Rapid (<5 min) identification of pathogen in human blood by electrokinetic concentration and surface-enhanced Raman spectroscopy.

This study reports a novel microfluidic platform for rapid and long-ranged concentration of rare-pathogen from human blood for subsequent on-chip surface-enhanced Raman spectroscopy (SERS) identification/discrimination of bacteria based on their detected fingerprints. Using a hybrid electrokinetic mechanism, bacteria can be concentrated at the stagnation area on the SERS-active roughened electrode, while blood cells were excluded away from this region at the center of concentric circular electrodes. This electrokinetic approach performs isolation and concentration of bacteria in about three minutes; the density factor is increased approximately a thousand fold in a local area of ~5000 μm(2) from a low bacteria concentration of 5 × 10(3) CFU/ml. Besides, three genera of bacteria, S. aureus, E. coli, and P. aeruginosa that are found in most of the isolated infections in bacteremia were successfully identified in less than one minute on-chip without the use of any antibody/chemical immobilization and reaction processes

Anti-Inflammatory and Anticoagulative Effects of Paeonol on LPS-Induced Acute Lung Injury in Rats

Paeonol is an active component of Moutan Cortex Radicis and is widely used as an analgesic, antipyretic, and anti-inflammatory agent in traditional Chinese medicine. We wanted to determine the role of paeonol in treating adult respiratory distress syndrome (ARDS). We established an acute lung injury (ALI) model in Sprague-Dawley rats, which was similar to ARDS in humans, using intratracheal administration of lipopolysaccharide (LPS). The intraperitoneal administration of paeonol successfully reduced histopathological scores and attenuated myeloperoxidase-reactive cells as an index of polymorphonuclear neutrophils infiltration and also reduces inducible nitric oxide synthase expression in the lung tissue, at 16 h after LPS administration. In addition, paeonol reduced proinflammatory cytokines in bronchoalveolar lavage fluid, including tumor-necrosis factor-α, interleukin-1β, interleukin-6, and plasminogen-activated inhibition factor-1. These results indicated that paeonol successfully attenuates inflammatory and coagulation reactions to protect against ALI

Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation

Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles). In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.Comment: 14 pages, 12 figure

Repression of btuB gene transcription in Escherichia coli by the GadX protein

<p>Abstract</p> <p>Background</p> <p>BtuB (B twelve uptake) is an outer membrane protein of <it>Escherichia coli</it>, it serves as a receptor for cobalamines uptake or bactericidal toxin entry. A decrease in the production of the BtuB protein would cause <it>E. coli </it>to become resistant to colicins. The production of BtuB has been shown to be regulated at the post-transcriptional level. The secondary structure switch of 5' untranslated region of <it>butB </it>and the intracellular concentration of adenosylcobalamin (Ado-Cbl) would affect the translation efficiency and RNA stability of <it>btuB</it>. The transcriptional regulation of <it>btuB </it>expression is still unclear.</p> <p>Results</p> <p>To determine whether the <it>btuB </it>gene is also transcriptionally controlled by trans-acting factors, a genomic library was screened for clones that enable <it>E. coli </it>to grow in the presence of colicin E7, and a plasmid carrying <it>gadX </it>and <it>gadY </it>genes was isolated. The <it>lacZ </it>reporter gene assay revealed that these two genes decreased the <it>btuB </it>promoter activity by approximately 50%, and the production of the BtuB protein was reduced by approximately 90% in the presence of a plasmid carrying both <it>gadX </it>and <it>gadY </it>genes in <it>E. coli </it>as determined by Western blotting. Results of electrophoretic mobility assay and DNase I footprinting indicated that the GadX protein binds to the 5' untranslated region of the <it>btuB </it>gene. Since <it>gadX </it>and <it>gadY </it>genes are more highly expressed under acidic conditions, the transcriptional level of <it>btuB </it>in cells cultured in pH 7.4 or pH 5.5 medium was examined by quantitative real-time PCR to investigate the effect of GadX. The results showed the transcription of <it>gadX </it>with 1.4-fold increase but the level of <it>btuB </it>was reduced to 57%.</p> <p>Conclusions</p> <p>Through biological and biochemical analysis, we have demonstrated the GadX can directly interact with <it>btuB </it>promoter and affect the expression of <it>btuB</it>. In conclusion, this study provides the first evidence that the expression of <it>btuB </it>gene is transcriptionally repressed by the acid responsive genes <it>gadX </it>and <it>gadY</it>.</p

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results

Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments

Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platform-specific nature of off-road traversability. In this paper, we propose a novel self-supervised terrain traversability learning framework, utilizing a contrastive label disambiguation mechanism. Firstly, weakly labeled training samples with pseudo labels are automatically generated by projecting actual driving experiences onto the terrain models constructed in real time. Subsequently, a prototype-based contrastive representation learning method is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. As the iterative interaction between representation learning and pseudo label updating, the ambiguities in those pseudo labels are gradually eliminated, enabling the learning of platform-specific and task-specific traversability without any human-provided annotations. Experimental results on the RELLIS-3D dataset and our Gobi Desert driving dataset demonstrate the effectiveness of the proposed method.Comment: 9 pages, 11 figure