Structure of polydisperse inverse ferrofluids: Theory and computer simulation

By using theoretical analysis and molecular dynamics simulations, we investigate the structure of colloidal crystals formed by nonmagnetic microparticles (or magnetic holes) suspended in ferrofluids (called inverse ferrofluids), by taking into account the effect of polydispersity in size of the nonmagnetic microparticles. Such polydispersity often exists in real situations. We obtain an analytical expression for the interaction energy of monodisperse, bidisperse, and polydisperse inverse ferrofluids. Body-centered tetragonal (bct) lattices are shown to possess the lowest energy when compared with other sorts of lattices and thus serve as the ground state of the systems. Also, the effect of microparticle size distributions (namely, polydispersity in size) plays an important role in the formation of various kinds of structural configurations. Thus, it seems possible to fabricate colloidal crystals by choosing appropriate polydispersity in size.Comment: 22 pages, 8 figure

Magnetophoresis of nonmagnetic particles in ferrofluids

Ferrofluids containing nonmagnetic particles are called inverse ferrofluids. On the basis of the Ewald-Kornfeld formulation and the Maxwell-Garnett theory, we theoretically investigate the magnetophoretic force exerting on the nonmagnetic particles in inverse ferrofluids due to the presence of a nonuniform magnetic field, by taking into account the structural transition and long-range interaction. We numerically demonstrate that the force can be adjusted by choosing appropriate lattices, volume fractions, geometric shapes, and conductivities of the nonmagnetic particles, as well as frequencies of external magnetic fields.Comment: 24 pages, 7 figure

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Incorporation of a selective sigma-2 receptor ligand enhances uptake of liposomes by multiple cancer cells

Background: The sigma-2 receptor is an attractive target for tumor imaging and targeted therapy because it is overexpressed in multiple types of solid tumors, including prostate cancer, breast cancer, and lung cancer. SV119 is a synthetic small molecule that binds to sigma-2 receptors with high affinity and specificity. This study investigates the utility of SV119 in mediating the selective targeting of liposomal vectors in various types of cancer cells. Methods: SV119 was covalently linked with polyethylene glycol-dioleyl amido aspartic acid conjugate (PEG-DOA) to generate a novel functional lipid, SV119-PEG-DOA. This lipid was utilized for the preparation of targeted liposomes to enhance their uptake by cancer cells. Liposomes with various SV119 densities (0, 1, 3, and 5 mole%) were prepared and their cellular uptake was investigated in several tumor cell lines. In addition, doxorubicin (DOX) was loaded into the targeted and unmodified liposomes, and the cytotoxic effect on the DU-145 cells was evaluated by MTT assay. Results: Liposomes with or without SV119-PEG-DOA both have a mean diameter of approximately 90 nm and a neutral charge. The incorporation of SV119-PEG-DOA significantly increased the cellular uptake of liposomes by the DU-145, PC-3, A549, 201T, and MCF-7 tumor cells, which was shown by fluorescence microscopy and the quantitative measurement of fluorescence intensity. In contrast, the incorporation of SV119 did not increase the uptake of liposomes by the normal BEAS-2B cells. In a time course study, the uptake of SV119 liposomes by DU-145 cells was also significantly higher at each time point compared to the unmodified liposomes. Furthermore, the DOX-loaded SV119 liposomes showed significantly higher cytotoxicity to DU-145 cells compared to the DOX-loaded unmodified liposomes. Conclusion: SV119 liposomes were developed for targeted drug delivery to cancer cells. The targeting efficiency and specificity of SV119 liposomes to cancer cells was demonstrated in vitro. The results of this study suggest that SV119-modified liposomes might be a promising drug carrier for tumor-targeted delivery. © 2012 Zhang et al, publisher and licensee Dove Medical Press Ltd