New Baitouweng decoction combined with fecal microbiota transplantation alleviates DSS-induced colitis in rats by regulating gut microbiota metabolic homeostasis and the STAT3/NF-κB signaling pathway.

AIM OF THE STUDY: We aimed to elucidate the synergistic effect and potential mechanism of New Baitouweng Decoction (NBD) combined with fecal microbiota transplantation (FMT) in rats with DSS-induced ulcerative colitis (UC). MATERIALS AND METHODS: Colitis was induced by 5% (w/v) dextran sulfate sodium (DSS) in drinking water for 7 days. NBD or NBD combined with FMT were administered to the colitis rats. Body weight and disease activity index were measured, and the colon histological change was imaged to further examine the efficacy of NBD and FMT. The specific effects of NBD on STAT3/NF-κB signaling pathway and gut microbiota in rats with UC were also investigated. RESULTS: The efficacy of NBD in combination with FMT was demonstrated by the lower disease activity index scores; increased tight junction proteins expression; and a lower expression of macrophage marker (F4/80) in colon tissues. NBD combined with FMT elevated the concentrations of short-chain fatty acids and inhibited activation of the JAK2/STAT3/NF-κB related proteins. Furthermore, 16SrDNA sequencing indicated that the gut microbiota in rats with UC was perturbed, in contrast to that in healthy rats. After treatment with NBD and FMT, the diversity and abundance of intestinal flora showed clear improvements. Spearman correlation analysis indicated a strong correlation between specific microbiota and fecal concentrations of acetate, propionate and butyrate. CONCLUSIONS: The protective mechanism of NBD combined with FMT may be linked to regulation NF-κB/STAT3 and restoration of the intestinal flora.

Vegetation and land classification method based on the background noise rate of a photon-counting LiDAR.

The changing of vegetation is a sensitive signature of global warming, and satellite photon-counting laser altimeters provide an effective way to monitor the changing of vegetation. Based on the background noise difference between vegetation-covered areas and bare lands, we proposed a classification method to distinguish vegetation-covered areas from the raw photons measured by photon-counting laser altimeters in relatively flat areas. First, a theoretical noise model was established considering the influence of the sunlight incident direction and reflection characteristics of different surfaces. Second, the thresholds from the proposed theoretical model were calculated and tested to classify the along-track land-cover types for the Ice, Cloud, and Elevation Satellite-2 (ICESat-2) photon-counting laser altimeter. Then, the study areas near Seattle and Romania in summer were selected and the classification method was verified to achieve an overall accuracy of over 77% (the strong beam) and over 76% (the weak beam) for both thresholds and areas. Our method utilized the noise photons with vegetation canopy reflection information, which are enormous in quantity and easy to extract compared to the signal photons. More importantly, this method reduces the requirements of the optical images (that are used as prior knowledge). The results show that using the noise photons of the weak beam may be more potential for the classification of vegetation and land than using the signal photons of the weak beam. We extended the research on the mechanism and application of ICESat-2 in forestry.

Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images.

Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information. The features of small targets may also be submerged by redundant top-level features, resulting in poor detection. To address these problems, we proposed a compact multi-scale dense convolutional neural network (MS-DenseNet) for aircraft detection in remote sensing images. Herein, DenseNet was utilized for feature extraction, which enhances the propagation and reuse of the bottom-level high-resolution features. Subsequently, we combined feature pyramid network (FPN) with DenseNet to form a MS-DenseNet for learning multi-scale features, especially features of small objects. Finally, by compressing some of the unnecessary convolution layers of each dense block, we designed three new compact architectures: MS-DenseNet-41, MS-DenseNet-65, and MS-DenseNet-77. Comparative experiments showed that the compact MS-DenseNet-65 obtained a noticeable improvement in detecting small aircrafts and achieved state-of-the-art performance with a recall of 94% and an F1-score of 92.7% and cost less computational time. Furthermore, the experimental results on robustness of UCAS-AOD and RSOD datasets also indicate the good transferability of our method.

A chip-based potentiometric sensor for a Zika virus diagnostic using 3D surface molecular imprinting.

The latest Zika virus (ZIKV) pandemic caused great international concern from explosively proliferating throughout the Americas. Currently, there is no vaccine to prevent Zika virus infection and available tests rely on antibodies or RNA. Unfortunately, antibody-based detection systems can result in false positive results and RNA-based detection systems are costly, time-consuming, and impractical for testing in remote regions. In this study, a potential point-of-care (POC) diagnostic system was developed using a chip-based potentiometric sensor to detect Zika virus using a 3D molecular imprinting technique. This chip-based potentiometric sensor system was able to detect 10-1 PFU mL-1 ZIKV in a buffered solution under 20 minutes without any sample manipulation. This sensor was tested against Dengue virus at clinical viral loads and showed no sign of cross-reactivity. When tested against human saliva samples containing clinical viral loads, this sensor was able to detect 10 PFU mL-1 ZIKV among the pool of bio-macromolecules. The high sensitivity and high selectivity demonstrated here proved that this lab-on-a-chip diagnostic has the potential to become a POC detection system for rapid and accurate screening of flaviviruses.

Design of a molecular imprinting biosensor with multi-scale roughness for detection across a broad spectrum of biomolecules.

The molecular imprinting technique has tremendous applications in artificial enzymes, bioseparation, and sensor devices. In this study, a novel molecular imprinting (MI) biosensor platform was developed for the detection of a broad range of biomolecules with different sizes. Previously this method has been applied to 2D molecular imprinting, where the height of the self-assembled monolayer (SAM) of around 2 nm limited the maximum dimensions of the molecule that can be imprinted to create template-shaped cavities. In order to match the size of the imprinted molecules with the height of the SAM, we propose a model for 3D molecular imprinting where the analyte is sequestered within a niche created by the surface roughness. The SAM is assembled on the walls of the niche, forming a 3D pattern of the analyte uniquely molded to its contour. Surfaces with multi-scale roughness were prepared by evaporation of gold onto electropolished (smooth) and unpolished (rough) Si wafers, where the native roughness was found to have a normal distribution centered around 5 and 90 nm respectively. Our studies using molecules with size ranging on a nanometer scale, from proteins of a few nanometers to bacteria of hundreds of nanometers, showed that when the size of the analyte matched the roughness range of the gold surface, the molecular imprinting process was optimized for the best biosensing performance. After optimization, the MI biosensor platform enabled the identification and quantification of a broad range of biomolecules with great discrimination abilities. Hemoglobin under different pH values and several mutated fibrinogen molecules can also be well differentiated through the test.

Quantitative real-time detection of carcinoembryonic antigen (CEA) from pancreatic cyst fluid using 3-D surface molecular imprinting.

In this study, a sensitive, yet robust, biosensing system with real-time electrochemical readout was developed. The biosensor system was applied to the detection of carcinoembryonic antigen (CEA), which is a common marker for many cancers such as pancreatic, breast, and colon cancer. Real time detection of CEA during a medical procedure can be used to make critical decisions regarding further surgical intervention. CEA was templated on gold surface (RMS roughness ∼3-4 nm) coated with a hydrophilic self-assembled monolayer (SAM) on the working electrode of an open circuit potentiometric network. The subsequent removal of template CEA makes the biosensor capable of CEA detection based on its specific structure and conformation. The molecular imprinting (MI) biosensor was further calibrated using the potentiometric responses in solutions with known CEA concentrations and a detection limit of 0.5 ng ml(-1) was achieved. Potentiometric sensing was then applied to pancreatic cyst fluid samples obtained from 18 patients when the cyst fluid was also evaluated using ELISA in a certified pathology laboratory. Excellent agreement was obtained between the quantitation of CEA obtained by both the ELISA and MI biosensor detection for CEA. A 3-D MI model, using the natural rms roughness of PVD gold layers, is presented to explain the high degree of sensitivity and linearity observed in those experiments.

A potentiometric protein sensor built with surface molecular imprinting method.

Surface molecular imprinting, as compared to molecular imprinted bulk polymers, has the advantages of higher re-occupation percentage of the reception sites, fast response, integration of sensing element and transducer, etc. In this study, a potentiometric protein sensor was developed based on the surface molecular imprinting technique. Using the self-assembled monolayers of alkanethiol with hydroxyl terminal groups as the matrix material, and target protein molecules as the template, the sensing layer was created on the surface of the gold-coated silicon chip-an electrochemical transducer. Potentiometric measurement demonstrated that the sensor could selectively detect myoglobin or hemoglobin molecules, either with or without the presence of other protein molecules in the same solution.