Improving the Accuracy of Cosmic-Ray Neutron Probe Estimate of Soil Water Content Using Multiple Detectors and Remote Sensing Estimates of Vegetation

The recently developed Cosmic-ray Neutron Probe (CRNP) for estimating soil water content (SWC) fills a critical measurement gap between point scale methods and large scale measurements collected from remote sensing. CRNP works by measuring the change in low-energy neutron intensity over time. However, the accuracy of CRNP to measure SWC is well known to be affected by other hydrogen sources (e.g. soil organic content, atmospheric water vapor, vegetation and surface water). This study focuses on the influence of rapidly growing vegetation in agricultural fields on the accuracy of the CRNP method. Here we use data from three long-term CRNP study sites in central Nebraska (Paulman Farms, est. 2015), eastern Nebraska (US-Ne3, est. 2011), and central Iowa (IVS, est. 2010) that span a natural precipitation gradient of increasing precipitation from west to east. All three fields grow maize and soybean depending on rotation. At each CRNP site both hourly moderated and bare neutron counts are recorded. Previous research has shown that the bare to moderated ratio may be a good indicator of changing vegetation conditions and useful as a correction to estimating SWC. In addition, I use the MODIS remote sensing dataset to calculate a widely used index to monitor vegetation, Green Wide Dynamic Range Vegetation Index (GrWDRVI or WDRVI). Finally, observed vegetation data from US-Ne3 was collected biweekly from 2003-2016 and used as a benchmark for the CRNP and remote sensing analyses. My results indicate that biomass data determined from remote sensing (GrWDRVI) closely follows in-situ sampling of biomass (R2=0.677 for Maize and R2=0.567 for Soybean). The driest site (Paulman Farms) showed the best relationship between bare to moderated (B/M) ratios and GrWDRVI with an R2 = 0.9188, while the wettest site (IVS) showed the worst relationship with R2 = 0.09. I found that local correction factors using B/M ratio and moderated counts removing the influence of vegetation changes can be derived, thus removing bias in the CRNP SWC observations. The improved algorithm for estimating SWC from CRNP will be beneficial for long term monitoring as well as validating remote sensing SWC products. More experiments with direct biomass observations and needed to fully understand the relationship between GrWDRVI, bare and moderated neutron counts, and in-situ biomass. Advisor: Trenton E. Fran

Learning Local Metrics and Influential Regions for Classification

The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem of multimodality, it is desirable to learn local metrics. In this short paper, we define a new intuitive distance with local metrics and influential regions, and subsequently propose a novel local metric learning method for distance-based classification. Our key intuition is to partition the metric space into influential regions and a background region, and then regulate the effectiveness of each local metric to be within the related influential regions. We learn local metrics and influential regions to reduce the empirical hinge loss, and regularize the parameters on the basis of a resultant learning bound. Encouraging experimental results are obtained from various public and popular data sets

Learning local metrics and influential regions for classification

The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem of multimodality, it is desirable to learn local metrics. In this short paper, we define a new intuitive distance with local metrics and influential regions, and subsequently propose a novel local metric learning algorithm called LMLIR for distance-based classification. Our key intuition is to partition the metric space into influential regions and a background region, and then regulate the effectiveness of each local metric to be within the related influential regions. We learn multiple local metrics and influential regions to reduce the empirical hinge loss, and regularize the parameters on the basis of a resultant learning bound. Encouraging experimental results are obtained from various public and popular data sets

Toward Certified Robustness of Distance Metric Learning

Metric learning aims to learn a distance metric such that semantically similar instances are pulled together while dissimilar instances are pushed away. Many existing methods consider maximizing or at least constraining a distance margin in the feature space that separates similar and dissimilar pairs of instances to guarantee their generalization ability. In this paper, we advocate imposing an adversarial margin in the input space so as to improve the generalization and robustness of metric learning algorithms. We first show that, the adversarial margin, defined as the distance between training instances and their closest adversarial examples in the input space, takes account of both the distance margin in the feature space and the correlation between the metric and triplet constraints. Next, to enhance robustness to instance perturbation, we propose to enlarge the adversarial margin through minimizing a derived novel loss function termed the perturbation loss. The proposed loss can be viewed as a data-dependent regularizer and easily plugged into any existing metric learning methods. Finally, we show that the enlarged margin is beneficial to the generalization ability by using the theoretical technique of algorithmic robustness. Experimental results on 16 datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in both discrimination accuracy and robustness against possible noise

Stochastic Optical Reconstruction Microscopy Imaging of Microtubule Arrays in Intact Arabidopsis thaliana Seedling Roots

Super-resolution fluorescence microscopy has generated tremendous success in revealing detailed subcellular structures in animal cells. However, its application to plant cell biology remains extremely limited due to numerous technical challenges, including the generally high fluorescence background of plant cells and the presence of the cell wall. In the current study, stochastic optical reconstruction microscopy (STORM) imaging of intact Arabidopsis thaliana seedling roots with a spatial resolution of 20–40 nm was demonstrated. Using the super-resolution images, the spatial organization of cortical microtubules in different parts of a whole Arabidopsis root tip was analyzed quantitatively, and the results show the dramatic differences in the density and spatial organization of cortical microtubules in cells of different differentiation stages or types. The method developed can be applied to plant cell biological processes, including imaging of additional elements of the cytoskeleton, organelle substructure, and membrane domains

Glycyrrhizin inhibits the invasion and metastasis of breast cancer cells via upregulation of expressions of miR-200c and e-cadherin

Purpose: To determine the inhibitory effect of glycyrrhizin (GLA) on cell invasion and metastasis in mammary carcinoma cells, and the mechanisms of actions involved.Methods: The effect of GLA at different concentrations on proliferation of breast cancer MDA-MB-231 and BT549 cells was assayed by MTT method. Transwell assay was used to determine the effect of GLA at different concentrations on invasiveness and metastasis of breast cancer MDA-MB-231 and BT549 cells. The influence of LGA on expressions of microRNA-200c and miR-200c was assayed by reverse transcriptase-polymerase chain reaction (RT-PCR).Results: There was no statistically significant difference in cell proliferation amongst cells treated with 5 and 20 μM GLA and untreated breast cancer cells. However, the proliferation of cells treated with 40 μM GLA was significantly reduced (p < 0.05). In the cell invasion and migration experiments, cell population transferred to the base of Transwell chamber in the two cell lines treated with GLA was markedly decreased, relative to cells without GLA treatment, while the number of cells decreased with increase in GLA concentration (p < 0.05). Results from image-pro-plus analysis revealed that the population of cells quantitatively crossing the Transwell compartment membrane decreased with increase in GLA concentration (p < 0.05). The expression of e-cadherin was increased by GLA treatment in a concentration-dependent manner. Moreover, GLA treatment led to significant changes in amounts of miR-200s a, b and c, with changes in miR-200c being the most significant (p < 0.05).Conclusion: GLA suppresses the invasiveness and metastasis of breast cancer MDA-MB-231 and BT549 cells via upregulation of the expressions of miR-200c and e-cadherin. These findings provide a theoretical basis for the development of new breast cancer drugs. Keywords: Glycyrrhiza, GLA, miR-200c, E-cadherin, Inhibition, Breast cancer cells, Invasion, Metastasi

Ginsenoside induces cell death in breast cancer cells via ROS/PI3K/Akt signaling pathway

Purpose: To study the influence of ginsenoside on breast carcinoma, and the mechanism of action involved.Methods: Different concentrations of ginsenoside were used to treat MCF-7 breast cancer cell line. Cell viability was measured by MTT assay, while protein expressions of p-Akt and p-PI3K were determined using Western blotting. The concentrations of reactive oxidative reactants and reactive oxygen species (ROS) were assessed using fluorescence immunoassay and immunofluorescence assay. The mechanism of action involved in ginsenoside-mediated apoptosis was determined based on ROS/PI3K/Akt signaling pathway.Results: There was no change in the inhibition of MCF-7 cell proliferation in control cells with time (p > 0.05). However, inhibition of MCF-7 cell proliferation in ginsenoside group was significantly higher than that in the control group (p < 0.05); furthermore, it increased with time and ginsenoside concentration. Apoptosis was markedly and concentration-dependently higher in ginsenoside-treated MCF-7 cells than in controls (p > 0.05). There were lower protein levels of p-PI3K and p-Akt in ginsenoside-exposed MCF-7 cells than in control group; the protein expressions  decreased with increase in ginsenoside concentration (p < 0.05). The expressions of ROS in ginsenoside-treated MCF-7 cells declined, relative to the untreated group; in addition, the expressions decreased with increase in ginsenoside concentration (p < 0.05).Conclusion: Ginsenoside suppresses proliferation of MCF-7 cell line, and exerts apoptotic effect on the cells via inhibition of the ROS/PI3K/Akt signal pathway. This provides a new approach to treat breast cancer. Keywords: Breast cancer cells, Ginsenoside, Apoptosis, ROS/PI3K/Akt signaling pathwa