Novel Cell type-specific aptamer-siRNA delivery system for HIV-1 therapy

The successful use of small interfering RNAs (siRNAs) for therapeutic purposes requires safe and efficient delivery to specific cells and tissues. Here we demonstrate cell type-specific delivery of anti-HIV siRNAs via fusion to an anti-gp120 aptamer. The envelope glycoprotein is expressed on the surface of HIV-1 infected cells, allowing binding and interalization of the aptamer-siRNA chimeric molecules. We demonstrate that the anti-gp120 aptamer-siRNA chimera is specifically taken up by cells expressing HIV-1 gp120, and the appended siRNA is processed by Dicer, releasing an anti-tat/rev siRNA which in turn inhibits HIV replication. We show for the first time a dual functioning aptamer-siRNA chimera in which both the aptamer and the siRNA portions have potent anti-HIV activities and that gp120 expressed on the surface of HIV infected cells can be used for aptamer mediated delivery of anti-HIV siRNAs

Two Regularization Models for Computed Tomography Image Reconstruction from Limited Projection Data

Computed tomography (CT) has been widely applied in medical imaging and industry for over decades. CT reconstruction from limited projection data is of particular importance. The total variation or l1-norm regularization has been widely used for image reconstruction in computed tomography (CT). Images in computed tomography (CT) are mostly piece-wise constant so the gradient images are considered as sparse images. The l0-norm of the gradients of an image provides a measurement of the sparsity of gradients of the image. However, the l0-norm regularization problem is NP hard. In this talk, we present two new models for CT image reconstruction from limited-angle projections. In one model we propose the smoothed l0-norm and l1-norm regularization using the nonmonotone alternating direction algorithm. In the other model we propose a combined l1-norm and l0-norm regularization model for better edge preserving

The Convergence of Two Algorithms for Compressed Sensing Based Tomography

The constrained total variation minimization has been developed successfully for image reconstruction in computed tomography. In this paper, the block component averaging and diagonally-relaxed orthogonal projection methods are proposed to incorporate with the total variation minimization in the compressed sensing framework. The convergence of the algorithms under a certain condition is derived. Examples are given to illustrate their convergence behavior and noise performance

Numerical Studies of the Generalized \u3cem\u3el\u3c/em\u3e₁ Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied

The roles of Eu during the growth of eutectic Si in Al-Si alloys

Controlling the growth of eutectic Si and thereby modifying the eutectic Si from flake-like to fibrous is a key factor in improving the properties of Al-Si alloys. To date, it is generally accepted that the impurity-induced twinning (IIT) mechanism and the twin plane re-entrant edge (TPRE) mechanism as well as poisoning of the TPRE mechanism are valid under certain conditions. However, IIT, TPRE or poisoning of the TPRE mechanism cannot be used to interpret all observations. Here, we report an atomic-scale experimental and theoretical investigation on the roles of Eu during the growth of eutectic Si in Al-Si alloys. Both experimental and theoretical investigations reveal three different roles: (i) the adsorption at the intersection of Si facets, inducing IIT mechanism, (ii) the adsorption at the twin plane re-entrant edge, inducing TPRE mechanism or poisoning of the TPRE mechanism, and (iii) the segregation ahead of the growing Si twins, inducing a solute entrainment within eutectic Si. This investigation not only demonstrates a direct experimental support to the well-accepted poisoning of the TPRE and IIT mechanisms, but also provides a full picture about the roles of Eu atoms during the growth of eutectic Si, including the solute entrainment within eutectic Si

Artificial Intelligence Approaches to Determine Graphite Nodularity in Ductile Iron

The complex metallurgical interrelationships in the production of ductile cast iron can lead to enormous differences in graphite formation and local microstructure by small variations during production. Artificial intelligence algorithms were used to describe graphite formation, which is influenced by a variety of metallurgical parameters. Moreover, complex physical relationships in the formation of graphite morphology are also controlled by boundary conditions of processing, the effect of which can hardly be assessed in everyday foundry operations. The influence of relevant input parameters can be predetermined using artificial intelligence based on conditions and patterns that occur simultaneously. By predicting the local graphite formation, measures to stabilise production were defined and thereby the accuracy of structure simulations improved. In course of this work, the most important dominating variables, from initial charging to final casting, were compiled and analysed with the help of statistical regression methods to predict the nodularity of graphite spheres. We compared the accuracy of the prediction by using Linear Regression, Gaussian Process Regression, Regression Trees, Boosted Trees, Support Vector Machines, Shallow Neural Networks and Deep Neural Networks. As input parameters we used 45 characteristics of the production process consisting of the basic information including the composition of the charge, the overheating time, the type of melting vessel, the type of the inoculant, the fading, and the solidification time. Additionally, the data of several thermal analysis, oxygen activity measurements and the final chemical analysis were included.Initial programme designs using machine learning algorithms based on neural networks achieved encouraging results. To improve the degree of accuracy, this algorithm was subsequently adapted and refined for the nodularity of graphite

Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution

This paper proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution. It explores the broad "middle spectrum" area between channel pruning and conventional grouped convolution. Compared with channel pruning, MSGC can retain most of the information from the input feature maps due to the group mechanism; compared with grouped convolution, MSGC benefits from the learnability, the core of channel pruning, for constructing its group topology, leading to better channel division. The middle spectrum area is unfolded along four dimensions: group-wise, layer-wise, sample-wise, and attention-wise, making it possible to reveal more powerful and interpretable structures. As a result, the proposed module acts as a booster that can reduce the computational cost of the host backbones for general image recognition with even improved predictive accuracy. For example, in the experiments on ImageNet dataset for image classification, MSGC can reduce the multiply-accumulates (MACs) of ResNet-18 and ResNet-50 by half but still increase the Top-1 accuracy by more than 1%. With 35% reduction of MACs, MSGC can also increase the Top-1 accuracy of the MobileNetV2 backbone. Results on MS COCO dataset for object detection show similar observations. Our code and trained models are available at https://github.com/hellozhuo/msgc.Comment: 13 pages, 11 figures, submitted to IEEEE Transactions on xx

Protective effect of dehydroandrographolide on obstructive cholestasis in bile duct-ligated mice

Background: Dehydroandrographolide (DA) is the main contributor to the therapeutic properties of the medicinal plant Andrographis paniculata (AP). However, it is unknown whether DA has a hepatoprotective effect on obstructive cholestasis in mice and humans. Methods: We administered DA to mice for 5 days prior to bile duct ligation (BDL) and for the 7 days. Liver function markers, liver histology and necrosis, compensatory responses of hepatocytes, liver fibrosis and the expression of hepatic fibrogenesis markers were evaluated in BDL mice and/or human LX-2 cells. Results: Mice treated with DA demonstrated lower levels of serum alanine transarninase (ALT), milder liver damage, liver necrosis and fibrosis formation than in vehicle control with carboxymethylcellulose (CMC) mice after BDL. DA treatment also enhanced the Mrp3 expression of hepatocytes but not Mrp4 following BDL. Further, DA treatment in BDL mice significantly reduced liver mRNA and/or protein expression of Tgf-β, Col1a1, α-Sma and Mmp2. This result was also supported by hydroxyproline analysis. The molecular mechanisms of DA treatment were also assessed in human hepatic stellate cell line (LX-2 cell). DA treatment significantly inhibited Tgf-β-induced Col1a1, Mmp2 and α-Sma expression in human LX-2 cells. These data suggested that DA treatment reduced liver damage through development of a hepatic adaptive response and inhibition of the activation of HSCs, which led to a reduction in liver fibrosis formation in BDL mice. Conclusions: DA treatment protected against liver damage and fibrosis following BDL and might be an effective therapy for extrahepatic cholestasis due to bile duct obstruction

Eutectic modification by ternary compound cluster formation in Al-Si alloys

Al-alloys with Si as the main alloying element constitute the vast majority of Al castings used commercially today. The eutectic Si microstructure in these alloys can be modified from plate-like to coral-like by the addition of a small amount of a third element to improve ductility and toughness. In this investigation the effects of Eu and Yb are studied and their influence on the microstructure is compared to further understand this modification. The two elements impact the alloy differently, where Eu modifies Si into a coral-like structure while Yb does not. Atom probe tomography shows that Eu is present within the Si phase in the form of ternary compound Al2Si2Eu clusters, while Yb is absent in the Si phase. This indicates that the presence of ternary compound clusters within Si is a necessary condition for the formation of a coral-like structure. A crystallographic orientation relationship between Si and the Al2Si2Eu phase was found, where the following plane normals are parallel: 011Si//0001Al2Si2Eu, 111Si//67¯10Al2Si2Eu and 011Si//67¯10Al2Si2Eu. No crystallographic relationship was found between Si and Al2Si2Yb. The heterogeneous formation of coherent Al2Si2Eu clusters inside the Si-phase is suggested to trigger the modification of the microstructure

Tumor-Derived Exosomal Protein Tyrosine Phosphatase Receptor Type O Polarizes Macrophage to Suppress Breast Tumor Cell Invasion and Migration

Tumor-derived exosomes, containing multiple nucleic acids and proteins, have been implicated to participate in the interaction between tumor cells and microenvironment. However, the functional involvement of phosphatases in tumor-derived exosomes is not fully understood. We and others previously demonstrated that protein tyrosine phosphatase receptor type O (PTPRO) acts as a tumor suppressor in multiple cancer types. In addition, its role in tumor immune microenvironment remains elusive. Bioinformatical analyses revealed that PTPRO was closely associated with immune infiltration, and positively correlated to M1-like macrophages, but negatively correlated to M2-like macrophages in breast cancer tissues. Co-cultured with PTPRO-overexpressing breast cancer cells increased the proportion of M1-like tumor-associated macrophages (TAMs) while decreased that of M2-like TAMs. Further, we observed that tumor-derived exosomal PTPRO induced M1-like macrophage polarization, and regulated the corresponding functional phenotypes. Moreover, tumor cell-derived exosomal PTPRO inhibited breast cancer cell invasion and migration, and inactivated STAT signaling in macrophages. Our data suggested that exosomal PTPRO inhibited breast cancer invasion and migration by modulating macrophage polarization. Anti-tumoral effect of exosomal PTPRO was mediated by inactivating STAT family in macrophages. These findings highlight a novel mechanism of tumor invasion regulated by tumor-derived exosomal tyrosine phosphatase, which is of translational potential for the therapeutic strategy against breast cancer