Antireflection self-reference method based on ultrathin metallic nanofilms for improving terahertz reflection spectroscopy

We present the potential of an antireflection self-reference method based on ultrathin tantalum nitride (TaN) nanofilms for improving terahertz (THz) reflection spectroscopy. The antireflection self-reference method is proposed to eliminate mutual interference caused by unwanted reflections, which significantly interferes with the important reflection from the actual sample in THz reflection measurement. The antireflection self-reference model was investigated using a wave-impedance matching approach, and the theoretical model was verified in experimental studies. We experimentally demonstrated this antireflection selfreference method can completely eliminate the effect of mutual interference, accurately recover the actual sample’s reflection and improve THz reflection spectroscopy. Our method paves the way to implement a straightforward, accurate and efficient approach to investigate THz properties of the liquids and biological samplesThe Fund from Hefei University of Technology (407-0371000019); Sichuan Province Science and Technology Support Program (No. 2016GZ0250); the Fundamental Research Funds for the Central Universities (Grant No. JD2017JGPY0006); National Natural Science Foundation of China (Grant No.51607050); MINECO (MAT2015–74381-JIN to B.P., RYC2014–16962 and CTQ2017-89588-R to P.dP.); Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016–2019, ED431G/09); European Union (European Regional Development Fund – ERDF)S

Neovascularization-directed bionic eye drops for noninvasive renovation of age-related macular degeneration

The current treatment of wet age-related macular degeneration (wAMD) relies on monthly intravitreal or intravenously injection of vascular endothelial growth factor (VEGF) inhibitor or photodynamic (PDT) agents to inhibit choroidal neovascularization. However, traumatic local therapy and exogenous long-distance fundus drug delivery often lead to secondary eye damage, low treatment efficiency, and immunogenic inflammation. Herein, inspired by the natural neovascular targeting ability of endogenous low-density lipoproteins (LDL), a noninvasive bionic nano-eye-drop with enhanced ocular penetrability and lesion recognizability is developed for enabling the PDT treatment of wAMD. Verteporfin (VP) as a laser-induced PDT agent is protected inside the hydrophobic core of reconstituted LDL (rLDL) vectors. 5-carboxyfluorescein (FAM) conjugated ste-penetratin (PEN, a transmembrane peptide) is anchored on the surface of the rLDL carrier, which enabled the nanoparticles (PEN-rLDL-VP) to cross the blood-retina barrier to realizing visual therapy. Following instillation, PEN-rLDL-VP can effectively deliver VP into neovascular that overexpress LDL receptors, which can respond to laser-induced PDT. Only with a single dose of the eye-drop and laser-induced PDT, the VEGF and proinflammatory intercellular adhesion molecule-1 (ICAM-1) proteins are significantly down-regulated in vivo, which implicates the neovascular inhibition and inflammation alleviation. This study presents an attractive non-invasive strategy for the PDT of wAMD

IGFBP-rP1, a potential molecule associated with colon cancer differentiation

<p>Abstract</p> <p>Background</p> <p>In our previous studies, we have demonstrated that insulin-like growth factor binding protein-related protein1 (IGFBP-rP1) played its potential tumor suppressor role in colon cancer cells through apoptosis and senescence induction. In this study, we will further uncover the role of IGFBP-rP1 in colon cancer differentiation and a possible mechanism by revealing responsible genes.</p> <p>Results</p> <p>In normal colon epithelium, immunohistochemistry staining detected a gradient IGFBP-rP1 expression along the axis of the crypt. IGFBP-rP1 strongly expressed in the differentiated cells at the surface of the colon epithelium, while weakly expressed at the crypt base. In colon cancer tissues, the expression of IGFBP-rP1 correlated positively with the differentiation status. IGFBP-rP1 strongly expressed in low grade colorectal carcinoma and weakly expressed in high grade colorectal carcinoma. In vitro, transfection of PcDNA3.1(IGFBP-rP1) into RKO, SW620 and CW2 cells induced a more pronounced anterior-posterior polarity morphology, accompanied by upregulation with alkaline phosphatase (AKP) activity. Upregulation of carcino-embryonic antigen (CEA) was also observed in SW620 and CW2 transfectants. The addition of IGFBP-rP1 protein into the medium could mimic most but not all effects of IGFBP-rP1 cDNA transfection. Seventy-eight reproducibly differentially expressed genes were detected in PcDNA3.1(IGFBP-rP1)-RKO transfectants, using Affymetrix 133 plus 2.0 expression chip platform. Directed Acyclic Graph (DAG) of the enriched GO categories demonstrated that differential expression of the enzyme regulator activity genes together with cytoskeleton and actin binding genes were significant. IGFBP-rP1 could upreguate Transgelin (TAGLN), downregulate SRY (sex determining region Y)-box 9(campomelic dysplasia, autosomal sex-reversal) (SOX9), insulin receptor substrate 1(IRS1), cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) (CDKN2B), amphiregulin(schwannoma-derived growth factor) (AREG) and immediate early response 5-like(IER5L) in RKO, SW620 and CW2 colon cancer cells, verified by Real time Reverse Transcription Polymerase Chain Reaction (rtRT-PCR). During sodium butyrate-induced Caco2 cell differentiation, IGFBP-rP1 was upregulated and the expression showed significant correlation with the AKP activity. The downregulation of IRS1 and SOX9 were also induced by sodium butyrate.</p> <p>Conclusion</p> <p>IGFBP-rP1 was a potential key molecule associated with colon cancer differentiation. Downregulation of IRS1 and SOX9 may the possible key downstream genes involved in the process.</p

Point Cloud Convolution Network Based on Spatial Location Correspondence

The study of convolutional neural networks for 3D point clouds is becoming increasingly popular, and the difficulty lies mainly in the disorder and irregularity of point clouds. At present, it is straightforward to propose a convolution operation and perform experimental validation. Although good results are achieved, the principles behind them are not explained—i.e., why this can solve the disorder and irregularity of point clouds—and it is difficult for the researchers to design a point cloud convolution network suitable for their needs. For this phenomenon, we propose a point convolution network framework based on spatial location correspondence. Following the correspondence principle can guide us in designing convolution networks adapted to our needs. We analyzed the intrinsic mathematical nature of the convolution operation, and we argue that the convolution operation remains the same when the spatial location correspondence between the convolution kernel points and the convolution range elements remains unchanged. Guided by this principle, we formulated a general point convolution framework based on spatial location correspondence, which explains how to handle a disordered point cloud. Moreover, we discuss different kinds of correspondence based on spatial location, including M-to-M, M-to-N, and M-to-1 relationships, etc., which explain how to handle the irregularity of point clouds. Finally, we give the example of a point convolution network whose convolution kernel points are generated based on the sample’s covariance matrix distribution according to our framework. Our convolution operation can be applied to various point cloud processing networks. We demonstrated the effectiveness of our framework for point cloud classification and semantic segmentation tasks, achieving competitive results with state-of-the-art networks

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

Automated truck platooning (ATP) has gained growing attention due to its advantage in reducing fuel consumption and carbon emissions. However, it poses serious challenges to highway bridges due to the load effect of multiple closely spaced heavy-duty trucks on the bridge. In China, ATP also has great application prospects in the massive and ever-increasing highway freight market. Therefore, the load effects of ATP on bridges need to be thoroughly investigated. In this study, typical Chinese highway bridges and trucks were adopted. ATP load models were designed according to the current Chinese road traffic regulations. The load effects of ATP on highway bridges were calculated using the influence line method and evaluated based on the Chinese bridge design specifications. Results show that the load effect of ATP on bridges increases with the increase in the gross vehicle mass and the truck platooning size but decreases with the increasing inter-truck spacing and the critical wheelbase. The Grade-I (best quality standard) highway bridges are generally capable of withstanding the ATP loads, while caution should be exercised for other bridges. Strategies for preventing serious adverse impacts of ATP load on highway bridges are proposed

Embedding differential privacy in decision tree algorithm with different depths

Differential privacy (DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining (DM) logic is considered in the DP implementation. However, although one-step DM computation for decision tree (DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo (MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation (previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large (e.g., ϵ = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity

Reinforcing Neural Network Stability with Attractor Dynamics

Recent approaches interpret deep neural works (DNNs) as dynamical systems, drawing the connection between stability in forward propagation and generalization of DNNs. In this paper, we take a step further to be the first to reinforce this stability of DNNs without changing their original structure and verify the impact of the reinforced stability on the network representation from various aspects. More specifically, we reinforce stability by modeling attractor dynamics of a DNN and propose relu-max attractor network (RMAN), a light-weight module readily to be deployed on state-of-the-art ResNet-like networks. RMAN is only needed during training so as to modify a ResNet's attractor dynamics by minimizing an energy function together with the loss of the original learning task. Through intensive experiments, we show that RMAN-modified attractor dynamics bring a more structured representation space to ResNet and its variants, and more importantly improve the generalization ability of ResNet-like networks in supervised tasks due to reinforced stability