Carboxymethyl chitosan-folic acid-conjugated Fe3O4@SiO2 as a safe and targeting antitumor nanovehicle in vitro

A synthetic method to prepare a core-shell-structured Fe(3)O(4)@SiO(2) as a safe nanovehicle for tumor cell targeting has been developed. Superparamagnetic iron oxide is encapsulated inside nonporous silica as the core to provide magnetic targeting. Carboxymethyl chitosan-folic acid (OCMCS-FA) synthesized through coupling folic acid (FA) with OCMCS is then covalently linked to the silica shell and renders new and improved functions because of the original biocompatible properties of OCMCS and the targeting efficacy of FA. Cellular uptake of the nanovehicle was assayed by confocal laser scanning microscope using rhodamine B (RB) as a fluorescent marker in HeLa cells. The results show that the surface modification of the core-shell silica nanovehicle with OCMCS-FA enhances the internalization of nanovehicle to HeLa cells which over-express the folate receptor. The cell viability assay demonstrated that Fe(3)O(4)@SiO(2)-OCMCS-FA nanovehicle has low toxicity and can be used as an eligible candidate for drug delivery system. These unique advantages make the prepared core-shell nanovehicle promising for cancer-specific targeting and therapy

Probabilistic Naming of Functions in Stripped Binaries

Debugging symbols in binary executables carry the names of functions and global variables. When present, they greatly simplify the process of reverse engineering, but they are almost always removed (stripped) for deployment. We present the design and implementation of punstrip, a tool which combines a probabilistic fingerprint of binary code based on high-level features with a probabilistic graphical model to learn the relationship between function names and program structure. As there are many naming conventions and developer styles, functions from different applications do not necessarily have the exact same name, even if they implement the exact same functionality. We therefore evaluate punstrip across three levels of name matching: exact; an approach based on natural language processing of name components; and using Symbol2Vec, a new embedding of function names based on random walks of function call graphs. We show that our approach is able to recognize functions compiled across different compilers and optimization levels and then demonstrate that punstrip can predict semantically similar function names based on code structure. We evaluate our approach over open source C binaries from the Debian Linux distribution and compare against the state of the art

Proteome analysis of Physcomitrella patens exposed to progressive dehydration and rehydration

Physcomitrella patens is an extremely dehydration-tolerant moss. However, the molecular basis of its responses to loss of cellular water remains unclear. A comprehensive proteomic analysis of dehydration- and rehydration-responsive proteins has been conducted using quantitative two-dimensional difference in-gel electrophoresis (2D-DIGE), and traditional 2-D gel electrophoresis (2-DE) combined with MALDI TOF/TOF MS. Of the 216 differentially-expressed protein spots, 112 and 104 were dehydration- and rehydration-responsive proteins, respectively. The functional categories of the most differentially-expressed proteins were seed maturation, defence, protein synthesis and quality control, and energy production. Strikingly, most of the late embryogenesis abundant (LEA) proteins were expressed at a basal level under control conditions and their synthesis was strongly enhanced by dehydration, a pattern that was confirmed by RT-PCR. Actinoporins, phosphatidylethanolamine-binding protein, arabinogalactan protein, and phospholipase are the likely dominant players in the defence system. In addition, 24 proteins of unknown function were identified as novel dehydration- or rehydration-responsive proteins. Our data indicate that Physcomitrella adopts a rapid protein response mechanism to cope with dehydration in its leafy-shoot and basal expression levels of desiccation-tolerant proteins are rapidly upgraded at high levels under stress. This mechanism appears similar to that seen in angiosperm seeds

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC

A generic approach for the study of higher-order mode propagation in circular ducts with simple aperture devices

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A generic approach for the study of higher-order mode propagation in circular ducts with simple aperture devices

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

TRANS-Net: Transformer-Enhanced Residual-Error AlterNative Suppression Network for MRI Reconstruction

International audienceSince deep priors could exploit more intrinsic features than handcrafted prior knowledge, unrolled reconstruction methods significantly improve image quality for fast magnetic resonance imaging (MRI) reconstruction with the combination of iterative optimization and deep neural network-based regularization terms. One popular way for unrolled methods is to employ the regularization penalty on the reconstructed result within the image space. Dissimilarly, in this article, we innovatively propose a reconstruction framework termed Transformer-enhanced Residual-error AlterNative Suppression Network (TRANS-Net) for MRI reconstruction to extend the unrolled methods. Unlike some existing model-based algorithms, TRANS-Net additionally deploys the regularization term on the error maps in the residual image domain, which greatly emphasizes the high-frequency information contained in the MR images. Besides, to further explore the global spatial correlation of the reconstructed image, the transformer module is adopted to enlarge the receptive field of deep priors regularized on the reconstructed images for better tissue recovery. The quantitative and qualitative results show that the proposed TRANS-Net method has superior performance to the state-of-the-art reconstruction algorithms in anatomical structure restoration and perceptual detail preservation