Saline and Alkaline tolerance of wetland plants — what are the most representative evaluation indicators?

The increasing discharge of wastewater containing inorganic salts, sometimes accompanied by high pH, has been a worldwide environmental problem. Constructed wetlands (CWs) are considered a viable technology for treating saline and/or alkaline wastewater provided that saline-alkaline tolerant plant species are selected and applied. The influence of both saline and alkaline stress on four wetland plant species during their seed germination, early growth, vegetative propagation and continued growth stages was evaluated by three experiments. Principal component analysis (PCA) was conducted for selecting representative indicators for evaluating the saline and alkaline tolerance of plants during vegetative propagation and plant growth stages. The saline and alkaline stress inhibited the vegetative propagation and plant growth of all tested plant species to varying degrees, therein the influences of saline-alkaline stress on plants were more marked than saline stress. The length of new roots, Na+ accumulation in plant tissue, Na+/K+ ratios in aerial tissue and the total dry biomass were selected as most representative indicators for evaluating the saline and alkaline tolerance of plants. Iris sibirica and Lythrum salicaria showed better saline and alkaline tolerance ability among tested species and could be grown in CWs for treating saline and/or alkaline wastewater

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu

Gluon Condensation Signature in the GeV Gamma-Ray Spectra of Pulsars

The accumulation of gluons inside nucleons, i.e., the gluon condensation, may lead to a characteristic broken power-law gamma-ray spectrum in high-energy nucleon collisions. Here we show that the observed spectra of at least 25 sources in the second Fermi Large Area Telescope Catalog of Gamma-ray Pulsars can be well fitted by such a broken power-law function that has only four free parameters. It strongly indicates that the gamma-ray emission from these pulsars is of hadronic origin, but with gluon condensation inside hadrons. It is well known that the quark-gluon distribution in a free nucleon is different from that in a bound nucleon. This work exposes the nuclear $A$-dependence of the gluon condensation effect, where $A$ refers to the baryon number. Our study reveals the gluon condensation under the condition of $A\to\infty$, which may open a new window for eavesdropping on the structure of compact stars on the sub-nuclear level.Comment: 12 pages (9 pages for main text), 5 figures, 1 table, accepted by PRD at https://journals.aps.org/prd/accepted/fd07cQ89M2118d20490d0d014fdd00616d4cdeb8

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins