped with water (CON), 2) CON plus 0.5-g fiber degrading enzymes (FDE), 3) CON plus 0.5-g protease (PRO), and 4) CON plus 0.5-g FDE and 0.5 g PRO (FDEPRO). The FDE contained about 62,000, 37,000, and 8,000 U/g of xylanase, cellulase, and β-glucanase, respectively, whereas activities in PRO amounted to 2,500,000, 1,300,000, and 800,000 U/g of acid, alkaline, and neutral proteases, respectively. Briefly, 50 g of DDGS or WM samples (n = 8) were mixed with 500-mL water with or without enzymes and steeped for 0 to 72 h at 37 °C with continuous agitation. The pH, concentration of monosaccharides, and organic acids in the supernatant and apparent disappearance (AD) of fiber in h. In WM, enzymes increased (P = 0.007) AD of NDF relative to CON at 72 h. Nonetheless, greater (P less then 0.05) AD of fiber was observed between 48 and 72 h. In conclusion, although there were differences in responses among co-products, fiber degrading enzymes increased release of fermentable monosaccharides from co-products at 12 to 24 h of steeping and these effects were not extended with the addition of protease.Twenty stock-type horses (589 ± 126 kg BW; 13 ± 8 yr) were used in a completely randomized design for 28-d to evaluate the impact of a joint supplement on gait kinematics, inflammation, and cartilage metabolism. Horses were stratified by age, sex, body weight (BW), and initial lameness scores and were randomly assigned to one of two dietary treatments consisting of either a 100-g placebo top-dressed daily to 0.6% BW (as-fed) commercial concentrate (CON; n = 10; SafeChoice Original, Cargill, Inc.), or an oral joint supplement (SmartPak Equine LLC) containing glucosamine, chondroitin sulfate, hyaluronic acid, methylsulfonylmethane, turmeric, resveratrol, collagen, silica, and boron (TRT; n = 10). Horses were group-housed with ad libitum access to coastal bermudagrass hay (Cynodon dactylon) and allowed to graze pasture 2 h/d. Horses were exercised progressively 4 d/wk at 45 min each. On days 13 and 27, blood was harvested followed by a 19.3-km exercise stressor on concrete. Horses traveled at the walk, with no mnd CS846 and PGE2 tended to decrease (P ≤ 0.10) from day 27 to 28, independent of dietary treatment. In conclusion, hock ROM at the walk and trot was most sensitive to dietary treatment. Supplementation did not alter biomarker concentration of collagen metabolites or systemic inflammation in the 28-d period, but a future study utilizing arthrocentesis may be warranted to specifically evaluate intra-articular response to dietary treatment.Aortic aneurysms are defined as dilations of the aorta greater than 50 percent. Currently, the only effective treatment for aortic aneurysms is surgical repair, which is recommended only to those that meet criteria. There is no available pharmaceutical therapy to slow aneurysm growth and thus prevent lethal rupture. The development of a number of murine models has allowed in depth studies of various cellular and extracellular components of aneurysm pathophysiology. The identification of key therapeutic targets has resulted in several clinical trials evaluating pharmaceutical candidates to treat aneurysm progression. In this review, we focus on providing recent updates on developments in murine models of aortic aneurysm. In addition, we discuss recent studies of the various cellular and extracellular components of the aorta along with the abutting aortic structures that contribute to aneurysm development and progression.Gene sets, including protein complexes and signaling pathways, have proliferated greatly, in large part as a result of high-throughput biological data. Leveraging gene sets to gain insight into biological discovery requires computational methods for converting them into a useful form for available machine learning models. Here, we study the problem of embedding gene sets as compact features that are compatible with available machine learning codes. We present Set2Gaussian, a novel network-based gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space, according to the proximity of these genes in a protein-protein interaction network. We demonstrate that Set2Gaussian improves gene set member identification, accurately stratifies tumors, and finds concise gene sets for gene set enrichment analysis. We further show how Set2Gaussian allows us to identify a previously unknown clinical prognostic and predictive subnetwork around NEFM in sarcoma, which we validate in independent cohorts.