Bartonbondesen7172

Typhoid toxin is an A2B5 toxin secreted from Salmonella Typhi-infected cells during human infection and is suggested to contribute to typhoid disease progression and the establishment of chronic infection. To deliver the enzymatic 'A' subunits of the toxin to the site of action in host cells, the receptor-binding 'B' subunit PltB binds to the trisaccharide glycan receptor moieties terminated in N-acetylneuraminic acid (Neu5Ac) that is α2-3 or α2-6 linked to the underlying disaccharide, galactose (Gal) and N-acetylglucosamine (GlcNAc). Neu5Ac is present in both unmodified and modified forms, with 9-O-acetylated Neu5Ac being the most common modification in humans. Here we show that host cells associated with typhoid toxin-mediated clinical signs express both unmodified and 9-O-acetylated glycan receptor moieties. We found that PltB binds to 9-O-acetylated α2-3 glycan receptor moieties with a markedly increased affinity, while the binding affinity to 9-O-acetylated α2-6 glycans is only slightly higher, as compared to the affinities of PltB to the unmodified counterparts, respectively. We also present X-ray co-crystal structures of PltB bound to related glycan moieties, which supports the different effects of 9-O-acetylated α2-3 and α2-6 glycan receptor moieties on the toxin binding. Lastly, we demonstrate that the cells exclusively expressing unmodified glycan receptor moieties are less susceptible to typhoid toxin than the cells expressing 9-O-acetylated counterparts, although typhoid toxin intoxicates both cells. These results reveal a fine-tuning mechanism of a bacterial toxin that exploits specific chemical modifications of its glycan receptor moieties for virulence and provide useful insights into the development of therapeutics against typhoid fever.OBJECTIVE We aimed to describe the clinical and economic burden attributable to carbapenem-nonsusceptible (C-NS) respiratory infections. METHODS This retrospective matched cohort study assessed clinical and economic outcomes of adult patients (aged ≥18 years) who were admitted to one of 78 acute care hospitals in the United States with nonduplicate C-NS and carbapenem-susceptible (C-S) isolates from a respiratory source. A subset analysis of patients with principal diagnosis codes denoting bacterial pneumonia or other diagnoses was also conducted. Isolates were classified as community- or hospital-onset based on collection time. A generalized linear mixed model method was used to estimate the attributable burden for mortality, 30-day readmission, length of stay (LOS), cost, and net gain/loss (payment minus cost) using propensity score-matched C-NS versus C-S cohorts. RESULTS For C-NS cases, mortality (25.7%), LOS (29.4 days), and costs ($81,574) were highest in the other principal diagnosis, hospital-onset subgroup; readmissions (19.4%) and net loss (-$9522) were greatest in the bacterial pneumonia, hospital-onset subgroup. Mortality and readmissions were not significantly higher for C-NS cases in any propensity score-matched subgroup. Significant C-NS-attributable burden was found for both other principal diagnosis subgroups for LOS (hospital-onset 3.7 days, P = 0.006; community-onset 1.5 days, P less then 0.001) and cost (hospital-onset $12,777, P less then 0.01; community-onset $2681, P less then 0.001). CONCLUSIONS Increased LOS and cost burden were observed in propensity score-matched patients with C-NS compared with C-S respiratory infections; the C-NS-attributable burden was significant only for patients with other principal diagnoses.PURPOSE To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks. METHODS Self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency, which enables sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects. The data were retrospectively undersampled, and reconstructed using SPIRiT, l1-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate reconstruction performance. RESULTS sRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and l1-SPIRiT, especially at high acceleration rates in targeted coronary MRI. Quantitative analysis shows that sRAKI outperforms these techniques in terms of normalized mean-squared-error (~44% and ~21% over SPIRiT and [Formula see text]-SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and l1-SPIRiT at rate 5). Whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and l1-SPIRiT, respectively. CONCLUSION sRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over l1 regularization techniques.Pallets are the most common equipment for transporting and storing goods. More and more companies are willing to rent pallets. Pallet rental companies need to transport pallets from their pallet rental service stations to customers and take these pallets back when they are unloaded. selleck compound Hence, managers should scientifically configure vehicles for their pallet rental service stations. The fleet size, which indicates the amount and types of vehicles, can significantly affect the efficiency and costs of empty pallet allocation. Therefore, an optimization model for fleet sizing and empty pallet allocation is proposed using the methods of mixed-integer programming and stochastic programming. The objectives of this model are to maximize the profits of pallet rental companies and minimize carbon dioxide (CO2) emissions from vehicles. A particle swarm optimization algorithm with inertia weight (IPSO) is developed to solve the proposed model because IPSO can avoid becoming trapped in local optima and is able to find a globally optimal solution within a reasonable number of iterations.