19 research outputs found

    Transcriptomic analysis of cell envelope inhibition by prodigiosin in methicillin-resistant Staphylococcus aureus

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    Methicillin-resistant Staphylococcus aureus (MRSA) is a leading threat to public health as it is resistant to most currently available antibiotics. Prodigiosin is a secondary metabolite of microorganisms with broad-spectrum antibacterial activity. This study identified a significant antibacterial effect of prodigiosin against MRSA with a minimum inhibitory concentration as low as 2.5 mg/L. The results of scanning electron microscopy, crystal violet staining, and confocal laser scanning microscopy indicated that prodigiosin inhibited biofilm formation in S. aureus USA300, while also destroying the structure of the cell wall and cell membrane, which was confirmed by transmission electron microscopy. At a prodigiosin concentration of 1.25 mg/L, biofilm formation was inhibited by 76.24%, while 2.5 mg/L prodigiosin significantly reduced the vitality of MRSA cells in the biofilm. Furthermore, the transcriptomic results obtained at 1/8 MIC of prodigiosin indicated that 235and 387 genes of S. aureus USA300 were significantly up- and downregulated, respectively. The downregulated genes were related to two-component systems, including the transcriptional regulator LytS, quorum sensing histidine kinases SrrB, NreA and NreB, peptidoglycan biosynthesis enzymes (MurQ and GlmU), iron-sulfur cluster repair protein ScdA, microbial surface components recognizing adaptive matrix molecules, as well as the key arginine synthesis enzymes ArcC and ArgF. The upregulated genes were mainly related to cell wall biosynthesis, as well as two-component systems including vancomycin resistance-associated regulator, lipoteichoic acid biosynthesis related proteins DltD and DltB, as well as the 9 capsular polysaccharide biosynthesis proteins. This study elucidated the molecular mechanisms through which prodigiosin affects the cell envelope of MRSA from the perspectives of cell wall synthesis, cell membrane and biofilm formation, providing new potential targets for the development of antimicrobials for the treatment of MRSA

    Simulation on Three-Dimensional Shock Interactions and Aerodynamic Heating Between Body and Wing

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    This study investigates the flowfield patterns and distributions of surface heat flux due to interactions among three-dimensional shock waves at the junction of the body and wing of an aircraft by solving Reynolds-averaged Navier-Stokes equations at a Mach number of 10 and attack angles ranging from 5 degrees to 20 degrees. The results indicate that the structures of wing/body-shock interactions vary significantly with test conditions. Four types of shock interaction patterns were observed: interaction-free, type I regular, type II regular, and Mach interactions. Once the flowfield of the shock interactions had been established, aerodynamic heating loads of the wing and body were affected by the flowfield structures. Wing/body-shock interactions produced uneven heat flux distributions on the surface and caused an abnormally high heat flux at a localized position. Five profiles of the distribution of heat flux were extracted to describe its characteristics on the surface according to the position and magnitude of the peaks of the localized heat flux. Induction-related factors that led to the peaks were classified into three types: reflected shock/boundary-layer interaction, contact surface impinging, and contact surface grazing.</p

    Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

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    Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes)

    A theoretical and computational study of the vibration excitation on the transition criteria of shock wave reflections

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    In this paper, we study the vibration excitation on the reflection of shock waves in hypersonic flows by using analytical and computational approaches. First, a theoretical approach is established to solve the shock relations which are further applied to develop the shock polar analytical method for high-temperature air. Then, a comparative investigation using calorically perfect gas model and thermally perfect gas model considering vibration excitation indicates an obvious change to the overall profile of the shock polar. The post-shock pressure increases within the strong branch of the shock polar while decreases within the weak branch due to vibration excitation of air molecules. A more notable phenomenon is the increase in the maximum deflection angle of the shock polar which can significantly influence the detachment criterion of shock reflection transition in high-temperature air flows. The shock polar analysis of shock reflection shows that the vibration excitation result in an obvious increase to the detachment criterion while a slight increase to the von Neumann criterion. A series of computations are conducted to confirm the above analytical findings on the shock reflection considering the vibration excitation. A slight difference of transition criterion between the theory and computations is found to be caused by the existence of the expansion fan which is an inherent flow structure. The proposed shock polar analytical method is proved to be an effective but simple approach for the study of shock wave reflections in hypersonic flows. (C) 2019 Elsevier Masson SAS. All rights reserved

    Assessing Green and Efficient Remediation at Waste Sites

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    Thousands of contaminated properties and waste sites in the United States cause groundwater pollution. Groundwater remediation systems often rely on electricity generated from non-renewable energy, namely burning fossil fuel. The Massachusetts Department of Environmental Protection (MassDEP) has been promoting energy efficiency and renewable energy sources to reduce greenhouse gas emissions associated with groundwater remediation. Our team utilized MA waste site data, MassDEP databases and remedial monitoring reports, a site visit, and interviews to determine if green and efficient energy applications are viable in the remediation process. Gains in energy efficiency from system component modifications and use of solar power can effectively reduce greenhouse gas emissions

    Shock relations in gases of heterogeneous thermodynamic properties

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    Shock relations usually found in literatures are derived theoretically under the assumption of homogeneous thermodynamic properties, i.e., constant ratio of specific heats, gamma. However, high temperature effects post a strong shock wave may result in thermodynamic heterogeneities and failure to the original shock relations. In this paper, the shock relations are extended to take account of high-temperature effects. Comparison indicates that the present approach is more feasible than other analytical approaches to reflect the influence of gamma heterogeneity on the post-shock parameters

    Spectral Characteristics of CN Radical (B - X) and Its Application in Determination of Rotational and Vibrational Temperatures of Plasma

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    The aim is to resolve the difficulties of measurement of temperature at several thousands of Celsius degrees for some unstable non-equilibrium gas flows. Based on the molecular spectroscopy theory and inherent molecular structure characteristics of the CN radical, the dependence of the spectral profile on the rotational temperature (RT), vibrational temperature (VT) and optical apparatus function are numerically explored within some certain ranges. Meanwhile, by comparing the numerically calculated spectra with the experimental spectra of the CN radical, the corresponding RT and VT of the plasma induced by the interaction of the laser pulse from an oscillated Nd:YAG laser with the coal target are determined, respectively. In addition, a short discussion on the thermodynamic state and the energy transfer process of the CN radical is also given

    Machine Learning Implementation in Education Technology

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    The goal of this project is to use machine learning to provide constructive feedback for student open responses. We are working with the ASSISTments team and their platform to help teachers give more helpful and timely feedback to their students. Using a dataset of open responses and corresponding grades, we were able to use machine learning to develop a model that can give what the most likely grade for an essay might be. Our model gives three suggestions for a given student response, and the teacher can pick the most appropriate one. We will further develop this using user testing with teachers that want to try out new features in ASSISTments
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